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What Type of Energy Structure Improves Eco-Efficiency? A Study Based on Statistical Data of 285 Prefecture-Level Entities in China

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  • Fan Zhang

    (School of Economics and Trade, Hunan University, Changsha 410006, China)

  • Nengsheng Luo

    (School of Economics and Trade, Hunan University, Changsha 410006, China)

  • Yanfei Li

    (School of Economics and Trade, Hunan University of Technology and Business, Changsha 410205, China)

Abstract

Increasing environmental pollution, resource depletion, and climate change have led to policymakers paying increased attention to the environmental and ecological impacts of economic activities. To establish which type of energy structure is most conducive to improving eco-efficiency, we use the super-efficiency data envelopment analysis (DEA) model to quantify the relationship between the two, based on the panel data of 285 prefecture-level cities in China from 2005 to 2016. The heterogeneity and spatial spillover effect on different types of cities are further discussed. Our findings suggest that energy structure optimization by reducing the proportion of coal energy is beneficial to improving ecological efficiency. However, the effect is nonlinear, showing an inverted U-shaped nonlinear change. The influence of energy structure optimization on ecological efficiency has a stronger effect on its improvement of resource-based and old industrial cities. Moreover, it has an obvious “local–neighborhood” spatial spillover effect. Additionally, the energy structure could be improved according to local conditions in different regions, such as the level of economic development, industrial structure, and resource endowment conditions. Furthermore, regional cooperation and coordination should be strengthened and consolidated, along with the positive spatial effects of high eco-efficiency cities. Especially in city clusters and metropolitan areas, the strengthening of cross-city cooperation in emission trading, environmental governance, and compensation is vital.

Suggested Citation

  • Fan Zhang & Nengsheng Luo & Yanfei Li, 2023. "What Type of Energy Structure Improves Eco-Efficiency? A Study Based on Statistical Data of 285 Prefecture-Level Entities in China," Sustainability, MDPI, vol. 15(11), pages 1-26, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:9130-:d:1164450
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    1. Plácido Moreno & Sebastián Lozano, 2014. "A network DEA assessment of team efficiency in the NBA," Annals of Operations Research, Springer, vol. 214(1), pages 99-124, March.
    2. Konstantinos Petridis & Alexander Chatzigeorgiou & Emmanouil Stiakakis, 2016. "A spatiotemporal Data Envelopment Analysis (S-T DEA) approach: the need to assess evolving units," Annals of Operations Research, Springer, vol. 238(1), pages 475-496, March.
    3. Qingyou Yan & Xu Wang & Tomas Baležentis & Dalia Streimikiene, 2018. "Energy–economy–environmental (3E) performance of Chinese regions based on the data envelopment analysis model with mixed assumptions on disposability," Energy & Environment, , vol. 29(5), pages 664-684, August.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Jie Wu & Liang Liang, 2012. "A multiple criteria ranking method based on game cross-evaluation approach," Annals of Operations Research, Springer, vol. 197(1), pages 191-200, August.
    6. Rashidi, Kamran & Farzipoor Saen, Reza, 2015. "Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement," Energy Economics, Elsevier, vol. 50(C), pages 18-26.
    7. Malin Song & Jun Tao & Shuhong Wang, 2015. "FDI, technology spillovers and green innovation in China: analysis based on Data Envelopment Analysis," Annals of Operations Research, Springer, vol. 228(1), pages 47-64, May.
    8. Necmi Avkiran & Lin Cai, 2014. "Identifying distress among banks prior to a major crisis using non-oriented super-SBM," Annals of Operations Research, Springer, vol. 217(1), pages 31-53, June.
    9. Hao Chen & Qiyan Wu & Jianquan Cheng & Zhifei Ma & Weixuan Song, 2015. "Scaling-up Strategy as an Appropriate Approach for Sustainable New Town Development? Lessons from Wujin, Changzhou, China," Sustainability, MDPI, vol. 7(5), pages 1-23, May.
    10. Wu, Qiyan & Zhang, Xiaoling & Shang, Zhengyong & Li, Zaijun, 2015. "Political-economy based institutional industry complex and sustainable development: The case of the salt-chemical industry in Huai’an, China," Energy Policy, Elsevier, vol. 87(C), pages 39-47.
    11. Necmi Avkiran & Kaoru Tone & Miki Tsutsui, 2008. "Bridging radial and non-radial measures of efficiency in DEA," Annals of Operations Research, Springer, vol. 164(1), pages 127-138, November.
    12. Zhang, Bing & Bi, Jun & Fan, Ziying & Yuan, Zengwei & Ge, Junjie, 2008. "Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach," Ecological Economics, Elsevier, vol. 68(1-2), pages 306-316, December.
    13. Konstantinos Petridis & Alexander Chatzigeorgiou & Emmanouil Stiakakis, 2016. "A spatiotemporal Data Envelopment Analysis (S-T DEA) approach: the need to assess evolving units," Annals of Operations Research, Springer, vol. 238(1), pages 475-496, March.
    14. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    15. Yu Yu & Weiwei Zhu & Qian Zhang, 2019. "DEA cross-efficiency evaluation and ranking method based on interval data," Annals of Operations Research, Springer, vol. 278(1), pages 159-175, July.
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