IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04236386.html
   My bibliography  Save this paper

Ambiguity, value of information and forest rotation decision under storm risk

Author

Listed:
  • Patrice Loisel

    (MISTEA - Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

  • Marielle Brunette

    (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Stéphane Couture

    (MIAT INRAE - Unité de Mathématiques et Informatique Appliquées de Toulouse - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

Storm is a major risk in forestry. However, due to the more or less pessimistic scenarios of future climate change, storm frequency is now ambiguous and only partially known (i.e., scenario ambiguity). Furthermore, within each scenario, the quantification of storm frequency is also ambiguous due to the differences in risk quantification by experts, creating a second level of ambiguity (i.e., frequency ambiguity). In such an ambiguous context, knowledge of the future climate through accurate information about this risk is fundamental and can be of significant value. In this paper, we question how ambiguity and ambiguity aversion affect forest management, in particular, optimal cutting age. Using a classical Faustmann framework of forest rotation decisions, we compare three different situations: risk, scenario ambiguity and frequency ambiguity. We show that risk and risk aversion significantly reduce the optimal cutting age. We also show that both scenario and frequency ambiguities reinforce the effect of risk. Inversely, ambiguity aversion has no effect. The value of information that resolves scenario ambiguity is high, whereas it is null for frequency ambiguity.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Patrice Loisel & Marielle Brunette & Stéphane Couture, 2023. "Ambiguity, value of information and forest rotation decision under storm risk," Post-Print hal-04236386, HAL.
  • Handle: RePEc:hal:journl:hal-04236386
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-04236386
    as

    Download full text from publisher

    File URL: https://hal.inrae.fr/hal-04236386/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Morag F. Macpherson & Adam Kleczkowski & John R. Healey & Nick Hanley, 2018. "The Effects of Disease on Optimal Forest Rotation: A Generalisable Analytical Framework," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 70(3), pages 565-588, July.
    2. Arthur Snow, 2010. "Ambiguity and the value of information," Journal of Risk and Uncertainty, Springer, vol. 40(2), pages 133-145, April.
    3. Loisel, Patrice, 2014. "Impact of storm risk on Faustmann rotation," Forest Policy and Economics, Elsevier, vol. 38(C), pages 191-198.
    4. Eric Nazindigouba KERE & Jérôme FONCEL & Marielle BRUNETTE, 2014. "Attitude towards Risk and Production Decision: An Empirical analysis on French private forest owners," Working Papers 201410, CERDI.
    5. Rupert Seidl & Mart-Jan Schelhaas & Werner Rammer & Pieter Johannes Verkerk, 2014. "Increasing forest disturbances in Europe and their impact on carbon storage," Nature Climate Change, Nature, vol. 4(9), pages 806-810, September.
    6. Camerer, Colin & Weber, Martin, 1992. "Recent Developments in Modeling Preferences: Uncertainty and Ambiguity," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 325-370, October.
    7. Philippe Bontems & Alban Thomas, 2000. "Information Value and Risk Premium in Agricultural Production: The Case of Split Nitrogen Application for Corn," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(1), pages 59-70.
    8. Michael Hoy & Richard Peter & Andreas Richter, 2014. "Take-up for genetic tests and ambiguity," Journal of Risk and Uncertainty, Springer, vol. 48(2), pages 111-133, April.
    9. Diego C. Nocetti, 2018. "Ambiguity and the value of information revisited," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(1), pages 25-38, May.
    10. Couture, Stéphane & Cros, Marie-Josée & Sabbadin, Régis, 2016. "Risk aversion and optimal management of an uneven-aged forest under risk of windthrow: A Markov decision process approach," Journal of Forest Economics, Elsevier, vol. 25(C), pages 94-114.
    11. Reed, William J., 1984. "The effects of the risk of fire on the optimal rotation of a forest," Journal of Environmental Economics and Management, Elsevier, vol. 11(2), pages 180-190, June.
    12. Sandrine Brèteau-Amores & Rasoul Yousefpour & Marc Hanewinkel & Mathieu Fortin, 2020. "Composition diversification vs. structure diversification: How to conciliate timber production and carbon sequestration objectives under drought and windstorm risks in forest ecosystems," Working Papers of BETA 2020-31, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    13. Marielle Brunette & Stéphane Couture & Jacques Laye, 2015. "Optimising forest management under storm risk with a Markov decision process model," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 4(2), pages 141-163, July.
    14. Rupert Seidl & Mart-Jan Schelhaas & Werner Rammer & Pieter Johannes Verkerk, 2014. "Correction: Corrigendum: Increasing forest disturbances in Europe and their impact on carbon storage," Nature Climate Change, Nature, vol. 4(10), pages 930-930, October.
    15. Ning Du & David V. Budescu, 2005. "The Effects of Imprecise Probabilities and Outcomes in Evaluating Investment Options," Management Science, INFORMS, vol. 51(12), pages 1791-1803, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Patrice Loisel & Marielle Brunette & Stéphane Couture, 2020. "Insurance and Forest Rotation Decisions Under Storm Risk," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(2), pages 347-367, July.
    2. Peter, Richard & Ying, Jie, 2020. "Do you trust your insurer? Ambiguity about contract nonperformance and optimal insurance demand," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 938-954.
    3. Thomas, J. & Brunette, M. & Leblois, A., 2022. "The determinants of adapting forest management practices to climate change: Lessons from a survey of French private forest owners," Forest Policy and Economics, Elsevier, vol. 135(C).
    4. Laure Cabantous & Denis Hilton & Howard Kunreuther & Erwann Michel-Kerjan, 2011. "Is imprecise knowledge better than conflicting expertise? Evidence from insurers’ decisions in the United States," Journal of Risk and Uncertainty, Springer, vol. 42(3), pages 211-232, June.
    5. Julie Thomas & Marielle Brunette & Antoine Leblois, 2021. "Adapting forest management practices to climate change : Lessons from a survey of French private forest owners," Working Papers hal-03142772, HAL.
    6. Jarisch, Isabelle & Bödeker, Kai & Bingham, Logan Robert & Friedrich, Stefan & Kindu, Mengistie & Knoke, Thomas, 2022. "The influence of discounting ecosystem services in robust multi-objective optimization – An application to a forestry-avocado land-use portfolio," Forest Policy and Economics, Elsevier, vol. 141(C).
    7. Couture, Stéphane & Cros, Marie-Josée & Sabbadin, Régis, 2016. "Risk aversion and optimal management of an uneven-aged forest under risk of windthrow: A Markov decision process approach," Journal of Forest Economics, Elsevier, vol. 25(C), pages 94-114.
    8. Christoph Bühren & Fabian Meier & Marco Pleßner, 2023. "Ambiguity aversion: bibliometric analysis and literature review of the last 60 years," Management Review Quarterly, Springer, vol. 73(2), pages 495-525, June.
    9. Diego C. Nocetti, 2018. "Ambiguity and the value of information revisited," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(1), pages 25-38, May.
    10. Cary Deck & Sebastian Ebert & Andreas Richter, 2018. "Special issue in honor of Harris Schlesinger: New developments in the study of risk preferences," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(1), pages 1-4, May.
    11. Stéphane Couture & Stéphane Lemarié & Sabrina Teyssier & Pascal Toquebeuf, 2024. "The value of information under ambiguity: a theoretical and experimental study on pest management in agriculture," Theory and Decision, Springer, vol. 96(1), pages 19-47, February.
    12. Lahno, Amrei M., 2014. "Social anchor effects in decision-making under ambiguity," Discussion Papers in Economics 20960, University of Munich, Department of Economics.
    13. Deegen, Peter & Matolepszy, Kai, 2015. "Economic balancing of forest management under storm risk, the case of the Ore Mountains (Germany)," Journal of Forest Economics, Elsevier, vol. 21(1), pages 1-13.
    14. Ali Jahani & Maryam Saffariha, 2022. "Tree failure prediction model (TFPM): machine learning techniques comparison in failure hazard assessment of Platanus orientalis in urban forestry," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 881-898, January.
    15. Rakotoarison, Hanitra & Loisel, Patrice, 2016. "The Faustmann model under storm risk and price uncertainty: A case study of European beech in Northwestern France," MPRA Paper 85114, University Library of Munich, Germany.
    16. Luo, Di & Mishra, Tapas & Yarovaya, Larisa & Zhang, Zhuang, 2021. "Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    17. Salmon, Timothy C. & Shniderman, Adam, 2019. "Ambiguity in criminal punishment," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 361-376.
    18. Noemi Pace & Giuseppe Attanasi & Christian Gollier & Aldo Montesano, 2012. "Eliciting ambiguity aversion in unknown and in compound lotteries: A KMM experimental approach," Working Papers 2012_23, Department of Economics, University of Venice "Ca' Foscari".
    19. Roxane Bricet, 2018. "Preferences for information precision under ambiguity," THEMA Working Papers 2018-09, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    20. Keck, Steffen & Diecidue, Enrico & Budescu, David V., 2014. "Group decisions under ambiguity: Convergence to neutrality," Journal of Economic Behavior & Organization, Elsevier, vol. 103(C), pages 60-71.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04236386. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.