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Hybrid method of mapping urban residential carbon emissions with high-spatial resolution: A case study of Suzhou, China

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  • Junyang Gao
  • Helin Liu
  • Yongwei Tang
  • Mei Luo

Abstract

Implementing carbon mitigation through urban spatial optimisation is a possible solution for alleviating global warming. However, the relationship between urban carbon emissions and urban spatial structure has not been well clarified, as adequate mapping of high-spatial-resolution urban carbon emissions from different sectors (particularly residential sectors), a precondition to solving the problem, has yet to be achieved. This study proposes a hybrid method of mapping the spatial distribution of urban residential carbon emissions on a 1 km × 1 km scale using multi-source data and exemplifies it via a case study of the Chinese city of Suzhou. The purpose of using this method is to differentiate residential carbon emissions by commuter population and home-based population, as the time they spend at home differs. The mobile signalling data of Suzhou were used to identify commuter and home-based populations. The number and spatial distribution of these two groups were then calibrated by referring to land use and O-D data. Using calibrated data, the proportion of electricity consumed by the two groups in different residential districts across the city was calculated. Total urban residential carbon emissions were then proportionally allocated to 1 km × 1 km grids. By validating estimated data against the data from the Statistical Yearbook, we found that the proximity level is higher than 93%. Furthermore, comparing these outcomes against the results estimated by using NTL data and the size of the identified population as the proxy data, it was observed that the results estimated by the hybrid method are of higher accuracy and stability.

Suggested Citation

  • Junyang Gao & Helin Liu & Yongwei Tang & Mei Luo, 2024. "Hybrid method of mapping urban residential carbon emissions with high-spatial resolution: A case study of Suzhou, China," Environment and Planning B, , vol. 51(1), pages 75-88, January.
    • Handle: RePEc:sae:envirb:v:51:y:2024:i:1:p:75-88
    DOI: 10.1177/23998083231167167
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    1. Xuemei Bai & Richard J. Dawson & Diana Ürge-Vorsatz & Gian C. Delgado & Aliyu Salisu Barau & Shobhakar Dhakal & David Dodman & Lykke Leonardsen & Valérie Masson-Delmotte & Debra C. Roberts & Seth Schu, 2018. "Six research priorities for cities and climate change," Nature, Nature, vol. 555(7694), pages 23-25, March.
    2. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
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