Analysis of influencing factors of industrial green and low-carbon transformation under the background of "double carbon": evidence from Sichuan province, China

Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure.Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province.Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.