Incorporating stakeholder preferences for land conservation: Weights and measures in spatial MCA

Abstract

Spatial multicriteria models may provide an equitable and efficient means for incorporating people's preferences in social decisions. However, in order for these tools to be effective, they should include criteria that are locally relevant and measurable in a spatial framework. This paper integrates measures of stakeholder preferences with GIS data in a spatial multicriteria framework for identifying high priority areas for land conservation. Individual participants' preference weights were measured using the Analytical Hierarchy Process. Individual preferences were aggregated into groups representing outside experts and local stakeholders. Aggregate preferences differed across groups, illustrating an affinity for local knowledge of stakeholders vs. universal broader issues by outside experts. The mapping of priority areas for conservation was relatively unaffected by the weights, mostly due to the lack of spatial measures for locally relevant criteria.

Introduction

Land conservation is becoming increasingly important as natural landscapes, agriculture, and rural characteristics are lost to development (Hymann and Leibowitz, 2000, Worldwatch Institute, 2003). To be effective with preservation activities under limited funding, conservation groups must target high priority lands by focusing on the integration of sound scientific criteria with support from local residents and landowners. Tools that help maximize consensus and minimize conflict among interest groups can lead to better decisions regarding land conservation programs. Multiobjective or multicriteria analysis (MCA) is a framework that can meet this need. The MCA framework enables the integration of goals, objectives, spatial data and stakeholder preferences in a systematic method. MCAs can facilitate community-based collaborative decision making by considering multiple attributes while avoiding some of the ethical, theoretical and practical shortcomings of conventional economic approaches (Prato, 1999, Munda et al., 1994).

One of the most important aspects of the MCA framework is its integration of people's preferences for attributes with objective measures of those attributes. It is through this integration that knowledge is incorporated in the framework. Several studies have focused on measuring the preferences of different stakeholder groups for alternative land uses (Duke and Aull-Hyde, 2002, Kline and Wilchems, 1996, Kline and Wilchems, 1998, Alho and Kangas, 1997), while other studies have focused on the sensitivity of land suitability coefficients to preference weights derived from a variety of methods (Triantaphyllou and Sanchez, 1997, French, 1986, Hartog et al., 1989, Alexander, 1989, Weber et al., 1988). The preference weights used in an MCA can greatly affect the results (Malczewski, 1999). Triantaphyllou and Sanchez (1997) found that the choice of multicriteria method is less important than the influence of weights on the results of an MCA. Prato (2003) recognized the significance of different preference weights by evaluating the effect of four hypothetical attribute weighting schemes on his MCA outcomes for ecosystem management of a river system.

Preference weights measured for different land management alternatives or conservation criteria can vary significantly across individuals and across groups these individuals represent. For example, Duke and Aull-Hyde (2002) found different rankings of land conservation objectives across Delaware's three counties based on a random sample of residents. Willett and Sharda (1995) showed the variability in rankings of water management objectives was significantly different among interest groups. Even when preferences are measured as group consensus, Cox et al. (2000) found variability in the rankings of development objectives across counties based on local government and business leaders that served as representatives for their respective counties.

This study focuses on the effect of people's preferences in conjunction with measurability of criteria in a spatial MCA framework that identifies and prioritizes areas for land conservation objectives. Community development projects often supplement ‘local’ knowledge with ‘expert’ knowledge by inviting experts external to the community to work with stakeholders internal to the community (Fraser and Lepofsky, 2004). Local knowledge is based on a familiarity with the history and geography of a place, whereas expert knowledge transcends the historical–geographic specifics of a place as a form of universal knowledge (Fraser and Lepofsky, 2004). While local knowledge is important to building consensus or identifying compromise, expert knowledge is often treated as having a universal sense of what is best for any place (Fischer, 2000, Skogen, 2003, Wondolleck and Yaffee, 2000). Experts are often more consistent in expressing their preferences for land conservation objectives; however, their rankings of the objectives may significantly differ from non-experts (Kangas et al., 1993, Kangas, 1994).

Another delineation of stakeholder groups is between the institutional members responsible for managing resources and local residents that are affected by their decisions. Planning agencies often consider themselves to be conduits of the voices of local residents (Fraser and Lepofsky, 2004). However, planning agencies may also be a conduit to resources external to a community (Kubisch et al., 2002). Therefore, even though members of an institution and local residents may share the same local knowledge of a place, institutional members' preferences may be significantly influenced by their relationship with the outside world.

We test for differences in preferences by separating participants into various groups, including outside experts vs. stakeholders and board members vs. local residents. The sensitivity of land prioritization to group aggregate preference weights is tested by comparing suitability indexes across the various groups using an integer mathematical program. We found significant differences in preference weights between outside experts and local stakeholders. However, the spatial MCA outcomes (rankings) were relatively insensitive to these weight differences. This was primarily due to the lack of objective, spatial measures for criteria representing local knowledge of place.