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Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

Author

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  • Jincai Zhao

    (School of Business, Henan Normal University, Xinxiang 453007, Henan, China)

  • Qianqian Liu

    (School of Geography Science, Nanjing Normal University, Nanjing 210023, Jiangsu, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, Jiangsu, China)

Abstract

Improving carbon efficiency and reducing carbon intensity are effective means of mitigating climate change. Carbon emissions due to urban residential energy consumption have increased significantly; however, there is a lack of research on urban residential carbon intensity. This paper examines the spatiotemporal variation of carbon intensity in the residential sector during 2001–2015, and then identifies the causes of the variation by utilizing the logarithmic mean Divisia index (LMDI) with the help of Microsoft Excel 2016 for 620 county-level cities in 30 Chinese provinces. The results show that high carbon intensity is mainly found in large cities, such as Beijing, Tianjin, and Shanghai. However, these cities showed a downward trend in carbon intensity. In terms of influencing factors, the energy consumption per capita, urban sprawl, and land demand are the three most influential factors in determining the changes in carbon intensity. The effect of energy consumption per capita mainly increases the carbon intensity, and its impact is higher in the municipal districts of provincial capital cities than in other types of cities. Similarly, the urban sprawl effect also promotes increases in carbon intensity, and a higher degree of influence appears in large cities. However, as urban expansion plateaus, the effect of urban sprawl decreases. The land-demand effect reduces the carbon intensity, and the degree of influence of the land-demand effect on carbon intensity is also clearly stronger in big cities. Our findings show that lowering the energy consumption per capita and optimizing the land-use structure are a reasonable direction of efforts, and the effects of differences in influencing factors should be paid more attention to reduce carbon intensity.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:8:p:3929-:d:532621
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    References listed on IDEAS

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    Cited by:

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    2. Gen Li & Shihong Zeng & Tengfei Li & Qiao Peng & Muhammad Irfan, 2023. "Analysing the Effect of Energy Intensity on Carbon Emission Reduction in Beijing," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    3. Yang Sun & Mengna Du & Leying Wu & Changzhe Li & Yulong Chen, 2022. "Evaluating the Effects of Renewable Energy Consumption on Carbon Emissions of China’s Provinces: Based on Spatial Durbin Model," Land, MDPI, vol. 11(8), pages 1-17, August.
    4. Xiaogang Song & Shufan Zhai & Na Zhou, 2024. "The Carbon Emissions from Public Buildings in China: A Systematic Analysis of Evolution and Spillover Effects," Sustainability, MDPI, vol. 16(15), pages 1-22, August.
    5. Yanfei Lei & Chao Xu & Yunpeng Wang & Xulong Liu, 2024. "Grid Model of Energy Consumption Using Random Forest by Integrating Data on the Nighttime Light, Population, and Urban Impervious Surface (2000–2020) in the Guangdong–Hong Kong–Macau Greater Bay Area," Energies, MDPI, vol. 17(11), pages 1-18, May.
    6. Guanghui Tian & Jianming Miao & Changhong Miao & Yehua Dennis Wei & Dongyang Yang, 2022. "Interplay of Environmental Regulation and Local Protectionism in China," IJERPH, MDPI, vol. 19(10), pages 1-21, May.

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