Environmental Decision-Making in Context: A Toolbox
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The committee chose a small set of tools for discussion in this section to illustrate particularly valuable attributes for informing sustainability concepts. The discussion considers how sustainability considerations are currently incorporated into the use of these tools, and how sustainability could be incorporated to a greater extent with additional research and development.
Our discussion of particular tools should not be interpreted to mean those tools are most appropriate, or that tools not discussed are inappropriate. The essence of their paper was to address three questions:. Later analysts for example, Greenberg et al. How can key local officials, expert staff, and the public be informed to reduce concern and increase trust and confidence?
Risk assessment is thus a tool for evaluating the relative merits of various options for managing risk. It can be applied in an engineered-systems context to assess possible effects due to a system failure e. It can also be applied in a public health context to address health effects resulting from exposures to chemical contaminants or some other stressor. Ecologic risk assessments evaluate the likelihood that adverse effects to ecosystems including plant or animal communities would result from exposures to environmental stressors.
Risk assessment is also applied to episodic natural events e. In general, EPA has focused its risk-based decisions on reducing risk in response to human or ecologic exposures to individual stressors usually single chemicals or pollutants in particular environmental media. Previous NRC studies have provided detailed advice on the risk assessment and risk management framework e. NRC elucidated a four-step process for risk assessment: hazard identification, dose-response assessment, exposure assessment, and risk characterization.
Risk assessments should provide a quantitative, or at least qualitative, description of uncertainty and variability consistent with available data. The information required to conduct detailed uncertainty analyses may not be available in many situations. In addition to characterizing the full population at risk, attention should be directed to vulnerable individuals and subpopulations that may be particularly susceptible or more highly exposed.
The depth, extent, and detail of the uncertainty and variability analyses should be commensurate with the importance and nature of the decision to be informed by the risk assessment and with what is valued in a decision. This may best be achieved by early engagement of assessors, managers, and stakeholders in the nature and objectives of the risk assessment and terms of reference which must be clearly defined.
The risk assessment should compile or otherwise characterize the types, sources, extent, and magnitude of variability and substantial uncertainties associated with the assessment. To the extent feasible, there should be homologous treatment of uncertainties among the different components of a risk assessment and among different policy options being compared. To maximize public understanding of and participation in risk-related decision-making, a risk assessment should explain the basis and results of the uncertainty analysis with sufficient clarity to be understood by the public and decision-makers.
The uncertainty assessment should not be a significant source of delay in the release of an assessment. Uncertainty and variability should be kept conceptually separate in the risk characterization. Uncertainty in quantitative risk assessments such as those carried out using computational models can arise from a lack or incompleteness of information, as well as incorrect information.
Uncertainty analysis is rooted in understanding the level of confidence associated with a particular decision and the causes of the uncertainties. Uncertainty analysis is typically quantitative, and at the simplest level can be implemented by considering ranges for model input data variables and parameters. Advanced uncertainty analysis methods could include simulation, in which statistical distributions for model values are used to produce a range of possible outcomes.
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As mentioned previously, variability is often considered along with uncertainty. It refers to actual differences in attributes due to heterogeneity or diversity in the system being considered. NRC also recommended principles for uncertainty and variability in a risk assessment context see Box but the principles are generally useful and relevant when considering the issues in the entire suite of EPA sustainability tools.
The Green Book found that the four-step risk assessment process, as envisioned by NRC , is an important component and tool used to inform decisions in the SAM approach. Risk assessment can be used to inform considerations of sustainability concepts by estimating whether, and to what extent, public health or the environment will be affected if an action is taken. The Green Book recommended that EPA include risk assessment as a tool, when appropriate, as a key input into its sustainability decision making. However, it is not always possible to address complex risk-related considerations quantitatively with the risk assessment approaches typically used by EPA.
The approaches EPA relies upon have important limitations, including requiring large amounts of information and analyses, being applied mostly to existing problems rather than striving to prevent potential future problems from occurring, and taking excessive amounts of time to execute—particularly at the national level—when data are lacking see NRC and b for further discussion of the limitations.
Framing adaptation economics in decision-making: a policy-led framework
Many of the broader public-health and environmental-health questions EPA is facing include multiple exposures to complex mixtures of chemicals. The traditional RA-RM approach does not adequately address this concern, particularly for communities that are especially vulnerable to environmental exposures by socio-economic stressors and disproportionate past exposures.
In recognition of the limitations in approaching these complex issues, EPA has attempted to widen the context in which risk assessment is performed to include the early consideration of a broad range of decision options, and the cumulative threats of multiple social, environmental, and economic stressors to public health and the environment.
In , EPA released guidelines for cumulative risk assessments that include combined risks posed by aggregate exposure to multiple stressors—aggregate exposure includes all routes, pathways, and sources of exposure to a given agent or stressor EPA However, there is substantial uncertainty in the approaches and the data for understanding outcomes for cumulative risks EPA b. New techniques are needed for broader characterizations of cumulative risks to better account for the full range of environmental stressors, particularly for environmental justice analyses see Chapter 6.
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A broadening of the risk assessment and risk management paradigm raises the need for screening-level risk-assessment tools such as databases, computer software, and other modeling resources NRC For example, the integration of risk assessment with LCA would allow EPA to consider a fuller range of issues relevant to a decision see discussion later in this chapter. Characterizing and reducing uncertainty throughout the risk analysis process is a major challenge. Given limited agency budgets, it is essential that EPA be more decisive about what outcomes are more likely to occur and those that are likely to be consequential in order for it to compare tradeoffs.
Without narrowing uncertainty, it is difficult to assess, in a broad manner, the advantages and disadvantages of options, for example, about processes used to manufacture a chemical or how to evaluate a proposed site for drilling for oil and gas production. EPA needs to quickly scope from possible events, to their likelihoods and then to their consequences in order to identify major hazards. There is also a challenge for risk managers to assess the effectiveness of investments in reducing risks from environmental exposures, rebounding from episodic events, and communicating these to stakeholders.
This will be especially important for decisions concerning climate adaptation. North et al. In addition, NRC assesses whether, and under what conditions, public participation achieves the outcomes desired. For consideration of impacts on a regional scale, for example, one needs to know not only the expected economic consequences of an event or exposures to stressors, along with their uncertainties, but also the consequences of investing in various levels of prevention. EPA and other major federal agencies have been stimulating research in these areas, but it has become even more imperative because of the increasing pace of emerging challenges see Chapter 6.
The committee further discusses the relationship of risk assessment and risk management decision making to sustainability approaches in Chapter 7. Economic benefit-cost analysis BCA 3 and cost-effectiveness analysis CEA have been used for many decades to organize and evaluate information in support of decision making. Many textbooks provide overviews and definitions e. The conceptual foundations are described in an OECD document as:.
For a project or policy to qualify on cost-benefit grounds, its social benefits must exceed its social costs. CEA is concerned with how to get societal benefits at the lowest cost possible. For example, reducing pollution or saving lives is qualitatively a benefit, and might be measured in terms of tons avoided or lives saved but neither are valued in dollars.
The key contribution is being able to describe "cost per unit of effectiveness" without full monetization. It is possible to have multiple endpoints of interest for cost-effectiveness comparisons so that more than one criterion can be evaluated. CEA differs from BCA in that it considers only the cost of achieving a given set of improvements and provides a metric for identifying the lowest cost strategy to achieve this given gain. CEA and BCA can provide useful information for decision making whether or not potential effects of interest are monetized.
However, when a decision is related to a regulation, which prescribes a level of control or expenditure, CEA would be more appropriate. Even when other criteria are used in decision making, BCA and CEA tools provide valuable information to decision makers. Indeed, a well-established reason for not adhering to a strict benefit-cost analysis occurs when the program or policy has significant distributional concerns, i.
This is a key consideration when thinking about sustainability. A second reason commonly given for not adopting a strict benefit-cost test is that there are times when all ecosystem or environmental benefits cannot be monetized so that a decision rule that uses only monetized values risks adopting a policy that does not improve human wellbeing.
This is another key tool in the context of sustainability and can be used as a general guide on how to qualitatively and quantitatively assess other sustainability metrics for example see discussion below on ecosystem services valuation. It can also help when developing analyses of tradeoffs. Specifically, best practices in BCA recognize that there is a hierarchy of aspects that can be monetized, some that can be measured but not easily monetized, and some for which even measurement remains a challenge.
BCA and CEA are best considered tools for organizing information in transparent ways so that decision makers can understand the ramifications of their actions, regardless of the ultimate decision criterion they employ in choosing an action. Thus, BCA can provide information to support decision making within all three pillars of sustainability. Sustainability has been defined in economics as a commitment to recognizing the welfare of future generations and to address intra-generational equity.
Ecosystem services and environmental decision making: Seeking order in complexity
Common distinctions are made between weak sustainability a commitment to maintain a nondeclining or given standard of living over time or strong sustainability a commitment to preserve the stock of critical natural assets such as exhaustible or slowly renewable natural resources. See Pezzey ; Solow ; Stavins, et al. Approaches that can be used to incorporate both weak and strong sustainability concepts in welfare analyses include:.
Farrow A recent and rather important development in the area of BCA is its emerging application to measures being considered for climate change mitigation. As mentioned previously, the sustainability tool for this purpose is SCC that is being used to incorporate the social benefits of reducing carbon dioxide CO2 emissions into BCAs of regulatory actions that may have otherwise small impacts, but when combined with many other small impacts, lead to large cumulative global impacts.
The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. The purpose of SCC estimates is to allow federal, state, and local agencies to evaluate and incorporate climate mitigation measures in their planning activities.
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The choice of discount rate is an important step in the computation of the SCC because it includes consideration of damages from climate change that are expected to occur far in the future, often to future generations. A scientific debate on the logic and ethical basis for choosing a discount rate in long time horizon problems has emerged Arrow et al. EPA research into the appropriate choice of discount rate for its policies and programs is important. See Box for additional details. The SCC values in dollars per metric ton of carbon dioxide are averaged values from the application of three peer-reviewed integrated assessment models IAMs ; an IAM is a sustainability analytics tool included in the Analytics report.
That fourth value is included to represent greater than expected effects of temperature change. Considerations in choosing the appropriate discount rate for evaluating environmental problems, such as climate change, that are long-term and intergenerational are mentioned elsewhere in this chapter. The substantial changes in revised model estimates over the short period of 3 years indicate the rapid increase in knowledge of the science and economics of climate change.
Equally important, the changes reflect many uncertainties involved in the model estimates, which should be reduced as data and models improve.