Assessing carbon footprints of cities under limited information
City carbon footprints have become an important tool for monitoring the progress of cities towards lowering their greenhouse gas emissions and contribution to climate change. Cities usually source a major part of their natural resource demand from their local, national and global hinterland, and cau...
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Published in: | Journal of cleaner production Vol. 176; pp. 1254 - 1270 |
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Main Authors: | , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Elsevier Ltd
01-03-2018
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Online Access: | Get full text |
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Summary: | City carbon footprints have become an important tool for monitoring the progress of cities towards lowering their greenhouse gas emissions and contribution to climate change. Cities usually source a major part of their natural resource demand from their local, national and global hinterland, and cause emissions across the whole global supply chain of a city's final demand. It is important that the data underlying carbon footprint assessments of cities capture these supply chains adequately and comprehensively. In this research, we determine the carbon footprints of four Chinese cities, Beijing, Shanghai, Chongqing and Tianjin based on different levels of data availability. Using these case studies, we show conclusively that city carbon footprint analyses must include input-output databases and associated calculus in order to avoid severe errors that arise from unacceptable scope limitations caused by the truncation of the footprint assessment boundary. We also show that city input-output databases must fulfil a number of requirements for city comparisons and for informed decision-making to be feasible. Our findings suggest that investment into multi-layered national input-output tables and datasets will be necessary to monitor progress of cities reducing their greenhouse emissions across the whole supply chain and to inform evidence based policy making that guides greenhouse abatement. |
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ISSN: | 0959-6526 1879-1786 |
DOI: | 10.1016/j.jclepro.2017.11.073 |