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Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants (2005)

Chapter: 5 Analytic Methods for Assessing Effects of New Source Review Rule Changes

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Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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5
Analytic Methods for Assessing Effects of New Source Review Rule Changes

INTRODUCTION

In principle, numerous methods could be used to assess the effects of the U.S. Environmental Protection Agency’s (EPA’s) recent changes to the New Source Review (NSR) rules. Some methods focus on the response of individual firms or facilities; some focus on entire industrial sectors; and some attempt to assess the responses of multiple sectors or the entire economy. An assessment of all of the factors of interest requires an evaluation of how firms, industrial sectors, or the economy will alter their investments and operations (including pollution control and pollution prevention) in response to changes in the NSR rules and the resulting changes in efficiency and pollutant emissions. The assessment also involves an evaluation of how the emission changes might affect air quality and human exposures and the resulting health consequences of those exposures.

The methods used in evaluating responses to changed NSR rules begin with assumptions about how individual firms or industries respond to regulatory incentives and constraints. In some cases, these assumptions are based on empirical information involving interpretations of historical data, surveys, case studies, or anecdotal reports. In more formal analyses, the assumptions usually also incorporate theoretical constructs that have been developed in the field of economics. The usefulness of

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

the alternative methods available and the selection of a particular one depends on the methods’ accuracy in representing responses to regulatory incentives and constraints, their sensitivity to the particular regulatory changes being assessed, and their ability to accurately estimate the outcomes of interest in the assessment.

Different indicators can be used to assess magnitudes and trends in pollution prevention and control, energy efficiency, emissions, air quality, and health effects (e.g., NRC 1999; Esty 2001; Hayward 2004). Table 5-1 lists possible indicators for each. Many of these indicators vary over time and space or from plant to plant, and some degree of averaging or smoothing may need to be done before the data can be analyzed. In many cases, the data currently are not available from a single comprehensive source (or even distributed among many sources), and thus incomplete data would be used for drawing inferences. Furthermore, the list of measures in Table 5-1 includes factors that are quantitative and directly indicative of the targeted outcome, such as the emissions from individual plants, industries, and states, as well as other factors that are more qualitative and difficult to measure, such as the rate of innovation for pollution prevention and control technology.

Because many of the outcomes and indicators in Table 5-1 are affected by a number of factors beyond the realm of the NSR rules (or even pollution control laws in general), such as economic conditions, government investment in research and development (R&D), fuel supplies and prices, and meteorological conditions, these other factors and data should also be considered in analyses that attempt to assess the likely impact of NSR rule changes on the outcomes of interest. Thus, any assessment involves (explicitly or implicitly) comparing two different estimates: an estimate of what would have happened had the rule changes not occurred and an estimate of what will happen with the rule changes. Both are subject to substantial uncertainty, and, as discussed in Chapter 6, it will be necessary to consider a range of possible scenarios for the economic and environmental assumptions that are applied to estimate and compare outcomes of the revised NSR rules with outcomes of the NSR rules before the revisions.

The remainder of this chapter reviews the major approaches and methods that have been, or might be, used to assess the impacts of changes in the NSR rules on the outcomes in Table 5-1 at the level of the firm, the industrial sector, and the economy. The committee considered it important to review the full range of methods available for this purpose to determine the extent to which the different approaches could assist in responding to the committee’s charge. This survey is deliberately broad

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

TABLE 5-1 Possible Indicators for Assessing the Outcomes of Interest

Outcome

Possible Indicators to Assess Outcome

Pollution control

Innovation in new technologies

 

Expenditures for research and development

 

Inventions and patents

Implementation of new technologies

 

Adoption by industry and utilities

Improvements in use (“learning by doing”)

 

Performance histories for selected technologies

Pollution prevention (source reduction)

Innovation, implementation, and improvements in industrial processes to be less polluting

Expenditures for research and development

Adoption by industry and utilities

Performance histories of selected technologies

Trends in emissions generated per unit of product produced

Life-cycle material-use impacts, considering economy-wide impacts through the supply chain and product delivery use, reuse, and disposal

Number of products introduced into commerce with reduced hazardous properties

Substitution of materials with less polluting substances

Energy efficiency

Innovation, implementation, and improvement in use of new technologies that enable energy efficiency in electricity generation and industrial processes

Energy efficiency of operating units and plants

Industry sector-wide energy use

Life-cycle energy-use impacts, considering economy-wide impacts through the supply chain and product delivery, use, reuse, and disposal

Emissions

Trends in emissions for individual units, plants, industries, states, regions, and the nation as a whole

Relationships between emissions and unit and plant operating costs and use

Life-cycle emission impacts

Air quality

Ambient concentrations of relevant emitted primary pollutants and pollutants formed in the atmosphere over various spatial and temporal scales.

Health effects

Human exposure and dose

Mortality and disease

Population incidence

Incidence for particular subpopulations (regional, socioeconomic)

Risks to highly exposed individuals

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

because different approaches are likely to be required for different industries, and because a final choice of methods has not been made. Furthermore, it is important to understand the general assumptions of each approach so that their practical and theoretical limitations are clear. Once the changes in emissions are estimated, other methods are used to estimate the resulting changes in ambient concentrations, exposures, dose, and human health and environmental risks. A preliminary assessment of the potential of these tools to be used in our evaluation of NSR rule changes is then provided. The assessment approaches discussed in this report will be relevant to the committee’s final report. No assessment results are provided in this interim report.

FRAMEWORKS FOR ASSESSING THE IMPACT OF REGULATION

In this section, we review the various approaches that can be used to estimate economic behavior in response to regulations at the level of the firm, the industrial sector, and the economy, as well as methods for evaluating the air-quality and public-health impacts of these responses. Where formal methods have been developed and applied, we identify the candidate models available, the types of variables that they estimate, the kinds of input data that they require, and their potential relevancy for evaluating the impacts of the recent changes in the NSR regulations on efficiency and emissions. In applying and interpreting these various models, important issues arise concerning the way statistical procedures are used and model uncertainty is interpreted. As such, we also briefly review key methods and issues for statistical estimation and uncertainty analysis.

Assessments of Individual Firm Behavior

Decisions to undertake plant maintenance and alterations and/or decisions to implement new or different pollution control technologies are made at the level of the individual firm or facility. Their decisions reflect the constraints and incentives of environmental regulation as well as economic and financial conditions, available information, alternative investment possibilities that compete for the firm’s resources, and individual firm preferences (including tolerance for risk).

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

Assessments of firm behavior in response to regulation can be based on anecdotal reports, directed case studies, surveys of multiple firms, and conceptual economic models. Anecdotal reports, case studies, and surveys have been done by EPA and specific state, environmental, and industry groups to address some of the issues concerning the likely impact of the NSR rules and rule changes. Although studies of this type have the potential to provide important information, they also are subject to bias depending on how issues are framed, survey participants are selected, and questions are asked (Yin 1994; Cox et al. 1995; Stake 1995). As part of our final report, we will evaluate the usefulness of a number of these studies for addressing the issues in our charge.

To the extent that their information is representative and pertinent, the insights from empirical studies of the type described above help to inform economic models that characterize and predict how firms will behave in response to different incentives. Economic models estimate behavior based on principles of rational choice and profit maximization (e.g., Tietenberg 2003; O’Sullivan and Sheffrin 2005). Process engineering models that estimate the performance (for example, efficiency), emissions, and cost, given alternative capital investments and operating decisions at individual facilities, can be included as a part of, or a precursor to, these models (e.g., Allen and Rosselot 1997; Lewin 2003).

Economic theory of firm behavior provides a useful window into how firms make choices and how they would likely alter their investment, input use, production, and emissions in response to changes in environmental regulations such as the NSR rule changes. Economists assume that firms exist to make profits and that their fundamental objective is to maximize profits by keeping costs low and revenues high. The effects of environmental regulation on firms’ decisions will depend on the stringency and form of the regulation and on the incentives that the regulation provides for firms to adjust their behavior (Magat 1978; Milliman and Prince 1989; Helfand 1991; Montero 2002).

The economics paradigm can be at odds with how business leaders might describe what motivates their actions. Firm managers often deny that they are motivated solely by profits, arguing that firms have other goals, such as maximizing market share or even broader social goals that guide their decisions. Indeed, the long-term economic performance of a company can be affected by its commitment to environmental quality. For example, many firms now recognize that consumer confidence and allegiance can be influenced by environmental performance, and that employee health and productivity are likewise affected (Grabosky 1994;

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

Hamilton 1995; Curcio and Wolf 1996; Anton et al. 2004). Nonetheless, a firm’s behavior is clearly disciplined by the realities of the market, and environmental commitments and controls can be costly. Firms that do not behave in ways that are consistent with profit maximization over the long term cannot succeed in a competitive market. The challenge for economic modeling is to characterize the firm’s resources, risks, costs, and profit opportunities that are most relevant to how a profit-maximizing firm responds to regulations and incentives. Data limitations and a lack of understanding of a firm’s constraints and opportunities can make the results of economic analyses highly uncertain, even if profit maximization is generally a good descriptor of firm behavior.

The profit-maximizing paradigm described below can inform many different methods of assessing how firms would be likely to respond to regulatory changes, including case studies, surveys of firms, and more formal econometric and simulation models. The key insight from this paradigm is that in understanding how firms will respond to regulation, it is important to understand the incentives created by different forms of regulation. This is particularly true when, as is the case with the NSR rules, the firm’s actions determine whether it will ultimately be subject to the cost of complying with a regulation.

Figure 5-1 is a hypothetical illustration of the trade-offs between cost and emission reductions for a firm considering different possible plant maintenance or alteration activities. The figure is simplified in several respects to highlight key implications. First, it represents a continuous and smooth range of alternatives when in fact there may be only a handful of discrete alternatives. Installation of a particular emission-control device is usually an all-or-nothing decision, and the curve is thus more properly characterized by a sequence of discontinuous steps. Second, the curve is not necessarily “drawn to scale,” exaggerating, in most cases, the costs of emission changes relative to the total cost of production. The graph identifies the emissions and costs associated with a set of possible plant maintenance and alteration decisions that a facility is assumed to be considering, while currently operating at location A, with relatively low total production costs but high emissions. The firm is considering a maintenance activity or alteration to the plant that would move it to nearby point M1, allowing it to operate with both lower cost and lower emissions. The change might result in modest improvements in operating efficiency or reliability that yield both cost and pollution benefitsa win-win outcome for the firm and the environment (this is the type of activity that proponents of the recent NSR rule changes hope to

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

FIGURE 5-1 A hypothetical illustration of options facing a firm operating with low costs/high emissions at point A, considering a maintenance activity that would result in a shift to M1, M2, M3, or M4 but that might also trigger an NSR that would require them to shift to the high-cost/low-emissions point labeled NSR. New facilities, with new technology, might be able to operate in the low-emissions/low-cost area denoted by “New Facility?”

encourage). However, if the firm fears that the proposed M1 change will trigger NSR, forcing it to move to the “NSR” location in the figure with much higher costs (and much lower emissions), it may elect to forego the M1 maintenance or alteration, thereby losing the opportunity to achieve the lower costs and the associated modest emission reductions.1

1  

Although NSR rules are intended to apply only to the case in which emissions of regulated pollutants are significantly increased, an activity of the type denoted by M1 still might trigger NSR—for example, with a system of linked producers, such as utility generators. In particular, consider a single boiler that could be improved so that it generates more and increases its emissions, but with a decrease in the overall emissions from the utility system because the modified plant is more efficient than the one that it replaced. Similarly, a multiplant firm

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

Other maintenance and alteration activities shown on the figure are also possible—opponents of the NSR rule changes fear that they would encourage more of the changes denoted as M3 and M4, resulting in higher emissions (regardless of whether they yield cost savings to the firm, although presumably, the cost-saving maintenance and alteration activities in M4 are more likely than those in M3). Even when the modest emission reductions achieved by M1 and M2 are lost in some cases, proponents of stricter criteria for triggering NSR argue that these criteria yield an overall net reduction in emissions. This happens because stricter rules encourage a number of these high-emitting plants either to make the major changes necessary to reach the low-emission levels of the NSR point on the curve (because they cannot continue to operate at the current point A without implementing the activity that now triggers NSR) or to be replaced by new facilities that do. Because of new, advanced technologies, these new facilities might even be able to achieve the lower emissions with much lower total costs, operating in the region denoted on the graph as “New Facility?”. Hart (2004) discusses how different regulatory policies can provide incentives for industry to adopt new vintages that lead to both reduced pollution and production growth.

In the next section, we discuss in more detail the formulation of conceptual models of profit-maximizing behavior that underlie the trade-off between reductions in costs and emissions illustrated in Figure 5-1.

Conceptual Models

Conceptual models provide a formal mathematical representation of how firms make choices to maximize profits. The most common assumption in these models is that firms operate in competitive markets where they take the prices of the products that they produce and of the inputs that they use as given. The profit-maximization problem involves finding the amount of inputs to use that maximizes total profits, given a production function that relates inputs to outputs. Emissions of pollution and the capital equipment used to reduce emissions can also be repre-

   

might have a least-cost strategy for decreasing emissions using modifications that rebalance production so that there is an increase in emissions at some plants, despite the net reduction in total emissions.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

sented. A simple model of firms’ behavior typically has the following structure:

  • Decision variablse: quantities of inputs to production that are used, including fuel, labor, capital, and pollution control equipment.

  • Parameters: input and output prices.

  • Constraints: what values of the decision variables are feasible or allowable. In an environmental assessment, the major constraints are as follows:

    • A production function that defines how inputs are transformed into output.

    • An emissions equation that calculates the amount of emissions resulting from using different combinations and amounts of inputs and relates these emission levels to emission limitations the facility must satisfy.

    • Other environmental constraints such as restrictions on fuel input use or the use of specific pollution abatement equipment that the facility must satisfy.

  • Objective function—a function that identifies the combination of decision variables that will maximize profits (revenues minus costs).

This model typically leads firms to produce output up to the point where the marginal cost of increasing production by a single unit is equal to the price at which the firm can sell its product. When the level of the firm’s output is known, the decision becomes one of selecting the mix of inputs that minimizes the firm’s cost of production given its production function. The solution to the cost-minimization problem, for a given level of production, can be used to determine a relationship between emissions and the total cost of production, such as the one shown by the dashed line in Figure 5-1.

Firms face environmental constraints that can take a variety of forms. An operating permit affecting a facility’s behavior typically imposes a cap on emissions from an operating unit within the facility. Often this cap is based on a desired maximum emission rate per unit of heat input and an assumption about maximum levels of fuel use. Environmental constraints can also take the form of requirements to install control equipment (including specific classes of control devices when technology-based rules are in place) that achieves required emission limits or requirements to use lower-polluting fuels, such as lower-sulfur coals in the generation of electricity. In some cases, firms can participate in a national or regional cap-and-trade program for emissions when the

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

constraint takes the form of a requirement that firms hold sufficient allowances to cover their annual or seasonal emissions of a particular pollutant. All these requirements impose costs on the firms that will influence the trade-offs that they make when determining how to produce their product and how much pollution to emit.

Regulated Markets

When, as was traditionally the case for electricity generation, the price of the firm’s output is determined by a regulator (e.g., a state Public Utility Commission) and not by the market, the firm’s profit-maximizing problem includes an additional constraint, and the product price is no longer a parameter in the model. Typically, regulators set regulated price equal to average cost, which provides weak incentives to minimize costs. Recognizing the weak incentive properties of average cost pricing, market regulators increasingly are relying on other forms of regulation such as capping product prices to provide regulated firms with an incentive to reduce costs. In particular, as the electric-power industry in several states has been making its way through the transition from monopoly regulation to competition, prices for electric power have been capped, providing strong incentives to reduce costs.

How regulators treat pollution control costs and other costs associated with environmental regulation in setting prices can have very important incentive effects on a firm’s choices over various options for complying with environmental regulation. Differences across state electric utility regulations in the treatment of emission allowances, costs of fuel switching, and costs of flue gas desulfurization (FGD) scrubbers had a definite role in shaping how electric utilities chose to comply with Title IV of the Clean Air Act (CAA) Amendments (Bohi and Burtraw 1992; Arimura 2002). Movement toward more competitive pricing of electricity generation will diminish the importance of these effects, but in certain regions of the country, such as the Southeast, deregulation of electricity-generation pricing is proceeding very slowly.

Differentiated Regulation

In the models discussed above, a firm has no influence on whether it is subject to a particular environmental regulation. For regulatory programs, such as new source performance standards (NSPS) and NSR, a

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

firm does not face the regulation until it takes a particular action. If a firm builds a new facility, then it knows that facility will be subject to NSR and possibly even stricter requirements (depending on where the facility is located). If a firm makes a major modification to an existing facility that is deemed to result in a “significant” increase in pollution, then it will be subject to NSR. The revisions to the NSR program that are the subject of this report affect the conditions under which NSR applies to an alteration at a facility.

To analyze such endogenously triggered regulation, a dynamic formulation of a firm’s profit maximization problem becomes more appropriate. Because major alterations to facilities are capital investments, the problem should be extended to include multiple periods and the firm’s objective should be restated as one of maximizing the present discounted value of future profits. Firms will compare discounted profits with and without the alteration and choose the course of action that appears to be the most profitable. Future costs with the alteration will include the costs of regulatory requirements triggered by NSR, and future costs without the alteration may include reduced levels of equipment reliability and other adverse outcomes. If the additional costs of complying with the NSR rule outweigh the benefits of the contemplated alteration, then the firm will not make the change. Being subject to the NSR rule may affect the payoff to the firm of different investment options and, in theory, could cause the firm to forego investments that would reduce emissions or improve energy efficiency at a facility, as illustrated in Figure 5-1.

The extent to which this has happened in practice is the subject of much debate. Firms and industries indicate instances when the potential to trigger NSR requirements made or might have made plant upgrades too costly to move forward. However, there is no way to independently corroborate such reports and rigorous statistical studies of this phenomenon do not exist, party because of lack of data and the difficulty of identifying the effects of NSR given all the varied influences on investment decisions. One recent empirical study that applied statistical methods analyzing possible effects of NSR rules, as distinct from NSR rule changes (List et al. 2004), is discussed later in this chapter. Several features of the NSR rule changes, including the change in the selection of test years for emission changes and the minimum expenditure threshold for major modifications, reduce the types of investments at existing plants that will trigger NSR. By removing certain types of expenditures from the category that triggers NSR, the rule might reduce the regulatory uncertainty facing the source and lowers the cost of many types of in-

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

vestments. At the same time, by lowering the costs of making modifications to existing plants, the rule change could encourage more firms to choose the modification option rather than invest in new, cleaner facilities.

The implications of different investment choices for plant-level emissions are impossible to anticipate with a conceptual economic model. A conceptual model can be used to illustrate conditions under which emissions increases might occur and when they will not. Then, given the necessary data, empirical analysis could be used to identify which conditions are relevant to a particular change. If a particular pollutant is capped nationwide (or within a region), then national (regional) emissions that are already at the level of the allowable cap will not rise with a relaxation of NSR applicability rules, although emissions could increase locally.

As stated above, conceptual economic models tend to be fairly simple representations of a firm’s behavior and choices. The models are intended to yield general economic insights about how firms might be expected to behave under certain conditions and assumptions and about how firms make trade-offs. The models can be used to generate hypotheses that could be tested later by econometric or other statistical methods. Imposing specific functional forms on the production and emission functions make it possible to develop a model that could be used for simulation purposes. However, the conceptual models do not provide much detail on how specific processes function, although, if necessary to address a particular question, such process models could be obtained from engineers and incorporated into economic models of behavior, as discussed later in this section.

Methods for Applying Conceptual Models

Econometric methods can be used to estimate the parameters of a profit function or cost function to test hypotheses generated by using conceptual models of a firm’s behavior and to quantify the size of the effects of concern. For example, Carlson et al. (2000) used a panel of generating-unit-level data from coal-fired electricity generators to estimate a cost function. In this model, total annual costs including pollution control costs depend on input prices including prices of different types of coal, the level of electricity production, the level of sulfur dioxide (SO2) emissions, a plant indicator variable, and a time trend to account for technological change. Input cost share equations and a model of SO2

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

emissions levels are estimated jointly with the total cost function. From the estimated cost function, they derived an equation for the marginal costs of emission reduction and used it to identify the efficient level of emission reduction at each plant and to solve for the market price of SO2 allowance prices under Title IV.

Estimating structural economic models can be quite data intensive, and often the requisite data on input prices, total costs, and economic profits for the relevant firm or plant are not readily available. In many cases, the best way to find the answer to an empirical question about how firms have responded to certain types of regulations or regulatory changes may not be by estimating a structural model. Instead, researchers use plant-level, firm-level, or, in some cases, more aggregate data to look at how environmental regulations and regulatory changes that were implemented in the past have affected economic activity and costs, productivity growth, and R&D and innovation.

Effects of Differential Regulation over Space and Vintage of Source

Research has also examined how differential regulations affect firms’ decisions about activities that could increase the stringency of the environmental regulations they face. One such decision is where to locate a new plant given differences in regulations across locations. Levinson (1996) used a conditional logit model and census facility-level data to study whether births of new manufacturing plants respond to differences in state environmental regulations and found that they do not. Using county-level information on ozone attainment status and plant-level information on facilities for four manufacturing sectors emitting high levels of volatile organic compounds (VOCs), Becker and Henderson (2000) studied the effects of differences in environmental regulations on where new plants choose to locate, sizes for new plants, and the timing of investments. They found that new plants are more likely to locate in attainment areas.

Only a few studies have used econometrics to look at the effect of differential regulation of sources due to vintage on economic decisions. An econometric study by Gruenspecht (1982) looked at the effects of

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

corporate average fuel economy (CAFE) standards2 on turnover of the automobile fleet and found that applying tighter standards to new cars reduces the rate of turnover of the existing automobile fleet. Nelson et al. (1993) studied the effect of NSPS and lowest achievable emission rate (LAER) standards on the age of installed capital of electricity generators and associated effects on emissions. They estimated an equation that relates the average age of the capital stock for a group of 45 electric utilities to measures of demand and input price growth and regulatory intensity. They found that differential regulations retard capital turnover but do not result in a significant increase in emissions.

A recent study by List et al. (2004) uses econometric models and nonparametric techniques to analyze the relationship between plant alteration and closure decisions and county attainment status as a proxy for stringency of NSR requirements.3 This study uses data from the industrial migration file maintained by the New York State Department of Economic Development from 1980 through 1990. The authors find that NSR appears to retard the rate of alteration of existing plants, but they find little evidence that NSR affects the closure of existing plants. Their study does not consider the effects of NSR on emission levels.

Process Engineering Models

Many of the modeling approaches described elsewhere in this chapter deal with multiple facilities and their interactions or use simplified characterizations of production technologies that merge multiple processes into a single-stage production function. However, such models often lack details about technology characteristics. For example, many life-cycle inventory and market analysis models use linear coefficients for the ratio of energy consumption to delivered units of a particular

2  

CAFE standards, which were initiated by Congress in the Energy Policy and Conservation Act of 1975, established mandatory fuel efficiencies in the form of required miles-per-gallon goals for fleets of passenger cars and light-duty trucks.

3  

The study by List et al. (2004) focuses on NSR rules in effect before the recent changes that are the subject of this report.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

product or for the ratio of emissions to a particular product. In reality, energy consumption and emissions at specific facilities can be a complex function of site conditions, feedstocks, process configurations, designs of each process area, operating practices, and maintenance, among others. Furthermore, when retrofit options are being evaluated, the availability of space at a site can severely constrain the location of new additions to a plant and thereby affect cost. Thus, there can be a need for a model or evaluation at the level of an individual plant, taking into account details of the plant’s major components. Such models can enable “what-if” analysis of changes in design, feedstock composition, and operations on efficiency, emissions, and cost at the level of an individual plant.

There are numerous plant-level modeling approaches, ranging from empirical to theoretical. An empirical approach typically involves fitting a regression equation or system of equations to available data regarding the inputs and outputs of individual process areas and linking the process area models together to describe an entire plant. A theoretical approach involves developing mass and energy balances for each process area including detailed chemistry (e.g., chemical kinetics) and physics (e.g., fluid flow) for each unit operation. For example, the furnace of a power plant could be simulated by using computational fluid dynamics coupled with a chemical mechanism that describes the combustion of fuel and formation of pollutants during combustion. Such a simulation would make it possible to describe the temperature field in three dimensions within the combustor and also dynamically. Such models can be both data and computationally intensive. If the same approach is applied to all process areas of a complex plant, the resulting model can be large and difficult to use in practice. Thus, the choice of an appropriate modeling approach depends on the objectives of the model.

Commercially available software tools, such as the Aspen Plus steady-state chemical process simulator, can be used to develop and apply simulation models of a wide variety of process plants. The user specifies key parameters of each unit operation and of the inlet streams. Thermodynamic databases describe the key physical and chemical properties of each chemical “component,” such as compounds. Aspen or Aspen Plus models have been developed for a variety of power-generation systems, including, for example, integrated gasification combined cycle systems (Frey and Rubin 1992). Cost models of process technologies can be developed by using built-in features of Aspen, or they can be developed separately and coupled with the performance model as subroutines. Aspen Plus simulation models require some software-specific

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

expertise to develop and run. It has been shown that simplified reduced-form models can be developed based on Aspen models, which in turn facilitate more rapid analyses useful for policy purposes (Frey and Bharvirkar 1998).

To be of practical use, process-engineering models of plants should be executable in a reasonably short period of time by users who are not experts in the model. An example of this type of model is the Integrated Environmental Control Model (IECM), which runs in a Windows environment and has a graphic user interface (Rubin et al. 1997). In the past, EPA developed and maintained a somewhat similar model, known as the Integrated Air Pollution Control System (IAPCS) (Radian 1999). However, IECM and IAPCS typically had a somewhat different technology focus, and IECM includes a distinguishing probabilistic simulation capability for quantifying uncertainty in inputs and outputs.

A key goal of plant-level models intended for policy applications is to capture salient details and key interactions among process areas without becoming unwieldy. One approach, used in the IECM and similar models, is to start with basic mass and energy balances for major “process areas” of the plant to describe, with adequate accuracy, the major mass and energy flows in the plant. For example, the major process areas of a new coal-fired electric power plant typically include the boiler, economizer, air preheater, particulate matter (PM) control device (typically a cold-side electrostatic precipitator or fabric filter), nitrogen oxide (NOx) control devices (typically a low-NOx burner and/or other combustion-based approaches and perhaps a postcombustion selective catalytic reduction system), an SO2 control strategy (e.g., the use of a low-sulfur fuel and/or postcombustion FGD), a steam cycle (including heat exchangers, steam drums, steam turbines, and condensers), and any other special considerations (e.g., mercury control using carbon injection). For each major process area of the plant, a separate mass and energy balance model is developed. The process areas are interconnected by the flow of mass and energy between them.

Plant-level models can be incorporated into a larger simulation framework. This has been done in the past, such as for the Advanced Utility Simulation Model, to support system-wide planning applications that take into account some of the details of design and operation of individual plants as well as system-wide considerations (e.g., Badger and Ojalvo 1988). Also, as noted later in the section entitled “Estimating Effects Across Multiple Sectors of the Economy,” process models for

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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production facilities are often included as a part of a life-cycle environment assessment for a given product or industry.

Assessment of Sector-Wide Response

General Framework for Sectoral Assessments

The response of a full industrial sector to regulation can be estimated with generalizations of the tools used for individual firms, including anecdotal reports and representative case studies or surveys. In addition, economic models are available to estimate the behavior of multiple plants or facilities that may or may not interact in some way in response to common constraints and incentives. These models are most frequently applied to electric-power generation, including short-term dispatch as well as long-term capital investment and technology adoption in response to future demand, prices, and regulation.

The purpose of sectoral assessments is to project the possible response of an entire sector of U.S. industry to scenarios concerning government policies, technological change, and economic conditions. The major difference between sectoral assessments and the individual firm analyses of the previous subsection is that sectoral assessments aggregate the actions of all firms within an industry while imposing certain consistency conditions that must be met by the market as a whole. These conditions usually require that markets clear—that is, that prices adjust so that supply equals demand for the sector’s inputs and outputs.4

In the case of outputs, an example of such a market clearing condition is that the quantity of electric power produced by a region’s power plants equals the quantity consumed by that region’s consumers, adjusted for net imports. By imposing such a condition, a sectoral analysis ensures that, for example, if one facility or set of facilities greatly increases their output (and emissions) in response to a change in NSR rules, then some other facilities will need to decrease their production (and possibly their emissions). A sectoral analysis can also account for price

4  

Sector models are also limited in their ability to track or predict other aspects of firm-specific behavior, such as the performance improvements that occur over time as a result of site-specific process adjustments or learning-by-doing.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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changes on demand so that, for instance, demand increases stimulated by lower prices could also consume and assimilate some of the increased production.

In the case of inputs, market clearing ensures that the aggregate demand by the sector for fuel or emissions allowances, for example, is consistent with the amount available. Continuing with the power industry example, a national cap on SO2 emissions under Title IV of the 1990 CAA Amendments implies that, if the cap is binding, an increase in emissions from one group of facilities must be matched by a decrease somewhere else or, because of allowance banking provisions, at some other time in the future. Conversely, if emission allowances have a positive price, then if a regulatory action forces a power plant to reduce its emissions, and the plant is allowed to sell the resulting excess allowances rather than surrender them, the result will be an increase in emissions at another location and/or at another time. This outcome would not occur if, as part of the settlement agreement imposed by the regulatory action, the allowances are eliminated.5 Because the supplies of some sectoral inputs, especially fuels, respond to price, increases in inputs demanded by one set of facilities can be met by a decrease in use by other facilities and by an increase in supply. Thus, for instance, if an emissions policy motivates a shift in fuel from coal to natural gas, prices for coal will fall, shrinking its supply, and prices for natural gas will increase, stimulating an increase in its supply. The resulting redistribution of fuel use (and emissions) among the nation’s power plants will reflect a balancing of supply and demand for the fuels and allowances. The purpose of sectoral

5  

In a statement regarding the surrendering of emission allowances as part of settlement agreements, a recent report of EPA’s Inspector-General’s office notes that (EPA 2004b)

When controls are installed, excess allowances of SO2 emissions are created, and it is vital that these allowances not be used. Consequently, all seven settlement agreements included an Emissions Trading Clause requiring the company not to use or sell any emission reductions. Also, all the settlement agreements required the surrender of allowances, except for Tampa Electric Power, which prohibited the selling and trading of SO2 allowances. If a facility is able to use allowances elsewhere at a plant or sell them to another facility, there will be no environmental benefit achieved.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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analyses is to project these shifts in a way consistent with the operation of the sector’s input and output markets.

However, sectoral assessments do not attempt to trace the effects of a policy change throughout all sectors of the economy. For instance, changes in energy use and emissions by railroads due to a change in demand for low-sulfur western coal by the power industry might be significant but would not be considered in a power sector assessment. Such indirect changes, however, are the focus of multisectoral assessments, discussed later in this chapter.

Figure 5-2 presents a basic framework that might be used to interpret the outputs of a sectoral analysis. The two axes represent the economic cost to society, excluding environmental costs, of producing and using a good and the environmental effects resulting from that production. Economic cost might include only the cost of production, if consumption is fixed, or, more generally, it could be the change in the net benefits of consumption if demand is responsive to price. Commonly, the environmental axis is measured in terms of tons of emissions per year because they are more easily estimated than the ultimate health and other effects. The two axes represent the aggregate effects for the entire industry, as opposed to the effects of an individual company’s or plant’s decision shown in Figure 5-1. As an example, trade-offs between SO2 emissions and power production costs for the entire U.S. electricity-generation system might be considered in this manner. Points on this plot might represent possible outcomes under different policies—for example, under different proposals for changes to the Title IV SO2 emissions cap. Point A might represent the present level of emissions and cost, and point B might indicate aggregate emissions and cost if the cap were reduced by 50%. The dashed line indicates a “production-possibility frontier” for the industry, representing the most efficient possible combinations of emissions and cost, given present technology. Points to the southwest (e.g., point C) are not feasible, and points to the northeast (e.g., point D) are inefficient, having higher costs or emissions than necessary.

However, such a two-dimensional plot can hide as much as it reveals for three reasons. First, it does not disclose the distribution of effects among different societal groups. A point on or near the frontier, such as point B, might have lower total emissions than some other point to its northeast, such as point D, but the former point actually might have higher emissions and impacts in some particular location. Second, the

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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FIGURE 5-2 Alternative sector-wide costs and emissions.

plot does not reflect the fact that environmental effects depend on the location and timing of emissions and not just the aggregate amount. Third, other potentially important policy objectives (not to mention other types of emissions) are not shown. Point D, for instance, might result in less disruption of coal-mining employment, which was a major policy driver in the 1978 revisions to power plant NSPS. In sum, there are many more than two dimensions to the impacts of policy scenarios, and these types of plots cannot tell the full story.

Methods for Sectoral Assessments

Sector-wide analyses can use a variety of methods to project the location of alternative scenarios in plots such as Figure 5-2. Two basic approaches are surveys and microeconomic simulations. Surveys, at their least systematic, might simply be nonrandom collections of anecdotes that are argued to be more or less representative of conditions facing, or actions by, firms in an industry. More desirable are sampling schemes in which the reported data are auditable and statistically representative. Surveys may address past actions of firms, or they might ask respondents to state how they would react to hypothetical conditions. In the latter case, no attempt is made, however, to ensure that the aggregate

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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future response of the sector is consistent with market clearing, and there are obvious dangers in the form of motivational and other biases.

There are two general sorts of sectoral simulation models (Andrews 1995). Top-down or econometric models are based on aggregate representations of sectoral responses that are estimated by statistical methods. For instance, the parameters for supply-and-demand curves for steel of a particular type might be estimated from historical data on prices, quantities produced and consumed, and input prices. Simulations are carried out by solving the resulting equations simultaneously under different input assumptions.

In contrast, bottom-up models (also called process-based or engineering economic models) instead represent the physical processes by which an industry converts inputs into outputs, and they usually include explicit variables describing investment and operating decisions of various types. As an example, a bottom-up model of steel production would explicitly account for the capacities of different types of steel production processes and the inputs they require (e.g., electric arc furnaces that use electricity to recycle steel scrap and integrated plants that make steel from iron ore, coal, and limestone using blast furnaces).

Bottom-up models compute market equilibria by assuming that companies operate and invest in production facilities to maximize profits, usually assuming conditions of perfect competition (such as perfect information and the absence of oligopolistic behavior). A well-known result is that a bottom-up model can simulate the result of a perfectly competitive market if it chooses the values of the operating and investment variables to maximize consumer benefits minus production costs. If demand does not respond to price, then production-cost minimization will also yield the competitive outcome.

The process detail of bottom-up models is what makes them useful for assessing the effect of environmental policy on production efficiency and emissions. First, efficiency and emission effects depend on the particular technology and inputs used to produce a product, which bottom-up models represent. Second, the health, ecosystem, and other effects of emissions depend on when and where the emissions take place, which bottom-up models can trace. Third, a regulation being studied may affect only particular operating or investment decisions for a subset of the processes or companies in an industry, a discrimination that can be represented in a bottom-up model.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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On the other hand, bottom-up models have two weaknesses. The first is their detailed data needs. Largely because of federal reporting requirements and extensive state regulation, the necessary information on input costs, process efficiencies, and consumption patterns is available for the electric power industry.6 However, this industry is an exception. For other industries that are less regulated and less concentrated and that produce a much more heterogeneous product, the data needed to build bottom-up models may be unavailable.

A second weakness can occur in the assumptions of rational profit-maximizing behavior and pure competition. Actual and modeled market behaviors can deviate significantly for several reasons: model simplifications, short-sighted decision making, non-economic objectives, market power, and inefficient market rules. Modelers have responded by developing approaches to sectoral modeling that recognize these market imperfections. Examples include agent-based models and oligopoly models. However, these approaches are not widely applied for policy making, in part because they often incorporate assumptions about how firms behave that are very difficult to verify.

Available data, relatively homogeneous outputs, and policy importance have made the agricultural and energy sectors the most common areas of application of bottom-up models. The use of bottom-up energy models by the federal government began with Project Independence in the 1970s, and both EPA and the U.S. Department of Energy have been enthusiastic consumers of such models since that time (Murphy and Shaw 1995). As an example of this type of model, electric power market simulation models generally have the following structure:

  • Decision variables: locations, capacities, operating levels, inputs, and emissions of pollutants from generation and transmission facilities.

  • Objective function: choose values of those variables to maximize consumer benefits minus investment and operation costs.

6  

Recent transfers of existing generating assets from regulated utilities to unregulated firms has reduced the amount of detailed financial data that are being collected for the electricity-generation sector.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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  • Constraints: what values of the variables are feasible:

    • For each location and time period:

      • Generation plus net imports must equal quantity demanded.

      • Generation and transmission quantities must be equal to or less than capacities.

      • Kirchhoff’s voltage law, and other physical relationships that determine power flows must be satisfied.

    • Local constraints on emissions and facility siting:

      • Emission caps defined over relevant regions and time periods.

      • Other regulatory restrictions.

Models that are created for specific markets, such as the Pennsylvania-Jersey-Maryland Interconnection, and that simulate short periods of time, such as operation over the next year, can represent much more detail about the transmission grid and the operation of particular generation plants. However, because power markets are national (and to some extent international) in scope,7 as is the SO2-emissions market, and because investments in this sector can have lifetimes of several decades, national models with a multidecade time horizon most commonly are used to analyze national energy and environmental policies. These models often also include explicit representations of supply and demand in fuel markets, whereas detailed regional models tend to treat prices of coal, natural gas, and other inputs as being fixed. The price that national models pay for their comprehensiveness is a necessarily simplified representation of power system operations—for instance, aggregating supply, demand, and emissions to census regions, as in the National Energy Modeling System (NEMS). This scale is too aggregate for detailed assessment of the health and other impacts of emissions because pollutant transport and transformation models require particular locations and timings for emissions. Therefore, the scenarios created by such national models on occasion are disaggregated to a county or similar scale.

7  

The U.S. power market is divided into three autonomous regions: the Eastern Interconnection, the West, and the Electric Reliability Council of Texas. These regions also have important connections to Mexico and Canada.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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Examples of national models that have been used to assess possible effects of NSR rule changes include EPA’s Integrated Planning Model (IPM) and NEMS of the U.S. Energy Information Agency. IPM is a large, bottom-up model of the U.S. electric power industry that EPA frequently relies on for analyzing the impact of present and proposed policies and regulations on that sector’s emissions and costs (EPA 2004s). It considers investment and operations on a multidecade time scale (e.g., 2005-2030), and its geographic disaggregation corresponds approximately to the National Electric Reliability Council regions. Similar generation facilities within a region are aggregated to limit model size. IPM represents the economics of power plant retrofits and retirements, which occur automatically when the benefits of these actions exceed the cost. Although in the long-run electricity consumption is price elastic, the model represents demand as fixed. This facilitates computation by allowing use of a cost-minimization objective and linear, rather than nonlinear, programming. This fixed demand assumption means that intersector competition is not considered.

NEMS, in contrast, provides less detail on the power sector but represents intersector interactions, such as natural gas-electricity substitution for household heating. NEMS is an interconnected suite of models for various components of the U.S. energy sector as well as models of the remainder of the U.S. macroeconomy and world energy markets (EIA 2003b). The model searches for a set of prices and quantities supplied and demanded that represents an equilibrium among modules representing oil and natural gas supply, natural gas transmission, coal supply, renewable fuels supply, electricity generation, petroleum fuels processing, and energy demands by residential, commercial, transportation, and industrial customers. The modules can also be run in stand-alone fashion—for example, for just the electricity sector subject to fixed energy demands. NEMS breaks down the results by nine census divisions and provides projections through the year 2025.

Electric power models of this type generally assume rational and perfectly competitive behavior by generators and that there are no obstacles to trade other than the transmission capacity limits. If the only environmental constraint is a total cap on emissions, then by definition the model will yield the lowest-cost solution for that level of emissions, which will be a point on the production possibility frontier in Figure 5-2.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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However, model solutions may not be on that frontier, because of other regulatory restrictions in the model, such as NSPS or local air-quality restrictions. Of course, actual market outcomes also will not be on the dashed frontier, because electricity markets are evolving at different rates across the United States, trade is not completely free, generator behavior may not be rational, and the model may be missing many actual constraints or it might misrepresent costs.

Future projections are necessarily highly uncertain because of unforeseen economic, legal, and technological changes. For example, model runs done by EPA to support development of the Title IV acid rain program projected higher SO2 compliance costs, more scrubbing, and less fuel switching than actually occurred, partly because prices and transport costs for western low-sulfur coal fell more quickly than anticipated. Therefore, sectoral models are best used to identify a range of possible outcomes and to gain insight into general relationships rather than to create specific numerical forecasts that, with current knowledge and methods, cannot be accurate in their details.

Costs, Productivity Growth, and Innovation

A number of econometric studies have looked at the effects of environmental regulation generally on costs, productivity growth, and innovation within specific industries and within the economy more broadly. Studies by Barbera and McConnell (1986) and Gray (1987) used industry-level data and found statistically significant negative impacts of regulation on productivity growth, but the effects are not necessarily large. Gollop and Roberts (1983, 1985) estimated firm-level cost functions and marginal abatement costs for coal-fired utilities to study the effect of SO2 regulation on productivity growth in the electricity sector as well as the regional effects of these regulations on the industry.

More recent studies of manufacturing industries take advantage of more-detailed plant-level data for manufacturing firms collected by the U.S. Census Bureau. In recent decades, the U.S. Census Bureau has made available plant-level economic data on manufacturing facilities collected as part of the quintennial Census of Manufacturers and Annual Survey of Manufacturers, which applies to larger facilities, making it feasible to use these more disaggregate data to look at how regulation has affected costs and productivity. For example, Greenstone (2002) used the plant-level database extending from 1972 to 1987 to examine the re-

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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lationship between nonattainment status and measures of economic activity, including employment, investment, and shipments for plants in polluting industries. He found that plants in nonattainment counties have lower employment, lower shipments, and lower total capital stock than analogous plants located in attainment areas, but the size of these effects is relatively small. Numerous other studies have used these data to study the effects of regulation on specific industries including pulp and paper (Gray and Shadebegian 2003) and oil refineries (Berman and Bui 2001).

Econometric methods have also been used to study the effects of environmental regulation on R&D and innovation; many of those studies are reviewed by Jaffe et al. (2003). The empirical studies present mixed results. Lanjouw and Mody (1996) found a significant positive relationship between pollution abatement expenditures and patenting activities. Jaffe and Palmer (1997) found a positive relationship between pollution abatement expenditures and R&D expenditures but no impact of the former on patenting. Taylor et al. (2003) determined that CAA regulations have had a significant impact on air-pollution-control innovation and patenting. Other studies looked at the effects of energy price changes and explicit efficiency standards on the nature of technological change in appliances, focusing particularly on the energy-savings character of the innovations (Newell et al. 1999).

Another modeling approach that can be used to explore the impact of regulation on environmental performance involves using “adaptive agents” to simulate the innovation and production activities of multiple firms competing in a product market. The decisions of the agents evolve over time in response to changing consumer preferences and demand and regulatory decisions affecting costs, prices, and profitability. These models have been used to explore the factors that affect the evolution of green products and processes (Teitelbaum 1998; Axtell et al. 2002; Bulla and Allada 2003). However, they are still in the early stages of research development, and none has yet been advanced to the point where detailed decisions on plant maintenance and replacement of the type that are important to NSR rule making can be evaluated.

Estimating Effects Across Multiple Sectors of the Economy

The sectoral assessments described in the section “Assessment of Sector-Wide Response” are concerned only with the direct effects of a policy on an industry and its immediate inputs and outputs. When firms

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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modify their production levels or product designs in response to regulation or other incentives, the effects of these decisions ripple through the economy and affect other industries, including those that provide their inputs, those that use their products, quantities shipped, and associated emissions. These indirect or ripple effects can be important.

One type of indirect effect results from substitution among sectors. For instance, a policy aimed at reducing emissions from a particular sector might result in increased prices for that sector’s output, shifting demands to other goods and services in the economy whose emissions then might increase. Electricity, for example, competes with natural gas, fuel oil, wood, and other fuels for home and water heating in various geographic markets in the United States, and policies that affect the price of electricity will influence the mix of fuels the residential sector consume. Another type of indirect impact is the result of upstream and downstream effects. As an example, the consideration of emissions only from the combustion of fossil fuels in power plants disregards emissions from other stages of the fuel cycle, including fuel extraction, transportation, and waste disposal.

Tracing indirect effects throughout the entire economy is the focus of life-cycle analyses and macroeconomic models that compute a general equilibrium outcome for multiple sectors of the economy. Each of these methods is briefly reviewed.

Studies that attempt to quantify ripple effects on the economy and the environment (as well as the direct effects from product manufacture) are referred to as life-cycle assessments (LCAs). The ripple effects occur “upstream” of the particular company, as modified orders to suppliers, their suppliers, and so forth. They also occur “downstream” of the production process because modified products and production quantity can result in changes in the emissions that occur during product use, reuse, recycling, and disposal.

LCAs can be performed by one of two methods: the process modeling approach or the economic input-output approach. The process method is the underlying principle behind a variety of LCA tools, most of which have been developed in Europe (Fruhbrodt 2004): the Society of Environmental Toxicology and Chemistry (SETAC) endorses this approach (Hendrickson et al. 1997). The process modeling approach requires that each aspect of a particular product’s life cycle be analyzed and documented. The models require extensive databases on materials and manufacturing and nonmanufacturing processes and use these to estimate a wide range of economic, technical, social, and environmental

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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impacts. Environmental impacts include factors such as resource depletion, acidification, eutrophication, global warming, human toxicity, freshwater aquatic toxicity, marine aquatic ecotoxicity, terrestrial toxicity, ozone layer depletion, tropospheric ozone creation, and radiation. Technical impacts include nonrenewable and renewable energy consumption and energy efficiency.

The second approach, based on economic input-output models, considers the full set of economic transactions between different sectors in the national economy. The general framework of the economic input-output model was developed by Nobel-Prize-winning economist Wassily Leontief, and it requires that a nation’s economy be divided into sectors (typically about 500). The inputs and outputs of these sectors are then defined by the 500 × 500 matrix that quantifies the economic transactions between each. The total transactions that ripple through the economy (all upstream flows) are computed for each unit of economic activity, and these can be adjusted linearly to produce estimates for various dollar amounts of output. With this framework, an economic input-output model is capable of determining the total economic activity and associated environmental impacts from any purchase amount of a particular product or service. Because of the comprehensive data provided by the U.S. Census Bureau, the economic input-output model is able to trace even seemingly unrelated and insignificant transactions such as office computer paper used at a manufacturing plant. The Bureau of Economic Analysis also develops work files that are used to extract specific data points from the large input-output matrix produced by the U.S. Census Bureau. A model that uses this approach is the Environmental Input-Output Life Cycle Assessment program (Green Design Initiative 2004).

Another method for looking at the broader economic effects of an environmental policy change is the use of macroeconomic analysis, implemented with a computable general equilibrium (CGE) model. In contrast to an input-output model, which assumes that inputs are used in fixed proportions, the CGE model allows firms to adjust their mix of inputs in response to changes in relative prices. If, for example, a change in environmental regulations increases the demand for a particular fuel, thereby increasing its price, the CGE model allows that effect to feed through to other sectors. Although this additional flexibility provides a better representation of how industries would respond to regulation-induced price changes, it comes at a cost in terms of sectoral detail. Most CGE models have only aggregate sectoral detail, dividing the entire economy into between 5 and 25 economic sectors. When augmented

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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with information on emission rates, CGE models can be used to look at the effect of the regulatory changes on direct emissions from regulated sectors and indirect effects on other sectors. CGE models have been implemented primarily at the scale of national economies and have been applied most frequently in the realm of environmental evaluation to address issues related to carbon control for climate change assessment, although national assessments have also been conducted for elements of the CAA (Jorgenson and Wilcoxen 1990a,b, 1993a,b; Manne et al. 1995; Fossati and Wiegard 2002).

Some models go further than just looking at emissions by incorporating air transport models that estimate impacts on pollutant concentrations and dose-response functions that translate air-quality changes into effects on human health and the environment. These models, known as integrated assessment models, often also include economic estimates of the monetary value of various changes in human health and environmental endpoints. These models have been used to analyze the benefits and costs of different environmental regulations including Title IV of the 1990 CAA Amendments (EPA 1997). More information on the type of modeling used to translate emissions into environmental effects is provided in the following section of this chapter.

LCAs, CGE, and integrated assessment models can provide useful information to evaluate the ripple effects of changes in production and demand that result from environmental regulation. However, given the difficulty in determining even the direct effects of NSR rules and rule changes on the production and emissions of regulated plants and industries, the use of a tool that translates these direct effects into estimates of changes in indirect economic activity and environmental emissions is premature at this time. As better estimates for direct effects are obtained and LCA and CGE tools are improved to allow more location- and plant-specific calculations, the use of these methods to estimate ripple effects should be considered.

Estimating Environmental and Public Health Impacts

Quantifying the influence of changes in emissions on public health and welfare is a complex, multistage process involving the integration of multiple data streams with physical, econometric, and behavioral theories using statistical models and expert opinion (NRC 2002). Models are needed to evaluate the causal pathways from the effects of NSR rule

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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changes, beginning with the relationship between changing plant emissions and ambient air pollution, followed by personal exposure, and then human health effects. Engineering, econometric, chemical, atmospheric, biomedical, and sociological theories and data are needed to inform these relations, but available empirical information is generally insufficient to the task. In some cases, data are not available; in others, data are incomplete or observational rather than experimental so apparent relations need to be adjusted. For example, in addition to changes in NSR rules, medium- to long-term trends in ambient pollution are influenced by emissions from other sources not affected by NSR rule changes and possible trends in weather or climate.

Trends in health outcomes likewise are affected by a variety of factors in addition to ambient air pollution, and even pollution effects can be modified by personal behavior (e.g., susceptible individuals may alter their behavior on high-pollution days). Additional complications arise from the need to assess relations over time and at relatively fine geographic scales. Because of the complexity of the relations and the relative lack of direct information, simulation models and complex statistical analyses are necessary to help sort out this causal network. These formal approaches are necessary to document assumptions, define and organize inputs and outputs, and, as much as possible, isolate the effects of NSR changes in the set of other candidate causes. A properly conducted and reported formal approach identifies relevant uncertainties and ensures that their influences are embedded in the outputs. Typically, the process begins with an estimate of how much emissions will change as an input, then estimates how these changes will affect exposure, and then estimates how these changes in exposure will affect human health.

Ambient Concentrations and Exposure Outcomes

Once changes in emissions have been estimated, atmospheric dispersion models are needed to relate emission changes to temporally and spatially indexed ambient concentrations and deposition. Pollutant fate and transport are affected by stack height and diameter, pollutant exit temperature and velocity, and other site characteristics, so differentiating among sources and source categories is important. Because relevant atmospheric conditions such as temperature, humidity, wind speed and direction, and background pollution levels vary both seasonally and spa-

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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tially, it is important to estimate where and when emission changes occur as well as the seasonal variations in these changes.

Assessments of the effects of changes in the NSR rules depend on estimating impacts of emission changes on ambient concentrations of primarily emitted pollutants such as SO2, carbon monoxide, and large or fine primary particles (PM10 and PM2.5, respectively) as well as secondarily formed fine PM (PM2.5) and ozone. In previous regulatory impact analyses of policies involving these pollutants, EPA applied multiple models with various degrees of sophistication, spatial coverage, and spatial resolution. For example, when evaluating the benefits and costs of the CAA (EPA 1999), EPA used the Urban Airshed Model (UAM) to evaluate ozone and combined the Regional Acid Deposition Model/Regional Particulate Model with the Regulatory Modeling System for Aerosols and Acid Deposition (REMSAD) to evaluate impacts on PM2.5, PM10, acid deposition, and visibility. Only a few historic episode dates were simulated and the geographic resolution was fairly coarse (56 × 56 kilometer grid spacing or greater over much of the country). More recently, EPA has used the Comprehensive Air Quality Model with Extensions (CAMx) for assessing ozone and is in the process of applying the Community Multi-scale Air Quality (CMAQ) model for its updated analyses of the Clean Air Interstate Rule.

A significant contributor to uncertainty in atmospheric modeling involves the formation and subsequent fate and transport of secondary pollutants (such as sulfate particles, nitrate particles, and ozone). The UAM captures many critical factors influencing ozone formation, including the spatial distribution of emissions of NOx and VOCs (including compositional information), spatially and temporally varying wind fields, diurnal variations of solar insolation and temperature, wet and dry deposition, and the Carbon Bond IV subroutine for chemical reactions among important species (EPA 1999).

But UAM has been shown to underestimate diurnal variability and has been recommended more for average patterns over longer time periods than for site-specific short-term estimates (Hogrefe et al. 2001). Similarly, REMSAD and related models contain modules for formation of secondary sulfates and nitrates, which depend on the relative ambient concentrations of sulfate, nitrate, and ammonium, solar insolation and temperature, wet and dry deposition processes, and other factors. Given the nonlinear and regionally varying relationship between changes in precursor emissions and changes in PM2.5 concentrations (West et al.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

1999), uncertainty beyond that of primary pollutant fate and transport should be anticipated.

Many uncertainties have been addressed in recent modeling efforts. Improved emission inventories, detailed gas-phase constituents, and hundreds of reactions have been incorporated in air-pollution models. Models with explicit microphysics and chemical thermodynamics have been developed that provide mechanistic descriptions of the partitioning of gas-phase pollutants to the particle phase. These provide a more accurate description of particle evolution (Seigneur et al. 1999). Basic aspects of secondary aerosol formation prompted by ozone photochemistry vary substantially among models, with few reproducing the observed afternoon maximum in particle growth (Pun et al. 2002). This critical feature of aerosol growth is common in many regions of the country, yet many widely used models do not adequately address it.

A fate-and-transport model with outputs used in a health benefits analysis does not have to accurately estimate at overly fine spatiotemporal scales. However, models are expected to perform well in estimating over long time frames and at relevant spatial scales. Local transport is insufficient, because studies have shown that a substantial portion of the health impacts of a source with an elevated stack can occur hundreds of kilometers from the stack (Levy et al. 2003). For secondary PM and ozone, such estimations are challenging, because detailed meteorologic and pollution data are required. Also, the models should be able to capture the time resolution that matches the evidence used to develop concentration-response functions. If 1-hour maximum ozone concentrations are associated with health outcomes, a model that lacks hourly concentration estimates will be deficient.

Estimation of spatiotemporal exposure gradients have relied on coupling physical models with data available from ambient monitoring stations coupled with statistical interpolation and smoothing models. The best of these provide space-time point estimates and relevant uncertainties using formal Bayesian models (Christakos et al. 2001).

Of course, personal exposures to pollutants can differ substantially from ambient concentrations. Efforts are being made to study the relationship between the two, but most epidemiologic, health effects studies have been based on data from monitors of ambient concentrations. Even though the locations of these monitors are not ideal for estimating population exposures (many were located to assess regulatory compliance), most health effects studies have relied on these data (NRC 2002). Many uncertainties in exposure relationships remain. For example, because

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

people generally spend most of their time indoors, many individual factors will influence the relationship between personal exposure and ambient concentrations. However, to understand the impacts of changes in NSR, we are concerned with personal exposures to air pollutants of ambient origin. For pollutants such as PM, personal exposure to air pollutants of ambient origin is highly correlated with ambient concentrations (EPA 2003b). However, there is some evidence that ambient levels of gaseous criteria pollutants may be more strongly correlated with personal PM2.5 exposures than with personal exposures to the gases themselves (Sarnat et al. 2001). In an exposure-health assessment, these and similar uncertainties should be documented and, to the extent possible, incorporated into the analysis.

Relating Ambient Concentrations and Exposure to Health Outcomes

To evaluate health impacts of concentration changes, concentration-response functions are developed for key health outcomes—ranging from mild morbidity effects to premature mortality. For most health outcomes, epidemiologic studies are used to develop the concentration-response functions, with animal studies and human experimental studies providing corroborating evidence for causality (NRC 2002). Many studies are available that employ a variety of approaches. Integrating findings across multiple published studies (research synthesis; meta-analysis) is generally preferred to selecting single “representative” studies. The synthesis should be based on an underlying model or models, including multistage models that incorporate site or study characteristics if heterogeneity in effects is present (Levy et al. 2000; Dominici et al. 2003). To the extent possible, it is important to evaluate the independent effects of the pollutant in question, usually by regression adjustment for co-pollutants. Proper treatment of these issues often requires advanced statistical methods.

A critical component in this stage of the analysis is the evaluation of whether thresholds for the health effect are anticipated, or, more generally, whether the concentration-response function deviates from linearity. Most key epidemiologic evidence to date has not detected thresholds or statistically significant deviations from linearity (Daniels et al. 2000; Pope et al. 2002), although these studies (and most studies) have low statistical power to address these issues. The approach by EPA generally has involved computing a baseline estimate assuming no threshold and conducting sensitivity analyses for selected plausible thresholds. This

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

has been considered a reasonable approach but is only one of many candidate options (NRC 2002).

Though some laboratory-controlled exposure studies of the short-term effects of air pollution are available, and there are a few “natural experiments” (Pope 1989; Friedman et al. 2001; Clancy et al. 2002; Marufu et al. 2004), most of the evidence on the health effects of ambient and indoor air pollution comes from observational studies that relate changes in health indicators to changes in exposure to air pollution. Estimates of acute exposure effects come from time-series studies. These studies relate short-term, within-location changes in air pollution to relative changes in death rates or other health outcomes. Because mortality rates is also associated with season, temperature, day of the week, and other pollutants, sophisticated statistical models using covariate adjustment and semiparametric regression are needed to adjust for long- and medium-term temporal variations and for other potential confounders. Estimates of long-term effects come from cohort studies. These studies follow individuals and use between-location variations in air pollution as the basis for estimating health effects. Both types of studies have their advantages and drawbacks, and research continues on reconciling estimates of effects (the cohort studies produce higher effects).

Fitting Models and Characterizing Their Uncertainty

As identified above, simulation-based and statistical models are needed to sort out key relationships in the chain from emissions to health effects. Sophisticated simulation approaches have been applied by EPA and in a wide variety of other contexts. Sophisticated statistical models are needed to integrate information from a variety of sources, gathered over different spatial and temporal scales, and with different degrees of measurement error, biasing and confounding influences (Rothman and Greenland 1998; Robins 1999; Robins et al. 1999; Mugglin et al. 2000; Pearl 2000; Zeger et al. 2000). Sensitivity analyses are especially important to quantify the robustness or fragility of conclusions to changes in model (or simulation system) form and inputs.

There are a variety of methods for quantifying uncertainty in model inputs and outputs and for dealing with structural uncertainties in models and scenarios. Morgan and Henrion (1990) and Cullen and Frey (1999) provide an overview of such methods. Typically, uncertainties in the

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

inputs to models can be quantified based upon statistical analysis of empirical data, the encoding of expert judgment in the form of probability distributions, or a combination of both. As an example, Bayesian methods provide an effective way to document assumptions; link information and expert opinion; guide analyses of complex, multilevel, nonlinear systems; ensure that all relevant uncertainties are incorporated and reported; and structure sensitivity analyses. Armitage et al. (2002) provide an excellent introduction; Carlin and Louis (2000) provide a more advanced treatment.

In any modeling or simulation exercise, two kinds of uncertainty operate: inherent stochastic (also called sampling uncertainty) and modeling uncertainty (also called nonsampling uncertainty) (see Morgan and Henrion 1990). The boundary between the two is fuzzy (some nonsampling uncertainties can be embedded in an overarching model), but a distinction can be made. Modeling uncertainties tend to dominate an assessment but generally are underexplored and underreported.

Although advanced statistical methods of the type described above are unlikely to be feasible for our evaluation (given both data and time limitations), we will attempt to highlight, at least qualitatively, key conceptual uncertainties in the modeling approaches that we use. Furthermore, EPA and others charged with addressing this issue over the long term should develop the capability for implementing these tools, as appropriate, in future assessments.

SUMMARY EVALUATION OF APPLICABILITY OF ANALYSIS METHODS FOR ESTIMATING IMPACTS OF CHANGES IN NSR RULES

Whether the formal methods described in this chapter will have sufficient sensitivity to the NSR rule changes under investigation to be able to estimate their effects accurately remains to be determined. Nonetheless, insights into the behavior of individual firms might help in estimating how individual facilities could respond to the incentives created by the rule changes. If recent historical evidence supports these behavioral models, this might then allow an assessment of at least the direction of the impacts of these changes on the outputs of concern (e.g., whether emissions are likely to increase or decrease) and possibly an estimate of the magnitude of the impact for typical facilities in different industrial sectors. Some models of the electricity-generating sector appear to be sufficiently detailed and sensitive to allow conclusions about the re-

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
×

sponses of individual facilities to be aggregated to the entire industrial sector. Long-term simulations with these models could allow a first assessment of how changes in NSR rules might affect technology adoption and emission trends. However, such a model would have to be subjected to thorough sensitivity analysis to see how much the conclusions change with different input assumptions and scenarios—for example, concerning the rate of innovation, the stringency of regional or national caps on pollutant emissions, surrenders of emissions allowances under NSR settlements, and the cost of alternative electricity-generation and pollution control technologies. Furthermore, models with the capacity for representing alternative technologies in a long-term simulation are not available for other sectors, and the time and resources available to the committee are not sufficient to support the construction of sector models for this purpose. For these other sectors, therefore, any generalization from the estimates of facility-level responses to estimates of industrial-sector responses will have to be undertaken more informally.

For the most part, the multisector models are even less able to represent the types of changes we are assessing than the sector models. Modifying the available models so that they can reflect these changes is substantially beyond the committee’s capacity or resources. Therefore, any intersector impacts will also have to be assessed informally, and any estimates of their direction or magnitude are likely to be highly uncertain.

The most appropriate way of assessing the impacts on health and other outcomes of any emission changes estimated on the basis of the above assessments will depend substantially on the amount and quality of information resulting from these assessments. In many cases, the human health impacts, for instance, are likely to depend on which specific facilities change their emissions in response to the rule changes, who is exposed to the emissions from these facilities, and the ambient air quality in the vicinity of these facilities before the alterations occur. It is unlikely that we will be able, at least in most cases, to make estimations with such specificity. Where we cannot, attempting to undertake sophisticated modeling of human health impacts would have little validity, and we probably will be able to do little more than indicate the likely direction and possibly the rough magnitude of these impacts, if any.

As discussed in Chapter 6, it will be necessary to consider a range of possible scenarios for the economic and environmental assumptions that are applied to estimate and compare outcomes from the revised NSR rules with outcomes from the NSR rules before to the revisions.

Suggested Citation:"5 Analytic Methods for Assessing Effects of New Source Review Rule Changes." National Research Council. 2005. Interim Report of the Committee on Changes in New Source Review Programs for Stationary Sources of Air Pollutants. Washington, DC: The National Academies Press. doi: 10.17226/11208.
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