Please use this identifier to cite or link to this item:
Full metadata record
|dc.description.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.||en_US|
|dc.title||Economic and energy impacts on greenhouse gas emissions: A case study of China and the USA||en_US|
|article.stream.affiliations||Chiang Mai University||en_US|
|Appears in Collections:||CMUL: Journal Articles|
Files in This Item:
There are no files associated with this item.
Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.