IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12359-d928183.html
   My bibliography  Save this article

Assessing the Drivers of Carbon Intensity Change in China: A Dynamic Spatial–Temporal Production-Theoretical Decomposition Analysis Approach

Author

Listed:
  • Xiaolei Liu

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Heng Chen

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Cheng Peng

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Mingqiu Li

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

Abstract

As carbon intensity (CI) can better reflect the coordinated relationship between carbon emissions and economic growth, the related research has gradually increased in recent years. To better explore the influence of production technology and spatial variations on CI disparities in China, this paper constructs a dynamic spatial–temporal production-theoretical decomposition analysis (DST-PDA) model to explore the dynamic spatial disparities and temporal variations of driving factors on CI in different regions. Moreover, this paper further investigates the impact of production-related factors, such as carbon emission technology’s change with regard to carbon intensity, and explores the benchmarking catch-up effect and the effort on reducing CI by setting benchmarks and dynamic comparative analysis, which could provide guidance for some underperforming regions. The main results are as follows: (1) The overall trends of CI increased from 2007–2019, and the northwest region had the largest growth rate. (2) Energy intensity was the dominant driver to reduce CI, and technological changes also played a great role in decreasing CI. Conversely, carbon emissions efficiency had negative effects on reducing CI. (3) The spatial variations of the contributions in factors to reduce CI have gradually increased. Resource-dependent development areas have great potential to reduce carbon intensity by improving energy and carbon emission efficiencies. The northwest has great potential to reduce CI by introducing advanced technologies. Some policies are proposed based on the results.

Suggested Citation

  • Xiaolei Liu & Heng Chen & Cheng Peng & Mingqiu Li, 2022. "Assessing the Drivers of Carbon Intensity Change in China: A Dynamic Spatial–Temporal Production-Theoretical Decomposition Analysis Approach," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12359-:d:928183
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12359/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12359/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meng, Bo & Wang, Jianguo & Andrew, Robbie & Xiao, Hao & Xue, Jinjun & Peters, Glen P., 2017. "Spatial spillover effects in determining China's regional CO2 emissions growth: 2007–2010," Energy Economics, Elsevier, vol. 63(C), pages 161-173.
    2. Ang, B.W & Zhang, F.Q, 1999. "Inter-regional comparisons of energy-related CO2 emissions using the decomposition technique," Energy, Elsevier, vol. 24(4), pages 297-305.
    3. Ning Chang & Michael L. Lahr, 2016. "Changes in China’s production-source CO 2 emissions: insights from structural decomposition analysis and linkage analysis," Economic Systems Research, Taylor & Francis Journals, vol. 28(2), pages 224-242, June.
    4. Chang, Yih F & Lin, Sue J, 1998. "Structural decomposition of industrial CO2 emission in Taiwan: an input-output approach," Energy Policy, Elsevier, vol. 26(1), pages 5-12, January.
    5. Liyun Chen & Qi Duan, 2016. "Decomposition analysis of factors driving CO2 emissions in Chinese provinces based on production-theoretical decomposition analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 267-277, November.
    6. Ang, B.W. & Su, Bin & Wang, H., 2016. "A spatial–temporal decomposition approach to performance assessment in energy and emissions," Energy Economics, Elsevier, vol. 60(C), pages 112-121.
    7. Liu, Bingquan & Shi, Junxue & Wang, Hui & Su, Xuelin & Zhou, Peng, 2019. "Driving factors of carbon emissions in China: A joint decomposition approach based on meta-frontier," Applied Energy, Elsevier, vol. 256(C).
    8. Duan, Cuncun & Chen, Bin & Feng, Kuishuang & Liu, Zhu & Hayat, Tasawar & Alsaedi, Ahmed & Ahmad, Bashir, 2018. "Interregional carbon flows of China," Applied Energy, Elsevier, vol. 227(C), pages 342-352.
    9. Wang, H. & Zhou, P., 2018. "Multi-country comparisons of CO2 emission intensity: The production-theoretical decomposition analysis approach," Energy Economics, Elsevier, vol. 74(C), pages 310-320.
    10. Wang, H. & Ang, B.W. & Su, Bin, 2017. "A Multi-region Structural Decomposition Analysis of Global CO2 Emission Intensity," Ecological Economics, Elsevier, vol. 142(C), pages 163-176.
    11. Pothen, Frank & Schymura, Michael, 2015. "Bigger cakes with fewer ingredients? A comparison of material use of the world economy," Ecological Economics, Elsevier, vol. 109(C), pages 109-121.
    12. Li, Hao & Zhao, Yuhuan & Qiao, Xiaoyong & Liu, Ya & Cao, Ye & Li, Yue & Wang, Song & Zhang, Zhonghua & Zhang, Yongfeng & Weng, Jianfeng, 2017. "Identifying the driving forces of national and regional CO2 emissions in China: Based on temporal and spatial decomposition analysis models," Energy Economics, Elsevier, vol. 68(C), pages 522-538.
    13. Mundaca, Luis & Markandya, Anil, 2016. "Assessing regional progress towards a ‘Green Energy Economy’," Applied Energy, Elsevier, vol. 179(C), pages 1372-1394.
    14. Wang, Shaojian & Xie, Zihan & Wu, Rong & Feng, Kuishang, 2022. "How does urbanization affect the carbon intensity of human well-being? A global assessment," Applied Energy, Elsevier, vol. 312(C).
    15. Zhou, P. & Ang, B.W., 2008. "Decomposition of aggregate CO2 emissions: A production-theoretical approach," Energy Economics, Elsevier, vol. 30(3), pages 1054-1067, May.
    16. Ang, B.W. & Xu, X.Y. & Su, Bin, 2015. "Multi-country comparisons of energy performance: The index decomposition analysis approach," Energy Economics, Elsevier, vol. 47(C), pages 68-76.
    17. 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).
    18. Guo, Bin & Geng, Yong & Franke, Bernd & Hao, Han & Liu, Yaxuan & Chiu, Anthony, 2014. "Uncovering China’s transport CO2 emission patterns at the regional level," Energy Policy, Elsevier, vol. 74(C), pages 134-146.
    19. 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.
    20. Chen, Jiandong & Xu, Chong & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2019. "Driving factors of CO2 emissions and inequality characteristics in China: A combined decomposition approach," Energy Economics, Elsevier, vol. 78(C), pages 589-597.
    21. Zhaohua Wang & Wei Liu & Jianhua Yin, 2015. "Driving forces of indirect carbon emissions from household consumption in China: an input–output decomposition analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 257-272, February.
    22. Liu, Nan & Ma, Zujun & Kang, Jidong, 2015. "Changes in carbon intensity in China's industrial sector: Decomposition and attribution analysis," Energy Policy, Elsevier, vol. 87(C), pages 28-38.
    23. Chong, ChinHao & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou & Li, Xu & Song, Shizhong, 2017. "LMDI decomposition of energy consumption in Guangdong Province, China, based on an energy allocation diagram," Energy, Elsevier, vol. 133(C), pages 525-544.
    24. Kim, Kyunam & Kim, Yeonbae, 2012. "International comparison of industrial CO2 emission trends and the energy efficiency paradox utilizing production-based decomposition," Energy Economics, Elsevier, vol. 34(5), pages 1724-1741.
    25. Zhou, P. & Zhang, H. & Zhang, L.P., 2022. "The drivers of energy intensity changes in Chinese cities: A production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 307(C).
    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. Shan Yang & Shangkai Zhu & Gao Deng & Huan Li, 2022. "Study on Influencing Factors and Spatial Effects of Carbon Emissions Based on Logarithmic Mean Divisia Index Model: A Case Study of Hunan Province," Sustainability, MDPI, vol. 14(23), pages 1-19, November.

    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. Bingquan Liu & Yue Wang & Xuran Chang & Boyang Nie & Lingqi Meng & Yongqing Li, 2022. "Does Land Urbanization Affect the Catch-Up Effect of Carbon Emissions Reduction in China’s Logistics?," Land, MDPI, vol. 11(9), pages 1-18, September.
    2. Wang, Hui & Li, Rupeng & Zhang, Ning & Zhou, Peng & Wang, Qiang, 2020. "Assessing the role of technology in global manufacturing energy intensity change: A production-theoretical decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    3. Liu, Bingquan & Shi, Junxue & Wang, Hui & Su, Xuelin & Zhou, Peng, 2019. "Driving factors of carbon emissions in China: A joint decomposition approach based on meta-frontier," Applied Energy, Elsevier, vol. 256(C).
    4. Wang, Qunwei & Hang, Ye & Su, Bin & Zhou, Peng, 2018. "Contributions to sector-level carbon intensity change: An integrated decomposition analysis," Energy Economics, Elsevier, vol. 70(C), pages 12-25.
    5. Liu, Xiao & Hang, Ye & Wang, Qunwei & Chiu, Ching-Ren & Zhou, Dequn, 2022. "The role of energy consumption in global carbon intensity change: A meta-frontier-based production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 109(C).
    6. Azam, Muhammad & Younes, Ben Zaied & Hunjra, Ahmed Imran & Hussain, Nazim, 2022. "Integrated Spatial-Temporal decomposition analysis for life cycle assessment of carbon emission intensity change in various regions of China," Resources Policy, Elsevier, vol. 79(C).
    7. Zhao, Zhibo & Shi, Xunpeng & Zhao, Lingdi & Zhang, Jinggu, 2020. "Extending production-theoretical decomposition analysis to environmentally sensitive growth: Case study of Belt and Road Initiative countries," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    8. Wang, Miao & Feng, Chao, 2018. "Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2652-2663.
    9. Meng Yang & Yisheng Liu & Jinzhao Tian & Feiyu Cheng & Pengbo Song, 2022. "Dynamic Evolution and Regional Disparity in Carbon Emission Intensity in China," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    10. Huang, Fei & Zhou, Dequn & Wang, Qunwei & Hang, Ye, 2019. "Decomposition and attribution analysis of the transport sector’s carbon dioxide intensity change in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 343-358.
    11. Wang, H. & Zhou, P., 2018. "Multi-country comparisons of CO2 emission intensity: The production-theoretical decomposition analysis approach," Energy Economics, Elsevier, vol. 74(C), pages 310-320.
    12. Ang, B.W. & Su, Bin & Wang, H., 2016. "A spatial–temporal decomposition approach to performance assessment in energy and emissions," Energy Economics, Elsevier, vol. 60(C), pages 112-121.
    13. Wang, H. & Zhou, P. & Xie, Bai-Chen & Zhang, N., 2019. "Assessing drivers of CO2 emissions in China's electricity sector: A metafrontier production-theoretical decomposition analysis," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1096-1107.
    14. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    15. Wang, H. & Ang, B.W. & Su, Bin, 2017. "A Multi-region Structural Decomposition Analysis of Global CO2 Emission Intensity," Ecological Economics, Elsevier, vol. 142(C), pages 163-176.
    16. Sueyoshi, Toshiyuki & Li, Aijun & Liu, Xiaohong, 2019. "Exploring sources of China's CO2 emission: Decomposition analysis under different technology changes," European Journal of Operational Research, Elsevier, vol. 279(3), pages 984-995.
    17. Song, Yi & Huang, Jian-Bai & Feng, Chao, 2018. "Decomposition of energy-related CO2 emissions in China's iron and steel industry: A comprehensive decomposition framework," Resources Policy, Elsevier, vol. 59(C), pages 103-116.
    18. Wang, H. & Zhou, P., 2018. "Assessing Global CO2 Emission Inequality From Consumption Perspective: An Index Decomposition Analysis," Ecological Economics, Elsevier, vol. 154(C), pages 257-271.
    19. Xu, Chong & Wang, Bingjie & Chen, Jiandong & Shen, Zhiyang & Song, Malin & An, Jiafu, 2022. "Carbon inequality in China: Novel drivers and policy driven scenario analysis," Energy Policy, Elsevier, vol. 170(C).
    20. Zhou, P. & Zhang, H. & Zhang, L.P., 2022. "The drivers of energy intensity changes in Chinese cities: A production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 307(C).

    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:19:p:12359-:d:928183. 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.