An automated system of emissions permit trading for transportation firms

New technologies such as artificial intelligence (AI) play important roles in transportation emissions trading platforms. Due to the complexity and stochastically changing prices of emissions, an effective algorithm is needed in these platforms to optimize the use of emissions. By setting an upper bound for buying and a lower bound for selling, such an algorithm can reduce trading risk and ensure the stability of trading platforms. In this study, we developed an automatic emissions permit trading system using a dynamic programming approach with selling and purchasing bounds for transportation firms with non-negligible fixed transaction setup costs. We partially characterize the optimal transportation and permit trading policies by exploiting a new mathematical property that is suitable for a two-dimensional control system. We attempt to elucidate the optimal coordination of permit trading and permit consumption for a transportation firm facing both the Markov price process and random demand during a multi-period planning horizon. We prescribe an optimal trading policy and propose a well-performed heuristic policy and a tight lower bound for the platform. We also show that the easily implemented heuristic policy would not significantly increase emissions. Our findings contribute to the literature and provide guidance to help transportation firms using AI platforms.