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The Influencing Effects of Industrial Eco-Efficiency on Carbon Emissions in the Yangtze River Delta

Author

Listed:
  • Zaijun Li

    (Research Institute of Central Jiangsu Development, Yangzhou University, Yangzhou 225009, China)

  • Xiang Zheng

    (Research Institute of Central Jiangsu Development, Yangzhou University, Yangzhou 225009, China)

  • Dongqi Sun

    (Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

A low-carbon economy is the most important requirement to realize high-quality integrated development of the Yangtze River Delta. Utilizing the following models: a super-efficiency slacks-based measure model, a spatio-temporal correlation model, a bivariate LISA model, a spatial econometric model, and a geographically weighted random forest model, this study measured urban industrial eco-efficiency (IEE) and then analyzed its influencing effects on carbon emission in the Yangtze River Delta from 2000 to 2017. The influencing factors included spatio-temporal correlation intensity, spatio-temporal association type, direct and indirect impacts, and local importance impacts. Findings showed that: (1) The temporal correlation intensity between IEE and scale efficiency (SE) and carbon emissions exhibited an inverted V-shaped variation trend, while the temporal correlation intensity between pure technical efficiency (PTE) and carbon emissions exhibited a W-shaped fluctuation trend. The negative spatial correlation between IEE and carbon emissions was mainly distributed in the developed cities of the delta, while the positive correlation was mainly distributed in central Anhui Province and Yancheng and Taizhou cities. The spatial correlation between PTE and carbon emissions exhibited a spatial pattern of being higher in the central part of the delta and lower in the northern and southern parts. The negative spatial correlation between SE and carbon emissions was mainly clustered in Zhejiang Province and scattered in Jiangsu and Anhui provinces, with the cities with positive correlations being concentrated around two locations: the junction of Anhui and Jiangsu provinces, and within central Jiangsu Province. (2) The direct and indirect effects of IEE on carbon emissions were significantly negative, indicating that IEE contributed to reducing carbon emissions. The direct impact of PTE on carbon emissions was also significantly negative, while its indirect effect was insignificant. Both the direct and indirect effects of SE on carbon emissions were significantly negative. (3) It was found that the positive effect of IEE was more likely to alleviate the increase in carbon emissions in northern Anhui City. Further, PTE was more conducive to reducing the increase in carbon emissions in northwestern Anhui City, southern Zhejiang City, and in other cities including Changzhou and Wuxi. Finally, it was found that SE played a relatively important role in reducing the increase in carbon emissions only in four cities: Changzhou, Suqian, Lu’an, and Wenzhou.

Suggested Citation

  • Zaijun Li & Xiang Zheng & Dongqi Sun, 2021. "The Influencing Effects of Industrial Eco-Efficiency on Carbon Emissions in the Yangtze River Delta," Energies, MDPI, vol. 14(23), pages 1-19, December.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8169-:d:695899
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    References listed on IDEAS

    as
    1. Wang, Zhaohua & Zhang, Bin & Liu, Tongfan, 2016. "Empirical analysis on the factors influencing national and regional carbon intensity in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 34-42.
    2. Tenente, Marcos & Henriques, Carla & da Silva, Patrícia Pereira, 2020. "Eco-efficiency assessment of the electricity sector: Evidence from 28 European Union countries," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 293-314.
    3. 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.
    4. Timo Kuosmanen, 2005. "Measurement and Analysis of Eco‐efficiency: An Economist's Perspective," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 15-18, October.
    5. Liu, Conghu & Gao, Mengdi & Zhu, Guang & Zhang, Cuixia & Zhang, Pan & Chen, Jianqing & Cai, Wei, 2021. "Data driven eco-efficiency evaluation and optimization in industrial production," Energy, Elsevier, vol. 224(C).
    6. Soumyananda Dinda, 2018. "Production technology and carbon emission: long-run relation with short-run dynamics," Journal of Applied Economics, Taylor & Francis Journals, vol. 21(1), pages 106-121, January.
    7. Lee, Taehwee & Yeo, Gi-Tae & Thai, Vinh V., 2014. "Environmental efficiency analysis of port cities: Slacks-based measure data envelopment analysis approach," Transport Policy, Elsevier, vol. 33(C), pages 82-88.
    8. Luc Anselin, 2001. "Spatial Effects in Econometric Practice in Environmental and Resource Economics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 705-710.
    9. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    10. Zhang, Fan & Deng, Xiangzheng & Phillips, Fred & Fang, Chuanglin & Wang, Chao, 2020. "Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    11. Wang, Zhaohua & Sun, Yefei & Wang, Bo, 2019. "How does the new-type urbanisation affect CO2 emissions in China? An empirical analysis from the perspective of technological progress," Energy Economics, Elsevier, vol. 80(C), pages 917-927.
    12. Wang, Zhaohua & Yin, Fangchao & Zhang, Yixiang & Zhang, Xian, 2012. "An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China," Applied Energy, Elsevier, vol. 100(C), pages 277-284.
    13. Yang, Lisha & Li, Zhi, 2017. "Technology advance and the carbon dioxide emission in China – Empirical research based on the rebound effect," Energy Policy, Elsevier, vol. 101(C), pages 150-161.
    14. Zhang, Chuanguo & Zhou, Xiangxue, 2016. "Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 943-951.
    15. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    16. Hans-Werner Sinn, 2008. "Public policies against global warming: a supply side approach," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 15(4), pages 360-394, August.
    17. Ertugrul, Hasan Murat & Çetin, Murat & Şeker, Fahri & Dogan, Eyüp, 2015. "The impact of trade openness on global carbon dioxide emissions: Evidence from the top ten emitters among developing countries," MPRA Paper 97539, University Library of Munich, Germany, revised 10 Mar 2016.
    18. Yang, Yuan & Cai, Wenjia & Wang, Can, 2014. "Industrial CO2 intensity, indigenous innovation and R&D spillovers in China’s provinces," Applied Energy, Elsevier, vol. 131(C), pages 117-127.
    19. Jeong, Kyonghwa & Kim, Suyi, 2013. "LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector," Energy Policy, Elsevier, vol. 62(C), pages 1245-1253.
    20. Wang, Qiang & Wu, Shi-dai & Zeng, Yue-e & Wu, Bo-wei, 2016. "Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1563-1579.
    21. Peng, Hongsong & Zhang, Jinhe & Lu, Lin & Tang, Guorong & Yan, Bingjin & Xiao, Xiao & Han, Ya, 2017. "Eco-efficiency and its determinants at a tourism destination: A case study of Huangshan National Park, China," Tourism Management, Elsevier, vol. 60(C), pages 201-211.
    22. Azam, Muhammad & Khan, Abdul Qayyum, 2016. "Testing the Environmental Kuznets Curve hypothesis: A comparative empirical study for low, lower middle, upper middle and high income countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 556-567.
    23. Ding, Suiting & Zhang, Ming & Song, Yan, 2019. "Exploring China's carbon emissions peak for different carbon tax scenarios," Energy Policy, Elsevier, vol. 129(C), pages 1245-1252.
    24. Liu, Zhao & Zhang, Huan & Zhang, Yue-Jun & Zhu, Tian-Tian, 2020. "How does industrial policy affect the eco-efficiency of industrial sector? Evidence from China," Applied Energy, Elsevier, vol. 272(C).
    25. 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.
    26. Zhang, Ning & Wei, Xiao, 2015. "Dynamic total factor carbon emissions performance changes in the Chinese transportation industry," Applied Energy, Elsevier, vol. 146(C), pages 409-420.
    27. Rui Zhao & Yiyun Liu & Zhenyan Zhang & Sidai Guo & Ming-Lang Tseng & Kuo-Jui Wu, 2018. "Enhancing Eco-Efficiency of Agro-Products’ Closed-Loop Supply Chain under the Belt and Road Initiatives: A System Dynamics Approach," Sustainability, MDPI, vol. 10(3), pages 1-15, March.
    28. J. Paul Elhorst, 2014. "Matlab Software for Spatial Panels," International Regional Science Review, , vol. 37(3), pages 389-405, July.
    29. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    30. Du, Kerui & Li, Pengzhen & Yan, Zheming, 2019. "Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 297-303.
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