Ecological and Environmental Economics

Abstract

New ideas or technologies are often advocated because of their purported improvements on existing methods. However, what is new is usually less well-known and less widely tested than what is old. New methods may entail greater unknown dangers as well as greater potential advantages. The policy maker who must choose between innovation and convention faces a dilemma of innovation. We present a methodology, based on info-gap robustness, to deal with the innovation dilemma. We illustrate the approach by examining the policy decisions for managing the Light Brown Apple Moth in California.

Keywords

Innovation, policy selection, robustness to uncertainty, info-gaps, Light Brown Apple Moth.

Abstract

In ecosystems driven by water availability, plant community dynamics depend on complex interactions between vegetation, hydrology, and human water resources use. Along ephemeral rivers—where water availability is erratic—vegetation and people are particularly vulnerable to changes in each other’s water use. Sensible management requires that water supply be maintained for people, while preserving ecosystem health. Meeting such requirements is challenging because of the unpredictable water availability. We applied information gap decision theory to an ecohydrological system model of the Kuiseb River environment in Namibia. Our aim was to identify the robustness of ecosystem and water management strategies to uncertainties in future flood regimes along ephemeral rivers. We evaluated the trade-offs between alternative performance criteria and their robustness to uncertainty to account for both (i) human demands for water supply and (ii) reducing the risk of species extinction caused by water mining. Increasing uncertainty of flood regime parameters reduced the performance under both objectives. Remarkably, the ecological objective (species coexistence) was more sensitive to uncertainty than the water supply objective. However, within each objective, the relative performance of different management strategies was insensitive to uncertainty. The ‘best’ management strategy was one that is tuned to the competitive species interactions in the Kuiseb environment. It regulates the biomass of the strongest competitor and, thus, at the same time decreases transpiration, thereby increasing groundwater storage and reducing pressure on less dominant species. This robust mutually acceptable strategy enables species persistence without markedly reducing the water supply for humans. This study emphasises the utility of ecohydrological models for resource management of water-controlled ecosystems. Although trade-offs were identified between alternative performance criteria and their robustness to uncertain future flood regimes, management strategies were identified that help to secure an ecologically sustainable water supply. Copyright © 2014 John Wiley & Sons, Ltd.

Author Keywords

ecohydrology; info-gap decision theory; species coexistence; robust decision making; arid; Tamarix usneoides; Acacia erioloba; Faidherbia albida

Abstract

Markov decision processes have become the standard tool for modeling sequential decision-making problems in conservation. In many real-world applications, however, it is practically infeasible to accurately parameterize the state transition function. In this study, we introduce a new way of dealing with ambiguity in the state transition function. In contrast to existing methods, we explore the effects of uncertainty at the level of the policy, rather than at the level of decisions within states. We use information-gap decision theory to ask the question of how much uncertainty in the state transition function can be tolerated while still delivering a specified expected value given by the objective function. Accordingly, the goal of the optimization problem is no longer to maximize expected value, but to maximize local robustness to uncertainty (while still meeting the desired level of performance). We analyze a simple land acquisition problem, using info-gap decision theory to propagate uncertainties and rank alternative policies. Rather than requiring information about the extent of parameter uncertainty at the outset, info-gap addresses the question of how much uncertainty is permissible in the state transition function before the optimal policy would change.

Keywords

Information-gap, Markov decision process, Reserve selection, Stochastic dynamic programming, Uncertainty

Abstract

Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost-efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an info-gap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost-efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade-off between expected performance and robustness, which suggested that the most robust strategy depended both on one’s budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance.

Keywords

Bayesian analysis; Burrowing owl (Athene cunicularia hypugaea); Conservation budgets; Decision analysis; Info-gap decision theory; Prairie dog (Cynomys ludovicianus)

Abstract

Info-gap decision theory facilitates decision making for problems in which uncertainty is large and probability distributions of uncertain variables are unknown. The info-gap framework allows the decision maker to maximize robustness to failure in the presence of uncertainty, where uncertainty is in the parameters of the model and failure is defined as the model output falling below some minimally acceptable performance threshold. Info-gap theory has found particular application to problems in conservation biology and ecological economics. In this study, we applied info-gap theory to an ecosystem services tradeoff case study in which a decision maker aiming to maximize ecosystem service investment returns must choose between two alternative land uses: native vegetation conservation or the establishment of an exotic timber plantation. The uncertain variables are the carbon price and the water price. With a “no-information” uncertainty model that assumes equal relative uncertainty across both variables, info-gap theory identifies a minimally acceptable reward threshold above which conservation is preferred, but below which plantation establishment is preferred. However, with an uncertainty model that allows the carbon price to be substantially more uncertain than the water price, conservation of native vegetation becomes an economically more robust investment option than establishing alien pine plantations. We explored the sensitivity of the results to the use of alternative uncertainty models, including asymmetric uncertainty in individual variables. We emphasize the general finding that the results of info-gap analyses can be sensitive to the choice of uncertainty model and that, therefore, future applications to ecological problems should be careful to incorporate all available qualitative and quantitative information relating to uncertainties or should at least justify the no-information uncertainty model.

Abstract

This study compares two widely used approaches for robustness analysis of decision problems: the info-gap method originally developed by Ben-Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate-altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info-gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them.

Keywords

Abrupt change; climate change; deep uncertainty; info-gap; robust decision making

Abstract

Decisions in ecological risk management for chemical substances must be made based on incomplete information due to uncertainties. To protect the ecosystems from the adverse effect of chemicals, a precautionary approach is often taken. The precautionary approach, which is based on conservative assumptions about the risks of chemical substances, can be applied selecting management models and data. This approach can lead to an adequate margin of safety for ecosystems by reducing exposure to harmful substances, either by reducing the use of target chemicals or putting in place strict water quality criteria. However, the reduction of chemical use or effluent concentrations typically entails a financial burden. The cost effectiveness of the precautionary approach may be small. Hence, we need to develop a formulaic methodology in chemical risk management that can sufficiently protect ecosystems in a cost-effective way, even when we do not have sufficient information for chemical management. Information-gap decision theory can provide the formulaic methodology. Information-gap decision theory determines which action is the most robust to uncertainty by guaranteeing an acceptable outcome under the largest degree of uncertainty without requiring information about the extent of parameter uncertainty at the outset. In this paper, we illustrate the application of information-gap decision theory to derive a framework for setting effluent limits of pollutants for point sources under uncertainty. Our application incorporates a cost for reduction in pollutant emission and a cost to wildlife species affected by the pollutant. Our framework enables us to settle upon actions to deal with severe uncertainty in ecological risk management of chemicals.

Keywords

Abrupt change; climate change; deep uncertainty; info-gap; robust decision making
Chemical management; Cost-effectiveness; Effluent limit; Information-gap decision theory; Robustness; Uncertainty

Abstract

Worldwide, we rely on introduced plants for the essentials of human life; however, intentional plant introductions for commercial benefit have resulted in invaders with negative environmental, economic or social impacts. We argue that plant species of low expected economic value should be less acceptable for introduction than species of high economic value if their other traits are similar; however, key traits such as likelihood of escape and costs of escape are often highly uncertain. Methods do not currently exist which allow decision makers to evaluate costs and benefits of introduction under uncertainty. We developed a cost-benefit analysis for determining plant introduction that incorporates probability of escape, expected economic costs after escape, expected commercial benefits, and the efficiency and cost of containment. We used a model to obtain optimal decisions for the introduction and containment of commercial plants while maximizing net benefit or avoiding losses. We also obtained conditions for robust decisions which take into account severe uncertainty in model parameters using information-gap decision theory. Optimal decisions for introduction and containment of commercial plants depended, not only on the probability of escape and subsequent costs incurred, but also on the anticipated commercial benefit, and the cost and efficiency of containment. When our objective is to maximize net benefit, increasing uncertainty in parameter values increased the likelihood of introduction; in contrast, if our objective is to avoid losses, more uncertainty decreased the likelihood of introduction.

Keywords

Commercial plant, Containment, Cost-benefit analysis, Information-gap decision theory, Invasive weed, Management

• Technical University of Denmark, Department of Environmental Engineering, 2800 Kgs. Lyngby, Denmark
• DHI Water Environment Health, Agern Alle 5, 2970 Hoersholm, Denmark
• ENM Consulting Engineers, 62-64 Grammou, 151 24 Maroussi, Athens, Greece
• HPC-PASECO Environmental Consultants, 22 Kykladon, 11361 Athens, Greece
• Danish National Environmental Research Institute, Frederiksborgvej 399, 4000 Roskilde, Denmark

Abstract

Resource costs of meeting EU WFD ecological status requirements at the river basin scale are estimated by comparing net benefits of water use given ecological status constraints to baseline water use values. Resource costs are interpreted as opportunity costs of water use arising from water scarcity. An optimization approach is used to identify economically efficient ways to meet WFD requirements. The approach is implemented using a river basin simulation model coupled to an economic post-processor; the simulation model and post-processor are run from a central controller that iterates until an allocation is found that maximizes net benefits given WFD requirements. Water use values are estimated for urban/domestic, agricultural, industrial, livestock, and tourism water users. Ecological status is estimated using metrics that relate average monthly river flow volumes to the natural hydrologic regime. Ecological status is only estimated with respect to hydrologic regime; other indicators are ignored in this analysis. The decision variable in the optimization is the price of water, which is used to vary demands using consumer and producer water demand functions. The price-based optimization approach minimizes the number of decision variables in the optimization problem and provides guidance for pricing policies that meet WFD objectives. Results from a real-world application in northern Greece show the suitability of the approach for use in complex, water-stressed basins. The impact of uncertain input values on model outcomes is estimated using the Info-Gap decision analysis framework.

© 2010 Elsevier B.V. All rights reserved.

Author keywords Hydro-economic models; Integrated water resources management (IWRM); Systems analysis; Water demand; Water value

Abstract

Substantial investment in climate change research has led to dire predictions of the impacts and risks to biodiversity. The Intergovernmental Panel on Climate Change fourth assessment report cites 28,586 studies demonstrating significant biological changes in terrestrial systems. Already high extinction rates, driven primarily by habitat loss, are predicted to increase under climate change. Yet there is little specific advice or precedent in the literature to guide climate adaptation investment for conserving biodiversity within realistic economic constraints. Here we present a systematic ecological and economic analysis of a climate adaptation problem in one of the world’s most species-rich and threatened ecosystems: the South African fynbos. We discover a counterintuitive optimal investment strategy that switches twice between options as the available adaptation budget increases. We demonstrate that optimal investment is nonlinearly dependent on available resources, making the choice of how much to invest as important as determining where to invest and what actions to take. Our study emphasizes the importance of a sound analytical framework for prioritizing adaptation investments. Integrating ecological predictions in an economic decision framework will help support complex choices between adaptation options under severe uncertainty. Our prioritization method can be applied at any scale to minimize species loss and to evaluate the robustness of decisions to uncertainty about key assumptions.

Abstract

Several parametric and non-parametric approaches have been developed to value financial assets. Yet, financial valuation techniques have only slowly percolated into disciplines concerned with the management of ecosystems. Particularly in forest management, decision-makers often find themselves confronted with extremely long time horizons and severely uncertain information. This requires careful valuation approaches, which are often underrepresented or even completely lacking in forest management. This paper gives a comprehensive overview on techniques for financial decision-making under uncertainty and develops future research needs.

First, we analyse different approaches from the expected utility framework as well as option pricing models and robust optimisation techniques as possible approaches to make decisions on forest investments and giving a short review regarding forestry-related applications. Afterwards we discuss the suitability of the presented approaches to support decisions in forestry and conclude that robust optimisation techniques should be developed further, especially since erroneous financial data is likely to occur, as well as deviations from the assumption of normality. Currently, the maximization of financial robustness is probably the most adequate approach for many long-term decisions in forestry, such as selecting the optimum tree species composition. Further development of this approach appears possible and necessary.

Finally, we come to the conclusion that even though it is intuitively clear that many long-term decisions should consider uncertainty, adequate financial valuation is not sufficiently developed within forest science. In the case of Central Europe, this may be an effect of ecological research dominating in forest science. Consequently, an intensification of the analysis of uncertainty in forest decision-making is necessary.

Keywords

Uncertainty, Diversification, Expected utility, Mean-variance, Option pricing, Stochastic dominance, Downside risk, Lower partial moment, Information-gap decision theory, Robust optimisation

Abstract

Benefit cost ratios (BCR) have been applied to conservation decisions for two reasons: cost-efficiency and transparency in decision making. Because BCRs are ratios of benefits to costs, the uncertainties associated with the two components (benefits and costs) are compounded. Therefore, BCRs can potentially involve more uncertainty than allocation strategies based solely on maximizing benefits. The robustness of decisions, defined here as the inverse of the number of misallocations due to uncertainties in benefits and costs of projects, is an unexplored component of applying BCRs to conservation decision making. To investigate the robustness to uncertainty of conservation investment with BCRs, we developed an information-gap model (info-gap) for using BCRs in selecting “portfolios” of conservation projects. Our model allows us to explore how uncertain we can be in our estimates of benefit and cost parameters while still selecting a portfolio that performs better than a critical threshold of misallocations perceived to be unacceptable. We first give a full theoretical description of our info-gap model formulation and then explore applications of the model to several hypothetical data sets.Copyright © ASCE 2011.