IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2631-d786629.html
   My bibliography  Save this article

Driving Factors of CO 2 Emissions in China’s Power Industry: Relative Importance Analysis Based on Spatial Durbin Model

Author

Listed:
  • Yuanying Chi

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China)

  • Wenbing Zhou

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China)

  • Songlin Tang

    (Economic School, Shandong Technology and Business University, Yantai 264005, China)

  • Yu Hu

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China)

Abstract

The low-carbon transformation of the power industry is of great significance to realize the carbon peak in advance. However, almost a third of China’s CO 2 emissions came from the power sector in 2019. This paper aimed to identify the key drivers of CO 2 emissions in China’s power industry with the consideration of spatial autocorrelation. The spatial Durbin model and relative importance analysis were combined based on Chinese provincial data from 2003 to 2019. This combination demonstrated that GDP, the power supply structure and energy intensity are the key drivers of CO 2 emissions in China’s power industry. The self-supply ratio of electricity and the spatial spillover effect have a slight effect on increasing CO 2 emissions. The energy demand structure and CO 2 emission intensity of thermal power have a positive effect, although it is the lowest. Second, the positive impact of GDP on CO 2 emissions is decreasing, but that of the power supply structure and energy intensity is increasing. Third, the energy demand of the industrial and residential sectors has a greater impact on CO 2 emissions than that of construction and transportation. For achieving the CO 2 emission peak in advance, governments should give priority to developing renewable power and regional electricity trade rather than upgrading thermal power generation. They should also focus on promoting energy-saving technology, especially tapping the energy-saving potential of the industry and resident sectors.

Suggested Citation

  • Yuanying Chi & Wenbing Zhou & Songlin Tang & Yu Hu, 2022. "Driving Factors of CO 2 Emissions in China’s Power Industry: Relative Importance Analysis Based on Spatial Durbin Model," Energies, MDPI, vol. 15(7), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2631-:d:786629
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2631/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2631/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Jinying & Li, Sisi, 2020. "Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model," Energy Policy, Elsevier, vol. 140(C).
    2. Jia, Zhijie & Lin, Boqiang, 2021. "The impact of removing cross subsidies in electric power industry in China: Welfare, economy, and CO2 emission," Energy Policy, Elsevier, vol. 148(PB).
    3. Tang, Songlin & Zhou, Wenbing & Li, Xinjin & Chen, Yingchao & Zhang, Qian & Zhang, Xiliang, 2021. "Subsidy strategy for distributed photovoltaics: A combined view of cost change and economic development," Energy Economics, Elsevier, vol. 97(C).
    4. Zhao, Pengjun & Zeng, Liangen & Li, Peilin & Lu, Haiyan & Hu, Haoyu & Li, Chengming & Zheng, Mengyuan & Li, Haitao & Yu, Zhao & Yuan, Dandan & Xie, Jinxin & Huang, Qi & Qi, Yuting, 2022. "China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model," Energy, Elsevier, vol. 238(PC).
    5. 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).
    6. Xu, Jin-Hua & Yi, Bo-Wen & Fan, Ying, 2020. "Economic viability and regulation effects of infrastructure investments for inter-regional electricity transmission and trade in China," Energy Economics, Elsevier, vol. 91(C).
    7. Ninpanit, Panittra & Malik, Arunima & Wakiyama, Takako & Geschke, Arne & Lenzen, Manfred, 2019. "Thailand’s energy-related carbon dioxide emissions from production-based and consumption-based perspectives," Energy Policy, Elsevier, vol. 133(C).
    8. Liu, Xianmei & Peng, Rui & Zhong, Chao & Wang, Mingyue & Guo, Pibin, 2021. "What drives the temporal and spatial differences of CO2 emissions in the transport sector? Empirical evidence from municipalities in China," Energy Policy, Elsevier, vol. 159(C).
    9. Wu, Haitao & Xu, Lina & Ren, Siyu & Hao, Yu & Yan, Guoyao, 2020. "How do energy consumption and environmental regulation affect carbon emissions in China? New evidence from a dynamic threshold panel model," Resources Policy, Elsevier, vol. 67(C).
    10. Tan, Zhongfu & Li, Li & Wang, Jianjun & Wang, Jianhui, 2011. "Examining the driving forces for improving China’s CO2 emission intensity using the decomposing method," Applied Energy, Elsevier, vol. 88(12), pages 4496-4504.
    11. Wang, Zhaoxia & Zhu, Han & Ding, Yan & Zhu, Tianli & Zhu, Neng & Tian, Zhe, 2018. "Energy efficiency evaluation of key energy consumption sectors in China based on a macro-evaluating system," Energy, Elsevier, vol. 153(C), pages 65-79.
    12. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2019. "Modelling dynamic impacts of urbanization on disaggregated energy consumption in China: A spatial Durbin modelling and decomposition approach," Energy Policy, Elsevier, vol. 133(C).
    13. Xie, Pinjie & Yang, Fan & Mu, Zhuowen & Gao, Shuangshuang, 2020. "Influencing factors of the decoupling relationship between CO2 emission and economic development in China’s power industry," Energy, Elsevier, vol. 209(C).
    14. Park, Jongmun & Yun, Sun-Jin, 2022. "Social determinants of residential electricity consumption in Korea: Findings from a spatial panel model," Energy, Elsevier, vol. 239(PE).
    15. Tang, Baojun & Li, Ru & Yu, Biying & An, Runying & Wei, Yi-Ming, 2018. "How to peak carbon emissions in China's power sector: A regional perspective," Energy Policy, Elsevier, vol. 120(C), pages 365-381.
    16. Lin, Boqiang & Bega, François, 2021. "China's Belt & Road Initiative coal power cooperation: Transitioning toward low-carbon development," Energy Policy, Elsevier, vol. 156(C).
    17. Tang, Liwei & He, Gang, 2021. "How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China," Energy, Elsevier, vol. 235(C).
    18. Croonenbroeck, Carsten & Palm, Marcel, 2020. "A spatio-temporal Durbin fixed effects IV-Model for ENTSO-E electricity flows analysis," Renewable Energy, Elsevier, vol. 148(C), pages 205-213.
    19. Pellini, Elisabetta, 2021. "Estimating income and price elasticities of residential electricity demand with Autometrics," Energy Economics, Elsevier, vol. 101(C).
    20. Espoir, Delphin Kamanda & Sunge, Regret, 2021. "Co2 Emissions and Economic Development in Africa: Evidence from A Dynamic Spatial Panel Model," EconStor Preprints 234131, ZBW - Leibniz Information Centre for Economics.
    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. Xin Xu & Yuming Shen & Hanchu Liu, 2022. "What Cause Large Spatiotemporal Differences in Carbon Intensity of Energy-Intensive Industries in China? Evidence from Provincial Data during 2000–2019," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    2. Liu, Xiaohong & Yang, Jiangjiang & Xu, Chengzhen & Li, Xingchen & Zhu, Qingyuan, 2023. "Environmental regulation efficiency analysis by considering regional heterogeneity," Resources Policy, Elsevier, vol. 83(C).

    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. Ping Lu & Zhihong Li & Ying Wen & Jianhui Liu & Yue Yuan & Ruiyu Niu & Yiran Wang & Liangliang Han, 2023. "Fresh insights for sustainable development: Collaborative governance of carbon emissions based on social network analysis," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(3), pages 1873-1887, June.
    2. Huaibo Yang & Chao Shi & Jianbo Li & Tianran Liu & Youwei Li & Yao Wang & Yueying Yang, 2022. "Has the Inter-Regional Power Transmission Promoted Economic Development? A Quantitative Assessment in China," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    3. Kun Chen & Yinrong Chen & Qingying Zhu & Min Liu, 2022. "The Relationship between Environmental Regulation, Industrial Transformation Change and Urban Low-Carbon Development: Evidence from 282 Cities in China," IJERPH, MDPI, vol. 19(19), pages 1-15, October.
    4. Xian’en Wang & Tingyu Hu & Junnian Song & Haiyan Duan, 2022. "Tracking Key Industrial Sectors for CO 2 Mitigation through the Driving Effects: An Attribution Analysis," IJERPH, MDPI, vol. 19(21), pages 1-16, November.
    5. Shuping Cheng & Lingjie Meng & Weizhong Wang, 2022. "The Impact of Environmental Regulation on Green Energy Technology Innovation—Evidence from China," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    6. Li, Jiaman & Dong, Kangyin & Wang, Kun & Dong, Xiucheng, 2023. "How does natural resource dependence influence carbon emissions? The role of environmental regulation," Resources Policy, Elsevier, vol. 80(C).
    7. Bowen Xiao & Chengyao Xu, 2023. "Can Policy Instruments Achieve Synergies in Mitigating Air Pollution and CO 2 Emissions in the Transportation Sector?," Sustainability, MDPI, vol. 15(19), pages 1-24, October.
    8. Xie, Pinjie & Gong, Ningyu & Sun, Feihu & Li, Pin & Pan, Xianyou, 2023. "What factors contribute to the extent of decoupling economic growth and energy carbon emissions in China?," Energy Policy, Elsevier, vol. 173(C).
    9. Liu, Fengqi & Kang, Yuxin & Guo, Kun, 2022. "Is electricity consumption of Chinese counties decoupled from carbon emissions? A study based on Tapio decoupling index," Energy, Elsevier, vol. 251(C).
    10. Chai, Jian & Tian, Lingyue & Jia, Ruining, 2023. "New energy demonstration city, spatial spillover and carbon emission efficiency: Evidence from China's quasi-natural experiment," Energy Policy, Elsevier, vol. 173(C).
    11. Xu, Jie & Lv, Tao & Hou, Xiaoran & Deng, Xu & Li, Na & Liu, Feng, 2022. "Spatiotemporal characteristics and influencing factors of renewable energy production in China: A spatial econometric analysis," Energy Economics, Elsevier, vol. 116(C).
    12. Pang, Qinghua & Dong, Xianwei & Zhang, Lina & Chiu, Yung-ho, 2023. "Drivers and key pathways of the household energy consumption in the Yangtze river economic belt," Energy, Elsevier, vol. 262(PA).
    13. Meng, Conghui & Du, Xiaoyun & Zhu, Mengcheng & Ren, Yitian & Fang, Kai, 2023. "The static and dynamic carbon emission efficiency of transport industry in China," Energy, Elsevier, vol. 274(C).
    14. Ping Cao & Xiaoxiao Li & Yu Cheng & Han Shen, 2022. "Temporal-Spatial Evolution and Driving Factors of Global Carbon Emission Efficiency," IJERPH, MDPI, vol. 19(22), pages 1-20, November.
    15. Hu, Hui & Qi, Shaozhou & Chen, Yuanzhi, 2023. "Using green technology for a better tomorrow: How enterprises and government utilize the carbon trading system and incentive policies," China Economic Review, Elsevier, vol. 78(C).
    16. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    17. Li, Xi & Yu, Biying, 2019. "Peaking CO2 emissions for China's urban passenger transport sector," Energy Policy, Elsevier, vol. 133(C).
    18. 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.
    19. Senhua Huang & Lingming Chen, 2023. "The Impact of the Digital Economy on the Urban Total-Factor Energy Efficiency: Evidence from 275 Cities in China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    20. Lanre Ibrahim, Ridwan & Bello Ajide, Kazeem & Usman, Muhammad & Kousar, Rakhshanda, 2022. "Heterogeneous effects of renewable energy and structural change on environmental pollution in Africa: Do natural resources and environmental technologies reduce pressure on the environment?," Renewable Energy, Elsevier, vol. 200(C), pages 244-256.

    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:jeners:v:15:y:2022:i:7:p:2631-:d:786629. 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.