Elsevier

Biological Conservation

Volume 143, Issue 7, July 2010, Pages 1737-1750
Biological Conservation

Development and application of a model for robust, cost-effective investment in natural capital and ecosystem services

https://doi.org/10.1016/j.biocon.2010.04.022Get rights and content

Abstract

Identifying good investments in environmental management is complex as several prioritization strategies may be used and significant uncertainty often surrounds cost, benefits, and agency budgets. In this paper I developed a model for robust portfolio selection based on preference programming to support cost-effective environmental investment decisions under uncertainty and applied it to the South Australian Murray-Darling Basin. Benefits and costs of 46 investment alternatives (called targets) for managing natural capital and ecosystem services were quantified and the associated uncertainty estimated. Thirty-six investment portfolios were selected using mathematical programming under four investment prioritization strategies (cost-effectiveness (E-max), cost-effectiveness including a suite of pre-committed (or core) costs (E-max), cost-only (C-rank), and benefit-only (B-rank)), three decision rules (pessimistic, most likely, and optimistic), and three budget scenarios (minimum, most likely, maximum). Compared to the optimally performing investment strategy E-max, the E-max and C-rank strategies only slightly reduced portfolio performance and altered portfolio composition. However, the B-rank strategy reduced performance by half and radically changed composition. Uncertainty in costs, benefits, and available budgets also strongly influenced portfolio performance and composition. I conclude that in this case study the consideration of uncertainty was at least as important as investment strategy in effective environmental decision-making. Targets whose selection was less sensitive to uncertainty were identified as more robust investments. The results have informed the allocation of AU$69 million in the study area and the techniques are readily adaptable to similar conservation and environmental investment decisions in other areas at a variety of scales.

Introduction

A perennial problem facing environmental agencies is how to allocate a limited budget across many worthy conservation, management, and restoration projects that enhance natural capital and ecosystem services (Prato, 2007, Wilson et al., 2007, Hajkowicz et al., 2009a). The need to consider the costs and multiple benefits of investment options and the uncertainty that surrounds these estimates makes systematic and efficient resource allocation a complex problem (Messina and Bosetti, 2003, Ehrgott et al., 2004, Liesiö et al., 2007, Liesiö et al., 2008, Phillips and Bana e Costa, 2007). Due in part to this complexity, environmental agencies have rarely considered both costs and benefits when setting investment priorities (Hughey et al., 2003, Ferraro, 2003, Polasky, 2008) and instead, have directed investment towards projects with greatest benefit, or lowest cost, or some other ad hoc objective (Ferraro, 2003, Newburn et al., 2005, Phillips and Bana e Costa, 2007). Overall, these investment strategies usually fail to achieve the most effective outcomes from environmental funds (Babcock et al., 1997, Wu et al., 2000, Crossman and Bryan, 2009). The need for agencies to prioritize the investment of scarce resources, satisfy due diligence requirements, and maximize the effectiveness of conservation funds has been widely recognised (Wu and Boggess, 1999, Ferraro, 2003, Polasky, 2008, Hajkowicz, 2009a, Wilson et al., 2009).

Similar capital budgeting investment problems routinely occur in management economics and finance when finite resources need to be allocated across a range of investment alternatives with the goal of maximizing benefits (Steuer and Na, 2003, Bana e Costa et al., 2006, Ho et al., 2007, Huang, 2008). Phillips and Bana e Costa (2007) divided the investment prioritisation and selection problem into the two elements of option appraisal and portfolio selection. Option appraisal involves calculating the costs and benefits, and ranking options. Costs and benefits are often measured in monetary terms and cost–benefit analysis used to evaluate the net gains in social welfare of an investment (Hanley and Barbier, 2009). However, conservation and environmental management investments in particular, typically accrue a complex and diverse suite of benefits. Many of these benefits (e.g. bequest and intrinsic values; Raymond et al., 2009) are not expressed in markets and are therefore not readily amenable to economic valuation (Hughey et al., 2003). For these reasons, the benefits of environmental investments are often evaluated in terms of multiple attribute utility (Keeney and Raiffa, 1976, Steuer et al., 2007, Hajkowicz et al., 2008). Multiple attribute estimates of benefits might not tell us anything about the net gains in social welfare of an investment, but they do enable effective comparison among competing alternatives (Hughey et al., 2003). Portfolio selection then involves allocating resources to those alternatives that offer the highest return on investment subject to budgetary and other constraints (Ferraro, 2003, Steuer and Na, 2003, Murdoch et al., 2007, Phillips and Bana e Costa, 2007).

Portfolio selection integrating both costs and benefits has been used to identify cost-effective spatial priorities for investment in biodiversity conservation (Ando et al., 1998, Balmford et al., 2000, Naidoo et al., 2006, Wilson et al., 2006, Wilson et al., 2009, Bottrill et al., 2008, Polasky et al., 2008, Underwood et al., 2009), restoration (Macmillan et al., 1998, Crossman and Bryan, 2006, Bryan and Crossman, 2008), and the enhancement of natural capital and ecosystem services (Crossman and Bryan, 2009, Nelson et al., 2009). Portfolio selection has also been widely used to identify cost-effective management priorities in conservation (Wu and Boggess, 1999, Wilson et al., 2007), water quality management (Alam et al., 2008, Hajkowicz et al., 2008, Bryan and Kandulu, 2009), natural resource management (Hajkowicz, 2007, Hajkowicz, 2009b, Crossman and Bryan, 2009, Marinoni et al., 2009), and enhancing ecosystem services (Prato, 2007). The studies cited above have shown that investment strategies which consider both costs and benefits may lead to substantially greater environmental benefits from limited budgets. However, few studies have considered the influence of uncertainty in such decision parameters as cost, benefit, and budget on the efficiency and composition of conservation investments or have provided a means for environmental agencies to select investment portfolios that are robust to this uncertainty.

Techniques proposed for portfolio selection and resource allocation under uncertainty include fuzzy simulation (Huang, 2008), info-gap theory (McDonald-Madden et al., 2008), Bayesian inference (Prato, 2007), contingent portfolio programming (Gustafsson and Salo, 2005), multiple criteria analysis (MCA), and preference programming (Kleinmuntz, 2007). Preference programming has been used to inform robust investment in research and development projects (see Lesiö et al., 2008), and offers significant potential for guiding investment in natural capital and ecosystem services under alternative investment strategies. Preference programming (Salo and Hämäläinen, 1992) has enabled the robust selection of portfolios despite incomplete information about the costs and benefits of investments (Liesiö et al., 2007, Liesiö et al., 2008). The dominance concepts and decision rules in preference programming provide a transparent basis for making investment decisions under uncertainty (Salo and Hämäläinen, 2004) that decision-makers are more likely to adopt (Kleinmuntz, 2007).

In this paper, I present a model for supporting cost-effective investment decisions for managing natural capital and ecosystem services under uncertainty, and describe its application in informing resource allocation by the South Australian Murray-Darling Basin (SAMDB) Natural Resource Management (NRM) Board (the Board). A natural capital and ecosystem services framework was used to provide a flexible basis for quantifying the diverse suite of benefits associated with achieving environmental targets. I quantified the benefit of 46 potential investment alternatives (or targets, Appendix A) for natural capital and ecosystem services in MCA workshops and simulated uncertainty using Monte Carlo simulation. Costs of achieving targets were quantified in dollar terms and the uncertainty estimated. Investment in targets was prioritized using four strategies: cost-effectiveness (E-max), cost-effectiveness including a suite of pre-committed (or core) costs (E-max), cost-only (C-rank), and benefit-only (B-rank). From preference programming, three decision rules (pessimistic, most likely, optimistic) and three budget scenarios (minimum, most likely, maximum) were used to assess the impact of uncertainty in the cost and benefit of targets, and in available budgets, respectively. I used mathematical programming to select 36 portfolios under each combination of investment strategy, decision rule, and budget scenario. The more robust investments were those selected for investment in more portfolios given the uncertainty in the investment problem. The Board’s use of the results to guide the investment of AU$69 million in environmental funds, and the adaption of the techniques to other jurisdictions at a variety of scales, is discussed.

Section snippets

Investment strategies

The principle of prioritizing investment based on value-for-money is “deceptively simple, uncontroversial, yet seldom used in organizations” (Phillips and Bana e Costa, 2007). Value-for-money, or cost-effectiveness, can be calculated using a benefit-cost ratio Bk/Ck where Bk is the benefit and Ck is the cost of target k. Maximally efficient E-max (Ferraro, 2003) portfolios may be selected simply by ranking investments in descending order and allocating resources until the budget is exhausted (

Study area

The SAMDB is an area of approximately 56,000 km2 (Fig. 1). Apart from the hilly eastern Mt. Lofty Ranges, the topography is mostly flat. The climate ranges from Mediterranean to semi-arid climate. The SAMDB’s high value ecological assets include the River Murray and its floodplain and lower lakes, Lake Alexandrina and Lake Albert, the Coorong estuary, and some 30,748 km2 of remnant native woodland and shrubland habitat. Both dryland and irrigated agriculture are common land uses in the region.

Cost-effectiveness

Appendix D details the costs and benefits of targets. Cost-effectiveness varied significantly between targets (Fig. 2) as evidenced by high standard deviations relative to the mean. The mean cost-effectiveness of targets was 1.04 (σ = 1.25) under the pessimistic decision rule, 2.45 (σ = 2.98) under the most likely, and 6.56 (σ = 8.68) under the optimistic decision rule. Cost-effectiveness also varied significantly within individual targets (Fig. 2) as evidenced by the mean difference in

Supporting environmental investment decision-making

In this study, the value-for-money principle was implemented by prioritising investment based on cost-effectiveness using the E-max strategy. Inclusion of core costs in the constrained E-max strategy selected some different targets for investment and achieved only slightly lower performance than E-max as core costs comprised only a small proportion of the total budget. Larger reductions in portfolio performance and changes in composition may be expected when environmental agencies commit

Conclusion

Environmental investment decisions are plagued by a lack of a clearly articulated investment strategy, and by significant uncertainty in investment decision parameters such as the costs and benefits of investment alternatives and available budgets. This study quantified the impact of investment strategies and uncertainty on the performance and composition of portfolios for enhancing natural capital and ecosystem services. I conclude that it is at least as important to consider uncertainty in

Acknowledgements

I am grateful to the SAMDB NRM Board, and CSIRO’s Sustainable Regional Development theme, Sustainable Agriculture Flagship, and Water for a Healthy Country Flagship for supporting and funding the research. I am also grateful for the support of the planning team at the SAMDB NRM Board and Darran King from CSIRO, and for the participation of the decision-makers and community of the SAMDB.

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