Decoupling analysis and scenario prediction of agricultural CO2 emissions: An empirical analysis of 30 provinces in China

Abstract

This study explores the decoupling status and the future trend of CO2 emissions from agricultural sector. First, this study uses the Log-Mean Divisia Index (LMDI) model to identify the driving forces that affecting the agricultural CO2 emissions from 2008 to 2017. Over the study period, the per capita cultivated area (PCA) and rural population (RP) were two main factors for increasing and decreasing agricultural CO2 emissions, respectively. Then the Tapio model was conducted to reflect the relationship between the agricultural CO2 emissions and agricultural output. Six decoupling statuses exited across provinces through the whole period. Strong decoupling status was observed in seven provinces, such as Hebei, while nine provinces experienced weak decoupling, such as Henan. Besides, coupling status still existed in fourteen provinces. Based on the provincial decoupling results, we establish three scenarios namely business-as-usual (BAU), median case of decoupling (MCD) and best case of decoupling (BCD) scenarios to estimate agricultural CO2 emissions in 2030. MCD scenario assumes that coupling status will not exit in provinces and all provinces could achieve strong decoupling in BCD scenario. Results reveal that China's agricultural CO2 emissions in 2030 will be 366.7 Mt, 224.9 Mt and 175.3 Mt under three scenarios, respectively. The agricultural CO2 emissions in MCD and BCD scenarios are 38.7% and 52.2% lower than those in BAU scenario in 2030, respectively. Inner Mongolia, Jilin, Jiangsu, Guangxi and Xinjiang should be given priority to promoting decoupling status under the MCD scenario due to their huge emission reduction potential.