Journal
JOURNAL OF CLEANER PRODUCTION
Volume 167, Issue -, Pages 571-581Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2017.08.159
Keywords
Carbon footprint by source; Indirect household carbon footprint; Residents-related attributes; 49 Japanese cities
Categories
Funding
- National Natural Science Foundation of China [71663025]
- Major Project of Economic and Social Development of Jiangxi Province, China [16ZD06]
- National Social Science Foundation of China [2015YZD16]
- NOW [467-14-003]
- project of Climate KIC: International Intelligence and Business Development Network on Circular Economy Business Opportunities with China (int-CEB)
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Cities accommodate more than half of the world's population and are responsible for most of the global environmental footprints of carbon and resource consumption. Hence, countermeasures to reduce the carbon footprint of cities is critical to confront global and national challenges on climate changes in the Post Paris Era. Particularly, carbon emissions arising from the residential sector are of great importance to urban emissions, considering their scale and contribution to the indirect emissions caused by supply chains. Despite much consideration on national or provincial household carbon footprints, there has been little discussion on city-level carbon emissions by source and their correlation with residents related attributes. Given such a circumstance, the present research explored the indirect household carbon emissions by source and its relationship with potentially affecting attributes through a case study on 49 Japanese prefecture capital-level cities (comprising 47 prefectural capitals and Kitakyushu City and Kawasaki City). We combined data from a national expenditure survey with the Embodied Energy and Emission Intensity Data for Japan Using Input-Output Tables (3EID) to calculate consumption-based carbon footprints. Furthermore, to investigate the correlation between the indirect household carbon footprint (IHCF) and residents-related attributes, we divided IHCF into seven sources (food, accommodation, recreation, education, medical service, energy, and transportation) and analyzed the relation with various attributes. The main results highlight that 1). Spatial unbalanced distribution is observed by IHCF types. 2). Each type of IHCF affected by different socio-economic variables according to stepwise regression analysis result. 3). We found that although total IHCF exhibited a comparatively weak correlation with the selected residents-related attributes, food, transportation, and education separately exhibited significant correlations. The insights obtained in terms of source categories and household related features of IHCF are expected to provide detailed information for future mitigation policies, i.e., city-level mitigation priority setting. (C) 2017 Elsevier Ltd. All rights reserved.
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