Policy Design and Performance of Emissions Trading Markets: An

For banking in emissions trading market, we compared the performance under banking .... This material is available free via the Internet at http://pub...
0 downloads 0 Views 2MB Size
Download PDF
Environ. Sci. Technol. 2010, 44, 5693–5699

Policy Design and Performance of Emissions Trading Markets: An Adaptive Agent-Based Analysis ZHANG BING, YU QINQIN, AND BI JUN* State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210093, PR China

Received November 21, 2009. Revised manuscript received May 24, 2010. Accepted June 1, 2010.

Emissions trading is considered to be a cost-effective environmental economic instrument for pollution control. However, the pilot emissions trading programs in China have failed to bring remarkable success in the campaign for pollution control. The policy design of an emissions trading program is found to have a decisive impact on its performance. In this study, an artificial market for sulfur dioxide (SO2) emissions trading applying the agent-based model was constructed. The performance of the Jiangsu SO2 emissions trading market under different policy design scenario was also examined. Results show that the market efficiency of emissions trading is significantly affected by policy design and existing policies. China’s coal-electricity price system is the principal factor influencing the performance of the SO2 emissions trading market. Transaction costs would also reduce market efficiency. In addition, current-level emissions discharge fee/tax and banking mechanisms do not distinctly affect policy performance. Thus, applying emissions trading in emission control in China should consider policy design and interaction with other existing policies.

1. Introduction Growing worldwide criticism has pressured China to reduce its air pollution to an acceptable level. In its 11th Five-Year Plan, the State Environmental Protection Agency (SEPA) set the target for reducing sulfur dioxide (SO2) emissions at 10% (22.95 million tons) by new desulfurization technologies and new environmental regulation instruments. Many policy options are available for SO2 reduction. Selecting costeffective policies is crucial for China to meet its air quality standards. Emissions trading using a market-based mechanism is considered effective in reducing emissions at the lowest possible economic cost. The key feature of emissions trading is that it allows regulated enterprises to transfer emission allowances to other enterprises. This transfer could lead to the distribution of emission reduction in enterprises that equate the marginal cost of emission reduction among enterprises and reduce total emissions control costs (1). Currently, this type of incentive mechanism is commonly used in the US, Europe, and other counties (2). Moreover, Article 17 of the 1997 Kyoto Protocol describing an international emissions trading system for greenhouse gases (GHGs) as one of the four cooperative mechanisms to achieve GHGs targets by 2008-2012 has ignited global interest (3, 4). * Corresponding author phone: 86-25-83592976; fax: 86-2583592976; e-mail: [email protected]. 10.1021/es9035368

 2010 American Chemical Society

Published on Web 06/30/2010

Emissions trading has successfully reduced total emission control costs and is regarded as one of most important instruments in pollution control. China started researching and piloting emissions trading programs in the 1980s. Since then, the government has conducted pilot programs on the compensated transfer of emission quotas. However, due to legal and regulatory constraints, limited experience, and implementation obstacles, these experiments have remained primarily conceptual. In the ninth Five-Year Plan period (1996-2000), significant progress was achieved when total emissions control (TEC) was promoted nationwide. Interest in emissions trading had grown noticeably during the 10th Five-Year Plan period, when TEC became more formal. In 1999, SEPA and the United States Environmental Protection Agency collaborated on a study to assess the feasibility of introducing the SO2 cap and trade program in China. This effort was initiated by significant discussions about the theories, conditions, foundations, and methods of cap and trade policy. Both institutions explored the opportunities and challenges in implementing SO2 emissions trading in the Chinese power sector with Benxi and Nanjing chosen as pilot areas. In 2002, SEPA initiated the “4+3+1” program by selecting four provinces (Shandong, Jiangsu, Shanxi, and Henan), three cities (Shanghai, Tianjin, and Liuzhou), and one company (China Huaneng Group) as pilot areas to pursue TEC and emissions trading (5). To date, limited emissions trading cases exist in the pilot areas, far from those observed in an active market. The maneuverability of the current emissions trading policy, conflict of different environmental policies, and administrative interference are considered main barriers to the success of the SO2 emissions trading programs in China (6, 7). In addition, other researchers have pointed out that institutional arrangement (or design) of the emissions trading policy has a decisive impact on cost-effectiveness based on ex ante analysis and ex post analysis in other counties (8). These design issues include transaction costs, spatial and temporal dimension mechanisms, initial allocations, market power, monitoring, enforcement, and so on (2, 9). Therefore, the issue “whether or not there is a need to implement emissions trading policy in China and how” should consider carefully. Since institutional arrangement or policy design will dramatically affect the cost-effectiveness of emissions trading (7), examining the cost-effectiveness of emissions trading market under different institutional arrangements or policy designs is crucial for designing an effective SO2 emissions trading policy in China. Traditional economic theories and analysis have only considered ideal representative participants in equilibrium states (10). In actual economic situations, the dynamic behavior and interactions with firms in the emissions trading system are complicated and often irrational (11). It is difficult to analyze the dynamically changing situations involving heterogeneous subjects when using static and homogeneous methods. A new policy analysis framework, an agent-based model, has been widely applied in artificial market simulation (10, 12-14) as well as in the emissions trading market (11, 15). This framework could account for heterogeneous and adaptive agents, dynamically changing situations, information asymmetry, and uncertainty. This paper establishes a bottom-up analytical framework of emissions trading policy under which the potential policy effects of SO2 emissions trading in the power sector of Jiangsu Province are studied in multiple levels. The next section presents the design for an SO2 emissions trading market of power plants in Jiangsu Province using an agent-based model. VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

5693

FIGURE 1. Flowchart of the Jiangsu SO2 emissions trading market. Under this model, every firm (agent) would act and interact in accordance with their rules, resulting in the performance of emissions trading policy. Different market mechanism designs and relevant performance of emissions trading markets are also examined.

2. Agent-Based Simulation Framework of SO2 Emissions Trading Markets 2.1. Emissions Trading Market. The process of a cap and trade system can be explained as follows: A regulator determines the scope of the system (regions, sectors, and so on) and sets a limit or cap on total emissions. Economic agents are provided with emission rights based on a certain standard, and each agent is required to hold the rights corresponding to its emissions. If an economic agent does not hold sufficient emission rights, it has to abate its emissions or acquire emission rights from agents with excess emission rights. According to recent practice of emissions trading in China (16), this research chose continuous double auction emissions trading market for further analysis. Double auction is a natural trading mechanism. In the double auction emissions trading market, sellers and buyers place the buy/sell limit order (including maximum/minimum price and the desired/ offered amount) at any time. When a new order matches the best opposite order in the queue, a trade is made (for the limit price of the counterpart); otherwise, the new order is placed on hold. Orders may also be canceled by their submitters (17, 18). All participants could learn from previous bid-asks and adjust their next bid-ask amount and price (Figure SI-3). 2.2. Agents. Power plants (agents) aim to abate SO2 emissions to a level below emission rights and maximize their profits. Unlike traditional economic analysis, agents behave based on their own local information. The information that each power plant depends on includes marginal profits (MP) function, SO2 emissions, emission rights, strategies, and other existing environmental regulations. Agents are free to trade permits at any time and may meet government 5694

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 15, 2010

standards by exercising pollution control and/or by possessing permits for their residual emission. This paper assumed all power plants are profit-maximizing agents; no agent exercises market power and perfect monitoring, and enforcement is available for the environmental regulator. The state variables of agents in emissions trading market are introduced in SI-A. 2.3. Agent Behaviors. As observed in actual emissions trading markets, emission permits are traded in the same manner as foreign exchange in exchange markets and stocks in stock markets. Many existing (conventional) economic models of such markets could also be applied to emissions trading markets. First, agents who wish to participate in emissions trading estimate their trade price and amount for the current period based on related information. They search for the right time to submit the order. The auction center matches the selling and buying orders according to double auction mechanisms (19). In addition, both agents could acquire and utilize information from previous auction outcomes and adjust their bidding strategies. Thus, each agent’s decision-making process consists of the following four main steps (Figure 1). 2.3.1. Step One: Think of an Order. When a firm participates in the emissions trading market for first time or has been waiting for a couple of trading rounds, it considers an order depending on MP, emissions allowance, market price of emission permits, and expected price of emission permits. The first stage is the determination of the emission amount that each firm expects to abate and trade in a year, based on the current market price (p). We assume that MPA is the MP of the firm when the amount of emission discharge (E) is equal to his allowance (A). There are three choices according to the current emission price of each firm. If the MPA of a firm is higher than the current emission price (Type A), the firm should be a buyer. If the MPA of a firm is lower than the current emission price (Type B), it should be a seller. If a firm has an MPA equal to the current emission price (Type C), it is neither a seller nor a buyer until the price changes. In the second stage, different agents make bid-asking strategies

(price and amount) according to the market price and their own information. The detail process of the second stage is described in SI-B. 2.3.2. Step Two: Make an Order. All agents have an order in mind; however, only some make an order in accordance with their strategies. We assume that each agent has a constant activation probability of 25% per round. In other words, 25% of the agents are randomly chosen to anticipate a bid in every round (20). 2.3.3. Step Three: Accept an Order. The buyer with the highest bid price is initially matched with the seller with the lowest asking price, provided both sellers and buyers exist and the highest bid price to buy is not lower than the lowest bid price to sell (19). The purchasing price would be the average of the highest bid price of buyers and the lowest bid price of sellers. In addition, if the bid amounts of the seller and the buyer do not match, then the bid of the buyer is matched with the seller for the quantity of tradable permits equal to the minimum of two amounts: the bidding amount of the buyer and the asking amount of the seller. The carryover amount to buy or sell is calculated, and the next pair is matched in the same manner. 2.3.4. Step Four: Learn from Previous Orders. A repeated auction constitutes an environment where agents could obtain and utilize information from previous auction results. The price convergence of zero intelligence traders is predictable from a priori analysis of the statistics of the system; therefore, more complex bargaining mechanisms or “intelligence” are necessary for traders. Consequently, a type of agent with simple machine-learning techniques was developed and referred to as zero intelligence plus (ZIP) agents (21). Further experiments have shown that ZIP agents outperform their human counterparts (22). Therefore, in this research, we endowed agents with ZIP ask-bidding strategies. According to ZIP strategies, agents in the emissions trading market adjust their profit margin and bid price according to their previous bidding. If there was a transaction in the last round and the agent was not the winner, or if there was no transaction, the agent decreases his profit margin in the current round. In contrast, the winner increases his profit margin in the current round (refer to SI-C for more information about ZIP mechanisms in emissions trading market).

in our model. Basic data such as the allowance of power plants, electricity price, and so on were collected directly from the power plants. Parameters such as SO2 removal rate, SO2 yield parameter, and so on were estimated from previous data (SI-D). 3.3. Policy Design Scenarios. The profits of power plants (agents) in the Jiangsu SO2 emissions trading market is affected by coal price (pc), electricity price (pe), transaction costs as well as existing emissions discharge fee/tax (pd), expected future price, and so on. Relevant policy design leads to distinct bid-ask strategies in the SO2 emissions trading market as well as its performance. Thus, different policy scenarios of transaction costs, emissions discharge fees/taxes, coal and electricity price, and banking were chosen to examine the performance of the Jiangsu SO2 emissions trading market. For the transaction costs, trading tax was taken into consideration. In accordance with the other double auction market, we set trading tax from 0.01 RMB/kg to 0.5 RMB/kg. For emissions discharge fee/tax, in addition to the current standard (1.26 RMB/kg), we chose a former standard (0.63 RMB/kg) and higher standard (1.89 RMB/kg) in the Jiangsu SO2 emissions trading market. We set an annual coal price increase of 10% from 2006-2010, and 5% from 2011-2020, according to the trend of coal price in China. The electricity price adjusts according to coal-electricity price linkage mechanism. For banking in emissions trading market, we compared the performance under banking and nonbanking scenarios (the policy design scenarios listed in Table SI-2). 3.4. Simulation Platform. The agent-based model of emissions trading was implemented in NetLogo, a platform suited for simulating spatial logic driven by the multiagent systems (MAS) and Cellular Automata (CA) approach (23). NetLogo is an agent-based modeling language and environment intended to model a range of phenomena, specifically complex systems with a large number of interacting agents. A standard NetLogo model consists of a world with a set of agents (generically known as “turtles”) and a suite of procedures specifying how the patches and turtles act and interact (SI-E, implementation of SO2 emissions trading market in NetLogo).

3. Market Design of the Jiangsu SO2 Emissions Trading Market

4.1. Completely Competitive Emissions Trading Market. To evaluate the proposed artificial market model and policy performance, we first examined the performance of a completely competitive emissions trading market. While marginal control costs were equated across all agents, the equilibrium price of the Jiangsu SO2 emissions trading market was set at 4.20 RMB/kg. When the market achieved equilibrium, TEC costs decreased to 4.24 × 109 RMB, a savings of 5.50 × 108 RMB (11.45% of TEC costs under the “command and control” scenario) (Figure SI-4). 4.2. Policy Design and Emissions Trading Market Performance. 4.2.1. Transaction Costs. We set different transaction cost scenarios, by converting trading tax from 0.01 RMB/kg to 0.5 RMB/kg according to other markets in China. In terms of the existence of robustness, we ran the model in every scenario 10 times. Figure SI-5 shows the evolution of market price under different trading taxes. The market prices in our model also converge on the equilibrium price quickly. However, the market price did not reveal significant distinctions under different trading taxes. The increase in trading tax depressed the transaction’s activity and heightened the total emissions control costs compared with the completely competitive market. Trading tax not only increased the cost of emissions trading but also blocked trading with a small amount. Figure 2 shows the total emissions trading amount under different trading tax

Based on previous analysis, we designed the SO2 emissions trading market of power plants in Jiangsu Province using a bottom-up approach. Every firm (agent) acts and interacts in accordance with market rules, resulting in the effective performance of emissions trading policy. 3.1. SO2 Emission Control in Jiangsu Province. Jiangsu is the only province in China that has both acid rain control and SO2 control zones. In the 10th Five-Year Plan (2000-2005), both the annual average SO2 concentration and acid rain frequency of Jiangsu Province increased. In 2005, acid rain frequency rose to 33.9%. In the new 11th Five-Year Plan (from 2006-2010), the central government required Jiangsu Province to reduce its SO2 emissions by 18% by the end of the Plan period. The electric power industry was asked to reduce its SO2 emissions from 0.739 million tons in 2005 to 0.55 million tons in 2010, a reduction rate of 25.6%. In this research, the SO2 emissions trading market of power plants in Jiangsu Province was examined. We collected the data of 45 power plants in the province for subsequent analysis. The aforementioned power plants account for 75.7% of electric power industry SO2 emissions in 2006 (see Table SI-1). 3.2. Model Parameters. Based on information about the 45 power plants, we laid down the econometric specifications for empirical tests and described the variables and parameters

4. Simulation Results

VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

5695

FIGURE 2. Total emissions trading amount under different transaction cost scenarios.

FIGURE 3. Total emission control costs under different transaction cost scenarios. scenarios. The total emissions trading amount declined along with the increase in trading tax. The total emissions trading amount when trading tax was 0.5 RMB/kg was 685,000 tons SO2, that is, 75% of the total emissions trading at a trading tax of 0.01 RMB/kg. Less emissions trading motivation and amount would lead to less market efficiency of the Jiangsu SO2 emissions trading market. While examining the total emissions control costs under different transaction cost scenarios, transaction cost was found to reduce emissions trading market efficiency significantly. Figure 3 shows the total emissions control costs under different trading taxes. When trading tax increased from 0.01 to 0.5 RMB/kg, the total emission control costs increased from 4.27 × 109 RMB to 4.33 × 109 RMB; the total cost saving rate declined from 10.57% to 9.14%. Thus, transaction costs would significantly reduce the market efficiency of the Jiangsu SO2 emissions trading market. 4.2.2. Emission Discharge Fee/Tax. To examine the performance of the Jiangsu SO2 emissions trading market under different emissions discharge fees/taxes, we controlled the 5696

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 15, 2010

transaction costs to zero and ran the model for only one year. The results under the three emissions discharge fee/ tax standards (0.63 RMB/kg, 1.26 RMB/kg, and 1.89 RMB/ kg) show that although emissions discharge fees/taxes reduced market price, they had little impact on the emissions trading amount and cost savings (Figure 4). Since agents should pay for the emissions discharge fees/taxes, the MP of permit would decline, as buyers would bid at a lower price. Under different emission discharge fees/taxes, the total emissions control costs of agents increased. The increased emission control costs would be levied by governments as emissions discharge fee/tax. Thus, the emissions discharge fee/tax did not affect the total social emissions control costs. In addition, the cost savings under the three emissions discharge fees/taxes standards were equal. The Jiangsu SO2 emissions trading market saved 5.50 × 108 RMB in emissions abatement costs (Figure 4). 4.2.3. Coal and Electricity Price. In accordance with the trend of coal prices in China, we ran the agent-based model from 2006-2020 with an annual coal price increase of 10%

FIGURE 4. Total emission control costs and market price under different emission discharge fee/tax scenarios (ECC_0 represents the emission control costs without emissions trading; ECC represents the emission control costs under emissions trading).

FIGURE 5. Cost saving and market price of the Jiangsu SO2 emissions trading market. from 2006-2010, and 5% from 2011-2020, while setting pd ) 1.26RMB/kg. We chose a scenario that permits are not allowed to bank. Along with the increase of coal price, the electricity price increment could not offset the costs brought by the increase of coal price according to coal-electricity price system in China. Thus, the marginal profits of power plants shrank, and the demand of permit would decline as well as the market price. In the Jiangsu SO2 emissions trading market, the market price decreased along with the coal price increase from 4.80 RMB/kg in 2006 to 2.79 RMB/kg in 2020 (Figure 5). On the other hand, less active emissions trading market would lead to less market efficiency. The cost savings of the Jiangsu SO2 emissions trading market declined to 3.01 × 107 RMB, which is 6% of the cost savings of 2006 (Figure 5). 4.2.4. Permit Banking. We compared the market efficiency of the Jiangsu SO2 emissions trading market when banking is allowed and when it is not. The coal price was also assumed to increase 10% from 2006-2010, and 5% from 2011-2020, with pd ) 1.26 RMB/kg. The market price of the two models also declined, while the market price when banking was allowed was smoother than when banking was not allowed

(see Figure 6). Banking would not affect the market efficiency of Jiangsu SO2 emissions trading market, and our independent two-sample t test did not reveal any significance (see Table SI-3). In addition, we also accounted for the total annual emission discharge and compared it with the total emission discharge standard to examine “temporal hot spots.” Results show that in 2008, 2011, and 2015, the emission discharge exceeded the discharge standard that would bring temporal hot spots (Figure SI-6).

5. Discussion Marketable permits are considered to be more flexible than other policy instruments because they allow changes in aggregate level of resource use when better information on resource stocks or resource flows becomes available. The performance of the Jiangsu SO2 emissions trading market is significantly affected by policy design and existing policies. If the market could trade fully without transaction costs, the market price in double auction markets would converge with the general equilibrium price as well as the market efficiency. However, the emissions trading market cannot avoid transVOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

5697

FIGURE 6. Permit price in banking and unbanking markets. action costs (24-26). Our results reveal that transaction costs have negligible influence on market price, which is different from certain analysis based on completely competitive market assumptions (27). However, transaction costs significantly reduce the amount of emissions trading as well as the market efficiency of SO2 emissions trading market. Although this research asserts that the emissions trading center or the electronic bulletin board performs well and reflects market conditions as well as reduces the searching costs, trading tax would also reduce the market efficiency of emissions trading. Thus, the government should set an acceptable trading tax for SO2 emissions trading market in China. Emission discharge fee/tax has been an alternative environmental economic instrument in China since 1982. Price-based (tax) and quantity-based (emissions trading) approaches would achieve equivalent welfare consequences under perfect information conditions. However, uncertainty regarding compliance costs causes the otherwise equivalent price and quantity controls to behave differently, leading to divergent welfare consequences (28). A hybrid policy in place of pure price or quantity controls is considered to be more efficient as a “safety valve” (29-32). Power plants in the Jiangsu Province should also pay for the emission discharge fee/tax. Our simulation results show that an increase in emissions discharge fee/tax depresses the emission permit price. Current emission discharge fee/tax does not affect the emissions trading amount and total emission control cost; rather, it transfers some profits from power plants to governments. However, superfluous environmental regulations will block the technology innovation of power plants (33) and increase administrative costs (34). Thus, the design of emissions trading policy should be combined with emissions discharge tax/fee policy. A low-level emissions discharge tax/fee is recommended for SO2 emissions trading policy in Jiangsu Province. China’s coal-electricity price system is the principal factor influencing the performance of its emissions trading market. Compared with coal prices, which are more market-oriented, China’s electricity prices are still controlled by the government based on the “cost-plus pricing” principle, which is used to determine the electricity price according to direct costs, indirect costs, and fixed costs, whether or not related to the production and sale of product/service. Thus, there is a price conflict between electricity price and coal price. To alleviate conflict, the National Development and Reform 5698

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 15, 2010

Commission of China has promulgated the “Mechanism of Coal Electricity Price Linkage”, which allows electricity price to vary with coal price, and stipulates that power plants should bear 30% of the costs caused by the coal price increase. Based on China’s coal-electricity pricing system, the continuous rise of coal price will greatly decrease the market price of emission permits and market activity, thereby reducing the performance of SO2 emissions trading market. Therefore, reforming the current coal-electricity price mechanism is essential to the success of SO2 emissions trading policy in China. Unlike other resources or materials, emission permits should be used or traded in a certain period - in a day, a season, or a year. The temporal pattern of emission rates, and whether the resulting concentrations accumulate or dissipate, could be important factors in designing any pollution control program (2). In the primary SO2 emissions trading scheme of Jiangsu Province, permit banking is allowed, while borrowing is not allowed (35). Our results reveal that permit banking does not lead to higher market efficiency in the Jiangsu SO2 emissions trading market. This result is different from that of other studies (36). However, banking is important in terms of promoting earlier reduction and flexible choice (37). Reducing the uncertainty of market price makes the market price fluctuation smoother. In addition, accumulating emission permits leads to “temporal hot spots” which should be treated carefully. The success of emissions trading programs depends on good policy design and integration with other policies. This research could help guide an emissions trading policy design that minimizes the total emission control costs. Four types of policy design factors were tested that yield significant shifts in policy performance. Thus, applying emissions trading in China should consider policy design factors and interaction with existing policies. We suggest that the emissions trading policy be pushed ahead with the reformation of coalelectricity price. Meanwhile, both low trading tax and emission discharge fee/tax are recommended according to previous analysis and permit banking should be allowed. In addition, we applied an agent-based modeling approach to illustrate the impact of policy design and existing policies on the performance of emissions trading policy. The behavioral rules of the agents were simplified under certain hypotheses. However, in actual emissions trading systems, agents should be more intelligent, and their action rules

should be more complex. Thus, the model of SO2 emissions trading market should be extended in future research. In this study, continuous double auction market mechanism and ZIP agents were adopted. The learning algorithm should be further adapted so that agents may maximize joint profits with other agents who belong to the same company as well as learn from other agents’ strategies in SO2 emissions trading market. In addition, perfect monitoring and enforcement was assumed so that the emission discharge of all the power plants will be lower than their available allowance. The violation and noncompliance of agents in SO2 emissions trading market should be tested to seek an effective enforcement mechanism.

Acknowledgments This paper is supported by the National Science Foundation of China (Grant No. 70903030) and the Science Foundation of Jiangsu Province (Grant No. BK2009250).

Supporting Information Available Additional information includes details on the state variables of agents (SI-A), emission control strategies in emissions trading market (SI-B), ZIP mechanism (SI-C), parameters in an agentbased model (SI-D), and implementation in NetLogo (SI-E) as well as supplementary tables and figures. This material is available free via the Internet at http://pubs.acs.org.

Literature Cited (1) Burtraw, D.; Evans, D. A.; Krupnick, A.; Palmer, K.; Toth, R. Economics of pollution trading for SO2 and NOX. Annu. Rev. Energy Environ. 2005, 30, 253–289. (2) Tietenberg, T. H. Emissions Trading: Principles and Practice; Resources for the Future: Washington, DC, 2006. (3) Soleille, S. Greenhouse gas emission trading schemes: a new tool for the environmental regulator’s kit. Energy Pol. 2006, 34 (13), 1473–1477. (4) Weber, C. L.; Matthews, H. S. Embodied environmental emissions in U.S. international trade, 1997-2004. Environ. Sci. Technol. 2007, 41, 4875–4881. (5) State Environmental Protection Agency (SEPA). Notice on beginning the pilots of promote the research program on China’s Sulfur Dioxide control and emissions trading policy, 2002. (6) Dudek, D. J.; Qing, H.; Zhang, J. Y. Environmental regulation capacity analysis: Report of SO2 control and emissions trading cases in China; Environmental Defense Fund: New York, 2004. (7) Tao, J. L.; Mah, D. N. Between market and state: dilemmas of environmental governance in China’s sulphur dioxide emission trading system. Environ. Plann. C Govern. Pol. 2009, 27, 175– 188. (8) Woerdman, E.; van der Gaast, W. Project-Based Emissions Trading: The Impact of Institutional Arrangements on CostEffectiveness. Mitigation and Adaptation Strategies for Global Change 2001, 6 (2), 113–154. (9) Antes, R.; Hansjurgens, B.; Letmathe, P. Emissions Trading: Institutional Design, Decision Making and Corporate Strategies; Springer: New York, 2008. (10) Tesfatsion, L. Agent-Based Computational Economics: A Constructive Approach to Economic Theory. In Handbook of Computational Economics; Judd, K. L., Tesfatsion, L., Eds.; 2006; pp 831-880. (11) Mizuta, H.; Yamagata, Y. Agent-based simulation and greenhouse gas emission trading. In Proceedings of the 2001 Winter Simulation Conference; 2001. (12) Walter, I.; Gomide, F. Electricity market simulation: multiagent system approach. In Proceedings of the 2008 ACM symposium on Applied computing; ACM: Fortaleza, Ceara, Brazil, 2008; pp 34-38. (13) Guerci, E.; Ivaldi, S.; Pastore, S.; Cincotti, S. Modeling and implementation of an artificial electricity market using agentbased technology. Phys. Stat. Mech. Appl. 2005, 355 (1), 69–76. (14) Veit, D. J.; Weidlich, A.; Krafft, J. A. An agent-based analysis of the German electricity market with transmission capacity constraints. Energy Pol. 2009, 37 (10), 4132–4144.

(15) Genoesea, M.; Sensfuss, F.; Mosta, D.; Rentza, O. Agent-Based Analysis of the impact of CO2 Emission Trading on Spot Market Prices for Electricity in Germany. Pac. J. Optim. 2007, 3 (3), 401–423. (16) Nie, T. Y.; Xu, W. Emissions trading in Chongqing, China. China Environment News; 2009. http://www.cenews.com.cn/xwzx/ zhxw/ybyw/200912/t20091228_629186.html (accessed Dec 30, 2009). (17) Cliff, D. Evolution of market mechanism through a continuous space of auction-types. In The IEEE Congress on Evolutionary Computation CEC2002; HI, 2001; pp 252-258. (18) Posada, M.; Herna´ndez, C.; Lo´pez-Paredes, A. Emissions Permits Auctions: an ABM Analysis. In The third annual conference of the European Social Simulation Association; Koblenz, 2005. (19) Nicolaisen, J.; Petrov, V.; Tesfatsion, L. Market power and efficiency in a computational electricity market with discriminatory double auction pricing. IEEE Trans. Evol. Comput. 2001, 5 (5), 504–523. (20) Walsh, W. E.; Das, G.; Tesauro, G.; Kephart, J. O. Analyzing complex strategic interactions in multi-agent games. In 8th Conference on Artificial Intelligence; Canada, 2002. (21) Cliff, D.; Bruten, J. Zero is not enough: On the lower limit of agent intelligence for continuous double auction markets; HP1997-141; Hewlett Packard Laboratories; 1997. (22) Das, R.; Hanson, J. E.; Kephart, J. O.; Tesauro, G. Agent-human interactions in the continuous double auction. In the International Joint Conferences on Artificial Intelligence (IJCAI); Seattle, USA, 2001. (23) Wilensky, U. NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, 1999. Available at http://ccl.northwestern.edu/netlogo/ (Aug 21, 2008). (24) Hahn, R. W.; Hester, G. L. Marketable permits: lessons for theory and practice. Ecol. Law Q. 1989, 16, 361–406. (25) Grubb, M.; Michaelowa, A.; Swift, B.; Tietenberg, T.; Zhang, Z. X. Greenhouse Gas Emissions Trading; The United Nations Conference on Trade and Development: Geneva, 1998. (26) Heller, T. C. Additionality, transactional barriers and the political economy of climate change. In On the Compatibility of Flexible Instruments; Jepma, C. J., Gaast, W. P., Eds.; Kluwer Academic Publishers: Dordrecht, 1999; pp 77-89. (27) Stavins, R. N. Transaction cost and tradable permits. J. Environ. Econ. Manag. 1995, 29, 133–148. (28) Weitzman, M. L. Prices vs. Quantities. Rev. Econ. Stud. 1974, 41 (4), 477–491. (29) Pizer, W. A. Combining price and quantity controls to mitigate global climate change. J. Publ. Econ. 2002, 85, 409–434. (30) Stranlund, J. K. A safety valve for emissions trading. University of Massachusetts Amherst Department of Resource Economics, Working Paper No. 2009-4; 2009. http://www.umass.edu/resec/ workingpapers (Feb 26, 2009). (31) Jacoby, H. D.; Ellerman, A. D. The safety valve and climate policy. Energy Pol. 2004, 32 (4), 481–491. (32) McKibbin, W. J.; Wilcoxen, P. J. Climate change policy after Kyoto: Blueprint for a realistic approach; The Brookings Institution: Washington, DC, 2002. (33) Oates, W. E.; Palmer, K.; Portney, P. R. Environmental regulation and international competitiveness: Thinking about the porter hypothesis. Resources for the Future Discussion Paper 94-02; Resources for the Future: Washington, DC, 1993. (34) Ellerman, A. D.; Schmalensee, R.; Joskow, P. L.; Montero, J. P.; Bailey, E. M. Emissions trading under the U.S. Acid Rain Program: Evaluation of compliance costs and allowance market performance; MIT Center for Energy and Environmental Policy Research:1997. (35) Jiangsu Provincial Environmental Protection Bureau (JSPEPB). Interim Procedures of SO2 emission trading of power plants in Jiangsu Province; 2002. Available at http://www.jshb.gov.cn/ jshb/showinfo/showinfo.aspx?infoid)4fe42a50-4dbb-4e0eae07-4aaa46a1b798 (Aug 23, 2008). (36) Cronshaw, M. B.; Kruse, J. B. Regulated firms in pollution permit markets with banking. J. Regul. Econ. 1996, 9, 179–189. (37) Phaneuf, D. J.; Requate, T. Incentives for investment in advanced pollution abatement technology in emission permit markets with banking. Environ. Resour. Econ. 2002, 22, 369–390.

ES9035368

VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

5699