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Temporal and Spatial Variability of Carbon Emission Intensity of Urban Residential Buildings: Testing the Effect of Economics and Geographic Location in China

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  • Qingwei Shi

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

  • Jingxin Gao

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

  • Xia Wang

    (School of Public Finance and Taxation, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Hong Ren

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

  • Weiguang Cai

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

  • Haifeng Wei

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

Abstract

The role of urban residential buildings (URBs) in the carbon reduction goal of China is becoming increasingly important because of the rising energy consumption and carbon emission of such buildings in the region. Considering the increasing spatial interaction of the carbon emission of URBs (URBCE) in the region, this study investigates the influence of climate and economic factors on the URBCE in North and South China. First, the URBCE is calculated by using a decomposition energy balance table based on the carbon emission coefficient of electric and thermal power, thereby improving the estimation of the basic data of URBCE. Second, the influence of economic and climatic factors on the URBCE intensity in 30 provinces of China is explored by using a spatial econometric model. Results show that the URBCE intensity in China had a spatial autocorrelation from 2000 to 2016. Climatic and economic factors have great differences in the degree and direction of influencing the URBCE intensity in the country. Formulating emission reduction policies for climate or economic zones is more scientific and effective than developing national policies. Among these factors, urbanization rate, climate, and GDP per capita have a significant positive impact on the URBCE intensity in the region, whereas other factors have varying degrees of negative impact. In addition, climate, consumption level, and building area have significant spatial spillover effects on URBCE intensity, whereas other factors do not pass the significance test. Relevant conclusions should be given special attention by policymakers.

Suggested Citation

  • Qingwei Shi & Jingxin Gao & Xia Wang & Hong Ren & Weiguang Cai & Haifeng Wei, 2020. "Temporal and Spatial Variability of Carbon Emission Intensity of Urban Residential Buildings: Testing the Effect of Economics and Geographic Location in China," Sustainability, MDPI, vol. 12(7), pages 1-23, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2695-:d:338652
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    References listed on IDEAS

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    3. Lisu Chen & Qiong Wei & Qiang Fu & Daolun Feng, 2021. "Spatiotemporal Evolution Analysis of Habitat Quality under High-Speed Urbanization: A Case Study of Urban Core Area of China Lin-Gang Free Trade Zone (2002–2019)," Land, MDPI, vol. 10(2), pages 1-21, February.
    4. Wang, Xiaolu & Tan, Yumin & Zhou, Guanhua & Jing, Guifei & John Francis, Emolu, 2024. "A framework for analyzing energy consumption in urban built-up areas based on single photonic radar and spatial big data," Energy, Elsevier, vol. 290(C).
    5. Haichao Feng & Ruonan Wang & He Zhang, 2022. "Research on Carbon Emission Characteristics of Rural Buildings Based on LMDI-LEAP Model," Energies, MDPI, vol. 15(24), pages 1-16, December.
    6. Qingwei Shi & Hong Ren & Weiguang Cai & Jingxin Gao, 2020. "How to Set the Proper CO 2 Reduction Targets for the Provincial Building Sector of China?," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    7. Yanyan Ke & Lu Zhou & Minglei Zhu & Yan Yang & Rui Fan & Xianrui Ma, 2023. "Scenario Prediction of Carbon Emission Peak of Urban Residential Buildings in China’s Coastal Region: A Case of Fujian Province," Sustainability, MDPI, vol. 15(3), pages 1-17, January.

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