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Forecast of Transportation CO2 Emissions in Shanghai under Multiple Scenarios
A reduction in CO2 emissions from transportation is of great significance to achieve the goal of “peak carbon and carbon neutrality” in China. For 2003–2019, this paper calculates the transportation CO2 emissions in Shanghai and constructs an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model for forecasting. The result shows that from 2003 to 2019, total and per capita CO2 emissions from Shanghai’s transportation sector increased, but the rate of growth decreased. Oil consumption was the main source of emissions, accounting for more than 92%. The study extended the STIRPAT model to analyze the driving factors for emissions. It shows that population size, passenger turnover, per capita GDP, transportation intensity, and energy intensity are positively correlated with emissions. Energy structure (the proportion of clean energy) has a negative impact, restraining growth. Under multiple scenarios, the forecast shows that Shanghai’s transportation sector can reach a CO2 emissions peak before 2030. However, overgrowth of the transportation sector should be avoided. Progress in green and low-carbon technology is particularly important to achieve China’s peak carbon goal. Shanghai should actively build an efficient green transportation system, continue to optimize the transportation energy structure, and promote green and low-carbon travel for residents.
Forecast of Transportation CO2 Emissions in Shanghai under Multiple Scenarios
A reduction in CO2 emissions from transportation is of great significance to achieve the goal of “peak carbon and carbon neutrality” in China. For 2003–2019, this paper calculates the transportation CO2 emissions in Shanghai and constructs an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model for forecasting. The result shows that from 2003 to 2019, total and per capita CO2 emissions from Shanghai’s transportation sector increased, but the rate of growth decreased. Oil consumption was the main source of emissions, accounting for more than 92%. The study extended the STIRPAT model to analyze the driving factors for emissions. It shows that population size, passenger turnover, per capita GDP, transportation intensity, and energy intensity are positively correlated with emissions. Energy structure (the proportion of clean energy) has a negative impact, restraining growth. Under multiple scenarios, the forecast shows that Shanghai’s transportation sector can reach a CO2 emissions peak before 2030. However, overgrowth of the transportation sector should be avoided. Progress in green and low-carbon technology is particularly important to achieve China’s peak carbon goal. Shanghai should actively build an efficient green transportation system, continue to optimize the transportation energy structure, and promote green and low-carbon travel for residents.
Forecast of Transportation CO2 Emissions in Shanghai under Multiple Scenarios
Liping Zhu (author) / Zhizhong Li (author) / Xubiao Yang (author) / Yili Zhang (author) / Hui Li (author)
2022
Article (Journal)
Electronic Resource
Unknown
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