Power Generation Sources and Carbon Dioxide Emissions in BRICS Countries: Static and Dynamic Panel Regression
DOI:
https://doi.org/10.13052/spee1048-5236.4143Keywords:
BRICS, climate change, CO2 emissions, electricity production sources, energy consumption.Abstract
Purpose: The threat of global warming has escalated as a result of industrialization, urbanization, population growth, and lifestyle changes in Brazil, Russia, India, China, and South Africa (BRICS). The amount of electricity generated by various sources is directly influenced by their respective carbon dioxide (CO2
) emissions. This study’s primary goal is to determine which sources are bad for the environment and which are not.
Methodology: Examining the impact of different energy generation sources on CO2
emissions using data from the BRICS. To analyze the data, pooled OLS and Generalized Method of Moments (GMM) are used, as well as Quantile Regression (QR).
Findings: We found that coal and gas power generation had a positive and large influence on CO2
emissions regardless of the method used. As compared to other emissions, coal-fired energy production has a more significant impact. In all regression models, hydroelectric and renewable energy generation can reduce CO2
emissions.
Originality: Identifying an empirical link between CO2
emissions and energy production sources is the study’s most significant accomplishment. To obtain solid results, the paper used a combination of QR and GMM techniques. The conclusions presented in this article have important environmental policy consequences. CO2 emissions can be reduced by reducing the consumption of fossil fuels and promoting the development of alternative energy sources such as hydroelectric, wind, and solar power.
Downloads
References
Abdallah, L., and El-Shennawy, T. (2013). Reducing carbon dioxide emis-
sions from the electricity sector using smart electric grid applications.
Journal of Engineering, 2013.
Al-Mulali, U. (2014). Investigating the impact of nuclear energy consump-
tion on GDP growth and CO2 emission: A panel data analysis. Progress
in Nuclear Energy, 73, 172–178.
Arellano, M., and Bond, S. (1991). Some tests of specification for panel data:
Monte Carlo evidence and an application to employment equations. The
review of economic studies, 58(2), 277–297.
Arellano, M., and Bover, O. (1995). Another look at the instrumental variable
estimation of error components models. Journal of Econometrics, 68(1),
–51.
Awosusi, A. A., Adebayo, T. S., Altuntas ̧, M., Agyekum, E. B., Zawbaa, H.
M., and Kamel, S. (2022). The dynamic impact of biomass and natural
L. C. Voumik et al.
resources on the ecological footprint in BRICS economies: A quantile
regression evidence. Energy Reports, 8, 1979–1994.
Aydin, M. (2019). The effect of biomass energy consumption on economic
growth in BRICS countries: A country-specific panel data analysis.
Renewable Energy, 138, 620–627.
Aytekin A. (2022). Energy, Environment, and Sustainability: A Multi-
criteria Evaluation of Countries. Strategic planning for energy and the
environment, 2022: Vol 41 Iss 3, 281–316.
Baloch, M. A., Mahmood, N., and Zhang, J. W. (2019). Effect of natu-
ral resources, renewable energy, and economic development on CO2
emissions in BRICS countries. Science of the Total Environment, 678,
–638.
Baltagi B. H. (2008) Forecasting with panel data. J Forecast 27(2):153–173.
Bashir, M. F., Ma, B., Shahbaz, M., and Jiao, Z. (2020). The nexus between
environmental tax and carbon emissions with the roles of environmental
technology and financial development. Plos one, 15(11), e0242412.
Bayazıt, Y. (2021). The effect of hydroelectric power plants on carbon emis-
sion: An example of Gokcekaya dam, Turkey. Renewable Energy, 170,
–187.
Bilgen, S., Kaygusuz, K., and Sari, A. (2004). Renewable energy for a clean
and sustainable future. Energy sources, 26(12), 1119–1129.
Blundell, R., and Bond, S. (1998). Initial conditions and moment restrictions
in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.
Buchinsky, M. (1994). Changes in the US wage structure 1963–1987: Appli-
cation of quantile regression. Econometrica: Journal of the Econometric
Society, 62(2), 405–458.
Cameron, A. C., and Trivedi, P. K. (2010). Microeconometrics using Stata
(Vol. 2). College Station, TX: Stata Press.
Canay, I. A. (2011).A simple approach to quantile regression for panel
data. The Econometrics Journal, 14(3), 368–386.
Chen, W., and Lei, Y. (2018). The impacts of renewable energy and techno-
logical innovation on environment-energy-growth nexus: New evidence
from a panel quantile regression. Renewable energy, 123, 1–14.
Cho, Y., Lee, J., Kim, T.Y. (2007). The impact of ICT investment and
energy price on industrial electricity demand: Dynamic growth model
approach.Energy Policy, 35, 4730–4738.
Cowan, W. N., Chang, T., Inglesi-Lotz, R., and Gupta, R. (2014). The nexus
of electricity consumption, economic growth, and CO2 emissions in the
BRICS countries. Energy Policy, 66, 359–368. enpol.2020.111339
Power Generation Sources and CO2 Emissions in BRICS Countries 421
Dantama, Y. U., Abdullahi, Y. Z., and Inuwa, N. (2012). Energy
consumption-economic growth nexus in Nigeria: An empirical assess-
ment based on ARDL bound test approach. European Scientific Journal,
(12).
Gasser, P. (2020). A review of energy security indices to compare country
performances. Energy Policy, 139, 111339.
Jin, T., and Kim, J. (2018). What is better for mitigating carbon emissions–
Renewable energy or nuclear energy? A panel data analysis. Renewable
and Sustainable Energy Reviews, 91, 464–471.
Menyah, K., and Wolde-Rufael, Y. (2010). CO2 emissions, nuclear energy,
renewable energy, and economic growth in the US. Energy Policy, 38(6),
–2915.
Nickell, S. (1981). “Biases in dynamic models with fixed effects.” Economet-
rica: J Econo Soc: 1417–1426.
Ozturk, I. (2017). Measuring the impact of alternative and nuclear energy
consumption, carbon dioxide emissions, and oil rents on specific growth
factors in the panel of Latin American countries. Progress in Nuclear
Energy, 100, 71–81.
Pao, H. T., and Tsai, C. M. (2010). CO2 emissions, energy consumption, and
economic growth in BRIC countries. Energy Policy, 38(12), 7850–7860.
Pao, H. T., and Tsai, C. M. (2011). Multivariate Granger causality between
CO2 emissions, energy consumption, FDI (foreign direct investment),
and GDP (gross domestic product): evidence from a panel of BRIC
(Brazil, Russian Federation, India, and China) countries. Energy, 36(1),
–693.
Paul, S., and Bhattacharya, R. N. (2004). CO2 emission from energy use
in India: a decomposition analysis. Energy Policy, 32(5), 585–593.
Performances. Energy Policy 139:111339. https://doi.org/10.1016/j.
Rahman, M. H., Majumder, S. C., and Debbarman, S. (2020). Examine the
Role of Agriculture to Mitigate the CO2 Emission in Bangladesh. Asian
Journal of Agriculture and Rural Development, 10(1), 392–405.
Rahman, M. M. (2017). Do population density, economic growth, energy use
and exports adversely affect environmental quality in Asian populous
countries?. Renewable and Sustainable Energy Reviews, 77, 506–514.
Roodman, D. (2009). How to do xtabond2: An introduction to difference and
system GMM in Stata. The Stata Journal, 9(1), 86–136.
Sebos, I., Progiou, A.G., and Kallinikos, L.E. (2021). Methodological Frame-
work for the Quantification of GHG Emission Reductions from Climate
L. C. Voumik et al.
Change Mitigation Actions. Strategic planning for energy and the
environment, 219–242.
Statista. Total population of the BRICS countries from 2000 to 2026 (in
million inhabitants). https://www.statista.com/statistics/254205/tot
al-population-of-the-bric-countries/Summit, F. B. (2013). Thekweni
declaration and action plan.
The Guardian (2012). World Carbon Emissions: the League Table of Every
Country. 21 June 2012.
Urry, J. (2015).Climate change and society.In Why the Social Sciences
Matter. Palgrave Macmillan: London, UK, pp. 45–59.
Wang, W., Mu, H., Kang, X., Song, R., and Ning, Y. (2010).Changes in
industrial electricity consumption in China from 1998 to 2007. Energy
Policy, 38, 3684–3690.
World Bank(2019).World development indicators. Accessed at: http://www.
worldbank.org/data/onlinedatabases.
Yang, L., and Lin, B. (2016). Carbon dioxide-emission in China’s power
industry: Evidence and policy implications.Renewable and Sustainable
Energy Reviews, 60, 258–267.
Yoo, S.H. (2005). Electricity consumption and economic growth: Evidence
from Korea.Energy Policy, 33, 1627–1632.
Yu, Z., Liu, W., Chen, L., Eti, S., Dinc ̧er, H., and Y ̈uksel, S. (2019).
The effects of electricity production on industrial development and
sustainable economic growth: A VAR analysis for BRICS countries.
Sustainability, 11(21), 5895.
Zhang, M., Mu, H., and Ning, Y. (2009). Accounting for energy-related CO2
emission in China, 1991–2006. Energy Policy, 37(3), 767–773.
Zhao, W., Cao, Y., Miao, B., Wang, K., and Wei, Y. M. (2018). Impacts of
shifting China’s final energy consumption to electricity on CO2 emission
reduction. Energy Economics, 71, 359–369.
Zhu, L., He, L., Shang, P., Zhang, Y., and Ma, X. (2018). Influencing
factors and scenario forecasts of carbon emissions of the Chinese power
industry: Based on a Generalized Divisia Index Model and Monte Carlo
Simulation. Energies, 11(9), 2398.