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

Wang, D; Liu, Y; Wang, Y; Shi, X; Song, X

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

Wang, D Liu, Y Wang, Y Shi, X

Song, X

Permalink

Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Energy Economics, 2020, 87
Issue Date:: 2020-03-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 11 Feb 2022

Adobe PDF

Download Accepted ManuscriptAdobe PDF (1.3 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, D
dc.contributor.author	Liu, Y
dc.contributor.author	Wang, Y
dc.contributor.author	Shi, X https://orcid.org/0000-0001-9653-7395
dc.contributor.author	Song, X
dc.date.accessioned	2020-09-20T23:47:03Z
dc.date.available	2020-09-20T23:47:03Z
dc.date.issued	2020-03-01
dc.identifier.citation	Energy Economics, 2020, 87
dc.identifier.issn	0140-9883
dc.identifier.issn	1873-6181
dc.identifier.uri	http://hdl.handle.net/10453/142780
dc.description.abstract	© 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.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Energy Economics
dc.relation.isbasedon	10.1016/j.eneco.2020.104709
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering, 1402 Applied Economics
dc.subject.classification	Energy
dc.title	Allocation of coal de-capacity quota among provinces in China: A bi-level multi-objective combinatorial optimization approach
dc.type	Journal Article
utslib.citation.volume	87
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
utslib.for	1402 Applied Economics
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/DVC (International)/Australia-China Relations Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-02-11T00:00:00+1000Z
dc.date.updated	2020-09-20T23:47:00Z
pubs.publication-status	Published
pubs.volume	87

Abstract:

© 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/142780