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


Woraphon Yamaka
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.
- Chiang Mai University Thailand
Energy impact, LASSO regression, TK1-9971, Ridge regression, Economics impact, Elastic net regression, Greenhouse gas emissions, Electrical engineering. Electronics. Nuclear engineering
Energy impact, LASSO regression, TK1-9971, Ridge regression, Economics impact, Elastic net regression, Greenhouse gas emissions, Electrical engineering. Electronics. Nuclear engineering
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).40 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
