Accounting for Active Management and Risk Attitude in Forest Sector Models

An Impact Study on French Forests


Given the importance of anthropogenic determinants in forest ecosystems within Europe, the objective of this paper is to link the evidence arising from biological models to socio-economic determinants, where the expected returns of forest investments represent the main driver. A micro-economic area allocation module is therefore coupled with an inventory-based forest dynamics module and a partial-equilibrium market module in a national-level forest sector model for France (FFSM++). Running long-term scenarios (until 2100), we show the implication of an active management policy on forest composition: when the most profitable option drives forest investments, coniferous forests are generally preferred over broadleaved ones. This result is, however, reappraised when the risk aversion of forest owners is explicitly considered in the model, given the higher risk associated with the former. We further show the strong stability of forest ecosystems that, due to the very long cycles, undergoes very small variations in volume stocks, even in scenarios where the initial forest regeneration is strongly influenced.

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  1. 1.

    The structure of the forest trade balance in France is strongly oriented toward net exports of primary products (215 Million USD, with exports 2.4 times the imports) and net import of transformed products (1912 Million USD, with imports 2.0 times the exports) [16].

  2. 2.

    This scenario is actually available in the Supplementary Material as areaIncrease.

  3. 3.

    Those forest/agriculture interactions in a context of climate change will be investigated in a future study within the framework of the ORACLE project. This study thus represents the first, yet necessary step in a broader analysis.

  4. 4.

    Given the very high fragmentation of the forest property in France (the average forest size by management unit is 4.44 ha [17]), we consider forest managers as price takers and myopic in terms of equilibrium effects: they know the current prices and the forecasted prices driven by exogenous factors like climate change, and they form heterogeneous expectations based on them, but they do not know the other forest managers’ expectations and hence they do not know how the sum of the expectations may influence the actual price in the future. They do, however, update their expectations using the newly available information as the model progress. In this regard, economic agents (here the forest managers) have what [30] would call “adaptative expectations”.

  5. 5.

    A proper comparison of gross margins would necessarily include information about the cost aspects in the expected returns, whereas the area allocation module currently only works with information about the revenue aspects, assuming similar costs between forest types. Using the supply function as an indicator of marginal costs may be a way to deal with this issue.

  6. 6.

    The full set of results, including regional ones, is available in the digital archive that accompanies this paper.

    Results for forest dynamics and markets are available in the “Complete model output” folder in the files “/output_{scenario_name}/results/forestData.csv” and “data/output_{scenario_name}/results/productData.csv”, respectively, and as pre-formatted tables and charts in file “results_complete.pdf”.

    Input data is located in the corresponding folder in the “ffsmInput.ods” spreadsheet and in the gis maps under “gis”.

    The complete source code of the model is available under a permissive open-source license (a modified GPL licence) under “Model source code”.

  7. 7.

    The active regeneration rate a R R is set to 0.5 here for all scenarios.

  8. 8.

    Since Eq. 10 is linear on r A, the average expected returns do not change because of a direct increase in its variance. However, Eq. 4 is not linear, and compared with a homogeneous risk aversion and a preference for coniferous forests, increasing variance leads to some switches to broadleaved forests that, in turn, affect coniferous volumes (−), prices(+) and, as a result, expected returns (+). However, we found this effect to be very marginal.

  9. 9.

    One of the reasons for this dualism in forest modelling is that the tools used are themselves different. Ecologists often use a general programming approach to build their models (C++, Matlab, Python, etc.), whereas economists often use programmes specialized in solving equilibrium problems like GAMS [6]. Our approach has been to instead use a generic programming language (C++) that gives us the flexibility required to build a complete forest dynamics and area allocation module with specialized software libraries, namely IPOPT [46], ADOL-C. [45], and ColPack [18], used to solve the Samuelson equilibrium and therefore build the market module.


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This work was supported by the French National Research Agency through the ARBRE Laboratory of Excellence, part of the “Investissements d’Avenir” Programme (ANR 11 – LABX-0002-01) and the ORACLE project (“Opportunities and Risks of Agrosystems & forests in response to CLimate, socio-economic and policy changEs in France (and Europe)” - ANR-10-CEPL-011).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Correspondence to Antonello Lobianco.

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Appendix A: Model notation

Appendix A: Model notation

Table 6 Commonly used index symbols
Table 7 Variables

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Lobianco, A., Delacote, P., Caurla, S. et al. Accounting for Active Management and Risk Attitude in Forest Sector Models. Environ Model Assess 21, 391–405 (2016).

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  • Forest sector modelling
  • Investments
  • Risk-aversion