Multidimensional measurements of approaches to forest sustainability assessments: an overview of models, approaches, and issues
This article provides an overview of concepts, models, measures, and dimensions of forest sustainability assessments. The methods described include models that are fully developed and have actually been applied to various application contexts, as well as two normative models, graph theory and cognitive mapping, that appear to have potential for more in-depth analysis of the interactions of sustainability indicators. The former is based on the principles of multi-criteria analysis (MCA), an approach that is capable of accommodating the amount of complexity and uncertainty inherent in the concept and practice of forest sustainability. The other methods make use of “softer” analysis that are suited particularly for the assessment and measurement of qualitative indicators of sustainability. The MCA models are capable of determining relative importance of indicators and estimating sustainability index values cased on the combined impacts of indicators. Measures of relative importance can be used to prioritise indicators or filter out those that are relatively unimportant. ‘Softer’ analysis methods such as cognitive mapping and graph theory can help examine the interactions between and among indicators and help identify critical domains, critical paths, and tactically and strategically important indicators, based on the strength of their connectivity with other indicators, either directly or indirectly. These measures of connectivity can provide useful insights that can help managers identify indicators in need of attention, monitoring or even mitigation when necessary. Concepts such as the use of sustainability thresholds and qualitative flags also discussed.
| Main Authors: | , |
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| Format: | Book Chapter biblioteca |
| Language: | English |
| Published: |
Kluwer Academic Publishers
2002
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| Subjects: | cognitive development, sustainability, criteria, models, |
| Online Access: | https://hdl.handle.net/10568/19187 https://www.cifor.org/knowledge/publication/1753 |
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