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Can fiscal decentralisation regulate the impact of industrial structure on energy efficiency?

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  • Qiu-Su Wang
  • Chi-Wei Su
  • Yu-Fei Hua
  • Muhammad Umar

Abstract

This study applies the slack-based measure-data envelopment analysis (SBM-DEA) method to measure energy efficiency and tests the spatial spill-over effects of fiscal decentralisation, industrial structure and energy efficiency using the spatial Durbin model. The results show that China's energy efficiency presents a clear geographical step distribution, and the Eastern and Western regions have higher energy efficiency than the Central region. Fiscal decentralisation has a positive effect on the energy efficiency of the Eastern and Central regions by upgrading the industrial structure. In addition, fiscal decentralisation has significant, positive externalities on the surrounding areas, promoting environmental protection and energy conservation in all regions of China. The results are in line with the tenets of environmental federalism (Oates & Schwab): through the allocation and transfer of industrial factors, fiscal decentralisation affects energy governance. Therefore, local governments should formulate policies and targets according to their regions’ different economic development levels, and the Central and Western areas, which have greater space for improvement and low energy efficiency, should receive attention to balance regional differences.

Suggested Citation

  • Qiu-Su Wang & Chi-Wei Su & Yu-Fei Hua & Muhammad Umar, 2021. "Can fiscal decentralisation regulate the impact of industrial structure on energy efficiency?," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 34(1), pages 1727-1751, January.
  • Handle: RePEc:taf:reroxx:v:34:y:2021:i:1:p:1727-1751
    DOI: 10.1080/1331677X.2020.1845969
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    Cited by:

    1. Yang Xu & Xiaodong Yang & Qiying Ran, 2024. "The Impact of Fiscal Science and Technology Expenditure on Digital Economy: A New Path to Economic Recovery in The Post-Pandemic Era," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 27(1), pages 83-98, March.
    2. Du, Xiaoxu & Tang, Zhenpeng & Chen, Kaijie, 2023. "A novel crude oil futures trading strategy based on volume-price time-frequency decomposition with ensemble deep reinforcement learning," Energy, Elsevier, vol. 285(C).
    3. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).

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