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Unfolding the interplay between carbon flows and socioeconomic development in a city: What can network analysis offer?

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  • Chen, Shaoqing
  • Xu, Bing
  • Chen, Bin
Abstract
There are overwhelming proofs of how urbanization contributes to the increase of carbon emissions. However, it has been unclear how structural and functional changes in urban carbon flows evolve with socioeconomic development in a long run, which is important for a more systemic and efficient carbon mitigation policy. The present study probes into the interaction between urban carbon metabolism and socioeconomic activities from a systems perspective. Taking Beijing as a case study, we model the dynamics between the changing carbon metabolism and variation in socioeconomic conditions over 1985–2030, based on a collection of system-based indicators from ecological network analysis. We find an “inverted V curve” carbon transition in Beijing, and the turning point occurred around 2010. This transition is widely observed in the variation in total embodied emission, total system throughflow, boundary flow and system capacity. Continuing improvement in efficiency is expected to lessen the pressure from carbonization in 2020 and 2030 without sacrificing the diversity of economic activities. We suggest that network analysis has a unique potential in unfolding the interplay between carbon transition and socioeconomic development that most “accounting approaches” fail to penetrate.

Suggested Citation

  • Chen, Shaoqing & Xu, Bing & Chen, Bin, 2018. "Unfolding the interplay between carbon flows and socioeconomic development in a city: What can network analysis offer?," Applied Energy, Elsevier, vol. 211(C), pages 403-412.
    • Handle: RePEc:eee:appene:v:211:y:2018:i:c:p:403-412
    DOI: 10.1016/j.apenergy.2017.11.064
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    References listed on IDEAS

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    Cited by:

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    3. Zhen, Wei & Zhong, Zhangqi & Wang, Yichen & Miao, Lu & Qin, Quande & Wei, Yi-Ming, 2019. "Evolution of urban household indirect carbon emission responsibility from an inter-sectoral perspective: A case study of Guangdong, China," Energy Economics, Elsevier, vol. 83(C), pages 197-207.
    4. Panyam, Varuneswara & Huang, Hao & Davis, Katherine & Layton, Astrid, 2019. "Bio-inspired design for robust power grid networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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    6. Xinhui Feng & Yan Li & Lu Zhang & Chuyu Xia & Er Yu & Jiayu Yang, 2022. "Carbon Metabolism in Urban “Production–Living–Ecological” Space Based on Ecological Network Analysis," Land, MDPI, vol. 11(9), pages 1-22, August.
    7. Chen, Shaoqing & Long, Huihui & Chen, Bin & Feng, Kuishuang & Hubacek, Klaus, 2020. "Urban carbon footprints across scale: Important considerations for choosing system boundaries," Applied Energy, Elsevier, vol. 259(C).
    8. Liu, Lirong & Huang, Guohe & Baetz, Brian & Huang, Charley Z. & Zhang, Kaiqiang, 2019. "Integrated GHG emissions and emission relationships analysis through a disaggregated ecologically-extended input-output model; A case study for Saskatchewan, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 106(C), pages 97-109.
    9. Sun, Chen & Song, Junnian & Zhang, Dongqi & Wang, Xiaofan & Yang, Wei & Qi, Zhimin & Chen, Shaoqing, 2023. "Tracing urban carbon footprints differentiating supply chain complexity: A metropolis case," Energy, Elsevier, vol. 282(C).
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