Russian energy projects in South Africa
From early 2019, South Africa and Russia have planned to increase their energy trade. Russia can become one of the world’s five largest energy exporters. This study examines of the cost of a kilowatt of electricity generated by coal power projects in South Africa and compares nuclear electricity with other types of green energy. This method must help to improve the management decision-making process in South Africa for energy exporta. Reasons for this persistence include the marketing strategies of Russian companies for seeking new markets in industrialised and postindustrial countries where, due to intensive competition, sales of Russian high-tech products are often unsuccessful. Renewable energy gives a chance to potentially reduce poverty in South Africa. The study concludes that imported crude oil is more suited to the needs of the refining industry of South Africa. The consumption for this type of energy in areas not concerning industry is insignificant and its increase is unlikely to be observed in the future.
• Nuclear energy is popular energy source in South Africa now.
• Provision of sustainable energy services helps to find the sources for economic growth.
• Renewable energy technologies have opportunity for reduce nuclear production in South Africa.
• Bio-energy can become the main source of energy in South Africa.
An, J., Dorofeev, M., 2019. Short-term FX forecasting: decision making on the base of expert polls. Investment Management and Financial Innovations 16(4), 72-85.
An, J., Dorofeev, M., Zhu, S., 2020. Development of Energy Cooperation Between Russia and China. International Journal of Energy Economics and Policy 10 (1), 134-139.
Ang, J.B., 2007. Are saving and investment cointegrated? The case of Malaysia (1965–2003). Applied Economics 39, 2167–2174.
Ang, J.B., 2008. Economic development, pollutant emissions and energy consumption in Malaysia. Journal of Policy Modeling 30, 271–278.
Ang, J.B., 2009. CO2 emissions, research and technology transfer in China. Ecological Economics 68, 2658–2665.
Apergis, N., Payne, J.E., 2009. CO2 emissions, energy usage, and output in Central America. Energy Policy 37, 3282–3286.
Apergis, N., Payne, J.E., 2010. The emissions, energy consumption and growth nexus: evidence from the commonwealth of independent states. Energy Policy 38, 650–655.
Apergis, N., Payne, J.E., Menyah, K., Wolde-Rufael, Y., 2010. On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecological Economics 69, 2255–2260.
Asongu, S.A., 2018. Comparative sustainable development in Sub-Saharan Africa. Sustainable Development, 1–14.
Asongu, S.A., Le, S., Biekpe, N., 2017a. Environmental degradation, ICT and inclusive development in Sub-Saharan Africa. Energy Policy, 111, 353–361.
Asongu, S.A., Le, S., Biekpe, N., 2017b. Enhancing ICT for environmental sustainability in sub-Saharan Africa. Technological Forecasting and Social Change, 1–8.
Atalay, E., Hortacsu, A., Roberts, J., Syverson, C., 2011. Network structure of production. Proceedings of the National Academy of Sciences, 108(13), 5199-5202.
Denisova, V., Mikhaylov, А., Lopatin, E., 2019). Blockchain infrastructure and growth of global power consumption. International Journal of Energy Economics and Policy 9(4), 22-29.
Elizarov, M., Ivanyuk, V., Soloviev, V., Tsvirkun, A., 2017. Identification of high-frequency traders using fuzzy logic methods. Proceedings of 2017 10th International Conference Management of Large-Scale System Development, MLSD 2017. https://doi: 10.1109/MLSD.2017.8109615i.
Ivanyuk, V., 2018. Econometric Forecasting Models Based on Forecast Combination Methods. Proceedings of 2018 11th International Conference Management of Large-Scale System Development, MLSD 2018. https://doi: 10.1109/MLSD.2018.8551825.
Ivanyuk, V., Soloviev, V., 2019. Efficiency of neural networks in forecasting problems. Proceedings of 2019 12th International Conference Management of Large-Scale System Development; MLSD 2019. https://doi: 10.1109/MLSD.2019.8911046.
Jaramillo, P., Matthews, H.S., 2005), Landfill-gas-to-energy projects: Analysis of net private and social benefits. Environmental Science and Technology, 39, 7365-7373.
Lisin, A, 2020c. Valuation of the activities of foreign banks in the Russian banking sector. Orbis, 15(45), 53-63. http://www.revistaorbis.org.ve/pdf/45/art5.pdf.
Lisin, A., 2020a. Biofuel Energy in the Post-oil Era. International Journal of Energy Economics and Policy, 10(2), 194-199. https://doi.org/10.32479/ijeep.8769.
Lisin, A., 2020b. Prospects and Challenges of Energy Cooperation between Russia and South Korea. International Journal of Energy Economics and Policy, Vol. 10 (3). https://doi.org/10.32479/ijeep.9070.
Lopatin, E., 2019a. Methodological Approaches to Research Resource Saving Industrial Enterprises. International Journal of Energy Economics and Policy 9(4), 2019, 181-187.
Lopatin, E., 2019b. Assessment of Russian banking system performance and sustainability. Banks and Bank Systems, 14(3), 202-211. doi:10.21511/bbs.14(3).2019.17.
Lopatin, E., 2020. Cost of Heating Pump Systems in Russia. International Journal of Energy Economics and Policy, Vol. 10 (3). DOI: https://doi.org/10.32479/ijeep.9056.
Meynkhard, A., 2019. Energy efficient development model for regions of the Russian Federation: Evidence of crypto mining. International Journal of Energy Economics and Policy 9(4), 2019, 16-21.
Meynkhard, A., 2019b. Fair market value of bitcoin: halving effect. Investment Management and Financial Innovations, 16(4), 72-85. https://doi.org/10.21511/imfi.16(4).2019.07.
Meynkhard, A., 2020. Priorities of Russian energy policy in Russian-Chinese relations. International Journal of Energy Economics and Policy 10 (1), 65-71. https://doi.org/10.32479/ijeep.8507.
Meynkhard, A., 2020b. Long-term prospects for the development energy complex of Russia. International Journal of Energy Economics and Policy, 10(3), с. 224-232.
Mikhaylov, A., 2018a. Pricing in oil market and using probit model for analysis of stock market effects. International Journal of Energy Economics and Policy, 2, 69–73.
Mikhaylov, A., 2018b. Volatility spillover effect between stock and exchange rate in oil exporting countries. International Journal of Energy Economics and Policy, 2018, 8(3), 321-326.
Mikhaylov, A., 2019. Oil and gas budget revenues in Russia after crisis in 2015. International Journal of Energy Economics and Policy 9(2), 2019, 375-380.
Mikhaylov, A., Sokolinskaya, N., Lopatin, E., 2019. Asset allocation in equity, fixed-income and cryptocurrency on the base of individual risk sentiment. Investment Management and Financial Innovations, 16(2), 171-181. doi:10.21511/imfi.16(2).2019.15.
Mikhaylov, A., Sokolinskaya, N., Nyangarika, A., 2018, Optimal carry trade strategy based on currencies of energy and developed economies. Journal of Reviews on Global Economics 7, 582-592. DOI: https://doi.org/10.6000/1929-7092.2018.07.54.
Milbrabdt, A.R., Heimiller, D.M., Perry, A.D., Field, C.B., 2014, Renewable energy potential on marginal lands in the United States. Renewable and Sustainable Energy Review, 29, 473-481.
Moiseev, N., 2017a. Forecasting time series of economic processes by model averaging across data frames of various lengths. Journal of Statistical Computation and Simulation, 87 (17). – P. 3111-3131.
Moiseev, N., 2017b. p-Value adjustment to control type I errors in linear regression models. Journal of Statistical Computation and Simulation, 87 (9). – P. 1701-1711.
Moiseev, N., 2017c. Linear model averaging by minimizing mean-squared forecast error unbiased estimator. Model Assisted Statistics and Applications, 11 (4). – P. 325-338.
Moiseev, N., Akhmadeev, B., 2017. Agent-based simulation of wealth, capital and asset distribution on stock markets. Journal of Interdisciplinary Economics, 29 (2). – P. 176-196.
Moiseev, N., Sorokin, A., 2018. Interval forecast for model averaging methods. Model Assisted Statistics and Applications, 18 (2). – P. 125-138.
Morgan, S.M., Yang, Q., 2001. Use of landfill gas for electricity generation. Practice Periodical of Hazardous, Toxic, and Radio Waste Management, 5(1), 14-24.
Morris, J.W., Barlaz, M.A., 2011. A performance-based system for the long-term management of municipal waste landfills. Waste Management, 31(4), 649-662.
Nyangarika, A., Mikhaylov, A. & Richter, U., 2019b. Oil price factors: Forecasting on the base of modified auto-regressive integrated moving average model. International Journal of Energy Economics and Policy, 9(1), 149-160. https://doi.org/10.32479/ijeep.6812.
Nyangarika, A., Mikhaylov, A. & Tang, B.-J., 2018. Correlation of oil prices and gross domestic product in oil producing countries. International Journal of Energy Economics and Policy, 8(5), 42-48.
Nyangarika, A., Mikhaylov, A., & Richter, U., 2019a. Influence oil price towards economic indicators in Russia. International Journal of Energy Economics and Policy, 9(1), 123-130. https://doi.org/10.32479/ijeep.6807.
Radosteva, M., Soloviev, V., Ivanyuk, V., Tsvirkun, A., 2018. Use of neural network models in market risk management. Advances in Systems Science and Applications, 18 (2), pp. 53-58. https://doi: 10.25728/assa.2018.18.2.582.
Sunchalin, A.M., Kochkarov, R.A., Levchenko, K.G., Kochkarov, A.A., Ivanyuk, V.A., 2019. Methods of risk management in portfolio theory. Espacios, 40(16).
Tazvinga, H. and Hove, T. 2010. Technical model for optimising PV/diesel/battery hybrid power systems, 3rd CSIR Biennial Conference, CSIR International Convention Centre: Pretoria, 1 August – 01 September 2010. Available at: http://researchspace.csir.co.za/dspace/handle/10204/4229.
Tazvinga, H., Thopil, M., Numbi, P.B. and Adefarati, T. 2017. Distributed renewable energy technologies. In Handbook of Distributed Generation: 3-67). Springer, Cham.
Trubnikov, V., Meynkhard, A., Shvandar, K., Litvishko, O., Titov, V., 2020. Medication market performance analysis with help of Analytic Hierarchy Processing. Entrepreneurship and sustainability Issues, 8(1), 899-916. http://doi.org/10.9770/jesi.2020.8.1(60).
Uandykova, M., Lisin, A., Stepanova, D., Baitenova, L., Mutaliyeva, L., Yuksel, S., Dincer, H., 2020. The social and legislative principles of counteracting ransomware crime. Entrepreneurship and Sustainability Issues 8(2), 777-798. http://doi.org/10.9770/jesi.2020.8.2(47).
Vigolo, V., Sallaku, R. and Testa, F. 2018. Drivers and barriers to clean cooking: A systematic literature review from a consumer behaviour perspective. Sustainability, 10(11): 4322.
Wang, C. and Nehrir, M.H. 2008. Power management of a stand-alone wind/photovoltaic/fuel cell energy system. IEEE Transactions on Energy Conversion, 23(3): 957-967.
Wang, Y., Diaz, D.F.R., Chen, K.S., Wang, Z. and Adroher, X.C. 2020. Materials, technological status, and fundamentals of PEM fuel cells–a review. Materials Today, 32: 178-203.
Wellinger, A., Murphy, J.D. and Baxter, D., 2013. The biogas handbook: science, production and applications. Elsevier.
Zakeri, B. and Syri, S. 2015. Electrical energy storage systems: A comparative life cycle cost analysis. Renewable and Sustainable Energy Reviews, 42: 569-596.
Zubakin, V.A., Kosorukov, O.A., Moiseev, N.A., 2015. Improvement of regression forecasting models. Modern Applied Science, 9 (6), 344-353.
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