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The importance of introducing spatial heterogeneity in bio-economic forest models: Insights gleaned from FFSM++

Author

Listed:
  • Antonello Lobianco

    (LEF - Laboratoire d'Economie Forestière - INRA - Institut National de la Recherche Agronomique - AgroParisTech)

  • Philippe Delacote

    (LEF - Laboratoire d'Economie Forestière - INRA - Institut National de la Recherche Agronomique - AgroParisTech, CEC - Chaire Economie du Climat - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres)

  • Sylvain Caurla

    (LEF - Laboratoire d'Economie Forestière - INRA - Institut National de la Recherche Agronomique - AgroParisTech)

  • Ahmed Barkaoui

    (LEF - Laboratoire d'Economie Forestière - INRA - Institut National de la Recherche Agronomique - AgroParisTech)

Abstract

Given the importance of anthropogenic determinants in forest ecosystems within Europe, the objective of the FFSM++ model is to link the evidence arising from biological models to socio-economic determinants, where the expected returns of forest investments represent the main drivers. Consequently, an inventory-based discrete-time Markov chain model of the forest resources is coupled with a partial equilibrium model of the market of forest products and with a microeconomic model of allocation of the harvested area to form a national-level forest sector model for France (FFSM++). In this paper, we present the model with emphasis on its spatial aspects, and we show that by only considering environmental heterogeneity and, therefore, the local characteristics of the forest under management, it is possible to realistically model management decisions such as forest investments. In particular, we propose an application that spatialises the forest growth rate, normally reported by inventory sources at the regional level, and we run long-term scenarios (until 2100) in order to simulate the effects on the forest dynamics of a potential increase in coniferous mortality in certain areas due to climate change when interactions between forest management strategies are explicitly considered.

Suggested Citation

  • Antonello Lobianco & Philippe Delacote & Sylvain Caurla & Ahmed Barkaoui, 2015. "The importance of introducing spatial heterogeneity in bio-economic forest models: Insights gleaned from FFSM++," Post-Print hal-01183732, HAL.
    • Handle: RePEc:hal:journl:hal-01183732
    DOI: 10.1016/j.ecolmodel.2015.04.012
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    References listed on IDEAS

    as
    1. Sauquet, Alexandre & Lecocq, Franck & Delacote, Philippe & Caurla, Sylvain & Barkaoui, Ahmed & Garcia, Serge, 2011. "Estimating Armington elasticities for sawnwood and application to the French Forest Sector Model," Resource and Energy Economics, Elsevier, vol. 33(4), pages 771-781.
    2. Sylvain Caurla & Franck Lecocq & Philippe Delacote & Ahmed Barkaoui, 2010. "The French Forest Sector Model: version 1.0. Presentation and theorical foundations," Working Papers - Cahiers du LEF 2010-04, Laboratoire d'Economie Forestiere, AgroParisTech-INRA.
    3. Antonello Lobianco & Philippe Delacote & Sylvain Caurla & Ahmed Barkaoui, 2014. "Introducing forest management in forest sector models: impact of active management and risk attitude on forest resources in the long term," Working Papers - Cahiers du LEF 2014-05, Laboratoire d'Economie Forestiere, AgroParisTech-INRA, revised Apr 2014.
    4. Alexandre Sauquet & Ahmed Barkaoui & Sylvain Caurla & Philippe Delacote & Serge Garcia & Franck Lecocq, 2011. "Estimating Armington elasticities for sawnwood and application to the French Forest Sector Model," Post-Print halshs-00688822, HAL.
    5. Sylvain Caurla & Philippe Delacote & Franck Lecocq & Ahmed Barkaoui, 2013. "Stimulating fuelwood consumption through public policies: an assessment of economic and resource impacts based on the french forest sector model," Post-Print hal-01072295, HAL.
    6. Caurla, Sylvain & Delacote, Philippe & Lecocq, Franck & Barkaoui, Ahmed, 2013. "Stimulating fuelwood consumption through public policies: An assessment of economic and resource impacts based on the French Forest Sector Model," Energy Policy, Elsevier, vol. 63(C), pages 338-347.
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    Citations

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    Cited by:

    1. Lundmark, Robert & Nolander, Carl & Olofsson, Elias, 2021. "Spatial production structure and input choices of forest products in Sweden," Forest Policy and Economics, Elsevier, vol. 128(C).
    2. Miguel Riviere & Sylvain Caurla & Philippe Delacote, 2020. "Evolving Integrated Models From Narrower Economic Tools : the Example of Forest Sector Models," Post-Print hal-02512330, HAL.
    3. Lungarska, Anna & Chakir, Raja, 2018. "Climate-induced Land Use Change in France: Impacts of Agricultural Adaptation and Climate Change Mitigation," Ecological Economics, Elsevier, vol. 147(C), pages 134-154.
    4. Lobianco, Antonello & Caurla, Sylvain & Delacote, Philippe & Barkaoui, Ahmed, 2016. "Carbon mitigation potential of the French forest sector under threat of combined physical and market impacts due to climate change," Journal of Forest Economics, Elsevier, vol. 23(C), pages 4-26.
    5. Huang, Wei, 2019. "Forest condition change, tenure reform, and government-funded eco-environmental programs in Northeast China," Forest Policy and Economics, Elsevier, vol. 98(C), pages 67-74.
    6. Claudio Petucco & Antonello Lobianco & Sylvain Caurla, 2020. "Economic Evaluation of an Invasive Forest Pathogen at a Large Scale : The Case of Ash Dieback in France," Post-Print hal-02625280, HAL.
    7. Miguel Riviere & Sylvain Caurla, 2020. "Representations of the Forest Sector in Economic Models [Les représentations du secteur forestier dans les modèles économiques]," Post-Print hal-03088084, HAL.
    8. Claudio Petucco & Antonello Lobianco & Sylvain Caurla, 2020. "Economic Evaluation of an Invasive Forest Pathogen at a Large Scale : The Case of Ash Dieback in France," Post-Print hal-03639337, HAL.

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    More about this item

    Keywords

    forest sector modelling; spatial model; bio-economic model; forest mortality;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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