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Exploring the complex relationship between industrial upgrading and energy eco-efficiency in river basin cities: A case study of the Yellow River Basin in China

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  • Wang, Ruonan
  • Xiao, Yi
  • Huang, Huan
  • Chang, Ming

Abstract

The synergistic advancement of industrial upgrading and energy eco-efficiency is crucial for the attainment of the Sustainable Development Goals. This study explored the coupling coordination relationship and its spatial-temporal evolution characteristics between industrial structure advancement and energy eco-efficiency, industrial structure rationalization and energy eco-efficiency in Yellow River Basin cities from 2010 to 2020. The super-SBM model and coupling coordination degree model were used to calculate the energy eco-efficiency and coupling coordination degree. Moran's I and Tobit model were used to analyze the spatial autocorrelation and the factors of coupling coordination degree. The coupling coordination degree of industrial structure advancement and energy eco-efficiency decreased from 0.535 to 0.489, with a growth rate of −8.60 %, showing a fluctuating downward trend. The coupling coordination degree of industrial structure rationalization and energy eco-efficiency decreased from 0.296 to 0.246, with a growth rate of −16.88 %, showing an "M" -shaped downward trend. The coupling coordination degree had a significant spatial correlation, and the global Moran's I value was stable around 0.2. Additionally, public transport and degree of opening-up can significantly promote the coordination relationship between industrial upgrading and energy eco-efficiency. These consequences can supply evidence for industrial and energy structure adjustment in river basin cities.

Suggested Citation

  • Wang, Ruonan & Xiao, Yi & Huang, Huan & Chang, Ming, 2024. "Exploring the complex relationship between industrial upgrading and energy eco-efficiency in river basin cities: A case study of the Yellow River Basin in China," Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:energy:v:312:y:2024:i:c:s0360544224032742
    DOI: 10.1016/j.energy.2024.133498
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    1. Hong, Qianqian & Cui, Linhao & Hong, Penghui, 2022. "The impact of carbon emissions trading on energy efficiency: Evidence from quasi-experiment in China's carbon emissions trading pilot," Energy Economics, Elsevier, vol. 110(C).
    2. Su, Yi & Fan, Qi-ming, 2022. "Renewable energy technology innovation, industrial structure upgrading and green development from the perspective of China's provinces," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    3. Qian Chi & Shenghui Zhou & Lijun Wang & Mengyao Zhu & Dandan Liu & Weichao Tang & Yaoping Cui & Jay Lee, 2021. "Exploring on the Eco-Climatic Effects of Land Use Changes in the Influence Area of the Yellow River Basin from 2000 to 2015," Land, MDPI, vol. 10(6), pages 1-16, June.
    4. Hübler, Michael, 2011. "Technology diffusion under contraction and convergence: A CGE analysis of China," Energy Economics, Elsevier, vol. 33(1), pages 131-142, January.
    5. Muratori, Matteo & Moran, Michael J. & Serra, Emmanuele & Rizzoni, Giorgio, 2013. "Highly-resolved modeling of personal transportation energy consumption in the United States," Energy, Elsevier, vol. 58(C), pages 168-177.
    6. Chuang, Ming Chih & Ma, Hwong Wen, 2013. "An assessment of Taiwan’s energy policy using multi-dimensional energy security indicators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 17(C), pages 301-311.
    7. Sharifi, Farahnaz & Nygaard, Andi & Stone, Wendy M. & Levin, Iris, 2021. "Green gentrification or gentrified greening: Metropolitan Melbourne," Land Use Policy, Elsevier, vol. 108(C).
    8. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    9. Tang, Zi, 2015. "An integrated approach to evaluating the coupling coordination between tourism and the environment," Tourism Management, Elsevier, vol. 46(C), pages 11-19.
    10. Du, Kerui & Cheng, Yuanyuan & Yao, Xin, 2021. "Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities," Energy Economics, Elsevier, vol. 98(C).
    11. Guo, Qiu-tong & Dong, Yong & Feng, Biao & Zhang, Hao, 2023. "Can green finance development promote total-factor energy efficiency? Empirical evidence from China based on a spatial Durbin model," Energy Policy, Elsevier, vol. 177(C).
    12. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    13. Nakamura, Koji & Kaihatsu, Sohei & Yagi, Tomoyuki, 2019. "Productivity improvement and economic growth: lessons from Japan," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 57-79.
    14. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    15. Haider, Salman & Mishra, Prajna Paramita, 2021. "Does innovative capability enhance the energy efficiency of Indian Iron and Steel firms? A Bayesian stochastic frontier analysis," Energy Economics, Elsevier, vol. 95(C).
    16. Fang, Chin-Yi & Hu, Jin-Li & Lou, Tze-Kai, 2013. "Environment-adjusted total-factor energy efficiency of Taiwan's service sectors," Energy Policy, Elsevier, vol. 63(C), pages 1160-1168.
    17. Xue, Yan & Tang, Chang & Wu, Haitao & Liu, Jianmin & Hao, Yu, 2022. "The emerging driving force of energy consumption in China: Does digital economy development matter?," Energy Policy, Elsevier, vol. 165(C).
    18. Chen, Nengcheng & Xu, Lei & Chen, Zeqiang, 2017. "Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models," Energy, Elsevier, vol. 134(C), pages 659-671.
    19. Ouyang, Xiaoling & Wei, Xiaoyun & Sun, Chuanwang & Du, Gang, 2018. "Impact of factor price distortions on energy efficiency: Evidence from provincial-level panel data in China," Energy Policy, Elsevier, vol. 118(C), pages 573-583.
    20. Luan, Bingjiang & Zou, Hong & Chen, Shuxing & Huang, Junbing, 2021. "The effect of industrial structure adjustment on China’s energy intensity: Evidence from linear and nonlinear analysis," Energy, Elsevier, vol. 218(C).
    Full references (including those not matched with items on IDEAS)

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