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• W2967597229 endingPage "118034" @default.
• W2967597229 startingPage "118034" @default.
• W2967597229 abstract "In this study, both the extended logarithmic mean divisia index (LMDI) model and the system dynamics (SD) model were used to explore determinants of CO2 emission change during 1995–2016 and to predict the emission mitigation potential from 2016 to 2030 in Shanghai (China). Some novel factors (e.g., private car ownership, urban travel structure, and income level) were chosen and added to the LMDI model. The combination of the LMDI and SD models might provide a new pathway for policymakers to cope with such sophisticated issues. The results showed that: (1) GDP per capita is the main positive driving force for CO2 emission growth, followed by population, average income level per capita, and car ownership per capita. Energy intensity is the main factor for carbon mitigation, followed by economic structure, residential energy intensity, and emission coefficient. (2) The additive effect of different scenarios is essential for emission control. (3) CO2 emissions and emission per capita would peak by 2025 at the level of 218.20 Mt and 8.83 t per capita, respectively. Tertiary industry and public travel model promotion, power generation structure, and primary energy structure optimization would facilitate emission mitigation in Shanghai, which could also be a reference for other similar mega-cities in developing countries." @default.
• W2967597229 created "2019-08-22" @default.
• W2967597229 creator A5040253386 @default.
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• W2967597229 date "2019-12-01" @default.
• W2967597229 modified "2023-10-10" @default.
• W2967597229 title "Coupled LMDI and system dynamics model for estimating urban CO2 emission mitigation potential in Shanghai, China" @default.
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• W2967597229 doi "https://doi.org/10.1016/j.jclepro.2019.118034" @default.
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