Features and influencing factors of carbon emissions indicators in the perspective of residential consumption: Evidence from Beijing,China |
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| Institution: | 1. School of Management and Economics, Beijing Institute of Technology, 100081 Beijing, China;2. Center for Energy & Environmental Policy Research, Beijing Institute of Technology, 100081 Beijing, China;3. Collaborative Innovation Center of Electric Vehicles in Beijing, 100081 Beijing, China;1. School of Management and Economics, Beijing Institute of Technology, 100081 Beijing, China;2. Center for Energy & Environmental Policy Research, Beijing Institute of Technology, 100081 Beijing, China;3. School of Economics and Management, Beijing Institute of Petrochemical Technology, 102617 Beijing, China;4. Collaborative Innovation Centre of Electric Vehicles in Beijing, 100081 Beijing, China;1. School of Geography and Oceanography Sciences, Nanjing University, Nanjing, China;2. The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing, China;3. CEES, Department of Biosciences, University of Oslo, Blindern, Norway;4. Information Center for Global Change Studies, Lanzhou Library, Chinese Academy of Sciences, Lanzhou, China;1. School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani, 12120, Thailand;2. College of Interdisciplinary Studies, Thammasat University, 2 Prachan Road, Phranakorn, Bangkok, 10200, Thailand |
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| Abstract: | This research establishes a residential indirect carbon emissions model through input–output structure decomposition analysis (IO-SDA) and LMDI, analyses the influencing factors affecting urban and rural residential carbon emissions indicators in Beijing through input–output tables from 2000 to 2010, and calculates the direct carbon emissions from residential consumption. As the results suggest, the total carbon emissions from residential consumption in Beijing showed volatility. Growing rural and urban differences in direct emissions, and for indirect emissions, mean that urban greatly exceeds rural in this regard. Rising per capita GDP and population, as well as intermediate demand and sectoral emissions intensity change induce growth in indirect emissions in both urban and rural settings: of which, per capita GDP contributes the most. Declining energy intensity contributes the most to emission reductions, followed by residential consumption rates, the rural to urban consumption ratio and consumption structure effects are much smaller. |
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| Keywords: | Residential consumption Carbon emissions indicators Input–output analysis LMDI decomposition |
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