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ABSTRACT
This paper analyses the rules for free allocation in the EU Emissions Trading System (EU ETS). The analysis draws on the empirical evidence emerging from two literature strands. One group of studies sheds light on the following questions: how efficient are free allocation rules in minimizing the risk of carbon leakage? Have they become more efficient over the trading periods? What are the technical limits to making them more efficient? Further: is firm behaviour affected by allowance allocation? Did specific provisions induce strategic behaviour with unintended effects? Studies from the second group estimate sectoral pass-through rates for the costs imposed by the EU ETS. Taking cost pass-through into account is necessary for properly targeting free allocation. The difficulty of accurately quantifying sectoral differences in cost pass-through ability, especially in manufacturing sectors (due to limited data availability and market heterogeneity), is the main hindrance to achieving further efficiency in allowance allocation. The new rules defined in the reform for Phase IV (2021–2030) nevertheless make some progress in this direction.
Key policy insights
The difficulty of accurately quantifying sectoral differences in cost pass-through ability is the main hindrance to efficient free allocation in minimizing carbon leakage risk.
In Phase IV (2021–2030), carbon leakage risk will be assessed more accurately thanks to: a) carbon intensity and trade intensity considered together through a combined indicator; b) possible use of more disaggregated data, and c) possible consideration of complementary qualitative assessments of abatement potential, market characteristics and profit margins.
It is expected that benchmarked allocation introduced in Phase III (2013–2020) has induced additional emission abatement, but there is still a lack of empirical evidence.
Acknowledgements
The work leading to this publication was co-financed by the EU LIFE Programme of the European Commission – Gran Agreement LIFE15 GIC/IT/000051 LIFE SIDE. This paper/article reflects only the authors’ view and the Agency is not responsible for any use that may be made of the information it contains.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The literature identifies four types of windfall profits under the EU ETS: a) from pass-through of opportunity costs; b) from overallocation; c) from exploiting the price differential between emission offsets issued under the Kyoto Protocol’s flexible mechanisms and emission allowances issued under the EU ETS (de Bruyn, Schep, Cherif, & Huigen, Citation2016); and d) from inframarginal rents, arising when marginal producers setting output market prices use more carbon-intensive technologies.
2 With benchmarking, an emission rate that characterizes emission-efficient production is used as the basis for allocations, with appropriate adjustments for the installation’s past or expected production levels.
3 By derogation, some member states, among those with a GDP per capita below 50% of the EU average, can continue free allocation to installations generating electricity in exchange for their modernization of the sector and for the diversification of the energy mix. In addition, the 2009 ETS Directive introduces a redistribution mechanism operating through cross-country allocation of the allowances to be auctioned. 88% of these are allocated to member states in proportion to their emission volumes in year 2005. 10% are distributed to the least wealthy member states as an additional source of revenue to help them invest in climate change mitigation and adaptation. The remaining 2% are given as a bonus to member states which, by 2005, had reduced their emissions by at least 20%, compared to their base year under the Kyoto Protocol.
4 In Phase III, this level is the highest between median production over 2005–2008 and median production over 2009–2010.
5 NACE is the statistical classification of economic activities in the EU.
6 Indirect emissions are those resulting from the generation of purchased electricity consumed by the entity.
7 The formula of free allocation in Phase III is reported in the Appendix.
8 The linear reduction factor applied in Phase IV will be 2.2%. The cap reaches in 2030 a level 43% below 2005 emissions. This level is consistent with the EU’s mitigation target for 2030 (40% reduction of overall GHG emissions below 1900 levels) set in the 2030 Climate and Energy Framework.
9 The strengthening of the price signal is also pursued through the enhancement of the Market Stability Reserve mechanism. For an analysis of how changes in different key parameters of allowance supply would impact on allowance price paths in Phase IV and beyond, see Perino and Willner (Citation2017).
10 According to the European Commission, of the 177 sectors currently classified as at risk, only about 50 will continue to be classified as such. However, the reduction in terms of emissions is smaller than the reduction in terms of sectors (European Commission, Citation2015).
11 Variations are relative to production levels considered for determining initial allocations. They are calculated as rolling two-year averages.
12 The interviews were carried out in 2009.
13 The sectors that are the most vulnerable are: other minerals, glass, iron and steel, and cement, the respective mean values of the vulnerability indicator ranging between 2.5 and 3.5. Other sectors, such as food and tobacco, fabricated metals, and vehicles, are significantly less vulnerable than the average.
14 The authors also indicate more specific factors, including cross-country sectoral differences in process emissions, recycling rate and product mix.
15 In the first two trading periods, defining free allocation rules for new and closing installations was the responsibility of member states. Most if not all member states, except Sweden, adopted closure provisions making free allocation conditional on continued production activity (Verde et al., Citation2018).
16 VECMs are supposed to be more accurate than SEMs because they control for dynamic interactions among all the independent variables. A second technical distinction can be made between the studies using electricity forward prices (typically, one-year ahead) and those using spot prices (one-day ahead). The choice of which prices to use for estimation is not neutral. On the one hand, spot prices are more volatile, less driven by fuel or carbon costs and more influenced by unforeseen events such as plant outages or weather shocks. On the other hand, greater data variation is useful for parameter identification (Hintermann, Citation2016).
17 Using larger shares of variable costs helps econometric identification of PTRs.
18 According to Reinaud (Citation2005), considering transportation costs and assuming full pass-through by European producers, carbon prices would need to be around €28/tCO2 for Chinese steel to compete with marginal steel production in Europe.