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Has the Fukushima accident influenced short-term consumption in the evolution of nuclear energy? An analysis of the world and seven leading countries

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  • Furlan, Claudia
  • Guidolin, Mariangela
  • Guseo, Renato

Abstract

In 2013 registered nuclear power consumption in several countries, including France, Germany, and other OECD members, declined. In this paper, we focus on nuclear consumption leaders and explore, through diffusion models, whether and to what extent Fukushima accident had a short-term effect on these countries' consumption dynamics. Safety checks, performed after the accident caused temporary shutdowns in production but not all of them were significant enough to modify nuclear energy evolution. Then, we compared the evolutionary behavior estimated through the entire time series and that obtained by excluding the last three observations (2011–2013): what would the forecasts have been before Fukushima? Significant short-term effects were identified in 2011–2013 at the global level, for France, and South Korea, while they have not been identified for the US, Germany, and Russia. About the medium-term evolution predicted by the models, we identified countries with declining consumption (the US, France, Germany and South Korea) and with increasing consumption (China, Russia, and Canada). At the global level a declining trend is predicted.

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  • Furlan, Claudia & Guidolin, Mariangela & Guseo, Renato, 2016. "Has the Fukushima accident influenced short-term consumption in the evolution of nuclear energy? An analysis of the world and seven leading countries," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 37-49.
  • Handle: RePEc:eee:tefoso:v:107:y:2016:i:c:p:37-49
    DOI: 10.1016/j.techfore.2016.04.004
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    as
    1. Csereklyei, Zsuzsanna, 2014. "Measuring the impact of nuclear accidents on energy policy," Ecological Economics, Elsevier, vol. 99(C), pages 121-129.
    2. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    3. Brandt, Adam R., 2010. "Review of mathematical models of future oil supply: Historical overview and synthesizing critique," Energy, Elsevier, vol. 35(9), pages 3958-3974.
    4. Glaser, Alexander, 2011. "After Fukushima: Preparing for a More Uncertain Future of Nuclear Power," The Electricity Journal, Elsevier, vol. 24(6), pages 27-35, July.
    5. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    6. Thomas, Steve, 2012. "What will the Fukushima disaster change?," Energy Policy, Elsevier, vol. 45(C), pages 12-17.
    7. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    8. Guseo, Renato, 2011. "Worldwide cheap and heavy oil productions: A long-term energy model," Energy Policy, Elsevier, vol. 39(9), pages 5572-5577, September.
    9. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    10. Bernard Pras & Gilles Laurent & Gary L. Lilien, 1994. "Research Traditions in Marketing," Post-Print halshs-00150675, HAL.
    11. Dalla Valle, Alessandra & Furlan, Claudia, 2011. "Forecasting accuracy of wind power technology diffusion models across countries," International Journal of Forecasting, Elsevier, vol. 27(2), pages 592-601, April.
    12. Dalla Valle, Alessandra & Furlan, Claudia, 2011. "Forecasting accuracy of wind power technology diffusion models across countries," International Journal of Forecasting, Elsevier, vol. 27(2), pages 592-601.
    13. Huenteler, Joern & Schmidt, Tobias S. & Kanie, Norichika, 2012. "Japan's post-Fukushima challenge – implications from the German experience on renewable energy policy," Energy Policy, Elsevier, vol. 45(C), pages 6-11.
    14. Csereklyei, Z., 2014. "Measuring the Impact of Nuclear Accidents on Energy Policy," 2014 Conference (58th), February 4-7, 2014, Port Macquarie, Australia 165825, Australian Agricultural and Resource Economics Society.
    15. Boccard, Nicolas, 2014. "The cost of nuclear electricity: France after Fukushima," Energy Policy, Elsevier, vol. 66(C), pages 450-461.
    16. Dalla Valle, Alessandra & Furlan, Claudia, 2014. "Diffusion of nuclear energy in some developing countries," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 143-153.
    17. Renato Guseo & Alessandra Valle, 2005. "Oil and gas depletion: Diffusion models and forecasting under strategic intervention," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(3), pages 375-387, December.
    18. Guseo, Renato & Mortarino, Cinzia & Darda, Md Abud, 2015. "Homogeneous and heterogeneous diffusion models: Algerian natural gas production," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 366-378.
    19. Meade, Nigel & Islam, Towhidul, 2015. "Modelling European usage of renewable energy technologies for electricity generation," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 497-509.
    20. Esteban, Miguel & Portugal-Pereira, Joana, 2014. "Post-disaster resilience of a 100% renewable energy system in Japan," Energy, Elsevier, vol. 68(C), pages 756-764.
    21. Guseo, Renato & Guidolin, Mariangela, 2015. "Heterogeneity in diffusion of innovations modelling: A few fundamental types," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 514-524.
    22. Albert C. Bemmaor & Janghyuk Lee, 2002. "The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates," Marketing Science, INFORMS, vol. 21(2), pages 209-220, November.
    23. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    24. Hayashi, Masatsugu & Hughes, Larry, 2013. "The Fukushima nuclear accident and its effect on global energy security," Energy Policy, Elsevier, vol. 59(C), pages 102-111.
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    5. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    6. Francesca Bitonti & Angelo Mazza & Salvatore Strozza, 2021. "Could the bass model be applied to Italian emigration?," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(3), pages 5-16, July-Sept.
    7. Nam, Hoseok & Konishi, Satoshi & Nam, Ki-Woo, 2021. "Comparative analysis of decision making regarding nuclear policy after the Fukushima Dai-ichi Nuclear Power Plant Accident: Case study in Germany and Japan," Technology in Society, Elsevier, vol. 67(C).
    8. Ding, Song & Li, Ruojin & Wu, Shu & Zhou, Weijie, 2021. "Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 298(C).

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