(514ab) A Game Theory Approach to Pollution Trading

Pollution trading is both a market-based and an environmental strategy which adds flexibility to the pollution abatement decision making. This strategy aims to satisfy environmental regulations at a cost lower than that required by treatment technology implementations. Former implementations of pollution trading intended to meet air quality standards with respect to SOx and NOx emissions; later, the effectiveness of the strategy was also demonstrated for pollution control in watersheds. The trading is based on the idea that some of the pollution dischargers (firms or organizations) have enough financial capacity and infrastructure to reduce their emissions below the required limits through treatment technology implementation; hence, if the pollutant concentration of their discharges is below the allowable limits, such organizations obtain credits, which can be sold. Simultaneously, organizations (pollutant sources) which are not able reduce its emissions due to the lack of infrastructure can meet environmental regulations by purchasing credits.

This work is concerned with pollution abatement in watersheds. Several mathematical programming approaches have been developed to optimize the decision making in the pollution trading strategy. In general, such approaches intend to minimize the global cost of technology implementation (the summation of technology implementation costs for every pollutant source). Costs associated to the purchase and selling of credits most of the times are not considered in the objective function. That is, in the overall context, it is assumed that the financial benefits obtained by some firms by selling credits are the same as the costs paid by other firms by purchasing such credits. These modeling assumptions are clearly not realistic. Most of the firms are not really concerned with the overall costs and benefits of the trading; they are concerned with their own costs and benefits. Therefore, the trading is really a market strategy where every pollutant source can cooperate or compete with any other source. That is, the approach can be represented as a mathematical game, where every source could be a player.

This work proposes optimization models that can guide pollutant sources to make optimal decisions in a polluting trading strategy. The mathematical models are formulated based on game theory. In particular, two approached have been analyzed. The first model considers a bilevel Stackelberg game; the second formulation involves a Nash equilibrium game represented as a Mixed Complementary Problem (MCP) with equilibrium constraints. Both of the formulations are solved through the GAMS modeling environment. The results include the decisions for each source of pollutants in order to satisfy global environmental regulations. To evaluate the performance of the optimization models, a mercury exchange case study is considered; a comparison is presented to analyze the differences among the solutions obtained by minimizing a global cost function and the solutions obtained by both of the game theory approaches.