Representations of the Forest Sector in Economic Models

Abstract

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.