Influential factors of carbon emissions intensity in OECD countries: Evidence from symbolic regression

• Published in: Journal of cleaner production Vol. 220; pp. 1194 - 1201
• Main Authors: Pan, Xiongfeng, Uddin, Md. Kamal, Ai, Bowei, Pan, Xianyou, Saima, Umme
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 20-05-2019
• Subjects: Carbon emissions intensity; CO2; Factors of emission; OECD; Symbolic regression; Symbolic regression; Factors of emission; Carbon emissions intensity; OECD; CO2
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2019.02.195

Carbon emissions intensity is used as a yardstick to evaluate the relative extent of carbon emissions of a country. To reduce carbon emissions intensity, it is crucial to find out the dominant factors that enhance it and take actions to manage them. This study is an attempt to employ symbolic regression method to explore the most influential factors of carbon emissions intensity in 34 OECD member countries using data from 1995 to 2014. Rooted in genetic programming, symbolic regression method can explore the inherent functional structures amidst the variables and could discover the influential factors robotically. The results confirm that the influential factors of carbon emissions intensity vary from country to country. GDP is found as the most frequent and important factor of carbon emissions intensity in 17 countries. Each of industrialization and technological innovation is the prominent factor in every 4 countries. And each of urbanization, total population, and foreign direct investment plays a pro-active role in 3 individual nations. Based on the empirical results, some policy implications are also conferred.
•Symbolic regression is applied to explore the influential factors of carbon emissions intensity.•Influential factors of carbon emissions intensity vary from country to country.•The most prominent factor of carbon emissions intensity is GDP followed by total population and foreign direct investment.