Optimizing an emission trading scheme for local governments: A Stackelberg game model and hybrid algorithm
Linda L. Zhang and
International Journal of Production Economics, 2017, vol. 193, issue C, 172-182
This study investigates a policy-making problem for a local government to implement an emission trading scheme by considering the interactive production decisions of firms in its administrative region. The market-based allowance trading price formed freely among the firms in the region is investigated by taking into account regional environmental bearing capacities. Under the scheme, the government sets the emission reduction target of the region and allocates tradable initial allowances to firms, and firms plan their production according to their allowances on hand. A Stackelberg game model is formulated to analyze the decisions of the government and firms aiming to maximize the social welfare of the region and maximize the profit of each firm. In view of the non-concavity and discreteness of the decision model for the government, we propose a hybrid algorithm to solve the game model efficiently. This algorithm consists of a polynomial time dynamic programming, binary search, and genetic algorithm. Results reveal that i) the Stackelberg game model greatly supports local governments' policy-making on the market-driven emission allowance trading scheme, and that ii) the social welfare is a great metric for policy-making decisions on environmental regulations. The market-driven emission trading scheme is an effective mechanism for local governments to induce emission reduction through green technology adoption by firms. However, governments should set their emission reduction targets appropriately because a tight or easy regulation policy significantly affects the environmental and economic benefits as well as the social welfare.
Keywords: Emission trading scheme; Local government; Social welfare; Game theory; Hybrid algorithm (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:193:y:2017:i:c:p:172-182
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