Study on the distribution of PM emission rights in various provinces of China based on a new efficiency and equity two-objective DEA model

Under the premise that the total emissions of PM(particulate matter) of air pollution is established, the allocation of PMemission rights in various provinces of China is the feasible way to governance the ash haze. In this paper, a data envelopment analysis (DEA) model that takes into account the objectives of efficiency and equity is constructed. On the basis of evaluating the PMemission performance of various provinces in China, the corresponding allocation steps are put forward. Then, the results of theDEAmodel and information entropy are compared. The research shows that: (1) under the same constraint conditions, the emission right of PMis allocated with DEA model, which leads to lower cost, less fluctuation of distribution amount and higher overall national performance. (2) the populationGinicoefficient method is used to evaluate and it is found that the Gini coefficient value is smaller according to the distribution results of DEA model, indicating that the PM emission right amount is more fairly distributed among provinces. The functions of this paper are as follows: (1) a new double-objective DEA model is constructed, which is suitable for multi-agent performance evaluation with multiple objectives; (2) a scheme to allocate PM among provinces is proposed, which can provide reference for the practice of haze reduction in China

Automated summary

Under the premise of establishing the total emissions of PM in air pollution, allocating PM emission rights in various provinces of China is found to be an effective way to govern haze. A DEA model that considers efficiency and equity objectives is constructed to evaluate performance and propose allocation steps. The results show that using the DEA model for allocation leads to lower cost, less fluctuation in distribution, and higher national performance. The study also indicates a fairer distribution of PM emission rights among provinces.