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dc.contributor.authorWoraphon Yamakaen_US
dc.contributor.authorRungrapee Phadkanthaen_US
dc.contributor.authorPichayakone Rakphoen_US
dc.description.abstractClimate change is the biggest 21st-century environmental challenge that impacts human communities, natural resources, and biodiversity. This study aims to study the economic and energy impacts on climate change measured by greenhouse gas emissions in China and the USA. Various factors are considered in this study; thus, the traditional regression analysis (OLS regression) may not be practical when the number of predictors is large, and multicollinearity exists. We suggest using three machine learning models, namely LASSO regression, Ridge regression, and Elastic net regression to deal with these limitations of the OLS method. Our results show that the impacts of economic factors for China and the USA. are slightly different. Chinese economic factors are found to increase greenhouse gas emissions, while there is a decrease in greenhouse gas emissions in the USA. However, we find strong evidence that renewable energy production leads to sustainable development in both the USA. and China.en_US
dc.titleEconomic and energy impacts on greenhouse gas emissions: A case study of China and the USAen_US
article.title.sourcetitleEnergy Reportsen_US
article.volume7en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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