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Research on the Strategy of Industrial Structure Optimization Driven by Green Credit Distribution

Author

Listed:
  • Guoping Ding

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China
    These authors contributed equally to this work.)

  • Jingqian Hua

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China
    These authors contributed equally to this work.)

  • Juntao Duan

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China
    These authors contributed equally to this work.)

  • Sixia Deng

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China
    These authors contributed equally to this work.)

  • Wenyu Zhang

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China
    These authors contributed equally to this work.)

  • Yifan Gong

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China
    These authors contributed equally to this work.)

  • Huaping Sun

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China
    School of Economics and Management, Xinjiang University, Urumqi 830046, China)

Abstract

Credit is an important means to promote economic development, while green credit is conducive to the sustainable development of industry. This paper aims to build a multiple linear regression model and a dynamic panel data GMM estimation model to analyze the important factors that affect the optimization of the industrial structure. We then use an analytic hierarchy process to explore the relationship between green credit and industrial optimization. We compare this with the optimization rate of the industrial structure according to the real data, and then obtain the effectiveness of the hierarchical analysis of the three major industries in the eastern, central and western regions. Finally, neural networks are used to forecast the total amount and distribution of green credit in 2021. The final results show that there are regional and industrial differences in the influence of green credit on industrial structure optimization, and in the process of using green credit to promote the optimization and upgrading of industrial structure.

Suggested Citation

  • Guoping Ding & Jingqian Hua & Juntao Duan & Sixia Deng & Wenyu Zhang & Yifan Gong & Huaping Sun, 2022. "Research on the Strategy of Industrial Structure Optimization Driven by Green Credit Distribution," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9360-:d:876435
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    References listed on IDEAS

    as
    1. Sun, Huaping & Geng, Yong & Hu, Lingxiang & Shi, Longyu & Xu, Tong, 2018. "Measuring China's new energy vehicle patents: A social network analysis approach," Energy, Elsevier, vol. 153(C), pages 685-693.
    2. Conrad, Jon M. & Nøstbakken, Linda, 2018. "Innovation and site quality: Implications for the timing of investments in renewable energy," Energy, Elsevier, vol. 148(C), pages 1173-1180.
    3. Liu, Wenfeng & Zhang, Xingping & Feng, Sida, 2019. "Does renewable energy policy work? Evidence from a panel data analysis," Renewable Energy, Elsevier, vol. 135(C), pages 635-642.
    4. Li, Yaoming & Zhang, Qi & Liu, Boyu & McLellan, Benjamin & Gao, Yuan & Tang, Yanyan, 2018. "Substitution effect of New-Energy Vehicle Credit Program and Corporate Average Fuel Consumption Regulation for Green-car Subsidy," Energy, Elsevier, vol. 152(C), pages 223-236.
    5. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2019. "Institutional quality, green innovation and energy efficiency," Energy Policy, Elsevier, vol. 135(C).
    6. Zhang, M.M. & Zhou, P. & Zhou, D.Q., 2016. "A real options model for renewable energy investment with application to solar photovoltaic power generation in China," Energy Economics, Elsevier, vol. 59(C), pages 213-226.
    7. Hochman, Gal & Timilsina, Govinda R., 2017. "Energy efficiency barriers in commercial and industrial firms in Ukraine: An empirical analysis," Energy Economics, Elsevier, vol. 63(C), pages 22-30.
    8. Uz, Dilek, 2018. "Energy efficiency investments in small and medium sized manufacturing firms: The case of California energy crisis," Energy Economics, Elsevier, vol. 70(C), pages 421-428.
    9. Liang, Yuanyuan & Yu, Biying & Wang, Lu, 2019. "Costs and benefits of renewable energy development in China's power industry," Renewable Energy, Elsevier, vol. 131(C), pages 700-712.
    10. Zhang, M.M. & Zhou, D.Q. & Zhou, P. & Chen, H.T., 2017. "Optimal design of subsidy to stimulate renewable energy investments: The case of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 873-883.
    11. Yang, Yong-cong & Nie, Pu-yan & Liu, Hui-ting & Shen, Ming-hao, 2018. "On the welfare effects of subsidy game for renewable energy investment: Toward a dynamic equilibrium model," Renewable Energy, Elsevier, vol. 121(C), pages 420-428.
    12. Chan, Ricky Y.K., 2010. "Corporate environmentalism pursuit by foreign firms competing in China," Journal of World Business, Elsevier, vol. 45(1), pages 80-92, January.
    13. Giorgio Petroni & Barbara Bigliardi & Francesco Galati, 2019. "Rethinking the Porter Hypothesis: The Underappreciated Importance of Value Appropriation and Pollution Intensity," Review of Policy Research, Policy Studies Organization, vol. 36(1), pages 121-140, January.
    14. Zeng, Shihong & Jiang, Chunxia & Ma, Chen & Su, Bin, 2018. "Investment efficiency of the new energy industry in China," Energy Economics, Elsevier, vol. 70(C), pages 536-544.
    15. Tian, Yuan, 2018. "Optimal policy for attracting FDI: Investment cost subsidy versus tax rate reduction," International Review of Economics & Finance, Elsevier, vol. 53(C), pages 151-159.
    16. Mundaca, Gabriela, 2017. "Energy subsidies, public investment and endogenous growth," Energy Policy, Elsevier, vol. 110(C), pages 693-709.
    17. Instefjord, Norvald & Nawosah, Vivekanand & Yang, Pei, 2016. "A contingent claims analysis of optimal investment subsidy," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 354-372.
    18. Justin Lin & Xifang Sun & Ye Jiang, 2013. "Endowment, industrial structure, and appropriate financial structure: a new structural economics perspective," Journal of Economic Policy Reform, Taylor and Francis Journals, vol. 16(2), pages 109-122.
    19. Justin Yifu Lin & Xifang Sun & Ye Jiang, 2013. "Endowment, industrial structure, and appropriate financial structure: a new structural economics perspective," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 16(2), pages 109-122, June.
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    1. Chuanjia Du & Chengjun Wang & Tao Feng, 2023. "The Impact of China’s National Sustainable Development Experimental Zone Policy on Energy Transition," Sustainability, MDPI, vol. 15(10), pages 1-21, May.

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