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Analysis of Interprovincial Differences in CO2 Emissions and Peak Prediction in the Yangtze River Delta
The Yangtze River Delta is the most populous and economically active region in China. Studying the reduction in CO2 emissions in this region is of great significance in achieving the goal of “peak carbon and carbon neutrality” in China. In this study, the Tapio decoupling and extended STIRPAT models were used to study the differences in provincial CO2 emissions characteristics and influencing factors in the Yangtze River Delta from 2001 to 2019. The results show that the growth rate of CO2 emissions was slower than that of economic development, which means that CO2 emissions and economic growth were in a state of weak decoupling. As found by ridge regression, the same factor has different impacts on CO2 emissions among provinces. The differences in these influencing factors were mainly caused by the imbalance of development in the Yangtze River Delta. Nine development scenarios were set out to predict the future trend of CO2 emissions based on economic development and carbon emissions technology using the extended STIRPAT model. It was found that low-carbon-emissions technology is conducive to controlling CO2 emissions in the Yangtze River Delta. In that case, the CO2 emissions would peak in 2029 at 1895.78~1908.25 Mt. Compared with the low-carbon-emissions scenarios, both the medium- and high-carbon-emissions scenarios are not conducive to achieving a carbon peak, with a 2~5-year delay in peak time and increasing emissions by 3.69~7.68%. In order to reduce the Yangtze River Delta’s CO2 emissions and pass the peak emissions as soon as possible, it is essential to not only optimize the energy structure, upgrade industries and promote the coordinated development of low-carbon technologies, but also promote emissions reduction in the transportation and construction fields and advocate for a low-carbon lifestyle among the public.
Analysis of Interprovincial Differences in CO2 Emissions and Peak Prediction in the Yangtze River Delta
The Yangtze River Delta is the most populous and economically active region in China. Studying the reduction in CO2 emissions in this region is of great significance in achieving the goal of “peak carbon and carbon neutrality” in China. In this study, the Tapio decoupling and extended STIRPAT models were used to study the differences in provincial CO2 emissions characteristics and influencing factors in the Yangtze River Delta from 2001 to 2019. The results show that the growth rate of CO2 emissions was slower than that of economic development, which means that CO2 emissions and economic growth were in a state of weak decoupling. As found by ridge regression, the same factor has different impacts on CO2 emissions among provinces. The differences in these influencing factors were mainly caused by the imbalance of development in the Yangtze River Delta. Nine development scenarios were set out to predict the future trend of CO2 emissions based on economic development and carbon emissions technology using the extended STIRPAT model. It was found that low-carbon-emissions technology is conducive to controlling CO2 emissions in the Yangtze River Delta. In that case, the CO2 emissions would peak in 2029 at 1895.78~1908.25 Mt. Compared with the low-carbon-emissions scenarios, both the medium- and high-carbon-emissions scenarios are not conducive to achieving a carbon peak, with a 2~5-year delay in peak time and increasing emissions by 3.69~7.68%. In order to reduce the Yangtze River Delta’s CO2 emissions and pass the peak emissions as soon as possible, it is essential to not only optimize the energy structure, upgrade industries and promote the coordinated development of low-carbon technologies, but also promote emissions reduction in the transportation and construction fields and advocate for a low-carbon lifestyle among the public.
Analysis of Interprovincial Differences in CO2 Emissions and Peak Prediction in the Yangtze River Delta
Siyu Zhu (author) / Ying Ding (author) / Run Pan (author) / Aifang Ding (author)
2023
Article (Journal)
Electronic Resource
Unknown
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