Evolving Integrated Models From Narrower Economic Tools: the Example of Forest Sector Models

Abstract

Integrated simulation models are commonly used to provide insight on the complex functioning of social-ecological systems, often drawing on earlier tools with a narrower focus. Forest sector models (FSM) encompass a set of simulation models originally developed to forecast economic developments in timber markets but now commonly used to analyse climate and environmental policy. In this paper, we document and investigate this evolution through the prism of the inclusion of several non-timber objectives into FSM. We perform a systematic, quantitative survey of the literature followed by a more in-depth narrative review. Results show that a majority of papers in FSM research today focuses on non-timber objectives related to climate change mitigation, namely carbon sequestration and bioenergy production. Habitat conservation, deforestation and the mitigation of disturbances are secondary foci, while aspects such as forest recreation and many regulation services are absent. Non-timber objectives closest to the original targets of FSM, as well as those for which economic values are easier to estimate, have been more deeply integrated to the models, entering the objective function as decision variables. Others objectives are usually modelled as constraints and only considered through their negative economic impacts on the forest sector. Current limits to a deeper inclusion of non-timber objectives include the models' ability to represent local environmental conditions as well as the formulation of the optimisation problem as a maximisation of economic welfare. Recent research has turned towards the use of model couplings and the development of models at the local scale to overcome these limitations. Challenges for future research comprise extensions to other non-timber objectives, especially cultural services, as well as model calibration at lower spatial scales.

Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood

“…At larger scales, economic activity and climate mitigation in the forest sector may be affected by disturbances (Lindner et al 2010, Seidl et al 2014), as well as smoke production (McKenzie et al 2014). Long‐term forecasts often rely on deterministic simulators where the inclusion of risk is a methodological challenge (Chudy et al 2016, Riviere et al 2020); Firelihood may provide a way to consider fire activity in such assessments, provided that they include relevant LULC factors for management.…”

A Bioeconomic Projection of Climate‐Induced Wildfire Risk in the Forest Sector

“…A recent exception is the national study by Delacote, Caurla and Riviere (2021), but disturbances were not addressed. Besides, natural disturbances have mostly been studied through the lens of the economic costs of prevention and recovery measures (Caurla et al., 2015; Niquidet et al., 2012; Prestemon et al., 2008) and remain a marginal topic in the forest sector modeling literature (Riviere et al., 2020). More generally, assessments of risks in forest sector modeling remain a challenge, largely owing to the models’ complexity and their deterministic nature (Chudy et al., 2016) that is neither suited to take into account stochastic events nor to quantify how uncertainty from climate projections spreads along the modeling chain.…”

The Loop Effect: How Climate Change Impacts the Mitigation Potential of the French Forest Sector

“…Their main strength is to endogenously represent feedbacks between timber markets, forest growth and owners' behaviours, all modelled in an economically consistent way. FSM have gradually expanded to include carbon and emissions accounting modules (Rivière et al, 2020; and have been used to assess mitigation measures based on sequestration (Guo & Gong, 2017;Latta et al, 2016), energy substitution effects (Latta et al, 2013a;Moiseyev et al, 2014) or both (Caurla et al, 2013;Kallio et al, 2013). FSM have also been used to evaluate the impacts of climate change on the forest sector, either using assumptions on climate change impact (Lobianco et al, 2016a) or through couplings with vegetation and circulation models (Joyce et al, 1995;McCarl et al, 2000;Sohngen et al, 2001;Perez-Garcia et al, 2002;Solberg et al, 2003).…”