Renewable Energy Substitution Model and Environmental Preservation
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Excessive usage of fossil fuels, which contain large hydrocarbons, worsens global warming, public health, and ecosystems. The rate of environmental degradation and greenhouse gas emissions is decelerated by switching from fossil fuels into renewables. This research applies time series method to estimate the electric power consumption of 112 countries. Then, a stochastic substitution model is introduced to estimate the contribution of the renewables in the electric power generation. The main part of the model relies on the Bayesian inference and pseudo random number generators (PRNGs) to update the statistical distribution of renewables in the substitution model. The results of the four types of substitution models emphasize on renewables investment as a way to accelerate the substitution and environment preservation.
KeywordsEnergy use forecasting Electric power consumption Bayesian inference Statistical substitution model
- 1.Reguly E. Paris climate accord marks shift toward low-carbon economy. Toronto, Canada: Globe and Mail; 2015.Google Scholar
- 2.GEA. Global energy assessments. International Institute for applied systems analysis. Cambridge: Cambridge Univ. Press; 2012.Google Scholar
- 3.IEA. World energy statistics. Paris: International Energy Agency; 2014.Google Scholar
- 8.Lee C-Y, Huh SY. Forecasting the diffusion of renewable electricity considering the impact of policy and oil prices: The case of South Korea. Appl Energy. 2017;69:207–17.Google Scholar
- 15.Ross S. A first course in probability. 8th ed. United States of America: Prentice Hall (PEARSON); 2010.Google Scholar
- 16.Gentle JE. Random number generation and monte carlo methods. New York; London: Springer; 2003.Google Scholar
- 17.Montgomery DC, Jennins CL, Kulahci M. Introduction to time series analysis and forecasting. Hoboken, New Jersey: WILEY-Interscience; 2008.Google Scholar
- 18.WHO. World Bank Open Data [Online]. 2017. data.worldbank.org.
- 19.IEA. International Energy Agency. [Online]. 2017. http://www.iea.org/statistics.
- 20.Reza Nadimi, Koji Tokimatsu. Potential energy saving via overall efficiency relying on quality of life. Appl Energy. 2019;233–234:283–99.Google Scholar