RESEARCH ARTICLE

Quantifying and mapping spatial variability of Shanghai household carbon footprints

  • Shangguang YANG 1 ,
  • Chunlan WANG 2 ,
  • Kevin LO , 3 ,
  • Mark WANG 4 ,
  • Lin LIU 1
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  • 1. Institute of Economic Development, East China University of Science and Technology, Shanghai 200237, China
  • 2. School of Social Development, East China Normal University, Shanghai 200062, China
  • 3. Department of Geography, Hong Kong Baptist University, Hong Kong, China
  • 4. School of Geography, University of Melbourne, VIC 3010, Australia

Received date: 26 Jun 2014

Accepted date: 24 Oct 2014

Published date: 02 Mar 2015

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

Understanding the spatial variability of household carbon emissions is necessary for formulating sustainable and low-carbon energy policy. However, data on household carbon emissions is limited in China, the world’s largest greenhouse gases emitter. This study quantifies and maps household carbon emissions in Shanghai using a city-wide household survey. The findings reveal substantial spatial variability in household carbon emissions, especially in transport-related emissions. Low emission clusters are founded in Hongkou, Xuhui, Luwan, Jinshan, and Fengxian. High emission clusters are located in Jiading and Pudong. Overall, the spatial pattern of household carbon emissions in Shanghai is donut-shaped: lowest in the urban core, increasing in the surrounding suburban areas, and declining again in the urban fringe and rural regions. The household emissions are correlated with a number of housing and socioeconomic factors, including car ownership, type of dwelling, size of dwelling, age of dwelling, and income. The findings underscore the importance of a localized approach to low-carbon policy-making and implementation.

Cite this article

Shangguang YANG , Chunlan WANG , Kevin LO , Mark WANG , Lin LIU . Quantifying and mapping spatial variability of Shanghai household carbon footprints[J]. Frontiers in Energy, 0 , 9(1) : 115 -124 . DOI: 10.1007/s11708-015-0348-8

Acknowledgments

The project was funded by the National Social Science Foundation (No. 11CRK005), the Ministry of Education Humanities and Social Science Foundation (No. 11YJA630176), the Shanghai Municipal Soft Science Foundation, the Australia Research Council (No. DP1094801), and the East China University of Science and Technology (No. WN1222011), the Fundamental Research Funds for the Central Universities (No. 222201422026). We are grateful to the two anonymous reviewers for helping to improve this work.
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