Explicating behavioral assumptions in forest scenario modelling – the behavioral matrix approach
Renats Trubins,
Ragnar Jonsson,
Ida Wallin and
Ola Sallnäs
Forest Policy and Economics, 2019, vol. 103, issue C, 70-78
Abstract:
Forest landscapes are too complex systems for the actors involved in policy making, including experts, to predict the consequences of different management options and policy measures without the aid of modelling tools. Forest sector models and forest management Decision Support Systems (DSS) are two major types of modelling tools that can be used for providing model-based support to forest policy development. Regardless of the modelling tool, policy makers and other concerned actors need to be aware of the behavioral assumptions, or implications, of a scenario in order to proceed to an assessment of what it takes to achieve, alternatively avoid it. A unified method or even a unified understanding of this problem is as yet lacking among forest scenario analysts. This paper presents an approach to facilitate the definition and communication of behavioral assumptions, primarily in DSS-based forest scenario modelling. At the core of the approach is the Behavioral Matrix (BM), a way of structuring forest management specifications. A case study in southern Sweden is presented as an example.
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1389934116303963
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:forpol:v:103:y:2019:i:c:p:70-78
DOI: 10.1016/j.forpol.2017.07.001
Access Statistics for this article
Forest Policy and Economics is currently edited by M. Krott
More articles in Forest Policy and Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().