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

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  • 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
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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Cai, Bofeng & Zhang, Lixiao, 2014. "Urban CO2 emissions in China: Spatial boundary and performance comparison," Energy Policy, Elsevier, vol. 66(C), pages 557-567.
    8. Ang, B.W. & Su, Bin, 2016. "Carbon emission intensity in electricity production: A global analysis," Energy Policy, Elsevier, vol. 94(C), pages 56-63.
    9. 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.
    10. 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).
    11. Li, Ke & Lin, Boqiang, 2017. "Economic growth model, structural transformation, and green productivity in China," Applied Energy, Elsevier, vol. 187(C), pages 489-500.
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
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