Setting the most robust effluent level under severe uncertainty: Application of information-gap decision theory to chemical management

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.     

Robustness and vulnerability of community irrigation systems: The case of the Taos valley acequias

Traditional economic and policy analysis theory has emphasized the implementation of private or public property rights regimes in order to sustainably manage natural resources. More recent work has challenged this approach by examining the strengths and weakness of common property governance of such resources. This paper contributes to this literature by analyzing the acequia irrigation communities in northern New Mexico. Through statistical analysis, we find that the acequias’ ability to maintain collective-action as estimated by a critical performance function, crop production, is aided by water sharing agreements and access to groundwater, and that it is hampered by property rights fragmentation and urbanization.     

Measures of preferred direction for environmental and ecological circular data

Circular or angular variables indicating direction or cyclical time can be of great interest to scientists studying ecology, biology or environmental issues. A common problem of interest in circular data is estimating a preferred direction and its corresponding distribution. This problem is complicated by the so-called “wrap-around effect” on the circle, which exists because there is no natural minimum or maximum. The usual statistics employed for linear data are inappropriate for directional data, as they do not account for its circular nature. Three choices for summarizing the preferred direction (the sample circular mean, sample circular median and a circular analog of the Hodges–Lehmann estimator) are discussed, with examples from environmental and ecological applications. Similar to the linear data case, the relative emphases of different methods sometimes yield different measures of preferred direction in the presence of outliers or lack of symmetry in the original data. Received: November 2003 / Revised: June 2004     

A distance-based uncertainty analysis approach to multi-criteria decision analysis for water resource decision making

The choice among alternative water supply sources is generally based on the fundamental objective of maximising the ratio of benefits to costs. There is, however, a need to consider sustainability, the environment and social implications in regional water resources planning, in addition to economics. In order to achieve this, multi-criteria decision analysis (MCDA) techniques can be used. Various sources of uncertainty exist in the application of MCDA methods, including the selection of the MCDA method, elicitation of criteria weights and assignment of criteria performance values. The focus of this paper is on the uncertainty in the criteria weights. Sensitivity analysis can be used to analyse the effects of uncertainties associated with the criteria weights. Two existing sensitivity methods are described in this paper and a new distance-based approach is proposed which overcomes limitations of these methods. The benefits of the proposed approach are the concurrent alteration of the criteria weights, the applicability of the method to a range of MCDA techniques and the identification of the most critical criteria weights. The existing and proposed methods are applied to three case studies and the results indicate that simultaneous consideration of the uncertainty in the criteria weights should be an integral part of the decision making process.     

Price-based vs. quantity-based environmental regulation under Knightian uncertainty: an info-gap robust satisficing perspective

Conventional wisdom among environmental economists is that the relative slopes of the marginal social benefit and marginal social cost functions determine whether a price-based or quantity-based environmental regulation leads to higher expected social welfare. We revisit the choice between price-based vs. quantity-based environmental regulation under Knightian uncertainty; that is, when uncertainty cannot be modeled with known moments of probability distributions. Under these circumstances, the policy objective cannot be to maximize the expected net benefits of emissions control. Instead, we evaluate an emissions tax and an aggregate abatement standard in terms of maximizing the range of uncertainty under which the welfare loss from error in the estimates of the marginal benefits and costs of emissions control can be limited. The main result of our work is that the same criterion involving the relative slopes of the marginal benefit and cost functions determines whether price-based or quantity-based control is more robust to unstructured uncertainty. Hence, not only does the relative slopes criterion lead to the policy that maximizes the expected net benefits of control under structured uncertainty, it also leads to the policy that maximizes robustness to unstructured uncertainty.     

Comparison of Critical Load Exceedance and Its Uncertainty Based on National and Site-specific Data

Critical loads have been used to develop international agreements on acidifying air pollution abatement, and within the UK and other countries, to develop national policies for pollution abatement. The Environment Agency (England and Wales) has regulatory obligations to protect sites of high conservation value from the threat of acidification, and hence requires a practical methodology for acidification assessments at the site-specific scale. The Environment Agency has therefore posed the question: Are the national critical load exceedance models sufficiently robust to form the basis for methods to assess harm to individual sites or are they only useful for national policy development? In order to provide one measure of the appropriateness of applying the models at the site-specific scale we incorporated estimates of uncertainty in both national and site-specific data into the calculation of critical load exceedance for individual sites. The exceedance calculations use data from a wide range of sources and the accuracy of the exceedance will be influenced by the accuracy of the input data sets. Using Monte Carlo methods to incorporate the uncertainty in the input data sets into the calculation a distribution of critical load exceedance values is generated rather than a single point estimate. This paper compares uncertainty analyses for coniferous forested sites in England and Wales using both national scale and site-specific data sets and uncertainty ranges.     

根据水/辛醇分配系数(KOW)估算有机化合物的吸着系数(KOC)

根据５５４ 种有机化合物的实测资料，研究了这些化合物水／辛醇分配系数（ＫＯＷ）值与土壤／沉积物吸着系数（ＫＯＣ）值之间的定量关系，建立了根据ＫＯＷ值估算有机化合物ＫＯＣ的定量模型，讨论了模型的误差和稳健性。研究证实，有机化合物的ｌｏｇＫＯＷ与ｌｏｇＫＯＣ之间存在很好的线性关系，根据已知ＫＯＷ值可以较准确估算ＫＯＣ值。对所研究的５５４ 种化合物，模型估算ＫＯＣ的平均误差为０．４８ 个对数单位，６２％ 的化合物误差小于０．５ 个对数单位。四种方式的ｊａｃｋｋｎｉｆｅ检验证明模型具有较高的稳健性。     

A multi-approach strategy in climate attribution studies: Is it possible to apply a robustness framework?

Attribution studies investigate the causes of recent global warming. For a few decades the scientific community generally adopted dynamical models – the so-called Global Climate Models (GCMs) – for such an investigation. These models show the essential role of anthropogenic forcings in driving the temperature behaviour of the last half century. In the last period even other (data-driven) methodological approaches were adopted for attribution studies. This allows the scientific community to compare the results coming from these different approaches and to possibly increase their robustness. For such a purpose, the paper explores the possibility of applying a robustness framework, so far used only in the case of multi-model GCM ensembles, to a strategy including models from different methodological orientations, assessing such an application especially in the light of the independence issue.     

Robust detection protocols for uncertain introductions of invasive species

Two important features of real-world port inspections of shipping containers for invasive species are the general absence of underlying economic considerations and the climate of severe uncertainty that surrounds the likelihood of invasive species introductions. In this article we propose and illustrate a method for determining inspection protocols that address both of these issues. We seek inspection protocols that are robust in the sense that they maximize the range of uncertainty over which the expected loss from the introduction of an invasive species plus the costs of inspections do not exceed some critical value. These inspection strategies are practical and provide ready alternatives to existing protocols.     

Misspecification of within-area exposure distribution in ecological Poisson models

Ecological studies enable investigation of geographic variations in exposure to environmental variables, across groups, in relation to health outcomes measured on a geographic scale. Such studies are subject to ecological biases, including pure specification bias which arises when a nonlinear individual exposure-risk model is assumed to apply at the area level. Introduction of the within-area variance of exposure should induce a marked reduction in this source of ecological bias. Assuming several measurements per area of exposure and no confounding risk factors, we study the model including the within-area exposure variability when Gaussian within-area exposure distribution is assumed. The robustness is assessed when the within-area exposure distribution is misspecified. Two underlying exposure distributions are studied: the Gamma distribution and an unimodal mixture of two Gaussian distributions. In case of strong ecological association, this model can reduce the bias and improve the precision of the individual parameter estimates when the within-area exposure means and variances are correlated. These different models are applied to analyze the ecological association between radon concentration and childhood acute leukemia in France.