ANALYSISMulti-attribute preference modelling and regional land-use planning
Introduction
Developing sustainable forest management policies has become increasingly complex in recent decades. There are many competing uses of forests, such as timber for industry, recreational opportunities, environmental benefits, and biodiversity, and other functions such as soil erosion control and groundwater retention. Policy makers attempt to increase the output of forest products to meet projected demands, independent of issues such as biodiversity, ecological integrity, and recreational potential (Gregory and Keeney, 1994). Developing effective forest policy requires that policy-makers take into account the multiple objectives of multiple stakeholders and their conflicting interests. Stakeholder participation in forest policy decision making helps to identify issues, develop alternative management options, and prioritize choices, and provides an opportunity for differing perspectives to be represented and conflicts to be reconciled.
The Australian Regional Forest Agreement (RFA) process initiated in 1992 is the most comprehensive and expensive planning exercise ever undertaken to manage the country's native forests (Dargavel et al., 2000). The main objectives of the RFA are to: (1) protect environment values in a Comprehensive, Adequate and Representative Reserve System; (2) encourage job creation and growth in forest-based industries; (3) manage native forests in an ecologically sustainable way (Commonwealth of Australia, 1999); and (4) increase public participation in decision making. The RFA programme undertook a Comprehensive Regional Assessment of natural, cultural, economic, and social values of the forest estate that formed the basis for the negotiation between the Commonwealth and State governments to balance environmental, social, economic, and heritage values (Coakes, 1998). RFAs are designed for 20 years in order to provide certainty for forest-based industry, conservation, and forest dependent communities, and are subject to periodic reviews.
The RFA process has been criticised for a number of reasons. Kirkpatrick (1998) contends that the RFA process has been an information gathering exercise rather than an explicit involvement in decision making. The participatory processes of RFAs have other problems, namely: (1) they are used by some stakeholders to advance their interests more effectively through the political process; (2) they reduce the autonomy of government agencies; and (3) they only allow a short time for community consultation (Dargavel et al., 2000). Coakes (1998) identified several institutional obstacles to social assessment in the policy process. Difficulties in implementing participatory approaches are further exacerbated by the lack of tested methods, which could facilitate and add rigour to stakeholder participation in the policy development process.
Multi-Attribute Decision Analysis (MADA) techniques can lead to well conceived and acceptable forest management plans and can improve the legitimacy of the process in formulating policy alternatives by explicitly recognising the conflicting, multi-attribute and incommensurable effects of decisions (Munda, 2000, Omann, 2000, Hajkowicz, 2007). MADA can be implemented using several approaches, such as the Analytic Hierarchy Process (AHP), Multi-attribute Value Theory and Multi-attribute Utility Theory, outranking methods and goal programming (Ananda and Herath, 2003, Ananda and Herath, 2005, Ananda, 2007). Since there are many reviews of MADA, a detailed review is not presented here (see Dyer et al., 1992, Hayashi, 2000). Ananda and Herath (2003) described how the AHP methodology can be used to formulate forest plans using a small sample of forest stakeholders. This paper extends that analysis and applies the AHP as a decision tool in regional forest planning in Northeast Victoria using a large sample of forest stakeholders.
The AHP has been applied to preference analysis in complex, multi-attribute problems (Kangas, 1993, Kangas, 1994, Varis, 1989). AHP has been widely applied to forest planning in experimental settings (Kangas, 1993, Ananda and Herath, 2003, Mau-Crimmins et al., 2005, Strager and Rosenberger, 2006). Many of these applications deal with a single decision maker or a handful of decision makers. The contribution of this paper is that it applies the AHP to a broader cross section of the general public.
The objectives of this paper are to:
- (1)
evaluate the relative importance of multiple attributes and forest management options using the Analytic Hierarchy Process (AHP);
- (2)
compare stated and predicted forest management choices of stakeholders; and
- (3)
draw implications for forest policy decision making in the context of the RFA process.
The rest of the paper is organised as follows. Section 2 provides details of the AHP and the practical implementation of the method. Section 3 presents the results of the AHP analysis. Section 4 compares the rankings based on stated preferences and AHP evaluation. Section 5 presents concluding remarks and policy implications.
Section snippets
Analytic hierarchy process
The AHP is a mathematical method for analysing complex decisions with multiple attributes (Saaty, 1977, Saaty, 1980). It aggregates separate performance indicators into an integrated performance indicator (Bouma et al., 2000). When applying AHP, a hierarchical decision schema is constructed by decomposing the decision problem into its decision elements. The preferences for the attributes are compared in a pairwise manner and numerical techniques are used to derive quantitative values from these
Analysis and results
The analysis was carried out at two levels: individual preferences and aggregated preferences. The data were further analysed using equal weights and self-assessed (participants) weights. Respondents were also asked to rank the options based solely on their intuition and rankings obtained were compared using non-parametric techniques such as Wilcoxon Sign Rank test and Spearman rho test. These results are discussed in the next section.
Predictive validity of AHP
The predictive validity of AHP was examined using nonparametric tests. The predictive validity describes the agreement between stated and predicted preferences. The stated preferences for forest management options are captured in ordinal ranking, and the predicted ranking is obtained by synthesising AHP pairwise comparisons in the individual analysis (Table 5). Table 8 compares the stated and predicted patterns of ranking and the difference between the two (stated minus predicted).
It is evident
Discussion and concluding remarks
Forest policy making in Australia is too complex and scientific or economic reasoning alone cannot determine the socially optimal forest use and management. Reaching agreement among stakeholders with diverse preferences and backgrounds is difficult. This paper evaluates the potential of the AHP in resolving stakeholders' disagreements. The paper examined forest stakeholders' objectives, attributes of forest management options, the relative importance of weights, and the ranking of forest
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