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Global competition dynamics of fossil fuels and renewable energy under climate policies and peak oil: A behavioural model

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  • Zeppini, Paolo
  • van den Bergh, Jeroen C.J.M.

Abstract

We develop a stochastic decision model to analyse the global competitive dynamics of fossil fuels and renewable energy. It describes coal, oil/gas, solar and wind. These differ not only in pollution intensities but also in profitability and innovation potential. The model accounts for the effect of learning curves, path-dependence and climate policies. Adoption shares endogenously affect agents' utility through increasing returns to adoption, learning, and a ‘peak oil’ capacity constraint. We find that peak oil induces a transition to coal rather than renewable energy, which worsens climate change. By introducing climate policies - such as a carbon tax, market adoption or R&D subsidies for renewables, and eliminating existing subsidies for fossil fuels - we identify potential transition patterns to a low-carbon energy system. Model analysis clarifies two main features of climate policies: which ones solve the climate problem, i.e. do not surpass the critical carbon budget; and how uncertain or variable are final market shares of energy sources.

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  • Zeppini, Paolo & van den Bergh, Jeroen C.J.M., 2020. "Global competition dynamics of fossil fuels and renewable energy under climate policies and peak oil: A behavioural model," Energy Policy, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:enepol:v:136:y:2020:i:c:s0301421519304859
    DOI: 10.1016/j.enpol.2019.110907
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    3. Sanjit Dhami & Paolo Zeppini, 2024. "Green Technology Adoption under Uncertainty, Increasing Returns, and Complex Adaptive Dynamics," GREDEG Working Papers 2024-20, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
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    8. Yuexiang Yang & Xiaoyu Zheng & Zhen Sun, 2020. "Coal Resource Security Assessment in China: A Study Using Entropy-Weight-Based TOPSIS and BP Neural Network," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    9. Wang, Kai-Hua & Kan, Jia-Min & Qiu, Lianhong & Xu, Shulin, 2023. "Climate policy uncertainty, oil price and agricultural commodity: From quantile and time perspective," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 256-272.
    10. Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
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    14. Sinha, Avik & Tiwari, Sunil & Saha, Tanaya, 2024. "Modeling the behavior of renewable energy market: Understanding the moderation of climate risk factors," Energy Economics, Elsevier, vol. 130(C).
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