3

Uncertainty in Technological and Economic Factors in EPA’s Decision Making

In Chapter 1 the committee specified three factors that affect decisions made by the U.S. Environmental Protection Agency (EPA): estimates of human health risks, technology availability, and economics (see Figure 1-2). As outlined in Chapter 1, the legal context within which a decision is made determines, to a large extent, which of those factors are considered by EPA. EPA’s analyses of uncertainty have traditionally focused on the uncertainties in human health risk estimates (discussed in Chapter 2), but uncertainties in technological and economic factors can affect EPA’s decisions. In this chapter the committee discusses the uncertainty in those factors. Although these three factors are not independent—for example, the estimates of human health risks influence the economic analysis, and technology availability contributes to economic analyses—the committee discusses them separately.

In addition to those three factors, other factors, including the political climate and social factors such as environmental justice and public sentiment, can also affect EPA’s decisions. Although there is uncertainty about such factors, that uncertainty is difficult, if not impossible, to quantify. Those factors, therefore, are discussed separately at the end of this chapter.

TECHNOLOGY AVAILABILITY

Technology Assessments

Congress recognizes that technological considerations—including the feasibility, impacts, and range of risk-management options—are key to



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3 Uncertainty in Technological and Economic Factors in EPA’s Decision Making I n Chapter 1 the committee specified three factors that affect decisions made by the U.S. Environmental Protection Agency (EPA): estimates of human health risks, technology availability, and economics (see Fig- ure 1-2). As outlined in Chapter 1, the legal context within which a decision is made determines, to a large extent, which of those factors are consid- ered by EPA. EPA’s analyses of uncertainty have traditionally focused on the uncertainties in human health risk estimates (discussed in Chapter 2), but uncertainties in technological and economic factors can affect EPA’s decisions. In this chapter the committee discusses the uncertainty in those factors. Although these three factors are not independent—for example, the estimates of human health risks influence the economic analysis, and technology availability contributes to economic analyses—the committee discusses them separately. In addition to those three factors, other factors, including the political climate and social factors such as environmental justice and public senti- ment, can also affect EPA’s decisions. Although there is uncertainty about such factors, that uncertainty is difficult, if not impossible, to quantify. Those factors, therefore, are discussed separately at the end of this chapter. TECHNOLOGY AVAILABILITY Technology Assessments Congress recognizes that technological considerations—including the feasibility, impacts, and range of risk-management options—are key to 73

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74 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY many of EPA’s regulatory issues and decisions, and many of the statutes that grant EPA its regulatory authority require the evaluation of the technology available to implement a proposed regulation. Exactly how Congress directs EPA to consider technological factors differs from statute to statute and, in some cases, from section to section in a single statute (see Box 3-1 for a description of different technology standards). Statutes typically require a consideration of the technologies likely to be feasible within a given time frame, and some require engineering costs (that is, the costs of purchas- ing, installing, and operating the technologies to meet the standard) to be considered when assessing feasibility.1 EPA typically considers—and court decisions have supported that consideration2—“technologies that are cur- rently available” to include technologies that can reasonably be anticipated to be developed in the future. The section of the Clean Air Act (CAA) related to mobile emission sources requires EPA to evaluate the “availability of technology, including costs”3 when considering revised tailpipe emission standards (Section 202), but to evaluate the “best technology that can reasonably be anticipated to be available at the time such measures are to be implemented”4 when considering standards for urban buses (Section 219). When EPA is deciding whether to list a pollutant as a hazardous air pollutant (HAP) under the CAA, it is only allowed to consider “adverse effects on human health or adverse environmental effects,”5 not cost or technical feasibility. When it sets standards for HAPs under the CAA, however, in addition to health risk assessments, it is required to take the maximum achievable control technol- ogy into account. The CAA also requires EPA to establish National Ambi- ent Air Quality Standards solely on the basis of health risks, and it is up to individual states to set implementation plans to achieve those standards. Under the Clean Water Act (CWA), EPA is required to develop effluent standards for sources of water pollution based on the “best practicable con- trol technology currently available”6 (BPT), or the “best available technol- ogy economically achievable”7 (BAT). The CWA establishes the framework for determining the BAT for conventional water pollution.8 In general, BAT effluent limits represent the best available economically achievable 1  The initial estimation of engineering costs could be considered part of the technology as- sessment or part of the benefit–cost assessment. Regardless, those costs will be included in the economic analysis in the regulatory impact assessment. 2  See, for example, Portland Cement Assn. v. Ruckelshaus, 486 F.2s 375 (D.C. Circ. 1973). 3  42 U.S.C. § 7521 (i)(2)(A)(i) (2012). 4  42 U.S.C. § 7554 (a) (2012). 5  42 U.S.C. § 7412 (b)(2)(B) (2012). 6  33 U.S.C. § 1311 (b)(1)(A) (2010). 7  33 U.S.C. § 1314 (b)(1)(B) (2010). 8  CWA, Pub. L. No. 107-377, Section 304(b)(2) (1972).

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UNCERTAINTY IN TECHNOLOGICAL AND ECONOMIC FACTORS 75 BOX 3-1 EPA Control Technology Categories Best Practicable Control Technology Currently Available “BPT is defined at Section 304(b)(1) of the [Clean Water Act (CWA)]. EPA sets Best Practicable Control Technology Currently Available (BPT) effluent limita- tions for conventional, toxic, and non-conventional pollutants. Section 304(a)(4) designates the following as conventional pollutants: biochemical oxygen demand (BOD5), total suspended solids, fecal coliform, pH, and any additional pollutants defined by the Administrator as conventional. The Administrator designated oil and grease as an additional conventional pollutant on July 30, 1979 (see 44 FR 44501).” Best Conventional Pollutant Control Technology “Best Conventional Pollutant Control Technology (BCT) is defined at Section 304(b)(4) of the CWA. The 1977 amendments to the CWA required EPA to iden- tify effluent reduction levels for conventional pollutants associated with BCT for discharges from existing industrial point sources. In addition to the other factors specified in section 304(b)(4)(B), the CWA requires that EPA establish BCT limita- tions after consideration of a two part “cost-reasonableness” test. EPA explained its methodology for the development of BCT limitations in a Federal Register notice.” Best Available Technology Economically Achievable “Best Available Technology Economically Achievable (BAT) is defined at Section 304(b)(2) of the CWA. In general, Best Available Technology Economically Achiev- able (BAT) represents the best available economically achievable performance of plants in the industrial subcategory or category. The factors considered in assess- ing BAT include the cost of achieving BAT effluent reductions, the age of equip- ment and facilities involved, the process employed, potential process changes, non-water quality environmental impacts, including energy requirements and other such factors as the EPA Administrator deems appropriate. EPA retains consider- able discretion in assigning the weight according to these factors. BAT limitations may be based on effluent reductions attainable through changes in a facility’s processes and operations. Where existing performance is uniformly inadequate, BAT may reflect a higher level of performance than is currently being achieved within a particular subcategory based on technology transferred from a different subcategory or category. BAT may be based upon process changes or internal controls, even when these technologies are not common industry practice.” SOURCE: EPA, 2012.

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76 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY performance of plants in the industrial subcategory or category. If the vari- ability of technologies within a category is too large, EPA can subdivide an industrial sector into more narrowly defined categories in order to examine available technologies in a more granular way. Under the Safe Drinking Water Act (SDWA),9 EPA must determine a nonenforceable maximum contaminant level goal (MCLG) for contami- nants solely on the basis of human health risks.10 In contrast, the enforce- able drinking-water standard promulgated under the SDWA—termed the maximum contaminant level (MCL)—is the “level that may be achieved with the use of the best available technology, treatment techniques, and other means that EPA finds are available (after examination for efficiency under field conditions, not solely under laboratory conditions), taking cost into consideration.”11 Thus, EPA must consider technological and eco- nomic factors when setting the enforceable drinking water standards (the MCLs) but not the MCLGs. Uncertainties in Technology Assessments There is inherent uncertainty in the analyses of both current and fu- ture control technologies. When assessing current technologies to establish a BAT, for example, EPA must consider such parameters as the cost of achieving BAT effluent reductions, the age of the equipment and facilities involved, the process employed, potential process changes, and non-water- quality environmental impacts. There may be only limited data available about any or all of those parameters; for instance, EPA might have the facility age for only a subset of facilities within a sector or subcategory of a sector. There can also be variability in those parameters within a sector or subcategory of a sector, which contributes to the uncertainty in EPA’s decisions. Using current technology for rulemaking, however, ignores that uncertainty, and it can lead to an underestimate of the level of control tech- nologies that could be implemented. For example, mobile source control technology has steadily improved over the past 30 years. A 2005 review of technology innovation included information about how the technologies for catalyst and fuels preparation continued to improve over time with progres- sively more stringent tail pipe emission standards (ICF Consulting, 2005). 9  SDWA, Pub. L. No. 93-523. 10  When the statutory framework requires that decisions be made solely on the basis of health effects, the focus is likely to be on protecting maximally exposed or sensitive individu- als. For such decisions there is no opportunity for other factors (for example, cost of mitiga- tion, loss to property, loss of employment, and social factors) to influence the selection of management options. 11  SDWA, Pub. L. No. 93-523.

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UNCERTAINTY IN TECHNOLOGICAL AND ECONOMIC FACTORS 77 The challenge is even greater when trying to predict what technologies might be available or in widespread use in the future and also to predict the effectiveness and costs of those technologies. For those predictions, analysts not only must estimate what technologies are currently available and the costs and amounts of emission reductions associated with those technologies, but also must model or somehow predict future developments in control technologies. The rate of innovation varies among sectors (Pavitt, 1984). An additional challenge is that past innovation curves might not re- flect future rates of innovation. As businesses begin to implement technolo- gies, there is a learning force at play that changes the rate of innovation. A regulation itself can also lead to an increase in the market size anticipated for a given technology, which provides a stimulus for investment in research and development for the technology. In other words, having regulations in place or on the horizon can lead to innovation in control technologies. In some sectors different businesses or entities will compete based on how efficiently they achieve a performance standard. This stimulates innovation and, as discussed later in this chapter, some analyses suggest that the inno- vation leads to costs that turn out to be less than estimated at the time of rulemaking (Morgenstern, 1997, 2011). Because there has been little study of innovation rates and the effects that EPA’s regulations have on those in- novation rates, there are insufficient data with which to develop models that account for those effects. Learning how technologies develop over time and how EPA’s regulatory decisions affect that development could help improve the agency’s models of technological factors and decrease the uncertainty in agency decisions. EPA sometimes has the statutory authority, or the precedent, to con- sider both available technologies and technologies that are anticipated to be available. There are greater inherent unknowns associated with anticipating technologies—which in some cases could be considered deep uncertainty. In some cases, however, the agency will select a current technology but build into the rule a review or evaluation at a future date to update the rule as technology advances (see Chapter 2 for more discussion). An alternative to this approach is the development of performance standards rather than specific technologies. Decision in the Face of Technology Uncertainties: EPA’s Highway Heavy-Duty Engine Rule In 1996 EPA was faced with uncertainties surrounding the technologies available to control emissions from highway heavy-duty engines. When considering regulations to control emissions of air pollution from highway heavy-duty engines (Control of emissions of air pollution from highway heavy-duty engines, 1996), EPA recognized that high concentrations of

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78 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY sulfur in diesel fuel led to corrosion caused by emission-control technologies that employ exhaust gas recirculation and that these sulfur concentrations were a major limiting factor to implementing emission-control technology. In its rulemaking EPA was faced with uncertainty about when and by what extent the concentrations of sulfur in fuel would decrease and about how low the concentration of sulfur would have to be in order not to affect emission-control technologies adversely. Given those uncertainties, EPA took a more adaptive strategy to rule- making. Rather than establishing a standard for emissions immediately, it established a timeline for future implementation of a standard and for evaluation of the effects of sulfur on control technologies in the interim. Specifically, in its rule EPA stated that “fuel changes could reduce the amount of emission control necessary for the engine, but . . . are probably not necessary to meet the proposed standards. However, this remains an area of uncertainty and is one of the issues which would be addressed fur- ther in the proposed 1999 review of the feasibility of the standard” (Con- trol of emissions of air pollution from highway heavy-duty engines, 1996, p. 33455). In other words, EPA identified the potential effects of changing fuel composition as an uncertainty in the sense that it was not known to what extent decreasing the sulfur content and other changes in diesel fuel would affect the ability meet proposed emission standards, and the agency indicated that it would further evaluate those effects in a 1999 review of the standard. EPA’s subsequent July 2000 document, Regulatory Impact Analysis: Control of Emissions of Air Pollution from Highway Heavy-Duty Engines (EPA, 2000c), reported that much lower concentrations of sulfur dioxide (a maximum of 30 ppm compared to then concentrations up to 500 ppm) would limit the corrosion caused by exhaust gas recirculation emission-control technologies. In a separate rule EPA later required a significant reduction in the amount of sulfur in diesel fuels beginning with the 2007 model year. During the decade between when the diesel engine standards were set in the 1990s and when the standards took effect in 2007, EPA developed companion regulations to change fuel composition and implemented demonstration programs, while engine manufacturers developed innovative emission con- trols as they were preparing for the regulatory change. EPA was faced with an uncertainty about how sulfur content affects emission controls and, therefore, allowed the industry time for additional research, development, and commercialization of control technologies before having a regulation become effective. Such an approach allowed for the evaluation of recent technological innovations and updated standards that reflected those in- novations. Scheduling such reviews of standards several years in the future can also motivate research and development.

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UNCERTAINTY IN TECHNOLOGICAL AND ECONOMIC FACTORS 79 ECONOMICS As discussed above, a number of the statutes and executive orders12 under which EPA operates require it to consider economics and economic analyses in its regulatory decisions. As described in EPA’s Guidelines for Preparing Economic Analysis (EPA, 2010), economic analyses combine various types of information, including information from the assessments of other factors discussed in this and the previous chapter, to provide “a means to organize information and to comprehensively assess alternative actions and their consequences” (pp. 1–2). The analyses inform decision makers of the costs associated with the various risks, the benefits of reducing those risks, the costs associated with risk mitigation or remediation options, and the distributional effects (see Box 3-2 for EPA’s definitions related to economic analysis). As with the other factors that affect EPA’s decisions, economic analyses have uncertainties. Those uncertainties contribute to the overall uncertainty in a decision, and EPA should consider them its decision-making process. This section discusses economic analysis and its uncertainty. The sec- tion begins with a brief overview of economic analysis in the regulatory setting. A number of texts and reports describe the use of economics and economic analyses in decision making in general (Gold et al., 1996; IOM, 2006; Sloan and Hsieh, 2012) and in environmental decision making in particular (Atkinson and Mourato, 2008; EPA, 2010; Pearce et al., 2006); the reader is referred to those sources for more detailed discussions. The committee then describes the uncertainties associated with those analyses, using examples to illustrate how EPA has evaluated and characterized un- certainty in economic analyses, followed by a discussion of the assessment of those uncertainties and reporting of those uncertainties. Economic Analysis Approaches Two of the main types of economic analysis are benefit–cost analy- sis (BCA; also called cost–benefit analysis) and cost-effectiveness analy- ses (CEA) (see Box 3-2 for definitions). In the context of environmental 12  For example, Executive Order 12866 requires analyses of the costs and benefits of “signifi- cant regulatory actions.” A “significant regulatory action” is defined as “any regulatory action that is likely to result in a rule that may: (1) Have an annual effect on the economy of $100 million or more or adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local, or tribal governments or communities; (2) Create a serious inconsistency or otherwise interfere with an action taken or planned by another agency; (3) Materially alter the budgetary impact of entitlements, grants, user fees, or loan programs or the rights and obligations of recipients thereof; or (4) Raise novel legal or policy issues arising out of legal mandates, the President’s priorities, or the principles set forth in this Executive order.”

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80 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY BOX 3-2 Definitions of Select Terms Used in Economic Analyses as Defined in Guidelines for Preparing Economic Analysis Benefit–cost analysis (BCA) A BCA evaluates the favorable effects of policy actions and the associated oppor- tunity costs of those actions. It answers the question of whether the benefits are sufficient for the gainers to potentially compensate the losers, leaving everyone at least as well off as before the policy. The calculation of net benefits helps ascertain the economic efficiency of a regulation. Benefit–cost ratio A benefit–cost ratio is the ratio of the net present value (NPV) of benefits associ- ated with a project or proposal relative to the NPV of the costs of the project or proposal. The ratio indicates the benefits expected for each dollar of costs. Note that this ratio is not an indicator of the magnitude of net benefits. Two projects with the same benefit–cost ratio can have vastly different estimates of benefits and costs. Benefits Benefits are the favorable effects society gains due to a policy or action. Econo- mists define benefits by focusing on changes in individual well-being, referred to as welfare or utility. Willingness to pay is the preferred measure of these changes, as it theoretically provides a full accounting of individual preferences across trade- offs between income and the favorable effects. Cost-effectiveness analysis (CEA) CEA examines the costs associated with obtaining an additional unit of an envi- ronmental outcome. It is designed to identify the least expensive way of achieving a given environmental quality target or the way of achieving the greatest improve- ment in some environmental target for a given expenditure of resources. regulations, the overall objective of both BCA and CEA is to compare dif- ferent regulatory options,13 different combinations of regulatory options, or the value of different regulatory options.14 In BCA both benefits and costs are expressed in monetary units, whereas CEA is intended to identify 13  Potential regulatory options include the option of taking no regulatory action. 14  That comparison, even in cost-effectiveness analysis, requires characterizing value in a common unit; one convenient measure of common value is money (Gold et al., 1996; Sloan and Hsieh, 2012).

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UNCERTAINTY IN TECHNOLOGICAL AND ECONOMIC FACTORS 81 Costs Costs are the dollar values of resources needed to produce a good or service; once allocated, these resources are not available for use elsewhere. Private costs are the costs that the buyer of a good or service pays the seller. Social costs, also called externalities, are the costs that people other than the buyers are forced to pay, often through nonpecuniary means, as a result of a transaction. The bearers of social costs can be either particular individuals or society at large. Distributional analysis Distributional analysis assesses changes in social welfare by examining the ef- fects of a regulation across different subpopulations and entities. Two types of distributional analyses are the economic impact analysis (EIA) and the equity assessment. Economic impact analysis (EIA) An EIA examines the distribution of monetized effects of a policy, such as changes in industry profitability or in government revenues, as well as nonmonetized ef- fects, such as increases in unemployment rates or numbers of plant closures. Social cost From a regulatory standpoint, social cost represents the total burden a regula- tion will impose on the economy. It can be defined as the sum of all opportunity costs incurred as a result of the regulation. These opportunity costs consist of the value lost to society of all the goods and services that will not be produced and consumed if firms comply with the regulation and reallocate resources away from production activities and towards pollution abatement. To be complete, an estimate of social cost should include both the opportunity costs of current consumption that will be foregone as a result of the regulation and also the losses that may result if the regulation reduces capital investment and thus future consumption. Total cost Total cost is defined as the sum of all costs associated with a given activity. SOURCE: EPA, 2010. the most effective use of resources without requiring the monetization of all benefits or costs. In 2003 the Office of Management and Budget (OMB) issued guidance on the development of regulatory analyses and the use of BCA and CEA. In that guidance OMB states that “major rulemaking should be supported by both types of analysis wherever possible” (OMB, 2003, p. 9). In 2011 EPA issued guidelines for economic analyses (EPA, 2010). In contrast to the OMB guidance, EPA’s guidelines focus on conducting BCAs for economic

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82 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY analyses, although they mention that CEA can be used to help identify the least costly approach to achieving a specific goal. BCAs are used to determine whether the benefits of a particular regula- tory option justify its costs. When making decisions using a strict benefit– cost rule, a regulatory agency will adopt only those projects or implement only those regulations for which the present value of the net benefit (benefit minus cost) is non-negative or the benefit–cost ratio (present value of ben- efits divided by present value of cost) is one or greater. Under that strict rule, all projects for which the net benefit is negative or the benefit-to-cost ratio is less than one would be rejected. Alternatively, BCA can be used to rank regulatory options by the size of their net benefits; regulators can then choose the option or options with the largest net benefit. That approach, however, places small-scale projects at a disadvantage relative to larger ones. To account for project size, some economic analysts prefer to use a benefit-to-cost (B/C) ratio; if the B/C ratio is greater than one, the project is accepted, and otherwise it is rejected. Description of Uncertainties in Economic Analyses Uncertainty in economic analyses can stem from determining what costs and benefits should be including in the analyses, in the estimates of the costs and benefits themselves, and in adjusting the costs and benefits to reflect that they will occur in the future (that is, discounting). The outcome of an economic analysis can vary greatly depending on the boundaries of that analysis, that is, on what is included in the analysis (Meltzer, 1997). Analyses can include mental health care costs and other health care costs, costs from lost employment, and a variety of other costs, such as the costs from the increased domestic violence that results from lost employment. In its guidelines, EPA (2010) considers social cost—which is described as the “total burden a regulation will impose on the economy” and “the sum of all opportunity costs incurred as a result of the regulation” (pp. xiv–xv)—to be the most comprehensive and appropriate measure of cost for a BCA. “Opportunity costs consist of the value lost to society of all the goods and services that will not be produced and consumed if firms comply with the regulation and reallocate resources away from production activities and toward pollution abatement” (p. xv). Social cost is narrower than the “total cost,” which is considered in a number of regulatory impact analyses and includes costs beyond the social costs. At the time of an economic analysis, the mean estimated values of an- ticipated benefits and costs and the corresponding net benefit or the benefit– cost ratio might indicate that a project is worth undertaking or that a rule is worth adopting, but after implementation the actual benefits and costs seen in retrospect can differ considerably from the estimated mean values

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UNCERTAINTY IN TECHNOLOGICAL AND ECONOMIC FACTORS 83 because of uncertainties at the time of the analysis. For example, if there is an unanticipated increase in energy prices after a decision, the actual cost of a decision could exceed the anticipated cost, and what was thought to be a project of positive net benefit may turn out to have a substantial nega- tive net benefit. That uncertainty, however, is rarely detailed in a BCA or CEA, let alone in the rationale for the decisions that use the BCA or CEA (NRC, 2002a). We discuss below the uncertainties associated with the estimates of costs—categorized broadly as compliance costs and the costs imposed across the entire economy—and benefits as well as the uncertainties related to discounting those costs and benefits. Cost Analysis Compliance Costs Compliance costs are the costs incurred in complying with a proposed regulatory rule, and they include those costs incurred by parties comply- ing with the regulations (for example, the costs to install emission-control technologies in an industrial facility). These compliance costs are borne by regulated entities, of which many, if not most, are in the private sector (EPA, 2010). Additional compliance costs include the government’s costs to monitor and enforce the rule and, more generally, activities to ensure compliance (Harrington et al., 1999). The increased costs borne by regu- lated entities and public agencies charged with enforcement may also be associated with increased costs borne by other private-sector entities and public agencies at different levels of government. An increase in enforce- ment effort will generally raise compliance rates and, subsequently, the costs borne by the regulated parties (Shimshack and Ward, 2005, 2008). Some research indicates, however, that the costs of “government administration of environmental statues and regulations” are “rarely considered in regula- tory cost estimates” (Harrington et al., 1999, p. 9). Measuring the capital costs and operating costs incurred by private parties in order to estimate compliance costs can be a difficult task for agencies with the responsibility of implementing a regulation. For example, under a cost-of-service regulation, an agency sets a price per unit of output and must gauge whether the regulated price it sets is sufficient to yield an adequate return to the regulated entity. At the same time, the agency must ensure that the price is not set so high that returns are excessive (Breyer, 1995). Compliance costs are often estimated using engineering models, with the models often being based on expert opinions of the relationships be- tween input use and outputs for a particular industry or application within

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96 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY In practice, discount rates of 3 and 7 percent are in widespread use for projects of only a few decades in duration (EPA/RTI International, 2008). Some research indicates that long-term rates are less than short-term rates, and therefore, for projects affecting more than one generation, such as those involving climate change, a lower discount rate or varying discount rates that are given by a schedule may be appropriate (Newell and Pizer, 2003, 2004). These “default” assumptions indicate the uncertainty inher- ent in this component of economic analyses. The most direct way of ac- counting for such uncertainties is to evaluate the benefits versus the costs of a particular decision using alternative discount rates and to judge how estimates of net benefits or benefit-to-cost ratios are affected and if they are affected sufficiently to affect the decision about project or rule adoption. As the committee indicated in Chapter 1, a sole focus on uncertainties in the risk assessment ignores possibly even greater uncertainties in key factors in decision making. Assessment of Uncertainties in Economic Analyses There are several methods for accounting for the uncertainty in eco- nomic analyses in a decision, ranging from the very simple to the highly complex. Simple solutions include inflating costs, deflating benefits, or both, and establishing a threshold for a regulatory action, such as only adopting those projects for which the projected benefit is 1.25 times the cost. That approach is similar to the use of defaults in estimating health risks and, similarly, has inherent uncertainties that EPA should consider when weigh- ing economic factors. An example of a more complex approach to dealing with uncertainty in economic analyses can be found in OMB’s guidelines on the development of regulatory analysis (OMB, 2003). Those guidelines recommend specifying the entire probability distribution of benefits and costs by, for example, using Monte Carlo simulations to assess the degree of uncertainty in estimates of benefits, costs, and benefit–cost ratios (OMB, 2003). Monte Carlo simulations are widely used in business applications and also, as mentioned in Chapter 2, for estimating human health risks (Marshall et al., 2009; Thompson et al., 1992). People’s decisions about when they can afford to retire provide an example of how Monte Carlo analyses can aid decision making. By run- ning multiple simulations using typical future consumption levels and the probability of different rates of return on investments and longevity, both of which are considered to be uncertain, Monte Carlo simulations can estimate the probability that an individual near retirement will outlive his or her as- sets, given the person’s initial asset mix. For a Monte Carlo simulation of a BCA or CEA, the input would be the range of potential benefits and the likelihood that each benefit would turn out to be the “true benefit” that

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UNCERTAINTY IN TECHNOLOGICAL AND ECONOMIC FACTORS 97 is actually seen. Similarly, the range of potential costs and the likelihood that each cost is the “true” or “actual” cost would be used to determine the probable cost. OMB’s guidelines (OMB, 2003) and the NRC report Estimating the Public Health Benefits of Proposed Air Pollution Regula- tions (NRC, 2002b) both recommend probabilistic modeling of uncertainty for BCAs and CEAs. Although such models are not commonplace in EPA’s estimates of benefits and costs (Jaffee and Stavins, 2007), the agency has used them at times. In a Monte Carlo analysis of benefits and costs, one calculates the probability that the actual net benefit will be negative or else that the net benefit will be less than a prespecified threshold amount. Reporting of Uncertainty A common problem when decision makers are faced with uncertainty related to a decision is how the uncertainty should be presented to the decision makers. Estimates of uncertainty in BCA and CEA are often pre- sented as aggregate numbers. Aggregates, however, do not by themselves help a decision maker identify the individual sources of uncertainty, but rather they indicate the overall level of certainty in the analyses. Knowing the specific sources of uncertainty can be as important as—if not more important than—documenting uncertainties in the aggregate. If the sources of uncertainty are identified, it may be possible to decrease the uncertainty by conducting further research or to refine the policy option in order to reduce the impact of the uncertainty. For example, if it is known that the uncertainty in a benefit valuation is due to heterogeneity, then the identifica- tion of the groups or stakeholders who would bear the burden of a higher- than-anticipated cost or of a lower-than-anticipated benefit because of the uncertainty might allow decision makers to design the initial proposed regulations to address or cope with those potential outcomes in advance. Although such analyses are possible, they are rarely done for BCAs and CEAs (NRC, 2002a). If the individual sources of uncertainty that contrib- ute to the overall uncertainty can be determined, uncertainty analyses in BCAs and CEAs can incorporate graphic representations—such as Tornado plots where the relative importance of the different sources of uncertainty are displayed sequentially—to provide an easily interpretable graphic dis- play of the sources of uncertainty (Krupnick et al., 2006). Arsenic in Drinking Water As discussed in Chapter 2, in 2001 EPA evaluated a recently enacted standard that would decrease the allowable concentration of arsenic in drinking water. When estimating the cost associated with implementing the proposed arsenic rule, EPA used a Monte Carlo simulation to forecast “a

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98 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY distribution of costs around the mean compliance cost expected for each system size category” (EPA, 2000a).18 The uncertainty analysis included only treatment costs, and it assumed a single commercial discount rate of 5 percent. In reviewing EPA’s cost estimates for the arsenic rule, the Arsenic Cost Working Group of the National Drinking Water Advisory Council noted that the “value of existing national cost estimates is now limited by the large uncertainty associated with the estimated outcomes” and recommended that the EPA “clearly explain the limitations of each estimate and quantify the uncertainty associated with the Arsenic Rule estimates” (Arsenic Cost Working Group to the National Drinking Water Advisory Council, 2001, p. 2). The advisory council also noted the need for “a more representative methodology to assess compliance cost,” noting the advantages of “an approach based on aggregated county, regional or state costs, coupled with extensive individual case analysis” (p. 2). Furthermore, the council recommended the use of a distribution of flows rather than the mean or median flow and noted the “significant uncertainty associated with EPDS [the number of entry points to the distribution system] determina- tion” (p. 3), and it recommended how EPA should examine information given that uncertainty. The relative lack or small amount of uncertainty analyses performed for the analysis of cost and benefits in the arsenic rule contrasts sharply with the extensive uncertainty analyses that were conducted for the human health risk estimates. EPA and the National Research Council conducted extensive analyses of the health data in order to estimate health risks, and that work included an extensive discussion of inherent uncertainties as well as quantitative assessments of how many of those uncertainties might affect the health risk estimates. Some of those limitations were outlined by the Arsenic Rule Benefits Review Panel of EPA’s SAB (EPA, 2000b). That panel noted that in the estimates for the number of cancer cases avoided there was nothing said about the uncertainty in the assumptions made about the lag time between the reduction of arsenic exposures and the reduction in risk. It also noted that the benefits and costs should be summarized in a manner that would indicate the variability in the benefits and costs associated with different sizes of water treatment facilities. It recommended that the age distribution of cancer cases avoided should be presented in order to allow readers to know the age distribution of those benefits, and it discussed the limitations of EPA’s valuation of avoided cancer morbidity and mortality, recommend- ing that the agency conduct more uncertainty analyses around those valu- ations, using sensitivity analyses or Monte Carlo analyses. 18  In that report EPA noted that, “Historically, most drinking water regulatory impact analy- ses used point estimates to describe the average system-level costs” (EPA, 2000a, pp. 6–17).

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UNCERTAINTY IN TECHNOLOGICAL AND ECONOMIC FACTORS 99 OTHER FACTORS A number of factors other than human health risks, technological avail- ability, and economics affect EPA’s decisions. Some of those factors and the uncertainty in estimates of them can be accounted for in estimates of human health risks (for example, the adverse health effects on sensitive popula- tions such as infants and children) or in economic analyses (for example, the value that the public places on having access to recreational space is often accounted for in BCAs). Other factors—such as environmental justice, political climate, and public sentiment—and their uncertainty, however, are not taken into account in the analyses of human health risk, economics, and technological availability, despite their influence on decisions. Executive Order 13563, issued in 2011, states that regulations are “to be based, to the extent feasible and consistent with law, on the open exchange of information and perspectives among State, local, and tribal officials, experts in relevant disciplines, affected stakeholders in the private sector, and the public as a whole.”19 As defined by EPA, public values “reflect broad attitudes of society about environmental risks and risk man- agement” (EPA, 2000d, p. 52). Public values can be specific to a certain geographic area or can apply to the nation as a whole. As such, public values affect both local- and national-level decisions and can affect EPA’s regulations. Several programs within EPA take community concerns into consideration, as required by either statute or executive order. The Resource Conservation and Recovery Act and the Comprehensive Environmental Re- sponse, Compensation, and Liability Act, commonly known as Superfund, address several issues related to public values. A community, for example, has an interest when it is chosen as a loca- tion for a hazardous waste facility or when an existing facility is permitted. EPA provides guidance for industries and agencies working with communi- ties likely to be affected by a hazardous waste site location; in that guid- ance the agency describes potential community concerns, such as concerns about the effects on quality of life, including concerns about preserving the community’s use of space, enjoyment and value of property, and sense of belonging and security, as well as promoting the economically sound pro- tection of resources (EPA, 2000e). The guidance does not provide advice on integrating those concerns into the decision-making process. Others define public participation as “the process by which public concerns, needs and values are incorporated into governmental and corporate decision mak- ing” (Creighton, 2005, p. 7). The broader aspect of public participation is recommended by others, including a National Research Council committee 19  Exec.Order No. 13563. Improving regulation and regulatory review. 76 FR 3821 (Janu- ary 21, 2011).

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100 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY (NRC, 2008) and EPA’s SAB Committee on Valuing the Protection of Eco- logical Systems and Services (EPA, 2009).20 The political climate can also affect EPA’s decisions. Although EPA is a scientific regulatory agency, its decisions take place in a broader context that includes more than just the scientific issues. As described by EPA (2000d), political factors that can affect decisions are “based on the in- teractions among branches of the Federal government, with other Federal, state, and local government entities, and even with foreign governments; these may range from practices defined by Agency policy and political ad- ministrations through inquiries from members of Congress, special interest groups, or concerned citizens” (p. 52). Thus the agency is influenced by a complex set of forces, not the least of which are the values, priorities, and direction of the President. No decision is based absolutely or purely on scientific analyses. Regardless of statutory directives, agencies often have broad discretion about when to act in the face of a risk, and a decision of when to act is influenced by executive branch leadership, congressional intention and attention, and the possibility of judicial intervention. Although social considerations such as environmental justice and the political climate affect EPA’s decisions and there is uncertainty in those factors and how they influence decisions, there is seldom any discussion concerning just how those factors and their uncertainty affect a decision. KEY FINDINGS • EPA considers a number of factors in addition to human health risks when making regulatory decisions. EPA’s legal authority and requirements predominately determine what factors it considers when making regulatory decisions. • Uncertainty is present in the assessment of all of the factors EPA that considers when making regulatory decisions, including tech- nology availability and economic factors. Those uncertainties, however, are rarely analyzed or explicitly accounted for in EPA’s regulatory decisions. Similarly, factors such as public sentiment, environmental justice, and the political climate influence EPA’s decisions, but the uncertainty in those factors is rarely accounted for in EPA’s decisions. EPA also does not discuss the uncertainty in any of those factors in its decision documents as thoroughly as it does the uncertainty in human health risk estimates. o  lthough different estimates of technology availability are some- A  times used, the uncertainties that stem from those estimates are 20  Chapter 6 further discusses the importance of stakeholder engagement and communication approaches for that participation.

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UNCERTAINTY IN TECHNOLOGICAL AND ECONOMIC FACTORS 101 not often carried through to the final outputs in a regulatory impact assessment. o  ncertainties in economic analyses are sometimes conducted, U but they are not necessarily presented. o  ncertainties in public sentiment and social factors, such as U environmental justice, are rarely accounted for explicitly in deci- sions, and their effects are rarely discussed. o  he political climate can affect assessments of regulatory op- T  tions. That effect of that climate on decisions is not always transparent, adding to the uncertainty in decisions. • The methods and use of uncertainty analyses for technological, eco- nomic, and other factors are not as well established as for the un- certainties in human health risk estimates. The committee’s review of the literature indicated that uncertainty analyses for economic analyses have been studied more than uncertainty analyses for the technological and social factors. RECOMMENDATION 2 The U.S. Environmental Protection Agency (EPA) should develop meth- ods to systematically describe and account for uncertainties in decision- relevant factors in addition to estimates of health risks—including technological and economic factors—in its decision-making process. When influential in a decision, those new methods should be subject to peer review. RECOMMENDATION 3 Analysts and decision makers should describe in decision documents and other public communications uncertainties in cost–benefit analyses that are conducted, even if not required by statute for decision making, and the analyses should be described at levels that are appropriate for technical experts and non-experts. RECOMMENDATION 4 The U.S. Environmental Protection Agency should fund research, con- duct research, or both to evaluate the accuracy and predictive capa- bilities of past assessments of technologies and costs and benefits for rulemaking in order to improve future efforts. This research could be conducted by EPA staff or else by nongovernmental policy analysts, who might be less subject to biases. This research should be used as a learning tool for EPA to improve its analytic approaches to assessing technological feasibility.

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102 ENVIRONMENTAL DECISIONS IN THE FACE OF UNCERTAINTY RECOMMENDATION 5 The U.S. Environmental Protection Agency should continue to work with stakeholders, particularly the general public, in efforts to iden- tify their values and concerns in order to determine which uncertain- ties in other factors, along with those in the health risk assessment, should be analyzed, factored into the decision-making process, and communicated. RECOMMENDATION 6 The U.S. Environmental Protection Agency should fund or conduct methodological research on ways to measure public values. This could allow decision makers to systematically assess and better explain the role that public sentiment and other factors that are difficult to quantify play in the decision-making process. REFERENCES Adamowicz, W., J. Swait, P. Boxall, J. Louviere, and M. Williams. 1997. Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. Journal of Environmental Economics and Manage- ment 32(1):65–84. Arsenic Cost Working Group to the National Drinking Water Advisory Council. 2001. Report of the Arsenic Cost Working Group to the National Drinking Water Advisory Council. Washington, DC: Environmental Protection Agency. Atkinson, G., and S. Mourato. 2008. Environmental cost–benefit analysis. Annual Review of Environment and Resources 33:317–344. Boyd, J., and A. Krupnick. 2009. The definition and choice of environmental commodities for nonmarket valuation. Washington, DC: Resources for the Future. Breedlove, J. 1999. Natural resources: Assessing non-market values through contingent valu- ation. Washington, DC: Congressional Research Service. Breyer, S. 1995. Breaking the vicious circle: Toward effective risk regulation. Cambridge, MA: Harvard University Press. Brookshire, D. S., and D. L. Coursey. 1987. Measuring the value of a public good: An empiri- cal comparison of elicitation procedures. American Economic Review 554–566. Control of emissions of air pollution from highway heavy-duty engines. 1996. Federal Register 61(125):33421–33469. Covello, V. T., D. B. McCallum, and M. T. Pavlova. 2004. Effective risk communication: The role and responsibility of government and nongovernment organizations. New York: Plenum Press. Creighton, J. L. 2005. The public participation handbook: Making better decisions through citizen involvement. San Francisco, CA: Jossey-Bass. Diamond, P. A., and J. A. Hausman. 1994. Contingent valuation: Is some number better than no number? Journal of Economic Perspectives 8(4):45–64. EPA (U.S. Environmental Protection Agency). 1995. Survey of control technologies for low concentration organic vapor gas streams. Washington, DC: EPA. ———. 2000a. Arsenic in Drinking Water Rule economic analysis. Washington, DC: Office of Ground Water and Drinking Water, EPA.

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