The Impact of Regulatory Independence on the Growth of Renewable Energies: A Panel Data Analysis of European Member States between 2013 and 2018

Carlos Gonzalez-Regalado1 , Diego Cebreros2 , and Eric Brousseau 1

1 Introduction

In electric systems, energy policy plays a crucial role in encouraging the adoption of renewable energies. Indeed, most investments in renewable energy power plants still rely on some subsidies (e.g., tax subsidies, feed-in tariffs, purchase obligations, and long-term contracts) to promote their development. The main motivation behind these support schemes is that experience effects will eventually make renewable technologies competitive with conventional generating technologies.

However, subsidy schemes and renewable generation have undermined the established market design, reducing the returns of conventional power plants and increasing the costs of balancing and capacity adequacy of electric systems (Newberry, 2016). The electricity production from solar and wind sources challenges the current "economic logic" of electricity markets: they are "intermittent" and have almost zero short-run marginal cost (Joskow 2011). In contrast, most conventional generating technologies (e.g., coal, gas, nuclear) are "dispatchable" and have higher short-run marginal costs that depend on their fuel and operation costs.

Much of the work on energy policy addressing the growth of renewable energy has implicitly assumed the institutional framework in which policymakers operate [START_REF] Blazquez | The renewable energy policy Paradox[END_REF]Lund 2009;Newbery et al. 2018;Tolmasquim et al. 2021). Nevertheless, the literature on institutional economics has shown evidence that the institutional framework might substantially affect the performance of economic policies (Scully 1988). Therefore, in this paper, we analyze the relationship between de jure and de facto power and its influence on renewable energy growth. While the former is the type of political power allocated by political institutions, the latter emerges from the ability to engage in collective action (i.e., in the form of lobbying or brute force) (Acemoglu and Robinson, 2006a). We use this theoretical relation from institutional economics as an entry point to analyze the relationship between National Regulatory Agencies (NRAs) with the executive power from European Member States (MS). Our research question is: to what extent do independent NRAs resist the growth of renewable energies in the EU electricity system?

In the EU, initially, most electric systems were controlled by nationally owned vertically integrated monopolies. With the liberalization reforms, MS took a step back on energy policy, enabling the NRAs to decide on the "economic rules" of the electric system [START_REF] Joskow | Lessons Learned from Electricity Market Liberalization[END_REF]. Indeed, NRAs were designed as specialized parties with the objective of establishing fair competition and stable rules for market participants. In this context, independence establishes the separation between the de-facto power of the MS and de-jure power of NRAs to steer the economic rules of electricity markets, with the objective to create a credible commitment to secure private investments.

Considering the fact that MS in the EU have committed to decarbonizing their electric system for 2050, not NRAs. Our thesis is that NRAs independence generates "resistance" to the speed at which renewable energies grow. For us, resistance means that NRAs will be cautious about how renewable energies might impact negatively on competition because their main objective is to provide a stable market that attracts investments to have a reliable and efficient electric system. For example, mechanisms such as renewable energy subsidies or changes in market design might be to the detriment of the position of incumbents which are the prominent investors in current electricity markets.

To test our thesis we perform an empirical analysis using panel data to measure the impact of regulatory governance on the renewable energy share and electricity tariffs in the EU member states. However, governance is not easy to measure, nor are publicly available indicators. Therefore, we use OECD regulatory governance surveys [START_REF] Brousseau | Comparative analysis of regulatory governance regimes in the OECD[END_REF] in combination with text-analysis metrics to derive governance indicators. This method uses data variability to identify the relevant features of a governance regime and the participants' weights assigned to each dimension.

Our results show that between 2013 and 2018, independent NRAs were negatively associated with the share of renewable energy sources (RES) in total energy consumption. Our contribution is two-fold. First, previous work analyzing the institutional context of policy makers [START_REF] Alesina | Reguation and investment[END_REF][START_REF] Égert | Regulation, institutions and aggregate investment: New evidence from OECD countries[END_REF][START_REF] Anderton | The impact of product market regulation on productivity through firm churning: Evidence from european countries[END_REF] focuses on the effects of the reforms (policy output) on economic performance (investments, factor productivity, and labor markets). We contribute to the empirical literature on the effects of the liberalization of utilities by separating the impacts of regulation per se from the impacts of governance arrangements that implement it. Second, we contribute to the literature on the drivers of decarbonization by discussing the policy implications that our results have for NRAs and environmental objectives. We suggest an ongoing tension between the will to support renewable energy development and guaranteeing low electricity prices to consumers through competition. Furthermore, we put our results in context, discussing the policy implications of the ambitious targets to decarbonize electricity systems in EU for 2050.

This study has eight sections. In the second section, we propose an overview of the emergence of NRAs from an EU perspective, considering the mandates of liberalization reform on their design. The third section provides the background on the tension between renewable energies and current market design. The fourth section describes our theoretical framework. Seciton five the data used and our empirical strategy. Section six presents our main results, while section seven discusses them in detail. Section eight offers our main conclusions.

National regulatory agencies in the EU

In Europe, the liberalization reforms aim to facilitate the integration of EU electric systems: incentivize the investments needed to develop transmission lines for exchanging electricity amongst MS and improve the system reliability and efficiency [START_REF] Joskow | Lessons Learned from Electricity Market Liberalization[END_REF]. These reforms required the privatization and vertical separation of formerly state-owned (national) monopolies, which, in many cases, controlled the totality of electric assets within a country. However, the unbundling of state-owned vertically integrated monopolies appeared insufficient to attract investments in the EU from the start of the liberalization process. Furthermore specialized wholesale markets were created, and the development of a common regulatory framework to guarantee fair competition.

However, the market power from the ancient monopolists was still relevant, and NRAs emerged to steer the market implementation and become technical referees to guarantee fair competition. The reasoning was that new investors needed stronger guarantees of fair treatment while competing with state-owned incumbents, as well as protection from shortterm political interests that could threaten their returns by changing market rules1 . Indeed, until the liberalization reforms, policy-making and enforcement were primarily in the hands of public bodies directly overseen by ministries. Therefore, politicians in power could have incentives to favor the state-owned (private) incumbent [START_REF] Stigler | The theory of economic regulation[END_REF] or to depart from long-term policy commitments in exchange for political support [START_REF] Majone | Temporal Consistency and Policy Credibility: Why Democracies Need Non-Majoritarian Institutions[END_REF].

Consequently, during the early 1990s, several European governments delegated regulatory functions to independent NRAs to signal policy stability and transparency to new investors [START_REF] Glachant | Regulating Networks in the New Economy[END_REF]. In addition, independent bodies could solve information gaps [START_REF] Glachant | Implementing incentive regulation and regulatory alignment with resource bounded regulators[END_REF], policy commitment concerns [START_REF] Gilardi | Policy credibility and delegation to independent regulatory agencies: a comparative empirical analysis[END_REF][START_REF] Gilardi | The formal independence of regulators: A comparison of 17 countries and 7 sectors[END_REF][START_REF] Gilardi | Chapter 14: The Independence of Regulatory Authorities[END_REF], arbitrate disputes, and monitor industry performance.

As shown in table 1, there are several European Directives that set overarching principles of competition on the NRA institutional framework. The regime was gradually implemented, with member states transferring regulatory tasks to NRAs (e.g., interconnection rate setting, monitoring duties) that could contribute to ensuring fair third-party access to the transmission and distribution segments (Article 4 of (2003/54/EC))2 . Later, the Third Electricity Package expanded the NRA regulatory powers/responsibilities by allowing them to modify contract terms and conditions, oversee interconnection issues, and solve disputes between market actors (Article 36 of (2009/72/EC)).

The Third Electricity Directive formally established independent NRAs in member states. First, it required member states to delegate regulatory responsibilities to a single national body, and second, it ruled on measures that granted this authority autonomy while limiting undue political influence on its decisions. The Directive, in particular, created limits between the NRA's top management and the government (and with the industry). Several rules, for example, prohibited top management from working for both the government and the NRA simultaneously or established "cooling-off" periods before NRA top management could join the industry. Additional measures restricted politicians' and political cycles' influence by creating fixed governing periods and limiting arbitrariness in executive management dismissal. Furthermore, the Directive required member states to ensure proper NRA operation by providing stable funds/human resources and allowing the NRA to administer its resources autonomously (Article 35 of (2009/72/EC)).

The CEP has deviated from the previous regime by implementing new sustainability mandates for the NRA operations. The NRAs are tasked with creating the technological and economic conditions that allow renewable electricity and "flexibility" services to participate in the market. In other words, NRAs' actions must guarantee the inclusion of additional renewable energy generation, electricity storage systems, and demand management mechanisms cost-efficiently. Yet, as discussed in the following section, these additional environmental requirements create contradictions between the market reform, the governance regime, and sustainability objectives.

3 Tension in the economic logic between renewables and conventional sources of electricity.

Since 2004, the share of renewable energies has doubled, reflecting the political commitment to decarbonization targets. Governments either supported the reduction in the investment costs in renewable generation (mainly for solar and wind units) or guaranteed positive and stable revenue streams through support mechanisms such as feed-in-tariffs / premiums, quotas, and other instruments. Figure 1 shows the positive evolution in the share of renewable energy as a percentage of total energy consumed in the 27 EU member states. The support instrument used the most was feed-in tariffs, which represented a subsidy of €53 billion in 2019 (EC, 2021). Because feed-in tariffs have supported renewable energies, and renewable energies reduce the price in wholesale electric markets, the subsidies from feed-in tariffs have become more expensive over time [START_REF] Henriot | Melting-pots and salad bowls: The current debate on electricity market design for integration of intermittent RES[END_REF][START_REF] Maclean | Energy system crossroads -time for decisions:uk 2030 low carbon scenarios and pathways -key decision points for a decarbonised energy system[END_REF]Council of European Energy Regulators, 2021).

The growth of renewable energy has been a main driver of several EU Directives ruling on decarbonization that have focused mainly on setting emission targets. These directives started in 2001 under the Kyoto Protocol, which agreed on a binding target for the EU to reduce 2012's emission levels by 5% to its 1990s emissions. The first renewable electricity However, the current wholesale market organization is designed to reward generators based on cost efficiency in a setting where generators have positive marginal costs (e.g., the fuel cost for thermic plants) and can produce energy at any given time. Plants with low marginal costs supply electricity to the network first. As the demand increases, generators with higher marginal costs deliver electricity until the demand is met. The marginal cost of the last generator participating in the market sets the wholesale price paid to all electricity suppliers. Therefore, additional renewable generation leads to lower wholesale electricity prices because of the zero marginal cost of producing an additional electricity unit. Low prices in the wholesale market may not provide enough incentives for new investors and may prevent current plants from covering their costs or not utilizing their total capacity [START_REF] Blazquez | The renewable energy policy Paradox[END_REF]. To correct this problem, European member states implemented onerous subsidy schemes to incentivize renewable generation. The importance of these subsidies has increased over time, as evidenced in fig. 2, which shows the extent of subsidies as a fraction of the cost of an electricity unit 3 .

The rise of renewable energy support has stressed the need for additional investments in the power system to handle the mismatch between unmonitored supply and market demand. In response, capacity markets have been developed to rectify the balance, focusing on compensating backup generation, frequently of a thermal nature 4 . Nonetheless, a notable drawback of these markets lies in their dependence on complex calculations grounded in various technical assumptions. Unfortunately, these assumptions, while considered effective in meeting the real needs of the power grid, have led to an excess of available power and consequently have raised the prices consumers pay. Empirical studies show a significant correlation between the share of renewables in the energy mix and higher electricity prices in the OECD countries [START_REF] Moreno | The electricity prices in the European Union. The role of renewable energies and regulatory electric market reforms[END_REF][START_REF] Da Silva | Assessing the determinants of household electricity prices in the EU: a system-GMM panel data approach[END_REF]. Estimations by authors, based on Eurostat and CEER:

Given the tension between renewable generation, the current market design, and, ul-3 For 2011, the share was calculated using the renewable energy support of 2010 and the generation and supply costs of 2011. The authors calculated the ratios. The data used in the numerator is the weighted average of the surveyed countries as calculated by the Council of European Energy Regulators (CEER) in their 2011, 2014, and 2018 Renewable Energy. 4 Capacity mechanisms usually rely on decentralized carbon-based generation plants that do not participate in the market.

timately, the liberalization objectives, we explore the role of NRAs in easing or stressing the tensions introduced by renewables, given the liberalization mandates. Although the reform set broad environmental principles for NRAs, they are mandated and accountable for reducing system costs, transmitting rents to customers, and encouraging new investments. The extent to which NRAs can carry out their mandates or choose to pursue other (environmental) objectives highly depends, among other elements, on the preferences of agencies, national governments, and European institutions [START_REF] Alesina | Bureaucrats or politicians? Part II: Multiple policy tasks[END_REF], and the NRAs' degree of autonomy from external intervention, discretion on resource usage and the breadth of powers delegated to execute their assignments.

4 Theoretical framework

Governance regime's characteristics

The assessment of governance regimes has focused on exploring whether NRAs carry out their mandates and objectives independently and on methodologies to estimate NRAs autonomy [START_REF] Thatcher | Theory and Practice of Delegation to Non-Majoritarian Institutions[END_REF][START_REF] Levi-Faur | The global diffusion of regulatory capitalism[END_REF]. [START_REF] Gilardi | Policy credibility and delegation to independent regulatory agencies: a comparative empirical analysis[END_REF][START_REF] Gilardi | The formal independence of regulators: A comparison of 17 countries and 7 sectors[END_REF] compared the governance of NRAs in economic and social sectors by assessing their decision-making independence (from the government and the industry). Following the literature on central bank independence, the author estimated a metric that considered the appointment length of agency authorities and provisions for their dismissal, budgetary autonomy, internal resource management, and delegated tasks as the most essential features of NRA independence5 .

However, recent studies suggest that even if independence is a fundamental dimension, other dimensions are needed to contrast regimes and study their impacts. [START_REF] Hanretty | Independence, Accountability and Perceived Quality of Regulators[END_REF] discuss the role of transparency and accountability in improving the regulatory decision-making process. They suggest that both features encourage bureaucrats to perform better in their duties and to stick to the agency mandates. Transparency in regulatory intervention has increased substantially in the electricity sector since the beginning of the market reform [START_REF] Pollitt | The European Single Market in Electricity: An Economic Assessment[END_REF]. The decision-making processes are open to public scrutiny, and regulatory authorities and staff are evaluated periodically by legislative bodies. However, these features are complex to measure because regulators differ in objectives and are embedded in different institutional systems.

Besides independence and transparency, the breadth of delegated powers is also relevant to describe and discriminate among governance regimes. OECD (2016) stresses the importance of regulatory levers to enforce market regulation, such as specifying network access conditions, setting prices and tariffs, settling disputes between market actors, and requesting information from market players. The availability of such powers increases the regulators' abilities to solve complex and unexpected problems [START_REF] Jamasb | Electricity Market Reform in the European Union: Review of Progress toward Liberalization & Integration[END_REF]. [START_REF] Jordana | Agency proliferation and the globalization of the regulatory state: Introducing a data set on the institutional features of regulatory agencies[END_REF] and [START_REF] Brousseau | Comparative analysis of regulatory governance regimes in the OECD[END_REF] used factor analysis and text analysis algorithms, respectively, to identify alternative regime arrangements. Both studies proposed alternative arrangements that partially contrast with mainstream studies (e.g., OECD ( 2016)). Jordana and co-authors propose a four-dimension model (Political independence, Managerial Autonomy, Regulatory Responsibilities, and Public Accountability) to discriminate between economic and social regulators worldwide. In contrast, Brousseau and Gonzalez-Regalado proposed a four-dimensional model focusing on economic European NRAs in network industries, including the electricity sector.

Their model uses detailed OECD surveys to follow the implementation of the current EU governance regime (part of the EU electricity market reform) across NRAs between 2013 and 2018. In detail, the model defines regime features as follows:

1. Independence from the government estimates the degree of separation between the regulatory agency and the executive's undue influence6 . The main objective of independence is to support the stability of market access and competition rules [START_REF] Gilardi | Chapter 14: The Independence of Regulatory Authorities[END_REF].

2. Discretion describes the agency level of "flexibility.". The dimension portrays an agency's freedom to manage its internal decision-making process and resources and to interpret its mandates.

3. Scope of market monitoring describes the regulator's ability to monitor/coordinate the competitive process, supervise the industry behavior, and manage economic incentives.

4. Transparency reflects the regulators' role in reducing information asymmetries between operators, public authorities, and users7 ; in particular, to ensure compliance and limit behavioral drift.

In the following sections, we use the model in Brousseau and Gonzalez-Regalado (2022) to associate differences in NRAs' governance regimes to energy mix heterogeneity across European countries.

The reform, system efficiency, and the environment

The literature evaluating market reforms and their impact on renewable electricity generation cannot showcase a significant relationship. Generally, market reforms improve member states' environmental performance but do not necessarily encourage new renewable generation units to enter the market. [START_REF] Pollitt | The European Single Market in Electricity: An Economic Assessment[END_REF] suggest that the decrease in pollutant emissions associated with market reforms results from the entry of efficient gas generators (replacing coal generators) and not new renewable units. Asane-Otoo (2016) finds similar results and suggests that the introduction of competition in generation and privatization attracted efficient (thermic) generators to the market and reduced emissions, but surprisingly, they did not find evidence of an increase in renewable generators. [START_REF] Nicolli | Energy market liberalization and renewable energy policies in OECD countries[END_REF] propose that the market reform, particularly the reduction of entry barriers and the access of new generators, reduced the incumbent's power to lobby against renewable support policy. Their empirical evidence displays that an extended market reform (OECD PMR index) positively correlates with public support for renewable energy. However, no empirical evidence of superior renewable energy generation is provided. This overview suggests that market reforms enhanced overall system efficiency but did not create market conditions that encouraged investment in renewable generation. Yet, the role of the sectoral governance regime on renewable generation has not been discussed. The following section analyzes the relationship between governance regimes and the electricity sector performance and proposes connections between governance regimes and renewable energy generation.

Hypothesis formulation

NRAs' actions depend on the interaction between the agencies' preferences (principles and mandates), their internal resources, and the interests of governments and industry players. In this context, the agencies' governance regimes have a strong influence on their outcomes and, hence, on industry performance. Governance features such as decision-making autonomy, agency discretion, and the breadth of delegated powers are expected to influence performance differently.

The market reform aims at incentivizing industry players to invest in competitive and integrated electricity systems. To deploy new investments, potential investors need a guarantee about their expected returns. The reform envisioned independent NRAs as bureaucratic bodies able to monitor and steer the market in a stable and predictable mode and reduce the likelihood of short-term politicians' opportunistic policy deviation [START_REF] Majone | Temporal Consistency and Policy Credibility: Why Democracies Need Non-Majoritarian Institutions[END_REF]. Consequently, regimes with independent NRAs are expected to attract superior investment levels. In this regard, [START_REF] Cubbin | The impact of regulatory governance and privatization on electricity industry generation capacity in developing economies[END_REF] finds a strong association between the presence of an independent regulator and superior electricity generation per capita. In some circumstances, the effect of independence is conditional on privatizing utilities. While [START_REF] Zhang | Electricity sector reform in developing countries: an econometric assessment of the effects of privatization, competition and regulation[END_REF]'s results indicate that independence characteristics are significantly associated with a higher generation per capital but only in the presence of private incumbents. This claim supports previous work by [START_REF] Zhang | Competition, regulation and privatisation of electricity generation in developing countries: does the sequencing of the reforms matter?[END_REF], who show that privatization increases sector per capital output only when an independent regulator is established before the reform. Independence is significantly associated with other sector performance measures, such as investment and firm leverage. Égert (2009) rely on a cross-section of 13 countries and six utility sectors in 2008 to show that the setup of an independent regulator and the application of incentive price regulation reduce return risk and, therefore, foster investments. Moreover, [START_REF] Cambini | Independent Agencies, Political Interference, and Firm Investment: Evidence from the European Union[END_REF] highlight that establishing an independent regulator positively impacts investments in 80 EU utilities, including 37 energy firms in distribution and transmission. After controlling for political and institutional variables, they found significant investments from the private sector increased between 1994 and 2004 because the setup of independent regulators enhanced policy credibility among investors.

The previous evidence supports the premise that independent NRAs commit to the reform's mandates, which has attracted investments that improved the overall system performance based primarily on monitorable sources. The inclusion of non-monitorable sources in the current market design stresses the system's efficiency by increasing its costs to match supply and demand8 . When conflicted with increasing system costs of renewables, which could translate into higher prices to consumers and efficiency losses (especially in the capacity market), independent NRAs serve the overall system efficiency. Thus, we expect that:

• Hypothesis 1: More independence is associated with a lower share of renewables in the energy mix.

The NRAs' ability to interpret its mandates relies on the institutional design of the agency, societal demands, and the stakeholders' preferences about those demands. In the case of decarbonization needs, a particular group (e.g., citizens) could advocate for a more decarbonized energy mix, while another group (e.g., consumers) could express concerns about the impact of decarbonization on the rise of electricity prices. The NRA's conduct would depend on its preferences about the societal demand and the power distribution among groups. Therefore, we do not expect a significant effect of discretion on the electricity mix.

• Hypothesis 2: More discretion should not be associated with a higher share of renewables in the energy mix.

The breadth of delegated powers granted to a regulatory agency (monitor and steer competition in the industry) describes in practice the NRA's capacity to accomplish its goals. Brousseau and Gonzalez-Regalado (2022) distinguish NRA's powers between the ones used to supervise competition (scope of market monitoring) and the ones to supply information to the industry and reduce information asymmetries between operators, users, and public authorities (transparency)9 . While political scientists have examined the reasons for governments to grant regulatory powers to NRAs (see [START_REF] Wassum | Explaining regulatory autonomy in EU network sectors: Varieties of utility regulation?[END_REF]), the effects of this breadth of powers on industry performance have been overlooked.

Several analyses have focused on the links between the breadth of NRA's powers and the agency's ability to conduct its work accurately. However, their results do not indicate the existence of a significant link. [START_REF] Hanretty | Independence, Accountability and Perceived Quality of Regulators[END_REF] examine the correlation between accountability, which partially overlaps with the NRA's capacity to reduce information asymmetries, and the perceived regulatory quality in energy, telecommunication, and transport. A regulatory quality measure was derived from self-administered surveys about the NRA's performance. The authors found a positive but non-significant relationship between the two variables. Similarly, [START_REF] Hanretty | Political Independence, Accountability, and the Quality of Regulatory Decision-Making[END_REF], using an ordered probit regression, did not find any correlation between accountability and regulatory quality (proxied by scores from the Global Competition Review) for 30 OECD competition authorities in 2005-2014.

These results indicate that regulatory powers do not affect the players' investment behavior. Principles, mandates, and policy commitment are determinants of investment decisions. As a result, we anticipate:

• Hypothesis 3: More market scope monitoring should not be associated with a higher share of renewables in the energy mix.

• Hypothesis 4: More transparency should not be associated with a higher share of renewables in the energy mix.

5 Data and methodology

Data

Because of data availability, our econometric analysis relies on 24 countries: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Germany, Denmark, Estonia, Spain, Finland, France, Greece, Hungary, Ireland, Italy, Luxemburg, Lithuania, Latvia, the Netherlands, Poland, Portugal, Sweden, Slovenia, and Slovakia. We analyze the European electric systems for the period 2013-2018. The appendix A gives a detailed summary of the data used and statistical characteristics, particularly table 4.

The share of electricity produced from renewable sources was extracted from the Statistical Office of the European Union (EUROSTAT) database. The share, in the numerator, includes hydropower, wind, solar, biomass, bioliquids, biogases, tide, wave, and ocean gross production. The denominator is the country's gross energy consumption in MWh 10 . Moreover, we constructed a second variable that captures the non-monitorable (i.e., solar and wind generation) sources' participation in the electricity mix to estimate whether the governance regimes conflict with these renewable sources.

To capture the NRAs' governance regime, i.e., independence, transparency (accountability), and Scope of action, we use the indicators computed by [START_REF] Brousseau | Comparative analysis of regulatory governance regimes in the OECD[END_REF]. The normalized scores range from 0 to 1 for each of these dimensions 11 . The metrics measure the evolution of the governance regime between 2013 and 2018.

Their metrics offer several advantages. First, they are derived from surveys on network sector governance regimes (electricity, gas, telecommunication, rail, and air transport) conducted by the OECD. The OECD (2016) provides the most comprehensive dataset about regulatory governance to date for both periods. Second, the characteristics and number of dimensions were defined using text-analysis algorithms 12 (word co-occurrence) to uncover latent dimensions using the data variability without predefined assumptions about the most contrasting differences among regimes. Third, their metrics allow comparing regimes over time, enabling the evaluation of whether changes in governance have implications for the industry's output. Finally, the metrics summarize scores based on the data variation and not in a predefined weighting scheme.

10 European Environmental agencies use this metric to track country decarbonatization target performance 11 The OECD estimates governance scores with the same survey [START_REF] Casullo | The 2018 indicators on the governance of sector regulators-Part of the Product Market Regulation (PMR) Survey[END_REF]. However, scores are not comparable across time because of significant changes in their questionnaire. Brousseau and Gonzalez-Regalado (2022) NLP method allows bypassing this time-comparability issue 12 Brousseau and Gonzalez-Regalado (2022) use a coding scheme to transform survey categorical responses into textual documents. Using topic modeling, textual sources uncover co-occurring clusters of terms named topics. Based on these clusters and their terms' semantic meaning dimensions are identified and defined.

To measure the extent and evolution of the liberalization reform, we constructed a market access variable that captures to what extent legal provisions support entry in the generation and retail segments. The estimation method (see Annex 2) follows the approach proposed by [START_REF] Brousseau | Comparative analysis of regulatory governance regimes in the OECD[END_REF]. The metric was estimated using information from the OECD Product Market Regulation survey for 2013 and 2018 [START_REF] Vitale | The 2018 edition of the OECD PMR indicators and database: Methodological improvements and policy insights[END_REF]. Similarly, we also utilize the OECD PMR index, which aggregates information on the industry entry barriers, market structure, vertical integration, and price controls. The data is available from 1975 to 2018.

In addition, our dataset considers national, sectoral, and institutional variables that affect the supply of renewable energy. Data on electricity consumption, production, and system capacity are obtained from EUROSTAT (2018) and International Energy Agency (2018), while the economic and demographic variables (GDP and population) from the Penn World Tables version 10 [START_REF] Feenstra | The next generation of the penn world table[END_REF]. The measures that capture the quality of institutions are gathered from various sources. The Freedom House Political Rights Score (Freedom House, 2018) accounts for political and institutional stability. Besides, the IHS-Market indicator and the Corruption Perception Index (CPI), from Transparency International (2018), measure the legal system's protection to prevent an arbitrary exercise of power. The society's preferences for environmental policies are estimated by the number of seats green parties hold in the legislative. The information is collected from the Comparative Political Data Set [START_REF] Armingeon | Comparative political data set 1960-2021[END_REF]. To proxy for the most prominent firm's decision power in the sector, we use the market share of the largest generation firm available in [START_REF] Vitale | The 2018 edition of the OECD PMR indicators and database: Methodological improvements and policy insights[END_REF]. Finally, the measure of the industry's ability to innovate is gauged by the number of patents per millions of habitants and extracted from the International Renewable Energy Agency (2018) database.

In addition, we construct three variables that portray the support (or opposition) to renewable electricity generation. The first measure accounts for the (lobbying) power that non-renewable technologies would have to preserve the current mix structure. Technologies with larger market shares are expected to spend more resources to keep the status quo. We followed [START_REF] Pfeiffer | Explaining the diffusion of renewable energy technology in developing countries[END_REF] and estimated the Hirschman-Herfindahl (HH) index for non-renewable technologies. 13 . The shares are extracted from EUROSTAT (2018). The second variable captures the extent to which lawmakers have produced public policies to support renewable generation. The variable is constructed by counting the years that particular policies or programs that support renewable generation (e.g., incentives or direct subsidies) have been in force 14 . More extended promotion of green energy should trigger entry in the generation segment [START_REF] Nicolli | Energy market liberalization and renewable energy policies in OECD countries[END_REF]. The third index accounts exclusively for the importance of policies that provide direct financial incentives to generators. 13 The variable is estimated as follows: lobbyingit = N i=1 share 2 i 14 We selected current active policies relevant to electricity generation at the national level. Policies that support consumers were not included in the indicator. If a policy has been active since 1976, in 2013, it was active for 37 years, while in 2018, it was active for 42 years.

This importance is proxied by the percentage of policies granting subsidies, loans, and tax incentives as a share of total renewable support policies. We use the International Energy Agency (2018) Policy Database to identify the policies, type, status, and duration period.

To measure consumer electricity prices, we use end-use consumer year-averaged prices (for industrial and household customers, excluded VAT). The data is gathered from Energy Prices and Taxes Statistics database (International Energy Agency, 2018).

Evolution of renewable shares, liberalization, and governance regimes

The shares of renewables have grown for our sample between 2013 and 2018. Figure 3 shows that disparities across countries remain over time despite this growth. This heterogeneity reflects differences in geographical location, national energy resources, and institutional and economic long-run conditions [START_REF] Bourcet | Empirical determinants of renewable energy deployment: A systematic literature review[END_REF]. As discussed in the following subsection, a fixed-effects model could account for these structural differences. The liberalization reform that started in the early 1990s kept pace during the 2000s, although at different paces. Most European OECD countries reduced regulatory barriers to firm entry and competition and implemented pro-competition regulations in their regimes. Figure 4 shows the evolution of the electricity market reform implementation between 2000 and 2018, measured by the OECD PMR indicator (each line represents a country in our sample). Since 2013, only a few countries have liberalized their markets further, while the majority have followed a steady trajectory.

In contrast, governance regimes experienced significant changes between 2013 and 2018. Figure 5 shows that NRAs are more independent (red) from the government and were granted higher levels of formality (reduced discretion in purple). However, regulators are still far from converging to a similar independence level. The variance of the score remains quite significant. In contrast, the transparency (green) and scope of market monitoring (blue) tasks increased their homogeneity over the period. Transparency increased while the scope of market monitoring remained similar to the previous period level.

The NRA governance model is a fundamental part of the liberalization reform in the European Electricity markets, as explained in section 2. This association challenges identifying the potential effects of governance on industry performance because changes in the governance regime might reflect a deepening of the liberalization reform. The considerable changes in the governance regimes and the relatively modest evolution of the liberalization reform between 2013 and 2018 provide a possibility to isolate the governance effects on performance.

Econometric model

The national electricity mix is the result of technical and political characteristics but also of various geographical, historical, and institutional factors that occasionally are unobserved for a researcher. The two-way fixed effect panel data model accounts for those unobserved factors that vary only over long periods. Besides, this model also controls for common shocks that could have affected electricity systems during specific periods 15 . ln(perf ormance i,t, ) = α + β 1 * govern_regime i,t + β 2 * regulatory_regime i,t + β 3 * controls i,t + µ i,t (1)

In eq. ( 1), the share of renewable energy and electricity price are our outcome variables (ln(perf ormance i,t, )). The effects of the governance dimensions (govern_regime i,t ), i.e., independence, discretion, scope of market monitoring, and transparency, on the outcome variables are captured by the term β 1 . Despite the liberalization reform's stability between 2013 and 2018, the term regulatory_regime i,t is introduced to capture the impact of any variation in the liberalization reform. The term β 2 captures the reform's effects.

Moreover, the error term structure µ i,t = δ i + ν t + γ i,t controls for unobserved electricity and renewable sector characteristics. The δ i term captures time-invariant system and national characteristics (e.g., the lasting aspects of geography and institutional traditions). 15 We checked for significant individual and time effects by testing if, jointly, all individual and time intercepts are different from zero in an OLS regression. Our results suggest that the fixed-effects model should be selected. Our specification also assumes a constant effect of independence over countries and time. However, we tested whether the overrepresentation of negative heterogeneous treatment effects might artificially negatively bias our average estimates. We follow de Chaisemartin and D'Haultfoeuille (2020) test to test if the effect direction comes from a true prior or is the result of overrepresentation. Our model is not driven by the issues presented on de Chaisemartin and D'Haultfoeuille (2020).

The ν t captures unobserved common shocks that could have affected electricity systems during a particular period, and the γ i,t term is an idiosyncratic individual error term.

In addition, we added a set of covariates controls i,t to account for other determinants of the energy mix, such as changes in demand (primary energy demand, income, energy security) and supply factors (policy support towards particular technologies, innovation, and specialization effects), and institutional quality (government effectiveness and the rule of law). The vector β 3 captures the effects of our control variables on system performance.

Results

Effect of governance regimes on renewable energy growth between 2013 and 2018

Table 2 shows the parameter estimates for the effects of the governance regime on the share of renewable energies. Models (1) to ( 4) test the individual effect of each dimension, while model ( 5) tests the overall regime effect.

Models ( 1) and ( 5) show a negative relationship between independence and the share of renewable energy after controlling for the rest of the dimensions. This estimate confirms the Hypothesis 1, which indicates that independent regulators are associated with a lower share of renewable electricity produced. At this point, we cannot explicitly distinguish between the independence granted to the regulator and its mandates (objectives). Therefore, we add a set of controls to account for other factors that might influence the share of renewables and the governance regime.

The rest of the dimensions do not seem to have a crucial influence on renewable generation. The effect of discretion is negative but is not statistically significant. Thus, we accept the Hypothesis 2. Transparency has a negative, but not significant, impact on renewable energy shares in models (2) and ( 5). In model ( 5), after controlling for the effects of the rest of the dimensions, the coefficient almost drops to zero. Thus, we accept Hypothesis 3. This result coincides with the one claimed by [START_REF] Koop | Political Independence, Accountability, and the Quality of Regulatory Decision-Making[END_REF], where no strong associations are found between transparency and regulatory or industry performance. Finally, the Scope of market monitoring shows a positive but non-significant coefficient in models ( 4) and ( 5). These estimates confirm Hypothesis 4. The positive effects could indicate that regulators with market steering tools can organize markets that attract renewable generation better. However, more research is needed to support this connection. measures the net electricity trade (exports -imports) as a proxy of electricity security. Years ren. laws: measures the political support for renewables by legislation. The metric sums the years that different laws are set in logs. Patenthundred: captures the effects of specialization in renewables. The measure is the ratio of patents and the number of people in a country. Discretion is excluded in model ( 5) due to multicollinearity with the other gov. dimensions. Heteroskedasticity robust standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

Confounding factors and additional controls

We assess confounding factors that might affect the share of renewables and the governance regime in table 2. First, we control for the effect of liberalization, which might influence the extent of the IRA model in a jurisdiction [START_REF] Gilardi | The formal independence of regulators: A comparison of 17 countries and 7 sectors[END_REF], and might foster renewable energies by removing entry barriers [START_REF] Nicolli | Energy market liberalization and renewable energy policies in OECD countries[END_REF]. The impact of market liberalization (mkt access) is positive and significant. Second, we consider the security of supply (trade balance). Countries with electricity deficits (more significant imports than exports) could be incentivized to set up independent regulators to foster investments. Besides, deficit countries might develop firm policies to support renewable generation since their territories might lack access to other generation capacities (hydro or nuclear plants), thus reducing their electricity deficit [START_REF] Pollitt | The European Single Market in Electricity: An Economic Assessment[END_REF]. We found that countries with positive trade balances (trade balance) are associated with larger renewable shares, yet all models' coefficients are insignificant. Finally, we control for the institutional environment and potential political capture following [START_REF] Baldwin | Global Renewable Electricity Policy: A Comparative Policy Analysis of Countries by Income Status[END_REF]. According to the authors, political capture threatens new investments by increasing the risk of expropriation. If political capture becomes large enough to hold up new investments, politicians set up independent regulatory agencies to attract new investments [START_REF] Gilardi | The formal independence of regulators: A comparison of 17 countries and 7 sectors[END_REF]. In our specification, we use FH pol rights to measure the institutional stability in a country (lower probability of investments being expropriated) and find that it is positively and significantly associated with larger renewable shares. In the robustness check section, we use alternative indicators to control for other institutional national characteristics, such as the rule of law and government quality.

We also controlled for the national efforts to support renewable generation. We measured this support by adding the years that different "green" policies have been in force (years ren. laws)16 . The variable's coefficient is positive and significant (as expected), as presented in table 2, model (2). Moreover, we incorporated electricity demand controls such as consumption and national wealth (income per capita). Regarding electricity demand, proxied by nominal GDP, we find a positive and significant effect. In other words, more consumption is associated with more significant electricity generation from all sources, but in particular from renewables17 ). However, more prosperous countries seem to prefer an energy mix that relies on thermic generation. This finding aligns with the argument that developed countries prefer to spend on thermic systems because renewable generation might not meet unexpected demand increases [START_REF] Cadoret | The political drivers of renewable energies policies[END_REF]. Similarly, we account for the (lobbying) power that non-renewable generators have to preserve their rents. High non-renewable lobbying power (IHH gen) should limit the entry of renewable generators. Our variable is negatively correlated with the share of renewables. Innovation effects (patenthundred ) are also positively correlated with a larger share of renewables.

In addition, we evaluated other market conditions associated with renewables, like electricity prices and market concentration. Systems with high prices might call for deeper regulatory reforms (including regulatory independence) or changes in the energy mix to reduce prices. Model (2) in table 5 indicates a positive but not significant association between prices and the renewable share. After adding prices to our specification, the coefficient of independence remains negative and significant. 18 . In markets with high concentration, the largest incumbent firm (through its market power) might attempt to maximize rents, either by capturing the NRA or by choosing a profitable energy source portfolio. Model (3) in table 5 shows a negative relationship between the share of the largest generator and the share of renewable energy, suggesting that more concentrated markets are connected to thermic generation. However, the coefficient size does not significantly change from the one in table 2, model (1) 19 .

Robustness check

Outliers

The distribution of our main variables indicates the presence of influential data points that might compromise our estimates. For instance, while the average share of renewable electricity lies around 20.1%, the share for Denmark stands more than two standard deviations away from the average. This issue is partially solved by our panel data specification that uses country within variation to identify the effect of governance regimes. However, some countries recorded significant changes (in our main variables) between 2013 and 2018, as shown in fig. 6. Countries like Malta (MT) and Luxembourg (LU) have considerably increased their shares of renewable generation, while Slovenia (SI) has improved its independence score performance compared to the rest of the sample 20 .

To verify the robustness of our estimates, we determine the extent to which they depend on those influential points by re-estimating model (1) in table 2, leaving out one country (data row) at a time 21 . The results in fig. 7 show the independence coefficients do not 18 The model also considers industrial consumer prices and displays similar results. The results are not reported 19 The incumbent with the largest share was not included in the main specification because the sample size drops by three countries. 20 On the vertical axis, we observe the change in the share of renewable energy (logs) from each country, while on the horizontal axis, we see the change in the independence score (logs). The figure highlights the presence of potential outlier observations that might drive our regression estimations. 21 Belsley et al. (1980) discuss the implications that outliers have depending on whether they are present in the outcome variable or independent variable. Variables that record extreme values in the dependent significantly vary from each other after excluding one observation, and all are significant at 10% level 22 . These outcomes suggest that influential observations do not drive either the direction or the significance of our estimates.

Policy and institutional channels

The policy and institutional environment (and its measures) might impact our results as well. Different aspects of national policies and institutions could add uncertainty to our estimations. Table 3 shows the estimation results with other policy and institutional variables used in the literature on renewable share determinants (surveyed by [START_REF] Bourcet | Empirical determinants of renewable energy deployment: A systematic literature review[END_REF]) to verify whether our estimates remain robust after using alternative policy and institutional measures.

Model (1) displays the coefficients of our baseline model. In model ( 2), we use the OECD PMR index to track changes in the market reform policies as an alternative to our mktaccess variable. The independence coefficient varies slightly, but it becomes less significant 23 . However, the reform's coefficient shows the expected negative sign (lower values of the PMR highlight a more liberalized market).

Model (3) accounts for national policy initiatives to reduce greenhouse gas emissions nationally. We use the variation of greenhouse gas emissions per capita growth rates as an effort proxy. The positive estimate suggests that countries with higher emissions growth rates tend to support higher renewable energy generation; however, the coefficient is not statistically significant at conventional levels. Social preferences might also play a role in supporting or limiting the implementation of decarbonization policies. In model (4), we add the variable Green Party to capture environmental preferences beyond government policies. The coefficient remains robust (at a 10% level) and suggests that countries, where green parties have more delegates are associated with larger renewable preference shares. We did not include this variable in our main specification because it reduces estimation accuracy more than improving the model fitting, as pointed out by the Akaike Information Criterion (AIC) 24 . variable, as in the case of Malta, influence the regression intercept but do not have an effect on the slope. The effects on the line from the independent variable observations like Slovenia or Bulgaria need more attention because they can alter the model slope and increase the occurrence of type 1 or type 2 errors. Following their argument, we removed one data row (country) from the model (1) to verify whether our estimates became insignificant. 22 In fig. 7, the y-axis displays the independence coefficient size, while the x-axis shows the excluded country.

The blue area shows the coefficient confidence interval (90%) for each estimated model. 23 We omitted the OECD PMR in our main specification because not all countries had a score. Missing country data was obtained by imputation. We decided to keep the variable as a robustness check. Different aspects of institutions are able to influence renewable energy investments. In model ( 5), we use the Transparency International Corruption Perception Index (CPI) as a proxy for government quality. Lower corruption levels should translate into betterfunctioning public administration. The independence coefficient slightly varies compared to model ( 1), yet the CPI coefficient is not statistically significant. In model ( 6), we use the measure of "Rule of Law" constructed by the IHS-Market, which captures how investments are protected from expropriation. The independence coefficient changes in magnitude, but the effects' direction and statistical significance remain. However, the IHS-Market coefficient becomes negative and non-significant, which is probably linked to the fact that countries in our sample tend to stick to EU standards. All the previous results conclude that our model is robust to different instruments to measure institutions.

Overlapping sample

Finally, we measure the share of renewables using the overlapping sample (22 countries from the price regression analysis presented in the discussion section (Section 7)25 ) and the generation of solar and wind sources as a share of total electricity production26 . In table 7 (in the appendix), the effect of independence in models ( 1) and (2) remains negative and significant at a 10% percent level. These parameter estimates suggest that the effect of the governance regime is present across different samples and renewable energy measures.

Policy implications

The EU has reinforced its commitment to improving environmental performance by introducing the Clean Energy Package (CEP) in 2019. This legislation sets environmental principles and mandates to member states and, more recently, to regulators while preserving the current market structure. In this context, we ask whether the new CEP's environmental principles and mandates provided to NRAs are compatible with the current market design. Our work proposes that the new environmental mandates will conflict with the liberalization (and NRAs) commitments, and additional actions are needed to reconcile the previous and more recent rulings.

Policymakers should consider expanding the understanding of the conflict between liberalization, market designs, and environmental objectives [START_REF] Pollitt | The European Single Market in Electricity: An Economic Assessment[END_REF]. The first step is determining whether the conflict between the NRA's competition commitments (measured by independence) and renewable energy generation has slowed down current environmen-tal targets. Most European member states achieved their environmental targets in 2020. According to the European Environmental Agency (2020), 22 Member States reached their 2020 targets, Belgium, Romania, and Slovenia remain close to meeting the target (< 1 %), and France and Poland were the countries furthest away from their 2020 target (over two percent below). At that stage, the conflict did not seem to affect the member states' decarbonization performance. However, it is uncertain whether the conflict's effects will remain negligible as the shares of renewable electricity grow. Future climate change targets will become more stringent as an unprecedented transformation in the energy system is necessary to meet the 32% renewable energy target set for 2030 and full decarbonization for 2050. As the share of renewables grows to meet the targets, the need for investments to match electricity supply and demand will increase, similarly to consumer bills. Thus, we expect the NRA tension to grow with the risk of decelerating the decarbonization pace.

If NRAs are not accountable for environmental performance, the independence negative impact will become more significant over time. A possible solution could be to evaluate the current NRA governance model and reconcile it with the environmental principles and mandates included in the CEP. According to [START_REF] Bartle | Independent economic regulation: A reassessment of its role in sustainable development[END_REF], coordination mechanisms between economic NRAs, the government, and other environmental stakeholders should be established without compromising the agency's independence. The NRAs' expertise and specialized knowledge place them as central actors in integrating renewable energies. There are three areas in which coordination can help NRAs to support decarbonization. First, NRAs can support information exchange between industry actors and provide their economic expertise to policymakers. The information flow can help decision-makers at any level to consider other actors' points of view and design optimal solutions. For example, economic NRAs' could share their experience on the undesired effects of policies. Second, NRAs may take an advisory role in policy elaboration. This should facilitate the detection of policy conflicts and the trade-off assessment between competition and environmental objectives. Finally, the information disclosure with stakeholders should be augmented beyond the economic scope of the NRAs' operation.

The proposed model "tweaks" have concerns. Investors could interpret NRA's involvement in environmental matters as a deviation threat from its competition principles and mandates. If the perceived threat is significant, some investors might decide to hold up their investments. Besides, the NRA's participation in non-economic advisory might reduce the levels of specialized knowledge and expertise that the agency can accumulate. Finally, agencies might fail legislative hearings and other types of evaluations because their performance assessment is currently based on economic results. NRA's accountability provisions should account for the tasks required to support environmental targets [START_REF] Bartle | Independent economic regulation: A reassessment of its role in sustainable development[END_REF].

Conclusions

In light of the challenges to achieve carbon neutrality on 2050, and the new "economic logic" that renewable energies have on current electric systems. This paper assessed the impact the institutional context in which policy makers operate, particularly focusing on analyzing the impact of independence, and other three indicators of NRAs govenance regimes (discretion, transparency, and scope of Market Monitoring) on the growth of renewable energies. Because of data availability we focus our analysis on the period between 2013 and 2018. Results show that NRA's independence negatively correlates with the growth of renewable energy share. In addition, we found that transparency is negatively correlated with electricity tariffs for industrial users.

The central argument of our work relies on the documented assumption that independent regulators were mandated to implement competition, reduce incumbent rents in favor of consumers, and ultimately trigger system reliability. Our findings suggest that independent regulators remained committed to their competition and efficiency mandates. This commitment conflicts with introducing renewables in the system because additional renewables increase system costs, e.g., excess power in the capacity market, which impacts competition, efficiency, and market stability.

Our main contribution is to provide robust quantitative evidence that between 2013 and 2018, the independence of NRAs decelerated the adoption of renewable power in the electric sector. Finally, we discuss the current and future consequences of not reconciling the current governance model and new environmental legislation. We found many arguments supporting that future climate targets should become a part of the NRA mandates.

Figure 1 :

 1 Figure 1: Evolution in the share of renewable energy as a share of gross energy consumption

Figure 2 :

 2 Figure 2: Renewable energy tariff support / electricity tariff ratio by type of consumer

Figure 3 :

 3 Figure 3: Distribution in the growth of renewable energies

Figure 4 :

 4 Figure 4: Evolution of entry barriers in the European Electricity markets

Figure 5 :Figure 6 :Figure 7 :

 567 Figure 5: Evolution of the IRA dimensions in Europe between 2013 and 2018

Table 1 :

 1 Summary of legal instruments for EU NRAs in Europe

		Legal origins
	Actors	Principles and tools	Delegation
		Competition -MS shall ensure	Delegation -ME shall designate a
		electricity markets are operated	single national regulatory authority
	Member States	following the competition, secu-	at the national level. (Article 35 Di-
		rity, and environmentally sustain-	rective 2009/72/EC)
		able principles.	(Article 3 Direc-
		tive 2003/42/EC; Article 3 Directive
		2009/72/EC).
		Non-Discrimination -If financial	Independence -Member states shall
		compensation (other forms) and ex-	guarantee the independence of the
		clusive rights that ME grants for	regulatory authority and shall en-
		fulfilling the obligations shall be	sure that it exercises its powers im-
		done in a non-discriminatory and	partially and transparently. Mem-
		transparent way (Article 3 Directive	ber states shall ensure that regula-
		2003/42/EC).		tory authority can take autonomous
				decisions independently from any po-
				litical body and has separate annual
				budget allocations (Article 35 Direc-
				tive 2009/72/EC)
		ME shall ensure the implementation
		of a system of third-party access
		to the transmission and distribution
		systems based on published tariffs.
		(Article 24 Directive 2003/42/EC)
		Monitoring -ME shall ensure the
		monitoring of the security of supply.
		Where MS consider it appropriate,
		governments may delegate this task
		to the regulatory authorities (Article
		4 Directive 2003/42/EC)
		Responsibilities -Regulatory author-	Independence -Regulatory author-
	NRAs	ities shall ensure non-discrimination, effective competition, and efficient	ities shall be wholly independent of the interests of the electric-
		market monitoring. (Article 36 Di-	ity industry (Article 35 Directive
		rective 2009/72/EC)	2009/72/EC)
		They should be responsible for: (a)	Accountability -Regulatory author-
		rules on the management and alloca-	ities shall submit formal decisions
		tion of interconnection capacity, (b)	to the relevant body in the Member
		the level of transparency and compe-	State regarding the methodologies to
		tition, (c) methodology to calculate	calculate tariffs. Member states may
		tariffs, and (d) help achieve in the	approve or reject the propositions
		most cost-effective way the develop-	of regulators (Article 36 Directive
		ment of secure, reliable and efficient	2009/72/EC).
		production of electricity (Article 36
		Directive 2009/72/EC).
				Authority -Regulators shall have
				the authority to require transmission
				and distribution system operators,
				to modify the terms and conditions,
				tariffs, and rules, to ensure that they
				are non-discrimination. Regulators
				may also act as dispute settlement
				authority between parties (Article 36
				Directive 2009/72/EC).

Table 2 :

 2 Fixed Effects: Impact of governance on renewable energies shares Controls: mkt access: a measure of market reform implementation related to granting new incumbents access to the natural monopoly segment of the industry (i.e., the network). Inst. Stability: Freedom House political rights score. The metric captures the extent to which political institutions are stable. Non-renew power : Incumbent lobbying power of non-renewable generators in the industry. Higher metrics show that a traditional incumbent firm has significant generation participation and, therefore, a high lobbying power.Demand(GDP): Gross domestic product measure in PPP USD of 2010. The measure proxies the electricity demand in a country. The correlation with primary electricity demand is above 0.98. Wealth: Income per capita: the variable measures average income in PPP USD 2010 per person. Trade balance:

		(1)	(2)	(3)	(4)	(5)
	Independence	-0.817**				-0.968*
		(0.334)				(0.531)
	Discretion		-0.203
			(0.317)
	Transparency			-0.371		0.098
				(0.496)		(0.504)
	Market				0.167	0.53
					(0.682)	(0.683)
	Liberalization	1.456**	2.306**	1.725*	2.091**	1.452**
		(0.616)	(0.864)	(0.902)	(0.787)	(0.583)
	Inst. Stability	0.038*** 0.041*** 0.039*** 0.040*** 0.039***
		(0.006)	(0.008)	(0.008)	(0.008)	(0.006)
	Non-renew power	-1.039** -1.328** -1.148** -1.297** -1.084**
		(0.312)	(0.351)	(0.358)	(0.371)	(0.312)
	Demand (GDP)	2.049**	1.739*	2.133**	1.887**	1.978**
		(0.861)	(0.843)	(0.977)	(0.910)	(0.852)
	Wealth	-2.136** -1.926** -2.271** -2.067** -2.051**
		(0.921)	(0.918)	(1.029)	(0.971)	(0.934)
	Trade balance	0.107	0.095	0.101	0.094	0.104
		(0.105)	(0.118)	(0.113)	(0.115)	(0.113)
	Years ren. laws	0.262*** 0.319*** 0.274*** 0.309*** 0.255***
		(0.058)	(0.085)	(0.092)	(0.087)	(0.073)
	Patenthundred	0.134**	0.125*	0.125*	0.123*	0.127**
		(0.056)	(0.067)	(0.067)	(0.070)	(0.053)
	Individual effects	yes	yes	yes	yes	yes
	Time effects	yes	yes	yes	yes	yes
	adj. R 2	0.87	0.842	0.846	0.841	0.868
	AIC	-141.4	-132.3	-133.5	-131.8	-139.5
	Observations	48	48	48	48	48
	F-statistic	33.137	18.331	23.049	24.334	36.048

Table 3 :

 3 Fixed Effects: Additional political and institutional covariates

		(1)	(2)	(3)	(4)	(5)	(6)
		Base	PMR	EGR	Green	Corrupt.I IHS-RL
	Independence	-0.817 * * -0.736 * -0.824 * *	-0.860 *	-0.898 * *	-1.309 *
		(0.334)	(0.383)	(0.329)	(0.423)	(0.363)	(0.649)
	OECD PMR		-0.0215 *
			(0.0118)
	Em.GR			0.0117
				(0.296)
	Green				0.0186 * *
					(0.00709)
	Corruption Index					0.0120
						(0.00710)
	Rule Law						-0.693
							(0.480)
	Time effects	yes	yes	yes	yes	yes	yes
	Controls	yes	yes	yes	yes	yes	yes
	adj. R 2	0.870	0.862	0.866	0.855	0.837	0.833
	AIC	-141.5	-138.9	-139.5	-136.6	-130.8	-129.6
	Observations	48	48	48	48	48	48
	F-statistic	33.14	100.2	30.49	43.16	11.96	16.27
	Model (1) shows the coefficients of the equation in M odel(1) -

Table 2 .

 2 OECD.PMR: score of market reforms calculated by the OECD between 0 (fully liberalized) and 6 (vertically integrated -state-owned industry). Em.EGR: emissions per capita in percentage annual growth rate. Green: number of shares of the green party in the legislative per person. FH political rights: Freedom house political right score. Corruption Index : Transparency international corruption index where higher scores stand for more transparent and efficient government. Rule of Law : IHS rule of law score. It represents the protection of private investments in a country. Heteroskedasticity robust standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 4 :

 4 Descriptive summary of variables used in the empiric analysisModel (1) of Table2is used in all specifications. The dependent variable Wind-Solar.SH in model (2) is the sum of wind and solar shares as part of the total electricity produced in GWh. The value varies between 0 and 1. Heteroskedasticity robust standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

	Type	Variable	Description (units)	Source	Min	Mean Max	SD
		name
	Main	Sh. renew-	Share of renewable		3.499	20.1	54.7	12.38
	dependent	ables		energy (%)				EUROSTAT
	variables	Ind.	elec-	Tariff	charged	for		4.245	4.8	5.47	0.272
		tric		consumption		of
				a	KWh	by	hou.
				(log(EUR/KWh))
	Secondary dependent variables	Sh. wind-gen Sh. solar	Share of renewable energy solar (%) energy wind (%) Share of renewable	EUROSTAT	0.021 0	9.5 2.15	45.8 7.82	10.75 2.194
		Hou.		Tariff	charged	for		4.879	5.44	5.96	0.267
		elecprice	consumption	of	a
				KWh by households
				(log(EUR/KWh))
		Independence Independence index	Brousseau	0.185	0.3	0.37	0.043
	Governance and reform variables	(0 -1) Market coord. Gov. index (0 -1) Transparency Transparency Scope index	& Gonzalez, 2020)	0.216 0.167	0.27 0.25	0.32 0.35	0.027 0.052
				(0 -1)
		Discretion	Discretion index (0-1)		0.144	0.24	0.34	0.048
		OECD		OECD PMR (1 -6)	OECD-	0.14	1.28	2.19	0.523
		PMR								PMR
		TA mkt ac-	Market access index	Constructed	0.11	0.15	0.2	0.023
		cess		(0 -1)					PMR sur-
										vey
	Electricity supply, demand,	Elec gen. Elec cons	Total electricity sup-ply (log GWh) Total electricity sup-	IEA OECD database	7.688 15.27	11 17.3	13.4 19.6	1.398 1.211
	and			ply (log GWh)
	security	Trade bal-	Elec. exports -im-		5.367	8.14	9.15	0.718
	controls	ance		ports (log GWh)
		GDP		GDP in const. (log 2017 million USD)	PWT ver10	24.3	26.6	29	1.318
		Income PC	GDP per capita (log		10.05	10.6	11.4	0.363
				USD/population)
		Years		Policy time that sup-	Constructed	2.485	4.19	5.66	0.773
	Renewable's	lawren		ports renewable gen-	IEA policy
	support			eration (log years)		database
	controls	Green		Green party seat in	Comparative	0	2.34	12.4	3.28
		Party		the Legislative			Political
										Dataset
		1-IHH		Diversification		of	Constructed	-0.441	0.47	0.72	0.248
		sshare		elec.		generation	IEA-
				sources (0 -1)			OECD
		1-IHH		Diversification		of	database	0.082	0.58	0.95	0.214
		sshare NR	elec. generation non-
				renewable sources (0
				-1)
		GHG em.	Greenhouse gas emis-		-28.72	0.10	38.10	9.49
		grate		sions growth rate (%)
	Institutions and gov-ernment efficiency controls	FH law FH rights FH index civil pol	Freedom House civil law scores (0-60) Freedom House pol. rights scores (0-40) scores (0-100) Freedom House total	Freedom in the World Report, 2021	44 28 72	55.9 38.1 93.9	60 40 100	3.471 2.29 5.567
		IHS RL	HIS rule of law index	IHS Markit	0.67	0.86	1	0.092
				(0-1)
		TI CPI	Transp. International	Transparency	43	67.4	91	14.07
				corruption perception	Interna-
				index (0-100)			tional

Table 8 :

 8 Fixed Effects estimation: Impact of governance regime in electricity price -complete tableModel (1) of Table2is used in all specifications. Heteroskedasticity robust standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

		(1)	(2)	(3)	(4)	(5)
	Independence	-0.116				0.225
		(0.291)				(0.438)
	Transparency		-0.512*			-0.767*
			(0.298)			(0.410)
	Scope			0.387		0.727
				(0.639)		(0.563)
	Discretion				0.31
					(0.303)
	Liberalization	0.91	0.626	1.069	0.8	0.761
		(1.176)	(1.280)	(1.179)	(1.440)	(1.286)
	Inst. Stability	-0.009	-0.0104	-0.0103	-0.0076	-0.0144
		(0.011)	(0.011)	(0.013)	(0.011) (0.0145)
	Wealth	-0.57*** -0.60*** -0.57*** -0.60*** -0.61***
		(0.176)	(0.174)	(0.184)	(0.176)	(0.184)
	Elec. Supply	0.243	0.297	0.184	0.249	0.221
		(0.183)	(0.198)	(0.197)	(0.208)	(0.200)
	Non-renew power	-0.013	0.006	-0.073	0.025	-0.094
		(0.106)	(0.112)	(0.152)	(0.135)	(0.148)
	Individual effects	yes	yes	yes	yes	yes
	Time effects	yes	yes	yes	yes	yes
	adj. R 2	0.828	0.845	0.83	0.834	0.851
	AIC	-144.5	-149.5	-145.1	-146.1	-149.8
	Observations	48	48	48	48	48
	F-statistic	27.87	32.1	32.96	30.59	28.66

Chaire Gouvernance et Régulation, Université Paris Dauphine-PSL

Chaire Armand Peugeot, Université Paris-Saclay, CentraleSupélec December 2, 2023Provisional version, do not quoteAbstract: Using a panel data analysis of 24 EU countries, we show that between 2013 and 2018, the degree of independence from the government of National Regulatory Agencies (NRAs) has a negative and robust relationship with the share of renewables in the electricity supply. Our results help us to derive some policy implications regarding the roles of governments and NRAs in the decarbonization of the electricity sector. We suggest an ongoing tension between environmental policy and the will to support renewable energy development, with the competition policy established on the NRA mandates since the liberalization reforms.

Investment in utilities requires the deployment of high-cost specialized capital. Under the presence of return risk (political instability), firms might not deploy optimal capital levels (underinvestment) or even not invest at all (hold-up problem).

The Second Electricity Package likewise included provisions for NRA independence, with a focus on limiting the possibility of incumbents' regulatory capture.

[START_REF] Gilardi | Policy credibility and delegation to independent regulatory agencies: a comparative empirical analysis[END_REF] operationalizes the independence dimension by constructing a weighted index that summarizes survey information by assigning different scores when agencies have or not a head/board appointment process, budget, and organizational levers, and the scope of regulatory powers.

[START_REF] Gilardi | Chapter 14: The Independence of Regulatory Authorities[END_REF] use a similar definition, in which describe independence as the institution that separates the government bureaucracy from the elected politicians

The dimension aggregates measures of the obligation for (public) reporting imposed on the RA and the reporting obligation imposed on the market players by the RA.

The "merit order" market design rewards generators in the queue line with marginal cost equal to or below the least efficient generation unit that entered the market. Assuming that the most efficient CO2generating units enter the queue, the CO2 units with higher operational costs will provide capacity services in case of need. This implies that balancing costs increase under additional intermittent renewable units that demand superior balancing capacity.

[START_REF] Wassum | Explaining regulatory autonomy in EU network sectors: Varieties of utility regulation?[END_REF] contrast regulatory powers between "hard" and "soft" competencies. "Hard" competencies are related to direct intervention capacity, as in the case of setting prices or price controls, emitting licenses, and issuing sanctions and penalties. "Soft" competencies describe the capability of the NRA to modify behavior through non-binding actions. Examples of these competencies are issuing industry and consumer standards, guidelines, and codes of conduct and collecting information from industry players.

We use a second variable to control for policy support. We estimated the share of policies that support subsidies from all the policies implemented in national legislation. Results in table 6 show no significant correlation between the variable and the share of renewable energy.

We measure electricity consumption by using GDP instead of primary electricity consumption because of their high correlation (Pearson correlation coefficient = 0.98) and because of data availability.

The price analysis includes Great Britain and Switzerland and excludes Bulgaria and Cyprus. Data availability drives this decision.

This measure is less sensitive to changes in electricity consumption patterns.

B Textual analysis construction variable

To measure the intensity of market reform, we assessed the regulatory environment using survey questions from the OECD PMR [START_REF] Vitale | The 2018 edition of the OECD PMR indicators and database: Methodological improvements and policy insights[END_REF]. Instead of using the categorical content of the survey, we tracked changes in replies by assessing the textual information embedded in the survey. We applied the following steps to the data.

1. We created a set of documents using the textual information from the survey. The textual information comes from the words used to express country replies. In the following question, "Are market prices regulated?", we registered "market prices are regulated" for a positive answer, and "prices are unregulated" for a negative one in our database.

2. We constructed text documents for every country and time with the previous information. Based on co-occurring terms in every document, we fit a topic model (LDA) and identify four groups of terms that describe the country's market reform, as shown in fig. 8.


Revant Kumar

Parminder Bhatia

Christopher Chow

Sentiment Analysis of Short Text -Sentence and User Representation Learning

Problem Statement & Motivation

The majority of existing approaches employ machine learning algorithms to build classifiers from tweets with manually annotated sentiment polarity. Most studies focus on designing effective features to obtain better classification performance.

Current methods work well for long paragraphs but they do not perform as well in sentences or short text. The objective of this project is to combine multiple existing word embedding schemes and neural network architectures to develop more powerful sentence models for sentiment classification.

The aim of a sentence model is to analyze and represent the semantic content of a sentence for purposes of classification or generation. The core of a sentence model involves a feature function that defines the process by which the features of the sentence are extracted from the features of the words or n-grams. Neural sentence models have a number of advantages. They can be trained to obtain generic vectors for words and phrases by predicting, for instance, the contexts in which the words and phrases occur. Through supervised training, neural sentence models can fine-tune these vectors to information that is specific to a certain task.

Existing work requires complicated networks which are at times tough to train and reproduce. In this paper, we demonstrate a sentiment analysis system that works on short texts such as Tweets. We also aim to incorporate Users Information in the Tweets to further enhance the system and make the tweets more personalized.

Related Work

Recently, there has been lot of work on sentence modeling and its application in other NLP tasks such as sentiment analysis, question answering, machine translation etc.

Neural Networks

• Neural Bag-of-Words (NBoW) Models: Projection layer that maps words, sub-word units or n-grams to high dimensional embeddings (vectors). The embeddings then combined component-wise with an operation such as summation. The resulting combined vector is classified through one or more fully connected layers.

• Recursive Neural Networks (RecNN): A model that adopts a more general structure provided by an external parse tree. At every node in the tree the contexts at the left and right children of the node are combined by a classical layer. The layer computed at the top node gives a representation for the sentence. • The trained weights in the filter m correspond to a linguistic feature detector that learns to recognize a specific class of n-grams. These n-grams have size n ≤ m, where m is the width of the filter. • Narrow type of convolution requires that s > m.

• A wide convolution ensures that all weights in the filter reach the entire sentence, including the words at the beginning and end of the sentence.

Dynamic Convolutional Neural Networks (DCNN)

• 1-D wide convolutions ensure that all weights in the filter reach the entire sentence, including the words at the beginnings and ends of the sentences. • Dynamic k-max pooling -k is a function both of layer depth and sentence length • Small filters at higher layers can capture syntactic or semantic relations between noncontinuous phrases that are far apart in the input sentence. • Folding operation to make it fully dependent across rows.

Recent work at Google Mind on Dynamic Convolution Neural Networks explores learning convolution filters at both the sentence and document level, hierarchically learning to capture and compose low level lexical features into high level semantic concepts. As DCNN models are based on the convolution operation, they are able to preserve ordering information between words in a sentence and between sentences in a document.

DCNNs are similar to the convolutional networks used in computer vision -with cascades of convolution, pooling and nonlinear transformations. However, DCNNs are specially adapted for some of the specifics of the sentence modeling task. They are particularly useful in Discourse Analysis, where relation between different sentences is also taken into account and also in tasks such as Text Summarization.

3 Approach and Techniques

Architecture (new)

The major objective of our network design is to incorporate the above advantages of DCNN with a simpler architecture that is easier to train and more scalable. Despite little tuning of hyper parameters, this simple model achieves excellent results on multiple benchmarks, suggesting that the pre-trained vectors are universal feature extractors that can be utilized for various classification tasks. Learning task-specific vectors through fine-tuning results in further improvements.

Sentence Representation

• We currently utilize f ilter sizes of 3, 4, and 5, each with a length 100 feature map.

• The sentence representation is obtained by concatenating these feature maps to yield 300 total features. • These features are fed into an MLP which consists of 300 inputs and hidden units and 2 outputs corresponding to sentiments. • Dropout (p=0.5) is also added at the penultimate fully connected layer.

Existing Algorithms and Optimizations

We have tried to incorporate various types of losses in our algorithm.

Cross Entropy: Word -Sentiment

We express the cross-entropy loss associated with the true label of the document: Optimizing against the above loss function maximizes the log probability according to the true label. We then backpropagate the error all the way through the word embeddings. Thus, the word representation achieved will also incorporate sentiment information which is not present in word2vec embeddings.

Lw i = y k log(y k ) + (1 -y k ) log(1 -y k ) + λ w (ϕ 2 )

Negative Sampling: User -Word

We also express a loss function incorporating user modelling, where u is the author of a given tweet i, and k is a random other user.

Lu i,u = log(σ(user u * tweet i )) + k log(σ(-user k * tweet i ))
The idea is that similar users will have similar values of sentiment. Two words may mean slightly different things for different users. Thus, great could really positive for some users while others might use great sarcastically or more freely in a variety of contexts. We have tried to use negative sampling here between the user and the tweet. Thus, if two users write similar tweets then their negative sampling will be low.

Negative Sampling: User -User

Finally, we can express a loss using user-user information. Let i be a user who follows user u and k be a random user who doesn't follow user u. The loss is:

Lu i u u = log(σ(user u * f ollower i )) + k log(σ(-user u * f ollower k ))
The idea is that similar users will have similar representations for the same word. We assume that a user will have similar representations as his followers which can help in determining the sentiment for a user according to loss 2. This loss helps in understanding user representations similarly to word2vec.

Thus the overall loss is given by:

L = Lw i + Lu i,u + Lu i u u

Experimental Design

A sentence (n words) is represented as a matrix of dimension n × d where d is the dimensionality for each word from word2vec.

A convolution operation involves a filter w ∈ R h×k , which is applied to a window of h words to produce a new feature. This filter is applied to each possible window of words in the sentence {x 1:h , x 2:h+1 , . . . , x n-h+1:n } to produce a feature map c = [c 1 , c 2 , . . . , c n-h+1 ], with c ∈ R n-h+1 . We then apply a max-overtime pooling operation [START_REF] Collobert | Natural language processing (almost) from scratch[END_REF] over the feature map and take the maximum value ĉ = max{c} as the feature corresponding to this particular filter. The idea is to capture the most important feature, one with the highest value for each feature map. This pooling scheme naturally deals with variable sentence lengths.

We have described the process by which one feature is extracted from one filter. The model uses multiple filters (with varying window sizes) to obtain multiple features. These features form the penultimate layer and are passed to a fully connected softmax layer whose output is the probability distribution over labels. We summarize the above process as follows:

• Input 4D tensor (MiniBatch,1,n,d)

• Weight 4D tensor (Number of features, 1, filter size, d)

• Max Pooling -one value per feature • Output of one convolution after max-pooling is (Number of features × 1).

• In this project, we use filters of length 3, 4 and 5, each with 100 features. Thus, each sentence is represented by a 300 element vector right before the fully connected softmax layer.

Model Variations

We experiment with several variants of the model.

• CNN-rand: Our baseline model where all words are randomly initialized and then modified during training. This should learn word embeddings like word2vec.

• CNN-static: A model with pretrained vectors from word2vec. All words -including the unknown ones that are randomly initialized -are kept static and only the other parameters of the model are learned.

• CNN-finetuned: Same as above but the pretrained vectors are fine-tuned for each task.

Results and Analysis

Using the above models, we have achieved various results and findings which we would like to discuss in the implementation order.

Data

Data used for evaluation is the SemEval 2013 Dataset for Twitter. The dataset consists of many tweets about many different topics. The dataset ground truth has three classes -positive, negative, and neutral. SemEval also provides unique user ID's per tweet, allowing us to employ userdependent learning as described in the loss functions above. We first acquire and clean the data and hash it into a dictionary for efficient lookup and training.

Framework

We used Theano which is implemented in python, as it gives freedom to design your model in details where we can modify and change our loss functions. Theano allowed us to rapidly prototype network flow graphs and play with their parameters Another advantage is that it calculates the derivatives automatically thus we don't have to implement back propagation every time.

Optimization Algorithm

We use stochastic gradient descent with a mini batch size of 50 units and we perform around 30 epochs. It takes around 8 to 10 hrs on 16 CPU Cluster.

The specific type of SGD used is a kind of SGD with momentum called Adadelta, as recent papers have shown good results with this algorithm in language-related tasks.

Results and Evaluation

Sentiment Loss

Using the negative log likelihood for the sentiment, we generated 300 feature values for each sentence. We also test the classification power of the representations with an SVM. Using the above representations, we obtain around 83-84% F -measure on Twitter sentiment data with the best CNN. We used the following activation functions:

• ReLU • Tanh • Sigmoid • Identity
Out of the above, Tanh and Rectified Linear Units gave the best results.

Dropout

Using a dropout layer in the MLP gave us 2 to 4 percent improvement. Dropout proved to be such a good regularizer that it was fine to use a larger than necessary network and simply let dropout regularize it. We were able to achieve about 86-87% accuracy using dropout.

Combined Loss

After combining all the losses associated with tweets and user-associated losses, as mentioned in the equation

L = Lw i + Lu i,u + Lu i u u
We observed that results we achieved were around 85-86%. Using all the losses could not improve the results which we achieved after dropouts. We have various hypotheses for why this might be.

• It may be that even though we are calculating per-user loss though negative sampling, one of the major problem in data is that most of the users had just 1 or 2 tweets. Thus it would have been difficult for the Lu i,u and Lu i u u loss to capture useful losses.

• Second reason is that even though we are given followers for a user , there may not be tweets for all of those followers, making inference about those followers difficult.

Static vs. Non-Static Representations

As we had inferred in the beginning we observed that using non-static representation of words gave us better results as compared to static representations.

The reason for this result is that word2vec representation are based on context where good and bad have similar representations. By backpropagating the losses till the words we are able to differentiate between those words more finely. • Sentiment Loss: Obtained around 83-84 percent F-measure. Tanh and ReLU units performed the best.

• Dropouts: Achieved about 86-87 percent accuracy using dropout.

• Combined loss: Achieved around 85-86 percent F-measure. Using all the losses could not improve the results which we achieved after dropout because each User had 1 -2 tweets and some followers had no tweets.

• Static vs Non-static: The reason for this result is that word2vec representation are based on context where good and bad have similar representations.

• User Embedding-Similarly we backpropagated the derivatives for the User embeddings which in itself can be used for many Social Computing Applications. • AdaDelta gave similar results but in less number of epochs.

Conclusions and Future Work

• Achieved great results with a simple architecture.

• Learned sentence representations can be used for further NLP tasks such as machine translation and question answering.

• Learned user embeddings which can be used on Social Data graphs.

• Results could be improved from User losses if distribution for User-Tweet ratio is higher.

Figure 1 :

 1 Figure 1: Time Delay Neural Networks

Figure 2 :

 2 Figure 2: DCNN Architecture

Figure 3 :

 3 Figure 3: Our Architecture

Figure 5 :

 5 Figure 5: Using User Loss

Figure 6 :

 6 Figure 6: Using Dropout