Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (5): 1063-1072.doi: 10.12082/dqxxkx.2020.190727

Previous Articles     Next Articles

Computation of Carbon Emissions of Residential Buildings in Wuhan and Its Spatiotemporal Analysis

JIA Tao1, YANG Shihao1, LI Xin2,*(), YAN Penggao1, YU Xuesong1, LUO Xi1, CHEN Kai1   

  1. 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
    2. School of architecture and civil engineering, Xiamen University, Xiamen 361005, China
  • Received:2019-11-29 Revised:2020-03-19 Online:2020-05-25 Published:2020-07-25
  • Contact: LI Xin
  • Supported by:
    National Natural Science Foundation of China(41971332)


Carbon emissions of residential buildings have an important impact on energy conservation policies, emission reduction strategies, and sustainable urban development. However, current studies mainly focus on carbon emission estimation for an entire city or a large region. There is a lack of consistent methods of carbon emission estimation for residential buildings. Thus, this paper proposes a method to calculate carbon emissions of residential buildings by the fusion of multiple datasets. Our method firstly uses a top-down based strategy to assign the total carbon emission to each urban block. Then it adopts a bottom-up strategy to establish an emission calculation model for each residential building by taking into account urban block planning factors, socioeconomic factors, and residential building morphological factors. This paper applies the proposed method to estimate carbon emissions of all residential buildings in Wuhan city. Our results show that: (1) Carbon emissions of residential buildings decreases from the central city to the suburbs, which is closely related to population distribution; (2) Carbon emissions of residential buildings are heterogeneous and exhibit a heavy-tailed distribution. For instance, there are 89% of residential buildings with carbon emission lower than the average of 1.28 ton and 11% of residential buildings with carbon emission higher than the average; (3) Residential buildings within the same urban block have slight difference in carbon emission with an average standard deviation of 7.66 ton, while residential buildings located in different urban blocks tend to have significantly different carbon emissions with an average standard deviation of 51.30 ton; and (4) Carbon emissions of residential buildings are more likely to be affected by plot ratios in planning factors, population density in socioeconomic factors, and shapes of residential buildings. Our method and experimental results can provide decision support for sustainable planning of urban residential areas.

Key words: residential building, carbon emission, nighttime light, spatiotemporal analysis, urban block, planning factor, socioeconomic factor, Wuhan