Scale, composition, and technique effects through which the economic growth, foreign direct investment, urbanization, and trade affect greenhouse gas emissions
By Genovaitė Liobikienė, Mindaugas Butkus. Renewable Energy, 2019
223 citations recorded by the source corpus.
Ecological Kuznets curve (EKC) reveals the relationship between GHG emissions and economic growth. However, there are other factors and different channels through which the economic growth affects environmental quality. Therefore, we aim to examine not only direct (scale) impact of previously analysed factors, such as GDP, trade, foreign direct investment (FDI), urbanization, industrialization, renewable energy consumption, and energy efficiency on GHG emissions, but also to model composition and technique effects of this impact. Proposed models were tested using the panel data of 147 countries between 1990 and 2012 and system GMM estimator. Estimation results provided an evidence of GDP’s positive and significant impact on GHG emissions and no evidence of scale effect of urbanization and FDI on GHG emissions. Moreover, we found statistical evidence of negative correlation between trade and GHG emissions and this contradicts to the leakage effect. Furthermore, our estimations did not reveal the composition effect for all analysed factors. In case of technique effect, the results showed that GDP, urbanization, and trade contributed to reducing carbon emissions only via energy efficiency but not via renewable energy. Therefore, countries should further seek that economic growth would be accompanied by technological development and faster growth of energy efficiency.
Subfields: Economics and Econometrics, Environmental Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/b821f4f8be4ad6435d54145323e70bbf6a1d3cf1
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Citations in the corpus
- A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018 (2021, background): “…in the last decade, following the long-term trend (Burke et al 2015, Yao et al 2015, Malik et al 2016, Sanchez and Stern 2016, Stern et al 2017, Chang et al 2019, Dong et al 2019, 2020, Liobikiene and Butkus 2019, Liu et al 2019, Mardani et al 2019, Pan et al 2019, Parker and Bhatti 2020).”