Effect of natural resources, renewable energy and economic development on CO2 emissions in BRICS countries.
By Danish, Muhammad Awais Baloch, N. Mahmood, Jian Zhang. Science of the Total Environment, 2019
872 citations recorded by the source corpus.
Economic development drives industrialization, which increased the value of the extracted natural resources. Excessive usage of natural resources, through agriculture, deforestation, and mining can affect the environment. In this regard, the present study investigates the effects of natural resources' abundance on carbon dioxide (CO2) emissions. The study uses annual panel data spanning from 1990 to 2015 in BRICS countries. The augmented mean group (AMG) panel algorithm, robust to crosssectional dependence and heterogeneity, infers the heterogeneous effect of natural resources on CO2 emissions among BRICS countries. Abundance of natural resources mitigates CO2 emission in Russia, but contributes to pollution in South Africa. In addition to this, natural resources help to form Environmental Kuznets Curve (EKC) hypothesis in Brazil, China, Russia, and South Africa. Finally, causality analysis suggested feedback hypothesis between natural resources and CO2 emissions.
Subfields: Economics and Econometrics, Environmental Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/0694e58ce752a1406e67f69096000704cd098a31
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In Economics and Econometrics
- G. B. Amegavi
- Mary Donkor
- Z. Langnel
- P. Donkor
- Mohammed Borhandden Musah
- S. Antwi
- I. A. Mensah
- J. Mensah
In Environmental Engineering
In Renewable Energy, Sustainability and the Environment
- G. B. Amegavi
- Mary Donkor
- Z. Langnel
- P. Donkor
- Mohammed Borhandden Musah
- S. Antwi
- I. A. Mensah
- J. Mensah
Citations in the corpus
- The connection between urbanization and carbon emissions: a panel evidence from West Africa (2020, background): “This estimator is, however, advantageous because it produces robust and reliable results (Driscoll and Kraay 1998; Jalil 2014; Sarkodie and Strezov 2019) and can be used for both short- and long-time-span data (Driscoll and Kraay 1998). Therefore, employing such an estimator could help yield unswerv”