IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10448-d894765.html
   My bibliography  Save this article

Does Environmental Regulation Improve Carbon Emission Efficiency? Inspection of Panel Data from Inter-Provincial Provinces in China

Author

Listed:
  • Pan Jiang

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China
    School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China)

  • Mengyue Li

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China)

  • Yuting Zhao

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China)

  • Xiujuan Gong

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China)

  • Ruifeng Jin

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China)

  • Yuhan Zhang

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China)

  • Xue Li

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China
    School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China)

  • Liang Liu

    (School of Economics and Management, Southwest University of Science and Technology, Mianyang 621010, China)

Abstract

This study aims to analyze the nonlinear relationship between environmental regulation and carbon emission efficiency and provide scientific reference for achieving the goal for carbon neutrality at a lower cost. Taking 30 provinces in China, using dual carbon policy as the research objects, the slacks-based measure–Malmquist–Luenberger (SBM–ML) index method was used to measure the carbon emission efficiency from 2009 to 2019 and a panel threshold regression model was established to explore the nonlinear effects of environmental regulation and carbon emission efficiency in each province. The results show that: (1) during the sample period, there is geographical variability in CEE, with the eastern coastal provinces having the highest CEE, followed by the central and western provinces, and the resource-dependent provinces having the lowest CEE and their energy consumption and utilization efficiency being significantly lower than other provinces; (2) when the energy consumption intensity is used as a threshold variable, the relationship between environmental regulation and carbon emission rate is an inverted “U” shape; and (3) when green technology innovation is used as a threshold variable, the relationship between environmental regulation and carbon emission rate is a “U” shape. This study provides a new perspective for improving carbon emission efficiency.

Suggested Citation

  • Pan Jiang & Mengyue Li & Yuting Zhao & Xiujuan Gong & Ruifeng Jin & Yuhan Zhang & Xue Li & Liang Liu, 2022. "Does Environmental Regulation Improve Carbon Emission Efficiency? Inspection of Panel Data from Inter-Provincial Provinces in China," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10448-:d:894765
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10448/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/16/10448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huijie Yan, 2015. "Provincial energy intensity in China: The role of urbanization," Post-Print hal-01457329, HAL.
    2. Morgenstern, Richard D. & Pizer, William A. & Shih, Jhih-Shyang, 2002. "Jobs Versus the Environment: An Industry-Level Perspective," Journal of Environmental Economics and Management, Elsevier, vol. 43(3), pages 412-436, May.
    3. Zhao, Xiaoli & Yin, Haitao & Zhao, Yue, 2015. "Impact of environmental regulations on the efficiency and CO2 emissions of power plants in China," Applied Energy, Elsevier, vol. 149(C), pages 238-247.
    4. 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.
    5. Kepplinger, D. & Templ, M. & Upadhyaya, S., 2013. "Analysis of energy intensity in manufacturing industry using mixed-effects models," Energy, Elsevier, vol. 59(C), pages 754-763.
    6. Zhang, Shengling & Wang, Yao & Hao, Yu & Liu, Zhiwei, 2021. "Shooting two hawks with one arrow: Could China's emission trading scheme promote green development efficiency and regional carbon equality?," Energy Economics, Elsevier, vol. 101(C).
    7. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    8. Wang, Guofeng & Deng, Xiangzheng & Wang, Jingyu & Zhang, Fan & Liang, Shiqi, 2019. "Carbon emission efficiency in China: A spatial panel data analysis," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    9. Wang, Shaojian & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    10. Joseph Aldy, 2006. "Per Capita Carbon Dioxide Emissions: Convergence or Divergence?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 33(4), pages 533-555, April.
    11. Pan, Xiongfeng & Uddin, Md. Kamal & Saima, Umme & Jiao, Zhiming & Han, Cuicui, 2019. "How do industrialization and trade openness influence energy intensity? Evidence from a path model in case of Bangladesh," Energy Policy, Elsevier, vol. 133(C).
    12. Xinghua Wang & Shunchen Wu & Xiaojuan Qin & Meixiang La & Haixia Zuo, 2022. "Informal Environment Regulation, Green Technology Innovation and Air Pollution: Quasi-Natural Experiments from Prefectural Cities in China," Sustainability, MDPI, vol. 14(10), pages 1-13, May.
    13. Guo, Ran & Yuan, Yijun, 2020. "Different types of environmental regulations and heterogeneous influence on energy efficiency in the industrial sector: Evidence from Chinese provincial data," Energy Policy, Elsevier, vol. 145(C).
    14. Teng, Xiangyu & Liu, Fan-peng & Chiu, Yung-ho, 2021. "The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model," Energy, Elsevier, vol. 216(C).
    15. Dargahi, Hassan & Khameneh, Kazem Biabany, 2019. "Energy intensity determinants in an energy-exporting developing economy: Case of Iran," Energy, Elsevier, vol. 168(C), pages 1031-1044.
    16. Joseph E. Aldy, 2007. "Divergence in State-Level Per Capita Carbon Dioxide Emissions," Land Economics, University of Wisconsin Press, vol. 83(3), pages 353-369.
    17. Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    18. Bian, Yiwen & He, Ping & Xu, Hao, 2013. "Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach," Energy Policy, Elsevier, vol. 63(C), pages 962-971.
    19. Minzhe Du & Jorge Antunes & Peter Wanke & Zhongfei Chen, 2022. "Ecological efficiency assessment under the construction of low-carbon city: a perspective of green technology innovation," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 65(9), pages 1727-1752, July.
    20. 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).
    21. Huayong Niu & Zhishuo Zhang & Yao Xiao & Manting Luo & Yumeng Chen, 2022. "A Study of Carbon Emission Efficiency in Chinese Provinces Based on a Three-Stage SBM-Undesirable Model and an LSTM Model," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    22. Grafton, R. Quentin & Kompas, Tom & Long, Ngo Van & To, Hang, 2014. "US biofuels subsidies and CO2 emissions: An empirical test for a weak and a strong green paradox," Energy Policy, Elsevier, vol. 68(C), pages 550-555.
    23. Yan, Huijie, 2015. "Provincial energy intensity in China: The role of urbanization," Energy Policy, Elsevier, vol. 86(C), pages 635-650.
    24. Lin, Boqiang & Wang, Miao, 2021. "What drives energy intensity fall in China? Evidence from a meta-frontier approach," Applied Energy, Elsevier, vol. 281(C).
    25. Christoph Trumpp & Thomas Guenther, 2017. "Too Little or too much? Exploring U‐shaped Relationships between Corporate Environmental Performance and Corporate Financial Performance," Business Strategy and the Environment, Wiley Blackwell, vol. 26(1), pages 49-68, January.
    26. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jingjing Qian & Chao Chen & Yun Zhong, 2022. "Environmental Regulation and Sustainable Growth of Enterprise Value: Mediating Effect Analysis Based on Technological Innovation," Sustainability, MDPI, vol. 14(21), pages 1-16, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Wei & Liu, Xuemeng & Wang, Die & Zhou, Jianping, 2022. "Digital economy and carbon emission performance: Evidence at China's city level," Energy Policy, Elsevier, vol. 165(C).
    2. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    3. Tingting Wu & Junjun Chen & Chengchun Shi & Guidi Yang, 2023. "Carbon Emission Efficiency and Reduction Potential Based on Three-Stage Slacks-Based Measure with Data Envelopment Analysis and Malmquist at the City Scale in Fujian Province, China," Sustainability, MDPI, vol. 15(16), pages 1-15, August.
    4. Edyta Sidorczuk-Pietraszko, 2020. "Spatial Differences in Carbon Intensity in Polish Households," Energies, MDPI, vol. 13(12), pages 1-21, June.
    5. Jing Xu & Dong Chen & Rongrong Liu & Maoxian Zhou & Yunxiao Kong, 2021. "Environmental Regulation, Technological Innovation, and Industrial Transformation: An Empirical Study Based on City Function in China," Sustainability, MDPI, vol. 13(22), pages 1-23, November.
    6. Feng, Yidai & Yuan, Huaxi & Liu, Yaobin, 2023. "The energy-saving effect in the new transformation of urbanization," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 41-59.
    7. Wang, Huiqing & Wei, Weixian, 2020. "Coordinating technological progress and environmental regulation in CO2 mitigation: The optimal levels for OECD countries & emerging economies," Energy Economics, Elsevier, vol. 87(C).
    8. Zhang, Wenyue & Li, Jianan & Sun, Chuanwang, 2022. "The impact of OFDI reverse technology spillovers on China's energy intensity: Analysis of provincial panel data," Energy Economics, Elsevier, vol. 116(C).
    9. Pan, Xiuzhen & Wei, Zixiang & Han, Botang & Shahbaz, Muhammad, 2021. "The heterogeneous impacts of interregional green technology spillover on energy intensity in China," Energy Economics, Elsevier, vol. 96(C).
    10. Shuangjie Li & Wei Wang & Liming Wang & Ge Wang, 2023. "Digital Economy and 3E Efficiency Performance: Evidence from EU Countries," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    11. Huang, Junbing & Lian, Shijia & Qu, Ran & Luan, Bingjiang & Wang, Yajun, 2023. "Investigating the role of enterprises' property rights in China's provincial industrial energy intensity," Energy, Elsevier, vol. 282(C).
    12. Fang, Tao & Fang, Debin & Yu, Bolin, 2022. "Carbon emission efficiency of thermal power generation in China: Empirical evidence from the micro-perspective of power plants," Energy Policy, Elsevier, vol. 165(C).
    13. Huang, Junbing & Du, Dan & Tao, Qizhi, 2017. "An analysis of technological factors and energy intensity in China," Energy Policy, Elsevier, vol. 109(C), pages 1-9.
    14. Feng Dong & Chang Qin & Xiaoyun Zhang & Xu Zhao & Yuling Pan & Yujin Gao & Jiao Zhu & Yangfan Li, 2021. "Towards Carbon Neutrality: The Impact of Renewable Energy Development on Carbon Emission Efficiency," IJERPH, MDPI, vol. 18(24), pages 1-23, December.
    15. Guimei Wang & Muhammad Salman, 2023. "The impacts of heterogeneous environmental regulations on green economic efficiency from the perspective of urbanization: a dynamic threshold analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9485-9516, September.
    16. Shichun Xu & Yiwen Li & Yuan Tao & Yan Wang & Yunfan Li, 2020. "Regional Differences in the Spatial Characteristics and Dynamic Convergence of Environmental Efficiency in China," Sustainability, MDPI, vol. 12(18), pages 1-21, September.
    17. Huang, Junbing & Chen, Xiang, 2020. "Domestic R&D activities, technology absorption ability, and energy intensity in China," Energy Policy, Elsevier, vol. 138(C).
    18. Liang Liu & Mengyue Li & Xiujuan Gong & Pan Jiang & Ruifeng Jin & Yuhan Zhang, 2022. "Influence Mechanism of Different Environmental Regulations on Carbon Emission Efficiency," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    19. Pan, Xiongfeng & Chu, Junhui & Tian, Mengyuan & Li, Mengna, 2022. "Non-linear effects of outward foreign direct investment on total factor energy efficiency in China," Energy, Elsevier, vol. 239(PD).
    20. Xianpu Xu & Bijiao Yi, 2022. "New Insights into the Impact of Local Corruption on China’s Regional Carbon Emissions Performance Based on the Spatial Spillover Effects," Sustainability, MDPI, vol. 14(22), pages 1-26, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10448-:d:894765. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.