Silviase/jgov_v1.4_full_ann huggingface dataset:
Table 1 The main features of PE and CGE models

CGE models	Partial equilibrium models
Sectoral coverage	Multi-sector analysis	Single-sector analysis
Relationships	Direct and indirect effects	Direct effects only
Modelling approach	Usually top-down	Usually bottom-up
Level of disaggregation between sectors	High, depends on a model's purpose	Not applicable
Level of disaggregation within sector	Usually low	Usually high

The main methodological differences between PE and CGE models explain the advantages and disadvantages of these models, as summarised in Table 1. However, the choice of the modelling approach, as well as, the geographical dimension (national, regional, etc.) and the time horizon of simulation studies is more driven by the specific objective of the study and by the policy question of interest, besides the author's inclinations, than by any such a 'strengths vs. weaknesses' criterion.

### 2.2 Key modelling assumptions

Few meta-analyses $ ^{2} $ (Repetto and Austin, 1997, Barker et al., 2002, Fischer and Morgenstern, 2005, Barker et al., 2006) use quantitative techniques and show that, despite the complexity of models assessing the ex-ante costs of climate change mitigation, the key assumptions explain most of the variation in cost estimates (see

Table 2). Thus suggesting that to make judgements as refers to the differences and reliability of the ex-ante cost estimates it is sufficient to focus only on the treatment of this relative small number of the key modelling assumptions.

These assumptions might be divided into three categories: (1) assumptions related to abatement targets (baseline, abatement target and time horizon for reaching $ CO_{2} $ reduction targets); (2) assumptions about structural modelling features (substitution possibilities, back-stop energy sources, technological change (TC), inclusion of climate and non-climate benefits $ ^{3} $ ); and (3) assumptions about policy modelling (availability of flexibility mechanisms, and carbon tax (auctioned permit) revenue recycling).

As the costs of emissions reductions needed to reach a certain target depends on both the starting and the ending point, the emission abatement level captures variations in both baseline emissions and the expected amount of abatement $ ^{4} $ . Baseline projections alone (economic system without any climate policy in place) depend on the projections for economic growth, fuel prices, energy efficiency, market imperfections and any other determinants of $ CO_{2} $ emissions $ ^{5} $ . Hence, different
baseline scenarios among models lead to different costs outcomes and should be kept in mind then analysing model results.

Table 2 Findings of meta-analyses

Repetto and Austin (1997)	Barker et al. (2002)	Fischer and Morgenstern (2003)	Barker et al. (2006)
Cost estimate	US real GDP loss	Global GDP loss	Marginal abatement costs for US	Gross world product	CO2tax rate/permit price
Salient assumptions	Abatement target	Abatement target	Abatement target	Abatement target	Abatement target
1. Abatement target6	Years to achieve specified abatement target	Years to achieve specified abatement target	Technology detail (bottom-up, top-down)	Model type (CGE/econometric)	Sectoral disaggregation
Intra-fuel and product substitution	Intra-fuel substitution	Disaggregation in regions, goods and energy supplies	Backstop technology	Induced TC
Back-stop technology	Back-stop technology	Back-stop technology	Climate benefit
Climate benefits	Climate benefits	Perfect foresight by consumers	Non-climate benefits
Non-climate benefits	Characterisation of international linkages	Induced TC
3. Policy	CO2tax (auctioned permit) revenue recycling	CO2tax (auctioned permit) revenue recycling	Kyoto flexibility mechanisms	CO2tax (auctioned permit) revenue recycling	Kyoto flexibility mechanisms
Kyoto flexibility mechanisms	Kyoto flexibility mechanisms (International joint implementation)

Note: Even though the impacts of the listed assumptions on the cost outcomes of the climate change models are self-explanatory, few meta-analyses deliver contradicting results. For instance, differently from other three meta-analysis, Fischer and Morgenstern (2005) find that the inclusion of a back-stop technology leads to higher marginal abatement costs.
that include the possibility to use flexibility mechanisms generate lower overall mitigation costs than models restricting their availability.

### 2.3 Concepts of costs

Defining the net costs of climate change policies is a very difficult task due to the disagreement and uncertainty surrounding the existence and the level of the indirect climate benefits of climate change policies $ ^{7} $ . Maybe for this reason, most climate change models (and all those we refer to) only focus on the economic costs of climate change mitigation.

Ex-ante economic costs (hereafter ex-ante costs) estimates of climate change policies are obtained through modelling simulation studies that compare projected outcomes for a particular sector, or the whole economy, with and without climate policy. When comparing the outcomes of modelling studies it is important to understand, firstly, whether the models measure partial (sectoral) costs, or general equilibrium costs, and, secondly, whether there are differences in their definitions of costs, and their relative merits and demerits.

Ex-ante costs of climate change policies are usually presented in three ways: as a measure of marginal abatement costs (the carbon tax or the estimated permit equilibrium price for $ CO_{2} $ emissions); as a change in GDP (in constant prices) between the outcomes under the two scenarios as a summary measure of macroeconomic costs; and finally, as a welfare measure incorporating both losses in producers' and consumers' surplus.

The equilibrium marginal abatement costs are the costs required to reach a defined $ CO_{2} $ target level set by a regulatory authority. It is important to understand that the permit price level does not per se create economic losses associated to the climate change policies and, as such, cannot be treated as a general measure of macroeconomic costs. For example, an increase in carbon tax should not be interpreted as evidence of output loss or a low carbon tax or an observable tradable permit level should not be treated as a sign of $ CO_{2} $ emission mitigation costs $ ^{8} $ .

To estimate the total macroeconomic costs associated with a given carbon reduction target, modelling studies usually use changes in GDP at constant prices because it captures all changes in the economy’s output and is comparable across countries and regions, as the measurement of GDP is standardised in many countries. The main shortcoming of this measure is that it does not account for the environmental effects of climate change policies, and as such cannot be used as a measure of welfare in the economy.

CO $ _{2} $ emission reduction costs arise not only because of the associated reduction in production as less energy becomes available to complement existing labour and capital, but also because of changes related with a shift to a less desired composition of products in consumption. Typically, the above described total output costs in terms of GDP capture the costs associated with the reduced volume of production, while effects on consumption are not accounted for. Theoretically, the welfare measure expressed in the Hicksian “equivalent variation” is a more socially
ferrous metals (metal ore roasting, pig iron, steel), the mineral industry (cement clinkers, lime, glass, ceramics) and the pulp and paper production - which are highly $ CO_{2} $ intensive and relatively exposed to domestic/international competition and hence are not able to pass through direct and/or indirect $ CO_{2} $ costs if they want to sustain their market share $ ^{10} $ .

Baron et al.(2008) $ ^{11} $ analyse the competitiveness aspects of the some above listed sectors: cement, iron and steel, oil refining and aluminium. Their ex-post analysis concludes that the currently available data does not reveal any impact of the EU ETS on cement trade in the period 2005-2006, but looking forward the EU ETS might have long-run impact by reducing investment and production capacity. The similar conclusion emerges for the iron and steel industry (steel sector was significantly overallocated). The EU ETS effects of the competitiveness were minimal on the European refining sector too, which benefited from the high mark-ups in the past. The European primary aluminium industry’s competitiveness was not affected, suggesting that “the high commodity price has overwhelmed any effect of the EU ETS on electricity costs” (Baron et al., 2008, p.34).

### 4. Ex-ante costs of the EU ETS

### 4.1 Taxonomy of the selected studies

There exist a number of studies devoted to predicting the costs of the EU ETS (see Table 3 for our selected studies $ ^{12} $ ). The modelling approach, the assumptions introduced and, thus, the cost estimates of each of the studies considerably depend on an objective of a particular study and the time a study was done. According to this, the selected research on the ex-ante costs of the EU ETS can be classified into two broad categories: (1) research analysing the cost-efficiency side of the EU ETS and (2) research analysing the equity side of the EU ETS. Additionally, an alternative classification is possible. The studies included in this analysis can be divided into (1) the studies carried out before the EU ETS legislation was enacted and (2) the studies undertaken after the EU ETS has come into effect in 1 January 2005. This latter classification overlaps with the former in the sense that general efficiency issues, such as the cost-effectiveness of the trading scheme, were more emphasised before the European policy on the EU-wide $ CO_{2} $ emission trading was approved; while equity issues, such as an optimal mechanism of permit allocation and the fair split of allowances between trading and non-trading sectors, attracted more attention during the pilot phase of the EU ETS.
Table 3 Summary of selected studies

Reference	Model	Modelling approach	Period (up to)	Geographical coverage	Major theme	Cost concept
Capros and Mantzos (2000)	PRIMES	PE	2010	EU 14 (Luxembourg excluded)	Efficiency	Total costs, MAC
IPTS (2000)	POLES	PE	2010	EU 15 (divided in 6 regions)	Efficiency	Total costs, MAC
ERM and Euroelectrics (2002)	GETS 3	PE	2017	EU 15 + Poland, Czech Republic, Norway, Switzerland, Hungary	Efficiency, equity	Relative total costs and relative average traded price
Sijm et al. (2002)	MARKAL-MATTER	PE	2010	EU 15 + Norway, Iceland, Switzerland	Efficiency	Total costs, MAC
Proost and Van Regemoter (2003)	GEM -E3	GE	2010	EU 15	Efficiency	Welfare change
Babiker et al. (2004)	EPPA-EU	GE	2010	EU 15 (devided in 9 regions)	Efficiency, distortions	Welfare change, MAC
COWI (2004)	GTAP-ECAT	GE	2010	EU 25	Efficiency, distortions	Total costs, MAC
Bohringer et al. (2005)	SIMAC	PE	2005	EU 15	Equity	Total costs, MAC
Bohringer and Lange (2005)	PACE	GE	n/a	Germany	Efficiency	Output, employment, consumption change
Kemfert et al. (2006)	GTAP-E	GE	2005-2006	EU 25	Efficiency, flexibility	MAC, Efficiency gains
Klepper and Peterson (2005)	DART	GE	2012	EU 15	Efficiency, flexibility	MAC, welfare loss
Russ et al. (2005)	POLES	PE	2025	EU (not defined)	Efficiency, flexibility	MAC, effort rate
Russ et al. (2005)	GEM-E3	GE	2025	EU (not defined)	Efficiency, flexibility	MAC, welfare loss
Peterson (2006)	DART	GE	2012	EU 25 (15 regions)	Efficiency, flexibility	MAC, welfare loss
Alexeeva and Anger (2007)	PACE	GE	2020	EU 27	Efficiency, flexibility	MAC, welfare loss
den Elzen et al. (2007)	FAIR-EU	PE	2020	EU 27	Efficiency, equity	Total costs, MAC
Stankeviciute and Criqui (2008)	POLES + ASPEN	PE	2020	EU 25	Efficiency, other energy and climate policies	Total costs, MAC

The early research (Capros and Mantzos, 2000, IPTS, 2000, ERM and Euroelectric, 2002, Sijm et al., 2002, Proost and Van Regemorter, 2003) focuses on one of the most important efficiency mechanisms – the international pollution trading – while it does not address the other two
must amendments be made for inflation or exchange rate, but care must be exerted to ensure that the underlying assumptions on the projected period, economic growth and demand for energy are reasonably comparable $ ^{13} $ . Another reason that makes a comparison of costs estimates challenging is the fact that early studies address policy options that somewhat differ from the actual EU ETS legislation approved by the European Commission in 2003. In addition to that, these studies estimate costs at different points in time, cover a different number of countries and, more importantly, use different costs definitions. Because of all these reasons a comparison of the costs estimates necessarily has many limitations.

From each of the studies included in this analysis we chose the cost measure relative to the scenario which most resembles the EU ETS. Each cost estimate is derived by comparing the results of the policy scenario (here, the EU ETS) to the reference scenario used as the benchmark. Sometimes both the reference and the policy scenario assume compliance with certain environmental targets. However, for the purpose of our study, the BAU scenario is more convenient than any other reference scenario, since the EU ETS is an actually working system, and its effects on the economy would be understood better if compared with the results of the BAU scenario where no environmental regulation is introduced. The comparison of the EU ETS scenario with other policy scenario would conceal the ex-ante costs of the EU ETS, if the costs estimate of reference policy scenario is not zero $ ^{14} $ .

To allow as clear a comparison as possible, we have adjusted the MAC and total output measures for exchange rate and inflation using the 2005 harmonised consumer price index.

## Marginal abatement costs

Most of the selected studies provide estimates of MAC that are equivalent to the equilibrium prices of tradable $ CO_{2} $ permits for the EU ETS scenario (see Figure 1 and Table 4). The MAC adjusted for inflation and exchange rate range from $ 0\ €_{2005} $ to $ 159\ €_{2005} $ . The average estimate of MAC provided by this representative sample of studies is $ 47\ €_{2005} $ which is far above the average actual market EUA price in the pilot EU ETS phase.
MAC per tonne of CO $ _{2} $ , EUR 2005

Figure 1 Ex-ante cost measures of the EU ETS: marginal abatement costs

Table 4 Marginal abatement costs UPDATE

Reference	Model	Modelling approach	Projected period (up to)	MAC per tonne of CO2 (EUR 2005)
Early studies
Capros and Mantzos (2000)	PRIMES	PE	2010	37.58
IPTS (2000)	POLES	PE	2010	55.30
Sijm et al. (2002)	MARKAL-MATTER	PE	2010	17.87
Babiker et al. (2004)	EPPA-EU	GE	2010	159.39
COWI (2004)	GTAP-ECAT	GE	2010	31.36
Late studies
Bohringer et al. (2005)	SIMAC	PE	2005	0.00
Kemfert et al. (2006)	GTAP-E	GE	2005-2006	1.89
Klepper and Peterson (2005)	DART	GE	2012	6.31
Russ et al. (2005)	POLES	PE	2025	61.29
Russ et al. (2005)	GEM-E3	GE	2025	102.89
Peterson (2006)	DART	GE	2012	6.01
Alexeeva and Anger (2007)	PACE	GE	2020	28.89
Den Elzen et al. (2007)	FAIR-EU	PE	2020	108.54
Stankeviciute and Criqui (2008)	POLES + ASPEN	PE	2020	35.60

## Total and average abatement costs

As there is no quick fix to correct for incomparable modelling features such as different baseline scenarios, different projection periods and different assumptions, the total cost of the predicted emission reduction from any one study cannot be directly compared to the total costs from another study. One way of making a useful comparison is to plot the total costs of $ CO_{2} $ reduction against the $ CO_{2} $ emissions reduction achieved (Ellerman et al., 2005). In so doing we can have a visual impression of the average cost per tonne of $ CO_{2} $ emissions reduced: total abatement costs divided by the reductions in $ CO_{2} $ emissions, which is given by the slope of the rays through the origin from each of the points in Figure 2.
Table summarises this information which was available for 7 selected studies.

Figure 2 Ex-ante cost measures of the EU ETS: total costs

The results presented in Figure 2 and Table 5 show that the $ CO_{2} $ abatement efforts fall in the range from 333 to 1,182 tonnes while the costs needed to reach such reduction targets vary from 7.7 to 55.4 billion € $ _{2005} $ . The average cost estimates are given in the last column of Table 7, they range from a maximum of 80 € $ _{2005} $ per tonne of $ CO_{2} $ removed (COWI, 2004) to a minimum of 21 € $ _{2005} $ per tonne of $ CO_{2} $ reduced (Capros and Mantzos, 2000, den Elzen, 2007).

Table 5 Ex-ante cost estimates of the EU ETS: average costs

Reference	Model	CO2 abatement (million tonnes)	Total abatement costs (billion 2005 EUR)	Average costs (EUR/tonne)
Capros and Mantzos (2000)	PRIMES	372	7.7	21
IPTS (2000)	POLES	638	20.3	32
Sijm et al. (2002)	MARKAL-MATTER	773	22.2	29
COWI (2004)	GTAP-ECAT	689	55.4	80
Bohringer et al. (2005)	SIMAC	333	18.7	56
den Elzen et al. (2007)	FAIR-EU	1182	24.8	21
Stankeviciute and Criqui (2008)	POLES + ASPEN	840	20.1	24

## Welfare measures

Few of the studies use a general equilibrium modelling approach and provide the costs of the EU ETS as a welfare change from the baseline welfare level. As displayed in Figure 3, welfare losses vary from -0.1 percent (Proost and Van Regemorter, 2003) to -3 percent (Alexeeva-Talebi and Anger, 2007) relative to the BUA welfare level.
Figure 3 Ex-ante costs of the EU ETS: welfare loss

### 4.3 What drives models results?

What drives these models’ results? Why do the ex-ante costs of the EU ETS differ so much among selected models? As mentioned before, according to Repetto and Austin (1997), Barker et al. (2002), Fischer and Morgenstern (2005) and Barker et al. (2006), the key assumptions that explain the different models’ results are the assumptions about the status quo (baseline) of the economic system; the assumptions about the structural modelling features; and the assumptions about the climate change policy (here, assumptions about the salient features of the EU ETS).
modelling studies should be more forward-looking and anticipating the policy process itself. At the same time, they should be as much backward-looking as possible, i.e. incorporate history which has been already learnt either by the policy of interest itself, or by previous similar policies.

This enquiry for more detailed anticipation of the economic fundamentals and policy options suggests that, in the rapidly changing economic and policy environment, it is more constructive to simulate the effects of the environmental policies in the short-run rather than in the long run. However, as most of our selected models lack these features, their outcomes, i.e. attempts to monetise the costs of the EU ETS, should be used as a policy guidance tool rather than a policy decision tool. The purpose should be such that the ex-ante work can be as reliable as possible and can be compared with the ex-post outcomes. When the similar assumptions are used for predicting and evaluating any environmental policy, predictions and evaluations can be parallel to each other allowing more room for a comprehensive discussion as to why differences or similarities of ex-ante and ex-post cost outcomes occur.

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