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7.2 Steps in Conducting Benefits Analysis

    As a practical matter, there are three primary steps to estimating the benefits of an environmental regulation.  First, the analyst must identify the reductions in human health and environmental damages expected to result from the regulation.  Next, these identified benefits must be quantified.  And finally, quantified benefits should be expressed in monetary terms to the extent possible.  In the sections that follow, these steps are described relative to the functional relationships presented above.
  7.2.1    Identifying Benefits
    Identifying the benefits of a regulation is analogous to identifying the reductions in damages to environmental service flows attributable to the rule.   The damages that can be avoided by reducing pollutant emissions fall into three broad categories (adapted from  Freeman, 1993):
  1. Direct damages to humans, including health damages and aesthetic damages;
    • Health damages result from human exposure to pollutants.  These damages include increases in the risk of death (mortality risk) or increases in the risk of experiencing an adverse health effect (morbidity risk).  Adverse health effects can be divided into acute effects such as headaches or eye irritation which generally last only a few days, and chronic effects such as emphysema or asthma which are generally associated with long-term illness.
    • Aesthetic damages result from contamination of the physical environment and include increased problems of odor, noise, and poor visibility.
  2. Indirect damages to humans through ecosystems, including productivity damages, recreation damages, and intrinsic or nonuse damages;
    • Productivity damages, including reduced productivity of farmland, forests, and commercial fisheries, result from pollution damages to physical environments which support these commercial activities.
    • Recreation damages result from the reduced quality of environmental resources such as lakes and rivers used for recreational activities.
    • Intrinsic or nonuse damages include losses in the value people associate with preserving, protecting, and improving the quality of ecological resources that is not motivated by their own use of those resources.
  3. Indirect damages to humans through nonliving systems, including damages to materials and structures (e.g., buildings and equipment) that are caused by pollution and can reduce the productivity of these assets.
 

7 Benefits Analysis

 7.0 Intro

 7.1 Economic
   Benefits: An
   Overview

 7.2 Steps in Con-
   ducting Benefits
   Analysis

 7.3 Benefits
   Transfer
    The process of identifying the benefits of a regulation is equivalent to qualitatively describing the first and second functional relationships presented in Figure 7-1.  That is, identifying benefits essentially involves describing the relationship between changes in pollutant emissions and ambient concentrations in environmental media and then describing the relationship between those ambient pollution concentrations and the services provided by the physical environment.  Figure 7-2 outlines the linkages between NOX emissions and environmental damages.
    As is evident from the amount of activity in the figure between pollution emissions and ambient concentrations, the fate and transport of pollutant emissions through the atmosphere usually involve a number of interactions between the emissions of interest and other atmospheric constituents.  In particular, NOx has both direct effects on human health and the environment and indirect (precursor) effects resulting from its interaction with other chemicals in the atmosphere. Therefore, care should be taken when describing the fate and transport of pollutant emissions through the environment.  Likewise, the relationship between ambient pollutant concentrations and environmental damages can also be complex.
  

Figure 7-2
  7.2.2    Quantifying Benefits
    Just as identifying the benefits of a regulation requires qualitatively describing the first and second functional relationships presented in Figure 7-1, quantifying benefits requires calculating the effects that changes in emissions have on environmental service flows.  This involves quantifying changes in emissions, using fate and transport models to estimate the corresponding changes in ambient concentrations of pollutants in environmental media, and then estimating dose-response or concentration-response relationships to translate these changes in ambient concentrations into quantitative changes in environmental damages.
  7.2.2.1  Quantifying Health Benefits
    In general, quantifying health benefits involves six steps.  These steps include determining the following:
    1.    the dose-response relationship for each health effect,
    2.    total exposure in the absence of the regulation,
    3.    the number of baseline cases for each quantifiable health effect,
    4.    total exposure with the regulation (for each regulatory alternative),
    5.    the number of cases for each quantifiable health effect with the regulation (for each regulatory alternative), and
    6.    the number of cases avoided as a result of each alternative.
Each of these steps is described below.
Step 1:  Determining the Dose-Response Relationship for Each Health Effect
    A dose-response relationship is an estimate of risk per unit of exposure to a pollutant.  For cancer assessments, dose-response has typically been modeled as a linear no-threshold relationship; that is, every unit of exposure contributes equally to aggregate risk.  For example, 100 units of human exposure result in a given amount of risk, regardless of whether there is a one-time exposure of 100 units to one person, ten exposures of one unit each to ten different people, or one-time exposures of one unit each to 100 different people.  However, the Agency has been moving toward the use of nonlinear health effects models.  These models may become more prevalent in future analyses.  For noncancer health effects, dose-response relationships may include a threshold and/or may be nonlinear with respect to the level of exposure.
Step 2:  Determining Baseline Exposure
    Human exposure to a pollutant is a function of ambient concentrations of that pollutant in environmental media.  Determining baseline exposure requires estimating two parameters.  First, analysts must identify the exposed populations (e.g., occupational groups, consumers of particular products, the general population living in a particular area) and the number of exposed individuals in each group.  Then one needs to determine the level, duration, route, and frequency of exposure (e.g., 100 ppm time-weighted average exposure for an 8-hour day, 50 days per year).
    At this point in the discussion, it is important to note that individuals may take action to reduce their exposure to harmful substances.  For example, individuals may purchase bottled water to avoid exposure to contaminants found in tap water.  Such averting behaviors affect the size of the exposed population.  Therefore, any exposure assessment should take into account individual actions designed to reduce exposure.  However, inclusion of such behavior is usually, at best, only implicit in the exposure assessment.  As a result, in the remainder of this section, the discussion of averting behavior in an economic analysis is limited to the use of averting expenditures as a proxy for individual WTP to avoid the health effects expected to result from human exposure to environmental contaminants (see Section 7.2.3.1).
Step 3:  Determining the Number of Baseline Cases for Each Quantifiable Health Effect
    The number of baseline cases for each quantifiable health effect is the product of the baseline number of people exposed, the amount (level, duration, and frequency) of baseline  exposure, and the dose-response relationship; that is, the baseline number of cases for each exposed population equals:
    Number exposed x Baseline exposure x Dose-response relationship.
Step 4:  Determining Exposure after the Regulation (for Each Regulatory Option)
    Each regulatory option may result in a reduction in the exposure level, a reduction in the exposed population, or some combination of the two.  Therefore, there are two dimensions to characterizing post-regulatory exposure.  First, it is necessary to estimate the impact of the option on exposure levels.  For example, a regulatory option may reduce (but not eliminate) the exposure to the pollutant from a particular exposure pathway.  Second, it is necessary to estimate the expected post-regulatory level of exposure for that exposure pathway.
Step 5:  Determining the Number of Cases for Each Quantifiable Effect with the Regulation (for Each Regulatory Option)
    In this step, Step 3 is repeated, using the post-regulatory estimates of exposure (number of people exposed; frequency, duration, and level of exposure) derived in Step 4.  Multiplying the new exposure estimates by the dose-response estimate yields an estimate of the post-regulatory number of cases.
Step 6:  Determining the Number of Cases Avoided as a Result of Each Regulatory Option
    To determine the number of statistical cases avoided as a result of a regulatory option, the number of post-regulatory cases (from Step 5) is subtracted from the baseline number of cases (from Step 3).  The difference is the quantified benefits of the regulation as follows:
Quantified
Health
Effects

=
Baseline Cases Resulting from Exposure to Pollutant

Post-Regulatory Cases Resulting from Exposure to Pollutant

    Estimating the concentration-response relationship provides an alternative to the six-step process outlined above.  Although estimating individual risk per unit of exposure and then multiplying this estimate by total exposure is the more accurate method of quantifying health effects, the data required for such an analysis are often not available.  In those cases, it may be possible to estimate the extent of health effects as a function of ambient concentrations of pollutants in the atmosphere.  This process involves the determining the following:
    1.    the concentration-response ratio for each health effect,
    2.    ambient pollutant concentrations in the absence of the regulation,
    3.    ambient pollutant concentrations with the regulation (for each regulatory alternative),
    4.    the number of cases for each health effect with the regulation (for each regulatory alternative), and
    5.    the number of cases avoided as a result of each alternative.
    This process differs from the six-step process described above only in that the estimation of a dose-response relationship and exposure for each health effect for each regulatory alternative are combined into one step—the estimation of a concentration-response relationship.  A concentration-response relationship is an estimate of the number of health effects associated with a given ambient pollutant concentration (e.g., number of asthma attacks associated with a particular concentration of ground-level ozone).  If the ambient concentration of the pollutant is known both with and without the regulation, then a concentration-response relationship can be used to estimate changes in the number of health effects resulting from the regulation.  Examples of concentration-response relationships for health effects developed in the literature include  Schwartz (1993),  Ostro and Rothchild (1989), and  Pope (1989).
  7.2.2.2  Quantifying Ecological Benefits
    Quantifying ecological benefits is similar to quantifying health benefits using concentration-response relationships.  In particular, when the effect of a regulation on the ambient concentrations of a pollutant can be estimated, and the relationship between ambient pollution concentrations and environmental service flows is known, then the ecological benefits of a regulation can be quantified.  For example, the National Crop Loss Assessment Network (NCLAN) developed concentration-response relationships linking ground-level ozone to leaf damage and reduced seed size in an effort to determine the effect of ozone on crop yields.  Other sources of concentration-response relationships for ecological damages include  Martin and Banzhaf (1994);  Heagle, Spencer, and Letchworth (1979);  Kopp, Vaughan, and Hazilla (1984);  Brewer and Ashcroft (1982);  Oshima et al. (1976);  Leung, Reed, and Geng (1982);  Foster et al. (1983);  Rowe and Chestnut (1985);  Clark, Henninger, and Brennan (1983); and  Keller (1985).
  7.2.2.3  Quantifying Benefits through Nonliving Systems
    Degradation of many materials, including metals, masonry, and paints, is accelerated by exposure to pollutants.  For example, corrosion rates for galvanized steel and zinc as well as erosion rates of carbonate stone have been studied and strongly linked to acidic deposition.  Building stone, which includes limestone and marble, is also affected by pollutant emissions.  Painted surfaces incur the largest economic losses from pollutant exposures ( Bernabo et al., 1988).  Although the physical links between pollutant emissions and materials damages are well established, the precise quantitative relationship between degradation rates and exposures are not.  The effectiveness of preventive maintenance has further complicated the assessment of pollution damages.  Therefore, quantifying the effects of pollutant emissions on materials and structures may be quite difficult.  Nonetheless, because reductions in materials damages could constitute a significant economic benefit of an air quality regulation, it is important to describe these effects to the greatest extent possible, even if only qualitatively.
  7.2.3    Monetizing Benefits
    Monetizing the benefits of a regulation involves estimating society’s willingness to pay (WTP) for quantified changes in environmental service flows.  In economics, WTP refers to the maximum amount an individual is willing to pay to acquire a benefit.  It is measured as the reduction in income required to return an individual to the level of utility he or she enjoyed prior to receiving the benefit.  An alternative measure of economic value is an individual’s willingness to accept (WTA) compensation to forego a benefit.  Conceptually, WTA is measured as the minimum compensation to required for an individual to achieve the same level of utility he or she would have attained if the benefit had been realized.  From a theoretical perspective, the appropriate measure (WTP or WTA) depends on the implicit property rights in the valuation context, and the two measures should be close in value.  WTP estimates have been generally preferred in the empirical economics literature.  The remainder of this section is devoted to describing the methods used to estimate WTP for environmental improvements.

  7.2.3.1  Monetizing Health Benefits
    Nonfatal Illness and Injury (Morbidity).  Economists use a number of approaches to monetize a change in the number of cases of a particular morbidity effect.  The four primary approaches are cost-of-illness (COI) methods, expressed preference methods, averting action methods, and hedonic wage and property value methods.  Of these, COI methods are most often employed in economic analyses of human health benefits.  Other methods include risk-risk tradeoffs, health state indexes, and damage award approaches.  The four primary approaches are examined below.
    Cost-of-Illness Approach.  Because of the difficulties associated with generating or using estimates of WTP for reductions in the risk of nonfatal illness or injury, analysts often prefer valuation methods based on the avoided costs of illness or injury.  The COI approach to morbidity valuation measures the direct and indirect costs resulting from a health effect.  Direct costs include such things as the value of goods and services used to diagnose and treat individuals suffering from the health effect.  Indirect costs consist primarily of foregone productivity measured by lost wages.  Total COI is the sum of direct and indirect costs.  Because the COI approach does not account for the full range of costs associated with an illness or injury (e.g., pain and suffering are not included), the results of these analyses should at best be viewed as lower-bound estimates of society’s WTP for reductions in such risks.  
    As mentioned above, the theoretically appropriate measure of economic benefits is society’s WTP to reduce the risk of a health effect (i.e., society’s WTP to avoid the risk prior to the actual occurrence of a health effect).  It is important to note that the COI approach measures costs after a health effect has occurred rather than an individual’s WTP to avoid the health effect in the first place.  In addition, the COI approach measures the costs to the individual (out-of-pocket costs and lost wages), the costs to the individual’s employer (in the case of paid sick leave), and the costs to third-party payers (payment of insured medical expenses or charity care), but not changes in individual well-being caused by the illness.
    There are two primary approaches to measuring COI:  the prevalence approach and the incidence approach.  The prevalence approach measures total costs of a particular illness or injury in the population for a given year.  In contrast, the incidence approach measures the cost of an individual case of illness from onset through recovery or death.  For assessing the benefits of OAQPS regulations, the incidence approach to measuring the cost of an illness or injury is most appropriate because the health benefits of OAQPS regulations are typically expressed in terms of the number of cases of a particular health effect avoided as a result of the regulatory action.  Use of the prevalence approach would be appropriate if the effect of a regulation eliminated a particular health effect.
    Expressed Preference Methods.  Expressed preference methods, including contingent valuation (CV) and conjoint analysis, can be used to elicit an individual’s WTP to avoid a given health effect.  CV techniques are the primary expressed preference method used to estimate health effects.  CV uses surveys to directly elicit an individual’s WTP to reduce the risk of a given health effect.  In particular, a CV survey asks each respondent about his or her personal characteristics, attitudes concerning the commodity being valued (reductions in the risk of an adverse health effect in this case), and WTP to acquire the commodity.  From the survey responses, analysts can estimate WTP to reduce the risk of an adverse health effect as a function of personal characteristics and attitudes.
    Although they are the most broadly applicable valuation methods, CV techniques remain controversial because of their hypothetical nature.  In particular, there are two primary criticisms of CV.  First, because an individual will not actually have to pay the amount he or she indicates in response to the survey, the individual may have little incentive to respond truthfully.  Second, critics contend that survey respondents often are unable to comprehend the commodity they are being asked to value; particularly when the commodity is a very small change in the risk of experiencing a health effect.  Proponents of CV assert that many of the problems raised by critics can be effectively controlled through good survey design.  Although OAQPS generally does not conduct original CV studies due to time and other resource constraints, analysts should be mindful of these criticisms when using existing CV estimates to monetize benefits.
    Rather than directly asking an individual his or her WTP, conjoint methods ask individuals to choose among different sets of alternatives.  Each alternative is broken down and described according to a common set of attributes, usually including the health effect of interest and some monetary measure.  The levels of these attributes are varied across alternatives.  By analyzing an individual’s choices across a number of pairs of alternatives, analysts can derive estimates of the trade-offs the individual is willing to make among the different attributes (including cost), thereby estimating the individual’s WTP to acquire a change in an attribute such as reduced risk of a specific health effect.  Although conjoint analyses show promise in valuing health effects, these techniques are not commonly used for such a purpose.
    Averting Action Methods.  In the face of a potential risk, individuals will often take defensive or averting action.  For example, in the case of groundwater contamination, averting action might include purchases of water filters, bottled water, and other alternative water supplies.  In the case of air pollution, averting actions may include such things as avoiding going out of doors.  These types of observable behavior can provide analysts with information about an individual’s WTP to avoid specific health risks; however, more generally they provide information about the costs of these behaviors, their relation to the source of such risk, and the magnitude of cost savings that would result from controlling the source.
    As noted above, examining averting behavior provides the analyst with information regarding individuals’ WTP to avoid exposure to a source of risk.  Averting actions taken in response to risk may also enter the regulatory analysis in the risk assessment to the extent that these actions reduce the overall exposure of the population to harmful pollutants.  If the exposure assessment of a regulatory analysis takes into account changes in averting behavior between the baseline and control scenarios, then the cost savings attributable to the reduced need for averting action can be considered one component of the benefits of the rule.  When examining expected reductions in averting behavior, the analyst must be careful not to double count.  In particular, analysts must estimate the benefits to those who, because of the rule, change their averting behavior separately from those who experience a reduction in exposure (and subsequent risk) without changing their own averting behavior.  To the former, averting expenditures measure potential cost savings resulting from the rule.  To the latter, averting expenditures provide an estimate of WTP to avoid exposure.
    Averting action methods are often preferred to expressed preference methods because they are based on actual behavior; however, measuring the benefits and costs of averting actions is often difficult.  First, to determine an individual’s WTP to avoid a specific health effect by examining his or her averting behavior, it may be necessary to determine the benefit the individual expects to receive as a result of his or her averting actions.  Second, averting actions are typically taken to avoid a health outcome by reducing the individual’s exposure to a harmful event.  Therefore, WTP measures estimated based on averting actions are usually employed as estimates of WTP to avoid exposure and not as measures of WTP to avoid a specific health effect.  Therefore, using averting action methods to value specific health outcomes of an OAQPS rulemaking may be difficult.
    Hedonic Methods.  Two common applications of hedonic methods are used to value changes in health risks:  hedonic wage models and hedonic property value models.  Hedonic wage models are based on the premise that, all else equal, an individual working in a risky occupation will require higher compensation for his or her labor than will the same individual working in a less risky occupation.  By examining wage differentials among individuals working in various occupations, analysts can estimate a worker’s WTA for exposure to higher levels of risk on the job.
    Hedonic property value models are based on the theory that the price of a residential property is equal to the discounted present value of the lifetime residential value of the home.  The price of the home can then be estimated as a function of its structural characteristics and the characteristics of the surrounding community.  By estimating housing prices over a range of properties, each with varying structural and community characteristics (including air quality), analysts can infer a household’s WTP for each of these characteristics.  For a good example of a hedonic study designed to estimate the value of avoiding environmental health risks, see  Mendelsohn et al. (1992).
    Fatality.  The benefits of OAQPS regulations can also include reductions in the risk of premature death.  The economics literature discussing the value of changes in fatality risks is rather extensive and provides a relatively strong basis for monetizing benefits when the number of deaths avoided as a result of a regulatory action can be calculated.
    Value of a Statistical Life (VSL).  Monetary estimates of changes in fatality risk are often expressed in terms of the VSL.  The term “value of a statistical life” is easily misinterpreted and should be carefully described when used in benefits analysis.  In particular, VSL refers to the WTP for reductions in the risk of premature death aggregated over the population experiencing the risk reduction; that is, VSL refers to the sum of many small reductions in fatality risks.  For example, if the annual risk of death is reduced by 1 in 1,000,000 for each of 2,000,000 people, then two statistical lives are saved each year as a result of the risk reduction measures.  If each individual is willing to pay $5 for the risk reduction of 1 in 1,000,000, then the value of each statistical life saved is $5 million.
    The basic assumption underlying the VSL approach is that equal increments in fatality risks are valued equally.  For example, a reduction in the risk of death of 1 in 1,000,000 is valued the same whether or not the original fatality risk was 1 in 100,000 or 1 in 1,000,000.  This assumption is generally defensible if the level of risk prior to the change is small (usually 1 in 100,000 or less) as is usually the case for fatality risks resulting from environmental hazards.  Because economic theory maintains that the marginal utility of risk reduction is an increasing function of the level of baseline risk, this assumption may not be valid for risks greater than 1 in 100,000.  For similar reasons, the VSL approach is only appropriate for marginal changes in the risk of death and should not be used to value more significant changes.  Because changes in individual fatality risks resulting from environmental regulation are typically very small, the VSL approach is usually acceptable for OAQPS benefits analyses.
    The literature indicates that empirical estimates of WTP for risk reduction are sensitive to whether the risk is borne voluntarily.  In general, it is believed that risk-averse individuals have a greater WTP for marginal risk reductions than those who choose risks voluntarily.  Because most VSL studies are based on wage compensation data, the results are applicable to changes in voluntary risks, and the population over which these values are estimated is often not representative of the population affected by a regulation; however, because, as noted above, fatality risks from both involuntary environmental hazards and occupational fatality risks tend to be quite small, using VSL estimates developed using wage compensation data is reasonable.
    In the past, most EPA analyses have used point estimates of VSL derived from the economics literature.  There are two alternatives to this approach.  First, analysts could apply a range of VSL values found in the existing literature to develop upper- and lower-bound estimates of the total value of lives saved as a result of a regulation.  This approach, however, often gives equal weight to very high and very low estimates that may be found infrequently in the literature.  Alternatively, analysts could use distributions of VSL values rather than point estimates or ranges.  Distributions give quantitative weights to the likely accuracy of the different VSL estimates based on the frequency with which they are found in the existing literature.
    In assessing the benefits of the CAA, analysts conducted a meta-analysis of VSL estimates found in the literature and fit the results to a Weibull distribution to characterize the range of possible benefit values ( EPA, 1996a).  This approach revealed that the majority of VSL estimates found in the literature tend to cluster in the range of $3 million to $7 million, with a central estimate of approximately $5 million.  A review of methods used by EPA in valuing changes in mortality risks conducted for OP by  Chestnut, Mills, and Alberini (1997) notes that there is an insufficient empirical basis to justify the disparity in VSL estimates chosen across programs within the Agency.  Although this report does not recommend specific VSL estimates to be used in EPA economic analyses, it does suggest that the central tendency of $5 million revealed in the CAA benefits analysis may provide the best starting point.
    Value of a Statistical Life Year (VSLY).  An alternative method of expressing reductions in mortality risk is the VSLY.  For example, if a regulation is estimated to save one statistical life among a population of working adults whose average life expectancy is 40 years, then the regulation would result in 40 life-years extended.  In general, there are two methods for valuing the number of life-years extended:
  • applying results from studies in which the WTP for a risk reduction is estimated as a function of age; and
  • annualizing VSL estimates using an appropriate discount rate and average life expectancy.
Both of these approaches have some fundamental shortcomings.  In particular, when deriving values of risk reduction as a function of age, the type of risk and the size of the marginal change in risk used to estimate the value of a risk reduction must closely match the risk reductions resulting from the regulation.  Annualizing VSL estimates does not provide an independent estimate of VSLY but simply rescales the VSL estimate.  Current research does not suggest a definitive method for estimating VSLY that is sensitive to such variables as current age, latency period, life-years remaining, and the social value of different risk reductions.
  7.2.3.2  Monetizing Ecological Benefits and Benefits to Materials and Structures
    Once changes in ecological service flows have been identified and quantified, analysts can use a number of valuation methods to monetize the benefits of a regulation.  The four primary methods for monetizing ecological benefits are
  • hedonic property value models,
  • travel cost models,
  • expressed preference methods, and
  • market models.
Table 7-1 shows the categories of benefits for which each of these valuation methods is most applicable.  Each of these methods is briefly described below.
    Hedonic Property Value Models.  As noted above, hedonic pricing theory maintains that the housing market functions as a market for environmental quality insofar as environmental quality is a characteristic of the property being purchased or of the community in which the property is located.  Although often used to assess WTP to avoid environmental health risks , hedonic property value models can be used to estimate the value individuals and households place on the perceived amenity and recreation benefits provided by the property.  In addition, hedonic methods could conceivably be used to estimate housing damages related to pollutant exposure; however, these methods have not been used for this purpose to date.
    Travel Cost Models.  Travel cost models have become a common method in the economics literature for estimating the benefits of environmental improvements to recreators.  For example, these models can be used to estimate anglers’ WTP to reduce toxic contamination of a water body that otherwise would be subject to a fish consumption advisory (see, for example,  Montgomery and Needelman, 1997).  Most often, travel cost models take the form of a discrete-choice model known as a random utility model (RUM) in which a recreator’s choice among a set of unique recreation sites is modeled as a function of the
  

Table 7-1
characteristics of that site, including environmental quality.  Because these models assume that the cost of reaching each site is a function of travel and time costs, researchers can exploit observed differences between travel distance and environmental quality to estimate the monetary value of each site characteristic.
    Expressed Preference Methods.  Analysts use two primary expressed preference methods to estimate the value of improvements in ecological systems.  These include CV studies and conjoint analyses.  The characteristics of these methods are described in Section 7.2.3.1, but it is important to note that only expressed preference methods can be used to estimate nonuse values.  Examples of CV studies used to estimate the value of environmental improvements are  Krutilla (1967) and  Kopp (1992).
    Market Models.  Market models can be used to assess the impact of changes in ecological services on both producers and consumers of market goods that rely on these services.  Producers often use natural resources as inputs into the production of goods and services.  To the extent that the quality of a natural resource such as groundwater is affected by regulations designed to reduce the ambient concentrations of pollutants in groundwater, so too will the cost and mix of inputs.  These changes in the input mix and production costs can be modeled using a firm’s cost and factor demand functions.  The resulting market supply function can be coupled with the demand for the good or service being produced to determine changes in producer and consumer surpluses.  In addition, when the market good of interest is an agricultural commodity, changes in increases in crop yield related to decreases in ambient pollutant concentrations can be directly analyzed by estimating changes in consumer and producer surpluses in the market for that commodity.
  7.2.3.3  Benefit Values per Unit of Emissions
    Often, the effect of a regulation on ambient concentrations of pollutants cannot be easily determined.  Likewise, it may be difficult to estimate the dose-response relationship or concentration-response relationship needed to quantify the effect of a regulation on environmental service flows.  As an alternative to identifying, quantifying, and monetizing the benefits of a regulation, it may be possible to forego the quantification step altogether and directly relate changes in pollutant emissions to monetized environmental benefits.  The two most commonly studied types of pollutants are criteria air pollutants and greenhouse gases.  The results of these studies are presented below.
    Criteria Air Pollutants.  In developing its RIA for the recently proposed particulate matter (PM) and ozone NAAQS, EPA estimated values for several impacts resulting from reductions in ambient levels of ozone and particulate matter (PM).  Although not entirely comprehensive, these estimates include health values (mortality and morbidity), visibility values, changes in agricultural productivity, and damages (soiling) to structures.
    In a more recent analysis of its proposed PM and ozone rule for controlling emissions from pulp and paper production ( EPA, 1997a), EPA used the PM and ozone NAAQS rule analysis to develop unit (i.e., per ton) estimates of the value of emissions reductions for volatile organic compounds (VOCs), PM, and sulfur oxides (SOX). This was accomplished by allocating the estimated total value of the NAAQS rule to each category of emissions and then dividing these values by the respective emissions reduction estimates.  The unit estimates and their suggested application are shown in Table 7-2.  Note:  These estimates and their applications are shown here for expository purposes only.  Future analyses using the benefit-per-unit-of-emissions approach should consider estimates most appropriate for the regulated pollutant of interest.
    Greenhouse Gas Emissions.  The global warming impacts of greenhouse gas emissions have been widely studied over the last decade.  These include impacts on agricultural production, sea level rise, biological diversity, fresh water supplies, and human health.  The
  

Table 7-2

range of uncertainty associated with estimates of these impacts is generally quite large, and relatively few of these studies have attempted to measure these impacts in monetary terms.  Nevertheless, a few studies do exist that provide order-of-magnitude estimates of monetary damages per unit of greenhouse gas emissions.  Table 7-3 summarizes a number of these estimates.  Most of these studies have used focused primarily on the global costs of sea level rise and damages to agriculture, although the later studies include more complex models of the interactions between economic activity and climate changes and have included estimates of nonmarket impacts.  It must be emphasized that these estimates are preliminary and are very sensitive to the assumptions and features of the respective models, such as the climate change model used, the discount rate used (usually between 2 percent and 6 percent), the time frame selected, and the future economic growth rates assumed.
    As with all benefits transfers, analysts should exercise caution when using estimates of the benefits per unit of emissions in an economic analysis of an air regulation.  In particular, analysts should clearly identify the benefits categories to which these values apply.  Analysts should also describe the methods used in the original empirical studies to estimate these
  

Table 7-3

benefits and the methods by which these study results were translated into benefit values per unit of emissions.  In addition, analysts should note the magnitude of emissions reductions estimated in the original study and the corresponding range of emissions reductions expected to result from the rule in question.  It is important to consider that, at the margin, the effects of emissions reductions may differ depending on the baseline level of emissions, and the magnitude of the planned emissions reduction.  Finally, analysts should consider the population affected by emissions reductions in the original study and the population expected to be affected by the rule in question.  It may be necessary to adjust the per-unit benefits to reflect such differences.  For a more detailed discussion of benefits transfer, see Section 7.3.

__________________
2 A dose-response estimate is a value that quantifies the increased risk of incidence of some health effect associated with a one-unit increase in exposure to a pollutant.  A concentration-response estimate is a value that quantifies the increase in some environmental damage associated with a one-unit increase in the ambient concentration of a pollutant.
3 For information about the design and results of the specific NCLAN experiments, refer to  Heck et al. (1982).
4 For a thorough discussion of the full set of methods used to value nonfatal health effects, readers are referred to  IEc (1997).
5 Analysts planning to use WTP estimates developed using the CV method are referred to the fall 1994 issue of the Journal of Economic Perspectives for a more thorough discussion of the debate over CV.  A detailed discussion of benefits transfer issues is presented in Section 7.3.
6 One exception is  Dickie and Gerking (1991) who use individuals’ expenditures on air conditioning and electric (rather than gas) stoves to model WTP to avoid acute health effects of air pollution.
7 It is important to note that VSL does not attempt to value the life of an identified individual.
8 See Chapter 10 of Freeman (1993) for a more detailed discussion of the diminishing marginal utility of risk reduction.

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