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Allocation of energy consumption among provinces in China: A weighted ZSG-DEA model

  • Siqin Xiong
  • , Yushen Tian
  • , Junping Ji*
  • , Xiaoming Ma
  • *Corresponding author for this work
  • Peking University

Research output: Contribution to journalArticlepeer-review

Abstract

To realize the sustainable development of energy, the Chinese government has formulated a series of national goals of total energy control and energy structure optimization. Under the national constraints, how to efficiently allocate the constrained total amount of energy consumption to each province is a fundamental problem to be solved. Based on a data envelopment analysis (DEA) model and a zero-sum game theory (ZSG), this paper constructs a weighted zero-sum game data envelopment analysis (ZSG-DEA) model to allocate the energy consumption quota. Additionally, this paper compares the results with the current administrative targets, to examine the efficiency and feasibility of each allocation mechanism. Finally, this paper employs the proposed model to determine the optimal energy structure for each province in China. The results indicate that by 2020, the national goal of energy structure adjustment will be realized, and energy structure will be diversified in most regions, whereas the coal-dominated status in primary energy consumption will not change. Additionally, the weighted ZSG-DEA model focuses on allocation efficiency while the government considers more regional economic disparity. Therefore, this study suggests a mixture of the two allocation mechanisms in accordance with specific conditions.

Original languageEnglish
Article number2115
JournalSustainability (Switzerland)
Volume9
Issue number11
DOIs
StatePublished - 18 Nov 2017
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Allocation
  • Energy consumption quota
  • Energy consumption structure
  • ZSG-DEA model

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