IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p829-d1032068.html
   My bibliography  Save this article

Study on the Temporal and Spatial Evolution of China’s Carbon Dioxide Emissions and Its Emission Reduction Path

Author

Listed:
  • Wei Shi

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Zhiquan Sha

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Fuwei Qiao

    (College of Economic, Northwest Normal University, Lanzhou 730070, China)

  • Wenwen Tang

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Chuyu Luo

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Yali Zheng

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Chunli Wang

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Jun Ge

    (Huaneng Pingliang Power Generation Co., Ltd., Pingliang 744000, China)

Abstract

Based on the total carbon emission data of 30 provinces and cities in China from 2000 to 2020, this paper used non-parametric kernel density estimation and traditional and spatial Markov probability transfer matrix methods to explore the temporal and spatial dynamic evolution characteristics of carbon dioxide emissions in China and then used a super-SBM model to calculate the carbon emission reduction potential of each province. The results showed that: (1) from 2000 to 2020, the total carbon emissions in China showed an upward trend of fluctuation, from 1.35 Gt to 4.90 Gt year by year, with an annual growth rate of 13.10%. (2) The core density curve showed a double peak form of “main peak + right peak,” indicating that a polarization phenomenon occurred in the region. (3) The overall trend of carbon dioxide emissions shifting to superheavy carbon emissions was significant, and the probability of transition was as high as 74.69%, indicating that it was challenging to achieve leapfrog transition in the short term. (4) Based on the principle of fairness and efficiency of provincial carbon emission reduction, mainland China’s 30 provincial administrative regions can be divided into four types. Finally, the carbon emission reduction path is designed for each province.

Suggested Citation

  • Wei Shi & Zhiquan Sha & Fuwei Qiao & Wenwen Tang & Chuyu Luo & Yali Zheng & Chunli Wang & Jun Ge, 2023. "Study on the Temporal and Spatial Evolution of China’s Carbon Dioxide Emissions and Its Emission Reduction Path," Energies, MDPI, vol. 16(2), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:829-:d:1032068
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/829/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/2/829/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fang, Kai & Tang, Yiqi & Zhang, Qifeng & Song, Junnian & Wen, Qi & Sun, Huaping & Ji, Chenyang & Xu, Anqi, 2019. "Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces," Applied Energy, Elsevier, vol. 255(C).
    2. Ang, B.W. & Su, Bin, 2016. "Carbon emission intensity in electricity production: A global analysis," Energy Policy, Elsevier, vol. 94(C), pages 56-63.
    3. Padilla, Emilio & Serrano, Alfredo, 2006. "Inequality in CO2 emissions across countries and its relationship with income inequality: A distributive approach," Energy Policy, Elsevier, vol. 34(14), pages 1762-1772, September.
    4. Radonjič, Gregor & Tompa, Saša, 2018. "Carbon footprint calculation in telecommunications companies – The importance and relevance of scope 3 greenhouse gases emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 361-375.
    5. Liu, S. & Xiao, Q., 2021. "An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model," Energy, Elsevier, vol. 224(C).
    6. Li, Ke & Lin, Boqiang, 2017. "Economic growth model, structural transformation, and green productivity in China," Applied Energy, Elsevier, vol. 187(C), pages 489-500.
    7. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    8. Lu, Heli & Liu, Guifang, 2014. "Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting," Applied Energy, Elsevier, vol. 131(C), pages 297-306.
    9. Wang, Bin & Yu, Minxiu & Zhu, Yucheng & Bao, Pinjuan, 2021. "Unveiling the driving factors of carbon emissions from industrial resource allocation in China: A spatial econometric perspective," Energy Policy, Elsevier, vol. 158(C).
    10. 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.
    11. 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.
    12. Bin Su & B. W. Ang, 2012. "Structural Decomposition Analysis Applied To Energy And Emissions: Aggregation Issues," Economic Systems Research, Taylor & Francis Journals, vol. 24(3), pages 299-317, March.
    13. Schipper, Lee & Murtishaw, Scott & Khrushch, Marta & Ting, Michael & Karbuz, Sohbet & Unander, Fridtjof, 2001. "Carbon emissions from manufacturing energy use in 13 IEA countries: long-term trends through 1995," Energy Policy, Elsevier, vol. 29(9), pages 667-688, July.
    14. Jahangir Alam, Mohammad & Ara Begum, Ismat & Buysse, Jeroen & Van Huylenbroeck, Guido, 2012. "Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis," Energy Policy, Elsevier, vol. 45(C), pages 217-225.
    15. Stephen Casler & Adam Rose, 1998. "Carbon Dioxide Emissions in the U.S. Economy: A Structural Decomposition Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 11(3), pages 349-363, April.
    16. Jiachen Yue & Huasheng Zhu & Fei Yao, 2021. "Does Industrial Transfer Change the Spatial Structure of CO 2 Emissions?—Evidence from Beijing-Tianjin-Hebei Region in China," IJERPH, MDPI, vol. 19(1), pages 1-23, December.
    17. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    18. Cai, Bofeng & Zhang, Lixiao, 2014. "Urban CO2 emissions in China: Spatial boundary and performance comparison," Energy Policy, Elsevier, vol. 66(C), pages 557-567.
    Full references (including those not matched with items on IDEAS)

    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. Román-Collado, Rocío & Cansino, José M. & Botia, Camilo, 2018. "How far is Colombia from decoupling? Two-level decomposition analysis of energy consumption changes," Energy, Elsevier, vol. 148(C), pages 687-700.
    2. Wang, Miao & Feng, Chao, 2017. "Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors," Applied Energy, Elsevier, vol. 190(C), pages 772-787.
    3. Yang, Xue & Su, Bin, 2019. "Impacts of international export on global and regional carbon intensity," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Lin, Boqiang & Tan, Ruipeng, 2017. "Sustainable development of China's energy intensive industries: From the aspect of carbon dioxide emissions reduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 386-394.
    5. Qingyou Yan & Yaxian Wang & Tomas Baležentis & Yikai Sun & Dalia Streimikiene, 2018. "Energy-Related CO 2 Emission in China’s Provincial Thermal Electricity Generation: Driving Factors and Possibilities for Abatement," Energies, MDPI, vol. 11(5), pages 1-25, April.
    6. Wang, Zhenguo & Su, Bin & Xie, Rui & Long, Haiyu, 2020. "China’s aggregate embodied CO2 emission intensity from 2007 to 2012: A multi-region multiplicative structural decomposition analysis," Energy Economics, Elsevier, vol. 85(C).
    7. Wen, Hong-xing & Chen, Zhe & Yang, Qian & Liu, Jin-yi & Nie, Pu-yan, 2022. "Driving forces and mitigating strategies of CO2 emissions in China: A decomposition analysis based on 38 industrial sub-sectors," Energy, Elsevier, vol. 245(C).
    8. 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).
    9. Zhu, Bangzhu & Su, Bin & Li, Yingzhu & Ng, Tsan Sheng, 2020. "Embodied energy and intensity in China’s (normal and processing) exports and their driving forces, 2005-2015," Energy Economics, Elsevier, vol. 91(C).
    10. Wang, Qiang & Han, Xinyu, 2021. "Is decoupling embodied carbon emissions from economic output in Sino-US trade possible?," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    11. Jialing Zou & Weidong Liu & Zhipeng Tang, 2017. "Analysis of Factors Contributing to Changes in Energy Consumption in Tangshan City between 2007 and 2012," Sustainability, MDPI, vol. 9(3), pages 1-14, March.
    12. He Li & Kevin Lo & Mark Wang & Pingyu Zhang & Longyi Xue, 2016. "Industrial Energy Consumption in Northeast China under the Revitalisation Strategy: A Decomposition and Policy Analysis," Energies, MDPI, vol. 9(7), pages 1-13, July.
    13. Jian Liu & Qingshan Yang & Yu Zhang & Wen Sun & Yiming Xu, 2019. "Analysis of CO 2 Emissions in China’s Manufacturing Industry Based on Extended Logarithmic Mean Division Index Decomposition," Sustainability, MDPI, vol. 11(1), pages 1-28, January.
    14. Su, Bin & Ang, B.W., 2020. "Demand contributors and driving factors of Singapore’s aggregate carbon intensities," Energy Policy, Elsevier, vol. 146(C).
    15. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    16. Edyta Sidorczuk-Pietraszko, 2020. "Spatial Differences in Carbon Intensity in Polish Households," Energies, MDPI, vol. 13(12), pages 1-21, June.
    17. 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.
    18. Chen Li & Heng Li & Xionghe Qin, 2022. "Spatial Heterogeneity of Carbon Emissions and Its Influencing Factors in China: Evidence from 286 Prefecture-Level Cities," IJERPH, MDPI, vol. 19(3), pages 1-29, January.
    19. Lin Boqiang & Kui Liu, 2017. "Using LMDI to Analyze the Decoupling of Carbon Dioxide Emissions from China’s Heavy Industry," Sustainability, MDPI, vol. 9(7), pages 1-16, July.
    20. Oludolapo A Olanrewaju, 2018. "Energy consumption in South African industry: A decomposition analysis using the LMDI approach," Energy & Environment, , vol. 29(2), pages 232-244, March.

    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:16:y:2023:i:2:p:829-:d:1032068. 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.