European Emission Trading Scheme and competitiveness: A case study on the iron and steel industry☆
Introduction
The European GHG Emissions Trading Scheme (ETS) is the largest cap-and-trade system worldwide and the most important European climate change mitigation policy in place. Its environmental effectiveness depends on the stringency of the overall emissions cap. However, with decentralised allocation – “national allocation plans” (NAPs) submitted by each member states and reviewed by the European Commission determine national caps which aggregate to make the EU cap – the environmental stringency is indirectly controlled.
In phase I of the directive (2005–2007), the number of allowances allocated was close to, or higher than the likely business-as-usual emissions during this period (Reilly and Paltsev, 2005, Schleich and Betz, 2005). This lack of stringency is largely fuelled by concerns about the competitive disadvantage for European economies vis-à-vis non carbon-constrained countries such as the U.S. and developing countries.
The debate on industrial competitiveness is blurred, however, by loosely defined wording such as “competitive disadvantage”, “competitive distortion” and “competitiveness” which can be interpreted very differently. For example, on a macroeconomic level, the very notion of competitiveness can be argued to be meaningless because exchange rates adjust over time to make up for “competitiveness distortions” experienced by a nation (Krugman, 1994). Yet on a micro level, individual industrial sectors and companies will lose or gain “competitiveness” — this can basically be reduced to two interpretations:
- 1.
a loss in domestic production, which in turn may induce industrial relocations, domestic employment losses and possibly leakage to pollution havens;
- 2.
a loss in profits, hence in stock value, of domestic firms.
It is essential to disentangle these two effects since, as we shall see, the ETS may impact them in very different ways.
The iron and steel industry sector is one of the most exposed among those covered by the EU ETS, since it is both highly CO2-intensive and relatively open to international trade (Quirion and Hourcade, 2004). Studies that have assessed the impact of the EU ETS in this sector (cf. Oberndorfer, 2006, and references therein) generally conclude to a modest decrease in EU production. Conversely most of these studies do not address the second aspect of competitiveness, i.e., profitability, one exception being Smale et al. (2006) who finds a positive impact.
However, these studies often do not report on the robustness of the results to the most obviously important parameters: marginal abatement cost, import, export and demand elasticities.
Debatable (and often implicit) modelling assumptions make it further problematic. First they generally do not make explicit, the rate of pass-through i.e. the share of an increase in marginal cost that is passed on to product prices. In addition, allowances are often assumed to be distributed on a lump-sum basis. As we will see, the latter assumption is not well-suited for modelling the EU ETS.
This paper assesses the impact of the EU ETS on both the production and profitability of the iron and steel sector by using a simple and transparent partial equilibrium model. Its simplicity allows us to vary key parameters and assumptions mentioned above and thus determine robustness and sensitivity of results to the different parameters. The parameters and assumptions that require more attention can therefore be identified.
We conclude that for the EU iron and steel sector in general, competitiveness losses, if any, are small. We prove this conclusion to be robust. Hence the tightening environmental stringency of the ETS in the second period should not be opposed on grounds of competitiveness losses. Our systematic sensitivity analysis allows us to identify important assumptions for every output variable. It turns out that the pass-through rates and updating rules, although most often implicit and not debated in existing analyses, are of major importance.
After outlining the model in the subsequent section, we present results on the competitiveness impacts of the EU ETS for central scenario in Section 3. Then, in 4 Sensitivity to classical parameters, 5 Sensitivity to key modelling choices, we test the sensitivity of these results by varying one by one the key assumptions pre-cited. In the Following section, all assumptions are varied together to quantify the overall uncertainty. Section 7 concludes.
Section snippets
Model
The purpose of this paper is to build a transparent model which provides a quantitative assessment of EU ETS impacts and allows testing the robustness of the results to various assumptions: marginal abatement cost, price elasticity of demand, trade elasticities, modelling of the EU ETS allocation method and pass-through rates. The latter is determined by a given representation of competition, together with the shape of the demand and supply curves. For example, a Cournot oligopoly of N
Assumptions
This section shows the mechanisms triggered by the CO2 price for a single set of parameters and modelling assumptions. The parameters (MAC curve parameters, price elasticity of demand θ, price elasticity of imports and exports σ, domestic and export pass-through rates PTX and PTD) are intermediate values taken from the literature. For modelling of the allocation method, we use the most common practice labelled “no updating”; we assume that the allocation in a given 5-year period does not depend
Sensitivity to classical parameters
In the 4.1 Marginal abatement cost curves, 4.2 Price elasticity of demand, 4.3 Price elasticity of imports and exports, we will vary sequentially the most obviously important parameters: MAC curve parameters, price elasticity of demand θ, and price elasticity of imports and exports σ.
Sensitivity to key modelling choices
Having checked the robustness of our results to the most obvious parameters, we now test the impact of often implicit yet debatable modelling assumptions: the pass-through rates and the allocation of allowances.
Total range of uncertainty
Up to now, we have varied only one parameter at a time to test the robustness of the qualitative results from our central scenario. In this section, we study every possible combination of parameters, i.e. 35 = 243 combinations. Assuming that every set of parameters value has the same likelihood – which overestimates the variance, compared to an assumption of a higher probability for medium values – we use the IPCC terminology for handling uncertainty: “very likely” means at least a 90%
Conclusions
The goal of the paper was to assess the competitiveness impact and the environmental effectiveness of the EU ETS in the iron and steel sector, while testing the robustness of the results to key assumptions: marginal abatement cost curve, price elasticity of demand, price elasticity of trade, pass-through rates and allocation updating rules. We address two dimensions of competitiveness: production and profitability.
A first conclusion is that production losses are weak, which is in line with the
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We would like to thank two anonymous referees, Thierry Bréchet, Jean-Charles Hourcade, Misato Sato, Henry Tulkens and participants at the 3rd CATEP workshop and CORE environmental seminar for their comments as well as the Institut français de l'énergie for financial support. The usual disclaimer applies.