Uncertainty and allocation decisions involving unique environmental resources

The implications of the treatment of uncertainty in the definition of optimal investment criteria for irreversible allocations of unique environmental resources are considered herein. This analysis argues that the single-project, orientation of conventional cost-benefit analysis can lead to inconsistent decisions because it fails to take account of the potential for risk pooling across projects. In the models discussed in this paper uncertainty arises because the planner must estimate the net benefits associated with a mix of developed and preserved natural environments. The results suggest that the criteria for optimal investment plans will be affected by those factors influencing this uncertainty. Both the scale and timing of optimal investments may be altered from those prescribed with the conventional (Fisher-Krutilla-Cicchetti rule) framework when the potential for risk pooling is introduced. Thus, it is important to consider as a part of the specification of the objective function the factors which give rise to uncertainty.     

Option value under income and price uncertainty

We present a rigorous examination of the sign of option value under income and price uncertainty. We state and prove three theorems on the sign of option value when preferences are state independent. If income is uncertain, the sign of option value is opposite the sign of the income elasticity of the good in question. The analysis of price uncertainty is in two parts: one covers the case of own price uncertainty and the other the case where the price of some other good is uncertain. In both cases the sign of option value is generally ambiguous.     

生态补偿的理论基础:一个分析性框架

总结分析了生态补偿的理论基础框架和基本理论依据。认为,自然资源环境利用的不可逆性是生态补偿的自然要求和生态学基础;环境资源产权权利界定是生态补偿的法理基础和制度经济学基础;公共物品属性是生态补偿政策途径选择的公共经济学基础;外部性的内部化是生态补偿的核心问题和环境经济学基础;自然资源环境资本论是生态补偿的价值基础和确定补偿标准的理论依据。     

Definition and properties of a coefficient of sensitivity for mathematical models

This paper contains a definition of a measure of the uncertainty inherent in the output of a mathematical model. The definition is essentially a formalisation of the kind of uncertainty measures that have been previously used in various modelling applications in many disciplines. The characteristics of the measure are described, and an example is given of its use with a particular mathematical model.     

Planning for robust reserve networks using uncertainty analysis

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence–absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data—erroneous species presence–absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.     

Resolving structural uncertainty in natural resources management using POMDP approaches

In recent years there has been a growing focus on the uncertainties of natural resources management, and the importance of accounting for uncertainty in assessing management effectiveness. This paper focuses on uncertainty in resource management in terms of discrete-state Markov decision processes (MDP) under structural uncertainty and partial observability. It describes the treatment of structural uncertainty with approaches developed for partially observable resource systems. In particular, I show how value iteration for partially observable MDPs (POMDP) can be extended to structurally uncertain MDPs. A key difference between these process classes is that structurally uncertain MDPs require the tracking of system state as well as a probability structure for the structure uncertainty, whereas with POMDPs require only a probability structure for the observation uncertainty. The added complexity of the optimization problem under structural uncertainty is compensated by reduced dimensionality in the search for optimal strategy. A solution algorithm for structurally uncertain processes is outlined for a simple example in conservation biology. By building on the conceptual framework developed for POMDPs, natural resource analysts and decision makers who confront structural uncertainties in natural resources can take advantage of the rapid growth in POMDP methods and approaches, and thereby produce better conservation strategies over a larger class of resource problems.     

Choosing ecosystem service investments that are robust to uncertainty across multiple parameters

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.     

Strengthening conservation science as a crisis discipline by addressing challenges of precaution,privilege, and individualism

Conservation science deals with crises and supports policy interventions devised to mitigate highly uncertain threats that pose irreversible harm. When conventional policy tools, such as quantitative risk assessments, are insufficient, the precautionary principle provides a practical framework and range of robust heuristics. Yet, precaution is often resisted in many policy arenas, especially those involving powerful self-interests, and this resistance is compounded by structures of privilege and competitive individualism in science. We describe key drivers and effects of such resistance in conservation science. These include a loss of rigor under uncertainty, an erosion of crisis response capabilities, and a further reinforcement of privileged interests in conservation politics. We recommend open acknowledgement of the pressures exerted by power inside science; greater recognition for the value of the precautionary principle under uncertainty; deliberate measures to resist competitive individualism; support for blind review, open science, and data sharing; and a shift from hierarchical multidisciplinarity toward more egalitarian transdisciplinarity to accelerate advances in conservation science. Article impact statement: Precautionary principle, privilege structures among disciplines, and culture of individualism link to effective conservation policy making.     

Application of the New Keystone-Species Concept to Prairie Dogs: How Well Does It Work?

Abstract: It has been suggested that the keystone-species concept should be dropped from ecology and conservation, primarily because the concept is poorly defined. This prompted Power et al. (1996) to refine the definition: keystone species have large effects on community structure or ecosystem function (i.e., high overall importance), and this effect should be large relative to abundance (i.e., high community importance). Using prairie dogs ( Cynomys spp.) as an example, I review operational and conceptual difficulties encountered in applying this definition. As applied to prairie dogs, the implicit assumption that overall importance is a linear function of abundance is invalid. In addition, community importance is sensitive to abundance levels, the definition of community, and sampling scale. These problems arise largely from the equation for community importance, as used in conjunction with removal experiments at single abundance levels. I suggest that we shift from the current emphasis on the dualism between keystone and nonkeystone species and instead examine how overall and community importance vary (1) with abundance, (2) across spatial and temporal scales, and (3) under diverse ecological conditions. In addition, I propose that a third criterion be incorporated into the definition: keystone species perform roles not performed by other species or processes. Examination of how these factors vary among populations of keystone species should help identify the factors contributing to, or limiting, keystone-level functions, thereby increasing the usefulness of the keystone-species concept in ecology and conservation. Although the quantitative framework of Power et al. falls short of being fully operational, my conceptual guidelines may improve the usefulness of the keystone-species concept. Careful attention to the factors that limit keystone function will help avoid misplaced emphasis on keystone species at the expense of other species.     

Uncertainty, learning, and the optimal management of wildlife

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.     

Forest loss,monetary compensation,and delayed re-planting: The effects of unpredictable land tenure in China

Over the past 65 years, forest tenure in China has oscillated unpredictably between private and village property regimes. This policy-induced uncertainty has distorted the harvesting decisions of individuals granted rights to grow trees and has lowered the value of China׳s forest output. We provide an analytical framework for assessing these effects quantitatively. Understanding the consequences of this policy-induced uncertainty is particularly important since China is currently engaged in an ambitious plan to increase its domestic supply of timber. To conduct this analysis, we extend the literature on forestry economics when there is a risk of loss due to forest fire or pests. We (1) take account of the possibility that replanting can only resume after an interval of uncertain length (with immediate replanting as a special Case); (2) investigate the effects of compensation for such losses based only on the net value of the stand of trees at the time of the loss; and (3) compare it to compensation that would leave the wealth and rotation decisions of the farmer unaffected by the presence of uncertainty.     

Worldviews,institutions and sustainability: An introduction to a co-evolutionary perspective

This work analyzes hierarchical and causal relationship between worldview, institutions, technology and economic behaviour under conditions of informational uncertainty.

In our pursuit of sustainable living, institutions function both as constraints and as primary instruments of change of socio-economic behaviour. The process of radical institutional change is contingent upon prior change in the dominant worldview, while its dynamics resembles that of a paradigm change in political and scientific arenas. Therefore, the timely and adaptive outcome of institutional change with respect to global environmental problems remains intrinsically uncertain.     

Bayes methods for combining disease and exposure data in assessing environmental justice

Environmental justice reflects the equitable distribution of the burden of environmental hazards across various sociodemographic groups. The issue is important in environmental regulation, siting of hazardous waste repositories and prioritizing remediation of existing sources of exposure. We propose a statistical framework for assessing environmental justice. The framework includes a quantitative assessment of environmental equity based on the cumulative distribution of exposure within population subgroups linked to disease incidence through a dose-response function. This approach avoids arbitrary binary classifications of individuals solely as 'exposed' or 'unexposed'. We present a Bayesian inferential approach, implemented using Markov chain Monte Carlo methods, that accounts for uncertainty in both exposure and response. We illustrate our method using data on leukaemia deaths and exposure to toxic chemical releases in Allegheny County, Pennsylvania.     

Hierarchical Bayesian Modelling of plant colonisation by winged aphids: Inferring dispersal processes by linking aerial and field count data

Understanding and modelling insect pest dispersal is an important prerequisite for designing integrated pest management programs. Nevertheless, studies investigating the dispersal of small insects in natural conditions remain scarce mainly because of the difficulty of tracking the movements of these organisms. Here we propose to use Hierarchical Bayesian Modelling (HBM) framework to gain knowledge on hidden processes that cannot be observed directly in natura, such as insect landing and insect mortality, through the definition of latent variables. An HBM describing crop colonization by winged aphids was fitted to a large dataset of field observations issued from a long term survey at a wide scale of both aerial and field densities of the bird cherry-oat aphid Rhopalosiphum padi. This study provides the first evidence that suction trap data are reliable proxies of aphid colonizing rates in cereal fields in autumn and can be a nice alternative to the very time-consuming crop sampling. The proportion of winged aphids landing in cereal fields is shown to vary between regions according to the degree of investment of local R. padi population in sexual reproduction. Results also indicate that under autumnal field conditions, less than 5% of winged aphids survive more than 10 days after landing. This HBM provides the basis of a predictive model for aphid crop colonization that fully accounts for all sources of uncertainty. It should be of great value to improve the trust of users in any decision making systems.     

Risk,Risk Aversion,and On-Farm Soil Depletion

This paper studies the production and attendant soil depletion choices of a risk-averse farmer in two related models. The first is a two-date model with uncertainty in both production and end-of-period land price. The second is a three-date model in which production and consumption choices are made in both periods, but there is uncertainty only in the second period. In both models, the paper identifies plausible conditions under which a higher level of initial farmer wealth and/or a lower level of production and land risk lead to a lower level of output and, as a result, a lower level of output-induced soil depletion.     

Policy options for toxics disposal: Laissez-faire,subsidization, and enforcement

This paper evaluates three second-best policies for dealing with the problem of illegal hazardous-waste disposal. The first is a laissez-faire policy; the second is a policy under which legal disposal of hazardous waste is subsidized; the third is a policy that discourages illegal disposal by increasing the expected penalty associated with illegal disposal. Using a simple computational model, I compute the optimum subsidy and the optimum enforcement budget, and determine the conditions under which each policy option is superior to others.     

Alternative policy instruments under uncertainty: A programming model of toxic pollution control

This paper presents the results of a programming model which examines the relative performance of different regulatory regimes under uncertainty in controlling toxic wastes discharged by industry to a central treatment plant when the central is subject to threshold-type losses in treatment performance. Four regulatory regimes are examined: non-market quantity, price, marketable permit, and mixed price-quantity. The theoretical framework of the model is based on the analysis of prices vs quantities by Weitzman in 1974 and its extension to the case of a discontinuous threshold effect. The data used are based on the situation of Bridgeport, Connecticut, which has a number of metal finishing firms.     

Conservation of Biodiversity in a World of Use

Abstract: Biodiversity conservation has become the stated objective of national governments, state agencies, local communities, and scientific organizations. Yet despite this attention the term biodiversity remains poorly defined. One of the unfortunate consequences of this lack of definition is a proliferation of claims that biodiversity can be both used and conserved. This claim is difficult to assess without a more precise way of defining biodiversity. We offer a heuristic framework for measuring the consequences of human use for biodiversity. Our definition of biodiversity includes three components: genetic, population/species, and community/ecosystem. Each component has its own three attributes: composition, structure, and function. Using this definition, we assessed the effects of different types of human use on the different components and attributes of biodiversity. We show that (1) different degrees of human use or alteration result in differential conservation of biodiversity components; (2) some components and attributes of biodiversity are more sensitive to human use than others; and (3) only extremely limited use or virtually no alteration will protect all components.     

Evaluating and managing wildlife impacts of climate change under uncertainty

Wildlife managers face the daunting task of managing wildlife in light of uncertainty about the nature and extent of future climate change and variability and its potential adverse impacts on wildlife. A conceptual framework is developed for managing wildlife under such uncertainty. The framework uses fuzzy logic to test hypotheses about the extent of the wildlife impacts of past climate change and variability, and fuzzy multiple attribute evaluation to determine best compensatory management actions for adaptively managing the potential adverse impacts of future climate change and variability on wildlife. A compensatory management action is one that can offset some of the potential adverse impacts of climate change and variability on wildlife. Implementation of the proposed framework requires wildlife managers to: (1) select climate impact states, hypotheses about climate impact states, possible management actions for alleviating adverse wildlife impacts of climate change and variability, and future climate change scenarios; (2) choose biological attributes or indicators of species integrity; (3) adjust those attributes for changes in non-climatic variables; (4) define linguistic variables and associated triangular fuzzy numbers for rating both the acceptability of biological conditions under alternative management actions and the relative importance of biological attributes; (5) select minimum or maximum acceptable levels of the attributes and reliability levels for chance constraints on the biological attributes; and (6) define fuzzy sets on the extent of species integrity and biological conditions and select a fuzzy relation between species integrity and biological conditions. A constructed example is used to illustrate a hypothetical application of the framework by a wildlife management team. An overall best compensatory management action across all climate change scenarios is determined using the minimax regret criterion, which is appropriate when the management team cannot assign or is unwilling to assign probabilities to the future climate change scenarios. Application of the framework can be simplified and expedited by incorporating it in a web-based, interactive, decision support tool.     

Estimating parameters of hidden Markov models based on marked individuals: use of robust design data

Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last 20 years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected-value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We have also implemented these models in program MARK. This general framework could also be used by practitioners to consider constrained models of particular interest, or to model the relationship between within-primary-period parameters (e.g., state structure) and between-primary-period parameters (e.g., state transition probabilities).