IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10176-d1180395.html
   My bibliography  Save this article

Study on Spatial and Temporal Characteristics and Influencing Factors of Carbon Emissions in the Urban Agglomeration of the Middle Reaches of the Yangtze River

Author

Listed:
  • Huang Zhang

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China)

  • Yidong Lei

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China)

Abstract

The industrial transfer of heavy industries such as non-metallic mineral manufacturing, metal smelting and manufacturing from the eastern coast of China to the central region is beneficial to the economic development of the central region on the one hand, but increases carbon emissions in the central region on the other hand. In February 2022, the National Development and Reform Commission approved the “14th Five-Year Plan for the Development of the Urban Agglomeration in the Middle Reaches of the Yangtze River”. This indicates that the urban agglomeration of the middle reaches of the Yangtze River is an important region for implementing green development in the central area. The spatial and temporal evolution of carbon emissions and influencing factors in this region are the foundation for achieving carbon peaking and the carbon neutrality goal. This paper calculates the total carbon emissions of the cities in the urban agglomeration of the middle reaches of the Yangtze River and uses models such as spatial autocorrelation, geographically weighted regression, and Geodetector to explore the spatial–temporal pattern of carbon emissions. The results show the following: (1) The total carbon emissions of the middle reaches of the Yangtze River urban agglomeration showed fluctuations during 2010–2020, and the carbon emission reduction effect is unstable. Additionally, the carbon emissions of the middle reaches of the Yangtze River city cluster show obvious spatial variability, but the high carbon emission area is always concentrated in Wuhan, and this remains unchanged. (2) In 2010, 2014 and 2017, population size was the most important factor affecting carbon emission divergence, and in terms of interaction, the interaction between energy intensity and GDP and urbanization is the reason for the increasing carbon emissions. (3) The influence of population size on carbon emissions decreases from north to south, the influence of energy intensity on carbon emissions shows a spread from the most influential region in the northwest to the centre and then to the northeast, and the GDP per capita has little influence on the difference of carbon emissions spatial distribution.

Suggested Citation

  • Huang Zhang & Yidong Lei, 2023. "Study on Spatial and Temporal Characteristics and Influencing Factors of Carbon Emissions in the Urban Agglomeration of the Middle Reaches of the Yangtze River," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10176-:d:1180395
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10176/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/13/10176/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ang, B.W., 2015. "LMDI decomposition approach: A guide for implementation," Energy Policy, Elsevier, vol. 86(C), pages 233-238.
    2. Al-mulali, Usama, 2012. "Factors affecting CO2 emission in the Middle East: A panel data analysis," Energy, Elsevier, vol. 44(1), pages 564-569.
    3. Mohammad Mafizur Rahman & Khosrul Alam, 2022. "CO 2 Emissions in Asia–Pacific Region: Do Energy Use, Economic Growth, Financial Development, and International Trade Have Detrimental Effects?," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    4. Knapp, Tom & Mookerjee, Rajen, 1996. "Population growth and global CO2 emissions : A secular perspective," Energy Policy, Elsevier, vol. 24(1), pages 31-37, January.
    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. Tianyi Zeng & Hong Jin & Xu Gang & Zihang Kang & Jiayi Luan, 2022. "County Economy, Population, Construction Land, and Carbon Intensity in a Shrinkage Scenario," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    2. Chen, Jiandong & Wang, Ping & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2018. "Decomposition and decoupling analysis of CO2 emissions in OECD," Applied Energy, Elsevier, vol. 231(C), pages 937-950.
    3. 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).
    4. Trotta, Gianluca, 2020. "Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis," Energy Economics, Elsevier, vol. 86(C).
    5. Juan Luo & Chong Xu & Boyu Yang & Xiaoyu Chen & Yinyin Wu, 2022. "Quantitative Analysis of China’s Carbon Emissions Trading Policies: Perspectives of Policy Content Validity and Carbon Emissions Reduction Effect," Energies, MDPI, vol. 15(14), pages 1-20, July.
    6. Hidab Hamwi & Rajeev Alasseri & Sara Aldei & Mariam Al-Kandari, 2022. "A Pilot Study of Electrical Vehicle Performance, Efficiency, and Limitation in Kuwait’s Harsh Weather and Environment," Energies, MDPI, vol. 15(20), pages 1-14, October.
    7. Wakiyama, Takako & Zusman, Eric, 2021. "The impact of electricity market reform and subnational climate policy on carbon dioxide emissions across the United States: A path analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    8. Román-Collado, Rocío & Colinet, María José, 2018. "Are labour productivity and residential living standards drivers of the energy consumption changes?," Energy Economics, Elsevier, vol. 74(C), pages 746-756.
    9. Jiandong Chen & Ping Wang & Jixian Zhou & Malin Song & Xinyue Zhang, 2022. "Influencing factors and efficiency of funds in humanitarian supply chains: the case of Chinese rural minimum living security funds," Annals of Operations Research, Springer, vol. 319(1), pages 413-438, December.
    10. Vikniswari Vija Kumaran & Siti Nurul Munawwarah & Mohd Khairi Ismail, 2021. "Sustainability in ASEAN: The Roles of Financial Development towards Climate Change," Asian Journal of Economics and Empirical Research, Asian Online Journal Publishing Group, vol. 8(1), pages 1-9.
    11. Ang, B.W. & Goh, Tian, 2019. "Index decomposition analysis for comparing emission scenarios: Applications and challenges," Energy Economics, Elsevier, vol. 83(C), pages 74-87.
    12. Oktay KIZILKAYA, 2017. "The Impact of Economic Growth and Foreign Direct Investment on CO2 Emissions: The Case of Turkey," Turkish Economic Review, KSP Journals, vol. 4(1), pages 106-118, March.
    13. Kristiana Dolge & Dagnija Blumberga, 2023. "Transitioning to Clean Energy: A Comprehensive Analysis of Renewable Electricity Generation in the EU-27," Energies, MDPI, vol. 16(18), pages 1-27, September.
    14. Wojciech Rabiega & Artur Gorzałczyński & Robert Jeszke & Paweł Mzyk & Krystian Szczepański, 2021. "How Long Will Combustion Vehicles Be Used? Polish Transport Sector on the Pathway to Climate Neutrality," Energies, MDPI, vol. 14(23), pages 1-19, November.
    15. Fernández-Amador, Octavio & Francois, Joseph F. & Oberdabernig, Doris A. & Tomberger, Patrick, 2023. "Energy footprints and the international trade network: A new dataset. Is the European Union doing it better?," Ecological Economics, Elsevier, vol. 204(PA).
    16. Mansor H. Ibrahim & Siong Hook Law, 2016. "Institutional Quality and CO 2 Emission–Trade Relations: Evidence from Sub-Saharan Africa," South African Journal of Economics, Economic Society of South Africa, vol. 84(2), pages 323-340, June.
    17. 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).
    18. Baležentis, Tomas & Li, Tianxiang & Chen, Xueli, 2021. "Has agricultural labor restructuring improved agricultural labor productivity in China? A decomposition approach," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    19. Haoran Wang & Toshiyuki Fujita, 2023. "A Review of Research on Embodied Carbon in International Trade," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    20. Sun, Xiaoqi & Liu, Xiaojia, 2020. "Decomposition analysis of debt’s impact on China’s energy consumption," Energy Policy, Elsevier, vol. 146(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:15:y:2023:i:13:p:10176-:d:1180395. 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.