Spatial evaluation of the nuclear power plant installation based on energy demand for sustainable energy policy
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DOI: 10.1007/s10668-023-03061-y
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- Amarawickrama, Himanshu A. & Hunt, Lester C., 2008.
"Electricity demand for Sri Lanka: A time series analysis,"
Energy, Elsevier, vol. 33(5), pages 724-739.
- Himanshu A. Amarawickrama & Lester C Hunt, 2007. "Electricity Demand for Sri Lanka: A Time Series Analysis," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 118, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
- Ustaoglu, E. & Sisman, S. & Aydınoglu, A.C., 2021. "Determining agricultural suitable land in peri-urban geography using GIS and Multi Criteria Decision Analysis (MCDA) techniques," Ecological Modelling, Elsevier, vol. 455(C).
- Ralph L. Keeney, 1987. "An Analysis of the Portfolio of Sites to Characterize for Selecting a Nuclear Repository," Risk Analysis, John Wiley & Sons, vol. 7(2), pages 195-218, June.
- Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
- Niklas Jensen-Eriksen, 2022. "Looking for cheap and abundant power: Business, government and nuclear energy in Finland," Business History, Taylor & Francis Journals, vol. 64(8), pages 1413-1434, October.
- Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
- Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
- Jewell, Jessica & Vetier, Marta & Garcia-Cabrera, Daniel, 2019. "The international technological nuclear cooperation landscape: A new dataset and network analysis," Energy Policy, Elsevier, vol. 128(C), pages 838-852.
- Shahzad, Umer & Doğan, Buhari & Sinha, Avik & Fareed, Zeeshan, 2021.
"Does Export product diversification help to reduce energy demand: Exploring the contextual evidences from the newly industrialized countries,"
Energy, Elsevier, vol. 214(C).
- Umer, Shahzad & Buhari, Dogan & Avik, Sinha & Zeeshan, Fareed, 2020. "Does Export product diversification help to reduce energy demand: Exploring the contextual evidences from the newly industrialized countries," MPRA Paper 103718, University Library of Munich, Germany, revised 2020.
- Erol, İsmail & Sencer, Safiye & Özmen, Aslı & Searcy, Cory, 2014. "Fuzzy MCDM framework for locating a nuclear power plant in Turkey," Energy Policy, Elsevier, vol. 67(C), pages 186-197.
- Pao, Hsiao-Tien, 2006. "Comparing linear and nonlinear forecasts for Taiwan's electricity consumption," Energy, Elsevier, vol. 31(12), pages 2129-2141.
- Kok, Besir & Benli, Hüseyin, 2017. "Energy diversity and nuclear energy for sustainable development in Turkey," Renewable Energy, Elsevier, vol. 111(C), pages 870-877.
- Lior, Noam, 2008. "Energy resources and use: The present situation and possible paths to the future," Energy, Elsevier, vol. 33(6), pages 842-857.
- Peter A. Lang, 2017. "Nuclear Power Learning and Deployment Rates; Disruption and Global Benefits Forgone," Energies, MDPI, vol. 10(12), pages 1-21, December.
- Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
- Roh, Seungkook & Choi, Jae Young & Chang, Soon Heung, 2019. "Modeling of nuclear power plant export competitiveness and its implications: The case of Korea," Energy, Elsevier, vol. 166(C), pages 157-169.
- Kim, Hoseok & Shin, Eui-soon & Chung, Woo-jin, 2011. "Energy demand and supply, energy policies, and energy security in the Republic of Korea," Energy Policy, Elsevier, vol. 39(11), pages 6882-6897.
- Chang, Da-Yong, 1996. "Applications of the extent analysis method on fuzzy AHP," European Journal of Operational Research, Elsevier, vol. 95(3), pages 649-655, December.
- Li, Zhao & Luo, Zujiang & Wang, Yan & Fan, Guanyu & Zhang, Jianmang, 2022. "Suitability evaluation system for the shallow geothermal energy implementation in region by Entropy Weight Method and TOPSIS method," Renewable Energy, Elsevier, vol. 184(C), pages 564-576.
- Javed Mallick, 2021. "Municipal Solid Waste Landfill Site Selection Based on Fuzzy-AHP and Geoinformation Techniques in Asir Region Saudi Arabia," Sustainability, MDPI, vol. 13(3), pages 1-29, February.
- Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
- Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
- Chen, Yuh-Wen & Wang, Chi-Hwang & Lin, Sain-Ju, 2008. "A multi-objective geographic information system for route selection of nuclear waste transport," Omega, Elsevier, vol. 36(3), pages 363-372, June.
- Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
- Peter A. Lang, 2017. "Nuclear power learning and deployment rates: disruption and global benefits forgone," CAMA Working Papers 2017-04, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Lior, Noam, 2010. "Energy resources and use: The present (2008) situation and possible sustainable paths to the future," Energy, Elsevier, vol. 35(6), pages 2631-2638.
- Pius Krütli & Michael Stauffacher & Thomas Flüeler & Roland W. Scholz, 2010. "Functional-dynamic public participation in technological decision-making: site selection processes of nuclear waste repositories," Journal of Risk Research, Taylor & Francis Journals, vol. 13(7), pages 861-875, October.
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Keywords
Nuclear power plant; Energy demand; Spatial evaluation; Geographic information system; Fuzzy analytic hierarchy process;All these keywords.
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