Environmental Decision Making and Risk Management for Groundwater Systems

With an eye to a specific application in New Zealand, Ms. Gough explores the use of risk management approaches for environmental decision making at strategic, policy, management and operational levels

Conservation Contracting in Heterogeneous Landscapes: an application to watershed protection with threshold constraints

A key issue in the design of land use policy is how to integrate information about spatially variable biophysical and economic conditions into a cost-effective conservation plan. Using common biophysical scoring methods, in combination with economic data and simple optimization methods, we illustrate how one can identify a set of priority land parcels for conservation investment. We also demonstrate a way in which conservation agencies can incorporate concerns about biophysical thresholds in the identification of their priority land parcels. We apply these methods using Geographic Information System data from a New York conservation easement acquisition initiative for water quality protection. Working Paper # 2002-01

A Multi-Factorial Risk Prioritization Framework for Food-Borne Pathogens

To lower the incidence of human food-borne disease, experts and stakeholders have urged the development of a science- and risk-based management system in which food-borne hazards are analyzed and prioritized. A literature review shows that most approaches to risk prioritization developed to date are based on measures of health outcomes and do not systematically account for other factors that may be important to decision making. The Multi-Factorial Risk Prioritization Framework developed here considers four factors that may be important to risk managers: public health, consumer risk perceptions and acceptance, market-level impacts, and social sensitivity. The framework is based on the systematic organization and analysis of data on these multiple factors. The basic building block of the information structure is a three-dimensional cube based on pathogen-food-factor relationships. Each cell of the cube has an information card associated with it and data from the cube can be aggregated along different dimensions. The framework is operationalized in three stages, with each stage adding another dimension to decision-making capacity. The first stage is the information cards themselves that provide systematic information that is not pre-processed or aggregated across factors. The second stage maps the information on the various information cards into cobweb diagrams that create a graphical profile of, for example, a food-pathogen combination with respect to each of the four risk prioritization factors. The third stage is formal multi-criteria decision analysis in which decision makers place explicit values on different criteria in order to develop risk priorities. The process outlined above produces a ‘List A’ of priority food-pathogen combinations according to some aggregate of the four risk prioritization factors. This list is further vetted to produce ‘List B’, which brings in feasibility analysis by ranking those combinations where practical actions that have a significant impact are feasible. Food-pathogen combinations where not enough is known to identify any or few feasible interventions are included in ‘List C’. ‘List C’ highlights areas with significant uncertainty where further research may be needed to enhance the precision of the risk prioritization process. The separation of feasibility and uncertainty issues through the use of ‘Lists A, B, and C’ allows risk managers to focus separately on distinct dimensions of the overall prioritization. The Multi-Factorial Risk Prioritization Framework provides a flexible instrument that compares and contrasts risks along four dimensions. Use of the framework is an iterative process. It can be used to establish priorities across pathogens for a particular food, across foods for a particular pathogen and/or across specific food-pathogen combinations. This report provides a comprehensive conceptual paper that forms the basis for a wider process of consultation and for case studies applying the framework.risk analysis, risk prioritization, food-borne pathogens, benefits and costs

Factors influencing the adoption of mobile services consumers' preferences using analytic hierarchy process

The rapid and widespread development of innovations in mobile services is changing societies and improving lives around the world. Due to lagging adoption, many of these new innovations have yet failed to generate revenue that was expected by mobile network operators, application and content developers. There are several factors which are affecting the service adoption by consumers. This paper aims to provide practitioners and academics, an insight on what consumers' preferences are by using an Analytic Hierarchy Approach (AHP). The objective of this paper is to identify factors influencing the adoption of the mobile services. In this study we have considered Payment Mode, Functionality, Added Value and PQCP (perceived quality, cost and performance) as the main service adoption factors. The survey results indicate that Functionality is the most important influencing factor for the respondents, followed by Added Value, PQCP and Payment Mode.Adoption,AHP,Mobile Value Services,Consumer's Preferences

Exploiting a Goal-Decomposition Technique to Prioritize Non-functional Requirements

Business stakeholders need to have clear and realistic goals if they want to meet commitments in application development. As a consequence, at early stages they prioritize requirements. However, requirements do change. The effect of change forces the stakeholders to balance alternatives and reprioritize requirements accordingly. In this paper we discuss the problem of priorities to non-functional requirements subjected to change. We, then, propose an approach to help smooth the impact of such changes. Our approach favors the translation of nonoperational specifications into operational definitions that can be evaluated once the system is developed. It uses the goal-question-metric method as the major support to decompose non-operational specifications into operational ones. We claim that the effort invested in operationalizing NFRs helps dealing with changing requirements during system development. Based on\ud this transformation and in our experience, we provide guidelines to prioritize volatile non-functional requirements

FQPA IMPLEMENTATION TO REDUCE PESTICIDE RESIDUE RISKS: PART II: IMPLEMENTATION ALTERNATIVES AND STRATEGIES

Implementation of the Food Quality Protection Act (FQPA) is fraught with difficulty due to the divergent perspectives and demands of stakeholders in the process. In "Part I: Agricultural Producer Concerns," the authors reviewed the concerns of food producers about potential FQPA threats to farm profitability, international competitiveness, consumer perceptions, and the development of pest resistance to remaining pesticides. Fortunately, lessons from past environmental policy and economic theory offer useful principles for how to implement the FQPA. This paper, "Part II: Implementation Alternatives and Strategies" addresses ways to accommodate producer concerns while meeting the policy mandate of reducing risk from pesticide exposure, especially for infants and children. In so doing, the authors are neither advocating nor criticizing this FQPA policy mandate; rather, they are providing policy analysis of alternative implementation strategies.Crop Production/Industries, Food Consumption/Nutrition/Food Safety,

Requirements Prioritization Based on Benefit and Cost Prediction: An Agenda for Future Research

In early phases of the software cycle, requirements prioritization necessarily relies on the specified requirements and on predictions of benefit and cost of individual requirements. This paper presents results of a systematic review of literature, which investigates how existing methods approach the problem of requirements prioritization based on benefit and cost. From this review, it derives a set of under-researched issues which warrant future efforts and sketches an agenda for future research in this area

Diagnosis and the management constituency of small-scale fisheries

Diagnosis and adaptive management can help improve the ability of small-scale fisheries (SSF) in the developing world to better cope with and adapt to both external drivers and internal sources of uncertainty. This paper presents a framework for diagnosis and adaptive management and discusses ways of implementing the first two phases of learning: diagnosis and mobilising an appropriate management constituency. The discussion addresses key issues and suggests suitable approaches and tools as well as numerous sources of further information. Diagnosis of a SSF defines the system to be managed, outlines the scope of the management problem in terms of threats and opportunities, and aims to construct realistic and desired future projections for the fishery. These steps can clarify objectives and lead to development of indicators necessary for adaptive management. Before management, however, it is important to mobilize a management constituency to enact change. Ways of identifying stakeholders and understanding both enabling and obstructive interactions and management structures are outlined. These preliminary learning phases for adaptive SSF management are expected to work best if legitimised by collaborative discussion among fishery stakeholders drawing on multiple knowledge systems and participatory approaches to assessment. (PDF contains 33 pages

Measuring the Contribution of Water and Green Space Amenities to Housing Values: An Application and Comparison of Spatially-weighted Hedonic Models

This study estimates the influence of proximity to water bodies and park amenities on residential housing values in Knox County, Tennessee, using the hedonic price approach. Values for proximity to water bodies and parks are first estimated globally with a standard ordinary least square (OLS) model. A locally weighted regression model is then employed to investigate spatial non-stationarity and generate local estimates for individual sources of each amenity. The local model is able to capture the variability in the quality of water bodies and parks across the county, something a conventional hedonic model using OLS cannot do.Land Economics/Use,