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Investing in energy forestry under uncertainty

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  • Di Corato, Luca
  • Gazheli, Ardjan
  • Lagerkvist, Carl-Johan

Abstract

Farmer's decisions to invest in renewable energy sources can contribute to lower greenhouse gas and mitigate climate change. However, it remains unclear how associated high sunk establishment costs, long-term commitment, highly uncertain net returns, and policy induced incentives could drive farmer's decision to afforest agricultural land. A real option model is used to theoretically frame the decision to switch from agriculture to energy forestry. Optimal investment timing is modeled and the functioning of government subsidies offered to speed up the switch to energy forestry is analyzed. The empirical analysis examines the establishment of new short-rotation coppice willow stands in Central East Sweden. It is shown that in the presence of volatile agricultural profits and high establishment costs, subsidies are needed to accelerate investment. We then examine the case of the municipality of Enköping and show that the combination of governmental subsidies for energy forestry with compensation for sewage sludge treatment provides an effective stimulus to investment in new willow stands which also has environmental benefits.

Suggested Citation

  • Di Corato, Luca & Gazheli, Ardjan & Lagerkvist, Carl-Johan, 2013. "Investing in energy forestry under uncertainty," Forest Policy and Economics, Elsevier, vol. 34(C), pages 56-64.
    • Handle: RePEc:eee:forpol:v:34:y:2013:i:c:p:56-64
    DOI: 10.1016/j.forpol.2013.06.001
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    References listed on IDEAS

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    Cited by:

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    2. Kostrova, Alisa & Britz, Wolfgang & Finger, Robert & Djanibekov, Utkur, 2016. "Real Options Approach And Stochastic Programming In Farm Level Analysis: The Case Of Short-Rotation Coppice Cultivation," 56th Annual Conference, Bonn, Germany, September 28-30, 2016 244864, German Association of Agricultural Economists (GEWISOLA).
    3. Luca Di Corato & Dimitrios Zormpas, 2022. "Investment in farming under uncertainty and decoupled support: a real options approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 876-909.
    4. Salles, Thiago Taglialegna & Nogueira, Denismar Alves & Beijo, Luiz Alberto & Silva, Liniker Fernandes da, 2019. "Bayesian approach and extreme value theory in economic analysis of forestry projects," Forest Policy and Economics, Elsevier, vol. 105(C), pages 64-71.
    5. Kostrova, Alisa & Britz, Wolfgang & Djanibekov, Utkur & Finger, Robert, 2016. "Monte-Carlo Simulation and Stochastic Programming in Real Options Valuation: the Case of Perennial Energy Crop Cultivation," Discussion Papers 250253, University of Bonn, Institute for Food and Resource Economics.
    6. Di Corato, Luca & Brady, Mark V., 2019. "Passive farming and land development: A real options approach," Land Use Policy, Elsevier, vol. 80(C), pages 32-46.
    7. Min Li & Apurbo Sarkar & Yuge Wang & Ahmed Khairul Hasan & Quanxing Meng, 2022. "Evaluating the Impact of Ecological Property Rights to Trigger Farmers’ Investment Behavior—An Example of Confluence Area of Heihe Reservoir, Shaanxi, China," Land, MDPI, vol. 11(3), pages 1-23, February.
    8. Gazheli, Ardjan & van den Bergh, Jeroen, 2018. "Real options analysis of investment in solar vs. wind energy: Diversification strategies under uncertain prices and costs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2693-2704.
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    11. Kozlova, Mariia, 2017. "Real option valuation in renewable energy literature: Research focus, trends and design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 180-196.

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    More about this item

    Keywords

    Real options; Investment analysis; Short-rotation willow coppice; Bioenergy policy;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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