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

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. (C) 2010 Elsevier Ltd. All rights reserved.