Frédéric Branger

Jean-Pierre Ponssard
email: jean-pierre.ponssard@polytechnique.edu

Oliver Sartor
email: oliver.sartor@iddri.orgiddri

Misato Sato
email: m.sato1@lse.ac.uk

EU ETS, Free Allocations and Activity Level Thresholds The devil lies in the details

Keywords: JEL Classification: D24, H23, L23, L61 Activity level thresholds, EU ETS, carbon trading, free allowance allocations, cement

This paper investigates incentives for firms to increase output above the activity level thresholds (ALTs) in order to obtain more free allowances in the EU Emissions Trading Scheme. While ALTs were introduced in order to reduce excess free allocation to low-activity installations, for installations operating below the threshold, the financial gain from increasing output to reach the threshold may outweigh the costs. Using installation level data for 246 clinker plants, we estimate the effect of ALTs on output decisions. The ALTs induced 5.8Mt of excess clinker production in 2012 (4% of total EU output), which corresponds to 5.2Mt of excess CO 2 emissions (over 5% of total sector emissions). As intended, ALTs do reduce overallocation (by 6.6million allowances) relative to a scenario without ALTs, but an alternative output based allocation would further reduce overallocation by 39.5million allowances (29% of total cement sector free allocation). Firms responded disproportionately to ALTs in countries with low demand, especially in Spain and Greece. The excess clinker output lead to increased EU clinker and cement exports, production shifting between plants and also an increase in clinker content of cement thus reducing the carbon efficiency of cement production.

Introduction

The justification for using free allocations in emission trading schemes has evolved over time. Historically, in schemes such as the U.S. acid rain program, it was introduced as a compensation mechanism for the owners of existing industrial assets for a change in the rules of the game (Ellerman et al., 2000). A lump sum transfer would be made to existing assets through a predetermined amount of annual free allocations for a given number of years. The free allocations offset the costs to purchase pollution permits on the market. Such methods are termed "grandfathering", "historic", "lump-sum" or "ex-ante" allocation. 5 New assets would not be allowed free allocations and thus would have to pay the full permit price on the market. As long as the free allocations are predetermined, all assets (old and new) would compete on the same playing field, the price of permits would provide the same opportunity cost for mitigating pollution, and in theory, the output price of the goods sold would incorporate the price signal for consumers.

More recently, free allocations have been explicitly used (or have been proposed to be used) as a way to strategically alleviate the risk of offshoring production and emissions (so-called "carbon leakage") for Energy-Intensive and Trade-Exposed (EITE) sectors such as cement, chemicals and steel. In the absence of border carbon adjustment, the implementation of which is considered as politically difficult, economic theory suggests that "output-based" allocation (OBA) should be used (e.g. Fischer and Fox 2007, Quirion 2009, Fischer and Fox 2012, Meunier et al 2012). Indeed an OBA scheme has been implemented within the Californian ETS which began in 2012 (California Air Resources Board, 2013). In contrast the EU ETS Phase 3 is unique in using a complex system. It combines an ex-ante calculation6 of an allocation and subsequent lump-sum transfer based on historic output (and multiplied by an emissions intensity benchmark) with a possible ex-post calculation and adjustment of this lump-sum according to rules related to actual capacity and activity levels as defined in Decision (2011/278/EU) (European Commission, 2011). Situations in which ex-post adjustments occur include the arrival of new entrants into the market, plant capacity extension/reduction, plant closure and partial cessation or recommencement of activity at an existing plant. These latter rules are governed by the activity level thresholds (ALT) 7 .

Qualitatively, ETS schemes with ALT approximate OBA: the amount of free allocations will vary with the activity level, and the overallocation profits8 associated with ex-ante schemes will be reduced. 9 The advantage of ALT rules is that they allow for a fixed cap (in fact a cap which will not exceed a predetermined amount for existing installations and the reserve for new entrants). One disadvantage is that they introduce an element of complexity in the scheme. Under these non-linear rules, the lump sum transfer of allowances to EITE sectors is reduced by 50%, 75% or 100% if the annual level of production of the plant falls below 50%, 25% or 10% respectively, of the historical activity level (HAL) of production that is used to determine the ex-ante allocation (European Commission, 2011).

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A second disadvantage is that the ALTs introduce distortions, which is the focus of this paper. A recent study on the EU ETS impacts on the cement sector 2005-2013 (Neuhoff et al., 2014) 10 found preliminary evidence through data analysis and comprehensive interviews with industry executives, that new ALTs introduced in 2013 provided cement installations the incentive to adjust output levels. The rationale is as follows. Since the free allocation in year t+1 is directly linked to output in year t, if output levels lie below the threshold levels, there may be an incentive to increase output in year t to achieve the relevant threshold (.10, .25, .50) and receive higher free allocations in year t+1. In this paper, such strategic adjustments of output motivated by ALTs is termed "gaming" behaviour, in line with the management literature (e.g. Jensen, 2001). Neuhoff et al. (2014) report that in interviews, company executives consistently confirm these practices where the regional cement market demand is insufficient to reach the minimum activity level. They identify three channels to marginally increase production in a plant which is producing below the threshold:

• Production shifting among local plants, i.e. reducing the production at a plant which is well above the threshold to increase the production at the plant which is below; this generates some transport costs 11 so that it can be too costly to be undertaken at a large scale;

• Exports of clinker to other markets so as not to perturb the local market while increasing production; this generates some cost in terms of export price rebate, since these exports would not naturally occur;

• Increase the clinker to cement ratio, i.e. incorporate within limits more clinker in cement instead of using less costly cementitious additives such as slag of flying ashes; this directly generates some cost.

The objective of the paper is to quantify the magnitude of these distortions, and discuss whether the disadvantages of ALTs balance the advantages.

Empirical studies on this subject remain limited. Most of these studies have examined the distortive effects of combined ex-ante allocations with ex-post new entrant and plant closure provisions. Pahle et al. (2011), Ellerman (2008) and Neuhoff et al. (2006) compared the new entrant provision relative to auctioning. These papers argued that new entrant provisions distort via their impact on investment decisions (essentially by acting as a subsidy). Meunier et al. (2014) compared this same provision with an output-based scheme whenever firms face an uncertain demand. They showed the entrant provision could induce excessive new investments in the EU cement sector. Fowlie et al. (2012) compare ex-ante schemes with closure rules with an output-based scheme and show that the lifetime of old inefficient plants would be unduly extended with the former. None of these studies has discussed the impacts of the possible distortions associated with the addition of "non-linear" ex-post adjustments to ex-ante allocation via the use of ALTs, such as introduced in the EU ETS Phase 3 (2013-2020).

While our analysis only concerns the cement sector, and has been done in a context of low carbon price, we think that its relevance may go beyond the sector context, and it could be potentially relevant to other EITEs. Altogether, we argue that the benefits of implementing """""""""""""""""""""""""""""""""""""""" """""""""""""""""""""

10 Three co-authors of this paper participated in this study and in conducting interviews that were carried out.

11 McKinsey (2008) estimate that transport costs for a tonne of clinker from Alexandria to Rotterdam are roughly €20/tonne, and that inland shipping costs are approximately €3.5/tonne per 100km and inland road transport was about 8.6€/ton per 100km.

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ALTs in terms of reduced overallocation profits does not outweigh the significant costs in the form of distortions, hence ALTs should be abandoned.

The paper is organized as follows. Section 2 discusses the EU ETS Phase 3 allocation rules, the predicted gaming behaviour from thresholds and the alternative allocation rules. Section 3 describes our conceptual framework for evaluating the effects of ALTs, the methodology, data sources, as well as the key assumptions involved in our analysis. Section 4 presents the results. Section 5 concludes and discusses some policy recommendations.

ETS free allocation rules and gaming of ALTs

The EU-ETS Phase 3 free allocation rules

In Phase 3 of the EU ETS, installations in sectors "deemed to be exposed to carbon leakage" are eligible to receive free allocation of emission allowances. The determination of the free allowances for each installation combines an ex-ante calculation, based on the historic output for existing installations (known as the "historical activity level" or "HAL" 12 ) or the initial capacity for new installations, with an ex-post calculation based on the ongoing activity level of this installation as defined in Decision (2011/278/EU) (European Commission, 2011). The ex-post calculation provides step wise adjustments intended to reflect changes in market volumes. These adjustments follow complex procedures.

For existing installations, the precise relationship that determines the next-period allocation from ex-ante and ex-post values is summarised by Equations 1 and 2 below. The amount of free allocations to an installation, i, at period t+1, for an eligible product, p is denoted A i,p,t+1 .

A i,p,t+1 = CSCF t x B p x HAL i,p x ALCF t+1 (q i,p,t /HAL i,p ), (1)
In equation ( 1) CSCF t is the uniform cross-sectoral correction factor 13 , B p is the benchmark for product p, 14 HAL i,p represents the historical activity level, q i,p,t represents the output of the eligible product in year t; and ALCF(q i,p,t /HAL i,p ) is the activity level correction factor. The latter factor defines a step wise function for the thresholds. It is defined as:

!"#$ !!! ! ! !"# = ! 1,!!!!!!!!!!!!!!! ! ≥ 0.5!!"#!!!!!!!!!!!!!!! 0.5,!!!!!!!0.25!!"# ≤ ! ! ! < 0.5!!"# 0.25,!!!!!!0.10!!!"# ≤ ! ! < 0.25!!"#! 0,!!!!!!!!0!!"# ≤ ! ! < 0.10!!"# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(2)
For new installations, the historic activity level is replaced by the capacity, to be precisely determined according to the rules. 15

Gaming and thresholds

Gaming behaviour refers to artificially increasing production to attain thresholds, in order to obtain more allowances. Consider a plant for which the "business as usual" activity level for year 2012 would be at say 40% of its historic activity level. Increasing production up to 50% of its historic activity level allows doubling the free allocation received. A rough """""""""""""""""""""""""""""""""""""""" """""""""""""""""""""

12 The benchmarked product-related historical activity level (HAL) is defined as maximum of the median annual historical production of the product in the installation (or sub-installation) concerned during either 2005-2008 or 2009-2010. (cf. Decision (2011/278/EU)). 13 This is determined by comparing the sum of preliminary total annual amounts of emission allowances allocated free to installations (not electricity) for each year over the period 2013-2020. In 2013 the CSCF is equal to 0.9427, then declines at 1.74% per year. 14 Product benchmarks in general reflect the average performance of the 10% most efficient installations in the sector or subsector in the years 2007-2008. The benchmarks are calculated for products rather than inputs Decision (2011/278/EU). 15 Guidance document n°7 in European Commission, 2011.

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calculation with a clinker plant illustrates the potential benefit of gaming. Suppose HAL refers to 1 Mt/year, the business as usual is 0.4 tons so that the plant needs to increase production by 0.1 tons to achieve the 50% threshold. At 9 €/t CO 2 in 2013, if the firm gets 100% of free allowances relative to HAL it is worth 6.5 M€; losing 50% allowances implies a loss of 3.25 M€. Suppose the emission intensity is say 0.8 t CO 2 /t of clinker. The increase in emissions is then equal to 0.080 t CO 2 which at 9 €/t CO 2 amounts to 0.72 M€.

In the presence of activity level thresholds, the net benefit of gaming in terms of allocations is the difference between the increased free allocations and the certificates needed to cover the increased production (in our case 2.53M€=3.25M€-0.72M€). The net benefit depends on the price of CO 2 , the benefit rising with the price. However, this artificial increase of production involves cost inefficiencies, which can be assumed increasing function of the extra production, independent of the CO 2 price but dependent on the plant. These cost inefficiencies can up to a point cancel out the gains from increased free allocation. This is shown in Figure 1, where gaming is undertaken only if the increased production to attain the threshold is less than ∆! ! . In our case, if the extra production of 0.1 ton of clinker does not involve cost inefficiencies of more than 2.53M€, gaming is profitable. Evidence of strong responses to thresholds -where small changes in behaviours lead to large changes in outcomes -has been found in the recent literature. Sallee and Slemrod (2012) find evidence that the automakers respond to notches in the Gase Guzzler tax and to mandatory fuel economy labels by manipulating fuel economy ratings in order to qualify for more favourable treatment. The management control literature also finds that managers tend to react strongly to the existence of a threshold. This is the case, for example, when bonuses depend on the achievement of a given level of sales for a sales manager, a given productivity indicator for a plant manager, a given return on investment for a business manager, a given level of the total shareholder return for a CEO, etc (Locke 2001). In a wellknown article, Jensen (2001) points out that such "gaming" behaviour is perfectly rational under threshold rules. He argues that these rules imply an agency cost which is largely underestimated and suggests that linear bonus schemes should be preferable.

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Alternative free allocation rules

The EU ETS Phase 3 rules can be compared with an ex ante allocation without ALTs similar to Phase 1 and 2 16 or an output-based allocation scheme. Under OBA, the next period allocation is determined according to an equation similar to equation (1) (with ! !,!,! ⟷ !"# !,! ×!"#$(! !,!,! !"# !,! )). The scheme therefore has no thresholds, and the historic activity level HAL is replaced by the previous year activity level ! ! so as allocations are altered on a continuous yearly production basis. In this paper, we will evaluate the impact of the ALTs by contrasting four scenarios, with their respective acronym:

( Ex-

Methodology and data

Conceptual framework

Since 2013 is the first year the threshold rule is in place, the 2012 activity level will directly determine the allocation allowances for 2013. Our analysis therefore focuses on outcomes of installations in the EU ETS in 2012.

The preliminary results in Neuhoff et al (2014) provided evidence of distortions due to the thresholds, based on interviews with industry executives and comparison between 2011 and 2012 data. The present study will attempt to quantify such distortions. This necessitates the elaboration of a counterfactual scenario for 2012 (what would have happened had the threshold rule not been implemented). A simple comparison between 2011 and 2012 would give inaccurate results because of underlying market trends e.g. cement consumption fell by 13% at the EU level between 2011 and 2012. Comparing with a counterfactual enables us to understand the magnitude of the excess output due to ALTs, and the corresponding excess emissions and overallocation profits. A straightforward caveat is that our results are then very dependent on the counterfactual. This is why it is constructed using a method as robust and unbiased as possible, based on historical data and econometrics (see Appendix C).

The cement sector

Our analysis focuses on the cement sector to investigate the magnitude of distortions arising from ALTs for three reasons. First, it ranks amongst the highest in terms of carbon intensity per value added thus, the effects of free allocation rules are magnified. Second, unlike chemicals and steel with many product categories and differentiated impacts, the cement sector is characterised by relatively homogeneous products and production processes. Thus inferring production (activity) from emissions is more straightforward (see further). Third, as the sector experienced a demand collapse in the order of 50% or more between 2007 and 2012 in several member states (Boyer and Ponssard, 2013), the ALT rules were likely to have been more a relevant factor for operational decisions during the period of investigation.

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The cement production process can be divided into two basic stages: production of clinker from raw materials and the subsequent transformation of clinker into cement by grinding with other mineral components. Clinker production accounts for the bulk of carbon emissions in cement production, and the reduction of the clinker-to-cement ratio is one of the most efficient ways of mitigation in the sector. Further, allocation under the EU ETS is based on a benchmark on clinker rather than cement or hybrid benchmark 17 .

Distinguishing between installations above and below thresholds

As described in Section 2.1, allocation is determined by q/HAL, however, data on installation activity levels (i.e. clinker output) are not publicly available. However, it is possible to infer clinker plants' activity levels inferred from plant level emissions data, which are available from the European Union Transactions Log (EUTL). That is, it is possible to use the observed ratio of publically-reported verified emissions (E) relative to the Historical Emissions Level (HEL), to proxy the share of unobserved activity level relative to Historical Activity Level (HAL) i.e. E/HEL ≈ q/HAL. [START_REF] Demailly | CO 2 abatement, competitiveness and leakage in the European cement industry under the EU ETS: grandfathering versus output-based allocation[END_REF] This approximation is possible owing to the very strong and direct relationship between production of clinker, a highly homogeneous product, and emissions. Indeed the emissions intensities of clinker production have changed only very marginally in the EU in recent years between 2005 and 2012 (GNR Database).

However, in distinguish between installations that are above or below thresholds (25% and 50% of q/HAL), an element of uncertainty is introduced due to the approximation of plant activity level based on emissions data. As detailed in A1., we ensure that installations are correctly identified using 2013 allocations data. This reveals whether or not the installation had seen its allocation reduced because of 2012 activity levels. 2013 allocation data also allowed us to obtain clinker carbon intensity at the plant level (see Appendix A.2). Therefore, we are able to assess production at the plant level through emissions (Appendix A.3).

Estimating counterfactual production and trade

For every installation, we estimate a counterfactual output level, to contrast with the observed one. To do so involves three steps and a number of assumptions (detailed in Appendix C). First, we use a simple fixed effects panel regression to predict clinker production (installation level fixed effects) using regional level cement consumption as the main explanatory variable. Counterfactual cement and clinker export levels are similarly predicted using regional level cement consumption as the main explanatory variable. We find that on average, if cement consumption in a region decreases by 1 Mt, clinker production tends to decrease by 0.65 Mt and clinker net exports increase by 0.16 Mt (see Appendix C.1). This assumes that changes are uniform across all installations in a region. We then relax this assumption and make corrections based on individual plants characteristics calibrated on historic data (see Appendix C.2). In developing the counterfactual, cement consumption and price are assumed to be independent of allocation rules, i.e. they would have been the same in 2012 whatever the allocation scheme considered in the paper. We return to this assumption in Section 3.6."

"""""""""""""""""""""""""""""""""""""""" """"""""""""""""""""" 17 The hybrid benchmark avoids the "clinker-cement paradox" (Demailly and Quirion 2006).". If the benchmarked product is cement, plants have an incentive to outsource clinker production. If it is clinker, the incentive to reduce the clinker-to-cement ratio is lost. In California, the benchmarked product is "adjusted clinker and mineral additives produced", which is equal to ! ! (1

+ ! !
), where ! ! ! is the clinker produced, ! is the clinker ratio and ! is the "mineral additives ratio" (limestone and gypsum consumed divided by cement produced). This system gives an incentive to use more mineral additives while preventing clinker outsourcing. [START_REF] Demailly | CO 2 abatement, competitiveness and leakage in the European cement industry under the EU ETS: grandfathering versus output-based allocation[END_REF] HEL is calculated in the same way as the HAL (Cf. Section 1) but using emissions as a proxy for clinker production activity.

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Having estimated counterfactual production levels by installation, we then proceed to estimate the number of free allowances (EUA) received at the plant level under the various scenarios. As an example, let us consider a plant [START_REF] Locke | Motivation by goal setting[END_REF] , which is functioning at 50% E/HEL and receiving 1 million EUAs. Suppose that our econometric model finds that the counterfactual activity of this plant is 40%. This plant would have received 0.4 million EUAs under OBA, 1 million EUAs under EX and EXALTG, 0.5 million EUAs under EXALTNG.

In this short example, we see that gaming from 40% to 50% allows obtaining 0.5 MEUAs more allowances, but involves 0.11 Mt CO 2 of additional emissions 20 , so that the net gain in terms of allowances is 0.39 MEUAs. To convert the various effects into monetary value, we assume a CO 2 price at 9€/EUA. We consider that the increased production is sold at marginal cost, and so has no impact on profits.

In summary, for the four different scenarios, we compute production, emissions and allocation. The net allowances (allocation minus emissions) is compared for the scenarios EX, EXALTNG, EXALTG and OBA. Comparing other scenarios to OBA gives an estimation of overallocation profits (in MEAUs or M€). The difference between EXALTG and EXALTNG gives the effect of gaming. Table 1 summarises how allocations and production are obtained under each scenarios.

Decomposing the destination of excess clinker produced

Which strategies do firms pursue, to increase output and gain free allocations when demand is low? We take a further look at the distortions from ALTs by assessing the relative importance of the different channels through which the excess EU clinker meets its destiny.

Comparing counterfactual net exports to real net exports gives the part of the excess clinker production which is destined for clinker exports and cement exports. Assuming no stockpiling, the remaining part is attributed to the change in the clinker ratio.

3.6.. Moderate and low demand countries

We suspect that the most important differences between scenarios EX and EXALTG will occur in countries in which cement and clinker consumption in 2012 fell well short of historical consumption level and hence ALT rules are relevant. The 26 EU ETS member states21 with ETS-participating clinker production plants, are divided into two groups (see Table 2). The first group of countries are where the average domestic cement consumption """""""""""""""""""""""""""""""""""""""" """"""""""""""""""""" [START_REF] Locke | Motivation by goal setting[END_REF] Caution, this plant does not have the same characteristics as the one in section 2.2 (in order to make computations easier) 20 Assuming that the plant has a clinker carbon intensity of 800kg CO 2 per ton of clinker.

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in 2011-2012 was less than 70% of 2007 levels. 22 We name this group "low demand" (LD) countries. We shall detail some of the results for Greece and Spain, two LD countries particularly affected by the downfall. The LD countries represented 51% of EU ETS cement emissions in 2008 and 40% in 2012. The remaining countries are classified as "moderate demand" (MD).

Key assumption

In terms of the construction of the counterfactual, our central hypothesis is that cement consumption and price are independent of allocation method. This assumption may at first appear at odds with the economic literature (Fischer and Fox 2007, Demailly and Quirion, 2009). Ex-ante free allocations would ordinarily not provide any protection against leakage (they are a lump sum transfer and firms marginal cost fully support the cost of carbon) while ex-post OBA allocations would (with OBA, firms receive free allocation proportional to their output the marginal cost is unchanged and there are no competitive impacts with respect to imports; this is the usual argument in favour of OBA). Cement consumption and price would then depend on the allocation scheme. This paper departs from this view. Rather, it assumes that firms adopt exactly the same pricing and production decisions in their home market in OBA and ex-ante allocation. This assumption is supported by a series of in depth interviews with cement sector actors (Neuhoff et al, 2014) to explicitly show why there has been no leakage. These interviews point out a number of reasons for such behaviour: The ex-ante free allocations have been obtained precisely to mitigate leakage thus a risk of losing future free allocations if regulators observe the ability to pass on the cost of carbon without observing leakage; the long term risk of attracting new entrants into the market from elevated prices (i.e. limit pricing); the risk of drawing the attention of competition authorities due to abnormal profit levels. This paper finds these arguments persuasive and thus assumes that the allocation rule has no impact on consumption and on prices of cement in the EU. The crux of our analysis concerns their impact on the production of clinker and cement.

Data

To examine the effects of ALTs on the cement sector, 246 clinker producing installations were identified as operating in 2010, 2011 and 2012. 24 Other variables are obtained at the country level as summarised in Table 3.

"""""""""""""""""""""""""""""""""""""""" """"""""""""""""""""" 22 The average of 2011 and 2012 was taken since both years are relevant to the analysis that follows here. 2007 is taken as the reference year since this was the year in which demand peaked in most EU Member States prior to the economic crisis of 2008.

23 "There are no clinker plants in Malta, Lichtenstein and Iceland. Emissions data on two clinker plants of Cyprus is available from 2012 only, hence cannot be used in this analysis. " 24 For this purpose, we rely heavily on the work carried out by Branger and Quirion (2014), which has developed an installation level dataset for the EU cement sector with clinker producing installations identified. 4. Results

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Impact of ALTs on the plant distributions

Figure 2 displays the distribution of plant activity levels for 2012 (EXALTG), the counterfactual production (EX, EXALTNG, OBA) and also the distribution in 2011 for comparison. In LD countries, there is a marked jump in installations operating around the 25% and 50% activity level thresholds in 2012, whereas the counterfactual distribution for these countries is not skewed at the thresholds. We find that in LD countries where 117 of the 246 cement installations are located, ALTs should have reduced free allocations in 42 of them, but due to gaming, it was reduced in only 20 installations in reality. Thus, in line with preliminary findings of Neuhoff et al (2014), these results show clearly that cement companies have indeed altered plant production levels in response to ALT rules. In MD countries, this response is noticeable but to a much less degree. The contrast between LD and MD shows the importance of the demand collapse in triggering this gaming behaviour.

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ALT impacts on clinker production and emissions

Table 4 gives the clinker production and the emissions for 2012 (EXALTG) and the counterfactual (EX, EXALTNG, OBA). The excess clinker production due to the introduction of thresholds rule is quantified. It represents an increase of 12% in LD countries, 21% for Spain and 42% for Greece. These increases are extremely large, even if the global impact at the EU level is more modest (5%). The increase in the clinker production translates into increases in emissions. Altogether we estimate that an additional 5.2 Mt CO 2 (+5 % for the

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sector as a whole) have been emitted by EU cement firms as a consequence of the strategic behaviour of cement companies25 . 4.3. Impact of gaming on plant distribution on the free allowances Table 5 gives the amount of EUA's that are allocated to cement installations under the four scenarios (EX, EXALTNG, EXALTG, OBA). If installations received 100% of their allowances regardless of their activity (i.e. the allocation under the EX scenario), then LD countries and MD countries would have received 74.5 and 70 million EUAs respectively. OBA allocations would lower allocations to 37.1 and 61.5 million EUAs respectively. The decrease in allocations is more significant for LD countries because the average activity is much lower. As explained, the scenario EXALTNG can be seen as an imperfect approximation of the OBA rule. If there had been no gaming, it would have set the allocations at 58.7 and 68.6 million EUAs. Thus for the cement sector as a whole, ALTs reduced overallocation in 2012 by 6.6 MEUSs compared to the scenario without ALTs. Had OBA been implemented instead, overallocation would have been further reduced considerably by 39.5 MEUAs, which corresponds to 29% of the total cement sector free allocation in 2012. The theoretical effect for the MD countries is negligible, as most of installations have an activity level superior to 50%. However for LD countries the theoretical effect of the threshold rule as an approximation of the OBA rule would have been more significant: a 42% (that is (74.5 -58.7)/(74.5 -37.1)) reduction should have been obtained. With gaming (EXALTG) a reduction of only 16% prevails (that is (74.5 -68.4)/(74.5 -37.1)). For Spain the percentages would respectively be 57% and 21%; and for Greece 71% and 24%.

Financial potential gain associated with gaming

In the calculation of the potential gain we assume that the increased production is sold at marginal cost, and so has no impact on profits. This gives an upper bound for the profits that could be achieved with gaming since it does not take into account the possible inefficiency costs: logistics cost for production shifting, extra sales expenditures and rebates for increased exports, opportunity cost for increasing the clinker to cement ratio).

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That there are inefficiency costs can be seen from the fact that not all plants achieved the 50% threshold, but some gaming was certainly worthwhile since a large proportion of plants did manage to get to the target.

To convert the increase in free allowances and the increase in emission rights into monetary value, we need to assume a CO 2 price. It should be clear that the amount of profitable gaming is dependent on the CO 2 price. We shall come back to this point in our discussion of the results. For LD countries, the potential gain of EX relative to OBA is estimated through the net increase of allowances which is 74.5 -37.1 Mt CO 2 and a EUA price 7.95€/t which makes 297 M€. With the introduction of the threshold rule this increase would have been only 172 M€ had the firms not gamed the scheme. The reduction is coming from the reduced amount of free allocations due to the downfall in market demand.

The gaming increases the amount of free allocations but increases emissions, bringing a potential gain at 208 M€, which represents an increase of 18% relative to 172 M€. 27 For Spain the per cent increase is 41% and for Greece it is 62%. These figures are substantial even though the carbon price was low at that time. This explains why firms indulge in the various inefficiencies described earlier to capture part of this gain.

4.5.

Where does the excess clinker end up? Indirect evidence revisited This section revisits the indirect evidence of excess clinker production proposed by Neuhoff et al. (2014). As noted, three channels have been identified, production shifting, exports increase and clinker ratio increase. a) Production shifting in multi-plants companies. Cement company executives in interviews reported that subsequent to the introduction of ALTs, it was frequent practice to arrange production levels across plants to ensure being above the threshold at as many units as possible (Neuhoff et al. (2014). We observe output behaviour consistent with these statements in several cement companies which have a number of plants producing close to the thresholds. Table 7 presents four examples 28 . In each of these firms in 2012, production (within the same geographical region) simultaneous falls in production in one plant which produced well above the threshold in 2011, and rises to above the threshold in another plant which was previously operating below the threshold.

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! = ! !!!" ! ! ! !!" !
, where ! ! is the clinker production, !" ! and !" ! net exports of clinker and cement, and ! ! the cement consumption (see Appendix B for explanation and Table 3 for data source).

Table 9 shows the clinker ratio for the MD countries, LD countries, Spain and Greece. The historical declining trend in the clinker-to-cement ratio has reversed in 2012. 15" " 4.6. Decomposing the channels for clinker disposal Our econometric model allows for deriving counterfactuals for net exports of clinker and cement (See Appendix C). Assuming no stockpiling, we can attribute the remaining excess clinker output to clinker ratio increase thereby decomposing into three destination channels. The cement consumption was remarkably low in 2012. Because of the consumption/export relationship established by the econometric model, clinker net exports would have risen anyway in 2012 compared to 2011 had the threshold rule not be implemented. This point need be taken into account in the analysis.

Table 10 details the results. Figure 3 provides a graphical representation. For LD countries, net exports of clinker increased by 6.2 Mt while our counterfactual is 4.6 Mt (+1.6 Mt); the net export of cement increased by 8.5 Mt while the counterfactual is 6.1 Mt (+1.7Mt of clinker embedded); this implies that 2.4 Mt of clinker went into the increased content of clinker in cement. This latter figure represents an increase of 4% relative to our counterfactual for the clinker to cement ratio as defined in the previous section.

Conclusions and policy options

An important change in the EU-ETS phase 3 for EITE concerns the introduction of the activity level threshold rule (ALT). The underlying rationale for its introduction is that it

(2" 0" 2" 4" 6"
All"LD" All"MD" All" Spain" Greece"

Mt'of'clinker'

Routes'of'excess'clinker'production'(Real'vs.' Counterfact)'Decomposition'

Clinker"Exports"Increase" Cement"Exports"Increase"

Clinker"Ratio" Total"Increase"

16" "

would reduce the overallocation profits in case of downfall in the demand: whenever the activity level of an installation falls below some threshold (50%, 25%, 10%) relative to its historic activity level used to allocate free allocations, the allocation would be reduced accordingly (50%, 25%, 0%).

Our ex post analysis of year 2012, the first year in which the threshold rule applies, focused on the cement sector, a sector in which approximately half the EU countries had experienced a significant downfall in consumption (LD countries). It provides a natural experiment to evaluate the consequences of this rule.

Our main conclusion is that while ALT did reduce to some extent overallocation profits, it also created operational distortions which lead to outcomes inconsistent with the low carbon transition of EU energy intensive industries. The reduction in overallocation profits is less than expected because of the gaming behaviour of the industry to achieve the thresholds, during periods of low market demand. Thanks to the elaboration of a counterfactual, we have been able to quantify that after the introduction of ALTs: the potential overallocation profit with gaming is 272 M€ (2 €/t clinker) and 228 M€ without gaming, while it would have been 365 M€ in the absence of ALT. The expected reduction in windfall profits due to the ALT is 38% while the actual reduction is 25%. The incentives are magnified in low demand countries, where profit with gaming is 208 M€ (3.8 €/t clinker) and 172 M€ without gaming, while it would have been 297 M€ without ALTs. We examined three ways in which firms' operations are altered in response to ALTs: shifting production among plants, increasing net exports of clinker and cement, increasing the clinker to cement ratio.

In the 2000's top management attention on the issues of climate change emerged as an important dimension of corporate social responsibility and a large number of companies got involved into proactive strategies to limit their own emissions (Arjalies et al., 2011). The EU-ETS positively contributed to turn this strategy into operational practise by putting a price on carbon. The distortions reported in our study are particularly detrimental in this respect: the production shifting goes against the restructuring of the assets to achieve scale economies, a key factor of cost efficiency in cement; the increased exports induce some relocation of foreign cement consumption in the EU, while the EU-ETS intention in giving free allocations was designed to reduce leakage, i.e. the relocation of EU consumption in foreign countries; the increase in the clinker to cement ratio goes against one of the main drivers to limit emissions in cement production. The introduction of ALT reversed the alignment of objectives between corporate social responsibility and the EU-ETS.

Our results have been obtained in a context of low carbon price, severe downfall in market demand, and large free allowances. A higher carbon price would make our results even more relevant, the higher the carbon price the higher the incentive to achieve the thresholds. 29 Had we observed growth, the threshold rule would have been inactive and the reserve for new entrants rule would have been the issue to be analysed. Anecdotic evidence 30 suggests likely distortions in that case as well and it would be interesting to carry out a rigorous analysis similar to this one.

"""""""""""""""""""""""""""""""""""""""" """"""""""""""""""""" 29 Take a EUA price at 20€/t a simple extrapolation for LD countries would bring up the potential wind fall profit to 236*20/9 = 524 M€. However if we assume that all plants achieve the 50% threshold, a reasonable assumption for a EUA price at 20€/t, it would go up to 583 M€. The expected reduction remains at 42% but the actual one drops to 22%. 30 If the historic activity level (HAL) refers to say a 60% capacity utilization rate, increasing production to 80% may not be beneficial since it will increase emissions with no increase in free allowances; in case of capacity expansion the detailed rule to determine the level of free allowances may induce an artificially high production during the period used to fix that level. Ref. private conversation with industry representatives.

17" "

These considerations suggest that the threshold rule should be abandoned for sectors such as cement for which carbon costs represent a significant share of production costs. This raises the question of what to put in its place for such sectors. Theory suggests that replacing free allocation with full auctioning and using border carbon adjustments offers the most efficient solution, yet politically this solution has not yet gained serious traction. Since the problem arises in part because the thresholds create an allocation system that fall between an ex-ante and ex-post scheme, one solution would be to move to full ex-post output-based allocation.

However, a number of issues must be carefully investigated before going in that direction. We can think of the following points: OBA implies the loss of an absolute cap for free allocations, OBA stifles any possibility for prices to be passed down the value chain, OBA may create a heavy administrative burden, and the declining trend in the caps to decarbonize the economy over the long term is incompatible with a benchmark to mitigate leakage. There are on-going discussions on how to circumvent these issues. For example the loss of demand side substitution incentives could be restored with a consumption charge (Neuhoff et al 2014). Output based scheme with hybrid benchmark has been implemented in California in 2012. An ex post study on this implementation would be welcome to see if, again, the devil lies in the details.

20" "

allocation data, which revealed whether or not the installation had seen its allocation reduced because its 2012 activity level fell below a threshold. Allocations in 2013 are equal to (cf equation ( 1)):

! !,!"#$ = !"!# !"!" ×! ! ×!"# ! ×!"#$ !,!"#$
Where !"!# !"#$ is the 2013 Cross Sectoral Correction Factor (0.9427), ! ! the clinker carbon intensity benchmark (766 kg CO 2 per ton of clinker), and !"# ! the Historical Activity Level of installation ! (in tons of clinker). Transforming the previous equation, where both !"# ! and !"#$ !,!"#$ are unknown, we obtain:

!"!# !"#$ × ! ! ! ! ×!"# ! ! !,!"#$ = 1 !"#$ !,!"#$ × ! !,!"# ! ! Noting ! !,!"# = !"# ! !"# !
(corresponding approximately to the clinker carbon intensity for the HAL producing years), and ! ! is the average clinker carbon intensity (863 kg CO 2 per ton of clinker, GNR, indicator 321) in 2008. We chose 2008 to proxy HAL production (not the highest level of production, which is 2007, but close).

The ratio at the left part of the equation can be computed with available data. On the right part, we have !"#$ !,!"#$ , which we want to find, and the ratio,!

! !,!"# ! !
, which is unknown as well but bounded and likely to be close to 1. Indeed, ! !,!"# varies in an extreme range from 720 kg CO 2 per ton of clinker to 1300 kg CO 2 per ton of clinker (and for the very large majority of the plants from 780 to 950 kg CO 2 per ton of clinker), which translates into a ratio ! !,!"# ! ! varying from 0.83 to 1.51 (and most likely from 0.90 to 1.10). Then, if the ratio, is comprised between 0.83 to 1.51 (respectively between 1.67 and 3.01, and between 2.64 and 4.80 31 ), we infer that !"#$ !,!"#$ = 1, (respectively 0.5 and 0.25).

This enabled catching out situations in which imperfections in the E/HEL measure as a proxy for the q/HAL would have led to a false conclusion about whether an installation was truly above or below its activity threshold in 2012. We found that the actual thresholds for the E/HEL measure that matched the 2013 allocation data were slightly lower in practice, at 22% and at 45%, rather than 25% and 50%. Discussion with industry experts revealed that there was a logical explanation for this systematic bias: clinker producers often have more than one kiln inside an installation that is treated as a single unit for free allocation purposes. When demand falls, it is common to concentrate production in the most efficient kiln(s). Thus emissions may fall by slightly more than overall clinker production, creating a slight downward bias in E/HEL as a measure of q/HAL in low demand countries. This bias could also be explained by the clinker carbon intensity improvement between HAL years and 2012.

A.2 Determination of clinker carbon intensity at the plant level

Once the !"#$ !,!"#$ has been determined at the plant level ! (see previous section), the plant clinker carbon intensity for HAL years, ! !,!"# , can then be obtained with the previous equation.

For 20 plants (out of 246), we found an unusual number (below 700 kg CO 2 per ton of clinker), possibly due to a capacity increase, and put instead a default value equal to ! ! .We """""""""""""""""""""""""""""""""""""""" """"""""""""""""""""" 21" " also set the default value ! ! when ! !,!"#$ = 0 (meaning !"#$ !,!"#$ = 0 or plant closure), making the computation impossible (15 plants).

We then correct the first approximation of clinker carbon intensity so as weighted average 32 clinker carbon intensity in big countries corresponds to GNR data in 2008 (818, 831, 832, 797, 847, 858, 849 and 842 kg CO 2 per ton of clinker for respectively Austria, Czech Republic, France, Germany, Italy, Poland, Spain and the United Kingdom). Finally we correct values of clinker carbon intensity in plants of other countries in the same way, so as the European weighted average clinker carbon intensity (! ! ).

A.3 Clinker production

Once clinker carbon intensity is estimated for each plant, clinker production can be obtained through emissions (! !,!,! = ! !,! ×! !,!"# , ). We assume that clinker carbon intensity does not evolve over time.

Further, we proxy the plant capacity with the formula

! ! = !"# !∈[!""#;!"#!] ! ! ! !,!""#
(maximal historical production).

B. Robustness check -Macro data consistency at the national level

If we denote the six different variables:

• Q K clinker production • Q C total cement production • NE K clinker net exports • NE C cement net exports • C C cement consumption • R clinker-to-cement ratio
We have two equations translating the conservation of cement on the one hand and the conservation of clinker on the other hand (neglecting stockpiling):

! ! = ! ! + !" ! ! ! = !×! ! + !" !
These equations must be verified for each country every year (for real of counterfactual scenario).

In this paper for real data, Q K , NE K , NE C and C C are obtained through different sources (see Table X), and Q C and R are re-computed (we have ! =

! !!!" ! ! ! !!" !
).

C. Counterfactual Production and Net Exports

C.1Macro level Our central hypothesis is that cement consumption is independent of allocation rules. Therefore, cement consumption would have been the same in 2012 had the threshold rule not been implemented.

Cement consumption is then our main variable to "predict" clinker production and net exports of clinker and cement. We do a panel regression based on years 2008 to 2011 (postcrisis, to avoid a time break):

"""""""""""""""""""""""""""""""""""""""" """""""""""""""""""""

32 The Weights are production, as multiplying plant emissions by this first approximation of clinker carbon intensity gives a first approximation of clinker production at the plant level (! !,!,!""# = ! ! !,!"# ×! !,!""# ).

22" "

∆! !",! = ! ! + ! ! ∆ !,!,! + ! !,! ∆!" !",! = ! ! + ! ! ∆! !,!,! + ! !,! ∆!" !",! = ! ! + ! ! ∆! !,!,! + ! !,!
∆! !",! (respectively ∆!" !",! , ∆!" !",! ! and ∆! !,!,! ) means variation of clinker production (respectively clinker and cement net exports, and cement consumption) between year ! -1! and year ! in region !.

Regions are almost identical to countries. In order to minimize measurement errors which would bias the regression, we regroup some small countries into larger entities which are coherent in terms of regional market: Baltic countries, Benelux, Norway-Sweden and Slovenia-Italy. There are then 20 different regions in the regression, and 3 time periods (2008-2009, 2009-2010, 2010-2011), so 60 points. The fit is very good for the clinker production (! ! = 0.73), good for the clinker net exports (! ! = 0.41) but not good for cement net exports (! ! = 0.01): changes in cement consumption do not predict changes in cement net exports. We find a positive term for ! ! and a negative term for ! ! as expected: a decrease in cement consumption involves a decrease in clinker production but an increase in clinker net exports. Regarding numerical figures, we find that on average, if cement consumption decreases by 1 Mt, clinker production decreases by 0.65 Mt and clinker net exports increase by 0.16 Mt. These relationships allow (small)

23" "

endogenous clinker ratio variations (mostly downward) through the previous macro equations.

For a region !, we then compute counterfactual net exports as:

!" !,!

!"#!!!" = !" !,! !"## + ! ! ∆! !,!,!"##!!"#! with ! ! = 0.162 and !" !,! !"#!!!" = !" !,! !"## + ! ! ∆! !,!,!"##!!"#! = !" !,! A prior estimation of the counterfactual activity is given using the previous macro relation. We suppose in a first time that the change in production is uniform across all installations belonging to the same region:

! ,! !"!!"#$" = ! !,!!! ×(1 + ! ! ∆! !,!,!;!∋! !! ! !,!,!;!∋! )
However the change in production may not be uniform in the different installations of a region. We thus investigate different potential biases. For that we regress the error (! !,! -! !,! !"!!"#$" ) on different plant-specific variables (we use estimations from 2009 to 2011):

! !,! -! !,! !"!!"#$" = ! ! + ! ! !"# !,!,!∋! + ! ! ℎ!"ℎ !,!,!∋! + ! ! !"#"!$%& ! + ! ! !"#$%#& ! + ! Variables are:

• !"# !,!,!∋! is a dummy variable equal to one if the activity of the plant in year ! -1 is lower than 10% of the average activity level of the region where the plant is (happens a bit more than 20% of the cases).

• ℎ!"ℎ !,!,!∋! is a dummy variable equal to one if the activity of the plant in year ! -1 is higher than 10% of the average activity level of the region where the plant is (happens a bit more than 20% of the cases)

• !"#"!$%& ! is the capacity of the plant (in Mt), proxied by the maximimum historical production.

• !"#$%#& !,! is a dummy variable equal to one if the plant is located near the coast (less than 50km, this was done thanks to the geolocalization of the plants in the EUTL data). It concerns 61 plants out of 246.

Counterfactual activities are re-estimated each year using the precedent year (errors are not piling up), so there is no suspicion for autocorrelation, and we use a standard regression. As the Breush-Pagan test reveals the presence of heteroskedasticity, we present robust standard errors obtained with Huber-White estimator. Results are displayed in Table 12. We find a significant bias for plants functioning at low activity levels. That is, with a uniform production change, we tend to underestimate the activity level of those plants by 7.6 percentage points on average. A reasonable explanation is that low activity plants are more maintained for profitability.

24" "

As there is no statistical significance for the parameter ℎ!"ℎ , we deduce that the production adjustment is made through all the other plants, and not only the high producing plants. Further, the parameter !"#"!$%& is not statistically significant: big or small installations adjust to the regional demand in the same way.

Finally, the parameter !"#$%#& is statistically significant and negative. It means that with a uniform production change, we tend to the activity of coastal plants. This is surprising as we could expect the opposite (coastal plants producing more, e.g. their production declining less, in order to export). It could simply mean that coastal plants production declines more than the other plants as a strategy of cement companies.

We use the results of this past regression to correct prior estimations with the distortion (only for !"#! and !"#$%#& which are statistically significant), then we renormalize activity at the region level to satisfy the macro change in production:

! !,! !"!!"#$%&$ = ! !,! !"!!"#$" + ! ! !"# !,!,!∋! + ! ! !"# !"# ! ! !,! !"!!"#$% = ! !,! !"!!"#$%&$ × ! !,! !"#$" ! !,!
!"#$%&$ Results of this method for years 2009 to 2011 are displayed in Figure 4 (comparing actual and estimated activity at the plant level). The uncertainties are relatively large, but there is no systematical bias in the estimation. That is, in our counterfactual scenario, some plants may wrongly be attributed an activity below the threshold, but the underestimation of allowances for these plants is compensated by an overestimation of allowances for others plants (for which we wrongly attribute an activity above the threshold).

25" "

0%" 25%" 50%" 75%" 100%" 125%" 0%" 25%" 50%" 75%" 100%" 125%"

Counterfactual'activity'

Real'Activity'

Figure 1 :

 1 Figure 1: The value of gaming. The installation engages in gaming when ∆! < ∆! ! . ! refers to the carbon intensity of the plant. Benefits are increased free allocations minus extra emissions.

Figure 2 :

 2 Figure 2: Distribution of installations according to their activity level (approximated by E/HEL) in 2012 for observed and counterfactual production.

"

  Counterfactual production respects the relationship ∆! !",! = ! ! ∆! !,!,! (with ! ! = 0.65) and is established at the micro level (plants). Explanations are given in the next section.C.2 Micro level (production)We name! !,! = ! !,! !"# !the activity of plant ! in year ! (ratio of emissions divided by historic emissions level). Invidual plant production of clinker is then given by ! !,!,! = ! !,! ×!"# ! ! !,!"# where ! !,!"# ! is the clinker carbon intensity of the plant (supposed time invariant).

Figure 4 :

 4 Figure 4: Predictions of E/HEL at year n based on E/HEL at year n-1 and respective change in consumptions for 2009-2011

Table 1 :

 1 Scenarios

	Scenarios	Allocations	Production
		Proportional to Activity	Counterfactual
	OBA	(HALxALCF <->q in Eq (1))	(explained in Appendix C)
		Independent of Activity
	EX	(ALCF=1 in Eq (1))	Same as OBA
		Hybrid
	EXALTNG	(Eq (1))	Same as OBA
	EXALTG	Same as EXALTNG	Actual 2012 Production

Table 2 :

 2 Moderate-(MD) and low demand (LD) countries in terms of cement consumption in 2012 relative to 2007 levels 23

	Low Demand (LD) Countries	Moderate Demand (MD) Countries
	Ireland, Spain, Greece, Bulgaria, Hungary,	Austria, Belgium, Czech Republic, Finland,
	Denmark, Portugal, Italy, Slovenia and Baltic	France, Germany, Netherlands, Norway,
	countries	Poland, Romania, Slovakia, Sweden and
		United Kingdom

Table 3 :

 3 Data sources

	Variable	Source
	Emissions and HEL	EUTL
	Clinker net exports	Eurostat (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#
	(NE	K )	Eurostat (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#,
				International Trade, EU Trade Since 1988 by HS2, 4, 6 and CN8).
				Data is originally given by country pairs. Total net exports are re-computed.
				Product category: "Cement Clinker" (252310)
	Cement net exports	Eurostat
	(NE	C )	Product category: Difference between "Cement, incl. cement clinkers" (2523) and "Cement Clinker" (252310).
	Cement consumption	1) Cembureau (2013) for the main European countries
	(C	C )	2) VDZ (http://www.vdz-online.de/en/publications/factsandfigures/cement-
				sales-and-consumption/, Table C10) for Baltic countries and Norway.
	Clinker	production	EUTL-derived estimation (through estimated clinker carbon intensity and
	(Q	K )	emissions, see A1). Where there were data gaps, supplementary data were obtained from several sources e.g.:
				•	National cement association data when reliable and exploitable, i.e.
				Spain
				(https://www.oficemen.com/Uploads/docs/Anuario%202012%281%29
				.pdf, p90)
				•	Germany (http://www.vdz-
				online.de/en/publications/factsandfigures/cement-data-at-a-glance/,
				table A2)
				•	France (http://www.infociments.fr/publications/industrie-
				cimentiere/statistiques/st-g08-2012, Table p7)
				•	Getting the Numbers Right database (GNR,
				http://wbcsdcement.org/GNR-
				2012/index.htmlhttp://wbcsdcement.org/GNR-2012/index.html,
				indicator 311a) for available countries (UK, Italy, Poland, Czech
				Republic, Austria)

Table 4 :

 4 Production and Emissions for the observed (EXALTG) and counterfactual (EX, OBA,

				EXALTNG) scenarios
				LD		MD	All	Spain	Greece
				countries	countries	countries
	Production (CF) in Mtons				48.7	79.3	127.9	13.2	3.9
	Production (observed) in Mtons			54.4	79.4	133.8	16.0	5.6
	Increased Production in Mtons			5.7	0.1	5.8	2.7	1.7
	Emissions (CF) in Mtons CO	2			42.7	67.5	110.2	11.4	3.5
	Emissions (observed) in Mtons CO	2	47.8	67.6	115.3	13.7	5,0
	Increased emissions in Mtons CO	2	5.1	0.1	5.2	2.4	1.5

Table 5 :

 5 The Free Allowances (MEUAs) under the four scenarios

	Allocations	LD countries	MD countries	All countries	Spain	Greece
	EX	74.5	70.0	144.5	23.6	8.7
	EXALTNG	58.7	68.6	127.4	15.8	4.6
	EXALTG	68.4	69.6	138.1	20.7	7.3
	OBA	37.1	61.5	98.6	10.0	2.9

Table 6

 6

	gives the potential profit associated with gaming for a CO	2 price at 7.95€/t, which
	corresponds to the average future price (December 2013) during year 2012. 26

Table 6 :

 6 Quantification of the monetary value of excess free allocations for the various scenarios.

	Millions of €
	relative to OBA	LD	MD	All	Spain	Greece
	EX	297	68	365	109	46
	EXALTNG	172	57	228	47	14
	EXALTG	208	64	272	67	23

Table 7 :

 7 Evidence of within-firm-country production shifting to meet thresholds Table8gives net exports of clinker and clinker embedded in cement from 2010 to 2012 for LD and MD countries. We observe a surge in clinker net exports in LD countries: 5.88 Mt in 2012, compared to 1.94 Mt and 1.56 Mt in 2010 and 2011 respectively. In contrast MD countries remained small net importers of clinker and no significant shift was observed in their trade patterns. Further analysis revealed that these clinker exports in 2012 were destined mainly to countries in Latin America and Africa, including Brazil, Togo, Ghana, Cameroon, Côte d'Ivoire, and Mauritania and Nigeria, which could have imported from Non EU various sources.

	Country-Company	Installation E/HEL 2011 E/HEL 2012
	Greece-W	1	34%	49%
	Greece-W	2	77%	66%
	Greece-W	3	11%	0%
	Spain-X	1	42%	50%
	Spain-X	2	57%	46%
	Spain-X	3	68%	56%
	Hungary-Y	1	41%	46%
	Hungary-Y	2	68%	50%
	Portugal-Z	1	34%	64%
	Portugal-Z	2	55%	51%
	Portugal-Z	3	71%	60%
	Note: We recall that if E/HEL>45%, then the installation is above the threshold
	b) Exports.

Table 8 :

 8 Clinker net exports in 2010, 2011 and 2012 in LD and MD countries in millions of tonnes

	LD Countries	2010	2011	2012
	Clinker	1.94	1.56	5.88
	Clinker in Cement	5.12	4.09	5.75
	MD Countries	2010	2011	2012
	Clinker	-0.87	-0.43	-0.37
	Clinker in Cement	2.39	2.78	2.63

Note: Source: Eurostat we use a common clinker ratio of 75% to compute clinker embedded in cement c) Clinker ratio. Another way excess clinker production might materialise is in a higher clinker-to-cement ratio. That is, firms could use more clinker to produce the same ton of cement. The clinker ratio can be recomputed at the macro level (state of group of states) with the formula!

Table 9 :

 9 Clinker-to-Cement Ratio in selected areas (source: authors' analysis)

	Clinker Ratio	2010	2011	2012
	MD Countries	76%	76%	77%
	LD Countries	74%	72%	74%
	Spain	79%	76%	82%
	Greece	76%	71%	75%

Table 10 :

 10 Real and counterfactual net exports of clinker and cement (Mt)

		Total		2012			2012
		Increase	Clinker Net Exports	Cement Net Exports	Clinker Ratio
		Production		Obse			Obse
	Region	Clinker	CF	rved	Diff	CF	rved	Diff*R Effect	Relative
	All LD	5.7	4.6	6.2	+1.6	6.1	8.5	+1.7	2.4	+ 4%
	All MD	0.1	0.4	-0.7	-1.1	3.3	2.7	-0.4	1.7	+ 2%
	All	5.8	5.0	5.5	+0.5	9.4	11.2	+1.3	4.0	+ 2%
	Spain	2.7	2.2	3.4	+1.2	2.2	2.6	+0.3	1.2	+ 7%
	Greece	1.7	0.5	1.8	+1.3	1.5	1.7	+0.2	0.2	+ 4%
				Figure 3: Routes of excess clinker

Table 11 :

 11 Regression results of the regional panel data regression

		∆! !!,! !	∆!" !",! !	∆!" !",! !
		(Clinker	(Clinker Net	(Cement Net
		Production)	Exports)	Exports)
	! !	0.646***
		(5.81)
	! !		-0.162**
			(2.36)
	! !			0.025
				(0.55)
	! !	0.73	0.41	0.01
	!	60	60	60
	Hausman Test	3.60 (0.06)	2.92 (0.09)	5.00 (0.02)
	Modified Wald Test for	2.4E6	7.5E6 (0.00)	2.6E6 (0.00)
	groupwise heteroskedascitity	(0.00)
	Note: *,** and *** means statistically significant at 10%, 5% and 1%
	Results are displayed in

Table 11 .

 11 Constant are not displayed (they are close to 0 and statistically insignificant). As suggested by the Hausman test (if p-value are low, fixed effects are preferred), we used a fixed effect model. As the modified Wald test reveals the presence of heteroskedasticity, we present robust standard errors obtained with Huber-White estimator.

Table 12 :

 12 Regression results of the plant level econometric model for production

	!"#	0.076***
		(4.78)
	ℎ!"ℎ	-0.105
		(1.24)
	!"#"!$%&	-0.005
		(0.48)
	!"#$%#&	-0.040***
		(3.40)
	constant	0.006
		(0.45)
	! !	0.06
	!	738

Note: *,** and *** means statistically significant at 10%, 5% and 1%

The term "grandfathering" is usually used in a narrow sense, whereby allocation is based purely on past emissions or output, whereas the other terms tend to also incorporate allocation methods such as the EU ETS Phase 3 rules, which is based on past production but is also multiplied by a benchmark.

Note that ex-ante and ex-post refer to whether the calculation of the freely allocated amount of allowances occurs prior to or following the production and emissions for which allowances are to be allocated.

New entrant provision and closing rules were already in place in Phases 1 and 2 of the EU-ETS. A closure rule is also used in the Californian ETS.

Overallocation profits can be distinguished from windfall profits, which refer to the profits from free allocation where emitters additionally profit from passing on the marginal CO 2 opportunity cost to product prices, despite receiving the allowances for free. Overallocation profits can occur even in the absence of cost pass through, if output fall short of historic levels.

Windfall and overallocation gains have been a persistent shortcoming of the use of ex-ante free-allocation mechanism in the EU ETS (e.g.[START_REF] Laing | The effects and side-effects of the EU emissions trading scheme[END_REF], Sartor et al. 2014[START_REF] Sandbag | Carbon Fat Cats 2011: The Companies profiting from the EU Emissions Trading Scheme[END_REF].

Note that Iceland, Liechtenstein, Malta have no listed clinker plants in the EUTL database, while data for Cypriote plants was not able to be exploited due to missing data.

This increase can be decomposed as 5.1 Mt CO 2 due to a scale effect (more production) and 0.1 Mt CO 2 due to an intensity effect (carbon-intensive plants being more used).

Source: ICE database (http://data.theice.com/MyAccount/Login.aspx)

Note that our methodology estimates the overallocation profit using the level of free allocations for year 2013 based on activity levels in year 2012 while their emissions in 2013, for which they have to pay certificates, depend on actual emissions in 2013, while we use the counterfactual for 2012.

We only display here groups of installations belonging to a country-company that are the most consistent with production shifting, but avoid cherry-picking individual installations. For the four cases, all installations of a certain country-company are displayed.

Acknowledgements

The authors wish to acknowledge the following people for their helpful comments and suggestions in the writing of this paper: Philippe Quirion, Andrei Marcu, Karsten Neuhoff, Bruno Vanderborght, Luca Taschini, DG Clima and the maintainers of the EUTL public registry, WBCSD and all the people who contribute to the Getting the numbers right (GNR) cement sector database and numerous people from the cement industry. Jean-Pierre Ponssard gratefully acknowledges the financial support from the ANR/Investissements d'avenir (ANR -11-IDEX-0003-02). Misato Sato gratefully acknowledges financial support from the Grantham Foundation and the ESRC through the Centre for Climate Change Economics and Policy.

Appendix A. EUTL Data computations'

A.1 Determination of the Activity Level Correction Factor (ALCF 2013 ) at the plant level The key challenge is to correctly distinguish installations that are above or below thresholds (25% and 50% of q/HAL), despite the limitation that activity levels have to be approximated using emissions data (E/HEL). To do so, we exploit the observations from the 2013


F Vienne

S Caro

L Désiré

J M Auberlet

F Rosey

E Dumont

Driving simulator: an innovative tool to test new road infrastructures

Keywords: Driving Simulator, validation, road design, road safety simulateur de conduite, validation, conception routière, sécurité routière

Driving simulators are high-tech and cost-effective tools that allow performing complex tasks such as designing, testing and studying road design and equipment, e.g., road markings and signs, public lighting, specific traffic lanes. For this, IFSTTAR (The French Institute of Science and Technology for Transport, Development and Networks) has developed various types of simulatorsstatic and dynamic car driving simulators, dynamic motorcycle riding simulators, a pedestrian crossing simulatorand is working on a bicycle simulator. As for any methodological tool, driving simulators must be handled in appropriate conditions. This article presents the pros and cons of this type of device based on IFSTTAR's experience of using simulators. The notion of validation of these tools is also tackled.

Introduction

Driving simulators are powerful tools which allow achieving complex tasks at a relatively low cost. There are several families of driving simulators which differ in how they are used. The most impressive simulators, developed by car manufacturers, serve to study the dynamics of vehicles and the validation of their components [START_REF] Watson | Design of Simulator Scenarios to Study Effectiveness of Electronic Stability Control Systems[END_REF]. In contrast, institutions dealing with driver training and road risk awareness use low-cost simulators to confront the drivers with various traffic conditions and to present them with crash scenarios. Finally, universities and research institutes dealing with transport and focussing in particular on the study of the behaviour of drivers and other road users (pedestrians, cyclists) implement driving simulation to approach a variety of questions such as the influence of medicine, the introduction of new driver assistance systems, the impact of new regulations, or the evaluation of road design solutions, on which we will focus.

Driving simulators indeed allow testing and comparing different existing or new road configurations or equipment. Thus, they allow determining how road design solutions are perceived and understood by the drivers, and which driving behaviour they generate. The managers of road networks are particularly interested in the acceptance of these solutions by the users, in their impacts on the speed and the side position of vehicles, or still in their impact on traffic safety. In the following sections, we begin with the description of driving simulators architecture; then we discuss advantages and limits of the driving simulators as a tool allowing the evaluation of road design solutions; we tackle the question of their validity and the construction of experimental protocols; we also present several examples of experiments which concern road design.

Architecture of driving simulators

Driving simulators immerse the participants (i.e., their drivers) into a virtual environment in which they move by controlling an interface (which can be a whole vehicle). The depiction of this virtual environment is generally multisensory, the objective being to bring to the participants the illusion that they are actually moving in this environment. Simulators thus consist mostly of elements of sensory stimulation (figure 1):  an interface, which may come in various forms, from the simple steering wheel to the complete vehicle with more or less advanced platforms, allowing the participants to move through the virtual environment and to interact with the virtual users;  haptic and inertial systems to improve the feedback;  a 3D visual rendering engine to compute the visual road environment, associated with a display system;  a 3D sound engine to improve the perception of traffic outside the field of vision;  a populating engine to immerse the participants among virtual users with realistic behaviours. There are a number of possibilities for research and development towards better sensory stimulation to improve the immersion of the participants:  high Dynamic Range (HDR) visual rendering for more realistic luminance and contrast levels;  stereoscopic display for improved distance perception and feeling of presence;

 inertial (on the dynamic platforms) and haptic feedback for improved control;  3D sound and the auralization for improved spatial perception of sounds.

Other areas of research deal with populating the virtual scene and aim at improving and at validating the behaviour of autonomous agents [START_REF] Espié | Archisim, A Behavioural multi-actors traffic simulation model for the study of a traffic model including ITS aspects[END_REF].

Advantages of driving simulation

There are different tools to study the impact of the road infrastructure and design on the behaviour of the users, such as roadside sensors, instrumented vehicles, and driving simulators. The latter present advantages compared with field systems, especially when it comes to the a priori evaluation of a project. Here are their main advantages in this respect.

Testing hazardous situations

Because simulators work with virtual environments, they provide total control over the simulated events. Thus, it is possible to present the participants with situations which would be difficult to study on a test track and even more on the road, either because they are hazardous or because they rarely occur. For instance, driving simulators have been used to study populations at risk such as elderly pedestrians (e.g., [START_REF] Lobjois | The effects of age and traffic density on road-crossing behavior[END_REF], reduced visibility conditions such as fog (e.g., [START_REF] Caro | Perception et contrôle de la distance intervéhiculaire en condition de brouillard : Etude sur simulateur[END_REF], or still situations of traffic congestion.

Control and reproducibility of the driving situations for all participants

Another advantage of the simulators is to be able to plan and control the events, by acting in particular on the behaviour of the autonomous agents and the elements of the infrastructure (e.g., traffic lights). The driving situations can thus be reproduced as many times as needed, which facilitates behaviour comparisons with various participants.

Easy scripting of non-existing road features

Simulators allow creating virtual environments supported by the technical information which describe the infrastructure (e.g., alignment, cross section). It can be the mock-up of a real environment or that of a future road project. And it is possible to add or modify road features for the purpose of evaluation (figure 2). Fig. 2. Sample road configurations and traffic conditions rendered on Ifsttar's driving simulator.

Recording traffic and vehicle data

To evaluate road design, the experimenters need data concerning the behaviour of the participants and their actions on the commands of the simulator (e.g., driving wheel, pedals, gear lever), as well as data concerning the traffic. The simulation software can supply such information since it manages the moves of the vehicle driven by the participants and as well as the actions of the various autonomous agents populating the scene. Contrary to most roadside or inboard observatories, information can be collected at a high frequency, with the possibility of fine data analysis.

Requirements when using driving simulators

Despite its advantages compared to field tests, driving simulators also imply requirements which must be taken into account when addressing the evaluation of road infrastructures. Some of them have to do with technical difficulties; others concern the behaviour which will be observed.

3D database

The visual environment plays an essential role in the evaluation of the infrastructure on driving simulators. Therefore, the study of an existing street or highway requires two things. On one hand, the topographic data must be available; it may be obtained from computer aided design software such as Autocad, or from geographic databases such as Google maps (e.g., [START_REF] Auberlet | The impact of perceptual treatments on lateral control: A study using fixed-base and motion-base driving simulators[END_REF]. On the other hand, the surfaces must be textured in order to faithfully reproduce the real environment. When it comes to studying new road features, they must be modelled and rendered as accurately as possible. Generating satisfactory 3D databases is often difficult and time consuming, but it is a necessary first step. Care should also be given to road signs: depending on display resolution, the participants may not be able to read them from as far as in the real world.

Interaction with different actors inside the virtual world

To favour the immersion of the participants, the simulated traffic and the agents in interaction, such as the pedestrians, have to behave in a realistic way. So, when investigating road features in connection with traffic (such as congestion signalling) the behaviour of the virtual road users ought to be realistic enough not to influence the behaviour of the participants. This also involves an important preparation.

Lighting

Among the different channels of the human sensory system, vision is the main source of information for a driving task [START_REF] Sivak | The information that drivers use: Is it indeed 90% visual?[END_REF]. Therefore, the computer graphics element of the driving simulation must receive special attention. Recent studies have been investigating the use of lighting systems, such as road lighting or pavement-embedded signals which provide information to the drivers. For such studies, photometrically calibrated HDR rendering and display enables accurate simulation of luminaires and headlamps (e.g., [START_REF] Petit | A High Dynamic Range Rendering Pipeline for Interactive Applications: in search for perceptual realism[END_REF].

Simulator sickness

One of the constraints when using simulators is the syndrome of simulator sickness (kinetosis). A common hypothesis concerning the origin of motion sickness is a conflict between visual and somato-sensory information. This syndrome appears particularly for urban driving (e.g., left-or right-turn situations) and concerns more particularly elderly drivers, a privileged target for our studies in road safety with the ageing of the population. Reducing simulator sickness is strategic, because it modifies the behaviour of those participants who feel it. Some solutions have been proposed, such as a questionnaire to identify those participants who are susceptible to motion sickness, or participant training to reduce the effects of kinetosis [START_REF] Werneke | What does the driver look at? The influence of intersection characteristics on attention allocation and driving behavior[END_REF].

Speed evaluation

On a driving simulator, it is easy to know the speed adopted by the participants all along the course. It is one of the reasons why speed is frequently used as an indicator to assess the impact of road design on the drivers' behaviour [START_REF] Chrysler | 36 : Design and evaluation of signs and pavement marking using driving simulators[END_REF]. There are several such examples of a priori evaluation of road configurations on a driving simulator [START_REF] Denton | The influence of visual pattern on perceived speed[END_REF][START_REF] Godley | A driving simulator investigation of perceptual countermeasures to speeding[END_REF]. Let us note however that the perception of speed can be biased on a simulator, so that we cannot consider that the practised speeds will be the same in real conditions [START_REF] Chrysler | 36 : Design and evaluation of signs and pavement marking using driving simulators[END_REF]. Indeed, the main information used by the drivers to estimate their own speed of travel lies in the variations in size and position of the objects in the road environment, as well as the level of texture [START_REF] Evans | Chapitre 8: Driver performance. Traffic safety[END_REF][START_REF] Manser | The influence of perceptual speed regulation on speed perception, choice, and control: Tunnel wall characteristics and influences[END_REF]. Peripheral vision is the main source of this information [START_REF] Evans | Chapitre 8: Driver performance. Traffic safety[END_REF], which explains the decrease of performance of the participants when estimating their own speed of travel with a limited field of vision [START_REF] Evans | Chapitre 8: Driver performance. Traffic safety[END_REF][START_REF] Manser | The influence of perceptual speed regulation on speed perception, choice, and control: Tunnel wall characteristics and influences[END_REF].

As we have just seen, driving simulators offer advantages but also imply requirements. These may ban certain researches or impose the use of specific material and software configurations. In order to better comprehend these constraints with respect to the possible uses of driving simulators, we now approach the question of the validity of the simulators and of the experiments which they serve.

Simulators and experiments validity

Several definitions of validity

To our knowledge, there is no standardized method to validate driving simulators; the validation process is sometimes limited to checking the conformity with the technical specifications from the designers or to an expert's opinion. We propose several definitions of the simulators' validity and discuss the consequences about the experiments' setup. We think it is essential to assess simulators based on the sensations they provide or on the behaviour they lead to. However, few works have been undertaken in this direction. [START_REF] Malaterre | Etude de la validité des simulateurs de conduite par comparaison de tâches réalisées en situation réelle et en simulation[END_REF] distinguish four levels of simulators' validity, which correspond to different kind of similarities between driving in virtual environments and driving in the real world: the physical, experiential, ethological, and psychological validity. Physical validity refers to the similarity between the sensory stimulations generated in the real environment and in the virtual environment. Perfect physical validity would imply, among other things, to be able to submit participants to prolonged accelerations, to be able to display images with high levels of luminance and contrasts, and to play a perfectly spatialized sound whatever the subjects' position. Since current systems are not technically able to produce such stimulations, the simulators' designers have to produce illusions, relying on detection limitations and ambiguities in the human sensory system. Researches are thus conducted to tune motion rendering algorithms (e.g., Colombet et al., 2010) or computer graphics algorithms (e.g., [START_REF] Petit | A High Dynamic Range Rendering Pipeline for Interactive Applications: in search for perceptual realism[END_REF] in order to generate sensations "similar" to those that would be experienced in real situations. This concept of sensations' similarity corresponds to the experiential validity, defined by [START_REF] Malaterre | Etude de la validité des simulateurs de conduite par comparaison de tâches réalisées en situation réelle et en simulation[END_REF]. Beyond the stimulations produced by the simulators and the sensations they provide, we can focus on the behaviour they lead to. Malaterre & Fréchaux thus defined the ethological validity, which deals with the similarity between the behaviours adopted on the simulators and in real situation, and corresponds to the absolute validity defined by [START_REF] Törnros | Driving behavior in a real and a simulated road-tunnel: a validation study[END_REF]. Absolute validity means that all the observables of the behaviour, such as speed, are identical to those observed in real situations. When variations in the driving conditions induce the same change of behaviour in the simulation as in the real world, we speak of relative validity [START_REF] Törnros | Driving behavior in a real and a simulated road-tunnel: a validation study[END_REF]. The results can then be interpreted qualitatively. Most (if not all) ethological validation studies deal with relative validity. By design (comparison with a control condition), experiments conducted on driving simulators generally presume the simulators' relative validity. Finally, psychological validity concerns the underlying processes which lead to the behaviour; however, it is difficult to identify these processes.

Research investigating driver behaviours, or the impact of road design on these behaviours, require simulators to have an ethological and experiential validity. In other words, these studies rely on similarity in both the behaviour of drivers and their experience, rather than the stimulations they are presented with. To our knowledge very few data are available regarding explicitly the validity in relation to the different types of driving simulator. A recent study [START_REF] Rosey | Impact of a driving simulator configuration on drivers' behavior: example with rural intersection studies[END_REF] showed the same trends in participants' behaviours on two different driving simulators, but higher speeds on the desktop simulator than on the full-cab one. Other studies [START_REF] Auberlet | The impact of perceptual treatments on lateral control: A study using fixed-base and motion-base driving simulators[END_REF][START_REF] Auberlet | The impact of perceptual treatments on driver's behavior: From driving simulator studies to field tests-First results[END_REF] showed that there is a correspondence between the effects of perceptual treatments on driver's behaviour, when studies are conducted with a static simulator, a dynamic simulator, and in a real situation, which goes in the way of relative validity.

Beyond the validity of the simulator as a tool, it must be emphasized that the processes which underlie the behaviour of drivers are particularly complex (e.g., [START_REF] Michon | A critical view of driver behavior models: What do we know, what should we do[END_REF] and may be influenced by many factors other than those related to the "driving simulator tool". Therefore, attention must be paid to such factors when experimenting on a driving simulator.

Relevant factors

In this section, we propose an inventory of the factors that influence the behaviour of the participants in a simulator experiment, whether or not these factors are related to the operation of the simulator. We propose to classify them in the following four levels: (1) access to information, (2) speed and trajectory control, (3) representative character of the situations, and (4) factors related to the individual and the context.

The first two levels concern those factors which affect the physical validity of a simulator. For the first level (access to information), it is the information provided to the participant under different sensory modalities in the virtual environment and the movement in this environment, including information provided by the cab of the simulator or device, through the speedometer or dashboard lights for instance. The second level (motion control) concerns the interface which enables participants to control their movement, i.e., their path, speed, and position relative to other vehicles. This level is based primarily on the dynamic model of the vehicle which is implemented in the simulator, the haptic feedback provided by the various commands (e.g., hardness of the pedals and steering wheel), not to mention the use of different actuators in the cab.

The third level (representative character of the situations) no longer concerns the simulator as a physical object but rather scenarios and virtual environments that are planned and presented. This level includes several components such as the richness of the visual scene, the way the virtual world is populated (density and behaviour of other users), or the frequency of extraordinary events. These are meaningful components which address processes at the highest levels for the participants. Thus, the behaviours of the participants tend to differ according to traffic density or in unusual situations.

Finally, the fourth level (related to the individual and the context) refers to anything that is outside the driving simulation itself. Participants are indeed required to drive a simulator in an environment that is foreign to them and under the scrutiny of the experimenter. This laboratory setting is totally different from the normal context of driving. The emotional state of the participants [START_REF] Jallais | When emotions disturb the localization of road elements: effects of anger and sadness[END_REF] and the events they have previously gone through in the near or distant past are also likely to influence their behaviour. Note that a large part of the boundaries of simulation experiments also applies to studies aboard actual vehicles and more broadly, to all experimental contexts.

Consequences for experimenters

Insofar as each of the previous factors is likely to influence the behaviour of the participants, they can affect the validity of the experiments carried out in terms of ethological validity. Experimenters must pay attention during the construction of the experimental protocol, and evaluate the potential impact of each factor depending on the purpose of the experiment. We could consider building a checklist to assess the potential impact of each of these factors, depending on the experimental conditions and objectives of the experiment. However, this approach involves having prior knowledge of the behaviour of the participants and of the parameters which are expected to influence it. Such precautions generally come with the methods used in experimental psychology, which we will not detail in this paper. Nevertheless, we list several components of the experiment the importance of which should be emphasized: the pre-experiment(s), the instructions given to participants, how participants are familiarised with the simulator, and the post-experimental interview.

The pre-experiment(s) and instructions given to the participants contribute to a common goal: to ensure that the participants actually encounter the studied driving situations and to verify that these situations were properly set up (proper choice of experimental conditions). For example, if for the purpose of the experimentation, the participants are expected to pass a vehicle in a specific area, the experimenter must make sure that the participants catch up with a vehicle, that they have the opportunity to pass it in the targeted zone, and that they understand that the manoeuvre is allowed, like in real life. This simple goal requires both a fine focus of the scenario and the choice of appropriate instructions. The instructions also provide the participants with some background on the driving situation, when the experimenter wants to draw their attention to a particular context (e.g., category of the road, day and time, passengers in the vehicle) which is likely to influence their behaviour.

The familiarisation phase appears to be an important part of each experiment with the simulator and the driving situation [START_REF] Auberlet | The impact of perceptual treatments on lateral control: A study using fixed-base and motion-base driving simulators[END_REF][START_REF] Rosey | Impact of a driving simulator configuration on drivers' behavior: example with rural intersection studies[END_REF]. It comes at the beginning of the experimentation, and should enable the participants to integrate the relationship between their actions on the controls of the simulator and the sensory and cognitive feedback that they receive. Their ability to control the simulator must therefore first improve, and then stabilize. Ideally, participants should have reached stability before the start of the test phase (where data will be collected) to prevent behaviour changes during the experiment. Therefore, it is desirable for the participants to face different types of stress (curves and speed changes more or less pronounced) during the familiarization phase, and for the nature and magnitude of the stresses to be adapted to the content and purpose of the experiment. For example, the participants should be confronted with sharp curves or narrow lanes during familiarization if the course of the experiment contains such features.

Discussion with the participants at the end of the experiment helps understand the mechanisms that they believe have dictated their behaviour, and point to possible misinterpretations. Without the absolute validity of the driving simulator, looking into the underlying mechanisms is necessary to make sure that the results apply to the actual driving, and to generalize to other driving situations.

Examples of simulator-based studies

This section presents several projects where Ifsttar's driving simulators were implemented to test new road features. In each case, it was necessary to improve the physical validity of the simulation platform with specific modules and technical developments in order to address the particular characteristics of the studied features.

Audio-tactile markings (ROADSENSE)

The ROADSENSE project aimed at defining and at optimizing the technical characteristics of warning audiotactile systems (e.g., rumble strips). The purpose of these systems is to prevent run-off-road accidents by warning the drivers with vibrations [START_REF] Rosey | État de l'art sur les systèmes audio-tactiles existants-Livrable 2.1. : Etat de l'art et définition fonctionnelle[END_REF]. Trials were conducted on a driving simulator, on a test track as well as on a road open to traffic in order to rate the performance of different systems (figure 3), and to evaluate drivers' perception. Since audio-tactile markings produce a warning when the tire of the vehicle runs over them, it was necessary to provide visual, vibratory and acoustic feedback to the participants. This called for a new vibro-acoustic rendering module. Rendered vibrations and sounds were measured with accelerometers and microphones and compared to measurements in the field; agreement between these measurements indicated satisfactory physical validity.

Intelligent road studs (INROADS)

The aim of the FP7 project INROADS (INtelligent Renewable Optical ADvisory System) is to improve the efficiency of the road network through the development of an intelligent LED road stud system, with integrated communication and sensor systems, which can get some or all of its power needs from renewable sources * .

Again looking for physical validity, we determined the photometric characteristics of the studied LED road studs as well as the operating rules of the automaton which controls a group of those LEDs. Then, we simulated these particular signals so as to produce luminance levels on a calibrated display system that were similar to those in the real world; we also simulated other lighting sources in the scene such as the driver's and oncoming vehicle headlights, as well as road lighting. Finally, we modelled the automaton of this device, the one which allows controlling colour, display frequency, activation/deactivation of one or many LEDs.

* http://www.fehrl.org/?m=320

Thanks to these improvements in the visual rendering engine of our simulator, we were able to investigate potential applications of intelligent LED road studs at night (see [START_REF] Shahar | Toward smart active road studs for lane delineation[END_REF]. The focus of this study was on the visibility, the legibility and in general terms the benefits of such LED-based systems. We used a simulator having three Full-HD 47-inch screens. The central view of the driving simulator (figure 4) was a HDR (High Dynamic Range) screen which provided a luminance range much wider than classical screens [START_REF] Seetzen | High dynamic range display systems[END_REF]. Luminance was measured with a luminance meter and calibrated in order to match the real situation. Moreover, subjective judgements (questionnaires) indicated a good experiential validity. This new functionality of Ifsttar's simulator will allow us investigating night-time driving conditions.

Lateral position at crest curves (VIZIR)

A study of the impact of different perceptual countermeasures on the lateral control of passenger cars on a straight rural road, especially when driving on a crest vertical curve, was conducted in the framework of the VIZIR project (in association with CETE-NC, LAMIH, SETRA). Perceptive difficulties were hypothesized because most of the accidents involve human errors and because almost half of all fatal collisions in Europe are classified as single-vehicle run-off-road or head-on collisions, which related them to trajectory control [START_REF] Rosey | Impact of perceptual treatments on lateral control during driving on crest vertical curves: A driving simulator study[END_REF].

The mock-up of an existing two-lane rural road was created for the purpose of the experiment. Ifsttar's fixedbase driving simulator served to test various types of treatments at a crest curve (figure 5), in order to evaluate their impact on lateral positioning [START_REF] Rosey | Impact of perceptual treatments on lateral control during driving on crest vertical curves: A driving simulator study[END_REF].

This experiment showed that the two treatments which best helped the drivers stay at the centre of their lane, with the lowest variability in lateral position, were the rumble strips (although they were rendered without vibration feedback) and the paved roadsides. Their influence was observed upstream and downstream of the crest curve. The same experiments, with the same 3D database, were conducted with the motion-base simulator of LAMIH [START_REF] Auberlet | The impact of perceptual treatments on lateral control: A study using fixed-base and motion-base driving simulators[END_REF]; measurements were also made on a real highway [START_REF] Auberlet | The impact of perceptual treatments on driver's behavior: From driving simulator studies to field tests-First results[END_REF]. The agreement between the results of the different conditions showed that relative validity had been achieved.

Conclusion

This paper recalls the advantages of driving simulators, particularly for the purpose of evaluating road design and treatments, compared to road-side or in-vehicle behaviour observation devices. It also raises some questions about the validity of driving simulation, which is seldom tackled, despite the fact that it determines the ability to generalize the results of the simulator experiments to the real traffic situations.

In order to have similar behaviours on a simulator and on the road, we need to target ethologic validity (ideally, psychological validity would allow exploring the processes which determine the behaviour). This is often done indirectly by assuming that it will derive from experiential or physical validity (e.g., section 5.1 & 5.2). It is also done by comparing behaviours on a simulator with behaviours in situ for a limited number of controlled reference conditions, assuming that the validity applies to other conditions (e.g., section 5.3). Even when a driving simulator is validated, the capacity to obtain valid results with it still depends on the manner in which it is implemented. This aspect is even less documented than validity itself for applications in the field of road safety and operations. However, driving simulators have proven their worth (Keith et al., 2005), and given that the demand is still high for testing innovative road configuration and equipment (see for example Transportation Research Circular E-C110, 2007, related to new technologies for highway design)probably thanks to the development of the systemic, user-centred approach of road safety, further research into driving simulator validity and operation should be encouraged.

Fig. 1 .

 1 Fig. 1. Simplified representation of a driving simulator.

Fig. 3 .

 3 Fig. 3. Illustrations of the audio-tactile systems tested: the first line contains pictures of systems in the real world; the second line shows snapshots of their virtual reconstruction.

Fig. 4 .

 4 Fig. 4. High Dynamic Range rendering of night-time driving conditions with road lighting (left) and LED studs (right).

Fig. 5 .

 5 Fig. 5. Snapshots of four experimental perceptual treatments investigated on a driving simulator.

Acknowledgments

The ROADSENSE project, led by Aximum, and the VIZIR project, led by Ifsttar, were funded by the French Research Agency (ANR). The INROADS project, led by TRL, was funded by the European Commission. The question of driving simulator ability to study road design was tackled in Ifsttar-Cerema's I2V initiative.