Efficiency and equity in regional coal de-capacity allocation in China: A multiple objective programming model based on Gini coefficient and Data Envelopment Analysis

Gang Ma, Xu Li and Jianping Zheng

Resources Policy, 2020, vol. 66, issue C

Abstract: Both governments and enterprises are giving increasing attention to creating efficient policies for coal de-capacity allocation across Chinese regions. In contrast to existing studies, we consider both efficiency and equity in the allocation of coal capacity reduction for streamlining the implementation of coal de-capacity reform. First, the coal production capacity and overcapacity in the coal production regions was estimated using the boundary production function model. Then, we constructed a multiple objective programming model based on the Gini coefficient and Data Envelopment Analysis (Gi-DEA), and applied it to coal de-capacity allocation. The main results are as follows: (1) The coal production capacity in China exceeded 5.7 billion tons by the end of 2015, and coal overcapacity is prevalent across the country. (2) The optimal allocation plan based on the Gi-DEA model achieve efficiency across the regions, and the Gini coefficient is 0.231, indicating this allocation scheme is efficient and fair. Specifically, the large coal production regions should undertake the majority of coal de-capacity, such as Shanxi, Shandong, and Guizhou, while the small and old coal production regions might undertake a heavier capacity reduction burden, such as Fujian, Guangxi, and Jiangxi. (3) The multiple objective programming model, Gi-DEA, using common weights, achieved overall efficiency of the DMUs, and generated a unique allocation plan.

Keywords: Coal de-capacity allocation; Gini coefficient; Data envelopment analysis; China (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:66:y:2020:i:c:s0301420719307822

DOI: 10.1016/j.resourpol.2020.101621

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