تجارت کیفی آب با اطلاعات نامتقارن، عدم قطعیت و هزینه های معامله:شبیه سازی بر اساس عامل تصادفی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|19006||2013||31 صفحه PDF||سفارش دهید||17190 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Resource and Energy Economics, Volume 35, Issue 1, January 2013, Pages 60–90
We examine the efficiency of emissions trading in bilateral and clearinghouse markets with heterogeneous, boundedly rational agents making decisions under imperfect and asymmetric information, and transaction costs. Results are derived using a stochastic agent-based simulation model of agents’ decision-making and interactions. Trading rules, market structures, and agent information structures are selected to represent emerging water quality trading programs. The analysis is designed to provide a strong test of the efficiency of trading occurring through the two market structures. The Differential Evolution algorithm is used to search for market trade strategies that perform well under multiple states of the world. Our findings suggest that trading under both bilateral and clearinghouse markets yields cost savings relatively to no trading. The clearinghouse is found to be more efficient than bilateral negotiations in coordinating point–nonpoint trading under uncertainty and transaction costs. However, the market under both structures is unlikely to achieve or even approximate least-cost pollution control allocations. Expectations of gains from water quality trading should, therefore, be tempered.
Emissions trading revolutionized air emissions regulations in the United States in the 1990s. The most prominent example is the US cap-and-trade emissions trading scheme for sulphur dioxide (SO2) emissions established under the 1990 US Clean Air Act Amendments. A new frontier for emissions trading is water quality protection, where the mechanism is commonly referred to as water quality trading (WQT). The US Environmental Protection Agency (EPA) issued policy guidelines for development of WQT programs in 2003 and has invested in the development of markets through funding of demonstration projects and technical assistance (US EPA, 2003 and US EPA, 2007). WQT programs are being considered or are in various stages of development in several states (US EPA, 2011). Prominent examples are the recently initiated nutrient trading programs for point and nonpoint sources in Pennsylvania and the Greater Miami River watershed in Ohio. The fundamental economic case for emissions trading is that market transactions can achieve pollution targets cost-effectively in markets that environmental regulators can construct without knowing the polluters abatement costs (Crocker, 1966, Dales, 1968 and Montgomery, 1972). A key question is whether WQT programs, especially those involving nonpoint sources, can live up to the expectations of regulatory cost savings (Horan and Shortle, 2011 and Ribaudo and Gottlieb, 2011). The successful large national cap-and-trade air emissions markets “…work roughly as the textbooks describe” (Joskow et al., 1998). The textbook vision requires that emissions (i) can be accurately metered for each regulated emitter, (ii) are substantially under control of the polluter (i.e., non-stochastic), (iii) that the spatial location of emissions within the market does not affect environmental outcomes, and (iv) that the market is perfectly competitive (Ellerman, 2005). Nonpoint emissions do not satisfy the first three requirements because they are by definition unobservable at the source, inherently stochastic, and the spatial location of emissions is important to water quality impacts. These factors are not fatal to the development of WQT markets, but they do imply that an optimally designed water quality trading framework that includes nonpoint sources will differ significantly from the textbook model in ways that limit potential cost-savings from a perfectly competitive market (Horan and Shortle, 2011). The fourth requirement, perfectly competitive markets, is also not characteristic of WQT markets that have been developed to date (Ribaudo and Gottlieb, 2011 and Woodward et al., 2002). Perfectly competitive markets require a large number of traders, all with perfect information but without market power, trading a homogeneous good. Regulation of water pollution at small spatial scales (e.g., stream segments, small watersheds) will often imply “thin” markets with limited numbers of potential participants. Traders can be highly heterogeneous in their economic activity, economic size, and contribution to pollution loads. For example, likely participants in a point–nonpoint nutrient trading market could range from small farms to large treatment works. Further, pollution emissions even within a specific category (e.g., nitrogen) can be highly heterogeneous in relevant water quality characteristics (e.g., nitrogen type, time and place of release, etc.). These characteristics eliminate the development of highly organized competitive exchange markets in which traders routinely participate as price-taking buyers or sellers (Woodward et al., 2002). The performance of WQT markets must, therefore, be understood within the context of market structures that are plausible for the problem. In this paper, we explore the validity of least-cost allocations as a prediction for WQT markets that capture key features of emerging nutrient markets. In addition, we examine the impacts on market outcomes and efficiency of two market structures, transaction costs and selected trading policy parameters. Because of the small number and nascence of point/nonpoint WQT programs, a robust ex post assessment cannot be conducted. Our analysis is based on an agent-based model (ABM) that is constructed to simulate the outcomes of trading within a set of trading rules and market structures consistent with developing US markets for nutrient trading between point and nonpoint sources. The agent-based modeling approach stands in contrast to the common use of cost-minimization models for ex ante analysis of pollution trading, which assume that markets are perfectly competitive and will achieve the least-cost allocation in equilibrium ( Hanley et al., 2007). Agent-based models allow the assumptions of perfect competition to be replaced by more realistic assumptions about individual behavior, information structures, and coordination mechanisms ( Duffy, 2006, Roth, 1995 and Roth, 2002). The ABM developed for this study explicitly captures characteristics of WQT markets under uncertainty that lead to complex decision-making and coordination. As our ABM evaluates a large number of possible realizations of the decision-making process under uncertainty, simulated trading outcomes, as a result, are stochastic. We, therefore, have a stochastic ABM. Based on actual and anticipated developments, we consider two types of market structures, bilateral negotiations and a clearinghouse (Morgan and Wolverton, 2005 and Selman et al., 2009). In both, agents must decide whether to participate in trading, and if they do, they must find trading partners, in the case of bilateral negotiations, and select bidding strategies in both bilateral and clearinghouse markets. Mistakes in these decisions may result in suboptimal participation and coordination failure. Agents are heterogeneous in their costs and relative size, have imperfect information about their own costs and the costs of others, which can be diminished but not eliminated by investments to improve their information, and are boundedly rational. Transaction costs are explicitly considered. Within this context, a strong test for the potential efficiency of trading is created using a large sample of agents’ trading strategies. These strategies are generated by the Differential Evolution (DE) algorithm (Storn and Price, 1997) to discover ‘robust’ trading strategies that maximize the probability of achieving efficient market outcomes under multiple states of the world (reflecting economic parameter variations). The stochastic ABM is formulated to capture a lower bound complexity in trading. When combined with a powerful search algorithm like DE for finding low cost trading strategies, the bias towards lower bound complexity increases the likelihood that tested WQT markets succeed. Under such conditions, finding poor performance would strongly imply that the efficiency of trading in real world markets, in which agents’ behaviors and interactions are much more complex, is likely to be even worse than the idealized case. We begin by presenting the structure of the agent-based model and then describe agents’ trade strategies in both bilateral and clearinghouse markets. The remaining sections describe the measures used to evaluate market performance, the computational simulation experiments, and lastly the results and policy implications.
نتیجه گیری انگلیسی
In this paper we use a stochastic agent-based simulation to explore the efficiency of trading within thecontextoftwoplausiblemarketstructures(bilateralnegotiationsandaclearinghouse).Themodel isconstructedtobeconsistentwiththerulesthatgovernnutrienttradingbetweenpointandnonpoint sources in the US. We are especially interested in testing whether least-cost solutions are likely and design the research to provide a strong test of the likelihood of efficient allocations in the two marketstructures.Thisisachievedby(i)developingatradingmodelthatcapturesessentialfeaturesofagent’s choices (e.g., market participation, pricing decisions and matching) and decision environments (cost uncertaintyandtransactionscosts)butavoidingcomplexitytotheextentpossible(lowerboundcom- plexity), and (ii) using a powerful algorithm for discovering robust trade strategies that minimize the expected social costs of pollution abatement. Our findings indicated that, within the set of trading rules considered, the clearinghouse is more efficient than bilateral negotiations in coordinating point–nonpoint trading. The results also show thattradinginbothmarketstructuresyieldsnetcostsavingsrelativetoautarkyinallcasesonlywhen the trading strategies are driven by the mechanisms of DE algorithm to a good set of agents’ choices. However, both bilateral trading and the clearinghouse market are unlikely to achieve or even approx- imateleast-costpollutioncontrolallocations.Thisimpliesthatamarketoutcomeinducedbycomplex interactions among agents in both market structures under many sources of uncertainties might not be the least-cost solution to the planner’s deterministic optimization problem. In other words, the least-cost solution is not a good prediction of the market outcome. As a result, market designs based on the assumption that the market can achieve the least-cost solution, ignoring the presence of com- plexities in decision making and interactions are not meaningful in practice. Expectations about what thesemarketscanattainandofgainsfromWQTshould,therefore,betempered.Moreover,transaction costs and trading policy parameters are found to have discernible impacts on the performance of the market. Our results, when interpreted with prior literature on these markets, point to a need for agencies to assist in the development of trading. This assistance could entail the development of trading insti- tutions (e.g., clearinghouses rather than relying on bilateral trading), and provision of information to help agents avoid trading strategy mistakes. Finally, while our analysis has been motivated by interests in water quality trading, our results apply more generally to trading in environmental management contexts with similar characteristics. Suchcontextscanbeexpectedasuseofthetradingmechanismisextendedtosmallerscalesandmore complex pollutants than those traded in large regional or national air emissions markets.