Emilie Alberola
email: emilie.alberola@caissedesdepots.fr

Julien Chevallier

Benoît Chèze
email: benoit.cheze@u-paris10.fr

The EU Emissions Trading Scheme : Disentangling the Eects of Industrial Production and CO 2 Emissions on Carbon Prices

Keywords: L11, L16, Q48, Q54 EU ETS, Emissions Trading, Carbon Pricing, CO 2 Emissions, Industrial Production

This article critically examines the impact of industrial production for sectors covered by the EU Emissions Trading Scheme (EU ETS) on emissions allowance spot prices during Phase I (2005-2007). Using sector production indices and CO2 emissions compliance positions dened by a ratio of allowance allocation relative to baseline emissions, we show that the eect of industrial activity on EU carbon price changes shall be analysed in conjunction with production peaks and compliance net short/long positions at the sector level. The results extend previous literature by showing that carbon price changes react not only to energy prices forecast errors and extreme temperatures events, but also to industrial production in three sectors covered by the EU ETS: combustion, paper and iron.

Introduction

In the current global ght against climate change, the European Union took the lead of environmental policy making by implementing the world's largest emissions trading scheme for CO 2 emissions, which came into operation on January 1, 2005. This article analyses the EU Emissions Trading Scheme (EU ETS) during its Pilot Phase (2005)(2006)(2007) by focusing on the empirical relationship between CO 2 allowance price changes 1 and economic activity in sectors included in the scheme. [START_REF] Springer | The Market for Tradable GHG Permits under the Kyoto Protocol: a Survey of Model Studies[END_REF] and [START_REF] Christiansen | Price determinants in the EU emissions trading scheme[END_REF] identify the main carbon price drivers as being economic activity, energy prices, weather conditions and policy issues. Besides the eects of energy prices, temperatures and institutional events on EU carbon prices, this article opens the "black box" of economic activity, with a particular emphasis on disentangling econometrically potential impacts ranging from the production to the environmental spheres on carbon price changes.

In theory, the carbon price is function of marginal abatement costs that vary depending not only on industrials' emissions abatement options, but also on the relation between emissions caps and counterfactual CO 2 emissions resulting from business-as-usual production growth forecasts. Thus, EUA price changes may be aected by economic activity 2 of various sectors covered by the EU ETS for two main reasons. First, industrials are able to inuence the market price through their choice of emissions abatements options 3 . Second, according to many market observers, industrials have hedged their allowances based on actual production during 2005-2007. To our best knowledge, none empirical study has yet explored the expected impacts of the variation of industrial production in EU ETS sectors on carbon price changes. Although, several studies detail the impacts of EU carbon prices on competitiveness in the power sector [START_REF] Reinaud | CO 2 Allowance and Electricity Price Interaction: Impact on Industry's Electricity Purchasing Strategies in Europe[END_REF]) and for the iron and steel industry (Demailly and Quirion (2007)). In this paper, we analyze ex-post the impacts of industrial production variation on carbon price changes for all sectors at the EU 27 level.

At the EU-wide level, the total number of allowances allocated is determined by Member States (MS) negotiating with industrials and after the validation of the European Commission (EC). As soon as the rst National Allocation 1 EU CO 2 allowance price changes are dened as the rst log-dierenced carbon price series pt = ln (Pt/P t-1 ), with Pt the daily EU allowance spot price at time t.

2 In our empirical analysis, the potential eects of economic activity on EUA price changes are analyzed using industrial production indices. Thus, in the remainder of the paper, we refer to the variation of industrial production.

3 Industrials face a choice between dierent abatement possibilities ranging from investment in simple end-of-pipe technologies reducing emissions at the end of the production line, to heavy investments in complex clean technology systems that necessitate production process changes. Information on marginal abatement costs is however very diuse and hardly disclosed by covered installations. As pointed out by [START_REF] Ellerman | Over-Allocation or Abatement? A Preliminary Analysis of the EU ETS Based on the 2005-06 Emissions Data[END_REF], allowance oversupply and early abatement concerns need to be balanced against the analysis of veried emissions relative to allowances allocated at the installation level. Thus, we examine the relationship between economic activity, as measured by industrial production indices, and carbon price changes based on two kinds of dummy variables. First, we use an indicator of allocation stringency, dened as the actual allocation relative to baseline emissions to capture the extent to which each sector records a net short/long position. Second, we identify production peaks, dened as the variation of industrial production above a specic threshold, to estimate the eects of economic activity in conjunction with industrial production indices. To fully decompose the net eects on carbon price changes, we also take into account the potential interaction between the two latter dummy variables and the industrial production index for each sector.

Compared to previous literature, this article extends Mansanet [START_REF] Mansanet Bataller | CO 2 Prices, Energy and Weather[END_REF] and [START_REF] Alberola | Price Drivers and Structural Breaks in European Carbon Prices 2005-2007[END_REF] by emphasizing other EU carbon price drivers than energy prices, temperatures and institutional events. Our results feature that three sectors may be identied as having a statistically signicant eect on carbon price changes: the combustion, iron and paper sectors which total 80% of allowances allocated in the EU ETS. While it has been possible

to decompose the analysis between simple dummy variables and the interaction variable only in the case of the combustion sector, this nding is the most interesting one since the combustion sector amounts to approximately 70% of allowances allocated.

The remainder of the article is organized as follows. Section 2 details the empirical relationship tested between the variation of industrial production in EU ETS sectors, emissions caps and carbon price changes. Section 3 presents the data and the econometric specications. Section 4 contains the empirical results and a discussion. Section 5 concludes with a summary of the main results.

4 NAPs determine the total quantity of allowances allocated to installations.

3 2 Industrial Production and Emissions Compliance: Potential Impacts on Carbon Price Changes

The EU ETS, the largest multi-country and multi-sector greenhouse gases emissions trading scheme world-wide, concerns large energy-intensive CO 2 emitting installations from nine industries accross its 27 MS. The aim of the EU ETS is to convey appropriate price signals to industrial operators who can select a combination of capital investments, operating practices and emissions releases to minimise the sum of abatements costs and allowance expenses [START_REF] Noll | Implementing Marketable Emissions Permits[END_REF]).

While allowance supply is xed by each MS through NAPs, allowance demand is function of the level of industrial participants' CO 2 emissions. Thus, the market equilibrium is driven by the transfer from installations with a long allowance position to installations with a short allowance position.

In the short run, a large number of factors may inuence industrial CO 2 emis- sions such as fuel (brent, coal and natural gas) and power (electricity) prices, weather conditions (temperatures, rainfall and wind speed) and economic activity [START_REF] Springer | The Market for Tradable GHG Permits under the Kyoto Protocol: a Survey of Model Studies[END_REF]

Industries in the EU ETS

Let us rst details the classication of industries covered by the EU ETS, as well as the variation of their production during 2005-2007. 5 Comments at the country-level arise from additional industrial production indices taken from Eurostat that are not reported due to space constraints. This section provides a brief description of the institutional features concerning allowance allocation and emissions monitoring in the EU ETS. [START_REF] Betz | Emissions Trading: Lessons Learnt from the 1st Phase of the EU ETS and Prospects for the 2nd Phase[END_REF], [START_REF] Leseur | Overview of European National Allocation Plans Research Report #8[END_REF] and [START_REF] Ellerman | Over-Allocation or Abatement? A Preliminary Analysis of the EU ETS Based on the 2005-06 Emissions Data[END_REF] provide a detailed analysis of NAPs during 2005-2007.

Classication of Industries

National

MS have distributed allowances to installations based on guidelines provided by the EC8 . The allocation process has thus followed a top-down structure in three layers:

1. Allocation at the macro level: the most important allocation decision from a macro perspective concerns the total number of allowances to be created, i.e. the setting of the cap. The sum of the 25 NAPs conditions the overall scarcity of emissions allowances and the environmental performance of the European policy. Each MS decides on its total amount of allowances allocated based on the coherence with its commitment under the Kyoto Protocol and the validation by the EC.

2. Allocation at the sector level: total allocation is based on emissions forecasts for sectors covered/not covered by the scheme, eorts to reduce past emissions during 1990-2002 and potential for emissions reduction.

MS have dierentiated between the combustion (power generation) sector, which was more constrained during the allocation process with respect to its potential for CO 2 emissions reduction, and other covered sectors. The 3. Allocation at the installation level: the approach adopted was free allocation. Allocation depends on average historical emissions of the installation during 2000-2002 and its share in sector emissions.

Allocation data at installation and sector levels collected on each national registry are transferred to the CITL. Three sectors collected more than 7% of allowances: cement, iron and reneries.

Other sectors represent only 1% of the EU allowance allocation.

Having detailed the allowance allocation process from the aggregated to the installation levels, we turn our attention to yearly compliance results recorded during 2005-2006 in the next section.

Veried Emissions and Yearly Compliance Results

Compliance with the emissions cap is measured at the installation level by the dierence between the yearly amount of allowances allocated and actual emissions during the commitment year. This annual balance, termed as compliance, ties agreggated from seven countries : Austria, France, Germany, Italy, Poland, Spain and the United Kingdom [START_REF] Trotignon | European CO 2 Market and the CITL : The Trial Period Under Scrutiny[END_REF]). Installations from the combustion sector are grouped into electricity production, heat and cogeneration, and other combustion activity. In these MS, the electricity production sector exhibits a short position by -8.4% in 2005 and by -10.3% in 2006. Based on the disentanglement of the power sector from the combustion sector described earlier, [START_REF] Trotignon | Technical Memorandum on Analysis of the EU ETS Using the Community Independent Transaction Log[END_REF] and [START_REF] Trotignon | European CO 2 Market and the CITL : The Trial Period Under Scrutiny[END_REF] conrm that allowance demand comes mainly from power generation installations, and allowance supply from other sectors. Electricity production plants are the biggest installations in the EU-ETS, whereas others are smaller installations and potential allowance sellers. Table 3 details allocation and emissions volumes expressed in MtCO 2 . The combustion sector and its power sector sub- activity dominates EU ETS emissions, followed by the cement, reneries and iron sectors.

Note that compliance at the sector level does not necessarily reect the situation at the installation level: a sector may be net long and the majority of its installations net short. However, we may draw the insight that, at the EU ETS level, the power sector is globally on the demand side while other sectors are on the oer side. Based on this detailed analysis of yearly compliance results, we attempt to link their expected impacts with industrial production on carbon price changes in the next section.

Linking the Potential Impacts of Industrial Production and Yearly Compliance Results on Carbon Price Changes

The purpose of this section consists in detailing explicitly the channels through which EUA price changes may be aected by industrial production in the various EU ETS sectors. As stated above, the variation of industrial production has a major impact on CO 2 emissions and therefore on allowance demand and sup- ply from covered installations. However, the link between EUA price changes, industrial production and yearly compliance results is complex to disentangle. or by pooling allowances within the company with a balance between its dierent installations. Thus, short (long) installations with less (more) allowances than veried emissions become potential buyers (sellers). As a result, the EUA price is driven by the scarcity of allowances on the market at the installation level, even if the market records a global allowance surplus.

Note the equilibrium market price may also be aected by exibility mechanisms such as banking and borrowing provisions 11 and the inow of project mechanism credits 12 . Yet the study of those price drivers goes beyond the scope of this paper and is left for further research.

On late April 2006, rst disclosures of some MS revealing net long positions on the allowance market caused a sharp fall in carbon prices by 54% within 10 In the EU ETS, two categories of market participants may be distinguished: nancial brokers and industrial operators on exchanges or on over-the-counter.

11 MS allowed industrials to bank surplus allowances for potential later compliance use and to borrow allowances from next yearly allocation within Phase I. See [START_REF] Alberola | European Carbon Prices and Banking Restrictions: Evidence from Phase I[END_REF] for an exhaustive discussion on this topic.

12 During the EU ETS Phase II (2008)(2009)(2010)(2011)(2012), European installations could use to achieve their emissions compliance credits from CDM (Clean Development Mechanism ) and JI (Joint Implementation ) projects, but in a limited proportion dened by each NAP. Thus, installations operators could use credits to meet up to 13.4% of their emissions commitments in average during 2008-2012. The delivery of credits on EU industrials accounts will be possible as the connection between the European and International transactions registries, respectively the Community Transaction Log and the International Transaction Log, will be eective in 2008. From Figure 7 and 8, EU ETS sectors may be categorized in four types of groups: one group with a positive variation of industrial production and a net long position, one group with a positive variation of industrial production and a net short position, one group with a negative variation of industrial production and a net long position and one group with a negative variation of industrial production and a net short position. Therefore, the logic at stake to disentangle the potential impacts of industrial production and yearly compliance positions on EUA price changes is the following: if a sector combines a net short (long) position and/or an increasing (declining) variation of activity, then this sector is net buyer (seller) of allowances and the impact on the allowance price shall be positive (negative) 13 .

Based on this suggested causal relationship, two questions are further examined in the next section: which sectors have had a statistically signicant inuence on EUA price changes during 2005-2007? among those sectors, is it possible to disentangle the eects of industrial production peaks, yearly compliance events and the interaction between them?

3 Data and Econometric Specication

We present rst data for the carbon price, energy prices, temperature events and compliance breaks that have been previously identied as carbon price drivers in the literature. Second, three variables are introduced to disentangle the potential eects of industrial production on the carbon price : sector production indices and dummy variables representing production peaks, compliance results

and squeeze probability around yearly compliance events. Third, econometric specications are detailed. 13 The disentangling analysis is more ambiguous in intermediate cases with increasing (declining) variations of activity and long (short) compliance positions. We introduce those spreads because power operators pay close attention to them as well as to the dierence between them. The dark spread is the theoretical prot that a coal-red power plant makes from selling a unit of electricity having purchased the fuel required to produce that unit of electricity. The spark spread refers to the equivalent for natural gas-red power plants. The equilibrium between these clean spreads represents the carbon price above which it becomes protable for an electric power producer to switch from coal to natural gas, and below which it is benecial to switch from natural gas to coal. As long as the market carbon price is below this switching price, coal plants are more protable than gas plants -even after taking carbon costs into account. This switching price is most sensitive to changes in natural gas prices than to coal prices changes [START_REF] Kanen | Carbon Trading and Pricing[END_REF]. These three protability indicators are used to determine the preferred fuel in power generation. For more details on energy variables used in this econometric analysis, see [START_REF] Alberola | Price Drivers and Structural Breaks in European Carbon Prices 2005-2007[END_REF].

Data

Note that we are able to alleviate endogeneity concerns among energy prices variables with the following arguments. In Western Europe, the natural gas market is mainly characterized by long-term contracts that range in duration from twenty to twenty-ve years 16 . Similarly, the coal is bought through longterm contracts [START_REF] Joskow | The Performance of Long-Term Contracts: Further Evidence from Coal Markets[END_REF]). Since those contracts do not have the same determinants, they does not appear to be endogenous with the determination of other energy prices variables included in our model such as the electricity price 17 .

By inuencing energy demand, temperatures conditions may have an impact on EUA price changes. Numerous studies, which highlighted the eect of tem-14 Since January 2008, this trading platform has replaced Powernext carbon. 15 The methodology is available at http://www.caissedesdepots.fr. Cited January 2008. 16 For instance, 86% of natural gas consumption in France is covered by long term contracts (MEDAD ( 2007)). See also [START_REF] Brown | What Drives Natural Gas Prices?[END_REF] for a detailed discussion on the drivers of natural gas prices.

17 See [START_REF] Chevalier | Gas and Electricity: A Challenge for Europe and for France[END_REF] for a detailed study of those determinants.

peratures on energy prices, indicate that only both temperatures increases and decreases beyond certain thresholds can lead to increases in power demand 18 .

Warmer summers increase the demand for air conditioning, electricity, and the derived demand for coal. Colder winters increase the demand for natural gas and heating fuel. As a result of increasing (decreasing) their output, power generators will see their CO 2 emissions levels increase (decrease) which should in return increase (decrease) the demand for allowances.

Extreme temperatures events are derived from the daily data of the Bluenext Weather index 19 , expressed in

• C, for Spain, France, Germany and the United Kingdom. W in07 is the cross product of the dummy variable characteristic of January and February, 2007 and the absolute value of the deviation from its seasonal average of the European temperature index 20 . This latter kind of interaction variable aims at testing the non-linearity of the relationship between temperatures and carbon price changes highlighted in previous literature, and may be interpreted as unanticipated temperatures changes.

The compliance break dummy variable is constructed by using the unit root tests with endogenous structural breaks developped by [START_REF] Lee | Minimum LM Unit Root Test with Two Structural Breaks[END_REF] and [START_REF] Lee | Testing the Null of Stationarity in the Presence of a Structural Break[END_REF]. This procedure statistically identies the compliance break as going from April 25 to June 23, 2006. On late April 2006, rst disclosures of the Netherlands, Czech Republic, France, and Spain revealing long positions caused this sharp price break of 54% within four days.

On May 15, 2006 the EC conrmed that veried emissions were about 80 Mt CO 2 or 4% lower than the 2005 yearly allocation. This break is included in our regressions using a dummy variable break.

To better take into account the impact of information revelation, we propose to use an additional cross-product variable, psq, that captures the allowance squeeze probability around yearly compliance announcements. This variable is constructed using the following two variables. Dif sq computes at time t the number of days remaining before the yearly compliance event. This variable may be interpreted as a proxy of the allowance squeeze probability. Sq is a dummy variable which takes the value of one during the period going from March, 30 to April, 30 of each year 21 , i.e. about fteen days before the ocial EC announcement 22 , and zero otherwise. The information embeded within the allowance squeeze probability appears especially relevant for industrials only around the yearly compliance announcement. Thus, the potential eect of the 18 For an extensive literature review on this topic, see [START_REF] Li | Electricity Use Sensitivity to Climate and Climate Change[END_REF];. 19 Until January 2008, these indices were labelled as Powernext weather.

20 W in07 = winter2007 * T emp_AbsDeviation 21 Note that for the 2005 compliance event, we rule out from the construction of the dummy variable the four days of strong EUA price adjustment that ocurred starting on April 24, 2006.

22 Indeed, the EC is bound by law to disclose the results of veried emissions by May, 15 of each year at the latest (see Directive 2003/87/CE). allowance squeeze probability, as proxied by dif sq, should only be analyzed during the fteen days before the ocial EC announcement, as captured by sq. This is why, instead of using the variable dif sq, we prefer to work with psq, which corresponds to the cross-product of the two previous variables: psq = dif sq * sq.

Sector Production Indices

In order to measure how the variation of production in EU ETS sectors may aect EUA price changes through the need of allowances to cover their yearly emissions, we use industrial production indices. Since CO 2 emissions levels are not directly observable at the installation level According to the decomposition of sectors required by the CITL, the following industries indices are collected: paper and board; iron and steel; coke ovens; reneries; ceramics; glass; cement; metal and electricity, gas, steam and hot water production (combustion). As explained above, the electricity sector represents 73% of allowances allocated in the combustion sector. Thus, the choice of the index of production and distribution of electricity, gas and heating in this article covers the main part of industrial production in the combustion 23 See [START_REF] Ellerman | Over-Allocation or Abatement? A Preliminary Analysis of the EU ETS Based on the 2005-06 Emissions Data[END_REF] for an extensive discussion.

sector. Each industrial production index has a base 100 in 2000 and is seasonally adjusted. These data are then resampled to convert monthly indices to daily frequency 24 (see IEEE (1979) for reference).

Let us discuss two preliminary concerns with the use of sector production indices. First, the choice of production indices over product prices is motivated by the fact that we want to assess the impact of the level of industrial production on EUA prices changes through an estimate of sector emissions levels. Thus, we concentrate our analysis on production quantities 25 . Second, endogeneity between energy prices and production indices is not likely to be an issue since both kinds of variables do not overlay each other 26 . Besides, the matrix of crosscorrelations between sector variables is reported in Table 8 (see the Appendix).

If the explanatory variables in the model are highly correlated (multicollinearity), the reported regression coecients may be severely distorted and thus the results are not reliable. Table 8 shows that no correlation is over around 0.6% in absolute value. This correlation matrix does not reveal serious problematic multicollinearities.

As detailed in Section 2, two mains reasons may explain the likely inuence of sector production on carbon price changes: industrial production peaks and the emissions yearly compliance at the sector level. Hence, in order to disentangle these two eects, we compute three kinds of dummy variables for each of the nine EU ETS sectors. The rst dummy variable concerns emissions compliance results. Recall that a given sector may be either net short or long in each yearly compliance. Thus, the dummy variable sectcompl 27 equals one if the sector is in an annual net short position and zero otherwise. The second dummy variable aims at capturing the eect of production peaks at the sector level: a production peak is dened by the variation of 1% in absolute value of the sector production index under consideration 28 . Thus, the dummy variable sectpeak 29 equals one if the sector encounters a monthly positive production peak and zero otherwise.

Of course, there is no reason for the dierential eect of the net short/long position dummy sectcompl to be constant across the two categories of produc- 24 We use the Matlab interpolation function by L. Shure. 25 Conversely, the price of goods traded in EU ETS sectors is used in analyses of the impact of the EU ETS on the competitiveness of sectors covered by the scheme [START_REF] Reinaud | CO 2 Allowance and Electricity Price Interaction: Impact on Industry's Electricity Purchasing Strategies in Europe[END_REF], Demailly and Quirion (2007)). 26 For instance, the electricity price does not appear to be correlated with the combustion production index since it covers only two thirds of electricity production as explained in Section 2.

27 Sect refers to the sector under consideration. = comb, iron, paper, coke, ref in, ceram, glass, cement, metal. 28 This threshold has been xed considering the average level of monthly variation of production over the two years. We experimented with a wide range of other proxies of industrial production, such as variations with higher thresholds over several months. We only found measures of production peaks to be statistically signicant as such.

Sect

29 Sect refers to the sector under consideration. = comb, iron, paper, coke, ref in, ceram, glass, cement, metal. tion peaks variable sectpeak and conversely. Therefore, in order to capture the likely interaction eect between these two qualitative variables, we compute a third type of dummy variable which is the cross-product between the two latter dummies. For instance, combcomplpeak = combcompl * combpeak is the product of the dummy variables characteristic of the net short position and the production peaks in the combustion sector.

Sect

Energy prices variables and sector indices have been transformed to "onestep ahead" forecast errors to take into account unexpected changes in market conditions [START_REF] Helfand | Testing for dynamic eciency of the Sulfur Dioxide Allowance Market[END_REF]). Usual stationarity tests were conducted and reveal that all energy price series are stationary when taken in rst dierence. Thus, all price series are integrated of order 1 (I(1)) 30 . Table 7 presents descriptive statistics for energy and sector variables.

Econometric Specication

The role played by industrial production and compliance positions on EUA price changes is now estimated. Following the discussion presented in Section 2, two distinct specications are introduced. The rst specication aims at identifying which production indices in EU ETS sectors have a potential impact on carbon price changes. The second specication attempts to disentangle, among those statistically signicant sectors, the potential impact of production peaks and compliance net short/long positions.

Does the Variation of Industrial Production in EU ETS Sectors

Impact EUA Price Changes?

On top of energy variables, temperatures events and compliance breaks that were previously identied as carbon price drivers in the literature, we include all sector production indices that may also have an eect on EUA price changes.

This rst step consists in identifying the reduced form model with only sector production indices that signicantly impact EUA price changes.

The estimated model is: 30 A journal of stationarity tests may be accessed upon request to the authors.

p t = α + β(L)p t + δbreak + νpsq i,t + ϕ(L)ngas t + γ(L)coal t + ι(L)elec t + κ(L)dark t + λ(L)spark t + σ W in07 + ς(L)cement t + τ (L)ref in t + υ(L)coke t + ω(L)comb t + ξ(L)glass t + ψ(L)metal t + ζ(L)paper t + ρ(L)ceram t + χ(L)iron t + t (1)
For energy variables and compliance breaks, t is the time period under consideration, p t is the rst log-dierenced EUA price series, break is a dummy characteristic of the period after the structural break on April, 2006, psq i,t is the allowance squeeze probability for i = {1, 2} referring to the 2005 and 2006 compliance results, ngas t is the Natural gas price series, coal t is the Coal price series, elec t is the Electricity price series, dark t is the Clean Dark price series, spark t is the Clean Spark price series, W in07 is the extreme temperatures event for January and February 2007 and t is the error term.

For sector variables, cement t is the cement production index in the EU 27

which applies for all sectors; ref in t is the production index in the reneries sector; coke t is the production index in the coke ovens sector; comb t is the production index in the combustion sector (i.e. heating from electricity and gas); glass t is the glass production index; iron t is the production index in the iron and steel sector; metal t is the production index in the metallurgy sector; ceram t is the production index in the ceramics sector; and paper t is the production index in the paper and pulp sector. All energy price series and sector indices have been transformed to one-step ahead forecast errors as explained above. L is the lag operator such that L X t = X t-n where n is an integer and polynomes such as (L)X are lag polynomials.

As explained in Section 4, this rst specication allows us to identify three sector activities among the industries in the EU ETS that signicantly aect EUA price changes: combustion, iron and paper.

Thus, we take our analysis one step futher by investigating in the next section why those sectors impact EUA price changes. Two main reasons were highlighted above, i.e. the inuence of compliance positions and production peaks.

Do Sector Production Peaks and Compliance Positions Impact EUA Price Changes? A Disentangling Analysis

To disentangle the potential impacts of industrial production peaks and compliance positions on EUA price changes, we add to the signicant sector production indices the following three dummy variables: sectpeak i,t , sectcompl i,t and sectcomplpeak i,t . sect i is the industrial sector under consideration and i = {comb, iron, paper} corresponds either to the combustion, iron and paper sectors that were signicant after estimating the reduced model with all sectors in eq.( 1). We then estimate three equations which may be summarized as:

p t = α + β i (L)p t + δbreak 1 + νpsq i,t + ϕ(L)ngas t + γ(L)coal t + ι(L)elec t + κ(L)dark t + λ(L)spark t + σ W in07 + ω sect i,t + sectpeak i,t + ϑ sectcompl i,t + η sectcomplpeak i,t + t (2)
where sectpeak i,t is a dummy variable capturing monthly positive production peaks, sectcompl i,t is a dummy variable for the net short annual compliance position in the sector under consideration and sectcomplpeak i,t is an interaction variable capturing the impact of positive monthly production peaks and a net short compliance position in the sector under consideration. Other variables are explained in eq.( 1).

Estimation results of eq.( 1) and eq.( 2) are provided in the next section.

Results and Discussion

As highlighted by [START_REF] Seifert | Dynamic Behavior of CO 2 Spot Prices: Theory and Empirical Evidence[END_REF], the EUA spot price series exhibit jumps Taking into account this quite dynamic behavior for EU allowance prices and volatilities, and the dependence of the variability of the time series on its own past, Borak et al. (2006) and Benz and Truck (2008) recommend to address the problem of heteroskedasticity with GARCH models. Indeed, GARCH(p, q) models put forward by [START_REF] Bollersev | Generalized Autoregressive Conditional Heteroskedasticity[END_REF] capture the conditional variance based not only on the past values of the time series (p t ) t≥0 , but also on a moving average of past conditional variances which better ts the data. [START_REF] Paolella | An Econometric Analysis of Emission Trading Allowances[END_REF] conclude that the GARCH specication that provides the best likelihood-based goodness-of-t for the EUA return series is a GARCH(1,1) model with generalized asymmetric t innovation distribution. Thus, they justify to work at least with an asymmetric GARCH to characterize EUA price series returns, even if it does not provide fully satisfactory results for VAR forecasts.

We depart from [START_REF] Paolella | An Econometric Analysis of Emission Trading Allowances[END_REF] by choosing an asymmetric GTARCH(p,q) model [START_REF] Zakoian | Threshold Heteroskedastic Models[END_REF]) with a Gaussian innovation distribution 31 . As demonstrated by [START_REF] Gourieroux | Pseudo Maximum Likelihood Methods: Theory[END_REF], even in the presence of 31 See [START_REF] Alberola | Price Drivers and Structural Breaks in European Carbon Prices 2005-2007[END_REF] for the calibration of the autoregressive order and the moving non-Gaussian residuals which is standard for nancial time series, the choice of the probability distribution will not yield to biased estimates when estimating by Pseudo Maximum Likelihood (PML). Thus, our estimates will not be aected by any ill-chosen distribution assumption. The estimates covariance matrix is estimated with the BHHH algorithm [START_REF] Berndt | Estimation Inference in Nonlinear Structural Models[END_REF]).

This specication ts well with descriptive statistics of EUA price changes displayed in Table 7. First, the kurtosis coecient is by far higher than 3 which is the value of the kurtosis coecient for the normal distribution. This excess kurtosis denotes a high likelihood of outliers. Second, the skewness coecient is dierent from zero and negative which highlights the presence of asymmetry.

This asymmetry characterizes a lower level of volatility after price increases than after price decreases.

Estimation results are presented in Table 9 (see the Appendix). The quality of regressions is veried through the following diagnostic tests: the simple Rsquared, the adjusted R-squared, the p-value of the F -test statistic (F -stat),

the ARCH Lagrange Multiplier (LM) test, the Akaike Information Criterion (AIC) and the Schwarz Criterion (SC).

The Eects of Sector Production Indices

First, we test eq.( 1) with only energy variables, temperatures events and compliance breaks. In Table 9, regression (1a) shows the results for eq.( 1). Both the adjusted R-squared and the R-squared are included between 14.9% and 17.5%, and, as judged by the F-test P-value, the joint signicance of results is accepted at the 1% signicance level. The ARCH LM test does not reject at the 10% signicance level the null hypothesis of no autoregressive conditional heteroskedasticity in the residuals for this model.

For energy variables, natural gas and clean spark impact positively EUA price changes, whereas coal and clean dark have negative coecients. The natural gas coecient is positive and signicant at 1%. High levels of natural gas lead power operators to realise a switching of their fuel from gas to coal.

Natural gas price got higher from October 2005 to April 2006 and thereby inuenced positively the EUA price. Clean spark aects EUA price changes with a positive coecient signicant at 1%. During the two years, clean dark stays above clean spark indicating burning coal is more protable than natural gas, which increases allowances demand. As the most CO 2 -intensive variable, coal plays a negative role on carbon price changes at 1%. The rationale behind this analysis is that when confronted to a rise of the price of coal relative to other energy markets, rms have an incentive to adapt their energy mix towards average of the EUA price series. For temperatures events, win07 is signicant at 1% level 33 . Its negative coecient could be explained by the fact that on January-February, 2007 temperatures were hotter than the decennial seasonal average. Actually, this result leads to two main conclusions. First, extreme cooling days do have an impact on EUA price changes. Second, it is not temperatures themselves but deviations from seasonal average which have an impact on EUA price changes during extreme temperatures events 34 . When extremely cold events are colder (hotter) than expected, power generators have to produce more (less) than they forecasted which may conduct to an increase (decrease) of allowances demand to cover their CO 2 emissions beyond their emissions cap and nally to an increase (decrease) of EUA price changes. Thus, unanticipated temperatures changes seem to matter more than temperatures themselves when one tests for the inuence of climatic events on EUA price changes. For more details on the results comments, see [START_REF] Alberola | Price Drivers and Structural Breaks in European Carbon Prices 2005-2007[END_REF].

Second, we turn to the inclusion of sector variables. Compared to previous literature, the point here is to test whether industrial production indices signicantly impact EUA price changes besides other drivers highlighted in regression (1a), Table 9. Results of eq.( 1) are presented in Table 9, regression (1b). We only present the reduced form estimate of eq.( 1) 35 . Both the adjusted R-squared and the R-squared are, respectively, equal to 14.9% and 18%. The AIC and the SC both decrease. Therefore, the inclusion of sector variables appears more relevant in explaining EUA price changes. All diagnostic tests are validated for these estimates. First, the structural change dummy variable, break, now 32 Chow's test results may be obtained upon request to the authors. 33 Other temperatures events were also tested such as July, 2005 (abnormal hot season in Spain), January and February, 2006 (a relatively cold winter in Europe), July, 2006 (relatively hot in Europe), September and October, 2006 (hotter than seasonal averages). None of them turned out to be statistically signicant on the whole period.

34 Note this remark applies only for extremely cold days. 35 That is to say, we only keep the signicant sector variables, and to do so, we withdraw one-by-one the non signicant variables from eq.(1).

becomes not signicant. As the main comment, losing signicance on break suggests that the inclusion of sector production indices in our model contributes to a sharper explanation of carbon price changes. Note that the second indicator of the role of information revelation on this new market, the squeeze probability dummy psq 1 , is signicant at 1% level. Its positive sign reects a strong allowance demand from installation operators before 2005 compliance results, which contributes to increasing EUA price changes. The non signicance of psq 2 may be interpreted as an indication that before 2006 compliance results market participants had anticipated a lower level of CO 2 emissions compared to allowances allocated and more accurately hedged their allowances during that year. Thus, the allowance squeeze probability did not appear relevant. Those comments apply to the remainder of the paper.

Secondly, among the nine sectors included in the EU ETS, three sectors are statistically signicant at 1% level: combustion, iron and paper. As shown in Figure 3, combustion and iron gather around 78% of allowances allocated, with respectively 70% and 8%. Neither reneries nor cement were identied as having any impact on EUA price changes. Both sectors, with respectively 7.6% and 9.1% of allowances allocated, are characterized by a compliance breakdown among installations that equally splits between net long and net short installations [START_REF] Trotignon | Technical Memorandum on Analysis of the EU ETS Using the Community Independent Transaction Log[END_REF]). Therefore, a potential justication for these non-signicant results may come from a pool management of allowances between rms within sectors, so that the considered sectors are globally in compliance 36 .

The negative signs of the three signicant sector variables could be explained by the following arguments. As developed in Section 2, industrial sectors which record a higher (lower) production growth than their baseline projections over

2005-2007 are expected to exhibit higher (lower) CO 2 emissions than the level xed by their allocation, and thus to reveal a short (long) compliance position.

Therefore, short (long) sectors become potential buyers (sellers) of allowances which should have a positive (negative) impact on allowance price changes.

This explanation ts well the negative sign of comb. Indeed, whereas this sector exhibits an increasing variation of production during 2005, the variation of production is declining during 2006 (see Table 2).

By contrast, Table 2 indicates that iron and paper sectors record positive industrial production growth rates. Figures 1 and2 provide us with a clearer picture: the variation of production in the paper and iron sectors is clearly increasing during the whole period. At this stage, we cannot further explain the reason behind the negative coecients of paper and iron. As already mentioned in Section 2, other eects such as the net short/long compliance position may 36 The economic logic behind this presumed pooling behavior is left for further research.

explain the impact of industrial production in EU ETS sectors on EUA price changes. For instance, both iron and paper sectors have by far the highest net long position with respectively 20.4% and 18.3% in 2005 as shown in Figure 5.

Thus, they are potential net sellers of allowances. Concerning the combustion sector, it is worth underlining that it is the only short sector in 2006 which is eectively constrained by the EU ETS 37 , and thus in a position of a potential net buyer.

Therefore, we take the analysis one step further in the next section by disentangling the eect of production peaks and compliance positions on EUA price changes.

The Eects of Production Peaks and Compliance Positions

As explained in Section 3.2.2, we now estimate eq.( 2) for each of the three sectors which were signicant in eq.( 1) (regression (1b), Table 9): combustion, iron and paper sectors.

Analysis of the Combustion Sector

The combustion sector stands out as the most important sector for this study In Table 9, regressions (2a) and (2b) show the results of eq.( 2) for the combustion sector. The regression (2a) contains combcompl and combpeak whereas regression (2b) contains these latter dummy variables as well as the interaction variable, combcomplpeak. Concerning regression (2a), the adjusted R-squared and the R-squared are, respectively, equal to 10.7% and 13.9%. Given the fact that coecient estimates are stable for energy prices and extreme temperatures events variables, we do not comment them further. Note the stability of results for these latter variables coecients between eq. ( 1) and (2) estimates proves the robustness of our results (regressions (1a) and (1b), Table 9). This comment applies in the remainder of the paper.

The comb coecient remains negative in both estimates (regressions (2a) and (2b), Table 9). Besides, combcompl and combpeak coecients are both positive and signicant at 1% level. The sign of these two dummy variables is conform to arguments presented in Section 2. First, as presented in regression 37 See also Figure 5.

culties in assessing the gap between allowance allocation and industrial emissions forecasts, such analysis may only be conducted around compliance events.

The European Commission disclosed on April 2, 2008 the data on 2007 veried emissions from 94% of installations, revealing that the EU ETS records a surplus by 8% (162.5 Mt CO 2 ). With the diusion of 2007 compliance data, a complete ex-post analysis of the relationship between sectors economic activity and EUA price changes may be further detailed in terms of actual CO 2 emissions abatement for the whole period of the EU ETS Pilot Phase (2005)(2006)(2007).

To our best knowledge, this article constitutes the rst attempt to test the empirical relationship between industrial production and EUA price changes. the kurtosis and N the number of observations.

Plans 4 (

 4 NAPs) were drafted, there was a concern of allowance oversupply during 2005-2007. Although this situation was common knowledge among market participants, the EUA price pattern increased to around 30 = C on July 2005 and then experienced a high level of volatility on late April 2006, when EUA prices collapsed by 54% within four days. Academic and market agents usually agree that the information disclosure of lower than expected 2005 veried emissions by simultaneous MS is the main reason behind this fall.

  , Christiansen et al. (2005)). Previous empirical literature focused only on the impacts of the rst two factors (Mansanet Bataller et al. (2007), Alberola et al. (2008), Rickels et al. (2007)). Some potential factors are missing in recent studies of carbon price drivers, such as the impacts of banking restrictions, other climatic variables (wind speed, rainfalls), project mechanisms and economic activity. As developped by Alberola et al. (2008), political and institutional decisions concerning allowance allocation and yearly compliance announcements may be identied as driving basically EU carbon price changes during 2005-2007. In what follows, we detail how the achievement of the emissions cap depends on forecasts of industrial growth in the sectors covered by the EU ETS. More precisely, the extent to which veried CO 2 emissions are lower than allowances allocated needs to be balanced against an analysis of yearly compliance objectives that are xed ex ante and the variation of industrial production that occurs ex post. This section presents rst the industries covered by the EU ETS along with the sector variation of production during 2005-2007, then the emissions caps associated to each sector to be in compliance, and nally links the expected impacts of industrial production and yearly compliance results on carbon price changes.

Figure 1 :

 1 Figure 1: Variation of Industrial Production in Paper, Coke, Reneries, Glass and Ceramics Sectors in 2005 and 2006 Source: Eurostat

  allocation to the power sector was based on historical emissions projections of electricity demand and the expected variation of electricity generation mix. The allocation to non-electricity sectors was based on emissions projections during 2001-2006 by extrapolating historical emissions per sector, i.e. the annual growth rate between 1990 and 2001.

  Figure 3 provides an overview of allowance allocation breakdown in 2006 by industries. The combustion sector represents the largest share of installations in the EU ETS with 70% of the EU allocation. The combustion sector was dened in a dierent way by each MS and contains too many sub-activities. Trotignon and McGuiness (2007); Trotignon et al. (2008) classify between large electricity production plants, district heating facilities (cogeneration when details were available) and other installations. Figure 4 exhibits the identication of combustion installations by activities in the EU ETS. At the EU level, electricity production represents approximately two thirds of the allocation to the combustion sector, and other sectors (including heat production and cogeneration) around one third. In each MS, the share of electricity production allocation in the combustion sector depends basically on their energy mix. The non-combustion sectors gather 30% of total allocation.

Figure 3 :

 3 Figure 3: Breakdown of Allowance Allocation by Industry in 2006 Source: CITL, Trotignon et al. (2008)

Figure 6

 6 Figure 6 shows 2005 and 2006 compliance results for combustion subactivi-9Sector compliance is computed as the dierence between allocation and emissions as a percentage of allocation.

Figure 4 :

 4 Figure 4: Characteristics of the Combustion Sector in the EU ETS Source: CITL, Trotignon et al. (2008)

Figure 5 :

 5 Figure 5: Emissions Compliance Positions by EU ETS sectors during 2005-2006 Source: Trotignon et al. (2008)

First

  , we discuss the relation between industrial production and CO 2 emis- sions. Changes in the level of industrial CO 2 emissions depend on numerous factors. Several studies based on the decomposition analysis have investigated the factors behind changes of industrial carbon emissions in the particular case

Figure 6 :

 6 Figure 6: Emissions Compliance Position in the Combustion Sector during 2005-2006 Source: Trotignon et al. (2008)

  four days. Thus, the release of the 2005 emissions compliance had a structuring market eect. As stated above, emissions net short/long positions need to be balanced against the variation of industrial production. Table4presents the net compliance and the annual production growth rate recorded in each sector during2005-2006.

Figure 7 :

 7 Figure 7: Emissions Compliance Positions and Production Growth Rates of EU ETS Sectors in 2005 Source: Eurostat, CITL and Trotignon et al. (2008)

  3.1.1 Carbon Price, Energy Prices, Temperatures Events and Compliance Break Variables The database used in this article is provided by the Mission Climat of the Caisse des Dépôt which publishes a monthly analysis on the EU ETS called Tendances Carbone. It contains extensive and up-to-date information on carbon and energy market prices, industrial production and temperatures indices, and CO 2 emissions compliance positions, and was rst used for the determination of carbon price drivers and structural breaks during 2005-2007 in Alberola et al. (2008).

For

  the carbon price, we use the daily EUA spot price (P t in = C/tonne of CO 2 ) negotiated from July 1 st , 2005 to April 30, 2007 on BlueNext 14 . The sample period starts at the launch of the BlueNext market place and ends at the disclosure of the 2006 compliance results when the EUA price path asymptotically tends towards zero until the end of Phase I. For other energy prices, we use the daily futures Month Ahead natural gas price (ngas in = C/Mwh) negotiated on Zeebrugge Hub, the daily coal futures Month Ahead price (coal in = C/t) CIF ARA and the electricity Powernext contract (elec in = C/Mwh) of futures Month Ahead Base. We also use the Clean dark spread, clean dark expressed in = C/MWh and the Clean Spark Spread, clean spark expressed in = C/MWh both calculated by the Mission Climat of the Caisse des Dépôt 15 .

  during 2005-2007. This very steep volatility may be explained by the immature state of EU allowance market where investors lack of experience to build their expectations during the Pilot Phase. Therefore, ocial communications by the EC are essential to reach a better information ow on installations' net short/long positions. Such announcements have had a structuring eect on EUA price changes during both 2005 and 2006 compliance periods.

  25less CO 2 -intensive energy sources, which conducts to less need of EUAs. Carbon price changes are positively aected by the electricity variable at 5% signicance level. For the compliance break, the 2006 structural change dummy break is statistically signicant at 1%. This dummy variable refers to the sudden price collapse that occurred following the rst report of 2005 veried emissions with most of the adjustment being made in four days on April 25-29, 2006. It tends to prove there is a structural change caused by the disclosure of new information by the EC concerning installations' net long positions. It also highlights the importance of institutional information during 2005-2007 on this new commodity market. This analysis is conrmed by a Chow's test of structural change 32 .

  since it represents a mere 70.13% and 69.85% of total emissions at the EU level in 2005 and 2006 respectively (Trotignon and McGuiness (2007); Trotignon et al. (2008) ). The combustion sector is also of particular interest since it is the only sector characterized by the alternance of a net long position (+0.6% in 2005) and a net short position (-1.5% in 2006).

First,

  Section 2 details both the expected eect of EU ETS sectors industrial production and emissions compliance on EUA price changes: industrial sectors which result in a higher (lower) production growth than their baseline projections over 2005-2007 are expected, through their surplus (decit) of allowances, to record a short (long) compliance position and then to be potential buyers (sellers) of allowances which should have a positive (negative) impact on EUA price changes. Second, Sections 3 and 4 present an econometric analysis of EUA price drivers including energy prices, extreme temperatures events, institutional events and industrial production indices of each sector at the EU 27 Member States level. Two distinct specications are introduced in Section 3. The rst one aims at identifying which production indices in sectors covered by the EU ETS have a potential impact on carbon price changes. The second specication attempts to disentangle, among those statistically signicant sectors, the potential impact of both production peaks and compliance net short/long positions.The two most important results featured in Section 4 may be summarized as follows. First, we show evidence that only three among nine sectors have a signicant eect on EUA price changes from July 1, 2005 to April 30, 2007. These sectors are combustion, paper and iron and total 78% of allowances allocated. This result is especially interesting since the combustion sector is the largest sector of interest in the EU ETS with 70% of allowances allocated. Second, the analysis attempts to better understand why these three sectors stand out as being signicant by identifying through which channels variations of industrial production from EU ETS sectors may operate on EUA price changes. The role played by yearly compliance positions and production peaks on this new market is demonstrated. For each of the three sectors previously identied, the analysis conrms our intuitions: both the variation of production and the net short/long position are signicant and have the expected eects on CO 2 price changes. -dierenced EUA price series, all energy variables and sector production indices transformed to forecast errors, StdDev.the standard deviation, Skew. the skewness, Kurt.

Table 1 :

 1 The Decomposition of Industrial Sectors in the EU ETS

	Over 2005-2007, the EU ETS covers large CO 2 -intensive emitting plants from
	nine industrial sectors. It does not deal with diuse emissions from transport
	and agriculture, in order to keep the system simple and cost ecient. The
	Directive 2003/87/CE indicates the list of activities qualied by the EU ETS:
	the combustion sector with a rated thermal input exceeding above 20 MWh,
	mineral oil reneries, coke ovens, iron and steel and factories producing cement,
	glass, lime, brick, ceramics, pulp and paper. Table 1 gives details on those
	sectors which include approximately 10,600 installations.
	UNFCCC sectors	CITL Activities
	Energy		1. Combustion installations with a rated thermal input
			exceeding 20 MW;
			2. Mineral oil reneries;
			3. Coke ovens;
	Production	and	4. Metal ore (including sulphide ore) roasting or sintering
	processing	of	installations;
	ferrous metals		5. Installations for the production of pig iron or steel;
	Mineral industry	6. Installations for the production of cement clincker in ro-
			tary kilns with a production capacity exceeding 500 tonnes
			per day or lime in rotary kilns with a production capacity
			exceeding 50 tonnes per day;
			7. Installations for the manufacture of glass including
			glass ber with a melting capacity exceeding 20 tonnes
			per day;
			8.	Installations for the manufacture of ceramic prod-
			ucts by ring, in particular roong tiles, bricks, refractory
			bricks, tiles, stoneware or porcelain, with a production
			capacity exceeding 75 tonnes per day;
	Other activities	9. Industrial plants for the production of (a) pulp from
			timber or other brous materials (b) paper and board with
			a production capacity exceeding 20 tonnes per day.
	Source: EU Directive 2003/87/CE, Annex I
	Based on NAPs, which provide the list of installations, and the Community
	Independent Transactions Log (CITL), which is the European central admin-
	istrator registry that oversees all national registries, it is possible to identify
	installations and the classication of their manufacturing activities. The CITL
	keeps track of yearly allocation, yearly veried emissions, the ownership of al-
	lowances and records transactions between industrial accounts. The analysis of

Note that Romania and Bulgaria have joined the EU ETS on January 1, 2007.

The Greek NAP was the last approved by the EC on June 2005.

On January 2004, the EC issued guidance on the implementation of the allocation process governed by articles

to 11 and Annex III of the Directive 2003/87/EC.

Acknowledgements

The authors are grateful to the Mission Climat of Caisse des Dépôts for the use of their Tendances Carbone database. Helpful comments have been received from seminar audiences at the Workshop "Bank and Finance: the Impact of Global Threats" of the University of Lille 1, the CORE Environmental Workshop and the UKNEE Envecon 2008. The usual disclaimer applies.

(2a) (Table 9), with no interaction eects, ceteris paribus, the growth rate of EUA prices is higher (by about 0.5%) when the combustion sector record a short allowance position. As explained above, emissions net short/long position needs to be balanced with production peaks.

Comparing the positive coecient of combpeak (about 0.02) to the negative one of comb (about -0.07) allows us to improve our analysis on the impact of industrial production on EUA price changes. Recall that Section 2 details the expected eects of the variation of production: industrial sectors which record a higher (lower) production growth than their baseline projections over 2005-2007 are expected, due to their decit (surplus) of allowances, to be net buyers (sellers) of allowances and should have a positive (negative) impact on EUA price changes. This economic logic explains the positive coecient of combpeak: we observe in regression (2a) (Table 9) that the growth rate of EUA prices is higher (by about 2%) when the combustion sector encounters a positive production peak ceteris paribus. Moreover, the negative coecient of comb is explained by its declining variation of production during the whole period. This eect remains even after taking into account the positive eect of production peaks.

Note however that the coecient estimates of the two latter dummy variables may be biased because we do not take into account their likely interaction eects. In other words, the eect of combcompl and combpeak on mean p t may not be simply additive as in regression (2a) but multiplicative as well as specied in regression (2b). That is why we now compare the results of eq.( 2) estimates (regression (2a), Table 9) with those of the same equation (regression (2b), Table 9) which includes the interaction eects between the two dummies, combcomplpeak. The adjusted R-squared and the R-squared are, respectively, equal to 15.3% and 18.5%. Note the AIC and SC both decrease: the inclusion of the interaction variable therefore allows us to gain a better insight into the eects of industrial production and compliance position on EUA price changes.

Concerning the dummy variables, the two additive dummies combcompl and combpeak and the interaction variable combcomplpeak are still statistically signicant at 1% signicance level. Holding other variables constant, when the combustion sector exhibits a net short allowance position and encounters a positive production peak, the growth rate of EUA prices is higher by about 2.3% (0.0231=0.0513+0.0063-0.0345), which is between the value of 0.6% (the eect of combcompl alone) and 5% (the eect of combpeak alone).

The next section presents estimation results for the iron and paper sectors.

Analysis of the Iron and Paper Sectors

In this section, we detail the results for both iron (regression (3), Table 9) andpaper (regression (4), Table 9) sectors The adjusted R-squared are equal to 08.32% and 02.97% for respectively regressions (3) and ( 4). Although the adjusted R-squared statistic is known as being controversial, it is worth underlining the lowest value is achieved for the paper sector which totals the lowest level of allocation. The two sector variables for each estimate (iron, ironpeak, paper, paperpeak) are signicant at 1% level. Iron (regression (3)) and paper (regression (4)) have both a negative coecient estimate, whereas ironpeak (regression (3)) and paperpeak (regression (4)) have a positive sign.

As explained is section 4.1, the negative sign of iron (regression (3)) and paper (regression (4)) variables is not explained by their increasing variation of production, (respectively 2.61% in 2005 and 4.31% in 2006, and 0.62% in 2005 and 4.31% in 2006) but ultimately by their net long position on the whole period.

Thus, we are able to identify the predominant impact of the net long position over the increasing production trend eect as drivers of EU carbon prices as a potential justication of the negative coecients of iron (regression (3)) and paper (regression (4)). The reason behind the positive sign of paperpeak and ironpeak (regression (4)) is similar to what has been explained in Section 4.2.1 for combpeak (regression (2a)). When a sector has an increasing activity peak, then it becomes a potential net buyer which yields to a positive impact on the allowance price.

Summary and concluding remarks

Previous literature has identied energy prices, temperatures events and institutional information variables as EUA carbon price drivers during [START_REF] Christiansen | Price determinants in the EU emissions trading scheme[END_REF]-2007[START_REF] Mansanet Bataller | CO 2 Prices, Energy and Weather[END_REF], [START_REF] Alberola | Price Drivers and Structural Breaks in European Carbon Prices 2005-2007[END_REF], [START_REF] Rickels | The Determinants of Allowance Prices in the European Emissions Trading Scheme: Can We Expect an Ecient Allowance Market[END_REF]).

The analysis of EU ETS price drivers is taken one step further in this article by investigating i) whether variations of industrial production from sectors covered by the EU ETS also have an impact on CO 2 price changes and ii) through which channels these eects may operate.

As both the European