Multiple criteria decision support in forest management—the approach, methods applied, and experiences gained

doi:10.1016/j.foreco.2004.10.023

Forest Ecology and Management

Volume 207, Issues 1–2, 7 March 2005, Pages 133-143

https://doi.org/10.1016/j.foreco.2004.10.023 Get rights and content

Abstract

We discuss the benefits of using multiple criteria decision support (MCDS) methods in forest management, briefly present some MCDS methods recently applied in forestry, and summarize experiences gained from MCDS applications in forestry. Applications of MCDS methods of varying characteristics can be found in the management planning of multiple-purpose forestry. However, the tool to be used should be chosen to fit the planning process at hand. When choosing a method, compromises must often be made. For instance, simple and easily understandable methods may mean loss of attainable information and, correspondingly, deficient analyses. More versatile methods enable deeper analyses and more complete exploitation of available data, but typically they are hard to use and understand. Simple and straightforward MCDS methods are needed in participatory approaches and in planning via information networks. Some recent studies indicate that, especially for behavioural reasons, it would be useful to use more than just one MCDS method, or hybrid approaches, in many planning situations. A further conclusion has been that interactive use of the methods greatly improves the efficiency of the planning process.

Section snippets

The approach

Forest simulators and numerical optimisation have long been employed in forest management planning worldwide. Today's forestry, with multiple criteria and functions, and often with multiple stakeholders with conflicting interests, calls for more flexible and versatile decision support than can be gained using “traditional” simulation and optimisation tools alone. Generally, in decision-making processes, decision-makers rank a set of decision alternatives and choose the best according to their

The analytic hierarchy process

The analytic hierarchy process (AHP), originally developed by Saaty, 1977, Saaty, 1980, is a widely used MCDS method and perhaps the most popular in many fields, including natural resource management. Mendoza and Sprouse (1989), Murray and von Gadow (1991), and Kangas (1992), among others, have used AHP in forestry applications, and the number of applications is continuously increasing (e.g., Rauscher et al., 2000, Reynolds, 2001, Vacik and Lexer, 2001). AHP has also gained interest among

SMART

Simple Multi-Attribute Rating Technique (SMART) is a decision-support method developed at the close of the 1960s and early 1970s in the field of multi-attribute utility theory (von Winterfeldt and Edwards, 1986). In fact, several methods based on direct evaluation are involved in the family of SMART methods, of which various researchers have developed new versions over the years. SMART shares many similarities with the basic ideas of AHP; however, the central difference is that SMART does not

Experiences and conclusions on the use of MCDS methods in multiple purpose forestry

Planning situations and planning needs vary greatly. Acquiring decision support for tactical planning is different from acquiring it for strategic planning; for non-industrial private forest planning compared to public or industrial forestry; for an individual decision-maker compared to a consortium; for planning solely for wood production in comparison to ecosystem management planning; or when using quantitative criteria in comparison to qualitative criteria; etc. The brief descriptions of

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Multiple criteria decision support in forest management—the approach, methods applied, and experiences gained

Abstract

Section snippets

The approach

The analytic hierarchy process

SMART

Experiences and conclusions on the use of MCDS methods in multiple purpose forestry

Eur. J. Operation Res.

Eur. J. Operation Res.

Eur. J. Operation Res.

J. Math. Psych.

J. Environ. Manage.

J. Environ. Manage.

Tourism Manage.

Landsc. Urban Plann.

For. Policy Econ.

Eur. J. Operational Res.

Eur. J. Operational Res.

J. Environ. Manage.

Eur. J. Operational Res.

Eur. J. Operational Res.

J. Policy Model.

For. Ecol. Manage.

J. Environ. Manage.

Comput. Electron. Agric.

J. Mathemat. Psych.

Math. Comput. Model.

Socio-Econ. Plann. Sci.

Autom. Construction

For. Ecol. Manage.

Analysing uncertainties in experts’ opinions of forest plan performance

For. Sci.

Uncertainty in the expert predictions of the ecological consequences of forest plans

Appl. Stat.

Regression methods for pairwise comparisons data

An evaluation of multi-criteria methods in integrated assessment of climate policy

J. Multi-Criteria Decision Anal.

Multiple Criteria Decision Analysis: An Integrated Approach

Timber harvest scheduling problems: compromise programming and utility optimization models

Lecture Notes Econ. Math. Sys.

Modeling timber harvest scheduling problems with multiple criteria: an application in Spain

For. Sci.

Combined use of goal programming and the analytic hierarchy process in forest management

Multicriteria approval: application of approval voting concepts to MCDM problems

J. Multi-Criteria Decision Anal.