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Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector

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  • Chen, Huadun
  • Du, Qianxi
  • Huo, Tengfei
  • Liu, Peiran
  • Cai, Weiguang
  • Liu, Bingsheng

Abstract

Revealing the regional differences and drivers of provincial urban residential building sector carbon emissions (URBCE) is critical for regional collaborative emission reduction in achieving the “Dural carbon” targets. This study tries to innovatively construct a framework for the exploration of the spatiotemporal patterns and driving mechanism of Chinese URBCE in 30 provinces during 2000–2019, combining kernel density estimation, spatial autocorrelation analysis and temporal-spatial LMDI model. Results indicate that: (1) A vast majority of regions have undergone a sharp growth in URBCE, with the regional gap of URBCE gradually widening. (2) Besides, the spatial pattern of regional URBCE is basically stable, with High-High distributed in the Northeast and North China, and Low-Low concentrated in the Southwest. (3) Urban residential building floor space per capita, population, and urbanization are promotive driving forces, while carbon intensity and energy intensity are increasingly inhibiting. This study can provide policymakers a better understanding of URBCE and help them develop regionally-specific emission reduction strategies. Additionally, it offers fresh perspective on how to research the spatiotemporal disparities of carbon emissions in other sectors and nations, and could be of international significance.

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  • Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222029887
    DOI: 10.1016/j.energy.2022.126102
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    as
    1. Zhao, Jincai & Ji, Guangxing & Yue, YanLin & Lai, Zhizhu & Chen, Yulong & Yang, Dongyang & Yang, Xu & Wang, Zheng, 2019. "Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets," Applied Energy, Elsevier, vol. 235(C), pages 612-624.
    2. Siqi Zheng & Rui Wang & Edward L. Glaeser & Matthew E. Kahn, 2011. "The greenness of China: household carbon dioxide emissions and urban development," Journal of Economic Geography, Oxford University Press, vol. 11(5), pages 761-792, September.
    3. Long, Ruyin & Shao, Tianxiang & Chen, Hong, 2016. "Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors," Applied Energy, Elsevier, vol. 166(C), pages 210-219.
    4. Jincai Zhao & Qianqian Liu, 2021. "Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities," IJERPH, MDPI, vol. 18(8), pages 1-18, April.
    5. Zhang, Yue-Jun & Hao, Jun-Fang & Song, Juan, 2016. "The CO2 emission efficiency, reduction potential and spatial clustering in China’s industry: Evidence from the regional level," Applied Energy, Elsevier, vol. 174(C), pages 213-223.
    6. Nan Zhou & Nina Khanna & Wei Feng & Jing Ke & Mark Levine, 2018. "Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050," Nature Energy, Nature, vol. 3(11), pages 978-984, November.
    7. Wang, Shaojian & Liu, Xiaoping, 2017. "China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces," Applied Energy, Elsevier, vol. 200(C), pages 204-214.
    8. Chung, William & Kam, M.S. & Ip, C.Y., 2011. "A study of residential energy use in Hong Kong by decomposition analysis, 1990–2007," Applied Energy, Elsevier, vol. 88(12), pages 5180-5187.
    9. Jiang, Jinhe, 2016. "China's urban residential carbon emission and energy efficiency policy," Energy, Elsevier, vol. 109(C), pages 866-875.
    10. 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.
    11. Huo, Tengfei & Xu, Linbo & Feng, Wei & Cai, Weiguang & Liu, Bingsheng, 2021. "Dynamic scenario simulations of carbon emission peak in China's city-scale urban residential building sector through 2050," Energy Policy, Elsevier, vol. 159(C).
    12. Zhang, Yue-Jun & Da, Ya-Bin, 2015. "The decomposition of energy-related carbon emission and its decoupling with economic growth in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1255-1266.
    13. Wang, Shaojian & Fang, Chuanglin & Wang, Yang, 2016. "Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 505-515.
    14. 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.
    15. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    16. Li, Wei & Sun, Wen & Li, Guomin & Cui, Pengfei & Wu, Wen & Jin, Baihui, 2017. "Temporal and spatial heterogeneity of carbon intensity in China's construction industry," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 162-173.
    17. McNeil, Michael A. & Feng, Wei & de la Rue du Can, Stephane & Khanna, Nina Zheng & Ke, Jing & Zhou, Nan, 2016. "Energy efficiency outlook in China’s urban buildings sector through 2030," Energy Policy, Elsevier, vol. 97(C), pages 532-539.
    18. Liu, Jiaguo & Li, Sujuan & Ji, Qiang, 2021. "Regional differences and driving factors analysis of carbon emission intensity from transport sector in China," Energy, Elsevier, vol. 224(C).
    19. Wang, Shaobin & Liu, Haimeng & Pu, Haixia & Yang, Hao, 2020. "Spatial disparity and hierarchical cluster analysis of final energy consumption in China," Energy, Elsevier, vol. 197(C).
    20. Sadorsky, Perry, 2014. "The effect of urbanization on CO2 emissions in emerging economies," Energy Economics, Elsevier, vol. 41(C), pages 147-153.
    21. Huo, Tengfei & Ma, Yuling & Xu, Linbo & Feng, Wei & Cai, Weiguang, 2022. "Carbon emissions in China's urban residential building sector through 2060: A dynamic scenario simulation," Energy, Elsevier, vol. 254(PA).
    22. Li, Jun & Zhang, Dayong & Su, Bin, 2019. "The Impact of Social Awareness and Lifestyles on Household Carbon Emissions in China," Ecological Economics, Elsevier, vol. 160(C), pages 145-155.
    23. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.
    24. Poumanyvong, Phetkeo & Kaneko, Shinji, 2010. "Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis," Ecological Economics, Elsevier, vol. 70(2), pages 434-444, December.
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    Cited by:

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    2. Huo, Tengfei & Cong, Xiaobo & Cheng, Cong & Cai, Weiguang & Zuo, Jian, 2023. "What is the driving mechanism for the carbon emissions in the building sector? An integrated DEMATEL-ISM model," Energy, Elsevier, vol. 274(C).
    3. Huo, Tengfei & Du, Qianxi & Xu, Linbo & Shi, Qingwei & Cong, Xiaobo & Cai, Weiguang, 2023. "Timetable and roadmap for achieving carbon peak and carbon neutrality of China's building sector," Energy, Elsevier, vol. 274(C).
    4. Xin Yang & Yifei Sima & Yabo Lv & Mingwei Li, 2023. "Research on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak: A Case of Henan Province in China," Sustainability, MDPI, vol. 15(13), pages 1-18, June.

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