An LSTM-STRIPAT model analysis of China’s 2030 CO2 emissions peak

Abstract

To achieve China’s CO2 emissions targets, all Chinese provinces need to ensure that their CO2 emissions are maintained at a reasonable level to avoid the shortboard effect. This paper proposed an integrated method, the LSTM-STIRPAT, to predict the CO2 emissions in 30 provinces, and assess the drivers of a different region. We divide 30 provinces according to the prediction result into provinces with peak value(PWP) and provinces without peak value(PWTP) and found that (i) Inner Mongolia, Jiangxi, Shandong, Hainan, Chongqing, Guizhou, Qinghai, Xinjiang are failed to reach their CO2 emissions peak by 2030, but almost all provinces experienced a small peak in their carbon emissions from 2008 to 2013; (ii) The ranking of CO2 emissions influencing factors in the PWTP is energy intensity (+) > population density (+) > energy consumption (+) > urbanization rate (−) > GDP per capita (+) > ratio of secondary industry (+); the ranking of CO2 emissions influencing factors in the PWP is energy intensity (+) > ratio of secondary industry (+) > urbanization rate (−) > population density (+) > energy consumption (+) > GDP per capita (−); (iii) PWTP's CO2 emissions show a significant lag effect, of which the ratio of secondary industry accounts for the most significant impact. According to the research results, we put forward relevant targeted measures to achieve China's carbon emissions peak commitments in 2030: (1) PWTP should give priority to encouraging the development of technology and strengthening the utilization of new energy and renewable energy; (2) PWP should give priority to reducing energy intensity, optimizing the industrial structure and accelerating the process of urbanization; (3) CO2 emission reduction in PWTP is a long-term task, it is necessary to adhere to the optimization and adjustment of the industrial structure.