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Disaggregation Method of Carbon Emission: A Case Study in Wuhan, China

Author

Listed:
  • Minghai Luo

    (Wuhan Geomatics Institute, Wuhan 430022, China)

  • Sixian Qin

    (Wuhan Geomatics Institute, Wuhan 430022, China)

  • Haoxue Chang

    (Wuhan University, Wuhan 430079, China)

  • Anqi Zhang

    (Wuhan University, Wuhan 430079, China)

Abstract

Urban areas contribute significant carbon emissions. Evaluating and analysing the spatial distribution of carbon emissions are the foundations of low-carbon city development and carbon emissions reduction. In this study, carbon emission inventory was first constructed and carbon emissions in Wuhan were estimated on the basis of energy consumption. Second, the spatial distribution models of carbon emissions in different sectors were developed on the basis of the census of the Wuhan geographical conditions data and other thematic data. Third, the carbon emission distribution in Wuhan was analyzed at the central urban, functional, new urban, built-up, and metropolitan development area scale. Results show that the industry sector emits most of the carbon emissions in Wuhan, followed by the residential population. Carbon emissions in the metropolitan development area can stand for the true carbon emissions in Wuhan. Thus, a geographically weighted (GW) model was adopted to analyze the correlation coefficients between economical-social factors (gross domestic product, population density, road density, industrial land and residential land) and carbon emissions in the metropolitan development area. Comparisons with other studies show that the disaggregation method we proposed in this work, especially the adoption of geographical condition census data, can reflect the spatial distribution of carbon emissions of different sectors at the city scale.

Suggested Citation

  • Minghai Luo & Sixian Qin & Haoxue Chang & Anqi Zhang, 2019. "Disaggregation Method of Carbon Emission: A Case Study in Wuhan, China," Sustainability, MDPI, vol. 11(7), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2093-:d:220985
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    References listed on IDEAS

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    4. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
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    Cited by:

    1. Fangjie Cao & Yun Qiu & Qianxin Wang & Yan Zou, 2022. "Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data," IJERPH, MDPI, vol. 19(17), pages 1-17, August.

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