Abstract
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.
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Notes
- 1.
The PRNG is an algorithm to generate a sequence of numbers whose properties are approximately similar to the properties of sequences of random numbers.
References
Reguly E. Paris climate accord marks shift toward low-carbon economy. Toronto, Canada: Globe and Mail; 2015.
GEA. Global energy assessments. International Institute for applied systems analysis. Cambridge: Cambridge Univ. Press; 2012.
IEA. World energy statistics. Paris: International Energy Agency; 2014.
Deb C, Zhang F, Yang J, Lee SE, Shah KW. A review on time series forecasting techniques for building energy consumption. Renew Sust Energ Rev. 2017;74:902–24.
Shakouri HG, Nadimi R, Ghaderi SF. A hybrid TSK-FR model to study short-term variations of the electricity demand versus the temperature changes. Expert Syst Appl. 2009;36:1765–72.
Bassamzadeh N, Ghanem R. Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks. Appl Energy. 2017;193:369–80.
Rao KU, Kishore V. A review of technology diffusion models with special reference to renewable energy technology. Renew Sust Energ Rev. 2010;14:1070–8.
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.
Fisher JC. A simple substitution model of technological change. Technol Forecast Soc Change. 1971;3:75–88.
Sharif MN, Kabir C. A generalized model for forecasting technological substitution. Technol Forecast Soc Change. 1976;8:353–64.
Meade N. Technological substitution: a framework of stochastic models. Technol Forecast Soc Change. 1989;36:389–400.
Huh S-Y, Lee C-Y. Diffusion of renewable energy technologies in South Korea on incorporating their competitive interrelationships. Energy Policy. 2014;69:248–57.
Meade N, Islam T. Modelling European usage of renewable energy technologies for electricity generation. Technol Forecast Soc Change. 2015;90:497–509.
Kumar R, Agarwala A. Renewable energy technology diffusion model for techno-economics feasibility. Renew Sust Energ Rev. 2016;54:1515–24.
Ross S. A first course in probability. 8th ed. United States of America: Prentice Hall (PEARSON); 2010.
Gentle JE. Random number generation and monte carlo methods. New York; London: Springer; 2003.
Montgomery DC, Jennins CL, Kulahci M. Introduction to time series analysis and forecasting. Hoboken, New Jersey: WILEY-Interscience; 2008.
WHO. World Bank Open Data [Online]. 2017. data.worldbank.org.
IEA. International Energy Agency. [Online]. 2017. http://www.iea.org/statistics.
Reza Nadimi, Koji Tokimatsu. Potential energy saving via overall efficiency relying on quality of life. Appl Energy. 2019;233–234:283–99.
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Appendix: Parameters of the Linear Trend Model and Starting Year for Each Country
Appendix: Parameters of the Linear Trend Model and Starting Year for Each Country
Country name | Parameters | Starting point | Country name | Parameters | Starting point | ||
|---|---|---|---|---|---|---|---|
u | v | u | v | ||||
Australia | 37025.5 | 4991.5 | 1970 | Cyprus | 9.5 | 85.3 | 1972 |
Austria | 21612.5 | 1177.4 | 1970 | Georgia | 5987.8 | 212.5 | 1995 |
Belgium | 31231.5 | 1548.3 | 1970 | Kazakhstan | 40349.0 | 2318.1 | 1995 |
Canada | 227925.8 | 8757.8 | 1970 | Latvia | 4401.7 | 127.0 | 1993 |
Chile | 646.2 | 1960.0 | 1981 | Lithuania | 8970.6 | 107.2 | 1993 |
Czech Republic | 44719.0 | 624.5 | 1976 | Malta | 102.2 | 50.0 | 1971 |
Denmark | 31415.0 | 190.1 | 1976 | Romania | 47003.0 | 243.5 | 1996 |
Estonia | 6541.0 | 70.4 | 1990 | Russia | 707373.0 | 10568.0 | 1993 |
Finland | 64241.0 | 1171.8 | 1990 | Serbia | 29993.0 | 189.1 | 2004 |
France | 366221.0 | 6156.0 | 1990 | Tajikistan | 23747.0 | 274.1 | 1994 |
Germany | 371972.3 | 5628.0 | 1970 | Turkmenistan | 5744.6 | 269.8 | 1990 |
Greece | 7087.8 | 1321.3 | 1970 | Ukraine | 148404.0 | 486.6 | 1994 |
Hungary | 22339.8 | 427.9 | 1970 | Uzbekistan | 42834.0 | 248.6 | 1992 |
Iceland | 1240.4 | 2336.6 | 1989 | Algeria | 191.2 | 1030.6 | 1977 |
Ireland | 3021.5 | 584.9 | 1970 | Angola | 121.7 | 1674.6 | 2002 |
Israel | 562.1 | 1358.6 | 1973 | Botswana | 87.5 | 105.8 | 1981 |
Italy | 238657.0 | 4787.3 | 1990 | Cameroon | 209.9 | 196.2 | 1971 |
Japan | 908349.0 | 91.2 | 1990 | Ethiopia | 697.1 | 1028.2 | 2000 |
Luxembourg | 3034.3 | 118.7 | 1970 | Gabon | 179.2 | 33.4 | 1971 |
Mexico | 5513.0 | 5640.1 | 1971 | Ghana | 28.7 | 466.1 | 1972 |
Netherlands | 39069.1 | 1949.8 | 1970 | Kenya | 590.7 | 130.1 | 1971 |
New Zealand | 13607.2 | 718.2 | 1970 | Libya | 231.2 | 548.1 | 1977 |
Norway | 36060.0 | 4994.5 | 1970 | Mauritius | 11.7 | 87.9 | 1983 |
Poland | 83089.9 | 1516.4 | 1970 | Morocco | 100.0 | 670.0 | 1976 |
Portugal | 2185.5 | 1193.7 | 1970 | Mozambique | 1349.0 | 1875.1 | 1996 |
Slovenia | 9592.2 | 208.4 | 1990 | Namibia | 1087.7 | 118.2 | 1991 |
Spain | 128941.0 | 6909.8 | 1990 | Nigeria | 231.8 | 530.5 | 1974 |
Sweden | 126470.0 | 513.9 | 1983 | Senegal | 14.9 | 60.3 | 1975 |
Switzerland | 28675.8 | 901.8 | 1970 | South Africa | 50816.0 | 4818.9 | 1971 |
Turkey | 46.8 | 6092.3 | 1982 | Sudan | 249.3 | 979.6 | 2003 |
United Kingdom | 231957.0 | 3496.8 | 1970 | Togo | 35.9 | 17.4 | 1971 |
United States | 2000000.0 | 67849.0 | 1970 | Tunisia | 86.0 | 376.0 | 1976 |
Argentina | 6980.1 | 2461.1 | 1971 | Zambia | 4468.8 | 183.8 | 1971 |
Brazil | 670893.0 | 47010.0 | 1990 | Zimbabwe | 6329.8 | 100.1 | 1971 |
Colombia | 134459.0 | 3961.9 | 1990 | Bahrain | 87.2 | 706.1 | 1979 |
Costa Rica | 1790.4 | 2209.0 | 1984 | Iraq | 3827.8 | 817.4 | 1971 |
Cuba | 5862.7 | 229.3 | 1971 | Jordan | 233.0 | 383.7 | 1983 |
Dominican Republic | 157.6 | 497.3 | 1983 | Kuwait | 572.3 | 1392.2 | 1977 |
El Salvador | 66.2 | 130.9 | 1973 | Lebanon | 96.6 | 408.3 | 1979 |
Ecuador | 4966.6 | 878.6 | 1997 | Oman | 259.1 | 805.8 | 1990 |
Haiti | 136.6 | 9.2 | 1971 | Qatar | 1105.1 | 1686.5 | 1996 |
Honduras | 2685.3 | 423.6 | 1990 | Saudi Arabia | 4179.4 | 7216.9 | 1982 |
Jamaica | 1020.3 | 101.3 | 1971 | United Arab Emirates | 1415.6 | 4335.4 | 1991 |
Nicaragua | 896.4 | 101.7 | 1991 | Bangladesh | 627.2 | 1963.1 | 1993 |
Panama | 1686.1 | 235.4 | 1991 | Cambodia | 17.1 | 254.0 | 2002 |
Paraguay | 112858.0 | 1165.2 | 1990 | India | 1876.7 | 24690.0 | 1981 |
Peru | 7964.0 | 1282.3 | 1992 | Indonesia | 2622.5 | 6569.8 | 1988 |
Trinidad and Tobago | 118.1 | 184.6 | 1971 | Malaysia | 495.4 | 4346.4 | 1986 |
Uruguay | 13765.0 | 774.6 | 1990 | Mongolia | 2471.5 | 48.4 | 1985 |
Albania | 1790.9 | 711.9 | 1992 | Nepal | 62.7 | 159.8 | 1988 |
Armenia | 3431.5 | 111.9 | 1995 | Pakistan | 152.2 | 2325.5 | 1977 |
Azerbaijan | 16257.0 | 78.1 | 1990 | Philippines | 2116.2 | 1312.2 | 1971 |
Belarus | 29223.0 | 202.4 | 1993 | Singapore | 165.2 | 1312.7 | 1978 |
Bosnia and Herzegovina | 2549.3 | 486.7 | 1992 | Sri Lanka | 230.2 | 295.6 | 1981 |
Bulgaria | 28932.0 | 174.5 | 1970 | Thailand | 2494.6 | 5282.6 | 1983 |
Croatia | 9971.6 | 298.5 | 1990 | China | 54716.0 | 238844.0 | 1995 |
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Nadimi, R., Tokimatsu, K. (2019). Renewable Energy Substitution Model and Environmental Preservation. In: Hu, A., Matsumoto, M., Kuo, T., Smith, S. (eds) Technologies and Eco-innovation towards Sustainability II. Springer, Singapore. https://doi.org/10.1007/978-981-13-1196-3_22
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