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Influence of Clean Energy and Financial Structure on China’s Provincial Carbon Emission Efficiency—Empirical Analysis Based on Spatial Spillover Effects

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  • Ying Xie

    (Student Affairs, Chongqing Business Vocational College, Chongqing 401331, China)

  • Minglong Zhang

    (China Center for Special Economic Zone Research, Shenzhen University, Shenzhen 518060, China
    School of Economics, Shenzhen University, Shenzhen 518060, China)

Abstract

Clean energy is an essential means to limiting carbon emissions and improving economic transformation, and a market-oriented financial structure is the inevitable result of the deepening of supply-side financial reforms. Exploring whether clean energy enhances carbon emission efficiency (CEE) through financial structural adjustment is essential in formulating policies intended to achieve the dual goals of “carbon peaking” and “carbon neutrality”. As part of the evaluation of China’s provincial CEE using panel data of 30 provinces from 2000 to 2019, this paper adopts an improved nonradial directional distance function (NDDF), while empirically analyzing the influence of clean energy and a market-oriented financial structure on CEE using a spatial econometric model. The results indicate the following findings: (1) The provincial CEE in China is characterized by significant spatial autocorrelation. (2) A 1% increase in the integration of clean energy and a market-oriented financial structure leads to a 0.0032% increase in the local CEE and a 0.0076% increase in neighboring regions’ CEE through the spatial spillover effect. Clean energy can efficiently enhance CEE through the stock market, while it has a passive impact through bank credit. (3) The interactive effect between clean energy and a market-oriented financial structure varies according to the provincial CEE. From the 25th to the 90th quantiles, the role of clean energy in promoting CEE through the capital market is very significant, while clean energy inhibits CEE through bank credit in most provinces. Therefore, China’s clean energy development will bolster its competitiveness in the global market through a market-oriented financial structure that will bring economic development and environmental pollution into balance and provide a theoretical foundation for China’s double carbon reduction.

Suggested Citation

  • Ying Xie & Minglong Zhang, 2023. "Influence of Clean Energy and Financial Structure on China’s Provincial Carbon Emission Efficiency—Empirical Analysis Based on Spatial Spillover Effects," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3339-:d:1065405
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    References listed on IDEAS

    as
    1. Salim, Ruhul A. & Hassan, Kamrul & Shafiei, Sahar, 2014. "Renewable and non-renewable energy consumption and economic activities: Further evidence from OECD countries," Energy Economics, Elsevier, vol. 44(C), pages 350-360.
    2. Charfeddine, Lanouar & Kahia, Montassar, 2019. "Impact of renewable energy consumption and financial development on CO2 emissions and economic growth in the MENA region: A panel vector autoregressive (PVAR) analysis," Renewable Energy, Elsevier, vol. 139(C), pages 198-213.
    3. Ferreira, Ana & Pinheiro, Manuel Duarte & de Brito, Jorge & Mateus, Ricardo, 2018. "Combined carbon and energy intensity benchmarks for sustainable retail stores," Energy, Elsevier, vol. 165(PB), pages 877-889.
    4. Balsalobre-Lorente, Daniel & Shahbaz, Muhammad & Roubaud, David & Farhani, Sahbi, 2018. "How economic growth, renewable electricity and natural resources contribute to CO2 emissions?," Energy Policy, Elsevier, vol. 113(C), pages 356-367.
    5. Zheng, Huanyu & Song, Malin & Shen, Zhiyang, 2021. "The evolution of renewable energy and its impact on carbon reduction in China," Energy, Elsevier, vol. 237(C).
    6. Guo, Shen & Jiang, Zheng & Shi, Huimin, 2018. "The business cycle implications of bank discrimination in China," Economic Modelling, Elsevier, vol. 73(C), pages 264-278.
    7. Wang, Qiang & Wang, Lili, 2020. "Renewable energy consumption and economic growth in OECD countries: A nonlinear panel data analysis," Energy, Elsevier, vol. 207(C).
    8. Chen, Chaoyi & Pinar, Mehmet & Stengos, Thanasis, 2020. "Renewable energy consumption and economic growth nexus: Evidence from a threshold model," Energy Policy, Elsevier, vol. 139(C).
    9. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    10. Zhou, P. & Ang, B.W. & Han, J.Y., 2010. "Total factor carbon emission performance: A Malmquist index analysis," Energy Economics, Elsevier, vol. 32(1), pages 194-201, January.
    11. Shiyi Chen, 2011. "The Abatement of Carbon Dioxide Intensity in China: Factors Decomposition and Policy Implications," The World Economy, Wiley Blackwell, vol. 34, pages 1148-1167, July.
    12. Hsu, Po-Hsuan & Tian, Xuan & Xu, Yan, 2014. "Financial development and innovation: Cross-country evidence," Journal of Financial Economics, Elsevier, vol. 112(1), pages 116-135.
    13. Yeh, Chih-Chuan & Huang, Ho-Chuan (River) & Lin, Pei-Chien, 2013. "Financial structure on growth and volatility," Economic Modelling, Elsevier, vol. 35(C), pages 391-400.
    14. Ji, Qiang & Zhang, Dayong, 2019. "How much does financial development contribute to renewable energy growth and upgrading of energy structure in China?," Energy Policy, Elsevier, vol. 128(C), pages 114-124.
    15. 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.
    16. Taskin, Fatma & Zaim, Osman, 2001. "The role of international trade on environmental efficiency: a DEA approach," Economic Modelling, Elsevier, vol. 18(1), pages 1-17, January.
    17. Menyah, Kojo & Wolde-Rufael, Yemane, 2010. "CO2 emissions, nuclear energy, renewable energy and economic growth in the US," Energy Policy, Elsevier, vol. 38(6), pages 2911-2915, June.
    18. Fukuyama, Hirofumi & Weber, William L., 2009. "A directional slacks-based measure of technical inefficiency," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 274-287, December.
    19. Elhorst, J. Paul & Lacombe, Donald J. & Piras, Gianfranco, 2012. "On model specification and parameter space definitions in higher order spatial econometric models," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 211-220.
    20. Dogan, Eyup & Altinoz, Buket & Madaleno, Mara & Taskin, Dilvin, 2020. "The impact of renewable energy consumption to economic growth: A replication and extension of Inglesi-Lotz (2016)," Energy Economics, Elsevier, vol. 90(C).
    21. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    22. Liu, Guanchun & Zhang, Chengsi, 2020. "Does financial structure matter for economic growth in China," China Economic Review, Elsevier, vol. 61(C).
    23. Cheng, Zhonghua & Liu, Jun & Li, Lianshui & Gu, Xinbei, 2020. "Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces," Energy Economics, Elsevier, vol. 86(C).
    24. Inglesi-Lotz, Roula, 2016. "The impact of renewable energy consumption to economic growth: A panel data application," Energy Economics, Elsevier, vol. 53(C), pages 58-63.
    25. Beck, Thorsten & Levine, Ross, 2002. "Industry growth and capital allocation:*1: does having a market- or bank-based system matter?," Journal of Financial Economics, Elsevier, vol. 64(2), pages 147-180, May.
    26. Chen, Yang & Shao, Shuai & Fan, Meiting & Tian, Zhihua & Yang, Lili, 2022. "One man's loss is another's gain: Does clean energy development reduce CO2 emissions in China? Evidence based on the spatial Durbin model," Energy Economics, Elsevier, vol. 107(C).
    27. Fan, Meiting & Shao, Shuai & Yang, Lili, 2015. "Combining global Malmquist–Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China)," Energy Policy, Elsevier, vol. 79(C), pages 189-201.
    28. Paramati, Sudharshan Reddy & Ummalla, Mallesh & Apergis, Nicholas, 2016. "The effect of foreign direct investment and stock market growth on clean energy use across a panel of emerging market economies," Energy Economics, Elsevier, vol. 56(C), pages 29-41.
    29. Xueyang Liu & Xiaoxing Liu, 2021. "Can Financial Development Curb Carbon Emissions? Empirical Test Based on Spatial Perspective," Sustainability, MDPI, vol. 13(21), pages 1-19, October.
    30. Yeguan Yu, 2023. "The Impact of Financial System on Carbon Intensity: From the Perspective of Digitalization," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    31. Shijian Wu & Kaili Zhang, 2021. "Influence of Urbanization and Foreign Direct Investment on Carbon Emission Efficiency: Evidence from Urban Clusters in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 13(5), pages 1-22, March.
    32. Wen, Shiyan & Lin, Boqiang & Zhou, Yicheng, 2021. "Does financial structure promote energy conservation and emission reduction? Evidence from China," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 755-766.
    33. Xu, Le & Fan, Meiting & Yang, Lili & Shao, Shuai, 2021. "Heterogeneous green innovations and carbon emission performance: Evidence at China's city level," Energy Economics, Elsevier, vol. 99(C).
    34. Zhang, Ning & Choi, Yongrok, 2013. "Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis," Energy Economics, Elsevier, vol. 40(C), pages 549-559.
    35. Du, Kerui & Lu, Huang & Yu, Kun, 2014. "Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach," Applied Energy, Elsevier, vol. 115(C), pages 491-501.
    36. Bhattacharya, Mita & Paramati, Sudharshan Reddy & Ozturk, Ilhan & Bhattacharya, Sankar, 2016. "The effect of renewable energy consumption on economic growth: Evidence from top 38 countries," Applied Energy, Elsevier, vol. 162(C), pages 733-741.
    37. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    38. Dev R. Mishra, 2017. "Post-innovation CSR Performance and Firm Value," Journal of Business Ethics, Springer, vol. 140(2), pages 285-306, January.
    39. Du, Kerui & Lin, Boqiang, 2017. "International comparison of total-factor energy productivity growth: A parametric Malmquist index approach," Energy, Elsevier, vol. 118(C), pages 481-488.
    40. Chica-Olmo, Jorge & Sari-Hassoun, Salaheddine & Moya-Fernández, Pablo, 2020. "Spatial relationship between economic growth and renewable energy consumption in 26 European countries," Energy Economics, Elsevier, vol. 92(C).
    41. Qi, Tianyu & Zhang, Xiliang & Karplus, Valerie J., 2014. "The energy and CO2 emissions impact of renewable energy development in China," Energy Policy, Elsevier, vol. 68(C), pages 60-69.
    42. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    43. Raghutla, Chandrashekar & Shahbaz, Muhammad & Chittedi, Krishna Reddy & Jiao, Zhilun, 2021. "Financing clean energy projects: New empirical evidence from major investment countries," Renewable Energy, Elsevier, vol. 169(C), pages 231-241.
    44. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
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