IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v14y2025i3p510-d1602712.html
   My bibliography  Save this article

Regional Disparities and Driving Factors of Residential Carbon Emissions: An Empirical Analysis Based on Samples from 270 Cities in China

Author

Listed:
  • Xiangjie Xie

    (School of Urban and Regional Sciences, Shanghai University of Finance and Economics, 777 Guoding Road, Yangpu District, Shanghai 200433, China)

  • Jing Wang

    (School of Urban and Regional Sciences, Shanghai University of Finance and Economics, 777 Guoding Road, Yangpu District, Shanghai 200433, China)

  • Mohan Liu

    (School of Urban and Regional Sciences, Shanghai University of Finance and Economics, 777 Guoding Road, Yangpu District, Shanghai 200433, China)

Abstract

Residential carbon emissions (RCEs) have become a major contributor to China’s overall carbon emission growth. A comprehensive analysis of the evolution characteristics of regional disparities in RCEs at the urban level, along with a thorough examination of the driving factors behind RCEs and the convergence, is crucial for achieving carbon reduction goals within regions. This study calculates the RCEs of 270 cities in China from 2011 to 2019 based on multiregional input–output tables and explores the regional differences and spatiotemporal evolution characteristics of RCEs using the Dagum Gini coefficient decomposition method and kernel density estimation. On this basis, we examine the driving factors of RCEs using an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) econometric model and further analyze the convergence of RCEs by introducing a β-convergence model. The results are as follows: (1) The regional disparity of RCEs in China generally shows a wave-like declining trend, with the primary source of this disparity being the differences between city tiers. (2) Kernel density estimation shows that the greater the urban rank, the larger the regional disparity; the RCE distribution in third- and lower-tier cities is more concentrated. (3) Population density, population aging, and education level significantly exert a negative influence on RCEs, whereas economic development level, number of researchers, and number of private cars are positively correlated with RCEs. (4) Each urban agglomeration’s RCEs exhibits significant β-convergence, but the driving factors of RCEs and their convergence differ significantly across the urban agglomerations. This study provides targeted policy recommendations for China to achieve its emission reduction goals effectively. Each city cluster should tailor its approach to strengthen regional collaborative governance, optimize urban layouts, and promote low-carbon lifestyles in order to facilitate the convergence of RCEs and low-carbon transformation.

Suggested Citation

  • Xiangjie Xie & Jing Wang & Mohan Liu, 2025. "Regional Disparities and Driving Factors of Residential Carbon Emissions: An Empirical Analysis Based on Samples from 270 Cities in China," Land, MDPI, vol. 14(3), pages 1-29, February.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:510-:d:1602712
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/14/3/510/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/14/3/510/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miketa, Asami & Mulder, Peter, 2005. "Energy productivity across developed and developing countries in 10 manufacturing sectors: Patterns of growth and convergence," Energy Economics, Elsevier, vol. 27(3), pages 429-453, May.
    2. Zhao, Congyu & Jia, Rongwen & Dong, Kangyin, 2023. "Does financial inclusion achieve the dual dividends of narrowing carbon inequality within cities and between cities? Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    3. 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.
    4. Li, Jianglong & Chen, Chang & Liu, Hongxun, 2019. "Transition from non-commercial to commercial energy in rural China: Insights from the accessibility and affordability," Energy Policy, Elsevier, vol. 127(C), pages 392-403.
    5. Ying Long & Jiahao Feng & Aolong Sun & Rui Wang & Yafei Wang, 2023. "Structural Characteristics of the Household Carbon Footprint in an Aging Society," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
    6. Zhou, Zhongren & Wu, Wenliang & Chen, Qun & Chen, Shufeng, 2008. "Study on sustainable development of rural household energy in northern China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(8), pages 2227-2239, October.
    7. Jingchao, Zhang & Kotani, Koji, 2012. "The determinants of household energy demand in rural Beijing: Can environmentally friendly technologies be effective?," Energy Economics, Elsevier, vol. 34(2), pages 381-388.
    8. Gabriela Czarnek & Małgorzata Kossowska & Paulina Szwed, 2021. "Right-wing ideology reduces the effects of education on climate change beliefs in more developed countries," Nature Climate Change, Nature, vol. 11(1), pages 9-13, January.
    9. Xu, Bin & Lin, Boqiang, 2015. "How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models," Energy Economics, Elsevier, vol. 48(C), pages 188-202.
    10. Golley, Jane & Meng, Xin, 2012. "Income inequality and carbon dioxide emissions: The case of Chinese urban households," Energy Economics, Elsevier, vol. 34(6), pages 1864-1872.
    11. Yamei Chen & Lu Jiang, 2022. "Influencing Factors of Direct Carbon Emissions of Households in Urban Villages in Guangzhou, China," IJERPH, MDPI, vol. 19(24), pages 1-14, December.
    12. Kok, Rixt & Benders, Rene M.J. & Moll, Henri C., 2006. "Measuring the environmental load of household consumption using some methods based on input-output energy analysis: A comparison of methods and a discussion of results," Energy Policy, Elsevier, vol. 34(17), pages 2744-2761, November.
    13. Fan, Jianshuang & Zhou, Lin & Zhang, Yan & Shao, Shuai & Ma, Miao, 2021. "How does population aging affect household carbon emissions? Evidence from Chinese urban and rural areas," Energy Economics, Elsevier, vol. 100(C).
    14. Garg, Amit & Vishwanathan, Saritha & Avashia, Vidhee, 2013. "Life cycle greenhouse gas emission assessment of major petroleum oil products for transport and household sectors in India," Energy Policy, Elsevier, vol. 58(C), pages 38-48.
    15. Han, Hongyun & Wu, Shu, 2018. "Rural residential energy transition and energy consumption intensity in China," Energy Economics, Elsevier, vol. 74(C), pages 523-534.
    16. Dou, Yue & Zhao, Jun & Dong, Xiucheng & Dong, Kangyin, 2021. "Quantifying the impacts of energy inequality on carbon emissions in China: A household-level analysis," Energy Economics, Elsevier, vol. 102(C).
    17. Jaehyeok Kim & Hyungwoo Lim & Ha-Hyun Jo, 2020. "Do Aging and Low Fertility Reduce Carbon Emissions in Korea? Evidence from IPAT Augmented EKC Analysis," IJERPH, MDPI, vol. 17(8), pages 1-15, April.
    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. Wu, Shu & Han, Hongyun, 2022. "Energy transition, intensity growth, and policy evolution: Evidence from rural China," Energy Economics, Elsevier, vol. 105(C).
    2. Wei, Rui & Zhang, Wencheng & Peng, Shuijun, 2022. "Energy and greenhouse gas footprints of China households during 1995–2019: A global perspective," Energy Policy, Elsevier, vol. 164(C).
    3. Li, Meng & Jin, Tianyu & Liu, Shenglong & Zhou, Shaojie, 2021. "The cost of clean energy transition in rural China: Evidence based on marginal treatment effects," Energy Economics, Elsevier, vol. 97(C).
    4. Su-Mei Chen & Jia-Jia Ou & Ling-Yun He, 2021. "The Environmental and Health Impacts of Poverty Alleviation in China: From a Consumption-Based Perspective," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    5. Yuan, Baolong & Ren, Shenggang & Chen, Xiaohong, 2015. "The effects of urbanization, consumption ratio and consumption structure on residential indirect CO2 emissions in China: A regional comparative analysis," Applied Energy, Elsevier, vol. 140(C), pages 94-106.
    6. Qing Ni & Hua Dong & Antonios Kaniadakis & Zhili Wang & Chang Ge, 2025. "Investigating Older Adults’ Response to Climate Change," IJERPH, MDPI, vol. 22(2), pages 1-27, January.
    7. Yang, Xiaojun & Xu, Jintao & Xu, Xiaojie & Yi, Yuanyuan & Hyde, William F., 2020. "Collective forest tenure reform and household energy consumption: A case study in Yunnan Province, China," China Economic Review, Elsevier, vol. 60(C).
    8. Moises Neil V. Seriño & Stephan Klasen, 2015. "Estimation and Determinants of the Philippines' Household Carbon Footprint," The Developing Economies, Institute of Developing Economies, vol. 53(1), pages 44-62, March.
    9. Meng, Weilu & Yuan, Gecheng & Sun, Yongping, 2023. "Expansion of social networks and household carbon emissions: Evidence from household survey in China," Energy Policy, Elsevier, vol. 174(C).
    10. Liu, Tie-Ying & Lee, Chien-Chiang, 2020. "Convergence of the world’s energy use," Resource and Energy Economics, Elsevier, vol. 62(C).
    11. Zhang, Yimeng & Wang, Feng & Zhang, Bing, 2023. "The impacts of household structure transitions on household carbon emissions in China," Ecological Economics, Elsevier, vol. 206(C).
    12. Dong, Kangyin & Wang, Bo & Zhao, Jun & Taghizadeh-Hesary, Farhad, 2022. "Mitigating carbon emissions by accelerating green growth in China," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 226-243.
    13. You, Jing & Kontoleon, Andreas & Wang, Sangui, 2015. "Identifying a Sustainable Pathway to Household Multi-dimensional Poverty Reduction in Rural China," 2015 Conference, August 9-14, 2015, Milan, Italy 211865, International Association of Agricultural Economists.
    14. Tan, Caixia & Yu, Min & Wang, Jing & Geng, Shiping & Niu, Dongxiao & Tan, Zhongfu, 2022. "Feasibility study on the construction of multi-energy complementary systems in rural areas—Eastern, central, and western parts of China are taken as examples," Energy, Elsevier, vol. 249(C).
    15. Xie, Jun & Zhou, Shaojie & Teng, Fei & Gu, Alun, 2023. "The characteristics and driving factors of household CO2 and non-CO2 emissions in China," Ecological Economics, Elsevier, vol. 213(C).
    16. Xin Zhang & Chenhui Ding & Chao Liu & Xianzhong Teng & Ruoman Lv & Yiming Cai, 2023. "The Bilateral Effects of Population Aging on Regional Carbon Emissions in China: Promotion or Inhibition Effect?," Sustainability, MDPI, vol. 15(23), pages 1-17, November.
    17. Xibao Xu & Yan Tan & Shuang Chen & Guishan Yang & Weizhong Su, 2015. "Urban Household Carbon Emission and Contributing Factors in the Yangtze River Delta, China," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-21, April.
    18. Liu, Zhong & Zhou, Zuanjiu & Liu, Chang, 2023. "Estimating the impact of rural centralized residence policy interventions on energy poverty in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    19. Hongwu Zhang & Lequan Zhang & Keying Wang & Xunpeng Shi, 2019. "Unveiling Key Drivers of Indirect Carbon Emissions of Chinese Older Households," Sustainability, MDPI, vol. 11(20), pages 1-17, October.
    20. Liu, Zhong & Wang, Menghan & Xiong, Qinqin & Liu, Chang, 2020. "Does centralized residence promote the use of cleaner cooking fuels? Evidence from rural China," Energy Economics, Elsevier, vol. 91(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:jlands:v:14:y:2025:i:3:p:510-:d:1602712. 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.