Factors influencing the use of decision support tools in the development and design of conservation policy

https://doi.org/10.1016/j.envsci.2017.01.002Get rights and content

Highlights

  • Many factors can influence the use of decision support tools in conservation policy.

  • Alignment of decision support tools with policy objectives a key factor.

  • Also important was ability to accommodate and cope with missing data.

  • Less important were a champion in the agency, and time required to apply tool.

  • Other factors include ambiguity of policy objectives, transaction costs and communication.

Abstract

There are many examples of decision support tools used to analyse information with the intention of assisting conservation managers and policy makers in their decision making. We used structured interviews to collect information on seven case studies from Australia and New Zealand to identify the factors that led to the use (or non-use) of decision support tools when developing conservation policies. The interviews explored hypotheses derived from existing literature on the use of decision support tools in conservation policy. Qualitative analysis of the interviews indicated that key factors influencing the uptake of a decision support tool in conservation policy include the alignment of the tool with the objectives and context of a policy, and its ability to be useful even in the presence of missing data. Two other factors that had been suggested in past literature were not perceived by interviewees to be as important as the above two: the presence of a champion for the decision support tool within the management agency, and the time required to apply the tool. The interviews also revealed a number of additional factors that influenced use or non-use of decision support tools that we had not extracted from existing literature: ambiguity about policy objectives, the autonomy of the agency, and the employee time costs of applying the decision support tool.

Introduction

A decision support tool (DST) is a platform for integrating, analysing and displaying information to assist decision makers. In support of decisions for conservation management, a DST may provide insights into the consequences of different management strategies or approaches, identify the strategy that will optimise a specified objective, identify knowledge gaps, and provide transparency in decision making. Decision support tools can range from relatively simple to highly complex.

Many DSTs have been developed by researchers with the intention of assisting conservation managers and policy makers. For example, the Ecosystem Management Decision Support system has been widely applied to landscape analysis in the US (Reynolds et al., 2014). The Analytic Hierarchy Process uses pairwise comparisons to prioritise decisions, and has been applied to wide variety of environmental and other decision contexts worldwide (Omkarprasad and Kumar, 2006). Marxan (Ball et al., 2009) is a DST designed to identify a set of conservation areas that achieve a particular objective at minimum cost, and can explore trade-offs between conservation and socio-economic objectives. It is the most widely used and known DST for conservation planning, with 6078 users across 182 countries (see www.uq.edu.au/marxan). Another example, the Investment Framework for Environmental Resources (INFFER – Pannell et al., 2012), is a tool for developing environmental projects and prioritising them based on the criterion of value for money. The Framework has been trialled or used by well over half of Australia’s 56 natural resource management regions, as well as other conservation organisations in Australia (Roberts et al., 2012), New Zealand (Jones and McNamara, 2014), Italy (Pacini et al., 2013) and Canada (see www.inffer.com.au).

Despite the benefits of DSTs, it is often observed that they are underutilised, or not utilised at all, by the intended end users (Nilsson et al., 2008, McIntosh et al., 2011). Several reasons are cited in the literature, including: different timeframes between policy decision making and scientific research (Briggs, 2006, Cvitanovic et al., 2015); research results not providing the specific information needed to support management or policy (Pannell and Roberts, 2009, Addison et al., 2013); lack of trust in the researchers by policy makers (Gibbons et al., 2008, McIntosh et al., 2011); low capacity of policy makers to use the research outputs in decision making (Rogers et al., 2015); and the lack of a champion within the policy organisation to enable and encourage uptake of the research results (Mumford and Harvey, 2014).

There has been little past research evaluating reasons why DSTs are or are not used in conservation management. A rare example is Addison et al. (2013), who investigated common objections to the use of models in conservation decision-making, based on collating statements made by researchers in the published and grey scientific literature. A common objection reported in the studies reviewed was the policy maker’s preference for unstructured subjective judgements from experts, rather than predictive models. The key reason cited for this objection was the resource intensity (money and time) required to deliver useful results using these models.

McIntosh et al. (2011) identified the challenges for DST use in environmental management from the perspective of a group of international experts in environmental DST development. Their recommendations include: to find a champion within the policy-making organisation to promote the DST and to build capacity with the end users and stakeholders.

Past studies on DST adoption in conservation management have provided recommendations based on the researchers’ experience. This study investigated the policy maker’s perspective on the factors that led to the use (or non-use) of DSTs in the development of key conservation and environmental policies. Bridging the gap between the policy maker’s and the researcher’s perspectives could offer useful insights that will improve the uptake of DSTs in conservation decision making, and subsequently lead to more effective policy design.

We examined notable case studies in Australia and New Zealand, exploring the factors that facilitated or inhibited DST usage in policy and management, based on interviews with managers and policy makers. The selection of case studies was not intended to be representative of all possible conservation policies; however, they offer a diverse selection and have useful insights that may be transferable to other case studies and policies. The next section presents the criteria used for assessment of DSTs, a description of the case studies and an outline of the interview process. Section 3 provides results and Section 4 is a discussion of key findings and conclusions.

Section snippets

Factors that facilitate usage of decision support tools

To investigate the factors that influence the uptake and usage of decision tools, we gathered a team of Australian experts in decision support tool design and implementation. Through a literature review and facilitated discussion amongst the team, we identified a range of factors that are likely to promote or prevent the uptake of DSTs in environmental management and conservation decision making. These factors have elements in common with those identified in past studies of the uptake of

Results

The importance of each of the eight factors that facilitate usage of DSTs varied for each of the seven case study policies (Table 2). For example, for the South West Marine Reserve Network (SWMRN), the interviewees perceived that uptake of the relevant DST (Marxan) was Low. The facilitating factor “Tool is able to deal with missing information” was seen as Important by the interviewees, and as not being met by the DST. On the other hand, in the Southern and Eastern Scalefish and Shark Fishery

Discussion

The purpose of this study was to seek insights on policy makers’ views on the factors that lead to the use or non-use of DSTs during the development of conservation-related policies and programs. Decision support tools, like the Harvest Strategy Framework and Marxan, can be very useful to policy makers for clarifying priorities, and for exploring and presenting trade-offs. They can help to define boundaries to the choice set, and increase transparency. They can also facilitate engagement with

Funding

This work was supported by the ARC Centre of Excellence for Environmental Decisions and the Australian Government’s National Environmental Research Program (Environmental Decisions Hub and Marine Biodiversity Hub).

Acknowledgements

We thank the policy makers who volunteered their time to be interviewed for this study.

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