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Forecasting energy needs with logistics

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  • Modis, Theodore

Abstract

The logistic function is used to forecast energy consumed worldwide and oil production in the U.S. The logistic substitution model is used to describe the energy mix since 1965 presenting a picture significantly different from the one covering the previous 100 years. In the new picture coal gently gains on oil and hydroelectric gains on natural gas even if it is three times smaller. Finally, renewables—wind, geothermal, solar, biomass, and waste—grow exclusively on the expense of nuclear, and are poised to overtake it by the late 2030s. By mid-21st century, coal, oil, and natural gas still remain the main players of comparable size. Hydroelectric has almost doubled in size. The only significant substitution is that of renewables having replaced nuclear albeit remaining at less than a ¼ the size of the other three energy sources. U.S. oil produced by fracking is forecasted to cease by mid-21st century, while oil produced by traditional methods should continue on its slowly declining trend. US oil production is likely to represent less than 1% of the oil consumed worldwide by mid-21st century.

Suggested Citation

  • Modis, Theodore, 2019. "Forecasting energy needs with logistics," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 135-143.
    • Handle: RePEc:eee:tefoso:v:139:y:2019:i:c:p:135-143
    DOI: 10.1016/j.techfore.2018.11.008
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    References listed on IDEAS

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    1. Modis, Theodore, 2017. "A hard-science approach to Kondratieff's economic cycle," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 63-70.
    2. Modis, Theodore, 1994. "Determination of the Uncertainties in S-Curve Logistic Fits," OSF Preprints n53pd, Center for Open Science.
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    1. Leitão, João & Ferreira, Joaquim & Santibanez-González, Ernesto, 2022. "New insights into decoupling economic growth, technological progress and carbon dioxide emissions: Evidence from 40 countries," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "Energy Demand Forecast Models for Commercial Buildings in South Korea," Energies, MDPI, vol. 12(12), pages 1-19, June.
    3. Wu, Xianhua & Deng, Huai & Li, Hua & Guo, Yiming, 2021. "Impact of Energy Structure Adjustment and Environmental Regulation on Air Pollution in China: Simulation and Measurement Research by the Dynamic General Equilibrium Model," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    4. Tan, Xiujie & Wang, Banban & Wei, Jie & Taghizadeh-Hesary, Farhad, 2023. "The role of carbon pricing in achieving energy transition in the Post-COP26 era: Evidence from China's industrial energy conservation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

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