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Optimal Transition to Renewable Energy with Threshold of Irreversible Pollution

Author

Listed:
  • Noël Bonneuil

    (INED - Institut national d'études démographiques, EHESS - École des hautes études en sciences sociales)

  • Raouf Boucekkine

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

When cheap fossil energy is polluting and pollutant no longer absorbed beyond a certain concentration, there is a moment when the introduction of a cleaner renewable energy, although onerous, is optimal with respect to inter-temporal utility. The cleaner technology is adopted either instantaneously or gradually at a controlled rate. The problem of optimum under viability constraints is 6-dimensional under a continuous-discrete dynamic controlled by energy consumption and investment into production of renewable energy. Viable optima are obtained either with gradual or with instantaneous adoption. A longer time horizon increases the probability of adoption of renewable energy and the time for starting this adoption. It also increases maximal utility and the probability to cross the threshold of irreversible pollution. Exploiting a renewable energy starts sooner when adoption is gradual rather than instantaneous. The shorter the period remaining after adoption until the time horizon, the higher the investment into renewable energy.

Suggested Citation

  • Noël Bonneuil & Raouf Boucekkine, 2014. "Optimal Transition to Renewable Energy with Threshold of Irreversible Pollution," Working Papers halshs-01025470, HAL.
    • Handle: RePEc:hal:wpaper:halshs-01025470
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01025470
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    Cited by:

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    2. Jean-François Fagnart & Marc Germain & Benjamin Peeters, 2020. "Can the Energy Transition Be Smooth? A General Equilibrium Approach to the EROEI," Sustainability, MDPI, vol. 12(3), pages 1-29, February.
    3. Wu, Fei & Xiao, Xuanqi & Zhou, Xinyu & Zhang, Dayong & Ji, Qiang, 2022. "Complex risk contagions among large international energy firms: A multi-layer network analysis," Energy Economics, Elsevier, vol. 114(C).
    4. Noël Bonneuil & Raouf Boucekkine, 2016. "Viable Nash Equilibria in the Problem of Common Pollution," AMSE Working Papers 1624, Aix-Marseille School of Economics, France.
    5. Germain, Marc, 2020. "Limits to growth and structural change," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 204-221.
    6. Barbosa, Luciana & Nunes, Cláudia & Rodrigues, Artur & Sardinha, Alberto, 2020. "Feed-in tariff contract schemes and regulatory uncertainty," European Journal of Operational Research, Elsevier, vol. 287(1), pages 331-347.
    7. Haichao Wang & Giulia Di Pietro & Xiaozhou Wu & Risto Lahdelma & Vittorio Verda & Ilkka Haavisto, 2018. "Renewable and Sustainable Energy Transitions for Countries with Different Climates and Renewable Energy Sources Potentials," Energies, MDPI, vol. 11(12), pages 1-32, December.
    8. Vardar, N. Baris, 2024. "Optimal taxation of nonrenewable resources during clean energy transition: A general equilibrium approach," Mathematical Social Sciences, Elsevier, vol. 130(C), pages 10-23.
    9. Anastasiia Zaremba & Ekaterina Gromova & Anna Tur, 2020. "A Differential Game with Random Time Horizon and Discontinuous Distribution," Mathematics, MDPI, vol. 8(12), pages 1-21, December.
    10. Ekaterina Gromova & Anastasiia Zaremba & Shimai Su, 2021. "Time-Consistency of an Imputation in a Cooperative Hybrid Differential Game," Mathematics, MDPI, vol. 9(15), pages 1-14, August.
    11. Dmitry Gromov & Ekaterina Gromova, 2017. "On a Class of Hybrid Differential Games," Dynamic Games and Applications, Springer, vol. 7(2), pages 266-288, June.
    12. Arega Getaneh Abate & Rosana Riccardi & Carlos Ruiz, 2021. "Dynamic tariff-based demand response in retail electricity market under uncertainty," Papers 2105.03405, arXiv.org, revised Nov 2024.
    13. Marc Germain, 2020. "Limits to growth and structural change," Post-Print hal-03129992, HAL.
    14. Gärttner, Johannes & Flath, Christoph M. & Weinhardt, Christof, 2018. "Portfolio and contract design for demand response resources," European Journal of Operational Research, Elsevier, vol. 266(1), pages 340-353.

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    Keywords

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    JEL classification:

    • Q30 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - General
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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