To address climate change,the world needs deep decarbonization to achieve carbon neutrality(CN),which implies net-zero human-caused CO_(2) emissions in the atmosphere.This study used emission-side drivers,including so...To address climate change,the world needs deep decarbonization to achieve carbon neutrality(CN),which implies net-zero human-caused CO_(2) emissions in the atmosphere.This study used emission-side drivers,including socioeconomic and net primary productivity(NPP)-based factors,to determine the changes in CN based on vegetation carbon sequestration in the case of China during 2001-2015.Spatial exploratory analysis as well as the combined use of production-theoretical decomposition analysis(PDA)and an econometric model were also utilized.We showed that CN was significantly spatially correlated over the study period;Yunnan,Heilongjiang,and Jilin presented positive spatial autocorrelations,whereas Guizhou showed a negative spatial autocorrelation.More than half of CN declined over the period during which potential energy intensity(PEIE)and energy usage technological change were the largest negative and positive drivers for increasing CN.PEIE played a significantly negative role in increasing CN.We advise policymakers to focus more on emission-side drivers(e.g.,energy intensity)in addition to strengthening NPP management to achieve CN.展开更多
文摘To address climate change,the world needs deep decarbonization to achieve carbon neutrality(CN),which implies net-zero human-caused CO_(2) emissions in the atmosphere.This study used emission-side drivers,including socioeconomic and net primary productivity(NPP)-based factors,to determine the changes in CN based on vegetation carbon sequestration in the case of China during 2001-2015.Spatial exploratory analysis as well as the combined use of production-theoretical decomposition analysis(PDA)and an econometric model were also utilized.We showed that CN was significantly spatially correlated over the study period;Yunnan,Heilongjiang,and Jilin presented positive spatial autocorrelations,whereas Guizhou showed a negative spatial autocorrelation.More than half of CN declined over the period during which potential energy intensity(PEIE)and energy usage technological change were the largest negative and positive drivers for increasing CN.PEIE played a significantly negative role in increasing CN.We advise policymakers to focus more on emission-side drivers(e.g.,energy intensity)in addition to strengthening NPP management to achieve CN.
