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A model-based analysis of biomethane production in the Netherlands and the effectiveness of the subsidization policy under uncertainty

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  • Eker, Sibel
  • van Daalen, Els

Abstract

Biomethane is a renewable alternative to natural gas. It has the potential to increase the sustainability of the energy system and to help deal with supply problems. However, several factors make the future of biomethane production complex and uncertain, such as resource availability, demand, capacity installation, profitability and the competition between the biomethane and electricity sectors for sharing the available biogas and biomass resources. In this research, we study the dynamics of the Dutch biomethane production and analyze the effects of subsidization policy with a system dynamics model. The policy is tested under uncertainty with respect to three conflicting objectives, namely maximizing production and emission reduction, and minimizing costs. According to the results, the subsidization is crucial to develop biomethane production, and the performance of the policy is enhanced in terms of robustness and of meeting all three objectives satisfactorily when the policy is implemented for a long time, with relatively low subsidy prices. Besides, the subsidization policy is found to be most vulnerable to the producers’ uncertain investment response to profitability. In future research, different policy options such as subsidizing other biomass-based renewable energy options and policies affecting the biomethane demand can be tested.

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  • Eker, Sibel & van Daalen, Els, 2015. "A model-based analysis of biomethane production in the Netherlands and the effectiveness of the subsidization policy under uncertainty," Energy Policy, Elsevier, vol. 82(C), pages 178-196.
    • Handle: RePEc:eee:enepol:v:82:y:2015:i:c:p:178-196
    DOI: 10.1016/j.enpol.2015.03.019
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    7. Moallemi, Enayat A. & Elsawah, Sondoss & Ryan, Michael J., 2020. "Robust decision making and Epoch–Era analysis: A comparison of two robustness frameworks for decision-making under uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    8. Patrizio, P. & Chinese, D., 2016. "The impact of regional factors and new bio-methane incentive schemes on the structure, profitability and CO2 balance of biogas plants in Italy," Renewable Energy, Elsevier, vol. 99(C), pages 573-583.
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    10. Hidayatno, Akhmad & Jafino, Bramka Arga & Setiawan, Andri D. & Purwanto, Widodo Wahyu, 2020. "When and why does transition fail? A model-based identification of adoption barriers and policy vulnerabilities for transition to natural gas vehicles," Energy Policy, Elsevier, vol. 138(C).
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    14. Horschig, Thomas & Adams, P.W.R. & Gawel, Erik & Thrän, Daniela, 2018. "How to decarbonize the natural gas sector: A dynamic simulation approach for the market development estimation of renewable gas in Germany," Applied Energy, Elsevier, vol. 213(C), pages 555-572.
    15. Anna Pääkkönen & Kalle Aro & Pami Aalto & Jukka Konttinen & Matti Kojo, 2019. "The Potential of Biomethane in Replacing Fossil Fuels in Heavy Transport—A Case Study on Finland," Sustainability, MDPI, vol. 11(17), pages 1-19, August.
    16. Li, Yanan & Lin, Jun & Qian, Yanjun & Li, Dehong, 2023. "Feed-in tariff policy for biomass power generation: Incorporating the feedstock acquisition process," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1113-1132.
    17. Moallemi, Enayat A. & de Haan, Fjalar & Kwakkel, Jan & Aye, Lu, 2017. "Narrative-informed exploratory analysis of energy transition pathways: A case study of India's electricity sector," Energy Policy, Elsevier, vol. 110(C), pages 271-287.
    18. Kwakkel, J.H. & Cunningham, S.C., 2016. "Improving scenario discovery by bagging random boxes," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 124-134.
    19. Hu, Xing & Yu, Shiwei & Fang, Xu & Ovaere, Marten, 2023. "Which combinations of renewable energy policies work better? Insights from policy text synergies in China," Energy Economics, Elsevier, vol. 127(PA).
    20. Mostafaeipour, Ali & Bidokhti, Abbas & Fakhrzad, Mohammad-Bagher & Sadegheih, Ahmad & Zare Mehrjerdi, Yahia, 2022. "A new model for the use of renewable electricity to reduce carbon dioxide emissions," Energy, Elsevier, vol. 238(PA).
    21. Francesco Pasimeni, 2019. "Community-Based Adoption and Diffusion of Micro-Grids: Analysis of the Italian Case with Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(1), pages 1-11.
    22. Auping, Willem L. & Pruyt, Erik & de Jong, Sijbren & Kwakkel, Jan H., 2016. "The geopolitical impact of the shale revolution: Exploring consequences on energy prices and rentier states," Energy Policy, Elsevier, vol. 98(C), pages 390-399.
    23. Tianran Ding & Wouter Achten, 2022. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/352782, ULB -- Universite Libre de Bruxelles.
    24. Christopher Schmid & Thomas Horschig & Alexandra Pfeiffer & Nora Szarka & Daniela Thrän, 2019. "Biogas Upgrading: A Review of National Biomethane Strategies and Support Policies in Selected Countries," Energies, MDPI, vol. 12(19), pages 1-24, October.

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