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

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@Article{PAN:2019:JCP,

• author = "Xiongfeng Pan and Md. Kamal Uddin and Bowei Ai and Xianyou Pan",
• title = "Influential factors of carbon emissions intensity in {OECD} countries: evidence from symbolic regression",
• journal = "Journal of Cleaner Production",
• year = "2019",
• volume = "220",
• pages = "1194--1201",
• month = "20 " # may,
• keywords = "genetic algorithms, genetic programming",
• keywords = "genetic algorithms, genetic programming, carbon emissions intensity, symbolic regression, factors of emission, CO2, OECD",
• ISSN = "0959-6526",
• URL = "http://www.sciencedirect.com/science/article/pii/S0959652619305943",
• DOI = "doi:10.1016/j.jclepro.2019.02.195",
• abstract = "Carbon emissions intensity can be 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, the 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 each 4 countries. And each of urbanisation, 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",
• notes = "also known as \cite{PAN20191194}",

}

Genetic Programming entries for Xiongfeng Pan Md Kamal Uddin Bowei Ai Xianyou Pan

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