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The heavy industry is the main source of CO 2 emissions. Examining the influencing factors of the heavy industry's CO 2 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 CO 2 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 CO 2 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 CO 2 emissions. To complicate matters further, energy consumption structure and export dependence have an "N-shaped" nonlinear effect on CO 2 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. • This article examines CO 2 emissions in China's heavy industry. • Economic growth exerts an inverted "U-shaped" effect on CO 2 emissions. • Energy consumption structure has an "N-shaped" effect on CO 2 emissions. • Energy efficiency produces an inverted "U-shaped" effect on CO 2 emissions. [ABSTRACT FROM AUTHOR]

Based on China's 30 provinces penal data, from 1990 to 2017, this paper uses the quantile regression model to estimate the impact of fossil energy abundance and clean energy abundance on economic growth and carbon dioxide (CO 2) emissions. The findings show that fossil energy abundance exerts a greater impact on economic growth in the intermediate quantile provinces such as Hebei, Jilin, Liaoning, and Shandong because these provinces have larger petroleum processing and coal gas production industries. The CO 2 emissions in the upper 90th quantile provinces such as Liaoning, Shanxi, and Shandong, receive the biggest impact from fossil energy abundance because these provinces consume more coal and oil. However, the impacts of renewable energy abundance on economic growth in the 50th-75th, upper 90th, and 75th-90th quantile groups are greater, since their renewable energy industry is growing faster. The influence of renewable energy abundance on CO 2 emissions in all quantile groups is positive, meaning it does not play a prominent role in mitigating CO 2 emissions. Therefore, each quantile province should formulate specific policies to promote the growth of renewable energy and actively develop strategic emerging industries. • Fossil energy contributes more to economic growth in the middle-level quantiles. • The impact of fossil energy on emissions in the upper 90th quantiles is the strongest. • Renewable energy abundance does not play a prominent role in reducing emissions. [ABSTRACT FROM AUTHOR]