Suchergebnisse

Environmental decision‐support tools often predict a multitude of different human health effects due to environmental stressors. The accounting and aggregating of these morbidity and mortality outcomes is key to support decision making and can be accomplished by different methods that we call human health metrics. This article attempts to answer two questions: Does it matter which metric is chosen? and What are the relevant characteristics of these metrics in environmental applications? Three metrics (quality adjusted life years (QALYs), disability adjusted life years (DALYs), and willingness to pay (WTP)) have been applied to the same diverse set of health effects due to environmental impacts. In this example, the choice of metric mattered for the ranking of these environmental impacts and it was found for this example that WTP was dominated by mortality outcomes. Further, QALYs and DALYs are sensitive to mild illnesses that affect large numbers of people and the severity of these mild illnesses are difficult to assess. Eight guiding questions are provided in order to help select human health metrics for environmental decision‐support tools. Since health metrics tend to follow the paradigm of utility maximization, these metrics may be supplemented with a semi‐quantitative discussion of distributional and ethical aspects. Finally, the magnitude of age‐dependent disutility due to mortality for both monetary and nonmonetary metrics may bear the largest practical relevance for future research.

A third generation of environmental policy making and risk management will increasingly impose environmental measures, which may give rise to analyzing countervailing risks. Therefore, a comprehensive analysis of all risks associated with the decision alternatives will aid decisionmakers in prioritizing alternatives that effectively reduce both target and countervailing risks. Starting with the metaphor of the ripples caused by a stone that is thrown into a pond, we identify 10 types of ripples that symbolize, in our case, risks that deserve closer examination: direct, upstream, downstream, accidental risks, occupational risks, risks due to offsetting behavior, change in disposable income, macro‐economic changes, depletion of natural resources, and risks to the manmade environment. Tools to analyze these risks were developed independently and recently have been applied to overlapping fields of application. This suggests that either the tools should be linked in a unified framework for comparative analysis or that the appropriate field of application for single tools should be better understood. The goals of this article are to create a better foundation for the understanding of the nature and coverage of available tools and to identify the remaining gaps. None of the tools is designed to deal with all 10 types of risk. Provided data suggest that, of the 10 types of identified risks, those associated with changes in disposable income may be particularly significant when decision alternatives differ with respect to their effects on disposable income. Finally, the present analysis was limited to analytical questions and did not capture the important role of the decision‐making process itself.

Increasing residential insulation can decrease energy consumption and provide public health benefits, given changes in emissions from fuel combustion, but also has cost implications and ancillary risks and benefits. Risk assessment or life cycle assessment can be used to calculate the net impacts and determine whether more stringent energy codes or other conservation policies would be warranted, but few analyses have combined the critical elements of both methodologies. In this article, we present the first portion of a combined analysis, with the goal of estimating the net public health impacts of increasing residential insulation for new housing from current practice to the latest International Energy Conservation Code (IECC 2000). We model state‐by‐state residential energy savings and evaluate particulate matter less than 2.5 μm in diameter (PM2.5, NOx, and SO2 emission reductions. We use past dispersion modeling results to estimate reductions in exposure, and we apply concentration‐response functions for premature mortality and selected morbidity outcomes using current epidemiological knowledge of effects of PM2.5 (primary and secondary). We find that an insulation policy shift would save 3 × 1014 British thermal units or BTU (3 × 1017 J) over a 10‐year period, resulting in reduced emissions of 1,000 tons of PM2.5, 30,000 tons of NOx, and 40,000 tons of SO2. These emission reductions yield an estimated 60 fewer fatalities during this period, with the geographic distribution of health benefits differing from the distribution of energy savings because of differences in energy sources, population patterns, and meteorology. We discuss the methodology to be used to integrate life cycle calculations, which can ultimately yield estimates that can be compared with costs to determine the influence of external costs on benefit‐cost calculations.