Allocation of coal de-capacity quota among provinces in China: A bi-level multi-objective combinatorial optimization approach

Publisher:
ELSEVIER
Publication Type:
Journal Article
Citation:
Energy Economics, 2020, 87
Issue Date:
2020-03-01
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© 2020 Elsevier B.V. Coal de-capacity, or capacity cut, is an important part of China's energy transition. Formulating a quota allocation scheme for coal de-capacity is the key to realizing smooth exit of coal overcapacity. This study proposes a novel method of allocation of coal de-capacity quota among provinces, based on bi-level multi-objective combinatorial optimization. In this bi-level optimal allocation scheme (BOAS), the upper level is the central government and the lower level is the provincial governments. The results indicate that, because of the different costs of coal de-capacity in each province, the execution rate of each province for tasks assigned by the central government is quite different. Compared with the government allocation scheme (GAS) and the single-level optimal allocation scheme (SOAS), the growth rate of total factor productivity of the BOAS increases by 2.14% and 0.60%, respectively; the total de-capacity cost of BOAS has reduced 64 billion yuan and 19 billion yuan, respectively; and the environmental benefits of BOAS has increased 73 billion yuan and 71 billion yuan, respectively; the Gini coefficient of BOAS calculated by various indexes is less than 0.3, placing the scheme within the category of considerable or absolute fairness. In addition, the proposed allocation model truly reflects the complex dynamics of the game process of China's coal overcapacity governance system, and can provide a more effective decision-making reference for the Chinese government in formulating the allocation scheme of coal de-capacity.
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