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The trend in current and near future energy consumption from a statistical perspective

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  • Kadoshin, Shiro
  • Nishiyama, Takashi
  • Ito, Toshihide

Abstract

Energy consumption has increased remarkably over the past half century mainly due to increasing population and economic development. The influences of these two factors are considered. In most developed countries, such as Japan, France, Germany and Korea, the growth rate of energy consumption is due to economic development. The effects of population in Germany and Japan will substantially decline. In the USA, it is due to both factors as well as in the developing countries, such as China, India, Indonesia and Latin America. Economic success is more effective than increasing population in China, India and Indonesia, while both factors are roughly equal in Latin America. In Africa, though the growth rate depends on the effect of increasing population, its contribution to world energy consumption is small. On a worldwide scale, the growth rate of energy consumption will be affected by improving standards of living.

Suggested Citation

  • Kadoshin, Shiro & Nishiyama, Takashi & Ito, Toshihide, 2000. "The trend in current and near future energy consumption from a statistical perspective," Applied Energy, Elsevier, vol. 67(4), pages 407-417, December.
    • Handle: RePEc:eee:appene:v:67:y:2000:i:4:p:407-417
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    6. Wan, Kevin K.W. & Tang, H.L. & Yang, Liu & Lam, Joseph C., 2008. "An analysis of thermal and solar zone radiation models using an Angstrom–Prescott equation and artificial neural networks," Energy, Elsevier, vol. 33(7), pages 1115-1127.
    7. Yoo, Seung-Hoon, 2006. "Causal relationship between coal consumption and economic growth in Korea," Applied Energy, Elsevier, vol. 83(11), pages 1181-1189, November.
    8. Kroeze, Carolien & Vlasblom, Jaklien & Gupta, Joyeeta & Boudri, Christiaan & Blok, Kornelis, 2004. "The power sector in China and India: greenhouse gas emissions reduction potential and scenarios for 1990-2020," Energy Policy, Elsevier, vol. 32(1), pages 55-76, January.
    9. Askar A. Akaev & Olga I. Davydova, 2021. "A Mathematical Description of Selected Energy Transition Scenarios in the 21st Century, Intended to Realize the Main Goals of the Paris Climate Agreement," Energies, MDPI, vol. 14(9), pages 1-28, April.
    10. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    11. Bian, Yiwen & Yang, Feng, 2010. "Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon's entropy," Energy Policy, Elsevier, vol. 38(4), pages 1909-1917, April.
    12. Tso, Geoffrey K.F & Yau, Kelvin K.W, 2003. "A study of domestic energy usage patterns in Hong Kong," Energy, Elsevier, vol. 28(15), pages 1671-1682.
    13. Yang, Liu & Lam, Joseph C. & Tsang, C.L., 2008. "Energy performance of building envelopes in different climate zones in China," Applied Energy, Elsevier, vol. 85(9), pages 800-817, September.
    14. You, Jing, 2013. "China's challenge for decarbonized growth: Forecasts from energy demand models," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 652-668.
    15. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
    16. Dahl, Jaimee K & Buechler, Karen J & Finley, Ryan & Stanislaus, Timothy & Weimer, Alan W & Lewandowski, Allan & Bingham, Carl & Smeets, Alexander & Schneider, Adrian, 2004. "Rapid solar-thermal dissociation of natural gas in an aerosol flow reactor," Energy, Elsevier, vol. 29(5), pages 715-725.
    17. Paul-Razvan Șerban & Monica Dumitrașcu & Bianca Mitrică & Ines Grigorescu & Irena Mocanu & Gheorghe Kucsicsa & Alexandra Vrînceanu & Cristina Dumitrică, 2020. "The Estimation of Regional Energy Consumption Based on the Energy Consumption Rate at National Level. Case Study: The Romanian Danube Valley," Energies, MDPI, vol. 13(16), pages 1-18, August.
    18. Azadeh, A. & Asadzadeh, S.M. & Mirseraji, G.H. & Saberi, M., 2015. "An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 47-63.

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