We explore the implications of managing forests for the dual purpose of sequestering carbon and producing timber, using a model of the forest sector that includes a Hartman-based representation of forest owners’ behaviour as well as heterogeneity in environmental conditions. We focus on France, where recent policies aim at increasing the carbon sink and where the diversity of forests makes an analysis of spatial dynamics relevant, and we use recent estimates of the shadow price of carbon consistent with the country’s climate commitments. Results suggest that forests may sequester up to 550 MtCO2eq by 2100, driven by changes in harvest levels and species choice, whilst rotation lengths increase overall. A spatial analysis reveals a high spatial variability for these trends, highlighting the importance of considering the local context. Changes in investment patterns affect the spatial distribution of forest cover types: by the end of the century, a majority of regions comprise a larger share of older, multiple-species and mixed-structure forests. Whilst such an evolution may present benefits in terms of biodiversity, ecosystem services provision and resilience, it raises questions regarding the adequacy of such developments with current forest policy, which also aims at increasing harvest levels. An overall mitigation strategy for the forest sector would likely include incentives to energy and material substitution in downstream industries, which we did not consider and may interact with sequestration incentives.

Forest sector models encompass a set of models used for forest-related policy analysis. As representations of a complex human-environment system, they incorporate multiple facts from their target, the forest sector, which is usually understood as comprising forests, forestry and forest industries. Even though they pursue similar goals and display similarities, forest sector models show divergences in their representation of the forest sector. In this paper, we question and discuss the determinants behind the representation of facts in forest sector models, and try to highlight the reasons behind modelling practices. The forest sector’s boundaries are often unclear, and it comprises facts of different natures for which dynamics take place on different time and spatial scales. As a result, modelling practices vary, and both empirical data and theory play varying roles in representing facts. Early models were developed in the 1970s and find their roots in traditional forest economics, the economics of natural resources, econometrics, but also transportation problems and system dynamics. Because they developed within a small but well-connected field, early efforts were influential in shaping current practices. Numerical simulation and scenario analysis are used as means of enquiry into model worlds: in that, forest sector models are a classical example of model use in economics, and they constitute a good example of how simulation models have been developed for decision-support purposes. Forest sector modelling is heavily influenced by its applied uses, and policy contexts shape both questions asked and how facts are introduced in scenario storylines. Understanding the determinants of modelling choices is necessary to ensure sound modelling practices. Forest sector models are now used to address issues wider than timber production. Practices turn to integration into multi-model frameworks to expand the boundaries of the system studied, but also towards the use of qualitative methods as new ways of representing facts, in particular deep changes that quantitative models may not be able to capture.

Under the influence of climate change, wildfire regimes are expected to intensify and expand to new areas, increasing threats to natural and socioeconomic assets. We explore the environmental and economic implications for the forest sector of climate‐induced changes in wildfire regimes. To retain genericity while considering local determinants, we focus on the regional level and take Mediterranean France as an example. Coupling a bioeconomic forest sector model and a model of wildfire activity, we perform spatially explicit simulations under various levels of radiative forcing. By using a probabilistic framework, we also assess the propagation of several sources of uncertainty to the forest sector, considering both climate‐induced uncertainty and the intrinsic stochasticity of the fire process. By the end of the century, summer burned areas increase by up to 55%, causing moderate losses of merchantable timber and forest carbon stocks, with cascading impacts for industrial activities and climate mitigation in the forest sector. Implications for industries remain limited, but we observe price increases, especially for softwoods, as well as spatially differentiated changes in producer welfare. Inter‐annual fluctuations explain most of uncertainty in wildfire activity, but their impacts on the forest sector are quickly dampened. Over time, owing to the cumulative nature of wildfire impacts on forest resources, uncertainty related to climate warming, climate models’ response and stochasticity intrinsic to the wildfire phenomenon strongly increase in relative importance. Results reassert the need to consider multiple futures in prospective assessments, including uncertainty inherent to natural processes, often omitted in large‐scale economic assessments.

Modelling wildfire activity is crucial for informing science-based risk management and understanding fire-prone ecosystem functioning worldwide. Models also help to disentangle the relative roles of different factors, to understand wildfire predictability or to provide insights into specific events. Here, we develop a two-component Bayesian hierarchically-structured probabilistic model of daily fire activity, which are modelled as the outcome of a marked point process in which individual fires are the points (occurrence component) and the fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the Stochastic Partial Differential Equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and Generalized-Pareto distributions, also adjusted with INLA. The Fire Weather Index (FWI) and Forest Area are the main explanatory variables. Seasonal and spatial residuals as well as a post-2003 effect are included to improve the consistency of the relationship between climate and fire occurrence, in accordance with parsimonious criteria. A set of 1000 simulations of the posterior model of fire activity is evaluated at various temporal and spatial scales in Mediterranean France. The number of escaped fires (≥1ha) across the region can be coarsely reproduced at the daily scale, and is more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10 to 100 ha) can be predicted as well, but the accuracy decays with size, as the model uncertainty increases with event rareness. Local predictions of fire numbers or burnt areas likewise require a longer aggregation period to maintain model accuracy. Regarding the year 2003 -which was characterized by an extreme burnt area in France associated with a heat wave-, the estimation of the number of escaped fires was consistent with observations, but the model systematically underrepresents larger fires and burnt areas, which suggests that the FWI does not consistently rate the danger of large fire occurrence during heat waves. Our study sheds new light on the stochastic processes underlying fire hazard, and is promising for predicting and projecting future fire hazard in the context of climate change.

Environmental policy and the expansion of the bioeconomy sector has led to increased demand for wood and wood products, pressuring forests. In order to meet additional demand, foresters must either intensify forest management practices or alter forest area. One of the more observable shifts in forest management comes in the form of changes in land use or land cover. Yet despite the many short- and long-term consequences of land-use change, the environmental impacts of it are less explored in forestry than in agriculture. In this paper, we conduct a literature review over the period between 1993 and 2022 to better understand how the notions of land-use and land-cover change are included in environmental impact assessments related to the production of wood and wood products. Specifically, we identified five categories of impacts studied and found a surprising dichotomy in terminology between land-use/land-cover change and changes in forest management practices. We present general trends in the methods and indicators used and discuss potential methodological and conceptual challenges inherent to this literature. Our results are particularly important in light of the growing popularity of land-use and land-cover data in research, as we highlight how they have been integrated into existing environmental impact assessment methods and how we can improve them going into the future.