 Can a regional-level forest management policy achieve sustainable forest management?Yusuke Yamada Forest Policy and Economics, 2018, vol. 90, issue C, 82-89 Abstract: While deliberating a regional level forest management policy, one should note the probable existence of plural and independent decision-makers in the target region and the possibility that they may not fully understand or obey the intent of the regional policy. To achieve sustainable forest management, the relationships between the policy and the decisions of individual decision-makers with respect to forestry activities (for example, thinning or clear-cutting) need to be considered. The objective of this study is to determine the extent to which zoning, as a regional forest management method that affects individual management activities, influences future forest resources. A Bayesian belief network (BBN) model was used to model relationships between zoning and individual forestry activities. Through the construction of the BBN model with factors (nodes) such as zoning types, forestry activities, and forest stand conditions, a conditional probability table (CPT) was obtained. This CPT shows the degree of possibility of harvest for each forest stand. Individual forestry activities were simulated on the basis of the CPT. As a case study, this model was applied to the regional municipality of Ugo which is situated in the Tohoku district of Japan. Three types of zoning were examined: (1) no zoning covering for wood production, (2) the current zoning that is actually used, and (3) zoning planned to emphasize wood production. The volume of harvested wood and the statistics for the slope distribution where harvesting occurred were observed under each zoning type. Results showed that future forest resources varied under each zoning type. However, influencing the timing and location of individual forestry activities may enable appropriate regional-scale management to achieve sustainable forest management. Keywords: Forest zoning; Bayesian network model; Adaptive management; Decision making; Forest multiple functions (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:forpol:v:90:y:2018:i:c:p:82-89 DOI: 10.1016/j.forpol.2018.01.013 Access Statistics for this article Forest Policy and Economics is currently edited by M. Krott More articles in Forest Policy and Economics from Elsevier |
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