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Economic and energy impacts on greenhouse gas emissions: A case study of China and the USA

Authors: orcid Woraphon Yamaka;
Woraphon Yamaka
ORCID
Harvested from ORCID Public Data File

Woraphon Yamaka in OpenAIRE
Rungrapee Phadkantha; Pichayakone Rakpho;

Economic and energy impacts on greenhouse gas emissions: A case study of China and the USA

Abstract

Climate 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.

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Keywords

Energy impact, LASSO regression, TK1-9971, Ridge regression, Economics impact, Elastic net regression, Greenhouse gas emissions, Electrical engineering. Electronics. Nuclear engineering

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