Soutenance de thèse de Miguel Rivière
Du 22/02/2021 au 22/02/2021
De 14:00 à 16:00
Détails de l'événement :
Intitulé : “Prospective analysis in the forest sector when facing environmental challenges: insights from large-scale bio-economic modelling”,
La thèse a été réalisée à INRAE au BETA sous la direction de Philippe Delacote et Sylvain Caurla, au sein de l’école doctorale Agriculture, Alimentation, Biologie, Environnement, Santé (ABIES) d’AgroParisTech.
Forest policy increasingly mobilizes the forest sector to address environmental concerns. Owing to the forest sector’s complexity and time scales involved, simulation models are often used as research methods to explore the future. This thesis investigates the contributions of Forest Sector Models (FSM), bio-economic simulation models commonly used for prospective analysis, to this transition.
We first adopt a conceptual perspective and, through a parallel exploration of the early literature in forest economics and the epistemology of model use, we show that forest policy has been, and still is, a strong driver of FSM research, influencing representation processes in models as well as narratives used to drive research. We also highlight that the nature of facts within the forest sector, the local context, data availability and past practices are other important determinants of model-based research. We subsequently review more recent literature to assess the extent to which environmental issues have been addressed. While originally focused on timber production and trade, a majority of the research now focuses on goals such as renewable energy production or the conservation of biodiversity. The treatment of such objectives has however been unequal, and those closer to the models’ original target are treated more often and more deeply. On the contrary, modelling is hindered when economic values are hard to estimate or when models cannot handle spatialized data, hence objectives related to cultural and some regulation services are less commonly studied.
The remainder of the thesis addresses two aspects of climate change, namely mitigation and adaptation, and brings methodological contributions by leveraging two ways of overcoming obstacles to the investigation of environmental objectives with large-scale bio-economic models: model couplings and the consideration of local environmental conditions. Both chapters focus on France, where the diversity of local contexts makes analyses focused on the upstream forest sector relevant, and use the French Forest Sector Model (FFSM).
First, using the FFSM and Hartman’s model for optimal rotations with non-timber amenities, we investigate consequences for forestry and landscapes of management practices aiming at both producing timber and sequestering carbon. We show that, while postponing harvests can increase carbon stocks in the short-term, changes in management regimes and species choice yield additional benefits in the long-term. Over time, these changes lead to more diverse forest landscapes in terms of composition and structure, with potential implications for policy and environmental co-benefits. However, trends show a high level of spatial variability across and within regions, highlighting the importance of considering the local context.
In-situ carbon stocks are however exposed to risks of non-permanence. We assess implications for the forest sector of climate-induced changes in wildfire regimes, as well as implications for model projections of uncertainties related to these changes. To do so, we use a probabilistic model of wildfire activity, which we couple to the FFSM, and we carry out multiple simulations using various radiative forcing levels and different climate models. Although locally significant, wildfires’ impacts remain limited at the sectoral scale. Fires affect a limited amount of the resource every year but in a cumulative manner, and the influence of climate change is mostly witnessed in the latter half of the century. Inter-annual fluctuations in fire activity only marginally propagate to the forest sector, and most uncertainty comes from the choice of climate models and scenarios. Stochasticity in the fire process, although never predominant, accounts for a significant share of uncertainty. These results stress the importance of considering multiple possible outcomes and the inherent variability in environmental processes in large-scale model projections.