Modeling the impact of energy abundance on economic growth and CO2 emissions by quantile regression: Evidence from China

Ying Liu, Boqiang Lin () and Bin Xu

Energy, 2021, vol. 227, issue C

Abstract: 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 (CO2) 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 CO2 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 CO2 emissions in all quantile groups is positive, meaning it does not play a prominent role in mitigating CO2 emissions. Therefore, each quantile province should formulate specific policies to promote the growth of renewable energy and actively develop strategic emerging industries.

Keywords: Energy abundance; CO2 emissions; Economic growth; Quantile regression approach (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:227:y:2021:i:c:s0360544221006654

DOI: 10.1016/j.energy.2021.120416

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