The market for tradable GHG permits under the Kyoto Protocol: a survey of model studies

Energy Economics 25 (2003) 527–551

The market for tradable GHG permits under the Kyoto Protocol: a survey of model studies Urs Springer* ¨ ¨ Wirtschaft und Okologie Institut fur (IWOe-HSG), University of St. Gallen, Tigerbergstrasse 2, CH-9000 St. Gallen, Switzerland

Abstract This paper gathers results from 25 models of the market for tradable greenhouse gas (GHG) emission permits under the Kyoto Protocol. Due to diverging projections of emissions growth and different modeling approaches, the model results differ substantially. The average market volume is approximately 17 and 33 billion USD under global trading and Annex B trading, respectively. Including non-carbon GHG lowers compliance costs and permit prices. In the absence of the US, permit demand roughly equals ‘hot air’ from the former Soviet Union. These countries can increase their revenues from selling permits by restricting supply, which raises the permit price. 䊚 2002 Elsevier Science B.V. All rights reserved. Keywords: Climate change; Kyoto Protocol; Modeling; Tradable permits JEL classifications: F21; Q43

1. Introduction The Kyoto Protocol is the first international agreement on emissions of greenhouse gases (GHG) that contains explicit emission limits and timetables (Barrett, 1998; Grubb et al., 1999). In the Protocol, a group of industrialized countries agrees to stabilize or reduce its GHG emissions in the commitment period 2008–2012 by 5.2% on average (compared to their 1990 emissions level). The Kyoto Protocol provides spatial flexibility through the so-called Kyoto Mechanisms: countries may buy and sell their assigned amounts of emissions, which is called international *Tel.: q41-71-224-2330; fax: q41-71-224-2722. E-mail address: [email protected] (U. Springer). 0140-9883/03/$ - see front matter 䊚 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 4 0 - 9 8 8 3 Ž 0 2 . 0 0 1 0 3 - 2

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Fig. 1. Model types.

emissions trading (IET). Emission reductions achieved in projects in developing countries can be sold to an industrialized country under the rules of the clean development mechanism (CDM). Project-based trades between industrial countries are called joint implementation (JI). Together with the quantified reduction commitments, these mechanisms constitute an international system of tradable GHG permits (Hahn and Stavins, 1999). Today, there is still large uncertainty about this emerging market. How large will it be? What is the likely range of permit prices? Numerous studies have tried to quantify the costs of reaching the commitments of the Kyoto Protocol and the size of the market for tradable GHG emission permits. This paper gives an overview of the results and methods used in these studies. To our knowledge, this is the first comprehensive survey of studies of the global market for tradable GHG emission permits under the Kyoto Protocol. The range of models reported here is broader than those in Weyant (1999), covering integrated assessment models, computable general equilibrium (CGE) models, Neo-Keynesian macroeconomic models, energy system models, and hybrid models. Different model types as well as diverging projections of emissions growth are the main reasons for the large differences found between the model results. A common finding of all studies is that emissions trading lowers the cost of reaching the commitments of the Kyoto Protocol. Under an unrestricted global trading regime, costs are lower and the market volume smaller than under a trading scenario where only countries with quantified emission targets (Annex B countries) trade. Including all GHG in the analysis yields lower costs and permit prices than the models that focus only on carbon dioxide. Limits on emissions trading as advocated by the European Union have been found to increase abatement costs and raised concerns about market

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power. The withdrawal from the Kyoto Protocol of the largest emitter of GHG, the US, has a strong influence on the environmental effectiveness of the Kyoto treaty and the emissions trading market it establishes. Together with the generous credits for enhancements of agricultural and forest sinks granted to a few countries in Bonn and Marrakesh, the absence of the US implies that permit prices approach zero. However, Russia, Ukraine, and Eastern European countries can increase their revenues from selling emission permits by restricting permit supply, which raises the price of tradable GHG permits. Model results can indicate the potential range of permit prices and the likely size of the emerging market for tradable GHG permits. However, their limitations and implicit assumptions should be noted carefully. For this reason, the main model types are classified and briefly described in the next section of this paper. In Section 3, estimates of permit prices and quantities traded under a global trading regime are shown and compared to those under an Annex B trading regime. Section 4 explores the main reasons for the large divergence among the results. The potential effects of limits on emissions trading on market prices and trade volumes are discussed in Section 5. Finally, we assess the consequences of the absence of the US from the Kyoto Protocol (Section 6). Section 7 contains a summary. 2. Model types There are many ways of analyzing the effects of climate policy and international permit trading on economic systems. No model can deal with all aspects in detail, each focuses on a certain area and omits some factors. For this reason, comparisons should be made carefully. The models whose results will be presented below (Appendix A) can broadly be classified into five categories.1 Fig. 1 provides a schematic overview of the main categories and combinations between them. 2.1. Integrated assessment models Models like AIM, GRAPE, or RICE represent both physical and social processes over a long period of time. They help to answer the questions of whether, when and how to address the problem of climate change. Full-scale integrated assessment models consider human activities, atmospheric composition, climate and sea level change, and ecosystems, but many models do not cover all areas (Weyant et al., 1996). The economic components of these models belong to one of the categories described below. 2.2. Computable general equilibrium models CGE models determine the new equilibrium state of an economic system after an exogenous disturbance, e.g. the introduction of a carbon tax. Because aggregate data 1 As Grubb et al. (1993) note, there is ‘no universal or accepted way of classifying models’. Our classification distinguishes the three model types described by Hourcade et al. (1996) plus another two categories, integrated assessment models and emissions trading models.

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on all sectors of an economy are used, these models are also called top–down models. They are either static or dynamic. Static models compare a state—usually the status quo—with a single future state. In dynamic models, the investment in each period determines the capital stock of the next period and therefore its production structure and emission level as well. The main advantage of CGE models like EPPA, GEM-E3, and GREEN is their ability to capture the influence of energy policy on other industry sectors and often also on international trade. A major shortcoming is the assumption of perfect markets, which are supposed to be in an equilibrium at the starting point of the analysis. Furthermore, CGE models compute a new equilibrium after a disturbance, but they do not provide an accurate picture of the adjustment path and therefore underestimate transition costs. The assumption of a perfect labor market is one of the reasons why such models often do not find evidence for double dividends (Hourcade et al., 1996). Monetary policy and labor market imperfections are ignored by most models of this type, except for G-CUBED, which includes some of these factors. 2.3. Emissions trading models In several recent papers (Ciorba et al., 2001; Eyckmans et al., 2001; Holtsmark ¨ and Maestad, 2002; Jotzo and Michaelowa, 2002; Loschel and Zhang, 2002; Stevens and Rose, 2002), marginal abatement cost (MAC) curves are used to analyze IET. MAC curves are mostly generated by running a CGE model under a changing emissions constraint. This yields shadow prices, which can be plotted as a function of the level of abatement.2 MAC curves can also be estimated econometrically, as in Ciorba et al. (2001). The ECN model derives its MAC curves from energy system models. MAC curves are robust and convenient tools for analyzing IET. However, important spill-over effects (such as carbon leakage) cannot be captured by these emissions trading models. This is also true for partial equilibrium models such as the one used by Holtsmark and Maestad (2002). 2.4. Neo-Keynesian macroeconomic models Neo-Keynesian macroeconomic models also belong to the class of top–down models. Unlike general equilibrium models, they take monetary policy into account and allow for imperfect competition and unemployment. Aggregate output is modeled as a function of capital and labor inputs, and input–output tables are used to represent transactions between sectors (Hourcade et al., 1996). The term ‘NeoKeynesian’ refers to the fact that aggregate demand is the principal determinant of the size of the economy (Zhang and Folmer, 1998). Macroeconomic models are of limited use for long-term analysis and they tend to be highly complex and economyspecific (Grubb et al., 1999). 2

See Ellerman et al. (1998) for a detailed description.

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2.5. Energy system models These models represent the energy sector in much more detail than both CGE and macroeconomic models. Because they use disaggregated data of existing and emerging technologies, they are often referred to as bottom–up models. Through linear programming, these models determine an optimal technology mix under the specified restrictions. Examples of such engineering models are MARKAL and POLES. Most bottom–up models exist only on a national scale. Bahn et al. (1999, 2001), Kanudia and Loulou (1998) combine national energy system models to represent a system of international emission trading. Bottom–up models have two main disadvantages: first, the demand for energy is exogenously specified and independent of prices (Zhang and Folmer, 1998). Second, bottom–up models solely represent the energy sector. Therefore, they cannot take account of linkages of the energy sector with the rest of the economy (Grubb et al., 1993). To overcome this problem, engineering models are often linked to (or integrated in) CGE models. MERGE, for example, contains a bottom–up representation of the energy sector and a top–down perspective on the rest of the economy. 3. Permit prices and trade volumes All models discussed here show that emissions trading delivers large cost savings. In contrast to most studies, we do not report the results from no-trading scenarios. Rather, we focus on estimates of permit prices and quantities traded under different trading regimes and assumptions. 3.1. Global trading Permit prices and traded quantities estimated by 13 models for a full global trading scenario are shown in Table 1. Full global trading means that industrialized countries (Annex B countries3) and developing countries (non-Annex B countries) participate in the market for tradable GHG emission permits. Non-Annex B countries can sell emission credits that result from emission reductions below their businessas-usual scenario, which can be interpreted as an optimistic version of the CDM. All numbers refer to the base runs of the models, results from sensitivity analysis are not reported. The results in Table 1 are from models (or model runs) that consider only carbon dioxide (CO2) emissions. Models that also incorporate emissions of other GHG are presented in the next section. Except for MERGE, none of the models includes terrestrial sinks and only GRAPE includes transaction costs. Prices reported in dollar values of a different year are converted to 2000 USD 3 Annex B of the Kyoto Protocol contains the following countries: Australia, Austria, Belgium, Bulgaria, Canada, Croatia, Czech Republic, Denmark, Estonia, European Union, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Latvia, Liechtenstein, Lithuania, Luxembourg, Monaco, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland, USA.

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Table 1 Prices, quantities, and trade volumes for global trading—CO2 only Model

Permit price (2000 USDyton CO2)

Quantity (million tons CO2)

Trade volume (million 2000 USD)

AIM ECN EPPA G-CUBED GEM-E3 GRAPE GREEN MERGE MS-MRT PACE POLES RICE-98 R&S

12 4 8 7 8 13 7 22 10 14 6 5 1

1833 2119 3428 3318 – 1540 2427 – – – 2295 – 1214

21 996 8476 27 424 23 226 – 20 020 16 989 – – – 13 770 – 1214

9 8

2272 2207

16 639 18 505

Average Median

¨ Sources: Bernstein et al. (1999b), Bohringer (2000), Capros (1999), Criqui and Viguier (2000), Ellerman et al. (1998), Kainuma et al. (1999), Kurosawa et al. (1999), Manne and Richels (1999), McKibbin et al. (1999b), Nordhaus and Boyer (1999), Stevens and Rose (2002), Sijm et al. (2000), van der Mensbrugghe (1998), and own calculations.

at a rate of 1.6% per annum, which corresponds to the average yearly increase of the US producer price indices (manufacturing) for the period 1989–1999 (OECD, 2001). Quantities are stated in (metric) tons CO2 . If necessary, they were transformed from carbon to carbon dioxide by multiplication with the factor 44y12. The spectrum of estimated permit prices is very broad, ranging from 1 to 22 USD per ton CO2. Stevens and Rose (2002) (partly) explain their low price estimate by the fact that they do not use a CGE model that includes second order effects. The low estimate of the ECN model (Sijm et al., 2000) is due to the technologyoriented bottom–up approach which includes a considerable amount of options at negative cost. If negative cost options (and ‘hot air’4 ) are excluded, price (15 USD) and quantity estimates (1362 million tons) are very similar to the results of the other studies. Only eight models explicitly estimated the amount of permits traded. Quantities range from 1214 million tons CO2 (R&S) to 3428 million tons (EPPA). Here, the range of the results is smaller than with price estimates. Trade volumes are calculated by multiplying permit prices with the number of permits traded in each model. Trade volume estimates are much closer together than permit prices and quantities (except for the R&S model). Since low prices lead to 4 Along with the decline of industrial production, GHG emissions in the former Soviet Union have decreased strongly since 1990. Most models predict that Russia’s emissions will be below its assigned amounts during the first commitment period (Victor et al., 2001). This ‘hot air’ (emission reductions which did not result from any policy measures) can be sold to other countries or banked for future use.

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Table 2 Prices, quantities, and trade volumes for Annex B trading—CO2 only Model

Permit price (2000 USDyton CO2)

Quantity (million tons CO2)

Trade volume (million 2000 USD)

AIM ECN ENEA EPPA G-CUBED GEM-E3 GRAPE GREEN GTEM MERGE MS-MRT OXFORD POLES RICE-98 R&S WORLDSCAN

21 19 18 44 18 17 22 18 36 74 29 71 17 18 3 6

1467

950 2592

30 807 – 11 880 55 660 36 306 – 28 226 27 054 – – 53 708 76 254 24 939 – 2850 15 552

Average Median

27 19

1466 1467

33 021 28 226

– 660 1265 2017 – 1283 1503 – – 1852 1074 1467 –

Sources: Bernstein et al. (1999b), Bollen et al. (1999), Capros (1999), Ciorba et al. (2001), Cooper et al. (1999), Criqui and Viguier (2000), Ellerman et al. (1998), Kainuma et al. (1999), Kurosawa et al. (1999), Manne and Richels (2000), McKibbin et al. (1999b), Nordhaus and Boyer (1999), Stevens and Rose (2002), Sijm et al. (2000), Tulpule´ et al. (1999), van der Mensbrugghe (1998), and own calculations.

more permit trade and vice versa, the products of these two factors exhibit less variance. Under global trading, the average trade volume of all models is 16 639 million USD. For the sake of simplicity, the models use a scenario where all countries have to reach their Kyoto target in the year 2010. In reality, these targets have to be met on average between 2008 and 2010. This means that the trade volumes refer to annual trades, because permits have to be bought every year if a country’s emissions exceed its assigned amounts. 3.2. Annex B trading Under an Annex B trading scenario, prices are higher and traded quantities lower than in the global trading scenario (Table 2). Again, the results differ significantly: the lowest price estimate is 3 USD per ton of CO2 (R&S), the top estimates are 71 (Oxford) and 74 USD (MERGE). Relatively few estimates are very high which is reflected by the fact that the average price clearly exceeds the median price. The high permit price estimated by the Oxford model can again be attributed to the model type: Neo-Keynesian macroeconomic models are based on estimated values of how imperfect markets react to changes in their environment. These frictions and imperfections are not included in bottom–up models like ECN, that

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report much lower prices. However, model structures alone cannot always explain the results: WORLDSCAN, a CGE model, comes up with a very low price of 6 USD, whereas the EPPA model, another CGE model, produces the second-highest estimate (44 USD). As expected, the quantity traded is lower under Annex B trading because of the smaller number of potential sellers and a higher permit price. The average quantity traded is 1466 million tons CO2 under Annex B trading, compared to 2272 million tons under global trading. Interestingly, most models predict a higher trade volume in monetary terms under Annex B trading than under global trading. The average trade volume under Annex B trading is 33 021 million USD, about twice as much as under global trading.5 Thus, the size of the market for tradable GHG permits is comparable to the world market for copper with total annual exports worth 30 168 million USD (UNCTAD, 2000). Several authors have tried to determine the market share of the three Kyoto mechanisms (Table 3). Estimates of the value of annual permit trade under JI and IET range from 1727 million USD (Zhang) to 11 917 million USD (Haites, 1998). The market share of these two mechanisms is expected to lie between approximately a quarter and half of the global market for tradable GHG permits in the first commitment period. Emission reductions generated under the CDM have a larger share of the market, estimates range from 55 to 77%. In monetary terms, the CDM market volume is expected to lie between 3212 and 21 208 million USD. The market could be even more separated due to liability rules or political restrictions. Buyer liability, for example, is likely to lead to a differentiation of the market: permits from countries which are believed to comply with reduction obligations would benefit from a premium, whereas permits from countries with uncertain status would be paid a lower price (Baron, 1999). Furthermore, political constraints could be placed on the use of the Kyoto Mechanism by individual countries, for example by not allowing certain project types under the CDM, which may influence the demand for permits of specific project types. Probably the most difficult practical question regarding the project-based mechanisms JI and the CDM are emission baselines (Chomitz, 1998; Ellis and Bosi, 1999). The baseline represents the amount of GHG that would have been emitted without the implementation of a project. If baseline estimates are too low, the amount of permits resulting from a project will be understated and project costs overstated. If baseline estimates are too high, the project will generate excess permits which can lead to an overall increase in emissions solely due to emissions trading. If very complicated baseline methods are chosen, transaction costs may turn out so high that the cost advantage of the project-based mechanisms disappears. The model studies do not explicitly specify a baseline rule. Instead, they make assumptions about the costs of abatement options in non-Annex B countries based on the efficiency of technologies and environmental policies in these countries. 5

Note that not all models have been run under both scenarios.

Modely author

ECN EPPA G-CUBED GREEN HAITES POLES SGMa ZHANGa

IET and JI

CDM

Traded quantities (million tons CO2)

Market volume (million 2000 USD)

Market share (%)

Traded quantities (million tons CO2)

Market volume (million 2000 USD)

Market share (%)

880 774 1503 972 1192 986 1309 576

3520 6189 10 523 6802 11 917 5918 10 472 1727

42 23 45 40 36 32 44 35

1239 2651 1815 1456 2108 1606 1665 1071

4956 21 208 12 705 10 190 21 083 12 848 13 317 3212

58 77 55 60 64 68 56 65

Sources: Criqui and Viguier (2000), Ellerman et al. (1998), Haites (1998), MacCracken et al. (1999), McKibbin et al. (1999b), Sijm et al. (2000), van der Mensbrugghe (1998), Zhang (2000), and own calculations. a Including all GHG.

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Table 3 Market share of the Kyoto Mechanisms

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Therefore, all studies implicitly assume that there are no expensive baseline estimation methods. 4. What explains the wide divergence between model results? The results of the model studies given above differ substantially. Many authors warn that their results should be interpreted carefully and stress that they can merely indicate an order of magnitude and make relative, but not absolute statements about costs. But why do results differ so much? The answer is simple: because there are so many uncertainties involved. Future emissions as well as GDP and population growth have to be projected, technological and policy scenarios need to be determined, and supply and demand elasticities estimated. Given all these unknown model inputs, it is not surprising that the results differ considerably. However, we do not believe that the market for GHG permits is an exceptional case. Future prices are always hard to predict. Estimates of permit prices in the US SO2 market have turned out wrong, even though that market was much smaller and less complex (Ellerman et al., 2000). If attempts to predict prices regularly fail in the stock markets, why should they be more accurate in a completely new market like the one for tradable GHG emission permits? We distinguish two main causes for the differences between model results. The first category includes all kinds of projections: projections of GDP growth, emissions growth, technological change, etc. The second category are modeling issues. The scope of the analysis as well as modeling approaches differ widely among emissions trading models. Of course, all models are—more or less—sensitive to parameter choice.6 4.1. Projections The emission limitation and reduction commitments in the Kyoto Protocol refer to emission levels in 1990. This means that the actual amount of abatement equals 5.2% plus the average increase in GHG emissions from 1990 to 2010. Therefore, the most important projection for assessing the costs of reaching the Kyoto limits is predicted emissions growth. Table 4 shows estimates of CO2 emissions in 1990, the reference scenario (i.e. the non-intervention scenario) for 2010, the Kyoto targets, and the resulting actual reduction requirements from several models and the US Energy Information Agency. A first notable point is that every model uses a different figure for 1990 emissions. This is due to the fact that these numbers have been adjusted repeatedly by the Parties to the United Nations Framework Convention on Climate Change (UNFCCC), so that researchers got different estimates of 1990 emissions depending on the year they collected them. 6 CGE models are probably most sensitive to parameter choice. Especially the substitution elasticities between energy and labor as well as between different energy forms have a large influence on the results.

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Table 4 Reference scenarios for CO2 emissions (Annex B) Modely projection ECN EPPA GEM-E3 GTEM GREEN IEO 2000 POLES R&S WORLDSCAN

Emissions (million tons CO2) 1990

2010

Kyoto target

14 044 14 509 – – 16 804 14 315 14 450 12 612 –

15 664 18 172 – – 20 761 15 602 16 331 14 042 –

13 194 13 765 – – 16 009 13 599 13 673 11 981 –

Reduction required (%) y16 y24 y10 y25 y23 y13 y12 y15 y22

Sources: Bollen et al. (1999), Capros (1999), Criqui and Viguier ´(2000), EIA (2000), Ellerman et al. (1998), van der Mensbrugghe (1998), Sijm et al. (2000), Tulpule et al. (1999), Stevens and Rose (2002), and own calculations.

Different reference scenarios lead to the broad range of actual reductions required shown in the last column of Table 4. No study assumes less than 10% total average reduction, the upper bound is 25%. Projections of population growth, GDP growth, and the rate of technological progress also vary from model to model, but are not compared here in detail. 4.2. Modeling issues The major question regarding model structures is the paradigmatic controversy whether bottom–up or top–down approaches are more adequate for the analysis of energy markets (Grubb et al., 1993). In general, bottom–up studies have provided lower estimates of the costs of climate policy than top–down models. This is also true for the studies of emissions trading under the Kyoto Protocol. Bottom–up models represent the energy sector in great detail. Based on performance and cost information about existing (and sometimes future) technologies, costminimizing combinations of technologies are determined through linear-programming. The purpose of bottom–up models is to answer the question ‘At what cost?’ can we achieve a certain goal or implement a policy (Jacoby, 1998). Hourcade et al. (1996) use the metaphor of a ‘magnifying glass’ that is used to look at the energy sector. The consequence of focusing on the energy sector is that economy-wide effects and feed-back effects are not captured. Furthermore, many bottom–up models do not endogenize human behavior. In other words, consumer and producer reactions are not determined within the model, but by external assumptions. Top–down models have a broader perspective and cover all sectors of an economy (and many regions in world models). This enables them to capture cross-sectoral and feed-back effects. The question to be answered by top–down models is: ‘At

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what price?’ (Jacoby, 1998). Both macroeconomic and general equilibrium models use aggregate data. This leads to more robust results in the long-term, because aggregated variables are more reliable than disaggregated variables. Of course, the representation of the energy sector in top–down models is less accurate. Therefore, these models cannot account for all technical possibilities and the substitution potential they provide. All sorts of hybrid models have been developed recently, so the above characterization may pronounce the differences too strongly. Energy sector models usually include ‘negative cost’ or ‘no regret’ measures. These measures are changes in the (mix of) technologies that lead to energy savings that are higher than the costs of implementing and running the technology. Economists tend to treat this claim skeptically. One counter-argument is that correcting market imperfections and policy measures to make people realize these savings are not costless. In any case, the existence of ‘no regret’ measures has large implications for the supply side of an emissions trading market. If many such measures exist, an absolute reduction goal like the Kyoto obligation obviously becomes easier to achieve the more countries participate, providing reduction opportunities at no cost. Top–down and bottom–up models also differ in the treatment of technological change, a crucial issue for long-term problems like climate change.7 In earlier energy system models, technological change was only related to the technologies specified in the models. Recently, technological learning has been incorporated into several bottom–up models (Messner, 1997; Seebregts et al., 1999). Some top– down models with endogenous technological change have been developed as well (Goulder and Schneider, 1999; Goulder and Mathai, 2000; Van der Zwaan et al., 2002), but in those models of IET described here, technological change is (still) exogenous. The MERGE model, for example, specifies future carbon free technologies (back-stop technologies) and uses an autonomous energy efficiency index, which is 40% of the GDP growth rate for most regions. Hourcade et al. (1996) state that ‘the differences in results between top–down and bottom–up modeling analysis are thus rooted in a complex interplay among differences in purpose, model structure, and input assumptions.’ They conclude that despite the recent developments of hybrid models, the difference in perspective and thinking about energy markets remains. A major shortcoming of most models is the fact that they include only carbon dioxide emissions. This limitation of the scope of the models is mainly due to data problems. There is large uncertainty in the data on emissions of the other GHG controlled by the Kyoto Protocol8, particularly those from developing countries. Holtsmark and Maestad (2002), Jensen and Thelle (2001), MacCracken et al. (1999), Reilly et al. (1999), Sijm et al. (2000), Zhang (2000) consider all six GHG in their analysis of the Kyoto Protocol. Brown et al. (1999), Burniaux (2000), Manne and Richels (2000) analyze emissions of the three major GHG, namely 7

¨ See Grubb et al. (2002), Loschel (2002) for a survey. Besides carbon dioxide, the Kyoto Protocol controls emissions of methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF6). 8

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Table 5 Reference scenarios for emissions of multiple GHG (Annex B) Model

CICERO ECN EDGE GREEN GTEM IGSM SGM ZHANG

GHG

6 6 6 3 3 6 6 6

Emissions (million tons CO2) 1990

2010

Kyoto target

17 819 17 473 17 614 – 17 640 17 809 18 014 18 112

19 390 19 458 18 344 – 21 620 22 282 20 090 19 062

16 907 16 766 16 723 – 16 758 16 753 17 114 17 171

Reduction required (%) y13 y14 y9 y18 y22 y25 y15 y10

GHG: 6: CO2, CH4, N2O, PFC, HFC, SF6 ; 3: CO2 , CH4 , N2 O. Sources: Brown et al. (1999), Burniaux (2000), Holtsmark and Maestad (2002), Jensen and Thelle (2001), MacCracken et al. (1999), Reilly et al. (1999), Sijm et al. (2000), Zhang (2000), and own calculations.

carbon dioxide, methane and nitrous oxide.9 Table 5 shows the reference scenarios of these models and the corresponding reduction requirements. Including emissions of non-carbon gases raises both 1990 emissions and reference scenario emissions. Interestingly, estimates of 1990 emissions are quite similar across the studies. Projected emissions in 2010 range from 18 344 (EDGE) to 22 282 million tons carbon equivalent (IGSM), which implies reductions between 9 and 25%. Despite of their differences, the multi-gas studies indicate what the general consequences of including non-carbon gases are: first, the costs of compliance are reduced, even though reference case emissions in terms of carbon equivalents increase. This is possible because all non-CO2 GHG have a larger global warming potential than CO2.10 Since most of them are less costly to abate, some of the reductions will be shifted to other gases. Of course, carbon dioxide emissions still need to be cut. Lower costs imply a lower permit price. Estimates range from 11 to 38 USD per ton CO2 equivalent under Annex B trading. If trading takes place across the globe, prices are expected to lie between 3 and 8 USD per ton (Table 6). The low prices found by Jensen and Thelle (2001), Sijm et al. (2000), Zhang (2000) can be explained by the low emissions growth they assume. Second, the quantity traded is likely to increase. Sijm et al. (2000) estimate that global permit trade increases from 2119 million tons CO2 equivalent to 2365 million tons, if all GHG are included. However, Reilly et al. (1999) find that the amount of ‘hot air’ in the system is nearly 60% lower due to the growth of non-carbon gases in the former Soviet Union. This decrease in cheap permits could ultimately lead to lower quantities sold and purchased through IET. 9

The analysis of Manne and Richels (2000) covers only emissions of the US. See Manne and Richels (2000) for a discussion of the use and adequacy of global warming potentials in climate policy analysis. 10

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Table 6 Prices, quantities, and trade volumes—multiple GHG Model

GHG

Permit price (2000 USDyton CO2 equivalent)

Quantity (million tons CO2 equivalent)

Trade volume (million 2000 USD)

Annex B trading CICERO EDGE GREEN GTEM MERGE SGM

6 6 3 3 3 6

16 11 17 26 38 23

1459

23 344 – 32 844 – – 20 907

Global trading ECN SGM ZHANG

6 6 6

3 8 3

2365 2974 1646

– 1932 – – 909

7095 23 792 4938

GHG: 6: CO2, CH4, N2O, PFC, HFC, SF6 ; 3: CO2 , CH4 , N2 O. Sources: Brown et al. (1999), Burniaux (2000), Holtsmark and Maestad (2002), Jensen and Thelle (2001), MacCracken et al. (1999), Manne and Richels (2000), Sijm et al. (2000), Zhang (2000), and own calculations.

Once again, the effect on the trade volume is a priori not clear. Sijm et al. (2000) estimate that the trade volume is smaller under a scenario where all GHG are included than under a CO2-only scenario. On the other hand, the GREEN model including all GHG comes up with a higher trade volume in monetary terms than the version covering only carbon dioxide. Since it is rather unlikely that all noncarbon gases will be subject to a cap-and-trade system, reductions of these gases will probably enter the system through project-based crediting systems like JI and the CDM, increasing both the share of these mechanisms and reduction costs. Note that all studies simulate perfect tradable permit systems, i.e. they assume that governments set up national tradable permit systems and allow for international permit trading. However, the Kyoto Protocol leaves the Parties full sovereignty in their choice of instruments, and most governments will probably not choose tradable permits as their sole instrument of national climate policy (Hahn and Stavins, 1999). We should rather expect a multitude of rules and instruments, including ‘pure’ tradable permit systems, tax regimes, fixed quantity standards, and hybrid forms.11 Hahn and Stavins (1999) conclude that ‘a truly cost-effective IET program is not compatible with the notion of full domestic sovereignty regarding instrument choice’. This implies that the models discussed above understate the true costs of the Kyoto Protocol. Furthermore, the trade volumes reported above only represent international trade. Yet, total trade in GHG permits comprises international and national trade. If some countries implement a national emissions trading system, the total market volume will be larger than indicated by those numbers. 11 Pizer (2002), McKibbin and Wilcoxen (2002) advocate hybrid systems that combine a carbon tax with elements of permit trading.

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5. Supplementarity Article 17 of the Kyoto Protocol states that emissions trading ‘shall be supplemental to domestic actions’. To achieve this, the European Union proposed a quantified restriction on emissions trading which consists of an elaborate set of rules restricting both imports and exports of permits. The EU proposal turned out to be one of the main obstacles to an agreement with the US in the post-Kyoto negotiations. Several researchers examined the effects of various forms of restrictions on emissions trading under the Kyoto Protocol (Bollen et al., 1999; Bernstein et al., 1999b; Criqui et al., 1999; Ellerman et al., 1998; Kemfert, 2000). They find that such restrictions lead to diverging marginal costs among Annex B countries and higher total abatement costs. Export limits increase prices and import limits decrease prices, traded quantities are lower in both cases. The specific rule proposed by the EU12 is analyzed by Zhang (2001), Ellerman and Wing (2000), Holtsmark and Maestad (2002). Ellerman and Wing (2000) examine the consequences of the ceiling using MAC curves from the EPPA model. They find that when the however clause operates (i.e. domestic action is undertaken and can be demonstrated), only the export limit is binding. In that case, the ceiling reduces aggregate cost savings by 28%. Without the however clause, the gains from trading almost entirely disappear and only 18 megatons carbon dioxide are transferred at a price of 58 USD per ton CO2. Their main result is that ‘a concrete ceiling provides a coordinating mechanism for restricting demand that could be as effective as overt collusion among buyers or the exercise of market power by a significant importer’. In other words, supplementarity represents an ‘invitation to monopsony’ (Ellerman and Wing, 2000). Holtsmark and Maestad (2002) simulate the effect of the EU proposal in a partial equilibrium model. They find that ‘the proposal in practice almost exclusively will put limits on the seller side of the permit market’. This causes permit prices to rise from 16 to 26 USD per ton carbon dioxide. Permit sales from Russia decrease from 764 megatons CO2 equivalent to 153 megatons. Most countries are not affected by the ceiling and can purchase the desired quantity of permits. Contrary to the results of the other authors, Zhang (2001) finds that restrictions on permit trading reduce the international permit price, from approximately 3 to 1 USD per ton CO2 equivalent. The ceiling forces the US and Japan to achieve a much higher share of their reduction obligation through domestic action (67.7% 12 ‘Net acquisitions by an Annex B Party for all three Kyoto mechanisms together must not exceed the higher of the following alternatives: (a) 5% of: its base year emissions multiplied by 5 plus its assigned amount, divided by 2; (b) or 50% of: the difference between its annual actual emissions in any year of the period from 1994 to 2002, multiplied by 5, and its assigned amount. (c) Net transfers by an Annex B Party for all three Kyoto mechanisms together must not exceed: 5% of: its base year emissions multiplied by 5 plus its assigned amount, divided by 2. (d) However, the ceiling on net acquisitions and on net transfers can be increased to the extent that an Annex B Party achieves emission reductions larger than the relevant ceiling in the commitment period through domestic action undertaken after 1993, if demonstrated by the Party in a verifiable manner and subject to the review process to be developed under Article 8 of the Kyoto Protocol’. (UNFCCC, 1999, p.14).

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instead of 18.9% under free trading for the US, and 55.6% instead of 4.8% for Japan). Under the however clause, the share of domestic action is at least 50% for all regions and permit prices are slightly lower. Ellerman and Wing (2000) and Zhang (2001) use the same MAC curves. Thus, the discrepancy of their results must be due to different assumptions about emissions growth and the fact that the analysis of Zhang (2001) includes emissions of six GHG, whereas Ellerman and Wing (2000) consider only carbon dioxide. Ellerman and Wing (2000) consider their own emission forecast as ‘relatively high’. In contrast, the figures in the official national communications submitted to the UNFCCC used by Zhang (2001) are lower than most projections of emissions growth. 6. The Kyoto Protocol without the US At the sixth Conference of the Parties to the UNFCCC (COP-6) in November 2000 in The Hague, delegations failed to reach a compromise. The main obstacles appear to have been the supplementarity issue (Section 5) and the role of sinks. To obscure the failure, the conference was officially prolonged by 6 months. In March 2001, US President Bush said he considered the Kyoto Protocol fatally flawed and made it clear that the US did not intend to ratify it. After the withdrawal of the largest buyer of emission permits, the market for tradable permits for GHG suddenly looked very different. 6.1. The absence of the US and sink credits In the absence of the US, aggregate permit demand from Annex B countries is more or less equal to ‘hot air’ from the former Soviet Union and Eastern Europe (depending on emissions growth both in Russia and the other Annex B countries). Consequently, the Kyoto Protocol achieves almost no emissions reduction compared to a business-as-usual scenario and estimated permit prices fall drastically, reaching ¨ values between 0 and 12 USD per ton CO2 (Bernard and Vielle, 2001; Bohringer, 2002; Ciorba et al., 2001 Den Elzen and de Moor, 2002; Hagem and Holtsmark, 2001; Jotzo and Michaelowa, 2002; Manne and Richels, 2001; Nordhaus, 2001). Surprisingly and contrary to the predictions of a number of observers, the withdrawal of the US did not kill the Kyoto Protocol. Instead, the US objection made the remaining Annex B countries increase their efforts and, ironically, ‘freed up negotiators to accept provisions they had opposed when the US was viewed as the principal beneficiary’ (Babiker et al., 2002). To enter into force, 55 countries accounting for 55% of Annex B carbon dioxide emissions in 1990 must ratify the Protocol. When the US with a share of 36% of total emissions left the negotiation table, Russia, Japan, and Canada became pivotal players. Without them, the Protocol could not enter into force. This increased bargaining power is clearly reflected in additional sink allowances granted to those countries in Bonn (at COP-6 bis) and in Marrakesh (COP-7).

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In Bonn, upper limits on credits from sinks (forests and agricultural soils) were specified for all Annex B countries. These limits do not exceed 2 megatons carbon per year, except for Canada (12), Japan (13), and Russia (17). At the seventh Conference of the Parties, the limit for Russia was raised to 33 megatons annually (UNFCCC, 2001). These additional sink credits further lower the environmental effectiveness of the Kyoto Protocol and reduce the demand for permits from Annex B countries, which implies that permit prices approach zero. However, this would only be the case if Russia, Ukraine and the Eastern European countries sold all their excess permits in the first commitment period. Yet, such a scenario is unrealistic, since these countries can increase their revenues by restricting the supply of permits. 6.2. Market power and banking If Russia and the Ukraine form a cartel together with Eastern European countries, they can maximize their revenues by selling between 10 and 60% of their excess permits at a price between 5 and 22 USD (Table 7). Given the fact that the major foreign policy goal of most Eastern European countries is accession to the European Union, it is doubtful that they will form an effective cartel with Russia and the Ukraine. If Eastern Europe behaves as a price taker and only Russia and Ukraine exert market power, the models predict a lower supply of ‘hot air’ and permit prices in the remarkably narrow range of 7–12 USD per ton carbon dioxide.13 The Kyoto Protocol allows countries to use emissions rights (assigned amounts) which were not used in the first commitment period in subsequent periods (permit banking). If there is a second period and permit prices in that period are higher than prices in the first period, Russia and Ukraine could benefit from selling even less permits in the first period and save them for the second commitment period. Quantifying such an intertemporal optimization is only possible if assumptions about emission targets in the second commitment period and the number of participating countries are made, which is of course highly speculative.14 6.3. The commitment period reserve After the sixth Conference of the Parties, the European Union did not insist on quantitative limits on emissions trading any more. Yet, Parties agreed on a mechanism that limits the potential sales of ‘hot air’ to some extent: the commitment period reserve (CPR). The aim of the CPR is to prevent overselling. Without the reserve, a country could in principle sell all its assigned amounts at the beginning of the first commitment period, thereby making large profits, and not buy back the necessary emission rights later on. The CPR deters such a ‘rogue trader strategy’ which could 13 ¨ Loschel and Zhang (2002) also run a duopoly scenario where Eastern European countries as well as Russia and Ukraine restrict permit sales without forming a joint cartel. 14 See Manne and Richels (2001), Bernard et al. (2002).

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Model

Permit price (2000 USDyton CO2)

Share of hot air sold (%)

Trading

Sinks

Cartel: Russia, Ukraine, and Eastern Europe MACGEM PACE POLES POLES&ASPEN WORLDSCANa

22 17 19 5 5

17 40 36 10 60

World wide Annex B Annex B World wide World wide

None B B, M B, M B, M

Cartel: Russia and Ukraine GTEMb MIT-EPPAb POLES

12 7 11

55 50 34

World wide Annex B Annex B

B B, M B, M

¨ Sinks: Bonn (B), Marrakesh (M). Sources: Blanchard et al. (2002), Bohringer (2002), Babiker et al. (2002), Den Elzen and de Moor (2002), Eyckmans ¨ et al. (2001), Jakeman et al. (2001), Loschel and Zhang (2002), and own calculations. a Includes hot air from Kazakhstan (Emission scenario A1B). b Include non-carbon GHG.

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Table 7 Monopolistic behavior in the absence of the US

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lead to non-compliance as a result of emissions trading. This is achieved by requiring Parties to hold a fraction (Y) of its assigned amounts or a fraction (X) of its last reviewed inventory times five permanently in reserve (i.e. not to sell them) during the first commitment period.15 Missfeldt and Haites (2002) analyze a CPR with different values of X and Y. They find that the reserve must exceed 85% to be effective. According to their calculations, ‘a value of X close to 90% and of Y between 95 and 98% will maximize the effectiveness of the CPR in limiting possible non-compliance due to overselling while minimizing the number of Annex B countries subject to restricted sales of surplus quota’. Their analysis excludes permits generated via the CDM and assumes US participation. The CPR specification agreed upon by the Parties in Bonn is the following: an Annex B country must hold a reserve of (a) 90% of its assigned amount or (b) 100% of five times its most recently reviewed inventory, whichever is lower (UNFCCC, 2001), Baron (2001), Haites and Missfeldt (2001) find that this rule reduces the risk of overselling without imposing severe restrictions on emissions trading. Eyckmans et al. (2001) analyze the effect of the CPR in the absence of the US. They find that option (b) is less restrictive for Central Europe and the Former Soviet Union than option (a). If business-as-usual emissions in 2005 are taken as the last reviewed inventory, this group of countries could sell approximately 22% of its assigned amounts. The CPR restriction benefits the sellers, as the equilibrium permit price rises from 10 to approximately 14 USD per ton CO2. However, Eastern Europe and the Former Soviet Union could gain even more by forming a cartel and further restricting the amount of permits sold (see Section 6.2 above). 7. Summary This paper gathers results from 25 models of the market for tradable GHG permits under the Kyoto Protocol. Price estimates for a global trading regime with US participation range from 1 to 22 USD. If trading only takes place among Annex B countries, prices are expected to lie between 3 and 74 USD. Average annual permit trade is estimated to be 2272 and 1466 million tons CO2 under global trading and Annex B trading, respectively. The models predict an annual average market volume of approximately 17 billion USD (global trading) and 33 billion USD (Annex B trading) for the period 2008–2012. Possible explanations for the remarkably large differences between the model results are different projections of emissions growth and model structures. The above figures are from models that use only carbon dioxide emissions data. Including non-carbon GHG lowers abatement costs and thus permit prices and increases the demand for permits. A limit (concrete ceiling) on emissions trading as proposed by the European Union would increase total compliance costs and carbon leakage, and would make market power more of a serious problem. 15 Note that the CPR only represents a temporary restriction on emissions trading. At the end of the commitment period, all excess permits can be traded.

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The withdrawal of the US from the Kyoto Protocol changed the prospects of the international market for GHG emission permits drastically. If the sizeable sink credits granted to Canada, Japan, and Russia are taken into account, aggregate permit demand equals ‘hot air’ from the former Soviet Union, which implies a permit price close to zero. Yet, Russia, Ukraine and Eastern Europe can increase their revenues by restricting permit supply. According to recent model studies, revenues for such a seller’s cartel would be maximized at a permit price between 5 and 22 USD. Note that all economic models of IET simulate a perfectly designed, wellfunctioning, full-scale market under ideal conditions. Yet, the real market is likely to be separated, complicated, and subject to diverging national legislation. How such a realistic market will perform has not yet been analyzed much and certainly deserves further attention. Acknowledgments The author would like to thank Josef Janssen, Claudia Kemfert, Sokrates Kypreos, ¨ Harri Laurikka, Andreas Loschel, and Bernhard Raberger for helpful comments and ¨ discussions. Financial support by the Ministry of Environment Baden-Wurttemberg is gratefully acknowledged. Appendix A: Overview of models Acronym

Model name

Model description

References

AIM

Asian-Pacific Integrated Model

Integrated assessment model

Kainuma et al. (1999)

CICERO

Static partial equilibrium model, including non-carbon GHG

Hagem and Holtsmark (2001) Holtsmark and Maestad (2002)

ECN

Spread-sheet model using MAC curves based on energy system models such as MARKAL. Includes all GHG

Sijm et al. (2000)

EDGE

Multi-region, multi-sector, dynamic general equilibrium model incorporating sinks and non-carbon GHG

Jensen and Thelle (2001)

ENEA

Emissions trading model based on MAC curves estimated for each fuel and country using an almost ideal demand system approach

Ciorba et al. (2001)

EPPA

Emissions Projections and Policy Analysis Model

Multi-region, multi-sector, dynamic general equilibrium model

Ellerman et al. (1998) Ellerman and Wing (2000)

G-CUBED

Global General Equilibrium Growth Model

Multi-region, multi-sector dynamic general equilibrium model

McKibbin et al. (1999a) McKibbin et al. (1999b)

GEM-E3

General Equilibrium Model for Energy,

Multi-region, multi-sector, dynamic general equilibrium model

Capros (1999)

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Economy, and Environment Interactions GRAPE

Global Relationship Assessment to Protect Environment

Integrated assessment model

Kurosawa et al. (1999)

GREEN

General Equilibrium Environmental Model for assessing the economic impacts of limiting carbon emissions

Multi-country, multi-sector, dynamic general equilibrium model

van der Mensbrugghe (1998) Burniaux (2000)

GTEM

Global Trade and Environment Model

Multi-region, multi-sector dynamic general equilibrium model

Tulpule´ et al. (1999) Jakeman et al. (2001)

IGSM

Integrated Global System Model

Integrated assessment model; economic module: EPPA

Reilly et al. (1999)

Emissions trading model using MAC curves from GEM-E3

Eyckmans et al. (2001)

MACGEM MERGE

Model for Evaluating the Regional and Global Effects of greenhouse gas reduction policies

Dynamic general equilibrium model with aggregate production and cost functions. Bottom–up representation of the energy supply, top–down model covering other sectors

Manne and Richels (1999) Manne and Richels (2000) Manne and Richels (2001)

MS-MRT

Multi-Sector, MultiRegion general equilibrium Trade model

Multi-sector, multi-region, dynamic general equilibrium model

Bernstein et al. (1999a) Bernstein et al. (1999b)

OXFORD

Oxford economic forecasting model

Neo-Keynesian macroeconomic model

Cooper et al. (1999)

PACE

Policy Analysis based on Computable Equilibrium

Multi-region comparative-static general equilibrium model

¨ Bohringer (2000) ¨ Bohringer (2002)

PET

Pelangi’s Emissions Trading model

Spread-sheet model using, among others, MAC curves from EPPA

Jotzo and Michaelowa (2002)

Energy system model for 26 regions

Criqui et al. (1999) Criqui and Viguier (2000)

POLES RICE

Regional Integrated Climate and Economy Model

Integrated assessment model with aggregate production and cost functions

Nordhaus and Boyer (1999) Nordhaus (2001)

R&S

Rose and Stevens Model

Multi-region dynamic non-linear programming model

Rose and Stevens (2001) Stevens and Rose (2002)

SGM

Second Generation Model

Multi-sector, multi-region, dynamic general equilibrium model; includes all GHG

MacCracken et al. (1999)

WAGE

World Applied General Equilibrium Model

Multi-sector, multi-region, dynamic general equilibrium model

Kemfert (2000)

WORLDSCAN

Multi-sector, multi-region, dynamic general equilibrium model

Bollen et al. (1999) Den Elzen and de Moor (2002)

ZHANG

Spread-sheet model using MAC curves from EPPA

Zhang (2000) Zhang (2001)

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