Cap-and-Trade Emissions Regulation: A Strategic Analysis

Cap-and-Trade Emissions Regulation: A Strategic Analysis

Frank Cheng, Yagil Engel, Michael P. Wellman

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 187-193. https://doi.org/10.24963/ijcai.2019/27

Cap-and-trade schemes are designed to achieve target levels of regulated emissions in a socially efficient manner. These schemes work by issuing regulatory credits and allowing firms to buy and sell them according to their relative compliance costs. Analyzing the efficacy of such schemes in concentrated industries is complicated by the strategic interactions among firms producing heterogeneous products. We tackle this complexity via an agent-based microeconomic model of the US market for personal vehicles. We calculate Nash equilibria among credits-trading strategies in a variety of scenarios and regulatory models. We find that while cap-and-trade results improves efficiency overall, consumers bear a disproportionate share of regulation cost, as firms use credit trading to segment the vehicle market. Credits trading volume decreases when firms behave more strategically, which weakens the segmentation effect.
Keywords:
Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence
Agent-based and Multi-agent Systems: Noncooperative Games
Multidisciplinary Topics and Applications: Computational Sustainability
Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems