How to achieve a low-carbon transition in the heavy industry? A nonlinear perspective

Abstract

The heavy industry is the main source of CO2 emissions. Examining the influencing factors of the heavy industry's CO2 emissions is essential for this industry to achieve a low-carbon transition. Existing research literature often assumes that the relationship between the influencing factors and CO2 emissions is linear. In fact, most economic phenomena are characterized by fluctuations, due to the influence of business cycles. This often leads to more likely nonlinear relationships between economic variables. This paper uses nonparametric additive regression model to investigate CO2 emissions from China's heavy industry. The empirical findings show that economic growth, urbanization, and energy efficiency exert a “first promotion, then restriction” inverted “U-shaped” nonlinear effect on CO2 emissions. To complicate matters further, energy consumption structure and export dependence have an “N-shaped” nonlinear effect on CO2 emissions. Therefore, in the early stage, the government should actively optimize the industrial structure, strengthen the management of the real estate industry, and expand R&D investment. In the long run, the government should subsidize the heavy industry to expand clean energy consumption, and provide tax incentives to support the export of high-tech industrial products.