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Recent trends in biogas value chains explained using cooperative game theory

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  • Skovsgaard, Lise
  • Jensen, Ida Græsted

Abstract

In Denmark, since 2014, it has been possible to upgrade biogas to a gas grid and achieve support in line with biogas-based heat and power production. Since then, most new biogas production plants have chosen to upgrade their biogas. In this study, a mixed integer programming model is used to find the optimal biogas value chain, and cooperative game theory is used to understand real world observations compared to this study's results. Specifically, three profit allocation mechanisms are applied to allocate the profit between the heterogeneous owners in the value chain. It is found that Danish biogas plants should use large shares of manure combined with deep litter. Furthermore, it is found that input suppliers have relatively poor bargaining power in the profit allocation negotiations due to poor alternatives. This may explain why livestock farmers tend to receive little payment for their manure, and why they are hesitant to join biogas projects.

Suggested Citation

  • Skovsgaard, Lise & Jensen, Ida Græsted, 2018. "Recent trends in biogas value chains explained using cooperative game theory," Energy Economics, Elsevier, vol. 74(C), pages 503-522.
    • Handle: RePEc:eee:eneeco:v:74:y:2018:i:c:p:503-522
    DOI: 10.1016/j.eneco.2018.06.021
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    Cited by:

    1. Gao, Evelyn & Sowlati, Taraneh & Akhtari, Shaghaygh, 2019. "Profit allocation in collaborative bioenergy and biofuel supply chains," Energy, Elsevier, vol. 188(C).
    2. Mac Clay, Pablo & Sellare, Jorge, 2022. "Value chain transformations in the transition to a sustainable bioeconomy," Discussion Papers 323957, University of Bonn, Center for Development Research (ZEF).
    3. 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.
    4. He, Ke & Ye, Lihong & Li, Fanlue & Chang, Huayi & Wang, Anbang & Luo, Sixuan & Zhang, Junbiao, 2022. "Using cognition and risk to explain the intention-behavior gap on bioenergy production: Based on machine learning logistic regression method," Energy Economics, Elsevier, vol. 108(C).
    5. Olszewski, Robert & Pałka, Piotr & Wendland, Agnieszka & Majdzińska, Karolina, 2021. "Application of cooperative game theory in a spatial context: An example of the application of the community-led local development instrument for the decision support system of biogas plants constructi," Land Use Policy, Elsevier, vol. 108(C).
    6. Bramstoft, Rasmus & Pizarro-Alonso, Amalia & Jensen, Ida Græsted & Ravn, Hans & Münster, Marie, 2020. "Modelling of renewable gas and renewable liquid fuels in future integrated energy systems," Applied Energy, Elsevier, vol. 268(C).
    7. Kari-Anne Lyng & Lise Skovsgaard & Henrik Klinge Jacobsen & Ole Jørgen Hanssen, 2020. "The implications of economic instruments on biogas value chains: a case study comparison between Norway and Denmark," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(8), pages 7125-7152, December.

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    More about this item

    Keywords

    Cooperative game theory; Profit allocation; Mixed integer programming; Biogas; Biomethane; Value chain;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D9 - Microeconomics - - Micro-Based Behavioral Economics
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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