Keywords: correlation table

C.1 Wilcoxon rank sum tests between treatments for all periods (p-values) C.2 Wilcoxon rank sum tests between treatments in period 1 (p-values) . C.3 Wilcoxon rank sum tests between treatments in period 28 (p-values) . C.4 Wilcoxon rank sum tests between treatments in period rst (p-values) C.5 Wilcoxon rank sum tests between treatments in period last (p-values) C.6 Wilcoxon rank sum tests of signicant dierences in eort between payo structure (p-values) . . . . . . . . . . . . .

Béatrice Roussillon and Sabrina Teyssier for accepting to carry out an experiment and co-author a paper with me. This research would not have been nished without the input of colleagues who have read through my research and provided constructive criticism; my gratitude goes to Paolo Crosetto, Adélaïde Fadhuile, Aline Fugeray-Scarbel, Aurélie Level, Anna Risch, Stéphane Robin, Béatrice Roussillon, Carine Staropoli, Sabrina Teyssier, and Carole Treibich. I would also like to thank all those who provided feedback on my presentations at conferences. In particular, the French Association of Energy Economists for rewarding my research with the 2017 FAEE prize for Best Student Paper.

I would like to thank my fellow runners for our much needed breaks in the working day: Julien Chicot, Adrien Hervouet, Stéphane Lemarié, Aurélie Level, Nicolas Pasquier, and Stéphane Robin.

I would also like to thank the rest of my colleagues and fellow PhDs, too numerous to mention by name but no less important, for welcoming me to the lab from the very rst day.

I would like to thank Patricia Buckley for taking the time to read through this thesis and for correcting my increasingly Frenchied English.

I would like to thank my Grenoble family for all the fun times we've had over the years.

Finally, I would like to thank François Best. Always.

INTRODUCTION 0.1 Energy Transition and Residential Consumers

The transition to a green, CO2 neutral, renewable and sustainable society is one of the key challenges of the 21st century. In 2017, the temperature of our planet increased by 1.1

• C since the pre-industrial era and 2013-2017 were the ve warmest years on record, so far. This warming of the planet has had far-reaching consequences across the globe; from severe storms and ooding to deadly droughts and wildres which have had major economic impacts on human life. Human inuence is the main driver behind the increasing global and regional temperatures (World Meteorological Organization, 2018).

In order to combat increasing temperatures, governments across the world have set targets to reduce our impact on the planet. The European Union has set objectives to be achieved by certain dates. The EU as a whole is on track to meet its 2020 objectives of a 20% reduction in greenhouse gas (GHG) emissions compared to 1990 levels, a 20% share of renewable energy in the production mix, and a 20% improvement in energy eciency 2 . By 2030, the EU will further its energy strategy by aiming to reduce GHG emissions by 40%, to have a share of at least 27% of renewable energy sources (RES) in the production mix, and to achieve energy savings of at least 25% across all sectors (industry, commercial, transport, residential) 3 .

Concerning GHG emissions, the EU met its 2020 target in 2014, and estimates in 2016 suggest that GHG emissions in the EU are 23% below 1990 levels. However, the EU is currently falling short of its 2030 tra jectory (European Environment Agency, 2017c). Similarly, France is on target to meet its 2020 GHG emissions target, however, the rate of reduction in emissions is due to slow down, and France is unlikely to reach the 2030 target (European Environment Agency, 2017d).

Concerning renewable energy, the EU is on track to meet its 2020 objective however, growth in the share of RES is slowing, making the 2030 target dicult to reach (European Environment Agency, 2017b).

In order to meet the EU's renewable energy target, France has committed to achieving a 23% share of RES by 2020.

However, in 2016, France's RES share was at 15.6%, and at the current rate of progress, France will meet its 2020 objective in 2029 4 .

ENERGY TRANSITION AND RESIDENTIAL CONSUMERS 3

Concerning energy savings, in 2014, in the EU-28, residential energy consumption represented the third highest consuming sector with 25% of nal energy consumption. Between 2005 and 2014, nal energy consumption in the household sector fell by 14.8% (European Environment Agency, 2017a). However, in France, residential electricity consumption is the highest consuming sector representing 36% of nal electricity consumption, and between 2001 and 2017, residential electricity consumption in France increased by 12%, remaining relatively stable from 2011 (Réseau de Transport d'Electricité, 2018). In 2014, France needed to reduce its nal energy consumption (all sectors combined) by a further 7.5% in order to meet its 2020 target (European Environment Agency, 2017a).

Given that residential consumption is the highest consuming sector and that France is on track to meet only one of the EU 2020 targets within the next year (European Environment Agency, 2017d), there is clear potential for lowering consumption in the residential sector in France in order to achieve the national and European energy transition ob jectives.

A way of reducing GHG emissions is to move away from the use of high cost, inecient, polluting generators. These generators are typically used during peak periods when demand is particularly high.

In 2008 in France, just 6% of peak capacity was used during 1% of hours (Faruqui et al., 2010a). Increasing the share of RES, which is intermittent by nature, means that there will be electricity available at certain periods of the day and of the year when the sun is shining and when the wind is blowing. Both of these imply a change in the traditional functioning of the electricity market so that demand follows supply rather than supply following demand [START_REF] Strbac | Demand side management: Benets and challenges[END_REF].

In the future, the increasing integration of electricity produced from RES will be stored for use during peak periods. Consumers will charge their electric vehicles at times when electricity is in plentiful supply and is cheaper, to then be used at times when supply is constrained, and high demand means high prices. Currently, capabilities for storing electricity are limited and expensive [START_REF] Stephens | Smart Grid (R)Evolution: Electric Power Struggles[END_REF] and so other methods of encouraging demand to follow supply are needed.

In the absence of electricity storage, Demand Side Management (DSM) is a method for redistributing loads from peak to o-peak periods. In the residential sector, consumers can be encouraged to lower their consumption during peak periods to avoid connecting inecient generators to the grid. They can also be encouraged to increase their consumption during o-peak periods when there is a supply of INTRODUCTION renewable energy available. DSM in the residential sector requires that consumers' demand be exible and that consumers respond to incentives used to modify their behaviour. Signicant savings, both monetary and environmental, can be achieved if households are successfully incentivised to lower their peak demand.

While peak demand reduction is important for achieving the objectives of reduced emissions and for the integration of RES, it does not necessarily result in a reduction in overall demand which is necessary to meet the EU's third objective of energy savings. The eect on overall demand will depend on whether there are spillovers from the use of incentives to lower peak demand, on the demand during other periods (Allcott, 2011a), or by how much consumers increase their demand during o-peak periods after being incentivised to decrease their peak demand (Torriti, 2012).

A reduction in overall demand refers to a decrease in total energy consumption at any time of day or year. While such reductions can made be through improvements in energy eciency (Nearly Zero Energy Building standards, retrotting of older buildings, and use of energy ecient appliances), occupant behaviour is an important factor in reducing residential energy consumption. Building characteristics can account for 42% of a building's energy use whereas occupant characteristics and behaviour can account for 4.2% [START_REF] Santin | The eect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock[END_REF]. Though this may not seem like a large proportion, [START_REF] Gram-Hanssen | Ecient technologies or user behaviour, which is the more important when reducing households' energy consumption?[END_REF] nd that the electricity consumption of households living in similar houses (according to building characteristics) can vary by a factor of 5 and the heating consumption can vary by a factor of 2-3. Additionally, there is an energy-eciency gap where realised eciency gains are less than predicted gains. This is partly due to behavioural barriers [START_REF] Hirst | Closing the eciency gap: Barriers to the ecient use of energy[END_REF] and partly due to rebound eects when consumption increases following an improvement in energy eciency [START_REF] Greening | Energy eciency and consumptionthe rebound eecta survey[END_REF]. Given the variation in energy consumption and the increase in consumption after eciency gains, there is a need to inuence consumer behaviour and to encourage consumers to reduce their energy consumption. In the traditional electricity market in which consumers are passive and are unaware of their consumption, inuencing behaviour is a significant challenge. However, the introduction of smart meters in the residential sector is an important technological advancement that allows for the implementation of incentives to encourage households to lower their energy consumption.

The European Commission (2014a, p.8) has stated that "in sectors such as housing [...] there will be a need for a signicant acceleration of current eorts to tap the signicant unexploited potential. This will require large investments in the building 0.2 TECHNOLOGIES AND INCENTIVES 5 sector (that lead to lower running costs), framework conditions and information that encourage consumers to take up innovative products and services and appropriate nancial instruments to ensure that all energy consumers benet from the resulting changes." Across the EU, Member States have invested in the installation of smart meters in residential homes. Figure 1 shows the deployment strategies of the Member States by 2020. Faruqui et al. (2010a) estimate that the deployment of smart meters in the EU will cost 51 billion euros and that the operational benets 5 will recuperate 26-41 billion euros. The missing 10-25 billion euro investment in smart metering technology can be recovered through a reduction of residential energy demand, in particular during peak periods. Smart meters are key technological advancement for an electricity market in which consumers take a more active role in in energy consumption management but they alone are not sucient to encourage consumers to lower their demand. To motivate a behavioural change, consumers need to be appropriately incentivised.

Technologies and Incentives

Through the use of smart meters, dierent incentives can be delivered to consumers based on accurate consumption measurements. Incentives which target overall demand reduction are traditionally information based [START_REF] Darby | The eectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering[END_REF] -information on historic consumption or real-time feedback -or, more recently, based on insights from behavioural economics (Allcott, 2011b). Smart meters facilitate the use of nancial incentives such as dynamic pricing which is used to encourage lower peak demand [START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF]. The following sections provide an overview of the literature on these dierent incentives.

Feedback

One of the main advantages of smart meters is the ability to communicate realtime consumption data to consumers. By providing households with information on their energy consumption, households will be made more aware of their consumption habits and will make eorts to lower their consumption. Increasing households' awareness of their energy consumption is the rst step towards changing consump- tion habits [START_REF] Attari | Public perceptions of energy consumption and savings[END_REF].

Paper bills are the method of feedback traditionally used to inform consumers of their energy consumption and expenditure. [START_REF] Darby | The eectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering[END_REF] highlight that such feedback is useful for assessing the impact on consumption of investments in energy eciency, as opposed to behavioural changes. Such information does not provide 6 The map displays the results of Member States' cost-benet analyses (CBA), either positive, negative, unavailable or inconclusive, and the state of smart meter (SM) deployment. As of 2013, countries in solid green have ocially begun installation of SM, those in shaded green are planning to install SM after an ocial decision has been taken, those in red have decided against SM installation after a negative or inconclusive CBA, those in solid orange have not yet made a decision, and those in shaded orange have begun a selective installation. For example, in Germany SM installation is limited to new or renovated houses, to prosumers and high-consumption households [START_REF] Edelmann | Cost-benet analysis for the comprehensive use of smart metering[END_REF]. appropriate feedback on eorts to lower energy consumption as the data is received long after the behavioural change. For feedback to have an eect, the relationship between action and eect needs to be clear to households so that they can see how behavioural changes aect energy consumption [START_REF] Fischer | Feedback on household electricity consumption: A tool for saving energy?[END_REF].

In order for consumers to truly benet from the consumption information provided by the smart meter, they must be able to access the information soon after implementing a behavioural change. Data that is available online provides a greater depth of information on consumption levels and can potentially close the action and eect gap, but it requires that consumers have a computer with an internet connection, and that they log on to access the data. Studies of such feedback and its eect on energy consumption nd that few people log on to the online portals and the number of connections decreases during the course of the study [START_REF] Benders | New approaches for household energy conservationin search of personal household energy budgets and energy reduction options[END_REF], Vassileva et al., 2012[START_REF] Schleich | Eects of feedback on residential electricity demandndings from a eld trial in Austria[END_REF].

To further close the action-eect gap, households can use a device capable of interfacing with their smart meter which can provide them with real-time, accessible consumption information. These devices are commonly known as energy monitors, real-time monitors or in-home displays (IHD). They are dedicated platforms which provide real-time consumption data thus providing a direct link between action and eect. The use of IHDs can encourage a reduction in consumption so long as they are kept in visible locations in the home for easy and quick access to the data available, however this is not always the case and the novelty factor of consulting one's consumption in real-time tends to wear o [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Hargreaves | Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term[END_REF].

In order to engage households with the data provided, attention needs to be given to how data is presented, whether in monetary or energy terms [START_REF] Buchanan | Feeding back about ecofeedback: How do consumers use and respond to energy monitors?[END_REF], whether numerical or graphical displays are used [START_REF] Chiang | A laboratory test of the ecacy of energy display interface design[END_REF], or whether amount spent on consumption is displayed factually, or presented as a loss [START_REF] Bager | Making `Smart Meters' smarter? Insights from a behavioural economics pilot eld experiment in Copenhagen, Denmark[END_REF]. Such presentations and their eect on eort are further explored in Chapter 4.

Dynamic Pricing

Dynamic pricing refers to the adjustment of retail electricity prices to better reect wholesale costs of energy production. During times of peak demand, production costs are higher leading to higher retail prices which incentivise households to lower their demand during peak periods, and in some cases, to increase their demand during o-peak periods, in order to maintain supply and demand balance (Faruqui INTRODUCTION et al., 2009). There are dierent tari structures which are more or less dynamic, from time-variant, but static, time-of-use (TOU) pricing to dynamic real-time pricing (RTP). These dierent pricing programmes dier by degree of risk and possible reward [START_REF] Faruqui | Dynamic pricing and its discontents[END_REF]. A at-rate tari in which prices are xed no matter the time of consumption is `risk-free' as all kWh are consumed at the same price.

A TOU tari has a slightly greater associated risk but the risk remains much lower than that of critical peak pricing (CPP) or RTP. Figure 2 shows the risk-reward trade-o of dynamic pricing taris where risk refers to the exposure of consumers to volatile wholesale electricity market prices [START_REF] Faruqui | The ethics of dynamic pricing[END_REF].

Figure 2: Risk-reward trade-o in dynamic pricing rates (adapted from Faruqui (2012, p.17))

Dynamic pricing taris are eective at reducing energy consumption, particularly CPP and RTP taris when combined with energy monitors [START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF]. Indeed, in order for households to successfully respond to dynamic pricing, consumers require an IHD device to inform them of the changing prices [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF], particularly in the case of RTP. However, opponents of dynamic pricing argue that residential consumers should not be asked to support the volatility of electricity prices, particularly consumers who are vulnerable to changes in electricity supply (young children, the elderly, disabled people) [START_REF] Alexander | Dynamic pricing? Not so fast! A residential consumer perspective[END_REF], and that the peaks of demand are natural peaks due to the organisation of daily life which are dicult to shift [START_REF] Naus | Smart grids, information ows and emerging domestic energy practices[END_REF][START_REF] Hall | Cost-reective electricity pricing: Consumer preferences and perceptions[END_REF].

Nudging

The installation of smart meters and IHDs is not sucient to engage consumers in behavioural change. The majority of households show a lack of interest in IHDs and it is often only those who are already concerned by their energy consumption who pay attention to their IHD [START_REF] Buchanan | The question of energy reduction: The problem (s) with feedback[END_REF]. Increasing retail electricity prices to better match wholesale prices tends only to have an impact on those consumers who are fully informed and attentive to the price changes [START_REF] Jessoe | Smart Grid Handbook, chapter Utilization and Customer Behaviour: Smart Choices for the Smart Grid[END_REF]. Given this, there has been an increase in the use of tools from behavioural economics to increase consumer response to incentives. Such incentives are coined `nudges' and are predominately based upon the work of Nobel prize winners Richard Thaler (2017), and Daniel Kahneman (2002). [START_REF] Thaler | Nudge: Improving decisions about health, wealth, and happiness[END_REF] dene a nudge as follows:

"A nudge [...] is any aspect of the choice architecture that alters people's behavio[u]r in a predictable way without forbidding any options or signicantly changing their economic incentives. To count as a mere nudge, the intervention must be easy and cheap to avoid. Nudges are not mandates."

Nudging is based on the idea of libertarian paternalism which is an approach that steers individuals towards choices which are in their best interest and will increase their welfare without limiting their freedom to choose. It recognises that individuals make choices which are not in their best interest, choices which they would not make if they had complete information and unlimited cognitive capabilities [START_REF] Thaler | Libertarian paternalism[END_REF].

Households have been nudged to lower their energy consumption via the use of social and injunctive norms. Social norms are a type of feedback which compares a household's energy consumption to the consumption of their neighbours' [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF][START_REF] Nolan | Normative social inuence is underdetected[END_REF]. Injunctive norms add social approval of a household's consumption in relation to that of their neighbours' [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF]. For example, Opower 7 put this into practice with their Home Energy Reports (HER).

These are paper bills which include a comparison of one's own consumption to the average consumption of neighbouring households along with a smiley face (injunctive norm) if you are consuming less than your neighbours (Allcott, 2011b). Figure 3 provides an example of the use of social and injunctive norms in an Opower HER.

INTRODUCTION

Figure 3: A Home Energy Report from Opower

In the absence of injunctive norms, i.e.: only descriptive comparisons of consumption, households who are consuming less than the average, tend to increase their consumption. [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF] suggest that the use of a descriptive social norm provides a level from which it is undesirable to deviate. This level becomes the normal level of behaviour and so, being above or below is unwanted. This leads to a convergence towards the average which Schultz et al. call a boomerang eect.

Such behaviour can also be described by a licensing eect when engagement in a good deed, lowering consumption, licenses an individual to subsequently engage in a bad deed, increasing consumption [START_REF] Khan | Licensing eect in consumer choice[END_REF]. The inclusion of smiley faces is used to counteract these eects by providing social approval of desirable behaviour: lowering consumption.

If such relatively costless nudges are eective at reducing energy consumption, then they may be used as an alternative to costlier incentives such as dynamic pricing.

Before such a step is taken, the eect of nudges and pricing should be explored in order to determine the monetary value of such nudge (see Chapter 3).

Outline of the Thesis

Smart meters are being introduced to households to embolden consumers to take active roles in their energy management, and in turn, to help meet national and European climate change ob jectives. Currently France is not on target to meet all of its ob jectives within the next year. Furthermore, there are both advantages and disadvantages to the incentives used to encourage households to lower their demand as highlighted in the previous section. The central question of this thesis is thus:

How do residential consumers respond to incentives used to encourage them to lower their consumption?

OUTLINE OF THE THESIS 11

This question is addressed in four chapters, a preview of each is given below.

Chapter 1

The rst chapter of this thesis provides a review of the existing literature which has explored how residential consumers interact with and use the incentives described in section 0.2 to lower their consumption, either peak or overall. The objective of the rst chapter is to identify any issues that aect the successfulness of the dierent incentives at encouraging households to lower their consumption. Dierent barriers to the acceptance and the adoption of smart meters, IHDs and the incentives delivered by them are identied.

The objective of chapter 1 is to analyse the potential of smart meters to encourage residential consumers to lower their consumption through the use of nancial and non-nancial incentives.

The research question of this chapter is: What are the main barriers to the acceptance and adoption of smart meters and the incentives they can deliver?

The literature is organised into two main types of barriers: barriers to acceptance and to adoption, as these are two key obstacles to be overcome if households are to be incentivised to lower their consumption. Acceptance is the rst obstacle as households must initially be willing to accept the installation of smart meters in their homes, and the provision of feedback on their consumption, whether it be by continued paper bills, through online portals or IHDs. The installation of smart meters paves the way for the use of dynamic pricing which is predominately implemented on an opt-in basis.

After accepting smart meters and the associated incentives, the next obstacle to overcome is adoption. The installation of a smart meter, the presence of an IHD, and the changing of prices is not sucient alone for households to lower their consumption. They must make use of and engage with these dierent incentives.

The extensive literature highlights that the main barrier to the acceptance of smart meters is that households do not trust energy companies, that they are unsure of what smart meters are and how they can be used to benet consumers. Concerning dynamic pricing, households nd the taris to be complex and so few opt-in into dynamic pricing preferring to remain on the simpler, risk-free at-rate tari. With regard to households' adoption of smart meters and incentives, the ndings of the INTRODUCTION literature suggest that any eects on consumption are typically short-lived. Households tend to engage with feedback initially, but their interest wanes after a few weeks or months. Furthermore, households are constrained to respond to feedback within their personal comfort levels on which they are not willing to compromise, and by the inexibilities of daily life. In addition, the monetary savings resulting from lowering their consumption are rarely suciently high to encourage persistent behavioural changes.

The contribution of this chapter is a recent review of the experimental literature in order to identify the obstacles to using smart meters and associated incentives as a means to encourage households to lower their consumption.

Chapter 2

Given the wealth of eld experiments and pilot studies exploring how consumers respond to incentives (alone or in combination, across many dierent countries, and under many dierent experimental designs) and the increasing attention given to behavioural incentives such as nudging in recent years, the second chapter uses a meta-analytic approach to analyse the results of contemporary experimental studies which have explored the eect of incentives on residential energy consumption.

The objective of chapter 2 is to quantitatively analyse the existing experimental literature to obtain precise estimates of the eect of dierent incentives on residential consumption.

The research questions addressed in the second chapter are: Which incentives are most eective at encouraging households to lower their energy consumption? How does the design of the experimental study impact the eectiveness of dierent incentives at lowering residential energy consumption?

Meta-analysis is the practice of combining the results of many studies which explore a same objective (the eect of incentives on residential energy consumption) in order to obtain a more precise estimate of the true eect. The idea is that the combination of many estimates of an eect size leads to a better estimate of the true eect [START_REF] Stanley | Meta-regression analysis in economics and business[END_REF].

The focus for this chapter is studies conducted around the time of the "Smart Grid

Era" [START_REF] Mckerracher | Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays[END_REF]. The data collection is limited to this period in order to avoid distorting estimated eect sizes by using studies from previous decades 0.3 OUTLINE OF THE THESIS 13 when greater eects of incentives on energy consumption were found [START_REF] Ehrhardt-Martinez | Advanced metering initiatives and residential feedback programs: A meta-review for household electricity-saving opportunities[END_REF]. In the past, greater eect sizes were found due to dierent levels of consumer knowledge of energy consumption, and due to available technology and feedback methods. In recent years there have been more studies which have used incentives based on behavioural economic theory. In this chapter, such behavioural incentives are separated into those which provide only a social norm or descriptive comparison, and those which also include an injunctive norm. This is in order to determine whether there is a dierence in eect size due to boomerang or licensing eects [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF]. In addition, the meta-analysis seeks to provide a better estimate of the true eect size of dierent incentives by including results not only from peer reviewed journals but also from government and utility reports. This is in order to avoid the "le drawer problem" [START_REF] Rosenthal | The le drawer problem and tolerance for null results[END_REF].

Results show that accounting for the sample size of the original study mitigates publication bias in the sample. Thus, accounting for sample size, on average, a study testing the eect of an incentive on residential energy consumption will expect to nd a 2% decrease in consumption. This is a much lower eect than estimated in previous meta-analyses.

Providing households with feedback on their energy consumption in real-time or in monetary terms has the greatest eect: respectively, a 2.89% and 2.86% reduction in consumption. Eect sizes are aected by study design choices such as how participants are recruited into the study. Studies in which participants choose to take part nd greater reduction eects of incentives on energy consumption which suggests that a national roll-out of a particular incentive is likely to be less eective than eld experiments and pilot studies have shown.

This is of particular importance to policy makers.

This chapter contributes to the eld of research by providing an up-to-date analysis of the eects of dierent incentives on residential energy consumption. In particular, this meta-analysis focuses on recent studies, and includes a greater number of experiments using behavioural incentives. It reduces the problem of publication bias, often rife in meta-analyses, and nally it takes additional study design features into consideration compared to previous meta studies.

Chapter 3

Highlighted in chapters 1 and 2, is the idea that dynamic pricing can be politically dicult to implement [START_REF] Alexander | Dynamic pricing? Not so fast! A residential consumer perspective[END_REF], and that consumers nd the taris dicult to understand [START_REF] Layer | Perceived price complexity of dynamic energy taris: An investigation of antecedents and consequences[END_REF][START_REF] Schlereth | Why do consumers prefer static instead of dynamic pricing plans? an empirical study for a better understanding of the low preferences for time-variant pricing plans[END_REF]. Additionally, nudges in the INTRODUCTION form of social comparisons, both with and without injunctive norms, are eective at lowering consumption but can have the undesired licensing or boomerang eect where households who consume less than the average of their neighbours feel that they can increase their consumption [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF], Allcott, 2011b). In the third chapter, each of these incentives are explored in a laboratory environment in order compare their respective advantages and disadvantages in a controlled setting.

The objective of chapter 3 is to compare how individuals respond to a behavioural incentive and a nancial incentive in a stylised energy consumption game.

The research questions asked in this chapter are: Which incentives are more likely to increase socially optimal behaviour? What is the "price" of the nudge?

Chapter 3 describes an experiment based upon a common pool resource game applied to the context of residential energy consumption. [START_REF] Ostrom | Governing the commons[END_REF] describes a common pool resource as a resource system from which a ow of resources can be extracted.

The stock of resources is renewable and can be maintained so long as the amount being extracted does not exceed the rate of renewal. Collectively, it is best if everyone does not exceed their share of the renewable amount of the resource, however, each individual would like to extract more. Previous research has discussed how this framework can be applied to energy infrastructure [START_REF] Bäckman | The Nordic electricity system as a common-pool resource[END_REF][START_REF] Goldthau | Rethinking the governance of energy infrastructure: Scale, decentralization and polycentrism[END_REF][START_REF] Gollwitzer | Rethinking the sustainability and institutional governance of electricity access and minigrids: Electricity as a common pool resource[END_REF]. This framework is applied to residential energy consumption during a period of peak demand where maximum capacity is being used. Each household would like to consume as they see t, however, it would be benecial for everyone if all households made an eort to lower their consumption in order to avoid reductions in tension, brownouts and blackouts.

The results of the experiment show that both the use of a nudge and a price encourage individuals to behave in a more socially optimal manner, i.e.: reducing their consumption, than if there were no incentives. The nudge is understood quickly by individuals and has an immediate eect on consumption in the second period of the game after feedback is rst received. The price takes longer to have an eect, and it is not until the fourth period of play that individuals integrate the price into their decision making. The hypothesis behind the nudge is that individuals who are not behaving optimally, i.e.: over-consuming, will be encouraged to do so after receiving the nudge, however, the results show that these individuals do not respond to the nudge and continue to over-consume. This nding is of particular importance as it highlights a potential drawback of nudge methods.

OUTLINE OF THE THESIS 15

The contribution of this chapter is the application of the common pool resource framework to the electricity market within a laboratory experiment. A monetary evaluation is made for the nudge by setting a price which incentivises individuals to consume the amount observed under the nudge and seeing whether this price level successfully encourages the same level of consumption.

Chapter 4

Chapter 1 identies that households do not necessarily engage with the information provided by their IHD: when the information is displayed in energy units it is incomprehensible [START_REF] Raw | Energy demand research project: Final analysis[END_REF]Ross, 2011, Buchanan et al., 2014), when it is in monetary units, the potential savings are too small. Households feel that any eort they make to lower their energy consumption is not worth it as it only has a small eect of consumption and on monetary savings [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF], 2013[START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF]. Given the identication of these barriers, the nal chapter explores how incentives can be framed to encourage greater eort when individuals are asked to make small eorts, for small rewards akin to eorts to lower consumption.

The ob jective of chapter 4 is to explore the framing of incentives used to encourage small eorts when the rewards are small.

The nal research question is: How can information (on IHDs) be framed to incentivise eort provision?

Chapter 4 looks at how information can be framed to encourage individuals to make a small eort when the rewards are small. In the experiment, individuals are incentivised to make an eort to complete an articial, real-eort task over a number of periods by dierent ways of framing their payos -gain or loss framing, and by slight changes in the payo structure -whether payos are risk-free or risky.

The experiment builds on ndings from Prospect Theory [START_REF] Kahneman | Prospect theory: An analysis of decisions under risk[END_REF] which postulates that individuals make more eort when payos are framed as a loss (in particular when losses are risky), than when they are framed as a gain.

In the case of small rewards for small eorts, the present experiment nds no signicant treatment eects of gain or loss framing. On average, individuals provide the same level of eort whatever the frame. However, individuals will provide more eort when payos are relatively higher under both framing types. This suggests that when rewards are small, as in the case of actions to lower energy consumption, individuals are equally incentivised to make an eort under both gain and loss fram-

INTRODUCTION

ing. Increasing the size of the rewards associated to an action is key to encouraging individuals to make an eort.

This chapter contributes to the literature on gain and loss framing by looking at how Prospect Theory applies in a situation where individuals must make small eorts for small rewards, and by including an element of risk in the payo structure.

Finally, the thesis concludes with an overview of the four chapters and their ndings in relation to the central research question. The limitations of the research and its implications for policy makers, practitioners, and theorists are considered. The thesis ends with a discussion of avenues for further research.

Introduction

The average residential consumer has learnt to be a passive user of electricity. For this consumer electricity has an invisible quality; it arrives in the household through hidden wires and is consumed as part of daily life and routine which makes it difcult for consumers to connect their daily activities to specic amounts of energy consumption (Burgess andNye, 2008, Hargreaves et al., 2010). The ma jority of residential consumers have a limited idea of how much they are consuming for different purposes [START_REF] Darby | The eectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering[END_REF]. They tend to overestimate the energy use of lighting and other visible, low-energy uses whereas they underestimate the energy consumption of less visible, high-energy uses [START_REF] Attari | Public perceptions of energy consumption and savings[END_REF].

To add to this, the majority of residential consumers pay a xed-rate for their electricity. Under a at-rate structure, all consumed kilowatt hours (kWh) are charged at the same xed price; the consumer does not dierentiate between a kWh that is consumed at 7pm to one that is consumed at 4am. Yet these two kWh do not have the same costs of production. The kWh that are consumed during peak hours cost signicantly more to produce and to distribute [START_REF] Faruqui | The ethics of dynamic pricing[END_REF]. This lack of transparent pricing gives electricity an unlimited quality from the point of view of consumers; no matter how much they consume, no matter when they consume it, the price per kWh remains the same. Additionally, energy consumption is a relatively small part of a household's bills which further heightens the unlimited quality of electricity.

In reality, electricity is not invisible nor in unlimited supply. Electricity grids across the world are under pressure to supply enough to meet the growing demands of modern life. With the electrication of the home and the domestication of technology, energy needs have changed and energy networks cannot keep up (Verbong et al., 2013). This increased demand is putting great strain on generators of electricity and certain generators are used for only a few hours a year to meet the demand on high peak days. Across the EU 5-8% of electricity network capacity is used only 1% of the time (Faruqui et al., 2010a).

Across the world, countries are setting ob jectives to reduce humankind's impact on the climate by reducing CO 2 emissions, increasing the share of renewable energy in the production mix, and achieving greater levels of energy savings. Given these objectives, many countries are investing in the installation of smart meters in residential homes. The impetus behind the smart meter initiatives across the globe 20 CHAPTER 1: BARRIERS TO ACCEPTANCE AND ADOPTION is that residential consumers will be better informed of their energy consumption through more detailed feedback, and monetary incentives such as dynamic pricing taris can be more easily implemented. This use of greater information, and monetary incentives will help households to lower their consumption, and in turn environmental and supply objectives will be met.

This transition to a cleaner, sustainable energy system through the use of smart meters will require residential consumers to take on a more active role in the energy system. Households will be asked to respond to signals about the price and supply of electricity. Smart meters are the technological advancement which will help them to do so and their installation removes a technological barrier to the implementation of time-variant taris and to the delivery of real-time consumption information.

However, these environmental objectives can only be achieved through smart meter installation if consumers are willing to accept smart meters in their homes. Consumers are considered to be central to the success of changes taking place in the electricity grid, and as such, they are also considered to be one of the greatest barriers to smart meter implementation. Consumers' acceptance of smart meters will greatly inuence the success of installation (Verbong et al., 2013). Even if consumers accept the installation of smart meters and the use of dierent incentives, this is not sucient alone to lower energy demand. A reduction in energy consumption will only be achieved if consumers engage with the information and incentives provided and use them to modify their daily energy consuming behaviour [START_REF] Buchanan | The question of energy reduction: The problem (s) with feedback[END_REF]. This rst chapter discusses the dierent barriers to the acceptance and adoption of smart meters and incentives by residential consumers, beginning with a denition of smart meters and dynamic pricing.

Smart Meters

Smart meters are installed at the end-users' premises in the place of the traditional meter and allow for two-way communication between suppliers and end-users. On the supply side, the benets of smart meter installation include better eciency in electricity production, transmission and distribution, reduced fraud, greater bill accuracy, electricity outage detection, and integration of micro-generation, among others [START_REF] Krishnamurti | Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters[END_REF][START_REF] Darby | Smart Grid Handbook, chapter Smart Meters and Residential Customers[END_REF]. Faruqui et al. (2010a) estimate that the return on investment of these supply-side benets are worth between 26 On the demand side, residential consumers will benet from remote meter reading, real-time energy consumption information, and a greater control over one's own consumption [START_REF] Carroll | Reducing household electricity demand through smart metering: The role of improved information about energy saving[END_REF][START_REF] Darby | Smart Grid Handbook, chapter Smart Meters and Residential Customers[END_REF].

Smart meters correct a market failure of imperfect information as in the traditional electricity market, consumption data can be inaccurate [START_REF] Carroll | Reducing household electricity demand through smart metering: The role of improved information about energy saving[END_REF]. Consumers receive monthly bills based on an estimate of their consumption calculated by the energy company with meter readings taking place perhaps quarterly. Smart meters, in combination with an IHD or other enabling technology, allow the collection of real-time energy consumption data and the communication of this information to both the utility and the consumer. This gives the consumer more accurate and more frequent information about their consumption, and thus allows the consumer to take a more active role in their energy consumption. In addition to a greater depth of consumption information, dierent incentives can be delivered to consumers via their smart meter and IHD.

Dynamic Pricing

Smart meters also pave the way for the use of dynamic pricing which requires that consumers pay diering prices according to the real-time cost of electricity production. The logic behind dynamic pricing is to provide consumers with economic incentives to reduce, or to increase, their demand in order to maintain supply and demand balance in the electricity market [START_REF] Borenstein | Dynamic pricing, advanced metering, and demand response in electricity markets[END_REF]. The dynamic pricing taris currently used dier in degree of time-variability:

CHAPTER 1: BARRIERS TO ACCEPTANCE AND ADOPTION

Time-of-use pricing

Under Time-of-use (TOU), as depicted in g. 1.2, the price depends on the time at which electricity is being consumed. This could be the time of day or the time of year [START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF]. Typically TOU taris consist of two or three periods; o-peak and peak, and occasionally the shoulder or mid-peak period which transitions between the two [START_REF] Faruqui | The power of dynamic pricing[END_REF]. The peak hours depend upon location and daily rhythms. For example, hotter countries have a peak during summer afternoons when the sun is at its hottest, whereas colder countries have peaks in the early morning, or later in the evening. Under seasonal TOU taris, there will be higher rates in summer for hotter countries when air conditioners are in greater demand and higher rates in winter for colder countries when heating is in high demand. This type of tari is not technically a dynamic tari as it is xed ex-ante and does not depend on real-time electricity demand [START_REF] Faruqui | The power of dynamic pricing[END_REF]. prices increase substantially on days where electricity demand soars, known as critical event days. Such days are when the temperature is particularly low (high) in cold (hot) countries. On days where there is no critical event, prices either revert to TOU prices or to at-rate prices. This tari is designed to communicate the true, uctuating electricity costs to consumers during dierent periods [START_REF] Faruqui | The power of dynamic pricing[END_REF]. Consumers receive a price signal to incentivise them to reduce their consumption during periods when electricity production is reaching maximum capacity.

Customers are notied of the occurrence of critical events on a day-ahead or day-of 1.1 INTRODUCTION 23 basis. CPP carries more risk for consumers than TOU pricing as consumers will pay a much higher price if they cannot shift their demand, however it oers a greater reward; by shifting their consumption consumers can take advantage of the much lower priced o-peak periods [START_REF] Faruqui | Dynamic pricing and its discontents[END_REF]. [START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF] suggest that this type of tari may be viewed more favourably from a political or regulatory point of view as a PTR tari does not penalise consumers with a much higher price for consumption that they cannot shift. Despite the demand-side advantages to PTR, [START_REF] Faruqui | The power of dynamic pricing[END_REF] argue that if consumers eectively reduce their consumption as a result of PTR, then the energy companies will look to increase electricity prices in order to maintain their revenue stream.

Real-time pricing

Under Real-time pricing (RTP), depicted in g. 1.5, the electricity price faced by a consumer changes on a real-time basis, typically on an hourly basis, according to current demand. The prices are communicated to consumers on a day-ahead or hour-ahead basis [START_REF] Faruqui | The power of dynamic pricing[END_REF]. This pricing programme is the highest risk of the programmes described in this section, however, it has the highest potential reward compared to a standard tari [START_REF] Faruqui | Dynamic pricing and its discontents[END_REF]; consumers have the opportunity to move their consumption to much lower o-peak prices. or a hire car, when using a toll bridge. Yet, such pricing programmes have low penetration in the electricity market [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF].

Given that consumers have some level of familiarity with the monitoring of consumption and other activities, and with dynamic pricing, this rst section looks at the barriers to households' acceptance of smart meters and dynamic pricing.

Mistrust of Energy Companies' Intentions

With the arrival of smart meters trust issues have once again come to the forefront.

Consumers are wary of energy companies' motives in oering installation of smart meters and energy monitors as previous bad experience with their energy provider leads consumers to question the energy companies' motives in providing smart metering technology [START_REF] Hall | Cost-reective electricity pricing: Consumer preferences and perceptions[END_REF]. They feel that the energy companies may not oer a smart meter package that is in the interest of the household, but one that serves the energy companies' interest [START_REF] Kaufmann | Customer value of smart metering: Explorative evidence from a choice-based conjoint study in Switzerland[END_REF]. Consumers who feel that energy companies benet most from the use of smart meters are less positive about their installation in their homes [START_REF] Krishnamurti | Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters[END_REF]. Furthermore, householders doubt whether the energy companies will pass on the monetary savings to customers as they feel that the energy companies will maintain their prot margins [START_REF] Spence | Engaging with energy reduction: Does a climate change frame have the potential for achieving broader sustainable behaviour[END_REF]. As participants (n=72) in [START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF] suggest, energy companies' prots increase as consumers use more energy.

In interviews with relevant Dutch stakeholders 9 (n=37), Verbong et al. (2013) nd that interviewees expressed ambiguity as to whether smart meters are in the interest of end-users. The stakeholders emphasised that while there are advantages for consumers, energy companies have their own motivations and it is unclear as to whose interests are better served.

Stenner et al. ( 2017) conduct a survey (n=1499) to explore the eect of trust on Australian households' willingness to participate in direct load control. The authors nd that households' level of trust in their energy supplier greatly aects their willingness to participate, with those who explicitly express mistrust being much less 9 The stakeholders interviewed represented governmental organisations, electrical and gas utility companies, researchers of energy related consumer behaviour, and residents. CHAPTER 1: BARRIERS TO ACCEPTANCE AND ADOPTION likely to participate. Even when this lack of mistrust is addressed, via reassurances that their energy company is taking steps to "rebuild community support", the proportion of households willing to participate only increases by a marginal amount.

These trust issues continue once the smart meter has been installed, consumers are unsure of what energy companies will do with the substantial amount of data on their energy consumption behaviour and habits [START_REF] Richter | Which smart electricity service contracts will consumers accept? the demand for compensation in a platform market[END_REF]. A small-scale study which involved interviews and workshops with ve Dutch households found that participants are concerned that energy companies will use data for commercial means [START_REF] Naus | Smart grids, information ows and emerging domestic energy practices[END_REF]. Namely, that energy companies will be able to use the real-time data to market specic services and/or products to consumers. The 228 participants in [START_REF] Pepermans | Valuing smart meters[END_REF] were willing to pay a signicant amount to have a smart meter which had no eect on privacy.

This lack of trust increases the psychological costs that consumers face, as they must spend time monitoring energy companies' use of their data [START_REF] Gerpott | Determinants of willingness to pay for smart meters: An empirical analysis of household customers in Germany[END_REF].

Though trust issues are mostly viewed as a barrier to adoption of smart meters, participants (n=22) in [START_REF] Krishnamurti | Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters[END_REF] suggest that the increased accuracy of energy bills due to real-time feedback from smart meters provides energy companies with an opportunity to build trust with consumers. However, households could face increased bills if their consumption was previously underestimated [START_REF] Raimi | Understanding and beliefs about smart energy technology[END_REF].

Uncertainty Regarding Technology

Aside from questions of trust, uncertainty is also an important issue. [START_REF] Darby | Smart metering: What potential for householder engagement?[END_REF]. In an online survey of American consumers (n=305), [START_REF] Raimi | Understanding and beliefs about smart energy technology[END_REF] nd that less than 36% participants have heard of smart meters and smart grids, and more than 64% showed no understanding of what smart meters were and could do. Other American participants (n=22) in [START_REF] Krishnamurti | Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters[END_REF] confused the smart meter with the devices; expecting a smart meter to come with an energy monitor so that they can verify the accuracy of their energy bill and see appliance-specic feedback describing their energy consumption in detail.

In an online conjoint analysis, [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF] the dierent appliances that they use [START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF].

This uncertainty is unsurprising given how the traditional electricity market is set up; households are accustomed to being passive users of energy. Consumers are often unaware of how much they pay for their electricity, or of the tari they are on. This is particularly true of the older generation [START_REF] Barnicoat | The ageing population and smart metering: a eld study of householders' attitudes and behaviours towards energy use in Scotland[END_REF]. Alexander (2010) discusses the implications of dynamic pricing for residential consumers suggesting that it goes against years of policy aimed at reducing price volatility for residential consumers in the electricity market. Alexander argues that the true cost of dynamic pricing is not considered in eld experiments and pilot studies. In order to implement demand response consumers require new technology which has its own cost. Furthermore, there is a cost associated with changing consumption behaviour which is not factored into savings calculations.

This uncertainty can lead to confusion of the benets and risks of smart meter and dynamic pricing, leading to unrealistic expectations, (potentially in favour of energy companies), and disappointed consumers [START_REF] Krishnamurti | Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters[END_REF].

Complexity of Taris

In a Norwegian eld experiment, [START_REF] Ericson | Households' self-selection of dynamic electricity taris[END_REF] explores households' tari choice

between their standard at-rate tari and a CPP tari when oered smart meter technology to automatically measure their hourly consumption. Of the 2 300 households initially approached for the study, 295 households chose the CPP rate.

Similarly, both [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF] and [START_REF] Schlereth | Why do consumers prefer static instead of dynamic pricing plans? an empirical study for a better understanding of the low preferences for time-variant pricing plans[END_REF] nd that when choosing a tari, German participants (n=160, and n=779, respectively) are more likely to select a simple TOU tari with a low variation in price, as opposed to a dynamic RTP tari with a high price variation.

In order to determine how the complexity of taris aects contract choice, Layer et al. ( 2017) conduct an online choice experiment of German consumers (n = 664).

The sample is divided into those who enjoy facing complex decisions and those CHAPTER 1: BARRIERS TO ACCEPTANCE AND ADOPTION who do not. Of the four hypothetical taris proposed, at-rate, TOU, CPP, and RTP, the former perceive the CPP and RTP taris to be complex and perceive little complexity concerning the at-rate and TOU taris. The latter perceive all four taris to be complex. In particular, [START_REF] Layer | Perceived price complexity of dynamic energy taris: An investigation of antecedents and consequences[END_REF] nd that the more components a tari contains 10 , if the tari contains odd-endings to price values, and the use of percentages in taris leads to increased perceived complexity of taris.

It is perhaps the issues of uncertainty described above and tari complexity which lead households to favour their existing, time-invariant taris. Yoshida et al. (2017) nd that greater knowledge of energy conservation increases choice of TOU and CPP taris. Furthermore, after experimenting dierent taris in a smart home laboratory 11 for 8 weeks, three of the four participants in [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF] preferred the dynamic taris to the static taris with the exception of the most dynamic tari which included both varying prices and load limits.

Reluctance Towards Automation and Third-party Control

Another issue of contention for residential consumers is the amount of control that smart meters will allow energy companies over their personal consumption. Consumers feel that the installation of smart meters means relinquishing control of their environment [START_REF] Barnicoat | The ageing population and smart metering: a eld study of householders' attitudes and behaviours towards energy use in Scotland[END_REF]. [START_REF] Krishnamurti | Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters[END_REF] nd that American consumers believe that smart meters will be used by energy companies to control household energy use. For example, to cut o the supply to households which consume too much electricity.

In a survey of 139 Dutch households, participants stated they preferred manual control to automatic control. They prefer to make their own decisions regarding when to turn-o appliances, instead of allowing a smart meter to do this for them; they are not willing to lose control for the sake of convenience [START_REF] Leijten | Factors that inuence consumers' acceptance of future energy systems: The eects of adjustment type, production level, and price[END_REF]. On the other hand, [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF] nd that German participants (n=160) prefer a system in which smart appliances could react automatically to variations in prices rather than making the changes themselves. 10 Price components refers to the number of dierently priced periods. For example, a simple TOU tari would have two price periods: peak and o-peak. 11 The smart home laboratory is a 60 metre squared house that can be lived in. It is fully equipped with functioning appliances which can be controlled for the purposes of testing new energy management technologies. See [START_REF] Allerding | Organic smart home: Architecture for energy management in intelligent buildings[END_REF] cited in [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF] for more details.

BARRIERS TO ADOPTION 29

With respect to functionality Belgian participants (n=228) accept a trade-o between no automation and total automation 12 , preferring to monitor and self-programme the smart meter and device to automatically turn-o appliances that have been on stand-by for too long [START_REF] Pepermans | Valuing smart meters[END_REF].

Though some households are unlikely to allow third-party intervention to control their energy consumption, it may be unlikely that they make the necessary behavioural changes in order to reduce energy consumption (Verbong et al., 2013).

When comparing preferences for smart meter contracts, [START_REF] Pepermans | Valuing smart meters[END_REF] conclude that as third-party intervention, and thus the eect on privacy, increases, the less the participants value the smart meter. Additionally, the extent to which consumers are willing to allow automatic control is limited by comfort; consumers do not wish to sacrice their desired comfort level.

Barriers to Adoption

Assuming that consumers have accepted the installation of smart meters in their homes, the next issue to consider is whether consumers will use the information and incentives delivered by smart meters to reduce their energy consumption. Feedback from smart meters and dynamic pricing alone are not going to have an eect on residential energy consumption. They facilitate energy conservation by making energy visible [START_REF] Darby | Smart metering: What potential for householder engagement?[END_REF][START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Gerpott | Determinants of willingness to pay for smart meters: An empirical analysis of household customers in Germany[END_REF] and by reecting the costs of production in energy prices [START_REF] Faruqui | The ethics of dynamic pricing[END_REF]. However, households need to engage with the information and respond to the incentives provided in order to lower their consumption.

Limited Motivation of Monetary Savings

Participants in studies across the UK and Australia state that their main motivation for accepting smart meters and dynamic pricing is nancial [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Buchanan | Feeding back about ecofeedback: How do consumers use and respond to energy monitors?[END_REF][START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF][START_REF] Barnicoat | The ageing population and smart metering: a eld study of householders' attitudes and behaviours towards energy use in Scotland[END_REF][START_REF] Hall | Cost-reective electricity pricing: Consumer preferences and perceptions[END_REF]. Indeed, British participants (n=1 892) expect to receive nancial savings that are twice as large as the amount they are expected to pay for `smart services' 12 No automation refers to a smart meter and device set-up which only allows for monitoring of energy consumption by the consumer, whereas total automation refers to `dynamic management of appliances', i.e. the smart meter can send information to specic appliances to turn them o or on in response to demand or price signals (Pepermans, 2014, p.285).

CHAPTER 1: BARRIERS TO ACCEPTANCE AND ADOPTION [START_REF] Richter | Which smart electricity service contracts will consumers accept? the demand for compensation in a platform market[END_REF]. In [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF] when choosing between taris, German participants (n=160) were primarily motivated by monetary savings; expecting to save 50e-150e. Actual savings during the study were in the order of 20e-60e, at the lower end of these expectations.

Households participating in various UK studies were particularly interested in taking part in order to lower their energy bills.

In focus groups led by [START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF], participants stated that they were motivated to shift energy consumption via dynamic pricing for monetary reasons. However, the authors suggest that the monetary saving from changing the use of isolated energy consuming appliances is likely to be too small to induce behavioural changes from nancial incentives.

Indeed, a single isolated behavioural change, such as turning an appliance o standby, is unlikely to have a large monetary impact.

The behavioural changes that participants can make to lower energy consumption may have a small individual impact, but many changes can add up to a larger nancial saving. For participants (n=275) in [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF] the savings were not as much as they had envisioned; savings were in pennies rather than pounds. On the other hand, some participants (n=21) in [START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF] found that each little saving adds up, yet others felt that they were comfortably well-o to not bother with trying to save energy to lower their bills.

The presentation of consumption information can have an eect on how individuals engage with it. [START_REF] Bager | Making `Smart Meters' smarter? Insights from a behavioural economics pilot eld experiment in Copenhagen, Denmark[END_REF] consider how to frame consumption information so as to encourage a greater provision of energy savings eorts on the part of consumers. They nd that presenting expenditure on energy consumption as a salient loss compared to a statement of expenditure increases the percentage of energy savings that households achieve. Framing information as a loss of money invokes motivation to save energy in individuals as they do not wish to lose money that is theirs.

Understanding of Information on Display

In order for consumers to make the most out of the two-way communication capabilities of smart meters, they require an IHD to display their energy consumption in real-time. Indeed, German households feel that such a device is a necessity for dynamic pricing taris as without, they do not feel adequately informed in order to be able to make the appropriate changes to their behaviour [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF].

BARRIERS TO ADOPTION 31

In an investigation of the eectiveness of energy monitor displays, Chiang et al.

(2012) measured participants' (n=41) sensitivity to dierent presentations of consumption information (numerical, analogue or emotive) in both colour (red for high consumption, and green for low consumption) and black and white. Participants responded quickest to changes in information when presented numerically, and found the analogue information hardest to understand. Participants stated a preference for colour but this did not improve their performance.

Participants in [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF], [START_REF] Raw | Energy demand research project: Final analysis[END_REF] and [START_REF] Buchanan | Feeding back about ecofeedback: How do consumers use and respond to energy monitors?[END_REF] prefer consumption data to be displayed in monetary terms, rather than in energy units or CO2 emissions, as such information is more relatable and comparable. While it is understandable that monetary comparisons are more relatable for consumers, they may not be of much value if prices have changed across dierent time periods, in this case, energy unit comparisons would be of more use [START_REF] Darby | Smart metering: What potential for householder engagement?[END_REF].

In hypothetical consumption scenarios, UK participants (n=170) were presented with consumption data in either monetary terms, as energy units (kWh) or as CO2 emissions and then asked to think of ways to reduce their consumption. Spence et al.

(2014) nd that participants who see their hypothetical consumption in monetary or energy units are more likely to state nancial reasons as motivation for lowering their demand. Those who receive consumption information in terms of CO2 emissions are more likely to cite environmental reasons. While in this study, participants' motivations were clearly primed by the display treatment, the ndings highlight that dierent displays evoke dierent motivations. Interestingly, Spence et al. nd that those who were in the monetary display were most likely to say that lowering their energy consumption is not worth it.

On the other hand, after interviewing 28 Australian households, Strengers (2011, p.331) nd that IHDs focus too much on the numbers, on quantifying what can be "saved and shaved" rather than on what households can do to change their behaviour and ultimately to lower their consumption. However, British participants (n=21), interviewed by [START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF], say that they receive sucient general energy savings advice from other outlets that the IHDs do not add anything new.

Inexibility of Daily Routines

The principal objective of dynamic pricing is to lower consumption during peak periods when demand is much higher and much more costly to produce (Faruqui et al., 2010a). This supposes that households are willing and able to lower their demand during such periods. However, households feel that there is little that they can do to prevent the natural peaks of energy consumption (due to non-exible work days or ingrained energy consumption habits) without drastically changing their lifestyle [START_REF] Naus | Smart grids, information ows and emerging domestic energy practices[END_REF][START_REF] Hall | Cost-reective electricity pricing: Consumer preferences and perceptions[END_REF].

Participants (n=275) in a UK study were reluctant to lower consumption below their normal level and, in fact, when prompted to do so, participants became defensive.

They felt that they had no control over certain aspects of their energy consumption; certain appliances were necessities no matter how much they consumed (the denition of necessary appliances varied across households) and they were not willing to sacrice their quality of life to save a small amount on energy [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF].

In terms of how householders react to dynamic pricing, [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF] nd them willing to change certain behaviours and use certain appliances at o-peak hours, such as dishwashers, washing machines and tumble driers. However, they nd consumers unwilling, and potentially unable, to change the time of use of other activities related to comfort or entertainment. [START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF] describe energy consumers as willing to shift consumption of devices where energy consumption is not at the point-of-use, i.e. white goods, and unwilling to shift use of devices where consumption is at the point-of-use, i.e. showers and televisions. [START_REF] Ericson | Households' self-selection of dynamic electricity taris[END_REF] hypothesises that consumers who have consumption patterns that are favourable to dynamic pricing, (i.e. their consumption is low during peak periods), are more likely to accept such taris. Yet, such dynamic pricing will not have the desired demand reduction eect for these consumers as they have less demand to shift to begin with. These consumers will benet from dynamic pricing without being demand responsive. This is true of both British (n=160) and German (n=779) participants in choice experiments: those who consider that shifting consumption is an easy task, are more likely to adopt time-variant taris [START_REF] Buryk | Investigating preferences for dynamic electricity taris: The eect of environmental and system benet disclosure[END_REF][START_REF] Schlereth | Why do consumers prefer static instead of dynamic pricing plans? an empirical study for a better understanding of the low preferences for time-variant pricing plans[END_REF].

Novelty Factor of Consumption Information

A common theme in eld experiments and pilot studies using smart meters, energy monitors and dynamic pricing is that the behavioural changes made by households are short-lived. There is an initial novelty factor when households use the monitors frequently to begin with. Participants use energy monitors to identify a baseline level of consumption which a household deems to be their normal level of consumption.

Any deviations from this baseline are then identied and acted upon [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Strengers | Negotiating everyday life: The role of energy and water consumption feedback[END_REF][START_REF] Hargreaves | Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term[END_REF][START_REF] Buchanan | Feeding back about ecofeedback: How do consumers use and respond to energy monitors?[END_REF].

In [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF], this identication leads to reactive and pro-active behavioural changes. When energy consumption is unusually high, households identify and turn o appliances as necessary (reactive). In the longer term, they monitor individual appliances in order to determine which are inecient and need replacing (pro-active). The use of the monitor aects future consumption decisions, with households taking energy-eciency into greater consideration when purchasing new appliances. However, follow-up interviews 12 months later with 11 of the initial monitor users revealed that usage of the devices had greatly decreased, with three households having stopped using them altogether. The energy monitoring devices are rarely used by households in the longer term; they become part of the background of daily-life and are used to monitor abnormalities rather than to encourage demand reduction [START_REF] Hargreaves | Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term[END_REF]. This is corroborated by Schleich et al.

(2013) who report limited use of feedback via a web portal; 70% of German participants (n=276) reported that they consulted the portal once a month, and also by Ueno et al. (2006) who nd a decrease in the number interactions with an energy monitor a few weeks after installation.

Studies on dynamic pricing are not without questions as to the durability of demand response. [START_REF] Faruqui | Quantifying customer response to dynamic pricing[END_REF] nd that under TOU pricing, the demand response across two summers greatly decreases; 5.9% in summer 2003 to 0.6% in summer 2004. As the authors state, this result should be interpreted with caution however, as the sample size was small. Furthermore, it is not clear whether temperature variations across the two summers are accounted for. An Italian experiment nds that consumption increased under TOU pricing compared to at-rate taris (Torriti, 2012). This could be considered as a type of rebound eect where households respond to the lower o-peak price by increasing their consumption by more than they lower their consumption in the peak period.

Eect on Household Dynamics

A nal barrier of smart meters and dynamic pricing worthy of discussion is their eect on household dynamics. Household energy consumption is often discussed with the household being a single entity, however, households contain families which have varied compositions.

In [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF] it was mostly male household members who used the monitors and who participated in the interviews, with the females of the household reported as "uninterested". In fact, the monitors were seen to cause conict within households; as some individuals felt that their actions were being constantly monitored -how much energy was being consumed and how much money was being spent by another member of the household. Other interviews with households revealed that although the male member may be more likely to be the bill payer, it is often the female household members who are responsible for managing the daily activities and thus the energy consumption of the household [START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF].

Households with children and older people are less likely to sacrice comfort and convenience to lower their energy demand [START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF]. These types of households are less exible than others. Older generations in particular are more likely to spend more time at home, and they may have certain needs or health issues that require consuming energy [START_REF] Barnicoat | The ageing population and smart metering: a eld study of householders' attitudes and behaviours towards energy use in Scotland[END_REF].

Recommendations

In order for smart meters and the incentives that they can deliver to be eective at encouraging households to lower their consumption, the barriers to acceptance and adoption discussed above will need to be overcome. Recommendations for overcoming some of the barriers identied above are discussed in this section.

Energy companies should increase their eorts to rebuild consumer trust where it has been lost. Such eorts will need to be credible given that non-veriable attempts at trust building" are not sucient to increase consumers' vote of condence in energy companies [START_REF] Stenner | Willingness to participate in direct load control: The role of consumer distrust[END_REF]. Trust could be rebuilt by decreasing the uncertainty around smart meters, IHDs, dynamic pricing and other incentives. As consumers are typically unsure of how smart meters and IHDs or other monitors dier in their capabilities, being more transparent in explaining this new technology to consumers could be one avenue for trust rebuilding. In particular, as the 1.4 RECOMMENDATIONS 35 introduction of smart meters results in more reliable billing, some consumers whose consumption was previously undermeasured and underbilled will see an increase in their bill despite not changing their behaviour. To build trust with these consumers, energy companies could pledge to not increase consumer bills for a certain transition period after the installation of a smart meter due to more accurate measurement in order to allow households to familiarise themselves with the technology. This would be a similar practice to that of designing dynamic pricing taris to be revenue neutral [START_REF] Faruqui | The power of dynamic pricing[END_REF].

Given the increased complexity of dynamic pricing taris relative to at-rate taris, consumers need to be carefully informed of the detail and educated as to how such taris can be protable to them. In particular, energy companies should take care to limit the complexity of taris by reducing the number of dierent components, using even-numbered prices for dierent periods, and where possible providing consumers with savings in absolute amounts rather than in percentage form [START_REF] Layer | Perceived price complexity of dynamic energy taris: An investigation of antecedents and consequences[END_REF]. Furthermore, previous research has shown that consumers who are more familiar with dynamic pricing taris through educational campaigns or direct experimentation are more likely to be willing to accept such taris [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF]Paetz, 2013, Yoshida et al., 2017). Consumer participation in dynamic pricing contracts could therefore be increased through improved knowledge of these taris.

Engaging consumers with the information provided by smart meters and the incentives used to encourage them to lower their consumption is paramount to them being eective. Rather than encouraging energy savings eorts, simply stating how much money households are saving highlights that small monetary amounts are saved with each energy saving action and may serve to discourage energy saving eorts [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF]. Dierent presentations of consumption information invoke dierent motivations to save energy and dierent individuals respond dierently to these presentations [START_REF] Spence | Engaging with energy reduction: Does a climate change frame have the potential for achieving broader sustainable behaviour[END_REF]. Given this, it is unlikely that a one-size-ts-all approach would be as successful at encouraging reductions in consumption, a more individual approach may be appropriate. An additional segment to be considered is that of pro-environmental consumers.

Such consumers gain additional utility from using a smart meter and device due to the pro-environmental benets of reducing energy consumption and making an eort to slow global warming. Gerpott and Paukert (2013, p.486) suggest that certain consumers derive this additional utility due to a `warm glow' eect of giving.

That is to say, consumers receive utility from the act of helping others, in this instance, from helping the environment.

If these dierent segments of consumers can be identied, then appropriate technology and incentives can be oered to them such that these consumers will have the tools which are relevant to their characteristics, motivations and situation, which they can successfully engage with and use to lower their consumption. Therefore, rather than a one-size-ts-all approach to energy saving, a consumer segment specic approach is recommended.

That being said, a consumer segment that may not necessarily benet from the use of a smart meter and device to reduce energy use is the segment of consumers for whom energy consumption is already low, as they will have little scope to further reduce their demand [START_REF] Darby | Smart metering: What potential for householder engagement?[END_REF]. Hence, these consumers may not accept smart meter installation. Such consumers may be pro-environmental consumers who have already reduced their consumption through other mechanisms, or low-income consumers who may not have the means to consume large quantities of energy, nor the scope to further reduce their consumption without becoming fuel-poor. Consideration should 1.5 CONCLUSION 37 be taken when targeting this segment of consumers.

Finally, automation and third-party control was found to be a signicant barrier to acceptance. However, this may help to overcome the barrier to adoption concerning the inexibility of daily life. Given that households may nd it dicult to shift some consumption, the recommendation here is to focus on the demand that can be shifted, and to provide households with the technology that will allow for automatic peak demand shifting. Introducing automation and third-party control may also help to increase consumer trust in energy companies if the latter helps consumers to achieve energy savings with minimal eort. Such technology will not be readily accepted by all consumers and so the focus here should be on the technophiles and price-sensitive consumers.

Conclusion

Smart meters and dynamic pricing correct two market failures in the residential electricity market; smart meters make energy visible by providing consumption information, and dynamic pricing limits how much energy can be consumed by charging residential consumers prices which reect actual costs at a given time. This rst chapter has provided a qualitative review of predominately qualitative literature on how households and consumers perceive, interact with and use smart meters, energy monitors and dynamic pricing as tools and incentives to lower their energy consumption. Recommendations were also made to overcome some of these barriers.

Given that households have long been passive users of electricity, smart meters and energy monitors are the tools that households can use to become more aware of their energy consumption and are the technology via which dierent incentives can be communicated to households in order to encourage them to lower their demand for energy. Signals regarding the real-time price of electricity can be sent to households which will allow for electricity pricing which is reective of the real cost of electricity production at dierent times of the day and the year, thus diminishing the unlimited quality of electricity from the point of view of households.

The review of the literature has highlighted four key barriers to acceptance and ve key barriers to adoption of both smart meters and dynamic pricing.

First and foremost, households show low levels of trust in their energy provider.

Households mistrust energy companies' intentions concerning the installation of CHAPTER 1: BARRIERS TO ACCEPTANCE AND ADOPTION smart metering technology and their use of the data collected. Should signicant energy, and monetary savings, be made, households do not trust energy companies to pass on these savings to the nal consumer. Given that [START_REF] Stenner | Willingness to participate in direct load control: The role of consumer distrust[END_REF] nd a simple two-line sentence alleviates a small amount of misgivings that consumers have regarding their energy company, utilities should consider credible methods in which trust can be rebuilt between themselves and residential consumers.

Whether households trust their energy company or not, many remain uncertain of what smart meters do and how dynamic pricing of electricity works. Tied in with uncertainty is the issue of the complexity of dynamic pricing taris. Consumers who perceive taris to be complex are more likely to opt for a simple at-rate, or perhaps a two-period TOU tari. However, when the benets of such taris are explained to consumers, they are more willing to accept the taris which suggests that there is a problem of information. Through experience with the dynamic taris, consumers have a better understanding of how they can use them to save both energy and money, and are more likely to select such taris [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF]. Energy companies should work on eective communication strategies to better inform households about both technology and incentives as doing so can be used to build trust, and has been shown to increase consumers' willingness to accept smart meters and smart services, and dynamic pricing.

A consumer's household is their domain, it is where they make decisions regarding their consumption. Households perceive smart meters as a way for third-parties to gain control of their daily life. Some consumers are concerned that energy companies will foist external control and automation on them against their will. Other consumers prefer a degree of external control, within their personal comfort parameters, as making behavioural changes is seen to be an inconvenience. A segmented approach to the implementation of dierent technologies (from monitoring to automation and control) and of dierent incentives is recommended to increase consumer participation and engagement.

The section on barriers to adoption explored how households interact with and use smart meters and dynamic pricing to lower their energy demand. Regarding the display of consumption information on energy monitors, households have a preference for simple, monetary metrics. They are less interested in the amount of energy consumed in kWh, nor in the emissions created by their consumption. Though such information awakens consumers' environmental motivations for lowering their energy consumption [START_REF] Spence | Engaging with energy reduction: Does a climate change frame have the potential for achieving broader sustainable behaviour[END_REF]. Some consumers would prefer less focus on 1.5 CONCLUSION 39 the data and more on actions that can be taken to lower their consumption [START_REF] Layer | Perceived price complexity of dynamic energy taris: An investigation of antecedents and consequences[END_REF].

Although households may prefer monetary information, and are mostly motivated to lower their energy consumption for nancial reasons, some households are quickly discouraged to make further eorts to lower their consumption when they realise that energy saving actions do not necessarily result in sizeable monetary savings.

This should be taken into consideration when designing how monetary information is displayed on IHDs, and when communicating to households the benets of dynamic pricing programmes as with the latter, greater monetary savings are possible.

A signicant barrier to households adoption of dynamic pricing is the lack of exibility in their daily lives. Daily life is shaped in such a way that there are natural peaks in demand which are dicult for households to shift. Households who are perhaps most likely to choose dynamic pricing taris are those who have favourable consumption patterns and so are those who have less possibility to shift their demand [START_REF] Ericson | Households' self-selection of dynamic electricity taris[END_REF]. Households which have less favourable consumption patterns could benet from the introduction of automated responses to price signals in order to make saving energy simpler.

Finally, many studies have shown that there is a novelty factor at play in households which use energy monitors. Initially, households interact a great deal with their monitors in order to identify their normal level of consumption and any anomalous levels of consumption. However, this initial interest tends to disappear as households use their monitors less and less often. Keeping households engaged in their energy consumption management is key to achieving energy savings.

Introduction

Across the globe, countries are committing to increasing the share of production from renewable energy sources (RES) (United Nations, 2017). This transition is facilitated by the upgrading of the grid to a smarter, more ecient, more reliable network in which RES can be more easily integrated [START_REF] Gungor | Smart grid technologies: Communication technologies and standards[END_REF]. The movement from a fossil fuel dependent energy system to one based on production from RES requires a re-imagining of the way in which residential consumers interact with the electricity grid. Rather than supply following demand, as is the traditional operation of electricity markets, the intermittent nature of production from RES calls for a greater level of exibility in demand in order for demand to follow supply.

Previous demand reduction strategies have focused on increasing energy eciency 14 as a way to lower consumption. However, despite a 33% increase in energy eciency (European Environment Agency, 2016), residential energy consumption in the EU has increased by 9% between 1990 and 2013

15 . The increase in consumption can be associated to the rebound eect and the focus on energy eciency as end rather than a means to achieving energy demand reduction [START_REF] Maréchal | Getting a (sustainable) grip on energy consumption: The importance of household dynamics and `habitual practices[END_REF].

Another strategy for reducing electricity demand focuses on ways to incentivise residential consumers to modify their electricity consuming behaviour. The installation of smart meters as part of the wider smart grid infrastructure, provides two-way communication between the household and the energy company, via the use of an energy monitor, and allows residential consumers to take an active role in the management of their electricity consumption. In the traditional electricity market, the residential consumer is a passive user for whom electricity is invisible and readily available [START_REF] Darby | The eectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering[END_REF][START_REF] Burgess | Re-materialising energy use through transparent monitoring systems[END_REF][START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF]. In the new market, the residential electricity consumer is better informed and more conscious of how much they consume.

Consumers have long been aware of the need to turn o unused lights, to not leave appliances on standby, to unplug chargers, to name a few, however due to a lack of information on the impact of such actions on consumption, consumers have not necessarily had the impetus to act. With the technological improvements being made to the grid, consumers can receive appropriate incentives to lower their electricity 14 Such energy eciency measures include the installation of home insulation, and the upgrading of old appliances to more energy ecient appliances, among others. 15 In 2014, the European Environment Agency (2017a) report the rst decrease in total household energy consumption since 1990 of 4%. In all previous years, household energy consumption has increased compared to 1990 levels, peaking in 2010. consumption.

The incentives that are tested in pilot studies and eld experiments fall under two categories: monetary incentives, and non-monetary incentives. Monetary incentives include information on monetary expenditure on energy, and pricing strategies. Such incentives allow households to better connect their consumption with its costs and encourage them to modify their behaviour to lower their costs. In the case of pricing strategies such as dynamic pricing, increasing the cost of electricity should, according to standard economic theory, incentivise households to consume less.

Non-monetary incentives can be further categorised into personal feedback on consumption, and social feedback. Personal feedback refers to information on a household's own consumption, which can be delivered in real-time via an energy monitor, made accessible on an online portal, or delivered as a monthly bill. Personal feedback also includes advice on how to reduce energy consumption, whether this is general advice or advice tailored to a particular household. By providing consumers with electricity consumption information and informing them of the consequences of increased consumption, rational consumers will make the decision to lower their electricity demand [START_REF] Frederiks | Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour[END_REF]. In reality, individuals do not behave rationally and so providing a greater level of information and monetary incentives may not be sucient to encourage all consumers to modify their behaviour.

The sub-category of social feedback refers to comparisons of a household's consumption with that of other households. Such incentives are based on theories in behavioural economics and psychology which suggest that individuals use heuristics, or rules-of-thumb, to simplify complex decision making [START_REF] Samson | The Behavioral Economics Guide[END_REF]. In the current context, households are informed of their consumption compared to the average consumption of their neighbours and receive social approval of their behaviour when they consume less than their neighbours via the use of positive reinforcement [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF]. This chapter uses a meta-analysis approach to explore the strategies and public policies which employ such incentives in eld experiments and pilot studies in order to evaluate the eect of the dierent incentives on households' energy consumption behaviour. The ob jective is to combine the results of many studies to provide a better estimate of the true eect of the dierent incentive types on residential energy consumption.

The current meta-analysis adds to literature on meta-analyses which explore incentives for reducing household electricity consumption by including solely recent 2.1 INTRODUCTION 45 studies, those published from 2005 up to 2016, the time of data collection. By focusing on this time period, named the "Smart Grid Era" [START_REF] Mckerracher | Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays[END_REF], a more accurate estimate of the eect of an incentive on current electricity consumption is calculated. Additionally, the present analysis includes studies from both peer reviewed literature and utility and government reports in order to have as varied a database of studies as possible as the ob jectives of those carrying out the experiments are not the same. Academic researchers have a nal objective to publish their research, whereas those working for utilities and governments seek to determine the return on investment in incentives. It can be argued that experiments with larger sample sizes provide more robust results, often the utilities have the means to run large trials of dierent incentives. Finally, if only peer reviewed articles are taken into consideration, there may be an issue of bias in the selection of studies used for the meta-analysis. The issue of publication bias is assessed in this chapter.

Compared to previous meta-analyses, a ner level of detail regarding the dierent incentives is used. In particular, the incentives regarding social feedback are separated into those which provide comparative feedback alone and those which also include approval or disapproval of behaviour as the former has been shown to result in a boomerang eect where households who consume less than their neighbours increase their consumption [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF]. The nal added-value of the present meta-analysis is the inclusion of a greater level of study design variables, such as how households are recruited into the study, and how they are assigned to the treatment groups. Studies which recruit participants on an opt-in basis and do not randomise assignment to treatment groups may be subject to selection bias as those households who have favourable consumption patterns or are predisposed to lower energy consumption are more likely to take part [START_REF] Alexander | Dynamic pricing? Not so fast! A residential consumer perspective[END_REF][START_REF] Ericson | Households' self-selection of dynamic electricity taris[END_REF][START_REF] Buchanan | The question of energy reduction: The problem (s) with feedback[END_REF].

The following section describes the dierent incentives used in the experimental literature and sets out the hypotheses which will be tested. This is followed by a discussion of previous meta-analyses and reviews in Section 2.3. Section 2.4 describes the data collection method, the model used and the variables of interest.

Section 2.5 presents the results, Section 2.6 discusses the results and nally, Section 2.7 concludes.

Incentives for Lowering Electricity Consumption

The principal strategies employed to incentivise households to reduce their consumption can be separated into monetary and non-monetary incentives. In this section, the dierent strategies used in the literature are described and the hypotheses that will be tested are stated.

Monetary Incentives

Monetary incentives can be separated into one of two categories: electricity cost information and pricing strategies. Monetary information is included here as although it is not a direct monetary incentive, such incentives display information in monetary terms thus informing households of how much they are spending on electricity or how much they are saving. By providing households with information as to how much their electricity consumption costs (as opposed to information on the amount of electricity consumed) households can see the monetary benets of reducing their electricity consumption. In interviews with households participating in electricity conservation eld experiments, residents preferred to receive feedback in monetary terms as this is considered to be more relatable and more comparable than energy units (Hargreaves et al., 2010, Raw and[START_REF] Raw | Energy demand research project: Final analysis[END_REF].

Further, with the installation of smart meters in residential homes, a major technological barrier to the implementation of pricing strategies such as dynamic pricing has been lifted. Dynamic pricing provides consumers with economic incentives to reduce (increase) their electricity consumption during peak (o-peak) periods by better aligning the retail price of electricity with the wholesale price in order to maintain supply and demand balance in the electricity market [START_REF] Borenstein | Dynamic pricing, advanced metering, and demand response in electricity markets[END_REF]. Such pricing taris are eective at reducing demand during periods of high demand but are not necessarily eective at reducing overall demand (Torriti, 2012).

However, such strategies can have spillover eects when behaviour to reduce consumption during a peak period carries on into o-peak periods (Allcott, 2011a).

Such pricing strategies are therefore included in the present meta-analysis.

Hypothesis 1a: Pricing strategies reduce electricity demand.

INCENTIVES FOR LOWERING ELECTRICITY CONSUMPTION 47

Hypothesis 1b: Monetary information reduces electricity demand.

Non-monetary Incentives

Non-monetary strategies refer to those which provide households with more detailed information on their electricity consumption. In the experimental literature, this type of incentive can be categorised into personal feedback and social feedback.

Personal Feedback

Personal feedback provides households with data on their own electricity consumption with comparisons to consumption during a dierent period, such as the previous day, month, or year. Such feedback is received in a number of ways: through detailed electricity bills (see [START_REF] Carroll | Reducing household electricity demand through smart metering: The role of improved information about energy saving[END_REF][START_REF] Schleich | Eects of feedback on residential electricity demandndings from a eld trial in Austria[END_REF], online via a website or email (see [START_REF] Benders | New approaches for household energy conservationin search of personal household energy budgets and energy reduction options[END_REF], Ueno et al., 2006, Gleerup et al., 2010, Vassileva et al., 2012, Mizobuchi and Takeuchi, 2013[START_REF] Schleich | Eects of feedback on residential electricity demandndings from a eld trial in Austria[END_REF], Harries et al., 2013, Houde et al., 2013), in real-time via a monitor in the home (see Van Dam et al., 2010[START_REF] Grønhøj | Feedback on household electricity consumption: Learning and social inuence processes[END_REF][START_REF] Alahmad | A comparative study of three feedback devices for residential real-time energy monitoring[END_REF], Carroll et al., 2014[START_REF] Schultz | Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms[END_REF].

The provision of information on individual electricity consumption allows households to develop a greater awareness of their electricity consumption. By comparing their consumption from one period to another, such information allows households to see which behaviours result in increased consumption, so that they can follow their electricity consuming activities and determine when and how they consume the most electricity, and thus when and how to reduce their consumption.

Hypothesis 2a: Individual feedback on electricity consumption reduces electricity demand. Hypothesis 2b: Real-time feedback on electricity consumption reduces electricity demand.

A further type of personal feedback that households may receive is advice on how to lower their consumption tailored to their particular situation (both building and household characteristics) (see Allcott, 2011b[START_REF] Ayres | Evidence from two large eld experiments that peer comparison feedback can reduce residential energy usage[END_REF], Costa and Kahn, 2013) or more general electricity savings tips (see Ueno et al., 2006[START_REF] Mountain | Hydro One Networks Inc.: TOU Pricing Pilot Project Results[END_REF], Van Dam et al., 2010[START_REF] Raw | Energy demand research project: Final analysis[END_REF].

For example, Allcott (2011b) provides households with "action steps" based on their actual energy use and household characteristics, on how they can lower their energy consumption. In Mountain (2008, p.31), participating households are provided with a list of 10 generic energy savings tips including, "Run your dishwasher during opeak hours", and "Turn monitor o instead of using a screen saver while you are not using a computer".

Hypothesis 3a: Personalised advice on how to save electricity reduces electricity demand.

Hypothesis 3b: Electricity savings tips reduce electricity demand.

Social Feedback

Social feedback refers to information on others' electricity consumption, such as neighbours or similar households. It is an intervention which has been increasingly explored in recent experimental studies and uses the notions of social and injunctive norms. A social norm refers to descriptive consumption feedback of personal consumption compared to that of other households. An injunctive norm reinforces whether a particular behaviour is socially approved or disapproved of. In the case of electricity consumption, an injunctive norm conrms whether a household's consumption is pro-social, i.e. whether the household is a low-consuming household [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF].

These two types of social feedback have been separated in the present analysis as there is evidence that solely descriptive comparative feedback leads to a boomerang eect where low-consuming households increase their consumption, converging towards the average [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF], Allcott, 2011b[START_REF] Ayres | Evidence from two large eld experiments that peer comparison feedback can reduce residential energy usage[END_REF]. The inclusion of injunctive norms reinforces the idea that households who consume less than average are engaged in pro-social behaviour and so they do not increase their consumption [START_REF] Cialdini | A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places[END_REF].

Such methods of feedback may be successful via two mechanisms: by creating competition within a neighbourhood, or by highlighting the social cost of energy consumption.

Regarding the latter, such social feedback may create a situation of conditional cooperation where households consume more (less) after learning that 2.3 PREVIOUS META-ANALYSES 49 others are consuming more (less) (Allcott, 2011b). Such behaviour is observed in public goods (Fischbacher et al., 2001, Frey and[START_REF] Frey | Social comparisons and pro-social behavior: Testing "conditional cooperation" in a eld experiment[END_REF] and common pool resource games [START_REF] Ostrom | Governing the commons[END_REF], Velez et al., 2009).

Hypothesis 4a: Social norms do not have an eect on electricity demand. Hypothesis 4b: Injunctive norms reduce electricity demand.

Previous Meta-Analyses

The eect of dierent feedback types and monetary incentives on electricity consumption has been studied by researchers and utilities alike since the 1970s, and as such, several reviews and meta-analyses have been undertaken (see [START_REF] Darby | The eectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering[END_REF][START_REF] Ehrhardt-Martinez | Advanced metering initiatives and residential feedback programs: A meta-review for household electricity-saving opportunities[END_REF], Faruqui et al., 2010b[START_REF] Delmas | Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012[END_REF][START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF][START_REF] Mckerracher | Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays[END_REF]. Focusing on the eect of pricing strategies, [START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF] nd that the more dynamic the pricing strategies 17 , the greater the amount of peak energy conserved, all the more so when enabling technology is used.

Delmas et al. (2013) provide the most recent analysis of studies from 1975 to 2012

nding that tailored advice and energy conservation tips are most eective at reducing energy consumption. The authors compare the average treatment eects of more robust studies (those which include a control group, demographic information and control for weather changes) to studies with fewer controls. They nd that more robust studies report a lower reduction in energy consumption [START_REF] Delmas | Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012[END_REF].

McKerracher and Torriti ( 2013) also look at how study design aects results by considering how participants are recruited to participate in studies and how this aects their eort to reduce their consumption. They group studies into three categories by sample size, use of representative sampling, and whether participants opt-in or opt-out. They nd that studies which use larger samples, representative sampling and opt-out participation show a smaller reduction in energy consumption. The present analysis goes further in exploring the eect of dierent levels of controls by comparing studies which use all controls to those which use fewer, and also by 17 Real-time pricing strategies are considered to be more dynamic as the price faced by nal consumers uctuates in line with wholesale prices. Time-of-use taris are less dynamic as the prices are xed for certain hours. Critical peak pricing and peak-time rebates fall in-between the two. Hypothesis 7: Average eect of incentives on electricity consumption is lower in Smart Grid Era compared to previous eras.

Method

Data Collection

In order to nd appropriate articles for this analysis, the following databases were searched: ScienceDirect, EconLit, Web of Science, SpringerLink, Econpapers, SSRN, NBER, for the following sets of keywords using Boolean logic:

• Keywords concerning type of consumption: electricity consumption, electricity demand, electricity usage, energy consumption, energy demand, energy usage, • Keywords concerning the level of consumption: residential, household, consumer, and;

• Keywords concerning the study type: pilot, trial, experiment, eld. Across all databases, after eliminating doubles, the search terms resulted in a list of 1,490 studies. The titles and abstracts of these studies were reviewed. In addition to the database search, the reference lists and the lists of citing articles for each selected article, as well as previous meta-analyses, were scanned for further relevant studies. This procedure resulted in a selection of 84 articles and 27 reports on the topic of using incentives to reduce residential electricity consumption. Each article and report was read and a nal selection of 24 articles and 15 reports were kept for the analysis.

The nal list of articles, those in which the treatment eect is reported as the change in electricity consumption of treated households compared to either a baseline or control group and details on why 72 papers were excluded can be found in Appendix A 18 . A coding protocol was implemented for the nal selection of 39 studies which involved an experimentation of the above incentives. The majority of articles came from economics, business, and energy journals. The reports are from utility and government websites as well as from consulting companies.

Figure 2.1 displays the geographical distribution of included studies. The ma jority of studies come from the United Kingdom and North America as these regions have been at the forefront of eld experiments and pilot studies on incentives to reduce electricity consumption. In addition, this could also be explained by the fact that one of the inclusion criteria is that the paper be written in English and that 18 The main reasons for excluding papers are: a dierent treatment eect measure was used (peak demand reduction, appliance level data), sample is non-residential, or studies were based on simulations or laboratory experiments.

METHOD 53

experiments carried out by national utilities and governments are likely to be written in the native language. This restriction could result in publication bias which will be assessed below.

Model and Estimation Method

Meta-regression analysis is a quantitative method of systematically analysing the results of empirical studies with a common objective. It goes beyond a literature review in that it allows the analyst to calculate a mean treatment eect across studies by discovering which variables lead to dierences in experiments which study the same treatment eect (Stanley andJarrell, 1989, Nelson and[START_REF] Nelson | The use (and abuse) of meta-analysis in environmental and natural resource economics: an assessment[END_REF]. Metaanalyses are used to estimate a more precise estimate of the true eect of a treatment than any single study can do alone [START_REF] Borenstein | Introduction to Meta-Analysis[END_REF].

Using notation from Nelson and Kennedy (2009, p.8), the following meta-regression model is estimated:

βi = α 0 + α 1 x i1 + ... + α K x iK + e i (2.1)
where (x i1 , ..., x iK ) is a vector of study characteristics, (α 1 , ..., α K ) are unknown parameters to be estimated, and e i is the normally distributed sampling-estimation error with zero mean and variance σ 2 i , ∀i = 1, ..., N .

This model can be estimated by ordinary least squares (OLS). However, given that in the sample of primary studies, there are treatment eects from studies of varied sample sizes, the method of estimation by OLS may lead to inecient and biased estimates. This bias can be mitigated by using White or Huber-White robust standard errors [START_REF] Sebri | A meta-analysis of residential water demand studies[END_REF].

Furthermore, the standard OLS approach may not be appropriate due to issues highlighted by [START_REF] Nelson | The use (and abuse) of meta-analysis in environmental and natural resource economics: an assessment[END_REF] and [START_REF] Stanley | Meta-regression analysis in economics and business[END_REF] which are prevalent in meta regression analysis such as publication bias, heterogeneity, heteroscedasticity and non-independence. Publication bias is an issue across much social science research when results that show a signicant eect are favoured for publication over those which do not. Heterogeneity is present due to either differences in the experimental design and methods used in the primary studies, or to dierences such as geographical location and historical context. The issue of heteroscedasticity arises from the inclusion of primary studies with dierent sample sizes, and nally, non-independence occurs when more than one observation is used from a single primary study. Each of these issues are a concern in the present meta-analysis and steps are taken to reduce their impact on the results as discussed below.

Other approaches used in meta-regression analysis to estimate the model in eq. (2.1) include using xed-or random-eects estimation (FEE and REE respectively)

19 .

FEE weights each treatment eect estimate by its precision squared, or the inverse of its variance. Furthermore, FEE assumes that all primary observations of treatment eects are drawn from the same population [START_REF] Stanley | Meta-regression analysis in economics and business[END_REF].

In the present sample, treatment eects are taken from primary studies from different countries which thus have dierent samples. Given such heterogeneity in the sample, [START_REF] Stanley | Meta-regression analysis in economics and business[END_REF] suggest that the REE is a technically more appropriate estimator as the weight used accounts for this heterogeneity.

In further research, [START_REF] Stanley | Neither xed nor random: Weighted least squares meta-analysis[END_REF] nd that the weighted least squares (WLS) estimator is preferable to both FEE and REE. The authors nd that under heterogeneity, WLS outperforms FEE, and in the case of publication or small sample bias, WLS does better than REE. Given the characteristics of the data used in the present meta-analysis, several approaches are taken to overcome the potential issues of publication bias, heterogeneity, heteroscedasticity, and non-independence.

Firstly, to limit issues of publication bias, both peer reviewed articles and reports from the grey literature are included in this analysis. In addition, after a description of the dataset and before any models are estimated, the selection of primary studies used in the meta-analysis is assessed for publication bias. This analysis leads to the conclusion that publication bias is present up to a factor of 2 and that using the 19 These terms refer to estimators used in meta-analysis and not to those used in panel data econometrics [START_REF] Stanley | Meta-regression analysis in economics and business[END_REF].

METHOD 55

sample size as a weight mitigates this problem.

Second, to tackle the sources of heterogeneity, a set of binary variables describing the study characteristics which are potential sources of heterogeneity are included in the regression (section 2.4.3 describes the variables used in the analysis), and the temporal context has been limited to primary studies published since 2005 representing the Smart-Grid Era [START_REF] Mckerracher | Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays[END_REF].

Next, to account for heteroscedasticity, the model in eq. (2.1) is estimated by WLS.

The preferred weight is the inverse standard error of the treatment eect, however, given that these are not always reported in the primary studies, a common approach is to proxy the standard error using the sample size [START_REF] Nelson | The use (and abuse) of meta-analysis in environmental and natural resource economics: an assessment[END_REF]Kennedy, 2009, Stanley and[START_REF] Stanley | Meta-regression analysis in economics and business[END_REF]. As such, the square root of the sample size is used as weights for the estimation following [START_REF] Delmas | Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012[END_REF], [START_REF] Sebri | A meta-analysis of residential water demand studies[END_REF] and Van Houtven et al. ( 2017) such that experiments with a larger sample are given more weight. Experiments with larger samples are considered to be more representative of the population and so the estimated eect is a better estimate of the true eect.

Finally, to address the non-independence of several treatment eects coming from the same primary study, the estimated standard errors are clustered by primary study.

Variables Dependent Variable

The variable of interest is the treatment eect reported in primary studies as the percentage change in electricity consumption as a result of the implementation of an incentive. When a control group is present in an experiment, the percentage change relative to the control group is used. If no control group is present, the percentage change relative to the baseline is used 20 . In the following analysis the dependent variable is referred to as the Average Treatment Eect (ATE).

Independent Variables

The independent variables refer to the type of intervention tested in the primary study and the controls used. As discussed above, there are pricing strategies: households receive a nancial reward which is directly linked to their electricity conservation eort. For example, changing prices are used to inuence consumers electricity 20 Presence of a control group is controlled for in the analysis to come.

CHAPTER 2: INCENTIVISING HOUSEHOLDS

consumption by aligning the retail price of electricity with the wholesale price. Or participating households are given feedback on how much their electricity consumption costs (monetary information).

Non-monetary strategies are separated into those which provide personal feedback, and those which provide social feedback of others' electricity consumption. Individual feedback refers to interventions where participants receive information on their current and previous consumption in energy units. This refers to consumption information that is in addition to the standard electricity bill, be it a more detailed bill, or consumption information on a website. Real-time feedback refers to the same type of information which is delivered in real-time via an energy monitor 21 . Households can also receive personalised advice specic to their living situation on how to lower their electricity consumption, or generic electricity savings tips.

Studies which provide social feedback are separated into those which provide social norms feedback: descriptive feedback of personal consumption compared to that of other households, and injunctive norms feedback which also provides social approval or disapproval of a household's consumption behaviour.

Finally, a set of control variables are included in the analysis: control group: presence of a control group; weather controls: whether weather is controlled for; demographic controls: the collection of demographic information; random: households are assigned randomly to control and treatment groups as opposed to choosing an intervention; opt-in recruitment: households choose to participate in the study; and duration: duration of study. These control variables are included in order to capture the heterogeneity between the dierent experiments. Furthermore, studies which include such variables control for changes in behaviour which cannot be explained by the use of an incentive alone. 21 Only data that are received via an IHD or monitor are considered to be real-time feedback in the present analysis. Real-time data are made available to households via websites (see [START_REF] Houde | Real-time feedback and electricity consumption: A eld experiment assessing the potential for savings and persistence[END_REF], however, the data are not accessible to consumers in real-time. They must log-on to the site in order to access the information. The incentives used in such experiments are included in individual feedback.

RESULTS

57

Results

Descriptive Statistics

The analysis covers 105 observations from 39 unique papers giving, on average, 2.7 observations per paper. In meta-analysis it is preferable to limit the analysis to one observation per study in order to reduce correlation between studies [START_REF] Nelson | The use (and abuse) of meta-analysis in environmental and natural resource economics: an assessment[END_REF]. However, given that some reports describe the results of more than one experiment, and also, due to the design of the sample experiments, doing so would greatly limit the number of observable treatment eects. To account for potential heterogeneity due to several observations being taken from one study, in the following analysis, standard errors are clustered by study.

Table 2.2 provides descriptive statistics of the independent and dependent variables for the full sample. Within the sample of studies selected for this analysis, individual feedback is the most experimented treatment representing 70% of the observations and 77% of the studies. Compared with previous meta-analysis, the share of studies involving a form of social feedback (social norms or injunctive norms) has increased. The injunctive norms treatment represents 27% and 26% of the observations and studies, respectively.

Concerning the design of the primary studies, the ma jority use a control group for comparison and control for demographic dierences in the sample population, 90% and 85% respectively. Fewer studies (59%) control for variations in the weather. 68% of observations randomly assign sub jects to a treatment but this is not a practice adopted in all studies, 49%. Opt-in recruitment is the more common method of recruitment, 67% of observations and 69% of studies.

Average Treatment Eects

Table 2.2 also provides both a non-weighted and weighted ATE by incentive. The ATE are weighted using study sample size as frequency weights following Schmidt and Hunter (2014) which gives more weight to studies with larger samples. The ATE across all incentives is 3.37% reduction in consumption. The weighted ATE takes into consideration the diering sample sizes in each study and equates to a 1.85% reduction in electricity consumption. This means that, on average, an incentive in a typical electricity conservation study will result in electricity savings of slightly less than 2%. In the sample of studies selected, the eect of incentives on electricity consumption ranges from an 22.2% reduction [START_REF] Kendel | The diusion of smart meters in France: A discussion of the empirical evidence and the implications for smart cities[END_REF] to a 13.69% increase (Torriti, 2012).

From table 2.2, it can be seen that real-time feedback and monetary information have the greatest eects on electricity consumption with a weighted ATE of 2.89% and 2.86%, respectively, indicating a reduction in consumption. Pricing strategies have the smallest eect on overall electricity consumption with a weighted ATE showing a reduction in consumption of 0.99%. A negative percentage indicates a reduction in electricity consumption, whereas a positive percentage change indicates an increase in electricity consumption. Table 2.5 provides the correlations between variables. There are no strong correlations between treatment eect and the treatment variables as treatment choice is typically random. Strong positive correlations can be seen between both the personalised feedback and the social norm and injunctive norm treatments, and strong negative correlation with opt-in recruitment as for these treatments, participating households took part in the study by default and opted-out if they did not want to take part. These studies are typically large-scale experiments led by utilities which have the means to carry out such studies (Allcott, 2011b[START_REF] Ayres | Evidence from two large eld experiments that peer comparison feedback can reduce residential energy usage[END_REF].

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59

Variables

(2) Presence of control group (=1) Collects socio-demographic data (=1) Random assignment to treatment (=1) Sample selection method (opt-in=1) The above graphical analysis indicates that the treatment eects reported in primary 2.5 RESULTS

(3) (4) (5) (6) (7) (8) (9) (10)

67

studies may be particularly aected by the presence of a control group, treatment assignment and sample selection methods.

In studies without a control group, the change in electricity consumption is compared within the same group of households between the treatment period and a baseline period. Whereas in studies with a control group, the change in consumption is compared both within the same group of households and between groups of households whose consumption is measured during the treatment and baseline periods; a dierence-in-dierence method. The latter studies allow researchers to account for additional factors which aect electricity consumption during the course of the study and appear to show a lesser treatment eect to the former.

Households who choose to participate in a study on electricity consumption may be particularly motivated to reduce their consumption. Those who participate in studies on an opt-out basis (which is arguably more representative of a national roll-out of such interventions) achieve much smaller levels of electricity reduction.

When households are randomly assigned to treatment groups, they achieve smaller electricity savings than when they are not. This would suggest that a tailored approach to treatment design corresponding to households existing motivations to change their electricity consumption is pertinent. Such motivations maybe monetary, environmental, or other.

The inclusion of weather controls and the collection of socio-demographic data does not appear to have a strong impact on the reported treatment eects.

The impact of these study design choices on the treatment eects will be further analysed in section 2.5.4.

Publication Bias Analysis

According to [START_REF] Card | Time-series minimum-wage studies: a metaanalysis[END_REF] there are three potential sources of publication bias in economic research: (1) a predisposition to accept studies which are consistent with the conventional view;

(2) an inclination to report models based on the presence of a conventionally expected results;

(3) a tendency to publish only statistically signicant results.

Potential publication bias in the sample of primary studies used in this meta-analysis can be analysed graphically using a funnel plot, as shown in g. 2.9. These graphs plot treatment eects against a measure of precision, such as the inverse standard error of the treatment eect or the square root of the sample size of the treatment The funnel plot in g. 2.9 plots treatment eect against the square root of sample size. The plot shows that the majority of treatments result in a reduction of electricity consumption. No studies from the grey literature report an increase in electricity consumption and there are more observations from peer-reviewed articles dispersed at the bottom of the funnel. The somewhat asymmetrical nature of the funnel plot suggests that there may be an issue of publication bias in the present sample due to results not being included in the analysis. [START_REF] Stanley | Could it be better to discard 90% of the data? A statistical paradox[END_REF] suggest that publication bias may be reduced and scientic inference improved by averaging the treatment eects of the top 10% of the funnel as these are the most precise estimates. Table 2.6 shows the non-weighted and weighted ATE for the full sample and the top decile according to the weight used 22 . 22 Where the inverse standard error is used as a weight, there are only 42 observations in the sample as the standard error is not available for all studies. This sub-sample is used as as a robustness check for issues of publication bias as the standard error is the preferred weight.

RESULTS

69

Comparing the ATE for the top 10% of the funnel and the full sample suggests that, on average, the eect of incentives on electricity consumption is overestimated by a factor of 2. When sample size is accounted for, as the weighted ATE shows, the distortion due to publication bias is greatly reduced and the dierence is not signicant (p = 0.8641).

As the inverse standard error is the preferred measure of precision, the non-weighted and weighted ATE of the 42 observations for which standard errors are reported or can be constructed are also given. The distortion due to publication bias is smaller for this subset of the sample when comparing ATE between the top 10% and the full sample (a factor of 1.8), and the dierence in values is not signicant (p > 0.1) once sample size is accounted for.

ATE (%)

Weighted ATE (%) The above correction for publication bias suggests that if present, any bias is small and not statistically signicant once sample sizes have been accounted for in calculating weighted average treatment eects. Nevertheless, it is prudent to test for the existence of such bias.

In the presence of publication bias, treatment eects are positively correlated with their standard errors [START_REF] Stanley | Meta-regression analysis in economics and business[END_REF]. This suggests that the size of an eect will depend on its standard error:

treatment_ef f ect i = β 0 + β 1 SE i + i (2.2)
To account for dierences in the primary studies, the equation is weighted by a measure of precision, ideally the inverse of its standard error [START_REF] Stanley | Could it be better to discard 90% of the data? A statistical paradox[END_REF]:

t i = β 0 (1/SE i ) + β 1 + v i (2.3)
where t i is the t-statistic of the treatment eect. As standard errors are not available for all observations, this equation is also constructed using the square root of sample size as the measure of precision:

CHAPTER 2: INCENTIVISING HOUSEHOLDS

treatment_ef f ect i /sample_size 0.5 i = β 0 (1/sample_size i ) 0.5 + β 1 + v i . (2.4)
In the presence of publication bias, treatment eects are positively correlated with their standard errors, and negatively correlated with sample sizes, as standard errors are inverse functions of sample size (Stanley andDoucouliagos, 2012, Schmidt and[START_REF] Schmidt | Methods of meta-analysis: Correcting error and bias in research ndings[END_REF]. Estimates of β 0 from eqs. (2.3) and (2.4) are an alternative correction of publication bias [START_REF] Stanley | Meta-regression analysis in economics and business[END_REF]. Table 2.7 shows the results of the estimations of the models in eqs. (2.3) and (2.4) for the sub sample of 42 studies for which the standard error is present and for the full sample using the square root of sample size as a proxy measure of precision.

Testing H 0 : β 1 = 0 is a test of whether publication bias is present, the funnel asymmetry test. If the coecient is signicantly dierent from zero then there is publication bias. In the rst specication (eq. ( 2.3)), the null hypothesis cannot be rejected. In the second and third specications when the sample size is used as a weight for both the sub sample and the full sample, the null hypothesis is rejected (p-values = 0.076 and 0.096, respectively). There is thus marginal evidence of publication bias in the full sample using the sample size as a proxy for provision.

A second test, the precision eect test, of whether there is a genuine empirical eect can be tested: H 0 : β 0 = 0. In both models, the null hypothesis is rejected, implying that there is a genuine empirical eect which merits further analysis.

Graphically, the funnel plot suggests that there is a potential issue of publication bias. When comparing the ATE of the full sample to the top 10% of the funnel, this bias is of a factor 2. Testing for publication bias suggests that publication bias is present in the full sample. However, accounting for sample sizes reduces the bias to a small and statistically insignicant amount. Therefore, a WLS estimation will be used to mitigate publication bias and to account for heteroscedasticity in the sample of primary observations, as discussed in Section 2.4.2.

Eects of Individual Incentives

The analysis of publication bias has shown such bias to be mitigated by taking sample sizes into consideration. The square root of sample size is therefore used as a weight in the following section in which the eects of the dierent incentives on 2.5 RESULTS

71

(

73

savings encouraged by the higher peak price [START_REF] Geelen | Empowering the end-user in smart grids: Recommendations for the design of products and services[END_REF][START_REF] Khan | Load forecasting, dynamic pricing and dsm in smart grid: A review[END_REF]). An explanation as to why monetary information does not have the predicted eect is that the possible savings are too small to be motivating [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF], or that households expenditure on electricity is small relative to their income (Faruqui et al., 2010b[START_REF] Schleich | Eects of feedback on residential electricity demandndings from a eld trial in Austria[END_REF].

In both the personal feedback and the full specication individual feedback has a signicant negative eect indicating a reduction in electricity consumption of 3-4 percentage points. When such feedback is delivered in real-time no additional signicant eects on electricity consumption are found. This could indicate that the eectiveness of feedback is captured in the individual feedback variable, or that real-time feedback reinforces the fact that individual actions to save energy do not amount to large savings [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF]. The use of savings tips indicates an increase in consumption of 4 percentage points. Generic advice on how to save electricity appears to not be eective at reducing consumption.

One reason for this is that householders generally know what they should do to reduce their consumption and that reminding them of such behaviours serves to crowd out any intrinsic motivation they had to do so.

In this meta-analysis, social feedback is separated into social norms and injunctive norms. Specication 3 shows that both types of social feedback result in a reduction in consumption of 4-5 percentage points. This provides new evidence of the eectiveness of such feedback compared to ndings in [START_REF] Delmas | Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012[END_REF] who found no signicant eect of such feedback. Since their meta-analysis, there has been an increase in large-scale studies of such incentives.

Across the ve specications, the 10% of studies which do not use a control group show a greater increase in electricity reduction of between 7.3 and 11.2 percentage points compared to those that do use a control group. This suggests that when electricity savings are calculated compared to a baseline of the same group, they may be overestimated. Duration of the study has a small signicant positive eect on electricity consumption in specications 1-3, and 5. This adds to the previous evidence that electricity conservation experiments are subject to attrition of the eects of incentives over time [START_REF] Delmas | Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012[END_REF]. The positive coecient on peer reviewed suggests that peer reviewed experiments are more conservative in their estimations of the eects of an incentive on electricity consumption than those from the grey literature.

Discussion

The meta-analysis presented in this paper provides a comparison of dierent incentives used in the experimental literature to incentivise residential consumers to lower their electricity demand. Contrary to previous analyses, it provides a comparison of contemporary experimental studies by focusing on studies from 2005 onwards, the "Smart Grid Era". Previous analyses risk overstating the potential of dierent incentives by including older studies [START_REF] Mckerracher | Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays[END_REF]. By restricting the time frame, the intention is to limit the analysis to studies with similar available energy monitoring technology, in order to avoid exacerbating issues of heterogeneity due to diering temporal contexts.

In order to avoid issues of publication bias, the present meta-analysis adopted a wide search method to collect data from both peer-reviewed and grey literature studies.

To verify the extent of the publication bias issue in the sample of studies used, a detailed analysis of the potential bias was carried out as a graphical examination of the potential publication bias suggested that this may be an issue. However, estimations of the amount of bias and tests of its presence have shown it to not be a signicant issue for the present sample of studies once sample size is accounted for. Furthermore, the precision eect test shows that there is a genuine underlying eect of interest.

In addition, the experimentation of new methods of encouraging households to lower their electricity demand are included in the present meta-analysis, namely the use of injunctive norms in addition to social norms. Furthermore, a greater level of study design controls are included to control for heterogeneity between studies. This provides an opportunity to disentangle the eects of such incentives and to carry out a more extensive comparison of the eects of dierent study methods on residential electricity demand.

The analysis has shown that on average and before taking into consideration primary study sample size, the dierent incentives result in a reduction in electricity consumption ranging from 2.22 to 4.69%. Across all incentives, a study on the effect of an incentive on electricity consumption can be expected to show a 3.37% reduction in electricity consumption. This eect is lower than reported in previous meta-analyses, however it is in line with the conclusion of [START_REF] Mckerracher | Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays[END_REF] that there is a downward trend in the size of conservation eects. Accounting for sample sizes, as the publication bias analysis suggests, a study will show on 2.6 DISCUSSION 75 average a reduction in consumption of 1.85%.

In terms of the level of controls used in the studies, compared to previous metaanalyses, there has been an increase in the number of studies using control groups, and controlling for demographic variables and weather variations which leads to lower, but arguably more reliable, estimates of the eect of incentives on electricity consumption. The two diering levels of study controls show a dierence in estimated electricity savings of 1.54 percentage points: more robust studies with more controls result in a lower average reduction in consumption of 2.17% compared to 3.71% for less robust studies. If such incentives are to be implemented at a national level, these more robust studies may be a better reection of the level of electricity savings that may be achieved. A graphical analysis showed that in studies in which households are randomly assigned to a treatment there is a smaller spread in treatment eects.

In studies in which households choose to participate, there is a greater spread of treatment eects. These households may have motivations to take part in electricity consumption eld experiments and pilot studies that are not necessarily accounted for in the experiment. These participants may be predisposed to make a greater eort than if the incentive were to be implemented at a national level [START_REF] Alexander | Dynamic pricing? Not so fast! A residential consumer perspective[END_REF][START_REF] Ericson | Households' self-selection of dynamic electricity taris[END_REF]. This implies that caution should be exercised when viewing the results of experiments in which participants self-select into a treatment.

While at the descriptive level, all incentives result in a reduction of residential electricity consumption on average, the econometric analysis shows that only certain incentives have a signicant eect once other variables are controlled for. Monetary-based incentives (pricing strategies and monetary information) tend to result in an increase in residential electricity consumption. Hypotheses 1a and 1b can be rejected for the present sample.

Individual feedback has a signicant eect at reducing electricity consumption, however, there is no signicant eect of real-time feedback. Real-time feedback is eective at reducing consumption, as shown in the descriptive analysis, however when other informational feedback and study design variables are controlled for, the eect is not signicant. Given that real-time feedback is often proposed in combination with other incentives, it may be dicult to isolate the eect of real-time feedback alone. Concerning the two types of guidance that can be given to households, personalised advice does not have a signicant eect on electricity consumption. However, savings tips are shown to increase electricity consumption. There is evidence to support hypothesis 2a, and to reject hypothesis 3b. There is inconclusive evidence to neither support nor reject hypotheses 2b and 3a.

Next, it was hypothesised that the use of social norms would have a signicant reduction eect on residential electricity consumption only in the presence of injunctive norms. Both the use of descriptive social norms and injunctive norms have a signicant negative eect on electricity consumption when other incentives are controlled for. There is evidence to refute hypothesis 4a, social norms alone do have the desired eect of reducing electricity demand. There is also evidence to support hypothesis 4b.

Hypothesis 5 refers to the eect of incentives with respect to sample size: as sample size increases, the eect of an incentive on electricity consumption falls. As g. 2.9

shows, there is greater variation in ATE when sample sizes are smaller, whereas with larger samples, the treatment eect is smaller. This provides some evidence to support hypothesis 5.

Similarly to previous meta-analyses, the sample set is separated by number of controls used. More robust studies are those which are deemed to be more representative of the population (use random treatment assignment and an opt-out method of recruitment) and which include greater controls of potential heterogeneity (use a control group, account for weather variation and collect socio-demographic data).

The more robust studies show a statistically signicant smaller ATE than the other studies. This provides evidence to support hypothesis 6.

Finally, hypothesis 7 refers to the downward trend in ATE over time. Previous metaanalyses found that incentives reduced electricity consumption by upwards of 6.4%.

The present analysis found an overall ATE of 3.37%, or a weighted ATE of 1.85%. This lends support to the hypothesis that the incentives used have a smaller eect on electricity consumption in the Smart Grid Era compared to the eras identied in previous meta-analyses.

Conclusion

This paper has provided an analysis of the eects of dierent incentives used in recent residential electricity consumption studies across the elds of economics, psychology, marketing and building research. This meta-analysis provides the most up to date assessment of recent experimental literature including newer methods of 2.7 CONCLUSION 77 incentivising consumers to lower their energy consumption.

On average, an incentive designed to reduce household electricity consumption will result in a reduction in consumption of 3.37%. Accounting for the dierent sized samples used in the individual studies, an incentive can be expected to reduce electricity consumption by 1.85%. This result indicates that electricity consumption reductions can be attained by incentivising households to make behavioural changes to reduce their electricity consumption.

In particular, less costly incentives such as informing households of their individual consumption 25 , or of the average consumption in their neighbourhood shows a greater level of reduction on electricity consumption compared to more costly incentives such as pricing strategies. This has important policy implications given that the latter incentive is often not readily accepted by consumers [START_REF] Alexander | Dynamic pricing? Not so fast! A residential consumer perspective[END_REF].

This indicates that lower-cost incentives may be sucient and that there is not necessarily a need to use costly pricing strategies when the ob jective is to reduce overall electricity consumption. Much focus in recent years has been on injunctive norm based incentives. One conclusion of this analysis is that descriptive social norms may be sucient on their own. The present meta-analysis faces certain limits. To begin with, the meta-analysis is as reliable as the primary studies included in the dataset. Certain primary studies found treatment eects which were much larger, in both the direction of reducing and of consuming more electricity. Such results should not necessarily be excluded from the dataset as they meet the criteria set out in section 2.4.1, however, they may inuence the ndings and conclusions of the analysis. Secondly, few experiments test the eect of a single incentive on electricity consumption as they often combine several incentive types. This makes it dicult to separate the eects of individual incentives on electricity consumption due to confounding eects. A third limit concerns the dierences in the design of the various studies that are not accounted for in the present study. For example, the composition of the samples in the primary studies is not necessarily identical: participants may have previously participated in similar studies, or the study may focus on a particular type of household.

For future research, this analysis highlights that it is important to undertake eld studies which are methodologically rigorous; studies which include control groups, control for demographic information and variations in weather. Including a control group and controlling for the weather provides a better estimate of the eect of an 78 CHAPTER 2: INCENTIVISING HOUSEHOLDS incentive as there may other factors which aect electricity consumption in addition to the incentive used. Furthermore, when individuals self-select into studies, and/or treatments, the reported eects of incentives are of a greater reduction in electricity consumption than when an opt-out or random treatment assignment approach is used. This provides support for the idea that a national roll-out of a particular incentive may not be the best approach as greater electricity savings can be attained if households are able to pick an incentive which is appropriate to them. A onesize-ts-all may not be the most eective. However, a tailored approach may not be feasible.

More research needs to be done in this area to determine whether households are able to pick appropriate incentives, and on the eect of tailored incentives on electricity consumption.

Abstract

The aim of demand response is to encourage consumers to be more exible with their energy consumption during peak periods. Using a contextualised common pool resource (CPR) framework, energy consumption choices are studied. Subjects choose how much to consume by deciding whether to use ve dierent appliances during 10 periods. The total consumption of these activities is the CPR contribution, and payos depend on personal consumption and the amount consumed by the group. In the nudge treatment, subjects are nudged towards the socially optimal level of consumption by the use of a happy or sad face if they are underconsuming or overconsuming. In the price treatment, a price is set to incentivise subjects to choose the level of consumption observed in the nudge treatment. The objective is to quantify the nudge via an equivalent price. Across all 10 periods, consumption is signicantly lower in treatment groups compared to control groups. There are implications for policy makers as the nudge treatment performs as well as an equivalent price without the implied loss of welfare, and is understood and integrated into subjects' decision making quicker than an equivalent price. However, the nudge reinforces existing consumption behaviour as those who over consume continue to over consume.

Introduction

After a discussion of households experiences with smart meters and dynamic pricing for both energy exibility and overall demand reduction, and an analysis of incentives used to encourage households to lower their electricity consumption, this chapter focuses on two particular incentives and their eectiveness for lowering peak energy demand. The meta-analysis has shown that monetary incentives are perhaps not best used to incentivise overall demand reduction; they are more eective at encouraging exibility [START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF] and that non-monetary incentives, such as social feedback, were shown to have a signicant eect at reducing overall electricity consumption. In this chapter, these two types of incentive are put to the test in a laboratory-based, stylised energy consumption game.

The main research hypothesis is that the management of end-use electricity consumption during peak periods is similar to the management of agents who use a common pool resource (CPR). Here, the CPR is the limited renewable energy sources which are sustained so long as electricity consumption does not exceed power capacities. Such an approach provides the possibility of exploring, in an experimental setting, the impacts of demand response tools on consumers' behaviour when they are placed in the social dilemma resulting from the need to balance supply and demand, while maintaining their desired level of consumption and comfort. Following [START_REF] Ostrom | Governing the commons[END_REF], and more recently [START_REF] Melville | The electric commons: A qualitative study of community accountability[END_REF] in the energy eld, this dilemma is the conict between the personal interest of consuming electricity without constraint, and the collective interest of maintaining power supply reliability.

This introduction provides the background on the impacts of increasing the share of renewable energy on power supply reliability, and consequently on the need for demand response programmes based on monetary, and non-monetary incentives or nudges. The principal objective of the experiment is to use a contextualised CPR game to explore the eect of nudges and peak prices on sub jects' consumption choices compared to when no policies are used, and to give a monetary value to the nudge.

The secondary objective is to compare sub jects' choice of which appliances to use and which electricity-consuming activities to take part in when faced with a need to reduce their demand. This section sets out the theory behind the CPR game used in the experiment, and Section 3.2 describes the experimental design. Section 3.3 presents and discusses the results, and Section 3.4 concludes and provides policy recommendations.

CHAPTER 3: DEMAND RESPONSE AS A CPR GAME

Renewable Energy and Demand Response Programmes

In the last two decades, there has been an increase in the share of renewable energy and in the number of distributed power generators (Renewable Energy Policy Network for the 21st Century, 2016). This calls for new strategies in the management of the electricity grid in order to maintain power supply reliability and quality, particularly at times when intermittent energy sources constitute a signicant part of total system capacity. This need is all the more important given that the European Union has set ambitious targets to reduce greenhouse emissions and to increase the share of renewable energy sources in the production mix by 2030 (European Commission, 2014a).

Reliable management of the electricity system requires a perfect balance between supply and demand in real time. Given the increase in renewable energy sources, this balance is harder to achieve as supply and demand levels can change rapidly and unexpectedly, in particular on high demand days and when natural conditions are unfavourable for the use of renewable energy sources. Moreover, the power generation infrastructure is highly capital intensive, such that demand side management may be one of the cheaper tools available for balancing supply and demand. Given the greater diculty of producing peak electricity, there is a need to have a more exible residential energy demand, particularly during peak periods. Demand response programmes, dened as the changes in electricity usage by end-use consumers from their normal consumption patterns in response to signals, are the main tool used or experimented in the management of the electricity grid [START_REF] Balijepalli | Review of demand response under smart grid paradigm[END_REF].

Current methods used to incentivise households to lower their energy demand include dynamic tari structures, informational incentives, or nudge-based incentives.

Under certain tari structures consumers face nancial incentives to reduce their energy demand as during certain hours or on days when demand is particularly high, the price of electricity is greater than at o-peak times. This increased price is designed to induce lower electricity use at times with high wholesale market prices or when system reliability is jeopardised [START_REF] Borenstein | Dynamic pricing, advanced metering, and demand response in electricity markets[END_REF], Faruqui et al., 2010b[START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Raw | Energy demand research project: Final analysis[END_REF]. Informational incentives involve providing the household with increased information on their consumption to allow them to make a more informed decision. Such incentives include information on how personal consumption compares from one day to another, or on a weekly or a monthly basis [START_REF] Benders | New approaches for household energy conservationin search of personal household energy budgets and energy reduction options[END_REF], Houde et al., 2013, Mizobuchi and Takeuchi, 3.1 INTRODUCTION 83 2013[START_REF] Schleich | Eects of feedback on residential electricity demandndings from a eld trial in Austria[END_REF], Carroll et al., 2014[START_REF] Schultz | Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms[END_REF]. Nudge based incentives go beyond simple information by changing the way the information is presented in order to exploit behavioural biases [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF][START_REF] Thaler | Nudge: Improving decisions about health, wealth, and happiness[END_REF], Allcott, 2011b[START_REF] Ayres | Evidence from two large eld experiments that peer comparison feedback can reduce residential energy usage[END_REF].

This experiment is particularly related to laboratory experiments which study the eect of monetary and non-monetary incentives, or nudges, on behaviour. These areas of literature are discussed below.

Monetary Incentives and Nudges in the Laboratory

In CPR laboratory experiments, monetary incentives are often modelled as taxes.

These are a rst best policy for managing behaviours which result in negative externalities [START_REF] Ballard | The marginal eciency eects of taxes and subsidies in the presence of externalities: A computational general equilibrium approach[END_REF]. In experimental games with negative externalities, studies have shown that taxes result in subjects performing at near optimal levels [START_REF] Plott | Externalities and corrective policies in experimental markets[END_REF][START_REF] Cochard | Eciency of nonpoint source pollution instruments: An experimental study[END_REF]. Yet, taxes are seldom accepted by the public. This can be explained by a preference for the status quo [START_REF] Cherry | The impact of trial runs on the acceptability of environmental taxes: Experimental evidence[END_REF], by tax aversion: individuals feel that negative incentives, such as taxes, impede their free-will and are controlling, and by framing: acceptance for taxes increases when the mechanism behind them is explained [START_REF] Kallbekken | Do you not like Pigou, or do you not understand him? Tax aversion and revenue recycling in the lab[END_REF][START_REF] Heres | Understanding public support for externality-correcting taxes and subsidies: A lab experiment[END_REF].

Given that monetary interventions such as taxes, and dynamic pricing in the context of electricity consumption, can be politically dicult to implement [START_REF] Alexander | Dynamic pricing? Not so fast! A residential consumer perspective[END_REF] as well as costly, policy makers have also used non-price interventions to inuence households to reduce their energy consumption, such as nudges.

A nudge is dened as a change to a choice setting which alters individuals' behaviour without removing any of the choices available to them nor aecting their economic incentives. Nudges are designed to incentivise individuals to pick an option that is in their best interest, an option which they would not necessarily choose for themselves [START_REF] Thaler | Nudge: Improving decisions about health, wealth, and happiness[END_REF]. While the idea of nudges is not recent, the term has certainly seen an increased level of interest in recent years. The nudge intervention used in this experiment relates to both information on suggested play as the feedback is based upon the optimal level of consumption, and on social approval as an element of whether an individual's consumption behaviour is approved of or not is included in the nudge.

Experiments using suggested play recommend a course of action to subjects con-

CHAPTER 3: DEMAND RESPONSE AS A CPR GAME

cerning their contribution to a public good or their extractions from a common pool resource. In a threshold public good game, [START_REF] Marks | Using suggested contributions in fundraising for public good[END_REF] and [START_REF] Croson | The eect of recommended contributions in the voluntary provision of public goods[END_REF] nd that suggesting a fair contribution to subjects before they decide on their contribution only results in the provision of the public good when preferences are heterogeneous. Dal Bó and Dal Bó (2014) nd that suggested play works only under the addition of an element of moral suasion; the idea that in a public good game, increasing your contribution to the maximum amount benets everyone.

In a CPR game, [START_REF] Delaney | Payments or persuasion: Common pool resource management with price and non-price measures[END_REF] suggest to groups what they should do to increase their payos using both informative and normative messaging and compare this to a subsidy. They nd that the subsidy is the most eective, followed by normative then informative messaging.

The authors note that it is unusual that the normative messaging treatment results in only a slight greater reduction in extraction level when compared to information alone given that previous research has found signicant eects on energy and water consumption reduction through the use of normative messages [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF], Allcott, 2011b[START_REF] Ayres | Evidence from two large eld experiments that peer comparison feedback can reduce residential energy usage[END_REF][START_REF] Ferraro | Using nonpecuniary strategies to inuence behavior: Evidence from a large-scale eld experiment[END_REF]. They suggest that the non-signicant dierence in the results may be due to small sample sizes (n=15). However, it may also be due to a certain level of overlap between the two treatments, as the information treatment also contains normative language. The two treatments, information and normative messaging should perhaps instead be viewed as a weak normative message and as a strong normative message, respectively.

Boun My and Ouvrard (2018) explore the impact of recommended play, or a nudge, and taxes on contributions to a public good for reducing pollution. They hypothesise that reaction to a nudge is greater when subjects are more sensitive to environmental issues. After measuring environmental sensitivity, subjects are split into groups according to whether they are more or less environmentally sensitive than average and are then faced with either a nudge; a statement of the socially optimal contribution to the public good, or a tax; a linear tax based upon the optimal contribution.

The tax treatment shows the greatest increase in contributions for both high and low environmentally sensitive groups, a 45% and 34% increase in contributions, respectively. They nd that the nudge divides sub jects according to their environmental sensitivity, with the least sensitive reducing their contribution by 29% compared to the baseline, and the most sensitive increasing their contribution by 14%. In In addition to suggested play, the nature of the nudge used in this experiment provides social approval or disapproval of an individual's behaviour in the game. The rationale is that social approval increases optimal behaviour in CPR games as subjects perceive utility (disutility) from social approval (disapproval) [START_REF] Rege | The impact of social approval and framing on cooperation in public good situations[END_REF]. There is mixed evidence as to whether social information and approval increases or decreases optimal behaviour in collective action games. It has been shown

both theoretically (Holländer, 1990, Fehr and[START_REF] Fehr | A theory of fairness, competition, and cooperation[END_REF] and experimentally [START_REF] Cialdini | Crafting normative messages to protect the environment[END_REF][START_REF] Rege | The impact of social approval and framing on cooperation in public good situations[END_REF][START_REF] Spraggon | A note on stochastic public revelation and voluntary contributions to public goods[END_REF] that such social norms can increase contributions in collective action games. In other experiments, social approval has been shown to reduce optimal behaviour [START_REF] Noussair | Public observability of decisions and voluntary contributions in a multiperiod context[END_REF]Tucker, 2007, Brent et al., 2017).

The social approval used in this experiment does not come from the other sub jects, but from the regulator who informs subjects via a happy or sad face whether they are consuming more or less than the optimal amount.

Theory of Common Pool Resources

In economics, goods are classied based upon their degree of excludability and rivalry. Formally, a common pool resource is dened as a stock of a natural or man-made resource system from which a ow of resource units can be withdrawn. The stock of a CPR is renewable and so the stock can be sustained so long as average withdrawal rates do not exceed average replenishment rates. The social dilemma of CPRs is that individuals would like to withdraw more than the sustainable amount of resource units from the stock and as such there is a conict between personal interest and collective interest.

π i = e -cx i + x i (a -bΣx i ) (3.1)
A rational, self-interested player invests an amount x i which maximises their prot:

max x i π(x i , Σx i ) = e -cx i + x i (a -bΣx i ) (3.2)
The rst order condition is:

-c + a -bx i -bΣx i = 0

(3.3)
Supposing that all agents are equal, a symmetric Nash equilibrium can be found such that x i = x j = x for all players i, j.

x i = (a -c) b(n + 1) (3.4)
This level of extraction maximises individual prots regardless of the eects of an individual's extraction on the group.

The socially optimal investment in resource extraction is the amount x which maximises the collective prot. Assuming symmetry, the player maximises:

3.1 INTRODUCTION 87

max x nπ(x) = n[e -cx + x(a -bnx)] (3.5)
The rst order condition is:

-cn + an -2bn 2 x = 0

(3.6)
which gives an optimal investment where:

x i = (a -c) 2bn (3.7)
This level of extraction takes into consideration the eect of each individual's consumption on the resource system.

The Nash equilibrium results in a higher level of extraction than the socially optimal amount, hence the social dilemma. One option to align private earnings with the social optimum, is to increase the cost of extraction c such that the Nash equilibrium and socially optimal level of extraction are equal. The cost of extraction c is increased by an amount d and its value is found by equating the Nash equilibrium and the socially optimal solution.

a -c -d b(n + 1) = a -c 2bn (3.8) d = (a -c)(n -1) 2n
(3.9)

This theoretical framework has been applied to residential electricity consumption [START_REF] Bäckman | The Nordic electricity system as a common-pool resource[END_REF][START_REF] Goldthau | Rethinking the governance of energy infrastructure: Scale, decentralization and polycentrism[END_REF][START_REF] Gollwitzer | Rethinking the sustainability and institutional governance of electricity access and minigrids: Electricity as a common pool resource[END_REF]. The electricity network (power stations, distribution centres, transmission lines) represents a man-made resource system and the resource units are the kilowatt hours which can be consumed.

In the short run, it can be considered that this system provides a stock of electricity units available to households. The stock of electricity is renewable in the sense that once electricity has been consumed it must be immediately reproduced in order to maintain supply and demand balance. Currently, generated electricity cannot be stored so the amount generated needs to correspond to the amount being consumed.

There is limited storage capability in generators which are able to maintain electricity supply for under a minute. Beyond a minute, the supply is unstable and there is a risk of blackouts due to drops in frequency and voltage [START_REF] Pratt | Smart Grid Handbook, chapter Demand Response[END_REF].

CHAPTER 3: DEMAND RESPONSE AS A CPR GAME

Given this, on on days of extreme weather, or when renewable energy resources supply electricity, there is risk of demand outstripping supply which implies a need to reduce the demand for electricity.

Individuals wish to consume electricity without constraint which is represented by the Nash equilibrium level of extraction described above. However, on days in which demand response is required, it would be better collectively if all individuals lowered their consumption. This level of consumption is represented within the CPR framework as the socially optimal amount.

Experimental Design

This section details the experimental design beginning with the parametric protocol and the dierent experimental treatments, followed by the hypotheses to be tested and a description of the participants and the procedure.

Experimental Parameters

The game concerns electricity consumption during 10 peak periods when demand can be greater than production. In the experiment, subjects form groups of four (n = 4) for 10 peak periods (t = 10). Sub jects remain in the same groups for the duration of the experiment. Each group makes up an electricity consumption system of four households which represent a neighbourhood or small society. In this context, the demand response challenge is represented as a repeated CPR game.

At the start of each period, each sub ject receives an endowment e = 100 ECU 29

which they can use to consume electricity (measured in energy units (EU)). In the control and nudge treatments each EU costs 1 ECU (c = 1). The cost of each EU changes in the price treatment (c = 3) as discussed below in section 3.2.1.2. Any ECU that the subject does not use to consume electricity is kept by the subject and included in their prot function. For every EU consumed, the subject receives a = 13 and every EU consumed costs b = 0.1 for all sub jects in the group regardless of who consumed it. Subjects' prot function is as follows:

π i = 100 -cx i + x i (13 -0.1Σx i ) (3.10)
29 ECU = Experimental Currency Units. The exchange rate is communicated to all subjects during the instruction phase and is 150 ECU = 1e.

EXPERIMENTAL DESIGN 89

Individually, subjects maximise their prot at the Nash equilibrium, x N E = 24 for an individual prot of 158 ECU. This level of consumption is greater, and the payo is lower than if sub jects maximised the collective gains. Collectively subjects should each consume x SO = 15 for an individual prot of 190 ECU. This represents the collective interest of lowering consumption by demand response.

In each period, sub jects must decide how much of their endowment to spend on consuming electricity by choosing whether or not to use ve dierent electrical items. Table 3.2 details the dierent levels of consumption that subjects can choose from.

Subjects are told that their electricity consumption brings them comfort (via a monetary gain) of 13 ECU for every unit consumed. The total consumption of their group leads to a reduction in personal comfort of 0.1 ECU for every unit consumed regardless of who consumes it.

This value represents the disutility of increased collective consumption due to voltage reductions and brief power cuts when demand is greater than supply. The greater the total consumption of the group, the greater the reduction in comfort.

Item

Nudge treatment

In the nudge treatment, sub jects are told that one way to avoid power cuts is to ask consumers to lower their consumption during peak periods. This implies a lower level of comfort (as the individual may lower their heating or use their washing machine at a dierent time, for example) but allows all individuals, including oneself, to avoid a much lower comfort level, i.e. a power cut, or a reduction in the quality of electricity distribution.

At the end of each period, sub jects receive additional feedback on their consumption.

If their choice of consumption is less than or equal to the level of consumption which minimises the reduction in comfort for the group, i.e.: the socially optimal level, they see a picture of a smiley face. If their consumption is greater than this level, then they see a sad face.

Price treatment

In the price treatment, subjects are told that voltage reductions and brief power cuts can be avoided by increasing the price of electricity in order to incentivise consumers to consume less during peak periods. The price for this treatment is calculated with respect to the average level of consumption observed in the nudge treatment.

The goal is to compare whether the price results in the same level of consumption as the nudge when the price implemented is designed to achieve the level of consumption observed in the nudge treatment. The average level of consumption observed in the nudge treatment is 19.07 across all periods. Given that subjects can only choose consumption in increments of 5, the price is calculated such that the Nash equilibrium consumption level in the price treatment is x N E,P i = 20.

a -c -d b(n + 1) = 20

(3.11)

13 -1 -d 0.1(4 + 1) = 20

(3.12) 30 The simulator is described to subjects during the explanation of the game phase.

EXPERIMENTAL DESIGN

91

d = 2 (3.13)
The price increase required to incentivise subjects to consume 20 EU is equal to 2.

The price of electricity for sub jects in the price treatment is thus equal to 3 ECU.

Subjects are told that each energy unit consumed during the peak period costs 3 ECU which is three times more expensive than in a normal period 31 .

In this treatment the subjects maximise:

max x i π(x i , Σx i ) = 100 -3x i + x i (13 -0.1Σx i ) (3.14)
The feedback given at the end of each period is the subject's level of consumption and their earnings for that period.

Hypotheses

Under the assumption that subjects are rational and self-interested, it is expected that players will choose the Nash equilibrium consumption amount in all treatments, i.e.: 25 in the control and nudge treatment, and 20 in the price treatment. Such players would not be inuenced by the nudge described above.

Previous experiments have shown that suggesting a course of action has a positive inuence on socially optimal behaviour (Dal Bó and Dal Bó, 2014, [START_REF] Delaney | Payments or persuasion: Common pool resource management with price and non-price measures[END_REF]Jacobson, 2015, Boun My and[START_REF] Boun My | Nudge and Tax in an Environmental Public Goods Experiment: Does Environmental Sensitivity Matter? Resource and Energy Economics[END_REF]. Other experiments have found that aligning the Nash equilibrium with the social optimum via the use of a tax (framed as a price increase in this experiment) is a rst best policy for dealing with social dilemmas in public good and CPR games [START_REF] Plott | Externalities and corrective policies in experimental markets[END_REF][START_REF] Ballard | The marginal eciency eects of taxes and subsidies in the presence of externalities: A computational general equilibrium approach[END_REF][START_REF] Cochard | Eciency of nonpoint source pollution instruments: An experimental study[END_REF]. However, such interventions are not always well-received by the public. In the context of electricity consumption, varying price structures or dynamic pricing also has its opponents [START_REF] Alexander | Dynamic pricing? Not so fast! A residential consumer perspective[END_REF]. This leads to the main hypotheses:

Hypothesis 1 Consumption choices in the nudge treatment will be lower than in the control treatment.

Hypothesis 2 Consumption choices in the price treatment will be lower than in the CHAPTER 3: DEMAND RESPONSE AS A CPR GAME control treatment.

Hypothesis 3 As the price level is xed according to the nudge result, consumption choices in the price treatment will be equivalent to those in the nudge treatment.

Furthermore, the positive impact of suggested play or a nudge is increased when an element of social approval or disapproval is included (Dal Bó and Dal Bó, 2014).

In the experiment, the nudge treatment is presented as an indication of what an individual's consumption is in relation to the level at which the loss in comfort is minimised for the whole group. The smiley and sad faces thus act as social approval or disapproval of a behaviour which aects the whole group. As such the following is hypothesised:

Hypothesis 4 Subjects who receive `happy face' feedback will not change their consumption in the following period (those who consume the optimal amount or less).

Hypothesis 5 Subjects who receive `sad face' feedback will lower their consumption in the following period (those who consume more than the optimal amount).

It has been shown in a previous experiment (Boun My and Ouvrard, 2018) that subjects' reaction to a nudge in an environmental setting depends on their environmental sensitivity. In addition, due to the nature of the CPR game and the interlinked eects of an individual's actions on the others in their group, altruism may also inuence a subject's choice of consumption. This leads to the following hypotheses:

Hypothesis 6 More environmentally sensitive and altruistic subjects will consume less than less environmentally sensitive and altruistic subjects in all treatments. Hypothesis 7 The dierence in consumption between more and less environmentally sensitive subjects will be greater in the nudge treatment than in the price treatment.

Participants and Procedure

240 sub jects took part in the experiment, during 12 sessions 32 in March and April 2017 at Grenoble Applied Economics Laboratory (GAEL). Each session lasted one 32 During the 8th session a technical problem occurred and so the results of this session are excluded from the analysis. The excluded session would have been in the price treatment.

EXPERIMENTAL DESIGN 93

and a half hours. Table 3.3 shows the number of subjects, groups, and sessions per treatment. The experiment was programmed using zTree software [START_REF] Fischbacher | z-tree: Zurich toolbox for ready-made economic experiments[END_REF]. For participating in the experiment, sub jects received a 10e show-up fee. In addition, sub jects earned 7e20 to 18e00, with average earnings across sessions of 12e30. The majority of sub jects were undergraduate students in various disciplines (67%), 59% were female sub jects, and the average age across subjects was 22 years.

Treatment

Number At the beginning of each session, subjects randomly chose a subject number and a computer post. Once the sub jects were seated, the experimenter read aloud all instructions 33 . These were also displayed on two screens at the front of the room which all subjects could see. After general instructions concerning condentiality, anonymity of data and the code of conduct were given, the experimenter described the context of the game. Subjects were told that the experiment would include several phases. The rst phase of the experiment was the CPR game. The second phase involved a risk aversion test 34 . [START_REF] Holt | Risk aversion and incentive eects[END_REF]. In the third and nal phase, subjects completed three questionnaires: the General Ecological Behaviour (GEB)

Scale 35 [START_REF] Kaiser | A general measure of ecological behavior[END_REF], an altruism questionnaire [START_REF] Costa | Revised NEO Personality Inventory (NEO-PI-R) and NEO Five Factor Model (NEO-FFI)[END_REF] and nally a demographic questionnaire. The GEB questionnaire is used to determine an individual's level of environmental sensitivity as this may impact their consumption choices in the game. A questionnaire on altruism is included as the nature of the game requires making a decision that aects other people, thus altruistic tendencies can be controlled for in the analysis.

The instructions for each phase were read aloud then the subjects completed the phase before listening to the instructions on the following phase. Before the begin- 33 An English translation of instructions is available in Appendix B. 34 Analyses on risk attitudes were not conclusive and so are not discussed further in the rest of the analysis. 35 Following Boun My and Ouvrard (2018), a shorter version of the GEB scale is used including 28 items. See Appendix B for details of the GEB and altruism questionnaires. ning of the CPR game phase, subjects completed a questionnaire to determine their understanding of the game. Subjects were informed of any wrong answers and had to correct them before advancing to the rst period of the game.

Results and Discussion

In this section, the results are described and discussed, beginning with descriptive statistics and a graphical analysis of group level consumption decisions, followed by non-parametric testing. Next, sub jects' individual choices analysed, for all treatments and specically for the nudge treatment according to the message received.

The eect of treatment on sub jects' welfare is also considered. Then, the results of the questionnaires used at the end of the experiment are described and the consumption decisions by type as identied by the questionnaires. Finally, the equipment choices made by subjects are assessed.

Average Consumption at the Group Level

The dynamics of average group consumption by treatment for each period is represented in g. 3.1. Table 3.4 summarises the average group consumption by treatment overall and in periods 1 and 2, as this is pre-and post-initial feedback. To further analyse the results, non-parametric tests on average group level consumption between and within treatments compared to the corresponding Nash equilibrium and to the social optimum are performed. The second part of table 3.4 gives these results.

In the absence of any policies, the control groups consume 23.49 on average. Though this level of consumption is close to the Nash equilibrium level of the initial game, it is signicantly dierent from 25 EU (p-value=0.0355, Wilcoxon signed-rank test).

When average consumption per period is tested, average consumption in the control group is not signicantly dierent from the NE in all but 3 periods. In periods 1, 5 and 9, average consumption is at its lowest and signicantly dierent from 25 for the control groups (p-values<0.05, Wilcoxon signed-rank test).

Result 1: In the absence of policy, subjects do not achieve the socially optimal level of consumption. The use of a nudge results in the lowest level of consumption of 19.07 on average across all 10 periods. This is to be expected given that the objective of the nudge is to encourage subjects to consume the optimal level of consumption of 15. In the rst period, all treatments start at a similar level of average consumption 36 . Given that in the nudge treatment, sub jects do not receive feedback until after having made their consumption decision, it is to be expected that average group consumption in the rst period will be similar between the nudge and control groups. In the nudge treatment, post-feedback, consumption is consistently lower compared to the control groups (p<0.01, Wilcoxon rank-sum test), thus supporting hypothesis 1. In g. 1.

it can be seen that after the initial feedback, the average consumption immediately decreases and from period 2, there is a signicant and permanent eect of the nudge policy as the average level of consumption under the nudge treatment is signicantly dierent to those of control groups.

Result 2: Average consumption in the nudge treatment is signicantly lower than in the control groups.

In the price treatment, when the price is increased such that consumers are incentivised to consume 20, (i.e. the observed level of consumption in the nudge treatment), the average group level of consumption is 21.09. This observed level of consumption is lower than that of control groups thus providing evidence to support hypothesis 2. In this treatment, subjects are aware of the price change prior to any decision making. Therefore, a signicant dierence between consumption decisions in the price treatment compared to control groups in the rst period should be expected, but this dierence is not signicant (p-value = 0.9194, Wilcoxon rank-sum test). The average group consumption is only consistently and signicantly dierent from the seventh period. It is also signicantly dierent in periods 3 and 5

(p-value<0.05, Wilcoxon rank-sum test). This suggests that it takes several periods for the sub jects to integrate the price increase into their decision making and that it is not until the seventh period that the price is fully integrated into their decision making process.

Result 3: Average consumption in the price treatment is signicantly lower than in the control groups from the seventh period.

Given that the price increase is designed to incentivise sub jects to consume the amount observed under the nudge treatment, there should not be signicant differences between the average group consumption decisions from the second period onwards between the nudge and price treatments. However, signicantly dierent levels of consumption in periods 2 and 3 (p<0.01, Wilcoxon rank-sum test) are observed. This suggests that subjects do not immediately integrate the price increase into their decision making. They require a few periods of play before they take into consideration the eect of the price increase on their consumption level. This result provides partial support for hypothesis 3, as consumption under the price increase is greater initially, and consumption choices in the two treatments are at similar levels from period 4.

Result 4: Consumption in the nudge and price treatments are statistically similar from the fourth period.

Finally, for all 10 periods, consumption across the three treatments is signicantly 98 CHAPTER 3: DEMAND RESPONSE AS A CPR GAME dierent (p<0.01, Kruskal-Wallis test). In both the nudge treatment and the control groups, the observed average levels of consumption are signicantly dierent from both the Nash equilibrium of 25 and the social optimum of 15 (p<0.05, Wilcoxon signed-rank test). Groups in the nudge and price treatments have an average level of consumption that is signicantly dierent from the control groups (p<0.01, Wilcoxon rank-sum test). Moreover, the average consumption observed in the nudge treatment is signicantly dierent from that observed in the price treatment (p<0.01, Wilcoxon rank-sum test).

The results described in this section are robust to panel data estimation as shown in table 3.5 which presents regression estimates of treatment eects. The specications have been estimated using panel data random eects estimation. Panel data methods are used as there are n subjects making a consumption decision in t periods.

Random eects estimation is preferable to OLS or xed eects estimation as it is more ecient than xed eects estimation, and given that the experiment uses a between-subject design, random eects estimation allows for the estimation of the time-invariant treatment variables [START_REF] Moatt | Experimetrics: Econometrics for experimental economics[END_REF].

The value of the constant represents the average group contribution controlling for dierent variables. All specications show a clear signicant eect of both the nudge and price treatments compared to the control groups. In specications 2 and 4, a period variable is included to control for variation during the game, however, the coecient is not signicant. In specications 3 and 4, dummy variables are added to specify whether the group under or over consumed compared to the optimal consumption in their treatment 37 . At the group level, there is no signicant eect on consumption due to under or overconsuming in the previous period. Given that feedback on under or over consumption is provided at the individual level and in the nudge treatment, this eect is explored in more detail in the following section. 100 CHAPTER 3: DEMAND RESPONSE AS A CPR GAME t-1 increases, sub jects increase their consumption in t. This could be indicative of a rebound eect where subjects who earn more, increase their consumption.

Average Consumption at the Individual Level

Specications 3, 4 and 7 show that individuals who underconsumed in t-1, reduce their consumption in t compared to optimally consuming individuals. Those who overconsume in t-1 continue to do so compared to optimally consuming individuals.

Once individual consumption type is controlled for, the signicant eect of the price treatment falls out as the price treats all individuals equally and does not dierentiate according to how an individual consumes (under, optimally, or over).

Finally, in specications 5-7, variables concerning subjects' sensitivity towards the environment and their level of altruism 38 are included. Individuals who are more sensitive to environmental issues consume less. Given the context of the CPR game as an electricity consumption decision, such individuals may have additional motivation to choose a lower level of consumption so as to decrease their hypothetical impact on the environment. There is no signicant eect of altruism on individual consumption choice. 38 The construction of these variables is explained in section 3.3.4

RESULTS AND DISCUSSION

101 [START_REF]I use energy-ecient bulbs[END_REF] (2)

(

CHAPTER 3: DEMAND RESPONSE AS A CPR GAME

The eect of the nudge on individual consumption decisions is also examined 39 .

The estimates are shown in table 3.7. Specication 1 includes only the feedback type, and subjects' level of environmental sensitivity and altruism is also included in specication 2. There is a signicant negative eect of the happy face feedback, and a signicant positive eect of the sad face feedback in both specications.

Subjects who under consume receive happy face feedback and sub jects who over consume receive sad face feedback. Compared to optimally consuming groups, this feedback has the eect of reinforcing an individual's behaviour in the previous period. With regard to the feedback received by subjects in the nudge treatment, both hypotheses 4 and 5 are rejected, as rather than nudging subjects towards the socially optimal level of consumption, the nudge employed in this experiment reinforces subjects' existing behaviour. Subjects who under (over) consume in the previous period tend to decrease (increase) their consumption in the present period. This suggests that while the nudge shows a decrease in average consumption at the group level, at the individual level the nudge may serve to reinforce behaviours that are already present. Individuals who overconsume and see a sad face in their feedback may feel that the nudge is a threat to their freedom to consume as they wish and so react to the feedback by demonstrating the behaviour that the nudge was trying to discourage [START_REF] Brehm | A theory of psychological reactance[END_REF][START_REF] Steindl | Understanding psychological reactance: New developments and ndings[END_REF].

Result 5: The feedback in the nudge treatment reinforces subjects' existing consumption behaviour.

At the individual level in the nudge treatment, environmental sensitivity and level of altruism have a signicant negative eect on consumption choice. More environmentally sensitive and altruistic individuals consume less compared to less environmentally sensitive and altruistic individuals.

Welfare Analysis

In this section we analyse the eect of the dierent treatments on sub jects' welfare, at both the group and the individual level. 42 In the nudge, price and control groups, 58%, 55% and 52% showed a high altruism level, respectively.

RESULTS AND DISCUSSION

107 Result 8: Individuals are aected dierently by the nudge treatment according to their level of altruism. In the price and control groups, there is no dierence according to their level of altruism.

Equipment Choices

This section looks at the hypothetical choices of subjects with regard to which electricity consuming activities they are willing to shift during peak periods. The consumption choices available to subjects are presented above in table 3.2. Figure 3.2

shows the share of sub jects willing to lower the temperature of their heating by treatment type across periods. Figure 3.3 shows the percentage of sub jects willing to turn o each of the other appliances, by treatment, in each period. The results in this section reect the results found in the eld, namely that individuals are willing to shift their use of appliances for which consumption is not at the point-of-use, and unwilling to shift their use of appliances for which consumption is at the point-of-use [START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF].

Figure 3.2 shows that the majority of sub jects are willing to lower their heating by 2

• . In the nudge treatment, after feedback has been received, there is an increase in the number of subjects choosing to lower their consumption by 2

• from 47% to 59%, and a decrease in those lowering the temperature by 1

• . The same can be observed for control groups but to a lesser extent. Of the subjects who choose to keep their heating at the same temperature, a greater percentage are present in the control groups and fewer in the nudge treatment.

In g. 3.3, across all treatments, subjects are most willing to shift their use of washing machines or dishwashers. Across the 10 periods of the game, just under 80% of subjects choose to turn o these machines across treatments. This share is Figure 3.3 shows electricity consuming entertainment activities to be the activity that subjects are least willing to shift, at least initially with three-quarters of sub jects choosing to turn on their televisions and computers in the rst period, across all treatments. However, as the game progresses this appliance choice sees an increase in the share of sub jects shifting its use.

The share of subjects willing to shift their use of cooking equipment is greater in the nudge treatment than in the price treatment and control groups, and remains around the 60% mark post initial feedback.

Concerning sub jects use of water heating, there is an increase in the share of subjects who turn o their water heater in the nudge and price treatments. Whereas, the share remains lower in the control groups.

Across treatments, the shift in use of appliances is most apparent in the nudge treatment. With a marked increase in the share of subjects turning o appliances post feedback in period 2. This trend is also visible to a certain extent in the price treatment, and much less so in the control groups.

Conclusion and Policy Implications

The experiment described in this chapter explored sub jects' responses to a nudge and a peak price based intervention in a contextualised CPR game. The experimental design allowed for a comparison of behaviour under a nudge policy and an equivalent price increase to an absence of policies. The nudge policy experimented concerned feedback on an individual's consumption choice in the form of a happy face if they consume the socially optimal amount or less, and a sad face if they consume more than the socially optimal amount. In addition, the experimental design provided an opportunity to examine sub jects' consumption choices regarding their use of dierent appliances as sub jects were asked to decide whether or not to use ve dierent appliances when deciding upon their level of consumption. The results of the experiment may be of interest to policy makers when considering the imple-110 CHAPTER 3: DEMAND RESPONSE AS A CPR GAME mentation of a nudge or a price based intervention designed to reduce households' energy consumption during peak periods.

In the absence of energy conservation policies, individuals do not achieve the socially optimal level of consumption. When a policy is introduced, a nudge or price increase, individuals signicantly reduce their consumption and it remains lower than that of individuals who do not experience any policy measures. Both the nudge and price increase result in a level of consumption that is halfway towards the optimal level compared to no policies. However, the nudge does so without the loss of both individual and group welfare that is associated with the price increase. Therefore, while both the nudge and price increase lead to a lower level of comfort due to the reduction in consumption, it can be concluded that although the nudge in itself is not sucient to achieve the social optimum, it performs as well as an equivalent price increase without the implied loss of welfare.

The experiment showed that the nudge was quickly and easily understood, and resulted in an immediate reduction in consumption in the period following initial feedback. On the other hand, individuals took longer to understand the eect of the increased price on their consumption and so took longer to integrate it into their decision making process.

The advantage of a nudge policy is that, at the group level, it results in an immediate and signicant reduction in consumption, however caution must be taken as the nudge in the present experiment reinforces the existing behaviour of individuals and divides the population into those who under or over consume, or who are more or less environmentally sensitive or altruistic.

In response to a happy face, individuals who under consumed previously tended to further decrease their consumption compared to individuals who consumed optimally, whereas, those who received a sad face tended to increase their consumption.

Collectively, these individuals compensate for one another's behaviour and so the nudge has an eect on average consumption. However, individually the nudge appears to encourage those who already under consume to consume less, and those who over consume to consume more. In practice, this could lead to a situation where low consuming households are further reducing their consumption to compensate for the increasing consumption of high consuming households. While this result has been obtained in a hypothetical consumption game, it is worth consideration when implementing such nudges in the eld.

It may be that overconsuming individuals see the nudge as a threat to their freedom

CONCLUSION AND POLICY IMPLICATIONS 111

to consume as they wish and so they react by demonstrating the discouraged behaviour. The nudge in the present experiment could be considered as an exogenous nudge; it is an outsider (the experimenter) who provides feedback in relation to an exogenously optimal level of consumption. The experimenter denes the parameters of the game and thus the optimal amount. Whereas in eld experiments an endogenous nudge (household consumption in relation to the average of the neighbourhood) is often tested. Given that nudges are a tool of "libertarian paternalism"

and that it is a third party who is nudging individuals towards a decision that is in their best interest, further research could look into how individuals respond to exogenous and endogenous nudges.

In addition to reinforcing existing consumption behaviour, the nudge had a greater reduction eect on individuals who are environmentally sensitive and show altruistic traits. Such individuals consumed less than their less environmentally sensitive and less altruistic counterparts.

The price increase showed no such eect. It would appear that the increase in price crowds out any existing motivation to reduce consumption due to environmental or altruistic tendencies.

Finally, the appliances that subjects are willing to shift their use of in order to reduce their consumption are considered. Subjects are most willing to turn o their washing appliances and prefer to continue to use their entertainment devices. Subjects are also willing to lower their heating in order to reduce their total consumption.

Further research could look into specialised nudges according to the types of electric appliances in each household.

Chapter 4

Gain

Abstract

When trying to save energy, households are required to perform small, repetitive tasks, e.g.: turning o lights, or turning appliances o standby. Each individual action has little eect on energy consumption and on household bills, however, they can add up to sizeable savings. This paper explores dierent methods of framing incentives to motivate subjects to perform a simple yet repetitive, real-eort task for a piece-rate payo. Each individual eort does not earn much for the individual, but combined the payo is signicant. A 2 by 3 design is used: either gain framed or loss framed incentives, crossed with either a control treatment with a xed payo, an ex-ante treatment with a low or high payo with equal probability, revealed to individuals prior to the task, or an ex-post treatment where the low or high payo with equal probability is revealed after completing the task. Individuals are expected to perform better under loss-framing. Results show little dierence in performance across treatments. Knowledge of the higher payo improves performance in the gain-framed, ex-ante treatment, and subjects perform signicantly better when the payo is higher in both ex-ante treatments.

Introduction

Smart meters are a key piece of technology in the implementation of various incentives used to encourage households to lower their consumption. An in-home display (IHD) retrieves data from a smart meter and provides households with feedback on their energy consumption in relation to the various incentives that can be implemented. Without such an interface between the consumer and the smart meter, it is dicult for households to act upon the information that the smart meter can provide [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF].

For the data displayed on IHDs to have the desired eect of reducing residential household consumption, an important consideration in the design of IHD interfaces is how to frame the incentives to make them more eective. Should households be incentivised with the carrot -shown how much they are saving, or with the stickshown how much they are paying?

Previous experiments exploring the eect of feedback provided by IHDs typically present information factually and consider the units of measurement of consumption information, whether to use energy units (kilowatt-hours) [START_REF] Parker | Pilot evaluation of energy savings from residential energy demand feedback devices[END_REF], Van Dam et al., 2010[START_REF] Nilsson | Eects of continuous feedback on households' electricity consumption: Potentials and barriers[END_REF], combined with monetary units [START_REF] Schleich | Eects of feedback on residential electricity demandndings from a eld trial in Austria[END_REF][START_REF] Alahmad | A comparative study of three feedback devices for residential real-time energy monitoring[END_REF], Carroll et al., 2014), or whether to use normative information by comparing a household's consumption to that of other similar households [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF], Allcott, 2011b[START_REF] Schultz | Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms[END_REF]. This information is presented factually, in terms of amount consumed or amount spent on consumption, however, little research has compared the framing of energy consumption information in terms of salient losses and gains.

To date, and to the best of the author's knowledge, two studies have explored the eect of explicitly framing expenditure on energy consumption as a loss. [START_REF] Bager | Making `Smart Meters' smarter? Insights from a behavioural economics pilot eld experiment in Copenhagen, Denmark[END_REF] nd that households under salient loss framing make more eort to shift their consumption than households under factual feedback. They reduce their overall demand by 7-11% compared to households without framed feedback. Bradley et al. ( 2016) create a loss frame by creating an incentive account for each household in their study in which an amount of money representative of each household's monthly budget is held. Households must then shift their energy consumption from peak to o-peak periods in order to retain as much of the incentive account as possible.

The authors nd that households in the incentive account treatment shifted more consumption during the experimental period compared to the pre-experiment period 116 CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES when they paid for consumption as normal and had no incentive account. Both of these studies have used salient, loss-framed feedback to incentivise households to shift and/or lower their energy consumption compared to either groups with a factual presentation of consumption feedback, or to a pre experimental period.

A characteristic of residential energy consumption is that eorts made to save energy often result in small savings. Indeed, households using IHDs have found that the information on daily expenditure is too small to encourage energy consumption reduction as a single behavioural change has a small impact on daily expenditure [START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF]. It is the accumulation of many small actions which amount to a larger eect on monetary savings. For some individuals the small individual monetary savings are not enough to encourage them to provide an eort [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF], whereas others appreciate that many, repeated actions can add up to signicant monetary savings [START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF].

In the more general (non energy related) experimental literature on gain and loss framing, there is evidence showing that individuals provide more eort under loss framing than under gain framing in both the laboratory [START_REF] Hannan | Bonus versus penalty: does contract frame aect employee eort?[END_REF][START_REF] Goldsmith | Incentives framing and task motivation: The intuitive appeal of gains and the actual ecacy of losses[END_REF][START_REF] Armantier | Framing of incentives and eort provision[END_REF][START_REF] Imas | Do people anticipate loss aversion?[END_REF], and the eld [START_REF] Fryer | Enhancing the ecacy of teacher incentives through loss aversion: A eld experiment[END_REF][START_REF] Hossain | The behavioralist visits the factory: Increasing productivity using simple framing manipulations[END_REF][START_REF] Armantier | Framing of incentives and eort provision[END_REF], to name a few.

Larger or lump-sum payos encourage greater eort provision under loss framing than under gain framing. In both hypothetical [START_REF] Hannan | Bonus versus penalty: does contract frame aect employee eort?[END_REF] and real-life [START_REF] Hossain | The behavioralist visits the factory: Increasing productivity using simple framing manipulations[END_REF] contract tasks, sub jects perform better under a loss-framed lump-sum payo for meeting a target. In an articial, real-eort task, Imas et al. (2016) nd that endowing sub jects with a t-shirt and taking it back if they do not meet a certain target is more eective than receiving the t-shirt after having met the target.

Similar results have also been found in the eld: in experiments in education, Fryer Jr et al. (2012) nd that endowing teachers with an incentive of $4000 has a larger eect on increased student grades than oering teachers the incentive at the end of the school year. In an experiment incentivising graders to grade papers, [START_REF] Armantier | Framing of incentives and eort provision[END_REF] nd that penalties out perform bonuses, and that a combination of both is most successful. They nd a U-shaped relationship between framing and eort; their sub jects increase their eort initially for both bonuses and penalties, but provide less eort when penalties are large. A large penalty has the eect of discouraging eort.

INTRODUCTION 117

On the other hand, recent studies exploring the eect of gain and loss framing to motivate eort for small payos are less conclusive. De Quidt et al. ( 2017) and [START_REF] Essl | Choking under time pressure: The inuence of deadline-dependent bonus and malus incentive schemes on performance[END_REF] do not nd signicant eects on eort provision due to framing. This is of particular interest for the present experiment as, as discussed above, encouraging residents to lower their energy consumption means motivating small eorts for small rewards.

Both of these studies provide sub jects with immediate feedback on their performance akin to the real-time feedback that IHDs provide on energy consumption. While De Quidt et al. ( 2017) and [START_REF] Essl | Choking under time pressure: The inuence of deadline-dependent bonus and malus incentive schemes on performance[END_REF] do not nd signicant eects of framing with feedback, other experiments using immediate feedback have found a signicant eect of loss-framing on eort compared to gain framing [START_REF] Goldsmith | Incentives framing and task motivation: The intuitive appeal of gains and the actual ecacy of losses[END_REF].

A further characteristic of residential energy consumption is that many variables aect household consumption, such as building characteristics, appliance characteristics, external factors (location, weather, temperature, etc.) in addition to occupant behaviour [START_REF] Kavousian | Determinants of residential electricity consumption: Using smart meter data to examine the eect of climate, building characteristics, appliance stock, and occupants' behavior[END_REF]. This can mean that similar sized households, in buildings with similar characteristics can have substantially dierent levels of electricity consumption [START_REF] Gram-Hanssen | Ecient technologies or user behaviour, which is the more important when reducing households' energy consumption?[END_REF]. Given the multitude of variables that aect energy consumption, when households make eorts to lower their demand, their eorts may not necessarily have the intended eect.

For example, a household may decide to lower their thermostat in order to save energy on heating during winter. However, factors outside of the households control may aect the payo of such an action. Suppose the household in question lives in an apartment surrounded by other apartments. They may save energy, and money, as a result of lowering their consumption, yet the temperature of their apartment may not fall drastically due to the residual heat from neighbouring apartments. On the other hand, suppose the household lowers the thermostat on a day which is colder than forecast. The household may not save as much energy as hoped. While these examples are admittedly anecdotal, they highlight that when acting to lower energy consumption, there is an element of risk as to whether the action will result in the desired monetary savings.

Furthermore, with the increasing share of renewable energy sources in the production mix, the future price of electricity is set to become more variable than at present. These diering prices for consumption at dierent times of the day vary in the amount of risk households will bear and the reward they provide households 118 CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES [START_REF] Faruqui | The ethics of dynamic pricing[END_REF]. [START_REF] Abeler | Reference points and eort provision[END_REF] explore the eect of risk on eort provision in the gain domain. Abeler et al. manipulate sub jects' reference points by proposing a payo in the form of either a piece-rate payo or higher/lower xed amount (with equal probability) in order to inuence sub jects' expectations of earnings. In a similar task to the present experiment, subjects provided more eort when the possible xed amount is higher. However, regardless of the size of the xed payo, sub jects provide eort up to the level of the xed amount though they could earn more under the piece-rate payo. The authors nd that expectations of payos form individuals' reference points such that if individuals expect to receive a low payo when they receive it, it is not perceived as a loss.

In two mixed frame experiments where sub jects faced risky payos, subjects received a piece-rate for performing simple additions to which a known amount was either added or taken away, with equal probability. Both [START_REF] Sloof | The eect of noise in a performance measure on work motivation: A real eort laboratory experiment[END_REF] and [START_REF] Corgnet | Revisiting the trade-o between risk and incentives: The shocking eect of random shocks? Management Science[END_REF] nd that subjects provide more eort when the additional gain/loss is higher, i.e.: when there is greater variability in the payo.

According to expected utility theory, there is no dierence in framing incentives positively (savings) or negatively (amount spent) as under isomorphic framing, the two incentives types should motivate the same provision of eort. On the other hand, prospect theory predicts that individuals make more eort when faced with negatively framed incentives than with positively framed ones as "losses loom larger than gains" (Kahneman and Tversky, 1979, p.279). This can be explained by the endowment eect [START_REF] Thaler | Toward a positive theory of consumer choice[END_REF] or reference point bias [START_REF] Kahneman | Anomalies: The endowment eect, loss aversion, and status quo bias[END_REF], whereby an individual considers a gain or a loss relative to their current endowment or reference point.

Prospect theory suggests that individuals derive greater utility from avoiding a loss than they do from gaining the same amount. Furthermore, the theory posits that individuals are risk-averse in gains and risk-seeking in losses [START_REF] Kahneman | Prospect theory: An analysis of decisions under risk[END_REF]. In a meta-analysis of 136 experimental papers, Kühberger (1998) corroborates the theory. Kühberger nds that risk aversion in gains and risk seeking in losses is particularly strong when reference points rather than outcomes are manipulated, i.e.: when starting points rather than nal earnings are manipulated. In the present experiment, starting points are manipulated by giving individuals in loss treatments an endowment from which their losses are subtracted.

These experiments have highlighted that individuals provide a greater level of eort 4. 1 INTRODUCTION 119 under loss-framing when payos are relatively large. When piece-rate payos are used the evidence is less clear. The particularity of energy consumption is that an individual, energy saving action has little impact on overall consumption (as such it is akin to a piece-rate payo ), yet together many repeated energy saving actions can amount to more substantial savings. When risk is added to payo structures, individuals provide more eort when the additional amount that they can gain or lose is greater.

The present experiment contributes to this literature by exploring how individuals respond to riskless, and risky payos under both gain and loss framing, when they can earn small payos for small eorts in an articial, real-eort, number counting task. Subjects are provided with immediate feedback which highlights the cumulative gains or losses as a result of subjects' eort. The present experiment captures some of the principal characteristics of residential energy consumption feedback via an IHD: small payos for small eorts which cumulate to sizeable earnings, direct feedback on performance, and both riskless and risky payos to reect outside factors which can aect the reward of a particular eort.

The results of the present experiment suggest that framing has little eect on eort provision when small eorts are rewarded with small payos. Under riskless payos, subjects provide more eort for larger payos regardless of the treatment frame.

Concerning subjects' eort throughout the game, their performance is lowest at the beginning of the game, then improves towards the end. Subjects' eort provision increases most signicantly in the riskless treatments when payos are revealed to subjects prior to eort provision.

Given that reducing energy consumption through changes in residential behaviour requires small, repetitive eorts for small rewards, the present experiment has shown that in order for subjects to provide an eort, any risk on the realisation of payos should be reduced, in particular under loss framing.

The rest of the chapter is organised as follows: in section 4.2 describes the real-eort task and experimental treatments, followed by a presentation of the hypotheses in section 4.3. Section 4.4 provides the results of the experiment and nally section 4.5 concludes.

CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES

Experimental Design

The experiment was designed to allow for a precise measure of eort provision which is exogenously inuenced by gain and loss framing, and by risky payos. A tedious, articial, real-eort task was used. Sub jects counted the number of ones in 9 by 9 tables of randomly ordered ones and zeros (Abeler et al., 2011, Essl and[START_REF] Essl | Choking under time pressure: The inuence of deadline-dependent bonus and malus incentive schemes on performance[END_REF]. The number of ones in each table varied between 28 and 45 (with 36 ones per table on average), and was randomly drawn prior to the rst experimental session and was the same for all subjects. All subsequent experimental sessions faced the same order of tables. The advantages of this task are that no special knowledge is required, learning possibilities are limited, and eort is easily measurable. In addition, experimenter demand eects are minimised as the task is articial and the outcome is of no intrinsic value to the experimenter.

The experiment consisted of 2 stages. At the beginning of the rst stage, instructions 45 were read aloud to sub jects, and subjects answered control questions. Prior to beginning the rst stage, subjects were informed that the second stage of the experiment involved answering a questionnaire.

In the rst stage of the experiment, subjects played 28 periods in each of which they had 80 seconds to count the number of ones in ve tables. Once sub jects had entered a number for the table on display, a new table appeared whether their answer was correct or not. If subjects ran out of time before completing the fth table of the period, then the period was over. Subjects were not informed of the current period number and nor was there a timer present. In total, sub jects saw up to 140 tables.

After each period of ve tables, sub jects received feedback on their performance for the period: how many tables they correctly counted and how much they earned, and feedback for the game so far: how much they earned. The number of tables which subjects correctly completed is used as a proxy for their eort provision.

In the second stage sub jects completed a questionnaire containing socio-demographic questions.

Treatments

A 2x3 experimental design was used to create six treatment groups. The treatments were divided into two frames: incentives were framed as either a gain or a loss. In the gain treatments subjects increased the size of their pot of earnings by correctly counting the number of ones in each table. In loss treatments subjects were endowed with the maximum earnings possible and their pot decreased for every incorrect or incomplete table. In other words, they maintained their pot if they correctly counted the number of ones in each table. The experimenter in each loss-framed session distributed the maximum earnings of 21e to each sub ject in real money and this was left on their table throughout the duration of the task. Within each frame, there were three payo structures: a xed payo known prior to eort provision, a low or high payo (with equal probability) known prior to eort provision, and a low or high payo (with equal probability) known after eort provision. Each of these payo structures will be described below.

Control

In the control treatments subjects could earn a xed payo for each correct table.

In the gain-control (GC) treatment subjects began with a pot of 0e and earned 15 cents per correct table. In the loss-control (LC) treatment, subjects began with a pot of 21e and lost 15 cents for each incorrect or incomplete table. Subjects gained or avoided losing money by counting the correct number of ones in each table.

Ex-ante

In the gain-ex-ante (GEA) treatment sub jects earned either 5 cents or 25 cents per correct table, with equal probability. In the loss-ex-ante (LEA) treatment subjects lost either 5 cents or 25 cents per incorrect or incomplete table, with equal probability. The per table payo remained the same for the 5 tables in a given period. The payo for a given in period was randomly drawn for each sub ject before subjects completed the tables and was displayed on the task screen.

In the ex-ante treatments, subjects were informed of the payo for the period before counting the number of ones. They knew the possible payo before making any eort.

Ex-post

In the gain-ex-post (GEP) treatment subjects earned either 5 cents or 25 cents per correct table, with equal probability. In the loss-ex-post (LEP) treatment subjects lost either 5 cents or 25 cents per incorrect or incomplete table, with equal probability. The payo stayed the same for all 5 tables in each period. The payo for a given period was randomly drawn for each subject before subjects completed the tables and was displayed on the feedback screen at the end of each period.

CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES

In the ex-post treatments, subjects were informed of the payo for the period after counting the number of ones, once the period had ended. They knew the payo after having made an eort. were 13-20 students per session and each session lasted one and a half hours. The experiment was programmed using zTree software [START_REF] Fischbacher | z-tree: Zurich toolbox for ready-made economic experiments[END_REF]. At the start of their session, subjects chose a subject number at random and a computer post. All instructions were read aloud by the experimenter and were displayed on two screens at the front of the room. Sub jects were told that the experiment would consist of two phases: (1) the experimental task, (2) the questionnaire. The rst phase began with a comprehension questionnaire which was corrected collectively before subjects started the experimental task. Instructions for the second phase were read aloud once all subjects had completed the rst phase.

HYPOTHESES 123 Hypotheses

According to expected utility theory, if subjects were rational and unaected by the framing of incentives and the risk associated to payo, then the above dierences in treatments would result in no signicant dierences in eort. The gain or loss framing would have no eect as the expected outcome is the same under each frame.

The dierent payo structures would have no eect on eort as the expected outcome is the same under each structure. The risk on payos in the ex-post treatments would have no eect as the expected payo is the same as in the control groups.

That being said, the above discussion of the literature has shown that individuals are inuenced by framing. Previous experiments have conrmed this idea [START_REF] Hannan | Bonus versus penalty: does contract frame aect employee eort?[END_REF][START_REF] Goldsmith | Incentives framing and task motivation: The intuitive appeal of gains and the actual ecacy of losses[END_REF][START_REF] Fryer | Enhancing the ecacy of teacher incentives through loss aversion: A eld experiment[END_REF][START_REF] Hossain | The behavioralist visits the factory: Increasing productivity using simple framing manipulations[END_REF][START_REF] Armantier | Framing of incentives and eort provision[END_REF][START_REF] Imas | Do people anticipate loss aversion?[END_REF]. However, some more recent studies have found no evidence of framing eects [START_REF] Hong | Framing manipulations in contests: A natural eld experiment[END_REF][START_REF] De Quidt | Bonus versus penalty: How robust are the eects of contract framing[END_REF][START_REF] Essl | Choking under time pressure: The inuence of deadline-dependent bonus and malus incentive schemes on performance[END_REF].

Prospect theory predicts that individuals behave dierently when faced with a gain or a loss; individuals attach a greater utility to avoiding a loss than they do to receiving an equivalent gain [START_REF] Kahneman | Prospect theory: An analysis of decisions under risk[END_REF].

Loss aversion may explain why framing incentives as gains and as losses aects behaviour dierently [START_REF] Kahneman | Prospect theory: An analysis of decisions under risk[END_REF]. When an incentive is negatively framed, an individual is given a payment from which an amount is withdrawn if their performance is not up to standard. This initial increase in income or utility provides the individual with an endowment. When faced with losing part or all of that endowment, prospect theory predicts that individuals will work harder to retain their endowment than if they were to begin with nothing and to make an eort to increase their endowment through bonuses. The loss of utility from moving away from the endowment amount is greater than the increase in utility of moving towards the same amount. This behavioural bias is known as the endowment eect [START_REF] Kahneman | Experimental tests of the endowment eect and the coase theorem[END_REF].

With positively and negatively framed incentives the reference point is dierent; individuals start from nothing or little and increase their income by making an eort.

Or they start with a certain endowment and make an eort to not lose it. The individual's point of reference for making their decision to make an eort or not is dierent under each incentive type. Each individual will also have a dierent reference point with regard to the base payment and the bonus or penalty (Kahneman,

x : V L (x) > V G (x).
As such, prospect theory predicts that individuals will expend more eort to avoid a loss of x than they will to gain x.

This leads to hypothesis 1:

Hypothesis 1: Subjects provide more eort under loss framing than under gain framing: subjects' eort provision will be greater in LC compared to GC, LEA compared to GEA, and in LEP compared to GEP.

Given the design of the present experiment, the only dierence in eort that a rational individual would provide is in the ex-ante treatments; individuals would 46 The value function used here is slightly modied from the original for explicative purposes [START_REF] Kahneman | Prospect theory: An analysis of decisions under risk[END_REF].

RESULTS AND DISCUSSION

125 provide more eort when the payo is higher. Indeed, the above studies which found signicant treatment eects are predominately studies which use large, rather than piece-rate incentives. This is further supported in Abeler et al. ( 2011) whose subjects provided more eort for higher payos.

This leads to hypothesis 2:

Hypothesis 2: Subjects provide more eort when payo amounts are higher: subjects' eort provision will be greater for GEA 0.25 compared to GEA 0.05 , and LEA 0.25 compared to LEA 0.05 . [START_REF] Kahneman | Prospect theory: An analysis of decisions under risk[END_REF] posit that individuals are risk averse towards gains and risk seeking towards losses. As [START_REF] Kühberger | The inuence of framing on risky decisions: A meta-analysis[END_REF] shows, this framing eect due to risk preferences is greater when reference points are manipulated. In the present experiment, under gain framing, subjects' initial reference point is 0e whereas under the loss frame, their initial reference point is an endowment of 21e.

This leads to hypotheses 3:

Hypothesis 3a: Subjects will provide more eort under risky payos compared to xed payos in the loss domain: subjects' eort provision will be greater in LEP compared to LEA and LC. Hypothesis 3b: Subjects will provide less eort under risky payos compared to xed payos in the gain domain: subjects' eort provision will be smaller in GEP compared to GEA and GC.

Results and Discussion

This section presents and discusses the results of the experiment in relation to the hypotheses set out above in section 4.3. The presentation of the results begins with the global treatment eects by frame, payo amount and by risk. This is followed by a discussion of the evolution of eort provision over time.

Eort Provision by Frame

The main variable of interest in the experiment is the number of correct tables in a given period. This variable is used as a proxy for the eort provided by sub jects in the experiment. Across all periods, subjects correctly completed most tables in the GEA and GC treatments, and least in the LEP treatment. Comparisons of average eort provision within payo structures and between framing show that there are no signicant dierences in eort provision due to framing. There is no clear trend in the evolution of average number of correct tables across treatments. Graphically, the trend lines are more disparate in the gain treatments, and more similar in the loss treatments.

Figure 4.3 displays the cumulative distribution functions of the total number of correct tables by completed by subjects in each treatment. The best subjects in each treatment correctly complete between 85 and 94% of the tables. There is more variation at the lower end of eort: the fewest number of tables correctly completed varies from 10 -35%. There is substantial overlap in the six CDFs, further indicating small dierences in eort provision across treatments. Graphically, the CDF of the LEP treatment stands out most: fewer subjects complete higher number of tables compared to the other treatments. 47 The average number of correct tables, standard deviations and associated Wilcoxon rank sum tests for periods 1 and 28 and for the rst and second half of the experiment are provided in Appendix C. Result 1: There is no framing eect: there are no signicant dierences between GC and LC, between GEA and LEA, nor between GEP and LEP (bar the nal period).

Eort Provision by Payo Amount

Given that there are two payo amounts in each of the Ex-Ante and Ex-Post treatments 48 , the average number of correct tables has been calculated for each payo amount (standard deviations in brackets). Table 4.4 shows the averages and the standard deviations of the variable of interest by payo amount for each of these treatments, as well as the results of Wilcoxon signed rank tests of signicant dier- 48 The expected payo in each of the four treatments with varying payos is ±0.15 in either a gain or a loss frame. The expected payo across all periods in each treatment is statistically indierent from ±0.15. Subjects perform better in the both GEA and LEA treatments when the gain or loss amount is greater. The dierence in eort is signicant (p < 0.01, Wilcoxon signed rank test) for both treatments. Subjects make more eort to earn, or to avoid losing, the larger payo amount when it is known. As expected, there is no signicant dierence in performance in the GEP and the LEP treatments as sub jects are only aware of the payo amount after having made an eort. The dierence in eort provision within the gain frame between payo amounts is not signicantly greater than that of the loss frame. Sub jects in the GEA treatment do not provide signicantly more eort for higher versus lower payos compared to the LEA treatment.

Result 2: Sub jects provide more eort for higher payos than for lower payos (when payos are known prior to eort provision).

Eort Provision by Risk

As described in Section 4.2.1, in treatments GEP and LEP subjects face risky payments of 5 or 25 cents per table in a given period with equal probability. The payo is revealed to subjects after they have completed the task. In treatments GEA and LEA, subjects face the same payo structure, however, the payo amount is revealed at the start of each period. Result 3: Within a frame (gain or loss) there are no signicant dierences in eort provision between payo structures due to risk. 49 The signicance stars in the Dierence column refer to Wilcoxon signed rank tests of signicant dierences in eort provision within a treatment by payo amount. 50

Evolution of Eort Provision

The above comparison of eort during the game shows few dierences across treatments. In order to further assess sub jects' eort during the experiment, the average number of correct tables is calculated for each block of 7 periods to provide 4 measures of eort across the experiment as shown in table 4.6. Figure 4.4 displays the average number of correct tables by treatment for a block of 7 periods, along with the associated condence intervals. Across all treatments, subjects' eort provision is lowest in the rst block of 7 periods and highest in the nal block. In the second and third blocks, sub jects' eort is relatively stable in treatments GEA, LEA and GEP, and decreases slightly in treatments GC, LC and LEP.

Table 4.7 provides regression estimates of the average number of correct tables in the blocks of 7 periods with respect to the second block.

The coecients on Periods 1-7 conrm the above discussion of g. 4.4, subjects' eort is lower in periods 1-7 compared to periods 8-14. The coecients on Periods 15-21 are not signicant in any of the treatments. There is no signicant change in subjects' eort between these two blocks of 7 periods. In the nal 7 periods, subjects' eort is signicantly greater in treatments GEA, LEA, and GEP compared to periods 8-14. In the other treatments, GC, LC, and LEP, subjects' eort increases in the second block compared to the rst, then remains at a similar level for the Subjects' improvement in correctly counting the number of ones in tables at the beginning and the end of the experiment, as well as across the two halves provides evidence of learning eects. As the experiment progressed, subjects became more adept at counting the number of ones in each table as the eort provision became routine.

Result 4: Subjects' performance increases between periods 1-7 and periods 8-14, before plateauing (GEA, LEA, GEP) or diminishing (GC, LC, LEP) in periods 15-21. Subjects' performance is highest in periods 22-28. 132 CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES [START_REF]I use energy-ecient bulbs[END_REF] (2) (3) (4) (

Individual Determinants of Eort Provision

This nal results section discusses which individual and study design characteristics have a signicant inuence on eort provision. The dependent variable is the number of correctly counted tables in the period. For robustness, both a cluster-robust OLS regression (odd numbered specications) and a panel data regression (even numbered specications) using random-eects estimation are run. Clustering standard errors by subject adjusts for dependence in observations. Using a panel data specication accounts for the fact that in the present data, there are n subjects making decisions in t periods. A random-eects estimator is used in order to account for the inuence of time-invariant variables on eort provision [START_REF] Moatt | Experimetrics: Econometrics for experimental economics[END_REF]. Table 4.8 provides the regression estimates 51 Specications 1 and 2 control only for treatments. As seen in the descriptive analysis, there are no signicant treatment eects. Specications 3 and 4 include design and individual variables which may have an eect on the dependent variable.

As suggested in section 4.4.4, sub jects' eort provision increases during the game;

the period variable has a signicant positive eect indicating that as sub jects gain experience with the task, their performance improves. The average number of ones per period controls for the diculty of the task; the more ones in the table, the harder the table. As expected, the coecient on this variable is signicantly negative; the harder, on average, the tables in a period are, the fewer tables sub jects complete in 51 A correlation The nal variables included in the regression refer to individual characteristics. Ability, as measured by subjects' performance in the rst period, is a key indicator of performance throughout the game. Sub jects who do better in the rst period are more likely to correctly complete more tables in the rest of the experiment. Degree of risk taking is a subject's response, on a scale from 1 (I avoid taking risks) to 5 (I love taking risks). This variable has no signicant eect on the number of correctly completed tables. Finally, gender and age of the subject are controlled for. Neither of these variables have a signicant eect. These results show that there are no signicant treatment eects due to framing. This may be due to the small sample sizes.

Conclusion

The objective of this chapter was to explore how incentives can be framed to encourage individuals to make small, repetitive eorts in a real-eort task, in a similar vein to encouraging households to make small, repetitive eorts to lower their energy consumption. Thus, the ob jective of this experiment was to better understand how the framing of incentives as either gains or losses aects sub jects' eort provision under piece-rate and risky payos. Subjects were asked to complete a repetitive and tedious task which required no particular skills: counting the number of ones in a 9 by 9 table.

Under gain framing, sub jects received a piece-rate payo for each completed table . In the loss-framed groups, sub jects were endowed with 21e and lost a piece-rate amount for every incorrect or incomplete table. An element of risk on payos was added to four treatments. Payos were either high or low with equal probability and the true payo was revealed to subjects either before or after eort provision according to the treatment. The dierent treatments presented subjects with identical payos under expected utility theory. However, they were framed in dierent ways in order to manipulate subjects' reference points via an endowment and via risk on the payo amount.

Based on ndings in previous experiments, and on prospect theory, sub jects were expected to provide more eort under loss framing, in particular in the loss framed ex-post treatment, as individuals derive more utility from avoiding a loss than from gaining the same amount. In addition, individuals are more risk seeking in the loss 4.5 CONCLUSION 135 domain, and more risk averse in the gain domain.

Results show that there is no overall eect of framing on eort provision in the present experiment; subjects provide the same level of eort between framing types within a payo structure. One explanation for such a result could be that most subjects are not susceptible to a loss frame as they realise it is a frame and so evaluate their earnings as a net payment rather than as a loss [START_REF] Harrison | Cumulative prospect theory in the laboratory: A reconsideration[END_REF]. Harrison et al. nd that this is more apparent when subjects use "house money" rather than earned money.

In the present experiment, every eort was made to endow sub jects with real money prior to beginning the loss-framed task; the money was left on sub jects' tables throughout the duration of the experiment. Subjects were aware that the 21e endowment would be exchanged for their actual earnings at the end of the experiment.

Subjects' evaluation of payos as a net payment rather than an avoided loss may explain why there are no signicant dierences in eort between each gain and corresponding loss treatment in the present experiment. However, previous experiments have employed the same approach of endowing sub jects with real money at the beginning of the experiment and have found that participants in loss-framed treatments provide signicantly more eort [START_REF] Hannan | Bonus versus penalty: does contract frame aect employee eort?[END_REF][START_REF] Goldsmith | Incentives framing and task motivation: The intuitive appeal of gains and the actual ecacy of losses[END_REF][START_REF] Imas | Do people anticipate loss aversion?[END_REF]. Furthermore, experiments in which subjects in loss-framed treatments have not been endowed with real money have also found signicantly more eort is provided under loss than under gain-framing [START_REF] Hossain | The behavioralist visits the factory: Increasing productivity using simple framing manipulations[END_REF].

In line with previous research, sub jects in ex-ante treatments provided more eort for the higher payo when it was made known to them prior to eort provision than for the lower payo. Sub jects display rationality and so when faced with a higher payo per table, they provided more eort and correctly counted more tables than when faced with a lower payo, in both gain and loss-framed treatments.

Regarding sub jects' behaviour under riskless and risky payos, the results of the present experiment do not show any signicant dierences in eort provision when payments are risky compared to when they are riskless under the same framing.

Perhaps sub jects correctly anticipated an expected payo of 15 cents, or the dierence in payos was not sucient and so subjects did not provide additional eort under risky payos.

Finally, subjects demonstrated learning and improvement during the experiment as their eort provision increased during the course of the experiment. This is particularly true of the ex-ante and ex-post treatments. However, subjects' eort 136 CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES provision plateaus or stagnates over the middle 50% of the game.

Implications, Limitations and Recommendations

The results of the present experiment have shown that there are limited framing eects on eort provision due to gain and loss framed incentives with and without risky payos. In particular, loss framing with risky payos is the least incentivising payo structure, and gain framing with risky payos revealed prior to eort provision is the most incentivising. This suggests that in designing IHDs, the element of risk regarding the size of energy savings needs to be controlled for as much as possible.

Households will make an eort for a higher payo and so in the context of energy saving behaviours, sub jects need to know whether a particular action will result in a lower or a higher payo as this will aect their willingness to provide an eort.

The laboratory setting of the present experiment provides a high level of internal validity which allows the experimenters to focus solely on the variable they wish to manipulate in order to ascertain its eect on eort provision. Subjects concentrated solely on the task of counting the number of ones in each table, any outside factors were controlled for. The only variables which were dierent from one treatment to another, were the framing and the payo structure.

In reality, when trying to save energy there are many other factors that will aect an individual's ability to provide the necessary eort to save energy. The use of risky payos attempted to reect such situations. Given the dierences in the laboratory setting and the context of energy consumption behaviour, it is dicult to generalise the results outside of the laboratory. That being said, if no signicant results were found in a controlled environment where subjects were solely focused on the task at hand, will there be signicant dierences in eort due to framing in the context of energy saving when there are many additional factors at work? On the other hand, the task was the only activity for sub jects to do for one and a half hours. Sub jects' complete concentration on the task regardless of framing may explain the lack of signicant treatment results.

The next steps for the present research are to increase the sample size in each of the treatments and to increase the saliency of the loss-frame by having subjects work for their endowment prior to completing the experimental task. By doing so, the hypothesis is that subjects will attach a greater utility to avoiding a loss of their endowment because they have had to work for it. This will avoid the possible issue 4.5 CONCLUSION 137 of subjects evaluating the net value of framing rather than seeing the loss-frame as a loss of their money.

Additionally, this experiment can be moved to the eld to determine whether subjects make an increased eort to avoid spending their own money on energy consumption when the loss is salient. This can be compared to the eort made under a salient gain frame and to a factual presentation of consumption information.

Conclusion 140 CONCLUSION

This thesis has explored the dierent incentives that are used to encourage residential consumers to lower their energy consumption in order to determine whether households accept these incentives and the technologies that deliver them, and whether these incentives are eective at reducing their consumption. This sub ject is of particular importance given that human activity is causing the atmosphere of our planet to heat up considerably which is having disastrous impacts across the globe. To limit humankind's future impact, governments across the world have set objectives to lower emissions, to increase the share of renewable energies in the production mix and to achieve energy savings. Residential consumers can do their part by lowering their overall demand, and in particular, their peak demand.

In recent years there has been an increasing interest in the use of behavioural incentives to encourage households to lower their consumption. Given the recency of the application of such incentives to residential energy consumption, there is a smaller amount of research on such incentives in an energy consumption context compared to traditional, nancial incentives. What's more, there are fewer studies which have collated the existing evidence on behavioural incentives. Furthermore, there are concerns as to the ecacy of behavioural incentives to motivate signicant and lasting changes to consumer behaviour. The present thesis has added to this body of research by exploring the previous literature and assessing the eectiveness of traditional and behavioural incentives on consumption, and by more closely scrutinising behavioural incentives in the laboratory.

To answer the central question, how do residential consumers respond to incentives used to encourage them to lower their consumption, the thesis was divided into four chapters. Firstly, a qualitative review of the existing literature answered the question of what are the main barriers to the acceptance and adoption of smart meters and the incentives that they can deliver. Secondly, a meta-analysis provided an updated exploration of the eectiveness of incentives at encouraging households to lower their consumption, and an examination of how the design of experiments can inuence the results. Given the ndings of the rst two chapters, the third chapter sought to further analyse consumer responses to both nancial and behavioural incentives in a controlled, laboratory setting in order to ascertain whether such incentives are able to encourage more socially optimal behaviour, and to quantify the response to a behavioural incentive. The fourth and nal chapter built on ndings from previous research which suggest that individuals make more eort to avoid losses than to receive gains, and looked at whether this is applicable to eort provision for saving energy which is characterised by small eorts for small rewards. This experiment explored ways in which information can be framed in order to incentivise eort provision.

In response to the research questions, Chapter 1 found that there exist many barriers to encouraging households to lower their consumption through incentives delivered via smart meters. These barriers need to be overcome in order for households to accept the installation of smart meters and associated technology in their home in the rst instance, and in the second, in order for them to be eective at motivating consumers to reduce their energy demand. If these barriers remain, households will not engage with smart meters and incentives, and the cost of investing in this technology will not be recuperated through energy and monetary savings.

Chapter 2 looked in detail at the eectiveness of dierent incentives using a metaanalysis approach to combine the results of recent eld experiments and pilot studies which have tested the eect of various incentives on residential energy consumption.

The meta-analysis found that accounting for sample size in primary studies provides more accurate estimates of the eect of incentives, and as such, on average, an incentive will show a reduction in consumption of the order of 2%. Incentives such as pricing strategies, which are primarily aimed at reducing peak demand, can also be eective at reducing overall demand so long as the incentive to reduce consumption during peak periods is not oset by the incentive to increase consumption during opeak periods. Reductions in peak demand are necessary to avoid the use of higher cost, polluting generators, however, if overall demand increases as a result of pricing strategies, the objectives of energy savings may not be met. Incentives based upon behavioural economics such as social feedback are also eective at encouraging a reduction in consumption on average. Such incentives are eective whether they are merely descriptive, or whether injunctive norms are also used. However, the latter shows a slightly larger reduction eect.

The estimate of the eectiveness of incentives was found to be greatly inuenced by the design of the study. In particular, the exclusion of a control group provides inated estimates of reductions in consumption than if a control group is present.

Studies without control groups compare the consumption of a same group of households before and after the implementation of an incentive and so do not perform a simultaneous control for additional factors which may aect consumption. Studies which use a control group provide both a comparison between households' consumption before and after the implementation of an incentive, and a comparison of a group of households during the same time period whose consumption is not inuenced by 142 CONCLUSION an incentive. The use of a control group provides a more robust estimate of the eectiveness that incentives would have in a natural, non-experimental setting.

Additionally, it is important to note that the ndings of eld experiments and pilot studies may overstate the eect that particular incentives would have in the case of a

nation-wide roll-out. In particular, when there are small samples of households who opt-in to the study, these households may be particularly disposed to make an eort to lower their consumption, whether this be for monetary, or environmental reasons, or even due to an interest in the enabling technology oered as part of the study.

As [START_REF] Spence | Engaging with energy reduction: Does a climate change frame have the potential for achieving broader sustainable behaviour[END_REF] have suggested, the type of incentive used, or the choice of unit used for the presentation of consumption information aects individuals' stated motivations to lower their consumption, and as shown in Chapter 3, this can also aect their consumption decisions.

In Chapter 3, individuals were either nudged to lower their consumption, or incentivised to do so by an increase in the price of hypothetical consumption within an experimental CPR game. The price incentive was most eective relative to the level of consumption each incentive was designed to encourage. However, the nudge had an immediate eect as the information was immediately understood by individuals. In the price treatment, individuals took longer to integrate the price into their decision making. This is reective of ndings from the literature discussed in Chapter 1 that pricing strategies such as dynamic pricing are complex for individuals to comprehend [START_REF] Layer | Perceived price complexity of dynamic energy taris: An investigation of antecedents and consequences[END_REF].

While both of these incentives were more eective at encouraging consumers to lower their consumption than an absence of policies, ex-post evaluations of individual characteristics including concern for the environment showed that such characteristics were aected by the incentive used to encourage individuals to lower their consumption. The consumption choices in the nudge treatment diered according to individual environmental sensitivity; those who were more sensitive to environmental issues had previously chosen to consume less. The use of a monetary incentive had no such eect and appeared to crowd out environmental characteristics, as individuals who were more sensitive chose to consume the same amount as those who were less sensitive to environmental issues. This nding, along with the discussions of Chapters 1 and 2, highlights how incentives can aect motivations and individual characteristics to lower energy consumption. If the use of monetary incentives crowds out any environmental characteristics or motivations to lower consumption, then this is of concern as Chapter 1 showed that consumers nd monetary displays of energy savings to not be particularly motivating as the monetary savings are often small [START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF][START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF].

The discussion and analysis of the literature in Chapters 1 and 2 highlighted potential problems with the use of behavioural incentives to encourage energy consumption reduction, namely that when told that they are consuming less than the average consumption of their neighbourhood, households increased their consumption [START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF]. This boomerang eect was mitigated by the inclusion of a smiley face to conrm to individuals that consuming less than the average is socially desirable (Allcott, 2011b). The nudge experimented in Chapter 3, which is presented dierently to the ones used in the eld, found another concerning impact. The nudge was successful at encouraging a reduction in average consumption, however it reinforced individuals' existing behaviour such that those who underconsumed compensated for those overconsumed. This is of particular concern as the use of such nudges may serve to divide consumers: those who are low-consumers make increased eorts to lower their consumption and those who are high-consumers do not.

As discussed above, monetary savings are not necessarily sucient to encourage energy saving eorts. With this in mind, Chapter 4 explored experimentally how the framing of information on earnings can encourage a greater provision of eort and found that individuals provide, on average, the same level of eort when incentivised to make a small eort for a small reward, however the earnings information is framed. When possible rewards are relatively larger, framing does not aect the eort provided by individuals in one direction or the other. When individuals are not sure of how much they will earn after an eort is made, i.e.: when earnings are risky, they tend to provide slightly less eort under loss framing. These ndings have shown that in order to encourage individuals to make small eorts for small rewards, as is typical of energy saving behaviours, the framing of the reward is not of utmost importance, rather it would be more eective to focus on the size of the amount that can be saved. In order to encourage, households to make an eort to lower their consumption, emphasis should be put on the fact that several eorts lead to a larger saving, as individuals provided more eort when the potential earnings were relatively higher. In addition, eorts should be made to minimise the risk on earnings so that households know that by doing a certain action they are sure to Monetary incentives, namely pricing strategies, are more suited to reducing peak demand than overall demand. In order for them to be truly eective, there needs to be an eort to better explain the taris so that perceived complexity does not slow their adoption, nor consumers' responsiveness. Behavioural incentives are another eective incentive, especially when injunctive norms are used alongside social norms. These incentives have the advantage over pricing strategies of being quickly understood but there may be unwanted consequences. When individuals are aware that they are being nudged towards a socially optimal behaviour, then they may react by performing exactly the behaviour that is undesirable. Information on energy and monetary savings is also eective, at least initially. However, there are limits to monetary motivation. Eorts to save energy result in small monetary rewards which are not necessarily sucient to be motivating. Dierent methods of framing small monetary rewards for small eorts does not increase eort provision.

Limitations

When undertaking this research, certain limitations were met. The methodology of meta-analysis used in Chapter 2 means being exhaustive in including all studies on the topic yet is limited by the availability of studies and the potential for available studies to only be those which show signicant eects. If studies which do not show signicant eects are not included in the meta-analysis, then the results of the analysis are biased. This limitation was overcome by analysing the extent to which publication bias was an issue in the sample of studies used, and by using methods to correct for it. The method of giving more weight in statistical analyses of those studies with larger samples was found to signicantly mitigate the publication bias issue.

Chapter 3 concerned energy consumption choices in a contextualised CPR game.

Subjects made decisions in a computer laboratory, and although their decisions had monetary consequences designed to reect the utility and disutility of consumption choices, their decisions did not impact actual consumption. On the other hand, in

Chapter 4 an entirely decontextualised game mimicking in a stylised manner certain characteristics of energy consumption and saving was used. Laboratory experiments benet from high internal validity -individuals make decisions in a highly controlled setting, however, they can lack external validity.

The ndings of the laboratory experiments presented in this thesis have provided an insight into behaviours in response to nudges and prices, and in response to the framing of information. They are not a substitute to the eld experiments and pilot studies analysed in Chapter 2, but rather, are a complementary approach to better understanding individual behaviour. The experiment in Chapter 3 highlighted an unexpected consequence of a nudge on dierent groups on individuals, and showed how responses to incentives can inuence individual characteristics. The experiment in Chapter 4 explored the provision of eort for small rewards in a less complex environment compared to real-life where eorts to save energy have wider impacts including impacts on comfort and on other individuals. The lack of framing eects in the laboratory environment raises the question of whether such framing eects will be found when the provision of eort is more complex. However, subjects in the experiment had only to focus on the task of counting the number of ones in a series of tables and so may not have been susceptible to framing as they were focused only on the task in hand.

An important requirement to verify the robustness of laboratory experiment results is replication. While, the experiment in Chapter 4 has replicated aspects of previous experiments and has drawn similar conclusions, namely that framing eects are not always found when earnings are small, the results of the experiment in Chapter 3 should be further researched. In particular, the eect of the nudge of reinforcing existing behaviour.

Implications

The implications of the present research are three-fold. Firstly, the dierent incentives are eective at encouraging households to lower their consumption to varying degrees. When policy makers decide which incentive or incentives to implement, they must rst consider the objective: overall demand reduction or peak demand reduction. The incentives should then be adapted to that objective bearing in mind that pricing strategies are most eective at reducing peak demand and that additional incentives should be used to counter any extraordinary increases in demand during the o-peak periods. Additionally, policy makers should consider that information on consumption in the form of nudges is quickly understood by consumers and so

CONCLUSION

can have an immediate eect on consumption whereas prices take a little longer to be integrated into individuals' decision making. This is of interest to policy makers as increased prices may cause individuals to be worse o for a time if they are unable to lower their consumption immediately in response to the price. Consumers may suer welfare losses before they are fully able to adjust their consumption in response to the price increase.

Secondly, policy makers should be aware of the barriers to the implementation of the dierent incentives and their potential undesirable consequences. In particular, they should be concerned by the individual eect of nudges typically used: as seen in the previous literature, descriptive comparisons may encourage low-consuming households to increase their consumption to the average amount, or, as seen in the present research, the inclusion of injunctive norms may reinforce existing behaviours such that low-consuming households reduce their consumption, compensating for high-consuming households who continue to increase their consumption.

Finally, the present research also has implications for the design of devices which display consumption information to households: the presentation of earnings in terms of gains or losses does not encourage greater eort provision under one or other framing.

It is the size of potential earnings and the reduction of risk on earnings which is of importance. This lack of framing eect for small earnings also has implications for theory, as it would seem that loss aversion does not cause individuals to provide greater eort under loss framing in the case of small earnings.

The amount that the individual can earn is simply too small to be aected.

Future research

The opening chapter of this thesis identies barriers to the use of incentives to encourage demand reduction of which some are then further analysed in the following chapters. Given that several barriers were identied, there is much scope for further research into the topic of this thesis.

Individuals are generally unaware of their consumption, or of the electricity tari that they are on. In turn this aects their decisions to opt-in into dynamic pricing contracts as individuals prefer to have a simpler contract as they are unaware of whether such a tari would be benecial to them and how they can change their behaviour to take advantage of the cheaper o-peak prices. An avenue for future research could be to see how individuals' tari choices change upon being informed of their own consumption and how they can make use of the dierent pricing levels, building on the research of [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF], [START_REF] Buryk | Investigating preferences for dynamic electricity taris: The eect of environmental and system benet disclosure[END_REF] and [START_REF] Layer | Perceived price complexity of dynamic energy taris: An investigation of antecedents and consequences[END_REF].

Monetary motivation has not been found to be a strong driver of changing consumption behaviour as the savings are generally small [START_REF] Hargreaves | Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term[END_REF][START_REF] Murtagh | 20:60:20: Dierences in energy behaviour and conservation between and within households with electricity monitors[END_REF]. Furthermore, the choice of dierent presentations of information awakens dierent motivations to save energy [START_REF] Spence | Engaging with energy reduction: Does a climate change frame have the potential for achieving broader sustainable behaviour[END_REF]. Another avenue for further research could be to provide individuals with displays of energy consumption information presented in dierent ways (monetary savings, energy savings, environmental savings, ...) then, via a choice experiment, measure their preferences for the dierent displays. This could then be further researched in the eld by comparing responses to incentives to lower consumption which are based on non-monetary calculations of the impact of saving energy on the environment, or on health, or other.

In addition to the barriers identied in Chapter 1, the experiments carried out in this thesis also opened up avenues for further research. In Chapter 3, the nudge resulted in the unintended eect of reinforcing existing behaviour. A possible explanation was found in reactance theory: individuals feel that their freedom to do as they wish is threatened and so they do the behaviour that is not encouraged [START_REF] Brehm | A theory of psychological reactance[END_REF]). Yet, this eect was not found in the eld experiments reviewed in Chapters 1 and 2. There is a dierence in the creation of the nudge used in the eld, and the one used in the present experiment. In the eld, behaviour is compared to an endogenous level, the average consumption of the neighbourhood, whereas in the laboratory experiment, behaviour is compared to an exogenously dened optimal level of consumption which is calculated by the experimenter. Individual responses to endogenous and exogenous nudges merits further research.

This thesis set out to explore how residential consumers respond to incentives used to encourage them to lower their consumption. Are there any questions? Phase 1: Electricity consumption behaviour

Electricity consumption

We consume electricity to satisfy our daily needs:

• To heat or to cool our homes via our central heating, or an air conditioner.

• To use our household appliances

• To use heat our meals or to keep them cool • To recharge our electronic equipment (mobile phones, computers) We are all electricity consumers, in particular in our homes, to a varying degree according to our daily needs and habits. To meet the demand for electricity, there are dierent actors in the electricity network, from production to distribution in residential homes. Regardless of the actors of production or distribution, there must always be a balance between production and consumption in order to maintain the functioning of the system. If there is an imbalance, then the system is interrupted and this could result in a power cut. For example, during the winter, the increased consumption by households could result in power cuts.

The study

This study focuses on such periods, known as peak periods when there is a risk that consumption is greater than the available production. These periods generally last for 4-8 hours, during the afternoon and evening.

Interruptions in the electricity network (voltage reductions, brown outs or black outs) equates to a reduction in comfort for all.

In order to avoid such interruptions in the electricity network (voltage reductions, brown outs or black outs), one solution is to ask consumers to reduce their consumption during the peak period. Of course, this reduction in consumption equates to a group: :-)

• Above the level which minimises the reduction in comfort felt by the group:

:-( At the end of the period, your earnings will be saved and you will start a new peak period, with a new endowment of 100 EU.

At the end of the 10 peak periods Phase 1 of the study is composed of 10 peak periods. In each period you have the same decisions to make with the same people in your electricity consumption group.

At the end of the study, your earnings in ECU for the 10 peak periods, will be added together and converted into euros at the following exchange rate : 150 ECU

= 1e

These earnings will be added to those of phase 2 and paid at the end of the study.

Are there any questions?

Before we begin, please answer a few questions to verify your understanding of the instructions.

Phase 2: Lottery For the nudge and control groups, the optimal consumption level is 60. In the price treatment, it is 80. For the nudge and control groups, the optimal consumption level is 15. In the price treatment, it is 20.

(3) (4)

(6) [START_REF]I use a cleaner made especially for bathrooms, rather than an all-purpose cleaner[END_REF] (1) Total number of correct tables in period 57 La carte présente les résultats des analyses coûts-avantages (ACB) des États membres, qu'elles soient positives, négatives, non disponibles ou non concluantes, ainsi que l'état d'avancement du déploiement des compteurs intelligents. A partir de 2013, les pays en vert foncé ont ociellement commencé l'installation de compteurs intelligents, ceux en vert hachuré prévoient d'installer des compteurs intelligents (une fois une décision ocielle a été prise), ceux en rouge ont décidé de ne pas installer des compteurs intelligents après une ACB négative ou non concluante, ceux en orange foncé n'ont pas encore pris une décision et ceux en orange hachuré ont commencé une installation sélective. Par exemple, en Allemagne, l'installation des compteurs intelligents se limite aux maisons neuves ou rénovées, aux consom'acteurs et aux ménages à forte consommation [START_REF] Edelmann | Cost-benet analysis for the comprehensive use of smart metering[END_REF].

FRENCH SUMMARY

Technologies et mesures incitatives

Grâce à l'utilisation de compteurs intelligents, diérentes incitations peuvent être offertes aux consommateurs en fonction de mesures précises de la consommation. Les incitations qui ciblent la réduction globale de la demande sont traditionnellement basées sur l'information [START_REF] Darby | The eectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering[END_REF] -informations sur la consommation historique ou de feedback en temps réel -ou, plus récemment, sur les connaissances de l'économie comportementale (Allcott, 2011b). Les compteurs intelligents facilitent l'utilisation d'incitations nancières tels que la tarication dynamique qui est utilisée pour encourager une baisse de la demande en période de pointe [START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF]

FRENCH SUMMARY Tarication dynamique

La tarication dynamique fait référence à l'ajustement des prix de détail de l'électricité pour mieux reéter les coûts de la production d'énergie. En période de pointe, les coûts de production sont plus élevés, ce qui entraîne une hausse des prix de détail qui incite les ménages à réduire leur demande pendant les périodes de pointe et, dans certains cas, à augmenter leur demande pendant les périodes creuses, an de maintenir l'équilibre entre l'ore et la demande [START_REF] Faruqui | The power of dynamic pricing[END_REF]. Il existe diérentes structures tarifaires plus ou moins dynamiques, allant d'une tarication variable dans le temps, mais statique, les tarications heures pleines-heures creuses, à une tarication dynamique en temps réel. Ces diérents programmes de tarication dièrent selon le degré de risque et le rendement possible [START_REF] Faruqui | Dynamic pricing and its discontents[END_REF]. Un tarif standard dans lequel les prix sont xés quel que soit le moment de la consommation est "sans risque" car tous les kWh sont consommés au même prix.

Le risque associé à une tarication heures pleines-heures creuses est légèrement plus élevé, mais le risque demeure beaucoup plus faible que celui d'une tarication de pointe critique ou d'une tarication dynamique en temps réel. La g. C.3 montre l'arbitrage risque-rendement des tarications dynamiques où le risque fait référence à l'exposition des consommateurs à la volatilité des prix du marché de gros de l'électricité [START_REF] Faruqui | The ethics of dynamic pricing[END_REF].

Les tarications dynamiques sont ecaces pour réduire la consommation d'énergie, en particulier les tarications du type Critical Peak Pricing ou Real-time Pricing lorsqu'ils sont combinés à des moniteurs d'énergie [START_REF] Faruqui | Arcturus: international evidence on dynamic pricing[END_REF]. En eet, pour que les ménages puissent réagir avec succès à une tarication dynamique, les consommateurs ont besoin d'un moniteur pour les informer de l'évolution des prix [START_REF] Dütschke | Dynamic electricity pricingwhich programs do consumers prefer?[END_REF], en particulier dans le cas de tarication en temps réel.

Toutefois, les opposants à une tarication dynamique défendent qu'il ne faut pas demander aux consommateurs du secteur résidentiel de soutenir la volatilité des prix de l'électricité, en particulier les consommateurs vulnérables aux changements de l'ore

INTRODUCTIONFigure 1 :

 1 Figure 1: Smart meter deployment strategies across the EU-27 by 2020 as of July 2013, (European Commission, 2014b) 6

  Figure 1.1 gives an example of a smart meter in deployment in France.

Figure 1 . 2 :

 12 Figure 1.2: Example of a TOU tari

Figure 1 . 3 :

 13 Figure 1.3: Example of a CPP tari

Figure 1 .

 1 Figure 1.4 represents a PTR tari.

CHAPTER 1 :Figure 1 . 5 :

 115 Figure 1.4: Example of a PTR tari

  of each individual control on energy consumption. Hypothesis 6: More robust studies (inclusion of control group, weather controls, demographic controls, opt-out recruitment, random assignment to treatment group) show a smaller reduction in electricity demand. Each of these reviews and analyses have covered studies across a long time period, from the 70s and 80s to the present. Ehrhardt-Martinez et al. (2010) nd trends in energy savings across two distinct periods; the Energy Crisis Era from the seventies to 1995, and the Climate Change Era from 1995 to 2010. McKerracher and Torriti (2013) identify an additional era, from 2005 onwards which they name the Smart Grid Era. The current paper seeks to better understand the eect of dierent interventions on energy consumption by considering solely studies from 2005 onwards so as to focus on the Smart Grid era. Studies conducted since 2005 are dierent to those conducted in the 70s and 80s. The more recent studies benet from technological advances in terms of the provision of treatments and the measurement of treatment eects. It would be erroneous to include results from such varied time periods. As Ehrhardt-Martinez et al. (2010, p.74) note, "studies that compare feedback-related savings across all four decades may result in inated expectations regarding potential energy savings today".

•

  Keywords concerning the type of incentive: Incentive, behaviour Informational feedback: smart meter, advanced metering, feedback, nudge, norm, Financial feedback: dynamic pricing, tari, time of use, critical peak pricing, real time pricing, peak time rebate, and;

Figure 2 . 1 :

 21 Figure 2.1: Geographical distribution of included studies

Figure 2 . 3 :Figure 2

 232 Figure 2.3: Treatment eects by presence of control group

Figure 2 . 5 :

 25 Figure 2.5: Treatment eects by collection of socio-demographic data

Figure

  Figure 2.6: Treatment eects by treatment assignment

Figure 2 . 7 :

 27 Figure 2.7: Treatment eects by sample selection method

  their set-up, Boun[START_REF] Boun My | Nudge and Tax in an Environmental Public Goods Experiment: Does Environmental Sensitivity Matter? Resource and Energy Economics[END_REF] create groups of either all highly environmentally sensitive subjects, or of less environmentally sensitive sub jects. This 3.1 INTRODUCTION 85 is perhaps not entirely reective of the situations where individuals interact with people of diering levels of environmental sensitivity.

A

  game of common pool resource extraction can be modelled as follows: a group of n players share a common resource. They each have an endowment e which can be used to invest in the extraction of the common resource. The amount invested in resource extraction by individual i is x i with Σx i the amount invested by the group. Extraction of the resource earns each player a for every unit extracted personally, minus b for every unit extracted by the group regardless of who extracts it. The parameter a represents the utility of consuming electricity in terms of increased comfort, the use of appliances without constraint, whereas the parameter b represents the disutility of all subjects' consumption of electricity in terms of voltage reductions and brief power cuts. The cost of investing in the extraction of the resource is c. Each player's prot depends on his own investment in extraction as well as the group investment:

  or not to use the dierent electrical appliances proposed, subjects are choosing to consume energy units in increments of 5. The choice of electricity consumption is made discrete to reect that in real life individuals consume electricity by turning appliances on or o. There are three levels of consumption for the heating choice; the same, 1

Figure 3 . 1 :

 31 Figure 3.1: Dynamics of average consumption by treatment

Figure 3 . 2 :

 32 Figure 3.2: Dynamics of heating usage by treatment

Figure 4 . 1 :

 41 Figure 4.1: A hypothetical value function(Kahneman and Tversky, 1979, p.279)

Figure 4 .

 4 Figure 4.2 shows the evolution of the average number of correct tables. A comparison is made between the payo structures within a treatment frame: loss or gain. The diculty as measured by the average distribution of ones in the ve tables of a period is underlaid and represented in grey. The peaks and troughs in the average number of correct tables correspond to relatively easy (fewer ones) and dicult (more ones) periods.

  Figure 4.2: Evolution of average number of correct tables and average diculty per period by treatment (comparison by frame)

  Figure 4.3: Cumulative distribution functions of number of correct tables in each treatment

Figure 4 . 4 :

 44 Figure 4.4: Evolution of average number of correct tables by block of 7 periods by treatment

  earn a xed amount. The ndings of the present research have helped to respond to the central research 144 CONCLUSION question: how do residential consumers respond to incentives used to encourage them to lower their consumption? Despite barriers to the implementation of smart meters and the incentives that they can deliver, residential consumers do respond to incentives and they are eective at encouraging a reduction in consumption. The dierent incentives are more or less eective depending on the reduction ob jective.

  les consommateurs doivent être incités de manière appropriée.

Figure C. 2 :

 2 Figure C.2: Stratégies de déploiement de compteurs intelligents dans l'UE-27 d'ici 2020 à compter de juillet 2013, (European Commission, 2014b) 57

Figure

  Figure C.3: Trade-o risque/récompense en matière de tarication dynamique (adapté de Faruqui (2012, p.17))

  58 met cela en pratique avec des factures papier qui compare la consommation d'un ménage avec la consommation moyenne des ménages voisins (norme sociale). On y trouve également un visage content (norme injonctive) si le ménage consomme moins que ses voisins(Allcott, 2011b). La g. C.4 fournit un exemple de l'utilisation de normes sociales et injonctives dans un facture d'Opower.

Figure C. 4 :

 4 Figure C.4: Une facture "Home Energy Report" d'Opower

  n'est qu'au cours de la quatrième période de jeu que les individus intègrent le prix dans leur prise de décision. L'hypothèse sous-tendant le nudge est que les individus qui ne se comportent pas de manière optimale en absence d'incitation, c'est-à-dire qui surconsomment, seront encouragés à baisser leur consommation après avoir reçu le nudge. Cependant, les résultats montrent que les individus ne répondent pas au nudge de cette manière et continuent à surconsommer. Cette constatation revêt une importance particulière, car elle met en lumière un inconvénient potentiel des nudges.La contribution de ce chapitre est l'application de la théorie des ressources communes au marché de l'électricité dans le cadre d'une expérience en laboratoire. La valeur monétaire du nudge est estimé en comparant le comportement des individus face au nudge au comportement face au prix équivalent. Le prix est xé pour que les individus soient incités à choisir le même niveau de consommation que celui qu'ils choisissent dans le traitement nudge. que l'information fournie par le moniteur n'incite pas nécessairement les ménages à réduire leur consommation : lorsque cette information est achée en unités énergétiques, elle est incompréhensible[START_REF] Raw | Energy demand research project: Final analysis[END_REF] Ross, 2011, Buchanan et al., 2014) ; lorsqu'elle est en unités monétaires, les économies potentielles sont trop faibles.Certains ménages estiment que l'eort qu'ils font pour réduire leur consommation d'énergie n'en vaut pas la peine car il n'a qu'un faible eet sur la consommation et entraîne peu d'économies monétaires[START_REF] Hargreaves | Making energy visible: A qualitative eld study of how householders interact with feedback from smart energy monitors[END_REF], 2013[START_REF] Goulden | Smart grids, smart users? the role of the user in demand side management[END_REF]. Le dernier chapitre tient compte de ces obstacles et cherche à déterminer comment des mesures incitatives pourraient être formulées pour encourager un eort plus important, lorsqu'on demande aux individus de faire de petits eorts pour de petites récompenses. Ce cas de gure s'applique en eet particulièrement aux eorts visant à réduire la consommation énergétique.L'objectif du chapitre 4 est d'explorer le cadre des mesures incitatives utilisées pour encourager les petits eorts lorsque les récompenses sont faibles.La dernière question de recherche est la suivante :Comment l'information (sur les moniteurs) peut-elle être formulée pour encourager la fourniture d'eorts ?Le chapitre 4 vise donc à déterminer comment l'information peut être structurée pour encourager les individus à faire un petit eort lorsque les récompenses sont faibles. Dans l'expérience, les individus sont incités à faire un eort pour réaliser une tâche articielle à eort réel sur un certain nombre de périodes. L'incitation prend la forme de paiements, qui sont présentés aux individus de manière diérente (sous forme de gains ou de pertes) et dont la structure est diérente (paiements sans risque ou à risque). L'expérience s'appuie sur les conclusions de la théorie des perspectives[START_REF] Kahneman | Prospect theory: An analysis of decisions under risk[END_REF] qui suggère que les individus font plus d'eorts lorsque les paiements sont présentés comme une perte (en particulier lorsque les pertes sont risquées), que lorsqu'ils sont présentés comme un gain. Dans le cas des petites récompenses pour de petits eorts, la présente expérience ne révèle aucun eet signicatif de la présentation des paiements sous forme de gains ou de pertes sur l'eort. En moyenne, les individus fournissent le même niveau d'eort quelle que soit la présentation. Toutefois, les individus fournissent plus d'eorts lorsqu'ils peuvent gagner ou perdre un montant élevé plutôt que faible. Cela suggère que lorsque les récompenses sont faibles, comme dans le cas des actions visant à réduire la consommation d'énergie, les individus font un eort équivalent dans le cadre d'une présentation sous forme de gains ou d'une présentation sous forme de pertes. Augmenter l'importance des récompenses associées à une action est essentiel pour encourager les individus à faire un eort. Ce chapitre contribue à la littérature sur la présentation des paiements sous forme de gains ou de pertes en examinant comment la théorie de la perspective s'applique dans une situation où les individus doivent faire de petits eorts pour de petites récompenses, et en incluant un élément de risque dans la structure des paiements. Enn, la thèse se termine par une vue d'ensemble des quatre chapitres et de leurs conclusions en relation avec la question centrale de la recherche. Les limites de la recherche et ses implications pour les décideurs, les praticiens et les théoriciens sont examinées. La thèse se termine par une discussion sur les pistes de recherche futures. Conclusion Cette thèse a exploré les diérentes mesures incitatives utilisées pour encourager les consommateurs du secteur résidentiel à réduire leur consommation d'énergie. L'objectif était de déterminer si les ménages acceptent ces mesures incitatives et les technologies qui y sont associées, et si ces mesures sont ecaces pour réduire leur consommation. Ce sujet revêt une importance particulière étant donné l'impact des activités humaines sur le réchauement de l'atmosphère de notre planète, qui a des eets désastreux dans le monde entier. Pour limiter l'impact futur de l'humanité, les gouvernements du monde entier se sont xé des objectifs de réduction des émissions, 216 FRENCH SUMMARY d'augmentation de la part des énergies renouvelables dans le mix de production et d'économies d'énergie. Les consommateurs du secteur résidentiel peuvent contribuer à cet eort en réduisant leur demande globale et, en particulier, leur demande de pointe. Ces dernières années, on observe un regain d'intérêt pour l'utilisation d'incitations comportementales pour encourager les ménages à réduire leur consommation. Compte tenu de l'application récente de ces incitations à la consommation résidentielle d'énergie, les recherches sur ces incitations dans le contexte de la consommation d'énergie sont moins nombreuses que celles portant sur les incitations nanciersnancières traditionnelles. De plus, il y a peu d'études qui ont rassemblé les données existantes sur les incitations comportementales. En outre, se pose la question de l'ecacité des incitations comportementales pour motiver des changements signicatifs et durables dans le comportement des consommateurs. La présente thèse contribue à ce corpus de recherche en explorant la littérature antérieure et en évaluant l'ecacité des incitations traditionnelles et comportementales sur la consommation, et en analysant plus nement les incitations comportementales en laboratoire. Pour répondre à la question centrale de savoir comment les consommateurs du secteur résidentiel réagissent aux incitations utilisées pour les encourager à réduire leur consommation énergétique, la thèse a été divisée en quatre chapitres. Premièrement, une revue de la littérature a permis d'identier les principaux obstacles à l'acceptation et à l'adoption des compteurs intelligents et aux incitations qu'ils peuvent orir. Deuxièmement, une méta-analyse a permis d'actualiser les connaissances sur l'ecacité des mesures incitatives pour encourager les ménages à réduire leur consommation, et de prendre en compte le fait que la manière dont sont conçues les expériences inuence les résultats. Compte tenu des conclusions des deux premiers chapitres, le troisième chapitre visait à analyser plus en détail les réactions des consommateurs aux incitations nancières et comportementales dans un environnement contrôlé en laboratoire an de déterminer si ces incitations sont capables d'encourager un comportement socialement plus optimal et à quantier la réactionFRENCH SUMMARY 217 à une incitation comportementale. Le quatrième et dernier chapitre s'appuie sur les résultats de recherches antérieures qui suggèrent que les individus font plus d'eorts pour éviter les pertes que pour recevoir des gains. Ce chapitre cherche à savoir si cela est applicable à la fourniture d'eorts pour économiser l'énergie, qui se caractérise par de petits eorts pour de petites récompenses. Cette expérience a permis d'explorer les moyens de présenter l'information an d'encourager la fourniture de l'eort. En réponse aux questions de recherche, le chapitre 1 a permis de constater qu'il existe de nombreuses barrières limitant les possibilités d'encourager les ménages à réduire leur consommation au moyen de mesures incitatives associées à des compteurs intelligents. Ces obstacles doivent être surmontés pour que les ménages acceptent l'installation de compteurs intelligents et de la technologie connexe à leur domicile dans un premier temps, et dans un deuxième temps, pour qu'ils soient en mesure de motiver ecacement les consommateurs à réduire leur demande énergétique. Si ces obstacles persistent, les ménages n'utiliseront pas de compteurs intelligents et n'adopteront pas les mesures incitatives, et le coût d'investissement dans cette technologie ne sera pas amorti par les économies énergétiques et monétaires qui pourraient être réalisées. Le chapitre 2 a examiné en détail l'ecacité de diérentes mesures incitatives à l'aide d'une méta-analyse combinant les résultats d'expériences récentes sur le terrain et d'études pilotes qui ont testé l'eet de diverses mesures incitatives sur la consommation énergétique résidentielle. La méta-analyse a révélé que la prise en compte de la taille de l'échantillon dans les études initiales fournit des estimations plus précises de l'eet des incitations et qu'en moyenne, une incitation entraînera une réduction de la consommation de l'ordre de 2%. Des mesures incitatives telles que les stratégies de prix, qui visent principalement à réduire la demande de pointe, peuvent également être ecaces pour réduire la demande globale tant que l'incitation à réduire la consommation pendant les périodes de pointe n'est pas compensée par une incitation à augmenter la consommation pendant les heures creuses. 218 FRENCH SUMMARY Des réductions de la demande de pointe sont nécessaires pour éviter l'utilisation de générateurs coûteux et polluants, mais si la demande globale augmente en raison des stratégies de tarication, les objectifs d'économies d'énergie pourraient ne pas être atteints. Les mesures incitatives fondées sur l'économie comportementale, telle que la comparaison sociale, sont également ecaces pour encourager une réduction de la consommation en moyenne. De telles incitations sont ecaces, qu'elles soient simplement descriptives ou qu'elles fassent également appel à des normes injonctives. Toutefois, cette dernière montre un eet de réduction légèrement plus important. L'estimation de l'ecacité des mesures incitatives a été grandement inuencée par le protocole de l'étude. En particulier, l'absence d'un groupe de contrôle fournit des estimations gonées des réductions de consommation par rapport au cas où un groupe de contrôle est présent. Les études sans groupe de contrôle comparent la consommation d'un même groupe de ménages avant et après la mise en ÷uvre d'une mesure incitative et n'eectuent donc pas un contrôle simultané des facteurs externes qui peuvent aecter la consommation au cours d'une expérience. Les études qui utilisent un groupe de contrôle fournissent à la fois une comparaison entre la consommation des ménages avant et après la mise en ÷uvre d'une mesure incitative, et une comparaison avec un groupe de ménages dont la consommation n'est pas inuencée par une mesure incitative, pendant la même période. L'utilisation d'un groupe de contrôle fournit une estimation plus robuste de l'ecacité que les mesures incitatives auraient dans un cadre naturel et non expérimental. De plus, il est important de noter que les résultats des expériences sur le terrain et des études pilotes peuvent surestimer l'eet que des incitations auraient dans le cas d'un déploiement à l'échelle nationale. En particulier, lorsqu'il y a de petits échantillons de ménages qui choisissent de participer à l'étude, ces ménages peuvent être particulièrement disposés à faire un eort pour réduire leur consommation, que ce soit pour des raisons monétaires ou environnementales, ou même en raison d'un intérêt pour la technologie oerte dans le cadre de l'étude. Comme l'ont suggéré Spence et al. (2014) le type d'incitation utilisé ou le choix de l'unité utilisée pour la présentation de l'information sur la consommation inue sur les motivations déclarées des individus à réduire leur consommation et, comme le montre le chapitre 3, cela peut aussi inuer sur leurs décisions de consommation. Au chapitre 3, les individus ont été encouragés à réduire leur consommation par le biais d'un nudge, ou incités à le faire par une augmentation du prix de la consommation hypothétique dans le cadre d'un jeu expérimental de ressources communes. L'incitation via le prix était la plus ecace pour atteindre le niveau de consommation que chaque incitation était conçue pour encourager. Cependant, le nudge a eu un eet immédiat puisque l'information a été immédiatement comprise par les individus. Dans le cas de l'incitation tarifaire, les individus ont mis plus de temps à intégrer le prix dans leur prise de décision. Cela reète les constatations tirées de la littérature dont il a été question au chapitre 1, à savoir que les stratégies de prix, telle que la tarication dynamique, sont complexes à comprendre pour les ménages (Layer et al., 2017). Ces deux mesures incitatives ont été plus ecaces pour encourager les consommateurs à réduire leur consommation que l'absence de politiques. Cependant, les évaluations ex post des caractéristiques individuelles, y compris la sensibilité environnementale, ont montré que les caractéristiques déclarées par les individus diffèrent selon l'incitation utilisée pour les encourager à réduire leur consommation. Les choix de consommation dans le traitement nudge dièrent selon la sensibilité environnementale individuelle ; ceux qui étaient plus sensibles aux questions environnementales sont ceux qui ont le plus réduit leur consommation. L'utilisation d'une incitation monétaire n'a pas eu cet eet diérencié en fonction des caractéristiques environnementales, car les personnes plus sensibles ont choisi de consommer la même quantité que celles qui étaient moins sensibles aux questions environnementales. Cette constatation, ainsi que les discussions des chapitres 1 et 2, mettent en lumière l'existence d'un lien entre les incitations, et les motivations et les caractéristiques individuelles. Si l'utilisation d'incitations monétaires écarte toute inuence des caractéristiques ou motivations environnementales sur la réduction de la con-220 FRENCH SUMMARY sommation, c'est préoccupant, car le chapitre 1 a montré que les consommateurs sont peu motivés par les économies monétaires liées à la réduction de leur consommation énergétique, celles-ci étant souvent faibles (Hargreaves et al., 2010, Murtagh et al., 2014). La discussion et l'analyse de la documentation aux chapitres 1 et 2 ont mis en lumière les problèmes potentiels liés à l'utilisation d'incitations comportementales pour encourager la réduction de la consommation d'énergie, à savoir que lorsqu'on leur dit qu'ils consomment moins que la consommation moyenne dans leur quartier, les ménages augmentent leur consommation (Schultz et al., 2007). Cet eet boomerang a été atténué par l'inclusion d'un smiley pour rappeler aux individus que consommer moins que la moyenne est socialement souhaitable (Allcott, 2011b). Le nudge expérimenté au chapitre 3, qui est présenté diéremment de ceux utilisés sur le terrain, a conduit à un autre impact inquiétant. Le nudge a réussi à encourager une réduction de la consommation moyenne, mais il a renforcé le comportement existant des individus, de sorte que ceux qui sous-consommaient ont compensé pour ceux qui surconsommaient. Cela est particulièrement préoccupant, car l'utilisation de tels nudges peut diviser les consommateurs : ceux qui sont peu consommateurs font des eorts accrus pour réduire leur consommation et ceux qui sont gros consommateurs ne le font pas. Comme expliqué dans le chapitre 1, les économies monétaires ne sont pas nécessairement susantes pour encourager les eorts d'économie d'énergie. C'est dans cette optique que le chapitre 4 a exploré expérimentalement la façon dont la présentation de l'information sur les gains peut encourager un plus grand eort. Le chapitre a également montré que les personnes fournissent, en moyenne, le même niveau d'eort lorsqu'elles sont incitées à faire un petit eort pour une petite récompense, quelle que soit la présentation des paiements (en gains ou en pertes). Lorsque les récompenses possibles sont relativement plus importantes, la présentation des paiements n'aecte pas l'eort fourni par les individus. Lorsque les individus ne sont pas sûrs du montant qu'ils gagneront après un eort, c'est-à-dire lorsque les gains sont risqués, ils ont tendance à fournir un peu moins d'eort dans le cas d'une présentation sous forme de pertes. Ces résultats ont montré qu'an d'encourager les individus à faire de petits eorts pour de petites récompenses, comme c'est le cas pour les économies d'énergie, la présentation des paiements n'est pas de la plus haute importance, il serait plutôt plus ecace de se concentrer sur le montant qui peut être économisé. An d'encourager les ménages à faire un eort pour réduire leur consommation, il convient de mettre l'accent sur le fait que plusieurs eorts combinés conduisent à des économies plus importantes, car les individus fournissent plus d'eorts lorsque les récompenses potentielles sont relativement plus élevées. En outre, il conviendrait de minimiser le risque sur les paiements an que les ménages soient assurés du montant qu'ils gagneront en réalisant une certaine action. Les résultats de la présente recherche ont permis de répondre à la question centrale de la recherche : comment les consommateurs résidentiels réagissent-ils aux incitations utilisées pour les encourager à réduire leur consommation énergétique ? Malgré les obstacles au déploiement des compteurs intelligents et des mesures incitatives qui y sont associées, les consommateurs résidentiels réagissent aux incitations et ces dernières sont ecaces pour encourager une réduction de la consommation. Les diérentes incitations sont plus ou moins ecaces selon l'ob jectif de réduction. Les incitations monétaires, à savoir les stratégies de prix, sont plus appropriées pour réduire la demande de pointe que la demande globale. Pour qu'ils soient vraiment ecaces, il faut mieux expliquer les tarifs an que la complexité perçue ne freine pas leur adoption, ni la réactivité des consommateurs. Les incitations comportementales sont une autre incitation ecace, en particulier lorsque des normes injonctives sont utilisées parallèlement aux normes sociales. Ces incitations ont l'avantage, par rapport aux stratégies de prix, d'être comprises rapidement, mais il peut y avoir des conséquences non désirées. Lorsque les individus se rendent compte qu'ils sont poussés vers un comportement socialement optimal, ils peuvent réagir en adoptant exactement le comportement qui n'est pas souhaitable. L'information sur les économies d'énergie et les économies monétaires est également ecace, du 222 FRENCH SUMMARY moins au début. Cependant, il y a des limites à la motivation monétaire. Les eorts d'économie d'énergie se traduisent par de petites récompenses monétaires qui ne sont pas nécessairement susantes pour être motivantes. Les diérentes méthodes de présentation des petites récompenses pour de petits eorts n'augmentent pas la fourniture d'eort. Limites Au cours de cette recherche, certaines limites ont été rencontrées. La méthodologie de la méta-analyse utilisée dans le chapitre 2 consiste à inclure de façon exhaustive toutes les études sur le sujet, mais elle est limitée par la disponibilité des études. Or, il est possible que les études disponibles ne soient que celles qui ont des eets signicatifs. Si les études qui ne montrent pas d'eets signicatifs ne sont pas incluses dans la méta-analyse, alors les résultats de l'analyse sont biaisés. Cette limite a été contournée en analysant les problèmes liés au biais de publication, et en utilisant des méthodes pour le corriger. La méthode consistant à donner plus de poids aux études portant sur des échantillons plus importants a permis d'atténuer de façon signicative le biais de publication. Le chapitre 3 portait sur les choix de consommation d'énergie dans un jeu de ressources communes contextualisé. Les sujets prenaient leurs décisions dans un laboratoire informatique et, même si leurs décisions avaient des conséquences monétaires conçues pour reéter l'utilité et la désutilité des choix de consommation, leurs décisions n'avaient aucune incidence sur la consommation réelle. D'autre part, au chapitre 4, un jeu entièrement décontextualisé imitant de manière stylisée certaines caractéristiques de la consommation et des économies d'énergie a été conduit. Les expériences de laboratoire bénécient d'une validité interne élevée -les individus prennent des décisions dans un environnement hautement contrôlé, mais elles peuvent manquer de validité externe. Les résultats des expériences de laboratoire présentées dans cette thèse ont permis FRENCH SUMMARY 223 de mieux comprendre les comportements en réponse aux nudges et aux prix, et en réponse à la présentation des paiements. Ils ne se substituent pas aux expériences de terrain et aux études pilotes analysées au chapitre 2, mais constituent plutôt une approche complémentaire pour mieux comprendre le comportement individuel. L'expérience du chapitre 3 a mis en évidence une conséquence inattendue d'un nudge donné à diérents groupes sur les individus, et a montré comment les réponses aux incitations et les caractéristiques individuelles sont liées. L'expérience du chapitre 4 a étudié la possibilité de fournir des eorts pour de petites récompenses dans un environnement moins complexe que dans la vie réelle, où les eorts d'économie d'énergie ont des impacts plus larges, notamment sur le confort et sur les autres individus. L'absence d'eets de la façon de présenter les paiements (sous formes de gains ou de pertes) en laboratoire soulève la question de savoir si de tels eets se manifesteront lorsque l'eort à fournir est plus complexe. Toutefois, les sujets de l'expérience n'avaient qu'à se concentrer sur une tâche qui consistait à compter le nombre de 1 dans une série de tableaux, et n'étaient donc peut-être pas sensibles à la présentation des paiements, car ils n'étaient concentrés que sur la tâche en cours. Une exigence importante pour vérier la robustesse des résultats d'expériences en laboratoire est la réplication. Bien que l'expérience du chapitre 4 ait repris certains aspects des expériences précédentes et ait tiré des conclusions similaires, à savoir que les eets de présentation des paiements (sous forme de gains ou de pertes) ne se manifestent pas toujours lorsque les gains sont faibles, les résultats de l'expérience du chapitre 3 devraient faire l'objet de recherches plus poussées. En particulier sur l'eet du nudge consistant à renforcer le comportement existant. Conséquences Les implications de la présente recherche sont de trois ordres. Premièrement, les différentes incitations sont ecaces pour encourager les ménages à réduire leur consommation à des degrés divers. Lorsque les décideurs décident des mesures d'incitation à mettre en ÷uvre, ils doivent d'abord tenir compte de l'objectif : réduction globale de la demande ou réduction de la demande de pointe. Les incitations devraient ensuite être adaptées à cet objectif en gardant à l'esprit que les stratégies de tarication sont les plus ecaces pour réduire la demande de pointe et que des incitations supplémentaires devraient être utilisées pour contrer les augmentations de la demande pendant les périodes creuses. En outre, les décideurs devraient tenir compte du fait que l'information sur la consommation sous forme de nudges est rapidement comprise par les consommateurs et peut donc avoir un eet immédiat sur la consommation, alors que les prix prennent un peu plus de temps à être intégrés dans le processus décisionnel des individus. Cela présente un intérêt pour les décideurs, car l'augmentation des prix peut aggraver la situation des individus pendant un certain temps s'ils ne sont pas en mesure de réduire immédiatement leur consommation en réaction à la hausse des prix. Les consommateurs peuvent subir une diminution de leur bien-être avant d'être pleinement en mesure d'a juster leur consommation en réponse à l'augmentation des prix. Deuxièmement, les décideurs politiques devraient être conscients des obstacles à la mise en ÷uvre des diérentes incitations et de leurs potentielles conséquences indésirables. En particulier, ils devraient prendre en compte l'eet individuel des nudges habituellement utilisés. Comme le montre la revue de littérature, les comparaisons descriptives peuvent inciter les ménages consommant peu à augmenter leur consommation jusqu'au niveau moyen ou, comme vu dans la présente étude, l'inclusion de normes injonctives peut renforcer les comportements existants, de sorte que les ménages peu consommateurs réduisent leur consommation, compensant l'impact des ménages très consommateurs qui continuent à accroître leur consommation. Enn, la présente recherche a également des implications pour la conception de dispositifs qui achent des informations sur la consommation aux ménages : la présentation des paiements en termes de gains ou de pertes n'a pas d'eet sur l'eort fourni. C'est le montant des paiements potentiels et la réduction du risque sur les paiements qui est importante. Cette absence d'eet de présentation pour les petites FRENCH SUMMARY 225 récompenses a également des implications théoriques, car il semblerait que l'aversion aux pertes n'incite pas les individus à fournir plus d'eorts lorsque les paiements sont présentés sous forme de pertes (par rapport à une présentation sous forme de gains) dans le cas des petites récompenses. Le montant que la personne peut gagner est tout simplement trop petit pour que la présentation ait un eet. Recherches futures Le premier chapitre de cette thèse identie les obstacles à l'utilisation d'incitations pour encourager la réduction de la demande énergétique, dont certains sont ensuite analysés plus en détail dans les chapitres suivants. Plusieurs obstacles ayant été identiés, les possibilités de recherche sur le sujet de cette thèse sont vastes. Les ménages ignorent généralement leur consommation ou ne connaissent pas le tarif de l'électricité qu'ils paient. Cela inue sur leur décision d'opter pour des contrats à tarication dynamique. En eet, les ménages préfèrent avoir un contrat simple car ils ne savent pas si un tarif dynamique serait avantageux dans leur cas, ni comment ils peuvent modier leur comportement pour proter des tarifs moins élevés en période creuse. Une piste de recherche future pourrait être d'étudier la manière dont les choix tarifaires des individus évoluent lorsqu'ils bénécient d'informations sur leur propre consommation et sur l'utilisation qu'ils peuvent faire des diérents niveaux de prix, en s'appuyant sur les recherches des autres. La motivation monétaire ne s'est pas avérée être un facteur clé du changement des comportements de consommation, car les économies monétaires sont généralement faibles. De plus, la manière dont est présentée l'information peut être mise en regard des diérentes motivations pour économiser de l'énergie. Une autre piste de recherche pourrait être de proposer aux individus diérents achages de l'information sur leur consommation d'énergie (économies monétaires, économies d'énergie, économies environnementales, ...) puis, à travers une expérience de choix, de mesurer leurs préférences pour ces diérents achages. Cela pourrait ensuite faire 226 FRENCH SUMMARY l'objet de recherches plus approfondies sur le terrain en comparant les réponses aux incitations à la réduction de la consommation basées sur des calculs non monétaires de l'impact des économies d'énergie : sur l'environnement, la santé, ou autre. Outre les obstacles identiés au chapitre 1, les expériences menées dans le cadre de cette thèse ont également ouvert la voie à de nouvelles recherches. Au chapitre 3, le nudge a eu pour eet involontaire de renforcer le comportement existant. Une explication possible a été trouvée dans la théorie de la réactance : les individus estiment que leur liberté de faire ce qu'ils veulent est menacée et ils adoptent donc un comportement qui n'est pas encouragé. Pourtant, cet eet n'a pas été observé dans les expériences de terrain analysées aux chapitres 1 et 2. Il y a une diérence entre la création du nudge utilisé sur le terrain et celui utilisé dans la présente expérience. Sur le terrain, le comportement est comparé au niveau de consommation moyen des ménages similaires (niveau endogène), alors qu'en laboratoire, le comportement est comparé à un niveau optimal de consommation déni de façon exogène, calculé par l'expérimentateur. Les réponses individuelles aux nudges endogènes et exogènes méritent des recherches plus approfondies. Cette thèse avait pour but d'étudier la manière dont les consommateurs du secteur résidentiel réagissent aux incitations utilisées pour les encourager à réduire leur consommation énergétique. Les consommateurs résidentiels réagissent positivement aux diverses mesures incitatives utilisées : les incitations entrainent une réduction moyenne de la consommation de pointe ou globale. La recherche a mis en évidence que diverses mesures incitatives peuvent avoir des conséquences indésirables sur la consommation au niveau individuel et que les mesures incitatives déclenchent diérentes caractéristiques qui peuvent inuer sur la façon dont les consommateurs réagissent à ces mesures.

  questioned German participants on their beliefs and expectations of dynamic pricing. They found that consumers are unsure of what dynamic electricity pricing is and what it can do. Of

	160 participants, just over half (53%) believed that dynamic pricing may result in a
	reduction in their energy use. Added to this uncertainty are diculties in calculat-
	ing peak and o-peak consumption; consumers do not know the energy demand of

Table 2

 2

	Authors	Objective	Time frame	Studies	Eect
		Eect of direct and
	Darby (2006)	indirect feedback on energy (gas and electricity)	1979-2006	38	Direct: -15% to -5% Indirect: -10% to 0%
		consumption
	Ehrhardt-Martinez et al. (2010)	Eect of dierent feedback consumption treatments on energy	1974-2010	57	-12% to -4%
	Faruqui et al. (2010b)	Eect on IHDs on energy consumption	1989-2010	12	-13% to -3%
	Delmas et al. (2013)	Reduction in energy consumption via dierent treatments	1975-2012	59	-55% to +18% Weighted ATE 16 : -7.4%
	Faruqui and Sergici (2013)	Peak demand reduction of time-varying prices.		34	-58% to 0%
	McKerracher and Torriti (2013)	Eect of IHDs on energy consumption	1979-2015	27	-5% to -3% ATE: -6.4%

.1 summarises the results of the previous reviews and meta-analyses discussed in this section.

Table 2 .

 2 Faruqui et al. (2010b) nd an energy reduction of 7% on average.[START_REF] Mckerracher | Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays[END_REF] perform a wider analysis of 27 peer and non peer reviewed studies between 1979-2011. The authors nd that as sample size increases, the reported treatment eect decreases. Additionally, they classify studies via sampling selection and recruitment method and nd that studies with more representative samples report lower percentages of energy reduction.Hypothesis 5: With larger samples, the reduction in energy consumption due to an incentive is smaller

1: Summary of results of previous reviews and meta-analyses Darby et al. (2006) reviews 38 feedback studies from 1979 to 2006 and concludes that, on average, direct feedback which is received immediately after the energy consuming behaviour is more eective than indirect feedback such as an energy bill. Both Faruqui et al. (2010b) and McKerracher and Torriti (2013) analyse the eect of real-time feedback, via an in-home display (IHD), on energy consumption. In a review of 12 pilot studies (1989-2010), Ehrhardt-Martinez et al. (2010) review 57 studies from 1974-2010 covering both feedback and dynamic pricing studies using advanced metering infrastructure. The authors conclude that feedback interventions result in a greater overall reduction in energy consumption than dynamic pricing which is more eective at decreasing demand at peak times.

Table 2

 2

	.2: Descriptive statistics and average treatment eects

Table 2

 2

	The primary studies are separated into those which use a higher number of controls;
	a control group, weather and demographic controls, randomly assign households to
	treatments, and use an opt-out method of recruitment, as such studies are assumed
	to show a more representative estimate of the true treatment eect. Studies which
	compare the treatment eect to a control group rather than the baseline of the
	same group of households, provide a more robust estimate of the treatment eect.
	The same applies to studies which use weather controls and collect demographic

.3: Comparison of weighted average treatment eects by literature type For comparison between the literature types, table 2.3 provides the weighted average treatment eects by study type, i.e.: whether the study is from a peer-reviewed journal or from the grey literature. In the sample of studies collected, there are no reports which use personalised feedback as an incentive. Across all incentive types, on average, a peer-reviewed study shows a weighted ATE of a 1.96% reduction, and a study from the grey literature shows a weighted ATE of a 1.71% reduction in consumption. Studies from the grey literature tend to show a smaller eect of an incentive on electricity consumption. Among the peer reviewed studies, the weighted ATE of the use of pricing strategies is an increase in electricity consumption of 2.31%, indicating that such strategies are more appropriate for reducing peak demand rather than overall demand. information. Studies which adopt a random treatment assignment method and an opt-out method of recruitment are more representative as they use samples in which households have not chosen their treatment method nor are sub ject to selection bias. Table

2

.4 gives the average treatment eects by robustness. More robust studies are those which include less. Of all the studies, 22% can be considered to be more robust. These studies have an ATE of a 2.17% reduction whereas the less robust studies have an ATE of a 3.71% reduction. These ATE are signicantly dierent (pvalue < 0.001, Wilcoxon signed rank test). The more robust studies show a greater reduction when sample size is taken into consideration.

  . The intuition is that the accuracy of the treatment eect increases with the level of precision. Studies with larger standard errors and smaller sample sizes are dispersed at the bottom of the graph, with the spread of treatment eects decreasing as standard errors decrease and sample sizes increase. In the absence of publication bias, the result is a symmetrical, inverted funnel shaped graph. On the other hand, if there is a publication bias, an asymmetrical funnel can result due to an absence of publications of non statistically signicant results(Egger et al., 1997, Sterne et al.,

	68		CHAPTER 2: INCENTIVISING HOUSEHOLDS
	2004).
		400
	Square root of sample size	100 200 300
		0
		-.2	-.1	0	.1	.2
			Effect size (%)
			Peer reviewed	Grey literature

groupFigure 2.9: Funnel plot of treatment eects versus sample size

Table 3

 3

		Excludable	Non-excludable
	Rivalrous	Private goods	Common pool resources
	Non-rivalrous	Club goods	Public goods
	Table 3.1: A classication of goods

.1 provides a general framework of the classication of goods according to these two criteria. A common pool resource is both rivalrous and non-excludable;

once it has been consumed by an individual, another individual cannot consume it, and it is costly to exclude individuals from consuming it. Such goods face a problem of over consumption as individuals wish to consume more than the amount which is sustainable.

  30 is available as well as a printed prot table. At the end of each period, subjects see how much they have consumed and their prot for the period.

• C cooler, or 2

• C cooler. Given the discretisation of the consumption amount, the Nash equilibrium is x i = 25 EU and the social optimum is x i = 15 EU. To assist subjects in deciding how many EU to consume, a 90 CHAPTER 3: DEMAND RESPONSE AS A CPR GAME simulator

Table 3

 3

	.4: Mean group consumption by treatment

Table 3

 3

	.6 shows the regression estimates of random eects specications of treatment
	and covariates on individual consumption choice. Specication 1 shows a signicant
	treatment eect for both the nudge and the price treatment at the individual level.

In even numbered specications, prot in t-1 is included and has a signicant but small positive eect on average individual consumption. As the amount earned in * p < 0.1, * * p < 0.05, * * * p < 0.01 Table 3.5: Average group consumption (random eects estimation)

Table 3

 3 Table 3.7: Eect of feedback on individual consumption in nudge treatment tests in table 3.9b. The p-values indicate that the levels are statistically dierent from one another between the nudge and the price treatments, and the nudge and control groups. Subjects are slightly more environmentally sensitive in the nudge treatment compared to the price treatment and control groups.

	3.3 RESULTS AND DISCUSSION					103
		(1)	(2)
	Under consumption :-) (t-1)	-2.317 * * *	-2.241	* * *
		(0.791)	(0.792)
	Over consumption :-( (t-1)	4.067	* * *	3.753	* * *
		(0.765)	(0.846)
	High Environmental sensitivity			-2.453	* * *
				(0.673)
	High Altruism			-1.732	* *
				(0.846)
	Constant	17.203 * * *	19.770	* * *
		(0.408)	(1.021)
	Observations	900	900
	R 2 Overall	0.115	0.142
	R 2 Within	0.004	0.004
	R 2 Between	0.825	0.601
	Standard errors in parentheses
	Robust standard errors clustered by group.

.8 compares the average observed welfare by treatment at the group and individual level.

We can see that both individually and at the group level, subjects are worst o in

39

Table B.3 in Appendix B shows the distribution of feedback types in the nudge treatment. * p < 0.1, * * p < 0.05, * * * p < 0.01 Table 3.9: Environmental sensitivity questionnaire results Table

3

.10a shows the average consumption decisions of individuals in each treatment according to their sensitivity to environmental issues. High environmental sensitivity is classed as greater than the average of the sample 41 . As can be seen from the table, overall and for each treatment, more environmentally sensitive subjects choose to consume less. The dierence in consumption level by environmental sensitivity is the greatest in the nudge treatment. This dierence is statistically signicant as shown in table 3.10b (p<0.01, Wilcoxon signed-rank test).

The average altruism scores are reported in table 3.11a across all sub jects and by treatment and the associated p-values of Wilcoxon rank-sum tests in table 3.11b.

The average scores on the altruism tests are signicantly dierent across the nudge and price, and the nudge and control treatments. They are not signicantly dierent between the price and control treatments.

  and Loss Framing of Incentives: Encouraging Individuals to Provide an Eort for Small Rewards Joint with Béatrice Roussillon 43 and Sabrina Teyssier 43 .

	This work has been partially supported by the Cross Disciplinary Program Eco-SESA 44
	receiving funding from the French National Research Agency in the framework of the "In-
	vestissements d'avenir program (ANR-15-IDEX-02).
	A preliminary version of this research was presented at a seminar at the University of Kent
	(UK, 2018).

43

Univ. Grenoble Alpes, CNRS, INRA, Grenoble INP, GAEL, 38000 Grenoble, France 44 See https://ecosesa.univ-grenoble-alpes.fr/

Table 4 .

 4 1 summarises the dierent payos in each treatment.

		Gain	Loss
	Endowment	0e	21e
	Control	0.15e	-0.15e
	Ex-Ante risk	0.05 or 0.25e	-0.05 or -0.25e
	Ex-Post risk	0.05 or 0.25e	-0.05 or -0.25e
	Table 4.1: Payos by treatment
	4.2.2 Participants and Procedure

The experiments took place during April and May 2018 at Grenoble Applied Economics Laboratory. 259 students took part in experiments across 16 sessions. There

Table 4

 4

	.2

Table 4 .

 4 3 provides the average number of correct tables and standard deviation in 126 CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES each treatment for all periods. The nal column shows the associated p-values of Wilcoxon rank sum tests of the dierence in the average number of correct tables between treatments over all periods 47 .

	Treatment	Average	Std dev.	p-value
	GC	3.23	1.14
				0.459
	LC	3.06	1.32
	GEA	3.27	1.16
				0.298
	LEA	3.10	1.24
	GEP	3.10	1.18
				0.349
	LEP	3.01	1.11
	Table 4.3: Number of correct tables overall and across all periods

Table 4 .

 4 5 displays average eort per treatment over all periods. Subjects perform marginally better in each ex-ante treatment compared to its corresponding ex-post treatment. There are no signicant dierences in eort provision according to risk 50 .

	Treatment	Average	Std dev.
	GEA	3.27	1.16
	GEP	3.10	1.18
	LEA	3.10	1.24
	LEP	3.01	1.11
	Table 4.5: Number of correct tables overall and across all periods

  Table C.6 in Appendix C gives the p-values of Wilcoxon rank sum tests of dierences in these average between payo structures 130 CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES

Table 4 .

 4 7: Regression estimates of average eort provision over blocks of 7 periods

		GC	LC		GEA	LEA	GEP	LEP
	Periods 1-7	-0.382	* * *	-0.394	* * *	-0.429	* * *	-0.324	* * *	-0.409	* * *	-0.521	* * *
		(0.105)	(0.114)	(0.091)	(0.100)	(0.067)	(0.086)
	Periods 15-21	-0.129	-0.099	-0.068	0.024	-0.025	-0.120
		(0.079)	(0.118)	(0.065)	(0.103)	(0.066)	(0.082)
	Periods 22-28	0.129		0.207	0.277 * * *	0.298	* * *	0.390	* * *	0.034
		(0.089)	(0.126)	(0.057)	(0.086)	(0.078)	(0.089)
	Constant	3.323 * * *	3.133	* * *	3.321	* * *	3.101	* * *	3.110	* * *	3.160	* * *
		(0.138)	(0.186)	(0.111)	(0.140)	(0.112)	(0.080)
	R 2	0.028		0.027	0.047	0.032	0.057	0.039
	Observations	868		812	1344	1344	1456	1428
	Standard errors in brackets are clustered by subject

  table is provided in table C.7 in Appendix C. Robust standard errors clustered by subject. Panel regression: balanced panels by subject and period.GC treatment used as reference level. Average number of ones per period: diculty measure. Ability: performance in rst period. Degree or risk taking: from 1 risk averse to 5 risk lover. * p < 0.1, * * p < 0.05, * * * p < 0.01

	134	4.4 RESULTS AND DISCUSSION CHAPTER 4: GAIN AND LOSS FRAMING OF INCENTIVES	133
	the period.
				(1)	(2)	(3)	(4)
				Cluster spec.	Panel spec.	Cluster spec.	Panel spec.
		LC	-0.165	-0.165	-0.108	-0.108
				(0.207)	(0.190)	(0.158)	(0.155)
		GEA	0.039	0.039	-0.006	-0.006
				(0.156)	(0.170)	(0.131)	(0.138)
		LEA	-0.127	-0.127	-0.170	-0.170
				(0.168)	(0.170)	(0.144)	(0.138)
		GEP	-0.128	-0.128	-0.088	-0.088
				(0.161)	(0.167)	(0.131)	(0.136)
		LEP	-0.219	-0.219	-0.185	-0.185
				(0.149)	(0.168)	(0.128)	(0.137)
		Period					0.027	* * *	0.027 * * *
								(0.002)	(0.001)
		Average number of 1's per period					-0.073	* * *	-0.073	* * *
								(0.005)	(0.005)
		Ability (t=1)					0.391	* * *	0.391 * * *
								(0.036)	(0.034)
		Degree of risk taking					-0.043	-0.043
								(0.043)	(0.040)
		Female					-0.073	-0.073
								(0.076)	(0.076)
		Age					-0.015	-0.015
								(0.017)	(0.015)
		Constant	3.227	* * *	3.227	* * *	5.100	* * *	5.100 * * *
				(0.122)	(0.132)	(0.447)	(0.409)
		Observations	7252	7252	7252	7252
		R	2	0.006			0.188
		R	2 Overall			0.006		0.188
		R	2 Within			0.000		0.082
		R	2 Between			0.016		0.361
		Standard errors in parentheses
		Clustered regression: Table 4.8: Regression estimates of eect of individual characteristics on average
		eort provision

  p < 0.10, * * p < 0.05, * * * p < 0.01 Table A.3: OLS estimation of treatment eects have completed the preceding phase. The session will not last longer than 1 hour 30 minutes.

	OLS estimation of treatment eects
		(1) Monetary Personal (2) feedback	(3) Social feedback	(4) Study design	(5) All incentives
	(e.g.: peak demand reduction, Pricing strategies 2.075 (1.354) appliance level data, median % change ) Non-residential sample Not a eld experiment Monetary information -0.333 (1.009) or pilot study Individual feedback -1.754 (1.260) (e.g.: a simulated study or laboratory experiment) Included under a dierent title Real-time feedback -1.253 (1.048) Gas and electricity consumption combined Experimental issues leading to missing data Savings tips 1.195 (1.338) Secondary data Personalised advice -1.690 (1.993)		17 11 8 4 3 1	1.651 (1.055) 1.980 (1.523) -2.116 (1.273) -2.565 * (1.310) 1.455 (1.292) -2.252 (2.069)
	Total Social norms			-1.161 (1.875)	72	-3.462 * (1.976)
	Table A.2: Reasons for studies exclusion from the analysis Injunctive norms -3.113 (2.825)	-1.942 (2.557)
	Control group	3.420 (3.131)	4.983 (3.116) (3.026) (3.201) 4.070 3.499	5.586 * (2.854)
	Weather controls	-0.003 (1.053)	0.294 (1.095) (1.342) (1.182) 0.380 0.405	-0.778 (1.339)
	Demographic controls -1.640 (1.623)	-1.665 (1.706) (1.802) (1.901) -1.576 -2.419	0.094 (1.603)
	Random assignment	-0.189 (0.990)	-0.343 (1.083) (1.302) (1.049) 0.154 0.135	-1.116 (1.299)
	Opt-in recruitment	-1.441 (1.495)	-1.652 (1.470) (2.739) (1.038) -3.063 -0.847	-4.224 (2.799)
	Duration	0.144 * * 0.164 * * * 0.135 * * * 0.117 * * * 0.215 * * * (0.058) (0.038) (0.045) (0.043) (0.056)
	Peer reviewed	1.982 * (1.112)	2.302 * * 2.301 * (1.106) (1.146) (0.971) 1.601	3.603 * * (1.391)
	Constant	-7.597 * * -7.410 (3.395) (4.423) (4.082) (3.765) -6.166 -6.732 * -7.455 * (4.016)
	Residential consumers respond pos-itively to the various incentives used in that they result in average reductions in peak or overall demand. The research highlighted that there may be unwanted Observations 105 105 105 105 105 Adjusted R 2 0.132 0.157 0.114 0.109 0.194 Standard errors in parentheses Standard errors are clustered by primary study. A negative coecient reads as a reduction in energy consumption.
	consequences of various incentives on consumption at the individual level, and that
	dierent incentives trigger dierent characteristics which can aect how consumers
	respond to the incentives.

*

Table B .

 B 1: Number of groups by consumption level (across all periods)

	Proportion of individuals by consumption type (un-
	der, optimal or over-consuming)
			Individual consumption
			Under	Optimal	Over	Total
		Nudge	190	316	494	1,000
			19.0%	31.6%	49.4%	100.0%
	Treatment	Price	234	295	271	800
			29.3%	36.9%	33.9%	100.0%
		Control	75	79	446	600
			12.5%	13.2%	74.3%	100.0%
		Total	499	690	1,211	2,400
			20.8%	28.7%	50.5%	100.0%

Table B

 B At the start of the rst phase, the total of your pot is 21e. For each period, the loss associated to each incorrectly completed table or table without an answer is 0e05 with 50% probability, or 0e25, with 50% probability. The random draw of the gain is valid for the ve tables within a period.. You lose 0e05 or 0e25 when you incorrectly complete the table, i.e.: when you have not counted the correct number of 1's or when you have not given an answer. You earn 0ewhen you correctly complete the table, i.e.: when you count the correct number of 1's in a table. Your earnings for the period are therefore: Number of correct tables x -0e05 OR Number of correct tables x -0e25At the start of the rst phase, the total of your pot is 0e. For each period, the gain

	a period. You win 0e05 or 0e25 when you correctly complete the table, i.e.: when Answers to comprehension questions Wilcoxon rank sum tests
	you count the correct number of 1's in a table. You earn 0ewhen you incorrectly Your task is to count the number of 1's in the table TRUE complete the table, i.e.: when you have not counted the correct number of 1's or GC LC GEA LEA GEP
	when you have not given an answer. Your earnings for the period are therefore, with There are 28 periods TRUE LC 0.459 50% probability: Number of correct tables x 0e05 OR Number of correct tables x 0e25 GEA 0.908 0.331 A period lasts for 80 seconds TRUE LEA 0.498 0.817 0.298
	GEP There are 5 tables in a period TRUE 0.489 0.723 0.310 LEP 0.098* 0.768 0.023**	0.994 0.452	0.349
	You have 80 seconds to count the number of 1's in 5 tables TRUE Table C.1: Wilcoxon rank sum tests between treatments for all periods (p-values)
	In each period, the gain associated to each correct table is 0e15 TRUE
			GC	LC	GEA	LEA	GEP
	In each period, the loss associated to each incorrect or incomplete table is
	0e15 TRUE	LC GEA	0.683 0.778	0.376
		LEA	0.726	0.362	0.753
	In each period, the gain associated to each correct table is 0e05 with 50% GEP 0.705 0.874 0.453 0.305
	probability, or 0e25, with 50% probability TRUE LEP 0.741 0.848 0.365 0.293	0.950
	associated to each correctly completed table is 0e05 with 50% probability, or 0e25, The random draw is valid for the 5 tables in a period TRUE Table C.2: Wilcoxon rank sum tests between treatments in period 1 (p-values)
	with 50% probability. The random draw of the gain is valid for the ve tables within GC LC GEA LEA GEP a period. You win 0e05 or 0e25 when you correctly complete the table, i.e.: when You know the random draw before the start of a period TRUE (1)
	you count the correct number of 1's in a table. You earn 0ewhen you incorrectly In each period, the loss associated to each correct table is 0e05 with 50% LC 0.763
	complete the table, i.e.: when you have not counted the correct number of 1's or probability, or 0e25, with 50% probability TRUE GEA 0.652 0.991
	when you have not given an answer. Your earnings for the period are therefore, with LEA 0.871 0.794 0.794
	The random draw is valid for the 5 tables in a period TRUE GEP 0.670 0.513 0.375 0.582 50% probability: Number of correct tables x 0e05 OR Number of correct Phase 1 Your task GC LC GEA LEA GEP Your task is to count the number of 1's in a table similar to the one below. tables x 0e25 At the start of the rst phase, the total of your pot is 21e. For each period, the loss associated to each incorrectly completed table or table without an answer is 0e05 with 50% probability, or 0e25, with 50% probability. The random draw of the LEP 0.041** 0.175 0.060* 0.041** 0.015** You know the random draw before the start of a period TRUE In each period, the gain associated to each correct table is 0e05 with 50% probability, or 0e25, with 50% probability TRUE Table C.3: Wilcoxon rank sum tests between treatments in period 28 (p-values) Variables
	A table is successfully completed if you count the correct number of 1's. gain is valid for the ve tables within a period.. You lose 0e05 or 0e25 when you The random draw is valid for the 5 tables in a period TRUE LC 0.407
	incorrectly complete the table, i.e.: when you have not counted the correct number of 1's or when you have not given an answer. You earn 0ewhen you correctly complete GEA 0.790 0.418 You know the random draw after the start of a period TRUE LEA 0.345 0.958 0.324
	the table, i.e.: when you count the correct number of 1's in a table. Your earnings GEP 0.260 0.914 0.231 0.839 The phase There are 28 periods in the rst phase. In each period, you are asked to count the In each period, the loss associated to each correct table is 0e05 with 50% LEP 0.152 0.833 0.075* 0.674 0.840 for the period are therefore: Number of correct tables x -0e05 OR Number probability, or 0e25, with 50% probability TRUE of correct tables x -0e25 Table C.4: Wilcoxon rank sum tests between treatments in period rst (p-values)
	.2: Number of groups by consumption level (across all periods) number of 1's in 5 consecutive tables. You have 80 seconds for each period. After The random draw is valid for the 5 tables in a period TRUE
	each period, you must reply to the following question: How many tables do you
	think you successfully completed? You know the random draw after the start of a period TRUE

  . Les sections suivantes donnent un aperçu de la littérature sur ces diérentes mesures incitatives. , mais elles exigent que les consommateurs disposent d'un appareil avec une connexion Internet et qu'ils se connectent pour accéder aux données. Des études évaluant ce type de feedback et son eet sur la consommation d'énergie révèlent que peu de personnes se connectent aux portails en ligne et que le nombre de connexions diminue au cours de l'étude[START_REF] Benders | New approaches for household energy conservationin search of personal household energy budgets and energy reduction options[END_REF], Vassileva et al., 2012[START_REF] Schleich | Eects of feedback on residential electricity demandndings from a eld trial in Austria[END_REF].

	FRENCH SUMMARY	203
	de comportement aectent la consommation d'énergie (Fischer, 2008).
	Pour que les consommateurs puissent vraiment bénécier des informations sur leur
	consommation fournies par le compteur intelligent, ils doivent pouvoir accéder à
	celles-ci peu de temps après la mise en ÷uvre du changement de comportement.
	Les données disponibles en ligne fournissent des informations plus détaillées sur les
	niveaux de consommation et peuvent potentiellement combler l'écart d'action et
	Feedback d'eetPour combler l'écart action-eet, les ménages peuvent utiliser un dispositif capable
	de s'interfacer avec leur compteur intelligent qui peut leur fournir des informations
	L'un des principaux avantages des compteurs intelligents est la possibilité de com-en temps réel et accessibles sur la consommation. Ces dispositifs sont communément
	muniquer des données de consommation en temps réel aux consommateurs. appelés moniteurs d'énergie. Ce sont des plates-formes dédiées qui fournissent des En
	fournissant aux ménages des informations sur leur consommation d'énergie, ils de-données de consommation en temps réel, assurant ainsi un lien direct entre l'action
	viendront plus conscients de leurs habitudes de consommation et feront des eorts et l'eet. L'utilisation de ces moniteurs peut encourager une réduction de la con-
	pour la réduire. Sensibiliser les ménages à leur consommation d'énergie est le pre-sommation à condition qu'ils soient placés dans des endroits visibles de la maison
	mier pas vers un changement des habitudes de consommation (Attari et al., 2010). pour un accès facile et rapide aux données disponibles. Cependant, ce n'est pas
	La facture papier est la méthode de feedback traditionnellement utilisée pour in-toujours le cas et le facteur nouveauté de consulter sa consommation en temps réel
	former les consommateurs de leur consommation et de leurs dépenses énergétiques. tend à s'atténuer au l de l'utilisation (Hargreaves et al., 2010, 2013). An de faire
	Darby et al. (2006) soulignent qu'un tel feedback est utile pour évaluer l'impact sur participer les ménages à l'utilisation des données fournies, il faut prêter attention à
	la consommation des investissements dans l'ecacité énergétique plutôt que pour la façon dont les données sont présentées, qu'il s'agisse de termes monétaires ou én-
	évaluer les eets dus aux changements de comportement. Ce genre d'information ne ergétiques (Buchanan et al., 2014), d'achages numériques ou graphiques (Chiang
	et al., 2012), de dépenses présentées de façon factuelle ou sous forme de perte (Bager

fournit pas de feedback approprié sur les eorts déployés pour réduire la consommation d'énergie, car les données sont reçues trop longtemps après le changement de comportement. Pour que le feedback ait un eet, la relation entre l'action et l'eet doit être claire pour les ménages an qu'ils puissent voir comment les changements and Mundaca, 2017). Ces présentations et leurs eets sur l'eort sont examinés plus en détail au chapitre 4.

  'une manière prévisible sans interdire aucune option ou modier sensiblement leurs incitations économiques. Pour compter comme un simple nudge, l'intervention doit être facile et peu coûteuse à éviter. Les nudges ne sont pas des mandats."Le nudging est basé sur l'idée du paternalisme libertaire qui est une approche qui oriente les individus vers des choix qui sont dans leur meilleur intérêt et qui augmenteront leur bien-être sans limiter leur liberté de choix. Le nudging reconnaît que les individus font des choix qui ne sont pas dans leur meilleur intérêt, des choix qu'ils ne feraient pas s'ils avaient une information complète et des capacités cognitives illimitées[START_REF] Thaler | Libertarian paternalism[END_REF].

	Nudging
	L'installation de compteurs intelligents et de moniteurs n'est pas susante pour in-
	citer les consommateurs à modier leurs comportements. La majorité des ménages
	montrent un manque d'intérêt pour les moniteurs. On constate que ce sont souvent
	seulement ceux qui sont déjà concernés par leur consommation d'énergie qui s'y in-
	téressent le plus (Buchanan et al., 2015). La tarication dynamique a tendance à
	n'avoir un impact que sur les consommateurs qui sont pleinement informés et at-
	tentifs aux changements de prix (Jessoe et al., 2016). Ainsi, les outils d'économie
	comportementale sont de plus en plus utilisés pour accroître la réaction des consom-
	mateurs aux mesures incitatives. Ces mesures incitatives sont appelées des nudges
	ou des coups de coude et reposent principalement sur les travaux des lauréats du
	prix Nobel Richard Thaler (2017) et Daniel Kahneman (2002).

[START_REF] Thaler | Nudge: Improving decisions about health, wealth, and happiness[END_REF]

dénissent un nudge comme suit : " Un nudge (...) est tout aspect de l'architecture de choix qui modie le comportement des gens 206 FRENCH SUMMARY dLes ménages ont été encouragés à réduire leur consommation d'énergie par le biais de normes sociales et injonctives. Les normes sociales sont un type de feedback qui compare la consommation d'énergie d'un ménage à celle de ses voisins

[START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF][START_REF] Nolan | Normative social inuence is underdetected[END_REF]

. Les normes injonctives a joutent de l'approbation sociale de la consommation d'un ménage par rapport à celle de ses voisins

[START_REF] Schultz | The constructive, destructive, and reconstructive power of social norms[END_REF]

.

Par exemple, Opower

See https://ec.europa.eu/clima/policies/strategies/2020_en

See https://ec.europa.eu/clima/policies/strategies/2030_en

This is calculated based on the share of renewable energy in France reported by the European Commission (see https://ec.europa.eu/info/business-economy-euro/economic-and-fiscal-policy-coordination/ eu-economic-governance-monitoring-prevention-correction/european-semester_en)

Operational benets include: remote meter reading, quicker detection of power outages, and fraud detection, among others.

Opower was an American company which provided software for utilities to use to analyse consumption data in order to encourage demand reduction. The company was acquired by Oracle Corporation in 2016.

The results of a cluster-robust OLS estimation are provided in Appendix A as a benchmark for the following WLS estimation.

Studies which used such incentives were included in the present meta-analysis as the primary authors also considered the eect of the incentive on overall household electricity demand.

Univ. Grenoble Alpes, CNRS, INRA, Grenoble INP, GAEL, 38000 Grenoble, France

See https://ecosesa.univ-grenoble-alpes.fr/

This is comparable to taris proposed by EDF at the time of the experiment; the highest peak price is approximately 3.5 times the standard tari (EDF, 2016).

This dierence is insignicant as tested non-parametrically using the Kruskal-Wallis test (p=0.9899).

The share of each type of group (under, optimal or overconsuming) is shown in table B.1 in Appendix B

In the nudge, price and control groups, 58%, 55% and 52% of subjects have high environmentally sensitivity, respectively.

est l'adoption an de savoir si ces dispositifs peuvent être ecaces. L'installation d'un compteur intelligent, la présence d'un moniteur et la tarication dynamique ne susent pas à elles seules à faire baisser la consommation des ménages. Ces derniers doivent s'engager à utiliser ces diérentes mesures incitatives. La littérature souligne que le principal obstacle à l'acceptation des compteurs intelligents est que les ménages ne font pas conance aux compagnies d'énergie. Ils ne savent pas ce que sont les compteurs intelligents et comment ils peuvent être utilisés au prot des consommateurs. En ce qui concerne la tarication dynamique, les ménages trouvent que les tarifs sont complexes et lorsqu'ils ont le choix, peu de ménages optent pour une tarication dynamique. Ils préfèrent rester sur le tarif standard qui est plus simple et sans risque. En ce qui concerne l'adoption par les ménages de compteurs intelligents et de mesures incitatives, les résultats de la littérature suggèrent que tout eet sur la consommation est généralement de courte durée. Les ménages ont tendance à répondre au feedback au début, mais leur intérêt diminue après quelques semaines ou quelques mois. De plus, les ménages sont contraints de répondre au feedback selon leur niveau de confort personnel sur lequel ils ne sont pas prêts à faire des compromis, et selon les rigidités de la vie quotidienne. En outre, les économies monétaires résultant de la baisse de leur consommation sont rarement susamment élevées pour encourager des changements de comportement persistants. La contribution de ce chapitre est une revue récente de la littérature expérimentale an d'identier les obstacles à l'utilisation des compteurs intelligents et des incitations associées comme moyen d'encourager les ménages à réduire leur consommation. Chapitre 2 Il existe une richesse des expériences de terrain et des études pilotes explorant comment les consommateurs réagissent aux incitations (seuls ou en combinaison, dans de nombreux pays et les nombreux protocoles expérimentaux) et une attention crois-

ACKNOWLEDGEMENTS vii

Acknowledgements

First and foremost, I would like to thank my supervisors, Daniel Llerena and Cédric Clastres, for their invaluable advice, guidance and good humour throughout this process.

I would also like to extend my thanks to the members of my thesis committee, Patrice Georon, Stéphane Robin, and Anne Rozan for their participation and comments on my work.

Additionally, I would like to express my gratitude to my colleagues who make it possible for us to carry out experimental research: Marie Cronfalt-Godet, Mariane Damois, Jean-Loup Dupuis, and Cédric Lanu. I would also like to oer thanks to

This work received funding from Innovacs 8 . A Meta-This work has been partially supported by Alpes Grenoble Innovation Research-POLE Edition 2015 and the Cross Disciplinary Program Eco-SESA 13 receiving funding from the French National Research Agency in the framework of the "Investissements d'avenir program (ANR-15-IDEX-02).

A preliminary version of this research was presented at the conferences of the Italian Association for Energy Economists (Italy, 2017), and the British Institute for Energy Economics (UK, 2018), and at a seminar at the University of St Gallen (Switzerland, 2018). 13 See https://ecosesa.univ-grenoble-

CHAPTER 3: DEMAND RESPONSE AS A CPR GAME price treatment, and better o in the nudge treatment. The increase in the peak price of electricity consumption results in a loss of welfare for individual subjects, or households, and for the group of sub jects, or neighbourhood or society. Given that in the nudge treatment, there is no change in price, subjects' welfare is not aected. As such subjects are nudged towards the optimum and so their welfare, both individually and at the group level, is greater than for control groups.

Treatment

Average We do not provide a welfare level for the price treatment for the socially optimal and Nash equilibrium levels of consumption as the price is designed such that the Nash equilibrium level of consumption is equal to 20 as observed in the nudge treatment. We provide the welfare associated with this level of consumption in the nal column. In this section, the results of the questionnaires completed after the CPR game regarding environmental sensitivity and altruism are detailed.

General Ecological Behaviour Scale

The GEB questionnaire measures an individual's environmental sensitivity [START_REF] Kaiser | A general measure of ecological behavior[END_REF]. Of the 28 items in the questionnaire, the mean score per item is 3.34 (std.

dev. = 0.22). Cronbach's α = 0.73 40 . The GEB scale is therefore acceptable.

The average environmental sensitivity level of sub jects overall, and per treatment is presented in table 3.9a, followed by the between treatment Wilcoxon rank-sum

Condentiality

In order to maintain your anonymity during any future data analysis, we will not ask for any personal information such as your name. The data collected during this study will remain anonymous and be used for research publications or other analyses. It will not be used for commercial purposes.

Code of conduct

Communication between participants is strictly forbidden. Including discussions of what should or should not be done during the study. Remain concentrated on your screen for the duration of the experiment. If you have any questions, or problems, raise your hand and an experimenter will come to you.

Payment and earnings

On your desk is an envelope containing 10e in exchange for your participation in this study. This amount belongs to you. In addition to this amount, and according to the decisions you make in the game, you can earn more money. You will be informed of your nal earnings at the end of the experimental session.

Organisation of the session

This session is composed of several phases. Before the start of each phase, instructions on the phase will be read aloud. A new phase will begin once all participants reduction in individual comfort. However, it avoids a greater reduction in comfort for all due to greater interruptions in the system.

In order to avoid such interruptions in the electricity network (voltage reductions, brown outs or black outs), one solution is to incentivise consumers to reduce their consumption by increasing the price during the peak period. Of course, this reduction in consumption equates to a reduction in individual comfort. However, it avoids a greater reduction in comfort for all due to greater interruptions in the system.

For this study, imagine that you are in a situation where you must decide on how much electricity to consume during a peak period. You will have to make this decision during 10 peak periods. This decision is based on whether or not you use the dierent electricity consuming appliances during the peak period. For the 10 peak periods, you will be placed in a group with three other people which constitutes an electricity consumption system.

Your task during the period

In each period, you will decide how much electricity to consume using your initial endowment of 100 Experimental Currency Units (ECU). In order to do so, you must decide whether to use or not several electricity consuming appliances, to each of which is associated a certain level of consumption. The consumption levels of each appliance will be presented on the next screen.

Electricity consumption choices

In each period, you must choose whether to use, or not, the following appliances:

1. Electricity heating, with three levels of use:

• No change = 15 Energy Units (EU)

• Lower temperature by 1 • C = 10 EU

• Lower temperature by 2 • C = 5 EU 2. Water heating:

• On = 5 EU

• O = 0 EU 159 3. Washing machine or dishwasher:

• On = 10 EU

• O = 0 EU 4. Cooking equipment (oven, hot plate)

• On = 10 EU

• O = 0 EU 5. Television or computer

For each period, you can therefore consume between 5 and 45 EU.

Earnings per period

Regardless of appliance usage, each EU is billed at the price of 1 ECU for all peak periods.

Regardless of appliance usage, each EU is billed at the price of 1 ECU for all peak periods.

Regardless of appliance usage, each EU is billed at the price of 3 ECU, which is 3 times more expensive compared to normal periods. With your initial endowment, you can consume up to 30 EU.

Your electricity consumption provides you with comfort via a monetary gain for each period: 13 ECU per consumed EU. However, your consumption in addition to the consumption of the three other people in your group has an impact on the equilibrium of the electricity network and thus on potential interruptions in supply.

The total consumption of the group results in a lower level of comfort for every one (including yourself ). The greater the total consumption of the group, the greater the reduction in comfort. Your earnings for the period thus depend on your own consumption and the impact that the total consumption has on the system.

Earnings simulator

To assist you in making your decisions, you have at your disposal a simulator with which you can simulate your earnings. You have 1 minute to do as many simulations as you wish before making your nal decision for the period. The use of the simulator is described in the following slide. In addition to the simulator, there is a table summarising all the possible earnings depending on your consumption choice (by column) and the total consumption of the other three people in your group (by row).

(Presentation of table )   (Presentation of simulator)

The end of the period At the end of each period, your earnings will be displayed on the screen. The part of your endowment which was not used for your consumption is integrated in this amount. At the end of the period, your earnings will be saved and you will start a new peak period, with a new endowment of 100 EU.

At the end of each period, your earnings will be displayed on the screen. The part of your endowment which was not used for your consumption is integrated in this amount. You will also be informed of how your consumption compares to the level of consumption which minimises the reduction in comfort felt by the group. This level is the same for all people in the group and is used as a reference for your own consumption. At the end of the period, your earnings will be saved and you will start a new peak period, with a new endowment of 100 EU.

At the end of each period, your earnings will be displayed on the screen. The part of your endowment which was not used for your consumption is integrated in this amount. At the end of the period, your earnings will be saved and you will start a new peak period, with a new endowment of 100 EU.

Your consumption during the period You will be informed via a smiley if your consumption for the peak period is:

• Equal or below the level which minimises the reduction in comfort felt by the APPENDIX B: APPENDIX TO CHAPTER 3 of winning 3e85 and a 9 in 10 chance of winning 0.10e. By clicking on the button associated with the chosen row, all the rows of Option A above your chosen row become green, and all the rows of Option B below your chosen row become blue.

The colour, green or blue, indicates that if that row is chosen randomly, you will win according to the probabilities in colour. Once you have made and conrmed your decision, the computer will randomly choose a row among the 10 rows. Then the computer will randomly choose one of the winnings of the option chosen for that row, according to the probabilities presented in the table. Your earnings will be displayed on the screen.

Are there any questions?

(Questionnaires and organisation of payment)

Thank you for participating in this experiment.

English Translation of Experiment Instructions

In black are the instructions in common for all treatments.

In brown are the additional instructions for the Gain Control treatment.

In green are the additional instructions for the Loss Control treatment.

In red are the additional instructions for the Gain Ex-ante treatment.

In blue are the additional instructions for the Loss Ex-ante treatment.

In orange are the additional instructions for the Gain Ex-post treatment.

In purple are the additional instructions in the Loss Ex-post treatment.

Study

This study is carried out by Grenoble Applied Economics Laboratory. During the study you will be asked to make decisions. If you have any comprehension problems, please do not hesitate to let us know.

Condentiality

In order to maintain your anonymity during the study and during any future data analysis, you were attributed a subject code. No personal data will be collected meaning it will be impossible to connect your answers during the study to your name. The data collected during this study will remain anonymous and be used for research publications or other analyses.

Code of conduct

Communication between participants is strictly forbidden. Including discussions of what should or should not be done during the study. Remain concentrated on your screen for the duration of the experiment. If you have any questions, or problems, raise your hand and an experimenter will come to you.

Organisation of the session

This study is composed of two phases. The rst phase includes:

1. The reading of the instructions At the start of the rst phase, the total of your pot is 0e. For each period, the gain associated to each correctly completed table is 0e05 with 50% probability, or 0e25, with 50% probability. The random draw of the gain is valid for the ve tables within

Random draw

The random draw is done by the computer before each period (5 tables) in order to determine the gain associated to a correctly completed table. You are informed of the result of this random draw before doing the task.

The random draw is done by the computer before each period (5 tables) in order to determine the loss associated to an incorrect or incomplete table. You are informed of the result of this random draw before doing the task.

The random draw is done by the computer before each period (5 tables) in order to determine the gain associated to a correctly completed table. You are informed of the result of this random draw after doing the task.

The random draw is done by the computer before each period (5 tables) in order to determine the loss associated to an incorrect or incomplete table. You are informed of the result of this random draw after doing the task.

Feedback at the end of each period

At the end of each of the 28 periods, you will be informed of:

• The number of correctly completed tables

• The gain associated to each table (if in gain treatments: GC, GEA, GEP)

The loss associated to each table (if in loss treatments: LC, LEA, LEP)

• Your earnings for the period (if in gain treatment: GC, GEA, GEP)

Your losses for the period (if in loss treatment: LC, LEA, LEP)

• Your total earnings for all the previous periods Are there any questions?

To start, please enter your subject code. Before we begin, please answer a few questions to verify your understanding of the instructions.

APPENDIX C: APPENDIX TO CHAPTER 4

Reminder (This slide was on display throughout the duration of phase 1.)

• Count the number of 1's

• 28 periods

• 1 period = 5 tables during 80 seconds

French Summary

Abstract This thesis examines how consumers respond to incentives used to encourage a reduction in their energy consumption. This necessary reduction stems from the need to reduce greenhouse gas emissions, increase energy production from renewable energy sources and achieve energy savings. These objectives require that residential demand be more exible in response to changes in supply and that energy savings be achieved by households.

The rst chapter explores the barriers to consumer acceptance and adoption of smart meters and the incentives that they provide. Signicant barriers exist and consumption reductions are far from being achieved. Limited motivation, lack of understanding of information on consumption and the rigidity of daily life are the main barriers preventing households from acting upon the incentives delivered via smart meters. The second chapter analyses the results of eld experiments and pilot studies on the impacts of dierent incentives on residential consumption. The results show that there are large variations and that, on average, an incentive will result in a 2% reduction in energy consumption. Real-time feedback and monetary information have the greatest eect. Finally, more robust studies report lower reduction eects. In the third chapter, a common pool resource game is used to explore individual responses to price and nudge-based incentives. Individuals are encouraged to reduce their consumption either by price increases or by smilies that reect their overconsumption. The price is most eective at encouraging the target level of consumption but takes longer to have an eect. The nudge is quickly understood but tends to reinforce overconsumption behaviours. The fourth chapter examines the eect of framing on eort provision. Individuals are asked to complete a simple and repetitive task for which they receive a piece-rate payo in the form of a gain or loss. Framing in the form of gains and losses is combined with three dierent payment structures: xed gain, low gain or high gain with an equal probability revealed before or after the eort is made. The results show that framing has no eect on eort provision, except for a high gain context announced before making the eort.