Cost curve based estimations (Tier 1 and 2)

In the case study applications for Tier 1 and Tier 2, the dimensions were defined as follows:

► Regional detail: we included data on individual countries.

► Sector detail: in Tier 1 no sector differentiation was analysed. In Tier 2 we carried out an ag-gregated analysis in which we differentiated between the combustion installations and the dustry sectors. In a further step, we did a disaggregated analysis in which additionally, four in-dustrial sectors (steel, non-metallic minerals, other transformation including the refineries and other industry) were distinguished.

► Temporal perspective: we analysed two variants: First, the analysis was based on average yearly data for the second trading period (2008-2012); Banking of certificates for later periods was not accounted for. Second, the analyses were carried out for the single year 2008, as the only year during the second trading period in which emissions exceeded allocations.

► Emissions and allocation data: The historic emissions and allocation data for use in Tier 1 and Tier 2 were taken from the EUTL, accessed via the ETS data viewer provided by the European Environment Agency. The EUTL contains data on free allocation and verified emissions by in-stallation and year for all inin-stallations regulated under the EU ETS. For consistency the data were adjusted with additional information from the report “Trends and projections in Europe 2013 – Tracking progress towards Europe’s climate and energy targets until 2020” (EEA 2013). Adjustments were necessary for changes in the scope of the EU ETS between the first and second period. Information on the amount of certificates sold or auctioned by the govern-ments was taken from the EEA report 2013.

► Counterfactual scenario: The counterfactual scenario was established based on the Business-as-usual (BAU) scenario from the POLES model with a carbon price of zero. Ideally, the BAU would be constructed by an ex-post model run, feeding the POLES model with observed devel-opments concerning factors that impact CO2 emissions such as e.g. economic development, but applying a carbon price of zero. However, this would have required the purchase of a com-pletely new model run, which was not feasible with the given budget. . Instead we used a BAU scenario that was constructed ex-ante. Not surprisingly, this scenario’s assumptions do not match observed developments. In particular the effects from the economic downturn on indus-trial production – and hence emissions – are substantial. Therefore, a correction was applied to estimate counterfactual emissions that are consistent with the observed developments of fac-tors that have an impact on emissions. The correction was applied for economic activity as well as for the share of low-carbon power generation (renewable and nuclear). In addition, model data were scaled up or down to match with the EUTL.

► Definition of the alternative policy scenario: To determine the fixed emissions budgets for the individual entities (sectors, countries) in the alternative policy scenario, we looked at their re-spective shares of freely allocated emission certificates in the second period (i.e. before trad-ing). In doing so, we also added the actual auctioned amounts to the electricity sector’s free al-location, in order to not underrate the permitted emission level for the electricity sector in the fictitious alternative policy scenario. This is based on the fact that in the 2^nd TP, both countries that account for the bulk of EU-wide auction amounts (Germany and UK) substantially reduced free allocation to electricity installations and did so to a much lesser extent for the industry sectors. It also takes into account the assumption that under an alternative policy scenario,

139 windfall profits from passing through “opportunity costs” of freely received allowances (which were a key argument for reducing the electricity sector’s fee allocation in the ETS) would not occur⁵¹; leaving only the abatement costs and their relevance for international competition as key arguments for a certain discrimination between electricity and industry sectors. Subtract-ing this emission budget from emissions in the counterfactual scenario yields abatement re-quirements for each entity. In case the abatement requirement was negative (i.e. the entity would be allowed to increase its emissions) the abatement requirement was set to zero. The abatement requirements of the remaining sectors were rescaled to ensure that the same abatement as in the ETS scenario is reached.

► Information on abatement costs: For Tier 1, linear abatement cost curves were constructed based on observed emissions prices and realized reduction quantities. For the Tier 2 approach, techno-economic marginal abatement cost curves are taken from the energy-system POLES model. For use within Tier 2, marginal abatement cost curves from POLES were available for the power sector and four industry sectors: steel, non-metallic minerals, other transformation (including the refineries) and other industry for all EU countries and for all years 2008-12. The curves consist of 50 equally-sized steps with regards to prices. For the calculations, the mar-ginal abatement cost curves for all years were scaled up or down to match the historic sector emissions data in the EU ETS as recorded in the EUTL. This operation was done for the year 2006, the first year of available baseline emissions in POLES. That is, baseline emissions in the marginal abatement cost curves for the combustion installations and the industry sector matched the historic emissions in these sectors for all individual countries and regions.

4.3.1.1 Linear abatement cost curve (Tier 1) analysis

Table 4-2 shows results for the application of the Tier 1 methodology (covering only trade between Member States in our analysis). At 1,520 m€ the EU ETS is estimated to have led to an efficiency gain of 38 % compared to the alternative policy scenario, estimated to cost a total of 2.450 m€.

Table 4-2: Calculation of abatement costs in the EU ETS and the alternative policy scenario for the Tier 1 approach

Reductions (Mt CO2) MAC (€/t CO2) Abatement costs (m€)*

ETS scenario

Seller 175 14.46 1 261

Buyer 36 14.46 258

Total 211 1 520

Alternative policy scenario

Seller 113 9.35 528

Buyer 97 39.46 1 922

Total 211 2 450

* calculated as reductions*MAC*0,5

51 For a more detailed explanation, see the Chapter “Estimation of cost savings by means of a model-based abatement curve (Tier 2 analysis)” Section 4.1.

140 4.3.1.2 Technology based cost curve (Tier 2) analysis

In the Tier 2 analysis, four different cases were investigated: On the one hand, the disaggregated anal-ysis in which trade is considered between the electricity sector and four industrial subsectors and on the other hand, the aggregated analysis in which only efficiency gains from trade between the electrici-ty sector and an aggregated industry sector are taken into account. For both approaches the analysis is carried out for both the single year 2008 and the average year of the 2^nd trading period. Table 4-3 shows the abatement costs for the Tier 2 approach for the four cases. In all four cases two methodolog-ical approaches to determine abatement have been followed: the Price Method in which the observed CO2-prices are applied to (corrected) POLES curves to derive total abatement and the Quantity Method in which total abatement is derived by subtracting observed verified emissions from the counterfactu-al (i.e. corrected POLES BAU). The totcounterfactu-al amount is then counterfactu-allocated to the sectors using the efficient split (i.e. same marginal abatement cost for both sectors).⁵²

For the ETS scenario for the average year of the 2^nd trading period, total abatement costs in the dis-aggregated case are equal to 1015 m€ for the price method, of which 840 m€ have to be borne by the electricity sector and 175 m€ have to be borne by the industry sectors. For the aggregated case, the calculated cost under the price method are similar with 1000 m€, of which 840 m€ originate in the electricity sector and 160 m€ in the industry sectors (see Table 4-3).

Applying the quantity method, the calculated costs are higher both in the disaggregated and in the aggregated case, with 1768 m€ (disaggregated) and 1406 m€ (aggregated) corresponding to higher abatement and hence a higher predicted price than under the price method. One reason could be that other factors or policies beyond the ETS price are influencing emission reductions. Even though we tried to correct for part of these factors, this correction is likely incomplete. Moreover, assumptions made when adapting the BAU and the MACCs could be wrong. Another reason could be that abatement technologies included in the abatement cost curve do not adequately reflect all available abatement options in the relevant sectors. Hence, the main reason for the inconsistency can most likely be at-tributed to the marginal abatement cost curves applied. The share of cost borne by the electricity sec-tor remains the same in the aggregated case. In the disaggregated case, the share of cost borne by the industry sector is higher under the quantity method. This corresponds to a higher share of the indus-trial sectors in total abatement. Beyond the effect mentioned above, this is likely impacted by a meth-odological aspect: emissions from the POLES models are scaled to match EUTL emissions in 2006 sep-arately for all four industry subsectors in the disaggregated analysis, but for the aggregated analysis the scaling is made on the aggregated level for the whole industry sector. As a result the industrial sector abatement cost curve is slightly steeper in the disaggregated calculations. Also counterfactual emissions in the disaggregated analysis differ from those in the aggregated analysis.

In the alternative policy scenario, higher emission reductions for the electricity sector result in higher total abatement costs of 1216 m€ under the price method and 1728 m€ under the quantity method in the aggregated case. In contrast, the industry sectors do not realise any emission reductions and hence do not bear any abatement costs on their side. In the disaggregated case under the quantity approach, too, abatement of the electricity is increasing and in aggregate, the industrial sectors mitigate less.

Several industrial sectors in some countries do not mitigate at all which results in higher total abate-ment costs in the alternative policy scenario. The cost add up to 2021 m€ for the price method and 3374 m€ for the quantity method in the disaggregated case. Hence, under the above assumptions, the efficiency gains from certificate trade amount to 18-19 % of total abatement costs in the alternative

52 It should be noted that all results depend on the marginal abatement cost curves used within the analysis. In this case, we relied on abatement cost curves generated with the POLES model. Abatement cost curves from other models could have been used as well and might lead to differing results. In a future study, comparisons of results based on different model based cost curves might be interesting.

141 policy scenario for the aggregated case and 48-50 % in the disaggregated case for the average year of the 2^nd trading period.

Table 4-3: Calculation of abatement costs in the EU ETS and the alternative policy scenario for the Tier 2 approach for the average year of the 2^nd TP (2008-2012)

Reductions (Mt CO2) Abatement costs (m€) Method disaggregated aggregated disaggregated aggregated ETS scenario

Electricity sector price 125 125 840 840

quantity 159 146 1390 1175

Industry price 24 23 175 160

quantity 36 27 378 231

Total price 148 148 1015 1000

quantity 195 173 1768 1406

Alternative policy scenario

Electricity sector price 126 148 1475 1216

quantity 167 173 2662 1728

Industry price 22 0 547 0

quantity 28 0 712 0

Total price 148 148 2021 1216

quantity 195 173 3374 1728

The analysis for the year 2008 results in higher abatement cost both in the disaggregated and aggre-gated case even though total abatement is slightly lower (see Table 4-4). The difference between the cost in the alternative policy scenario and the ETS scenario is smaller. The calculated cost savings from trade amount to 12-15 % of total abatement costs in the alternative policy scenario for the aggregated case and 23-39 % in the disaggregated case for the single year 2008. Higher values pertain to the price method.

Table 4-4: Calculation of abatement costs in the EU ETS and the alternative policy scenario for the Tier 2 approach for the year 2008

Reductions (Mt CO2) Abatement costs (m€) Method disaggregated aggregated disaggregated aggregated ETS scenario

Electricity sector price 115 115 1032 1139

quantity 147 132 1801 1550

Industry price 17 17 391 178

quantity 31 19 754 245

Total price 133 132 1424 1317

quantity 178 151 2555 1795

142 Reductions (Mt CO2) Abatement costs (m€)

Method disaggregated aggregated disaggregated aggregated Alternative policy scenario

Electricity sector price 113 132 1655 1547

quantity 153 151 2555 2040

Industry price 20 0 664 0

quantity 25 0 757 0

Total price 133 132 2319 1547

quantity 178 151 3312 2040

4.3.1.3 Short Summary

Given the uncertainties and assumptions, and given the different time periods considered in the Tier 1 and Tier 2 applications (average of 2008-2012 or single point of time, disaggregated and aggregated analysis) the resulting emissions reductions due to the ETS compared to the counterfactual scenario in Tier 1 and Tier 2 range from 133 to 211 Mt CO2 for the EU. At the same time, the associated cost sav-ings compared to an alternative policy scenario leading to identical emission reductions cover a span from 12% to 50% in these two Tier approaches. For more detailed descriptions of the approaches, assumptions and results please refer to the individual Tier-level reports within the ETS-5 research project (Cludius et al. 2016) and in this report.