1 Although the U. S. Supreme Court recently reaffirmed that the Clean Air Act's National Ambient Air Quality Standards ( NAAQS) should be set without regard to costs, the Court also recognized that the Clean Air Act calls on states and EPA to consider costs in implementing those standards. In addition, several Executive Orders require cost analysis of major regulatory actions.

2 Winston Harrington, Robert D. Morgenstern, and Peter Nelson, " On the Accuracy of Regulatory Cost Estimates," Resources for the Future Discussion Paper 99 18, January 1999.

1 Assessing the Impact of Progress and Learning Curves on Clean Air Act Compliance Costs Cynthia J. Manson, Matthew B. Nelson, and James E. Neumann Industrial Economics, Incorporated Cambridge, MA Revised July 12, 2002 INTRODUCTION The accuracy of EPA estimates of regulatory compliance costs has long been a primary focus of discussion among academics, regulators, regulated entities, and other stakeholders. 1 The EPA Office of Air and Radiation relies on cost estimates to design economically efficient strategies to implement all aspects of the Clean Air Act and its 1990 Amendments, including federally mandated stationary and mobile source controls that support NAAQS compliance; controls on hazardous air pollutants; measures to reduce emissions of stratospheric ozone depleting substances; and requirements to limit acid rain precursor emissions. Because of the central role of costs in the regulatory process, EPA is continually assessing methods for refining approaches to cost estimates.

One way to improve the accuracy of regulatory cost estimates is identify where systematic estimation biases may occur and attempt to correct for them. A study by Harrington, Morgenstern, and Nelson ( 1999) evaluated the accuracy of EPA and OSHA estimates of 25 ex ante regulatory cost estimates relative to ex post studies of actual costs, and concluded that initial cost estimates by EPA tend to overstate costs. 2 Based on the Harrington et al. analysis and on other studies of regulatory costs, in our own prior work we identified three key factors inherent in the production and adoption of compliance measures that suggest that direct compliance cost projections may more often be overestimated than underestimated. These factors include: " learning by doing," or learning curve impacts, which refer to efficiency gains related to cumulative production; innovation and 3 Cynthia Manson and Jim Neumann, " Assessing Prospective Estimates of the Costs of Clean Air Act Compliance: Are Costs Overestimated?" memorandum prepared by Industrial Economics, Incorporated for U. S. Environmental Protection Agency, Office of Policy Analysis and Review, June 29, 2001. Note that Harrington et al. ( 1999) identify several reasons for cost overestimation errors, including a built in conservative bias and inaccuracies in estimating the size of the affected universe. These factors also affect the specification of benefits. Without further information on case specific marginal benefit and cost curves, it is difficult to assess overall effects of such mis estimations on benefit/ cost ratios. IEc therefore focused on factors that affect only costs.

4 In reality, " learning" and " innovation" lie on a continuum. However, as we discuss below, incremental innovations which do not require new capital investments ( i. e., new processes) are generally reflected in progress curves. In contrast, more dramatic changes in processes, or significant shifts in energy or raw materials use, are less predictable and are not generally addressed.

5 Energy Information Administration, Assumptions for the Annual Energy Outlook 1997, U. S. Department of Energy, Washington, DC, January 1997, pages 58 and 63 64.

6 Other efforts include rules for Tier 2 highway vehicles ( 65 FR 6698, February 10, 2000, marine diesel engines ( 64 FR 73300, December 29, 1999) nonroad diesel engines ( 63 FR 56968, October 23, 1998) and highway diesel engines ( 62 FR 54694, October 21, 1997).

2 technological change; and cost reducing features of regulatory design. 3 Most existing Agency cost estimates do not make adjustments to account for these factors.

This report suggests an approach to incorporate learning curve impacts into relevant regulatory cost estimates. We focus specifically on the treatment of learning curves ( and the more comprehensive progress curves), rather than the potentially more dramatic but less predictable " innovations" which involve significant changes in process. Learning and progress curves, by contrast, measure incremental increases in efficiency that occur as cumulative production of a product increases. 4 These improvements result from a number of factors, including increased experience among employees and refinements to the manufacturing process and products. Learning and progress curves have been quantified in empirical data for over 50 years, are widely used in engineering and operations research literature, and have recently been incorporated into forecasts for emerging technologies at the Energy Information Administration. 5 In addition, a since 1997 several EPA regulatory efforts have incorporated progress curve impacts into future cost estimates. These efforts include analyses supporting the Tier 2 tailpipe regulations ( USEPA 1999), the Heavy Duty Diesel rule ( USEPA 2000a), and the Phase 2 Final Rule on Handheld Spark Ignition Engines ( USEPA 2000b). 6 This report first discusses the theoretical basis for identifying and estimating learning and progress curve impacts in the regulatory context, and then documents several cases where progress curves have affected estimates of compliance costs associated with Clean Air Act regulations. Finally, we summarize the potential implications and practical considerations involved in incorporating progress curve impacts into EPA compliance cost estimates. 7 Belkouai, ( 1986), p. 6. See also Dutton and Thomas ( 1986); Greening, et. al. ( 2001); McDonald and Schrattenholzer ( 2001); Montgomery and Day ( 1985).

3 THEORETICAL BASIS FOR ESTIMATING LEARNING AND PROGRESS CURVES Managers have long observed that per unit production costs decrease as cumulative production increases. Economists have suggested that the decrease results in part from " learning" within the producing organization as workers become more experienced. In 1936, T. P. Wright published the first study to quantify this trend. He observed that, on average, as cumulative output doubled in the aircraft production industry, labor and input requirements decreased to approximately eighty percent of their former cost.

According to the equation first presented by Wright, costs will decrease by a constant percentage ( the P value) with every doubling of cumulative production, reflecting a log linear function:

A 1 = A 0

XN Where:

A = Cost of Production X = Index of cumulative production N = log P / log 2 = learning index, where P is the rate of cost reduction as production increases In other words, Wright's " 80 percent rule" ( i. e., a P value of 0.8, or 80 percent) is associated with a 20 percent ( i. e., 100  80) reduction in costs. The 80 percent rule predicts that a product which costs $ 50 to produce when the 100th unit is in production will cost $ 40 to produce when the 200th unit is produced.

Since the 1930s, researchers have documented empirical cost trends that fit this log linear function in a wide range of industries, including the pollution abatement technology industry. While other functions have been developed to estimate learning patterns in specific circumstances, practitioners in industry generally agree that, in the words of Ahmed Belkaoui, " the log linear model has been and still is, by far, the most useful model." 7 Empirical studies performed over the past half century have also documented that a P value of approximately 80 percent appears to occur across a wide range of industries. As a result, the " 80 percent rule" is commonly used in the development of future cost estimates. EPA has recently undertaken a limited application of the 80 percent rule as part of its compliance cost estimates for activities associated with the Tier 2 regulations ( 1999), the Heavy Duty Engine/ Diesel Fuel rule ( 2000), the Phase 2 Final Rule on Handheld Spark Ignition Engines ( 2000), and other recent efforts. 8 As a result, analysts argued that the increasing mechanization of production would lead to a slower learning rate since " machines cannot ` learn' to run any faster" [ Belkaoui, p. 5( 1986)].

9 The study found a median P value of 77.5 percent, with over three quarters of the estimated P values between 70 and 90 percent [ Montgomery and Day, p. 4 ( 1985)].

10 The authors distinguish between progress curves and experience curves on the basis of the data used to establish the curve. Progress curves rely on cost information, while experience curves use price data. Cost data ( and progress curves) have the advantage of clarity; prices often reflect by market strategy and may not follow reductions in material or labor costs.

4 The 80 percent rule is well documented and widely accepted, but several issues surrounding its definition and application should be considered by EPA in developing an approach that will support a broader application of learning curve theory. Three key theoretical issues are: identifying an appropriate definition of " learning;" addressing variability among empirical estimates of learning; and considering other forces ( such as institutional " forgetting") that may affect the curve. Below we discuss the importance of these issues in a regulatory compliance setting. In addition, we outline several technical issues that affect the accurate assessment of learning and progress; these include the stage of product development, the costs affected ( e. g., fixed or variable), the existence of multiple progress curves, and the extent to which innovations and improved efficiencies such as energy efficiency will affect compliance behaviors.

Learning, Experience, and Progress Curves Early studies of learning curves focused mainly on " learning by doing" or " labor learning," or on the concept that employees learn to do their jobs more effectively and at lower cost with increasing experience. 8 During the 1960s, however The Boston Consulting Group ( BCG) firmly established the presence of an " experience curve" which expanded the definition of " learning" to include changes in " all costs of every kind required to deliver the product to the ultimate user.... There is no question that R& D, sales expenses, advertising, overhead, and everything else is included" ( IEA, p. 26). BCG's definition of learning was broader than previous analyses, but an 18 product experience curve cost analysis using Wright's log linear formula revealed that experience curves were almost identical to the more narrowly defined learning curve, with a median P value of 77.5 percent [ Wooley ( 1972)]. 9 Furthermore, in 1984 Dutton and Thomas argued that cost trends reflect more than just labor learning: their " progress curve" combines " changes in materials inputs, process or product technologies, or managerial technologies" with labor learning ( p. 235). Again, despite the shift to more general causes for cost reductions, the Dutton and Thomas survey of 108 samples provided a " basis for the widely publicized 80 percent progress curve" ( p. 237). 10 While there is no clear explanation for the unexpected consistency of results across different definitions of learning, researchers have postulated that Wright's learning curves may have captured more than labor learning.

Recent analyses tend to define costs comprehensively. In addition to labor learning, recent studies identify management techniques, technological innovation, economies of scope, and 11 Economies of scope present a different challenge than economies of scale because they cannot be attributed to a single technology, and are therefore very difficult to isolate. However, in a regulatory analysis it is reasonable to assume that these impacts will occur for any technology in a proportion equal to that reflected in industry P values.

12 For example, a firm with consistent annual production of 100 scrubbers will experience learning each year, but will only see cost changes related to economies of scale in the year that production is increased.

5 economies of scale as components of the progress curve ( Joskow and Rose, pp. 7 8; Montgomery and Day, pp. 6 7; McDonald and Schrattenholzer, p. 260). In a regulatory setting, the progress curve has the advantage of providing a single estimate for a number of forces that are known to affect costs but are difficult to isolate. However, it is important to consider the relationship between " progress" and two other forces which contribute to cost decreases: economies of scale and innovation.

Economies of Scale Like learning, economies of scale ( and scope) contribute to cost reductions, but predicting and quantifying their impacts is difficult, and isolating them from other components of " learning" is daunting. 11 As a result, many progress curves simply include economies of scale and scope as part of a cumulative effect on per unit cost production. McDonald and Schrattenholzer explain, " Given the data that are available, model inputs in which learning and scale economies are lumped into a single estimated progress rate may be simpler, as reliable, and therefore more useful than efforts to extract the two separate effects from the empirical data" ( 2001). The advantage to policy makers of a single, reliable method for assessing several forces is obvious: because regulatory costs focus on total compliance related costs, it is seldom necessary to estimate factors such as economies of scale in isolation.

However, authors such as Argote and Epple ( 1990) caution that studies that do not control for economies of scale can over estimate the effects of " learning" by incorporating one time gains due to scale and scope. 12 While McDonald and Schrattenholzer may be correct that controlling for economies of scale is difficult with available data, analysts should use caution in projecting aggressive estimates of future progress for technologies that are not likely to experience changes in scale or reflect changes in scope.

Innovation " Innovation" represents a broad range of process and input changes, from minor efficiency improvements to dramatic changes in product. Progress curves tend to reflect some technological innovation, including minor process adjustments that speed production, improve fuel efficiency, or reduce necessary inputs. These changes tend to be incremental and regular, and lead to cost reductions consistent with the progress curve [ Jacobsen, p. 150 ( 2001)]. Major innovations, 13 According to IEA, technology structural changes redraw the log linear graph in the shape of a double knee, reflecting a " transition period" where the slope of the progress curve ( the P value) is significantly higher.

14 In one area   energy technology   researchers are attempting to predict significant technological change, particularly within the long time frames relevant to climate change. Modeling and research efforts have focused on predicting increases in energy efficiency and emergence of new technologies, based in part on known energy price elasticities. Future research in this area may provide information that could be used to further refine regulatory cost analysis.

6 however, can significantly alter the cost of products or services and effectively " shift the curve." For instance, Henry Ford's production line completely changed the cost of producing automobiles, and Dow Chemical's development of the HCFC caused a dramatic drop in the cost of producing CFC free refrigerators. The International Energy Agency refers to these changes as technology structural changes. 13 These changes are difficult to foresee and do not conform to any pattern of gradual cost reductions. 14 While these shifts are unpredictable and are not factored into future progress curve projections, the resulting cost reductions provide what IEA labels " a beneficial surprise for the policy maker" ( IEA, 92). The record of Clean Air Act implementation over the past 30 years contains a number of examples of technological innovations ( e. g., HCFCs) that have increased the cost effectiveness of air pollution control.

Progress Curves in Regulatory Analysis While the literature reflects many definitions of " learning" and " progress," the broader progress curves identified by BCG, Dutton and Thomas ( 1984) and more recent studies are likely to be the most relevant for regulatory analysis, because they capture a range of cost impacts that are relevant to the projection of accurate regulatory costs. In addition to the economies of scale and scope and the incremental innovations discussed above, progress curves are likely to reflect concurrent adaptations such as:

Market Adaptations: Continuing changes in the structure of markets and practices related to financing and trade frequently contribute to changes in costs. Some of these may be adverse ( i. e., if raw materials become scarce) but controlled adaptations have tended to focus on increased efficiency.

Reduced Transaction Costs: Similar to market shifts, transaction cost changes may contribute to progress as costs associated with adopting technologies are reduced. Regulations and voluntary programs ( e. g., EPA's Energy Star) can be constructed to reduce such costs and thereby enhance progress. 15 Note because empirically based progress curves represent " net" changes in cost, use of these curves also accounts for the " costs" that are often associated with learning, such as the investment in management expertise, to the extent that these costs are reflected in the cost data used to develop the progress curves.

16 The authors write: " In any given industry, firms'progress functions, as well as progress rates, vary widely." Furthermore, " steepness of cost decline ( the P value) frequently is controllable via creative managerial efforts."

7

Changes in Energy Efficiency: Energy efficiency improvements ( either by utilities, manufacturers, or regulated entities) can have an important impact on costs, often by both " saving costs" and encouraging productivity increases.

While these forces are not specifically " learning" ( i. e., labor learning), they are inevitably reflected to some degree in the more broadly defined and measured progress curves and are, like economies of scale, difficult to isolate from labor learning. Furthermore, the empirical data supporting the existence of progress curves are likely to reflect all of these forces to some degree. The stability of results from quantitatively estimating progress curves from studies performed over the last 60 years suggests that over time, these changes and adaptations are relatively consistent in scope. Therefore, the careful application of a comprehensive " progress factor" in estimating the impacts of learning on regulatory compliance costs appears to be a more efficient initial approach than attempting to develop separate estimates for the impacts of multiple factors, except in cases where specific data suggest that exceptional events or forces are affecting costs. 15 Variance Among Progress Rates Although multi sector studies tend to reveal progress rates with P values in the 80 percent range, most progress studies also reveal significant variance in P values both across industries and for firms within the same industry. For instance, McDonald and Schrattenholzer's survey of learning in the energy technology industry identified P values ranging from 55 percent to 99 percent ( i. e., doubling of cumulative reduction could result in cost reduction that ranged from 45 percent to only 1 percent, respectively), with a median value of 83 to 84 percent [ McDonald and Schrattenholzer ( 2001)]. This within sector variance is notably higher than the variance across sectors.

Researchers have interpreted this wide range of progress rates at the firm level to indicate that production experience creates the opportunity for cost reduction, but not all organizations in an industry exploit this potential equally. It is generally believed that a company's progress rate, or P value, is a dependent variable which changes relative to the skill, creativity, and intelligence of management [ Dutton and Thomas ( 1984)]. 16 In contrast, a competitive industry should ultimately reflect the success of its competitive firms and will reveal broad progress trends, as " slower" players exit and more efficient firms continue to compete. 17 The general application of an industry level progress curve does not address the need for a separate analysis of small entity impacts; if dramatic progress takes place only at large companies in a specific industry, analysis required by the Regulatory Flexibility Act should address this.

18 The authors examined existing studies of manufacturing processes in industries including electronics, machine tools, and manufacture of automobiles, aircraft, steel, paper, and apparel.

19 In the absence of specific industry data, use of the 80 percent value assumes ( 1) that firms with rapid progress rates will not dominate the market over time, leading to a lower industry wide P values and ( 2) that the market remains competitive and barriers to entry are not unreasonable, since monopolies with low progress rates would lead to less cost reduction across an entire industry.

8 Variance among firm level progress curves is not generally problematic in a regulatory setting, because EPA cost estimates address industry wide impacts. 17 Therefore, defensible estimates of industry level progress are more important in predicting compliance costs. While studies reveal that progress rates range widely among firms and industries, these surveys also consistently show a bell shaped distribution of P values with both the median and mode P value in the 75 to 85 percent range; see Exhibit 1 for one such distribution, from Dutton and Thomas's work. Dutton and Thomas's survey of 108 manufactured products in multiple industries yielded both a median and mode P value of 81 percent ( i. e., a 19 percent cost reduction for each doubling of production). 18 McDonald and Schrattenholzer calculated a median P value of 83 to 84 percent, and the 75 to 85 percent range was most common. Montgomery and Day report that Wooley's survey found a median P value of 77.5, with over three quarters of the estimated P values between 70 and 90 percent.

In some cases more specific industry information may suggest a different conclusion. In Greening et al.' s survey of twelve pollution abatement technologies, six of the 12 preliminary P values calculated fell between 80 and 90 percent; the median for the group of 12 was 90 percent ( i. e., a 10 percent reduction in costs as cumulative production doubled). These distributions suggest that future industry wide production cost forecasts should generally reflect progress rates of between 75 and 85 percent, unless specific industry studies provide more exact estimates. 19 Note on Experience Depreciation and Organizational Forgetting While learning is nearly ubiquitous, some recent studies have focused on apparent exceptions where production costs increase ( or fail to decrease) over time. Several authors have established the possibility of both " experience depreciation" and " organizational forgetting" [ McDonald and Schrattenholzer ( 2000); Argote and Epple ( 1990); Benkard ( 2000)]. These authors argue that cumulative production may be affected by the departure of trained staff and imperfect transfer of knowledge from old employees to new employees, particularly when large layoffs are coupled with slowed production. McDonald and Schrattenholzer ( 2000) assert that " experience gained from units built last year results in greater current cost reductions than experience from 10 years ago." One example of a technology that could potentially have suffered from " forgetting" is photovoltaic solar technology, which languished after the era of high oil prices in the 1970s. However, because regulations generally tend to require increases in production and more rapid adoption of technologies, " forgetting" is unlikely to be a significant issue in regulatory analysis. 9 Exhibit 1 20 Note that " learning by using" is in fact a portion of the general process of learning at the regulated entity, and could conceivably be reflected in the products of this company. However, only the portion of production costs associated with compliance are relevant to the regulation.

10 Issues Related to Implementation of Progress Curves in a Policy Setting The theoretical and empirical basis is strong for incorporating progress curve impacts into estimates of future compliance costs. However, a number of issues related to progress curve " behavior" present key challenges to the effective implementation of progress curves. EPA has addressed several of these issues during implementation of progress curve estimates; in particular we examined the Agency's approach in developing the Tier II and Heavy Duty Engine/ Diesel Fuel rules. Below we provide a summary of the issues; Exhibit 2 presents a summary of EPA's approach in the Tier II regulatory impact analysis.

Product and Industry Stage of Development: Because the progress curve is dependent on cumulative production, an accurate projection of progress relies on an accurate estimate of the " life stage" of the technology in question. That is, if a regulation requires widespread application of a new or emerging technology ( as in the case of catalytic converters) to meet compliance requirements, then progress curve impacts on cost may be dramatic as cumulative production doubles rapidly. In contrast, if a regulation requires broader adoption of an existing technology with a considerable cumulative production history ( e. g., a MACT standard that requires smaller facilities to control emissions from conventional boilers), then changes in cost due to progress may be subtle. For this reason, it is important to identify the current cumulative installed base of a pollution control technology, as well as predicting accurately the number of regulated entities that will adopt the technology ( and therefore contribute to cumulative production) and the " doubling time."

Multiple Progress Curves in the Regulatory Compliance Context: In the context of regulatory compliance, more than one progress curve may be reflected in costs. For example, manufacturing costs for flue gas desulfurization units have decreased over time, reflecting progress and reducing capital costs to the regulated community ( assuming that market prices also decrease). In addition, regulated entities will likely improve the efficiency of their operations and maintenance related to the units and affected processes. These increases in efficiency represent a separate " learning by using" impact that would result in further cost reductions over time. 20 Because regulators are generally concerned with the end user of a new technology, consideration of the additive impacts of multiple progress opportunities may be warranted. 11 The extent to which the Agency should consider multiple progress curves will depend on the construct of the regulation, and the " age" of the affected technologies. If regulations will likely result in significant capital investments and process retooling to incorporate innovative technologies, then it may be reasonable to examine learning in several areas. If, however, a program emphasizes readily available or alternatively, " low maintenance" technology, then progress will likely be limited to only one process.

Identifying Costs Affected by Learning: Judicious application of progress curves is critical in developing defensible cost estimates; even the most expansive definitions of progress curves do not include all the costs borne by industry. For example, one time installation costs may not be sensitive to learning unless they are performed by a third party. Similarly, some monitoring related costs and other requirements may employ a very consistent approach and represent fixed costs that are relatively impervious to learning. Although the literature reveals several approaches to estimating progress, including the use of price, total costs, and marginal costs, an approach that limits application of the curve to marginal costs is the most defensible.

The Role of Energy Efficiency in Progress and Compliance: Economists have recently focused considerable effort on the accurate prediction of energy prices and the cost implications of efficiency as innovative technologies emerge. This issue is particularly crucial in the analysis of regulations to counter climate change, which involves predictions over long time horizons. However, energy prices and efforts to improve efficiency and conservation also have short term implications for costs, and it is important to consider the interplay between these activities and progress curves.

Specifically, regulations that increase the production cost may encourage regulated entities to adapt process innovations or efficiency measures ( e. g., reducing energy use to reduce NO x outputs to targeted limits). These measures represent low cost compliance approaches that provide considerable benefits ( and may reflect effective regulatory design). However, the impact on progress curve estimates may be to slow cost decreases by reducing the demand for units ( and therefore increasing " doubling time"). While this issue is essentially a challenge in estimating regulatory response and not in developing accurate progress curve estimates, it is important to consider compliance options and responses in developing progress curve doubling times and estimates. 12 Exhibit 2. EPA Progress Curve Estimation in the Tier II Regulatory Impact Analysis EPA has undertaken a limited application of the 80 percent rule in recent regulatory efforts, including the Tier II sulfur regulations ( 1999), the Heavy Duty Diesel rule ( 2000), and the Phase 2 Final Rule on Handheld Spark Ignition Engines ( 2000). In these efforts, the Agency adopted a general approach that employed the multi industry average " 80 percent rule," projecting that costs would decrease by 20 percent as cumulative production doubled. Recognizing that the 80 percent rule is a general average that may over or under state progress rates in specific industries, EPA also adopted a number of conservative assumptions in applying the curve, in order to assure that progress impacts were not overstated ( i. e., that costs would not be underestimated). Assumptions included:

Some processes were not included in estimates of progress: EPA did not apply progress related cost decreases to technologies ( e. g., evaporative systems) which were already widely available and therefore unlikely to be considerably affected by incremental decreases in learning. The 80 percent rule was applied only to newer technologies that were more likely to show considerable efficiency gains due to progress.

" Learning" would occur after the second year of production: EPA assumed that one year of production ( in which no learning would take place) formed the base " unit" of cumulative production. In other words, if 100 units were produced in the first year, then it was assumed that 100 units would be produced in the second year and the expected 20 percent drop in costs would occur at the beginning of the third year. This assumption establishes a relatively high cumulative production experience as the basis for doubling, which in turn reduces the rate of cost reduction ( i. e., because " doubling" cumulative production requires more effort). In reality, rapid production during the first ( or second) year could conceivably lead to considerable cost reductions earlier in the process.

Certain process costs were not subject to progress: EPA did not apply progress based cost reductions to fixed costs or to certain variable costs. For example, in the Tier II analysis catalyst precious metal costs were omitted from consideration due to high uncertainty in metals price forecasts.

In addition, specifically for the Tier II sulfur regulations, EPA limited its estimates of progress curve impacts to one " learning" cycle. The Agency did not fully apply Wrights log linear function, which would continue to reduce future production costs indefinitely. Instead EPA calculated only a single reduction in costs of 20 percent starting in the third year of the rule, consistent with the " first cycle" of the log linear function.

IMPACTS OF PROGRESS ON AIR POLLUTION CONTROL TECHNOLOGY COSTS: EXAMPLES The literature establishes that the impacts of progress on costs are essentially ubiquitous, occurring across a broad range of products and industries, including a number of pollution abatement technologies ( Greening et al.); progress curves have proven to be a reasonable way to model these impacts. Below we present three brief discussions of the existence of progress curves in specific air pollution control technologies. The first discussion examines existing estimates of progess curves specific to scrubber technology. We then discuss the issues related to developing progress functions for emerging NO x control technologies. Finally, we examine the broader support for " progress" that may be reflected in historical trends in the Vatavuk cost indices for various pollution control technologies.

Example 1: Sulfur Dioxide Control Technology 21 See Burtraw, Dallas. " Cost Savings Sans Allowance Trades? Evaluating the SO2 Emission Trading Program to Date", Resources for the Future, February 1996, and Institute of Clean Air Companies, Scrubber Myths & Realities, White Paper, May 1995.

22 Laitner, S. " Analyzing changes in Scrubber Costs over Time" unpublished memorandum, U. S. Environmental Protection Agency, Office of Atmospheric Programs.

23 The large difference between this progress rate and the dramatic progress rate of 47 percent calculated by Greening, et al. may be a result of different components that are included in 13 Flue gas desulfurization units ( i. e., " FGD technology," or " scrubbers") present a clear example of declining costs associated with compliance. Several research efforts have sought to explain the dramatic decreases in the costs related to sulfur control since the late 1980s. Authors such as Dallas Bertraw ( 1996) and reports by the Institute of Clean Air Companies ( ICAC) ( 1995) have documented the decreases in cost and have attributed a portion of costs to learning and incremental process innovations. 21 In addition, more recent work by Taylor, et al.( 2000) and Greening, et al.( 2001) have identified specific progress ratios for scrubber technologies. These articles identify P values for scrubbers of 83 and 88 percent, respectively. Finally, separate unpublished estimates by Laitner using different assumptions identify a progress curve for capital costs of 85 percent. 22 Scrubber technology presents an ideal opportunity for examining progress curves for two reasons: the technology has a decade long history of development and adoption, and the U. S. Department of Energy regularly publishes data tracking the number of installations, unit costs, and removal efficiency of scrubbers over time. The Taylor et al., Greening, et al., and Laitner research each employ EIA data; the different estimates of progress in these articles arise principally from differing time lines and differing definitions of affected costs.

Specifically, the two estimates developed by Laitner use data from Electric Power Annual to develop a progress curve based on total national cost trends in both capital and O& M costs per kilowatt ( kW) over the decade from 1985 to 1995. Cumulative production is estimated as the total installed capacity of megawatts ( MW) nationwide. In the Greening et al. study, the authors estimate a progress curve of 88 percent ( P value of 0.88). Incorporated into this estimate are a relatively conservative progress ratio for capital costs of 89 percent and a dramatic progress ratio for operating costs of 47 percent ( P values of 0.89 and 0.47, respectively C the 47 percent P value indicate a cost decrease of over 50 percent as cumulative production doubles). Laitner's other effort produces a capital cost related progress curve of 85 percent ( P value of 0.85), based on a more detailed regression analysis of four variables: size of FGD unit, efficiency of removal, percent of SO 2 removal, and cumulative production.

The research by Taylor et al. examines FGD system O& M costs at 88 plants that have reported at least twelve years of continuous operations data to EIA. Using the standard log linear progress function, the authors calculate an 83 percent progress ratio on FGD labor and maintenance, and supervision costs. The study does not consider changes in capital costs ( i. e., retrofits or upgrades) at the plants. 23 " operating costs" in the EIA data ( e. g., energy), and also could result in part from the different sample facilities and time frames examined.

24 The mean and mode of Dutton and Thomas' study is a P value of 81 percent; the standard deviation is 7.9 percent.

14 The 88 and the 85 percent progress ratios reported in Greening et al. and by Laitner suggest that progress impacts related to scrubber capital costs are considerable, but may be less significant than implied by the 80 percent " rule of thumb." However, both the 88 and 85 percent values are within one standard deviation of the mean ( P value of 81 percent) of the distribution of progress ratios developed by Dutton and Thomas ( 1984). 24 An examination of the three approaches, however, provides some useful insights for policy makers wishing to apply progress curves.

Both the sample and the specific focus on operating costs are relatively narrow in the Taylor et al. study; the progress curve identified in this effort is effectively limited to " learning by using" by regulated entities and does not consider changes in capital costs. In a regulatory analysis, this type of estimate could provide a useful estimate for application to a limited range of operating costs associated with compliance.

The Greening et al. study provides a " unified" estimate of progress that could be applied more broadly to both capital and O& M costs at regulated facilities. Although this estimate presents a more conservative progress rate than the Taylor study, its potential for relatively broad application would increase its impact on cost estimations.

Finally, the Laitner research suggests that an independent estimate of capital costs could be applied, possibly in conjunction with the Taylor estimates of operations cost impacts. Application of these two estimates would provide a more aggressive estimate of progress than the Greening estimates; careful discussion of the range of potential cost impacts and a sensitivity analysis could address these discrepancies.

The estimates of progress curves applicable to scrubber capital and O& M costs are consistent with other empirical studies of learning in that they identify clear patterns of cost reduction associated with cumulative production. In addition, the three estimates, though they focus on different aspects of progress, fall between 83 and 89 percent, well within the range of values that has been established in the literature. As a result, these studies support the validity of an approach that incorporates progress curve impacts into regulatory cost estimates. Moreover, while technology specific progress ratios clearly allow for a more refined discussion of costs, the general consistency between " scrubber progress" and the 80 percent " rule of thumb" is also notable. This consistency appears to support an approach that adopts the 80 percent rule as an initial estimate of impacts in cases where technologyspecific ratios are difficult or impossible to develop. 25 SCR and SNCR are " fundamentally similar in that [ they] both use an ammonia containing reagent to convert the NO x produced in the boiler to nitrogen and water. SNCR accomplishes this at higher temperatures ( 1700 degrees Fahrenheit to 2000 degrees Fahrenheit) in the upper furnace region of the boiler. In contrast, SCR operates at lower temperatures ( about 600 degrees Fahrenheit) by using a catalyst [ in the stack] to produce the desired reaction between ammonia and NO x. In practice, these differences mean that SNCR has lower capital costs and limited NO x reduction capability ( typically 30 to 40 percent but higher in some cases). SCR is more capital intensive but is capable of achieving much greater reductions ( up to 90 percent and higher)" ( Amar, 2000; Botsford, 2001).

15 Example 2: NOx Control Technology Oxides of nitrogen ( NO x), like sulfur dioxide, are a target of pollution control legislation and regulation in the United States today. Efforts to limit NO x emissions date back to the early 1970s, but prior to 1990 NO x requirements were largely limited to new or substantially modified sources. Only in the past decade, as a result of the 1990 Clean Air Act Amendments' particular emphasis on NO x emissions reductions, have American regulations pushed industry to develop new pollution abatement technology. As a result, American NO x control technological development has a short history, and it presents a good example of a new technology that may reveal a measurable progress curve ( Amar, 2000).

Initial NO x Control Development NO x control technology differs from most other pollution abatement technology in that its initial development occurred in Germany and Japan, not the United States. Prior to the passage of the 1990 Clean Air Act Amendments in the United States, Germany and Japan passed " technology forcing" NO x emissions regulations that obligated their utility industries to significantly reduce NO x emissions. During the 1980s the Germans and Japanese developed the first models of today's preferred NO x reduction technologies, including selective catalytic reduction ( SCR) and to a lesser degree selective noncatalytic reduction ( SNCR). 25 These technologies today are considered the " primary post combustion NO x control options commercially available" for use at utility plants ( Amar, 2000). By 1990, German and Japanese utilities had installed about 200 SCR units, representing about 40,000 MW of capacity. SNCR NO x control units, although developed after SCR, had been installed at a smaller but significant portion of European utilities ( Eskinazi, 1989; Amar, 2000).

As the Europeans and Japanese advanced SCR NO x control technology through the 1980s, they were able to increase SCR's effectiveness, reduce negative externalities, and develop a number of operational cost saving strategies ( e. g. leaving space in the reactor for future catalyst additions) consistent with the progress principle. By the end of the decade, the SCR technology had gone from experimental to the utility industry standard ( Amar, 2000). Although no detailed record of SCR system costs in Europe in the 1980s has been published, the decreasing cost of catalyst is well documented. According to Eskinazi et al.( 1989), catalyst costs generally dominate SCR system costs, so trends in the cost of the catalyst offer a good proxy for the cost of the overall SCR system. 26 Catalyst costs represent a major component of SCR operating costs, so any change in these costs will drive the SCR operating costs in a similar direction. In the late 1980s, increased competition in the European catalyst market is credited with driving down prices. This drop in price is likely associated, at least in part, with progress in the catalyst production industry as manufacturers achieved economies of scale and improved production processes.

27 Ammonia ` slip' refers to small, steady releases of ammonia to the atmosphere that may occur in SCR and SNCR systems during operation. Most jurisdictions in the United States have limited this slip to 5 ppm, and the state of Massachusetts has limited it to 2 ppm. These regulations have forced SCR and SNCR manufacturers in the U. S. to redesign the technology ( Botsford, 2001).

16 By 1990, with about 120 European SCR units in operation, the cost of catalyst had dropped by two thirds ( from $ 900 per cubic foot to $ 300 per cubic foot). 26 ( Eskanazi et al., 1989; Amar, 2000)

U. S. NO x Control With the passage of the 1990 Clean Air Act Amendments, the United States adopted substantially more stringent NO x regulations, and utilities began installing SCR and SNCR systems on their plants. The first full scale SCR system for a U. S. coal fired power plant went on line in 1995. As of 2000, 10 SCR and 20 SNCR systems were operational ( Amar, 2000). While Europe and Japan developed the SCR technology, application in the United States has demanded further innovation. U. S. coal's higher sulfur levels required design changes, and government regulation demanded much stricter control of ammonia slip. 27 While there is no comprehensive data set on the historical costs of SCR and SNCR in the United States, The Northeast States for Coordinated Air Use Management ( NESCAUM) has researched NO x control costs by contacting utilities and investigating cost trends on a case by case basis. NESCAUM data indicate that the first U. S. SCR and SNCR projects experienced difficulties with ammonia flow controls and distribution, ammonia slip, pressure loss, and increased outages in their early years, which is consistent with the initial experience in Germany and Japan. However, NESCAUM also notes that, " many of these problems have been overcome as plant operators move up the learning curve" ( Amar, 2000). The NESCAUM reports indicate both capital and operating cost reductions in NO x control technologies have occurred.

The NESCAUM case studies also suggest that learning and innovation have had a clear impact on actual capital costs. For instance, Southern California Edison retrofitted its boilers with SCR systems for $ 300 million, a full $ 650 million less than the firm's 1991 cost estimate. NESCAUM explains: " these savings are believed to largely be the result of staging in controls, thereby letting the more expensive technologies improve in cost and performance" ( Staudt, 1998). Similarly, Public Service of New Hampshire initially considered an SCR system too expensive as a NO x control option, but after a short time the SCR capital cost bids reached Public Service's budget capability ( Stradt, 1998). Finally, the capital costs of installing an SCR system at the Plymouth Co generation plant in Plymouth, NH were 35 percent lower than expected based on earlier estimates, leading NESCAUM 28 This power plant is a internal combustion, diesel fuel plant, which is not on the same scale as most public utilities. " SCR is the only commercially proven secondary NO x reduction method for lean burn gas engines and diesel engines," explains NESCAUM ( Amar, 2000b). It is unclear how the use of SRC technology to control NO x emissions from non utility NO x sources impacts the progress rate of SRC use at utilities. Spillover cost reductions may result from the experience at nonutilities or it may have no impact.

17 to conclude that this result " may also indicate that SCR technology is becoming less costly" ( Amar, 2000b). 28 Total operating costs have also dropped at plants with SCR and SNCR systems. Examples include the following:

 New England Power developed and installed a new system to control reagent utilization in its SNCR at Salem Harbor, an innovation that has provided a 60 percent reduction in reagent use and an annual savings of $ 600,000;

 Montaup Electric Somerset Station, " plant operators have, over time, gained more experience with operation of the [ SNCR] system," which has brought annual operational costs down by a total of over 60 percent ( Stradt, 1998).

As NESCAUM points out in recent analysis of NO x control costs, progress effects have not been factored into any of the major government and industry engineering cost estimates for SCR and SNCR since 1980. As a result, NO x control costs projections in 1982, 1985, and 1989 from have proven excessive. NESCAUM writes, " The difference between early, pre regulatory estimates and current costs is substantial: costs declined by 65 to 90 percent for SCR ( on a dollars per ton of NO x removed basis) and by approximately 65 percent for SNCR ( on a levelized cost basis). Although part of this differential is attributable to overly conservative assumptions in the pre regulatory period, inuse experience with these technologies and the innovations it inspired clearly play a major role" ( Amar, 2000). Exhibit 3 provides summaries of EPRI and NESCAUM cost estimates for SCR and SNCR.

Exhibit 3 SCR AND SNCR COST ESTIMATES SCR COSTS: ( 1999 Dollars; Based on a 500 MW, Wall Fired Boiler)

Study Capital Costs ($/ kW) Cumulative % Decrease $/ ton Cumulative % Decrease EPRI 1985 90 155 2,800 11,290 EPRI 1989 125 None 2,500 5,000 4 55 NESCAUM 1998 50 75 40 60 1,000 1,100 64 90 SNCR COSTS: ( 1999 Dollars) Exhibit 3 SCR AND SNCR COST ESTIMATES 29 Note that we assume that early cost estimates were accurate for the year in which they were reported; Amar suggests that initial costs may have been overestimated; if this is true then progress curves based on these estimates would also likely be high.

30 200 units were installed in Europe and Japan from 1970 to 1990, and 10 units were installed in the United States from 1995 to 2000 ( Amar, 2000). We know that at least four of these 210 units were installed in Japan before 1985 ( Staudt, 1998). Assuming that the 10 SCR units installed in the United States between 1995 and 2000 were installed at a rate of two per year, we conclude that in 1998 206 units had been fully installed. Thus, this calculation assumes that no new installation of SCR units occurred in Japan or Germany after 1990. Since neither country passed new " technology forcing" regulations after 1990, this assumption is probably accurate. We also assume 18 Study Capital Costs ($/ kW) Cumulative % Decrease $/ ton Cumulative % Decrease EPRI 1982 29 35 3.5 3.75 EPRI 1989 6 19 45 80 4 5 Increase NESCAUM 1998 15 48 57 1.25 69 75 Sources:

SCR estimates: Amar ( 2000), page V 5. EPRI 1985 is from Miller, EPRI Coal Combustion Systems Division et al. ( 1985). SO2 and NOx Retrofit Control Technologies Handbook. Palo Alto, Electric Power Research Institute: CS 4277 SR ; EPRI 1989 is from Eskinazi et al. ( 1989); NESCAUM 1998 is from Straudt ( 1998).

SNCR estimates: Amar ( 2000), page V 5. EPRI 1989 is from Eskinazi et al. ( 1989); EPRI 1982 is from EPA OAQPS ( 1983) Control Technologies for Nitrogen Oxides Emissions from Stationary Sources. Research Triangle Park, NC, U. S. EPA document: EPA 450/ 3 83 002; NESCAUM 1998 is from Straudt ( 1998).

Calculating a Progress Rate While the available data on actual cost decreases are limited, it is possible to use NESCAUM's total reported changes in cost to calculate a rough progress rate estimate for both SCR and SNCR using the log linear function and the evidence of decreasing costs. This curve is an approximation that reflects only two data points and does not attempt to correct for other " nonlearning forces, but it provides an initial indication of what progress curves for SCR and SNCR could look like. 29 To calculate an SCR progress rate based on the actual percentage cost decreases reported by NESCAUM, we must establish an estimate of cumulative production for the different point in time cost estimates. Based on NESCAUM data, we assume that there were four coal burning utility SCR units installed in the world ( including Europe and Japan) by 1985, and a total of 206 units installed by 1998.30 The total actual cost decrease from 1985 to 1998 of 65 to 90 percent reported by that no other units have been installed in other regions of the world. If more units have been installed, the progress rate we calculate would be too aggressive. For instance, if 500 units had been installed by 1998, the progress rate would be 72 to 86 percent, a few percentage points higher than the range we report here.

31 To project a conservative estimate of progress ( eliminating catalyst costs) one could use only the capital cost estimate changes reported by NESCAUM. The 40 to 60 percent decreases in capital costs over time correspond to progress curves of 91 and 85 percent, respectively. However, these estimates are likely to be overly conservative because they ignore the extent to which learning by using, learning by doing, and incremental innovation contributed to reductions in both the costs and the required quantities of catalyst.

32 According to Amar ( 2000), 300 SNCR units were installed worldwide by 2000. Assuming no units were installed until 1988, and then 25 were installed each year after that, we estimate that 250 units were installed worldwide by 1998.

33 According to Amar ( 2000), 20 SNCR units were installed in the United States by 2000, the first of which was installed in Salem, MA in 1992. Assuming 2.5 units were installed each year from 1992 to 2000, 15 units would have been installed by 1998. Thus, we assume 15 domestic units in this calculation.

19 NESCAUM, when considered as a function of cumulative production, indicates a progress rate between 83 and 67 percent ( i. e., cost reductions of between 17 and 33 percent, respectively, with each doubling of production). This range is likely to represent a relatively aggressive set of progress curve estimates, because it incorporates the significant changes in catalyst costs that may be due in part to factors other than learning and progress. 31 To calculate a progress rate for SNCR development on a worldwide scale, we assume that 250 SNCR units were installed in the world from 1982 to 1998.32 Again relying on the total cost decrease of 69 to 75 percent reported by NESCAUM, this technology's implied progress rate ranges from 86 to 84 percent, respectively. However, since SNCR did not fully develop in Japan and Germany before being introduced in the U. S., an alternative approach might include only U. S. installed units when calculating SNCR development progress in the United States ( i. e. assuming that there is no " shared" learning). Assuming that 15 SNCR units were installed between 1982 and 1998 within the United States, the implied progress rate for SNCR ranges from 70 to 74 percent. 33 In reality, SNCR in the United States has probably been influenced by the installation of units around the world. However, the quantity of domestically installed units is probably more influential in bringing down domestic costs, since knowledge and experience gained in Europe often does not reach U. S. utilities, and the specific coal content and local regulations create specific challenges to U. S. installation. Thus, the actual progress rate for SNCR is probably somewhere in the middle of the range that we have estimated ( i. e., 70 to 86 percent, indicating cost decreases of 30 to 14 percent, respectively, as production doubles). 20 Again, the progress ratios presented here are preliminary, and reflect the quality of the underlying estimates. However, as an initial exercise in estimating the potential impacts of learning, they do provide support for the use of an 80 percent progress factor in preliminary estimates of future pollution abatement technology costs.

Example 3: Vatavuk Air Pollution Control Cost Index Since the dollar value of goods and services changes over time, government and academics have developed cost and price indices in order to allow for the comparison of prices in one time to that of a different era. General indices, such as the Gross Domestic Product ( GDP) deflator, the Consumer Price Index ( CPI), and the Producer Price Index ( PPI) track the costs and prices of an extremely broad " basket of goods" in order to measure the relative purchasing power of a single dollar over time. These general indices are usually seen as a good measure of the rate of " inflation."

In some cases, analysts wish to track changes in the cost of certain goods ( e. g. the Marshall & Swift Equipment Cost Index records the price of equipment used in " process industries," such as paper and paint production). These costs are tracked in more specific indices that may or may not match the broader inflation indices, depending on the changes in the price or supply of specific raw materials and components.

In 1995, William M. Vatavuk of EPA's Office of Air Quality Planning and Standards ( OAQPS) developed cost indices for particular pollution control technologies. These indices, collectively known as the Vatavuk Air Pollution Control Cost Indices ( VAPCCI), are designed to be used " to escalate costs from the initial ( base) period ( first quarter 1994) forward to any quarter in the future" ( Vatavuk, 1995). For six volatile organic compound ( VOC) control technologies and one particulate matter ( PM) control technology ( wet scrubbers), the indices provide a comparative cost record from 1989 to 2001.

As part of our examination of progress, we analyze the Vatavuk Indices to determine whether the costs for VOC and PM control technologies are declining when adjusted for inflation. If the progress effect is present in this pollution control industry, we believe that correcting the Vatavuk Indices for inflation should reveal a decreasing cost trend for the pollution abatement technologies tracked by the Vatavuk Indices.

This effort is not designed to isolate the actual progress ratios of specific pollution control technologies for a number of reasons. First, indices show changes in price over time and do not provide information about cumulative production. Second, the indices do not reflect the total cost associated with manufacturing pollution control equipment. Third, some cost changes recorded by the indices may be the result of non progress related shifts in the marketplace ( i. e. changes in commodities markets or a drop in oil prices). Regardless, this index provides a well established record of pollution abatement costs that may provide strong qualitative evidence that a progress effect is active in the pollution control industry. 21 The Index William Vatavuk developed the VAPCCI because in his analysis he found that more general indices C such as the ( CPI), the ( PPI), and even the more specific Chemical Engineering Plant Cost Index C did not accurately reflect the relative costs of pollution control equipment over time. The " basket of goods" used in these indices simply did not match the inputs to pollution abatement technology, and these inputs did not necessarily follow more general cost trends ( Vatavuk, 2000).

To develop his index, Vatavuk used the cost data of individual basket items in the PPI to compile a new basket of goods which tracked the relative cost trends exclusively of each pollution abatement technology's components. He then created a relative cost trend for each technology by averaging each of the individual basket items, weighted each component in proportion to its overall impact on the technology cost ( OAQPS, 1995).

Like all indices, the VAPCCI is actually a list of relative values referenced to a specific month, quarter, or year. To convert the price of a good from one period of time to another, one uses the following equation:

Cost New = Cost Old  ( Index New / Index Old)

Vatavuk offers a good example: " Suppose that in first quarter 1994, a wet scrubber cost $ 100,000. Now estimate how much this scrubber would cost in first quarter 1995. If the 1994 ( old) VAPCCI value was 100.0, and the 1995 ( new) VAPCCI value was 109.9, then, the 1995 estimated equipment cost is:

Cost New = $ 100,000  ( 109.9/ 100.0) = $ 109,900" ( Vatavuk, 1995)

The Progress Effect Reflected in the Index While Vatavuk assumed that pollution abatement costs are relative to the cost of their inputs, his pollution control cost indices do not control for the progress effect. As Montgomery and Day ( 1985) note, industry specific and product specific indices " may capture a substantial portion of productivity gains in the industry." As a result, we expected that the VAPCCI would show a lower growth rate than inflation as measured by GDP and CPI if the progress effect was reducing costs in real terms as inflation caused current dollar increases. To assess this possibility, we compared the Vatavuk indices for technologies that have been tracked from 1989 to 2001 to CPI and the GDP deflator.

This analysis places the Vatavuk indices in comparison to both CPI and the GDP deflator because, while CPI is the most commonly used inflation index, Montgomery and Day ( 1985) suggest that the GDP deflator probably offers a more appropriate measure of " the changing value of money." The GDP deflator is not bound to consumer prices, and it measures a more comprehensive basket of goods than CPI ( e. g., the GDP includes changes in the cost of production inputs that are not sold 34 Exhibits 4 and 5 reveal, especially around the year 1994, that input costs can also rise relative to inflation due to lack of supply or other market forces. If, for instance, an earthquake destroyed a major catalyst production facility, the cost of catalytic incineration could rise rapidly due to a shortage of catalyst in the marketplace. The progress function assumes a steady supply of inputs in a competitive marketplace.

35 To assume that the " real" decreases in costs demonstrated in Exhibits 1 and 2 represent an actual progress curve, one would have to assume both that all pollution equipment production began in 1989 and that production rates of these technologies have been exactly constant since 1989.

22 directly to consumers, such as manufacturing equipment.) As a result, it is generally more conservative in its estimate of inflation than CPI ( for a comparison of the two indices, see Exhibit 6).

When compared to the change in cost as reflected in both CPI and GDP, the Vatavuk indices for catalytic incinerators, flares, gas absorbers, refrigeration systems, regenerative thermal oxidizers, thermal incinerators, and wet scrubbers all showed slower growth between 1989 and 2001, as demonstrated in Exhibits 4 and 5. The decrease in real costs from 1989 to 2001 ranged from 1.5 percent ( thermal incinerators) to 20.4 percent ( refrigeration systems) when compared to the GDP deflator. While this may initially seem modest when compared to the 20 percent reductions expected in the progress curves, it is important to note that the indices capture only changes in the component cost of each technology. A complete " progress curve" in the regulatory setting would also have to consider labor learning and assembly line improvements, as well as the impact of " learning by using" on the regulated community's operating costs. Thus, these real cost decreases are consistent with the presence of the progress curve in the pollution abatement industry.

Although all seven technologies that we examine do sink in cost when adjusted for inflation, Vatavuk emphasizes the difficulty in determining which cost influencing force ( e. g. raw material cost variation, labor shifts, and market forces) drives each cost down; however, he does identify two specific forces which drove down prices consistent with the progress effect. He writes, " With catalytic incinerators, a drop in catalyst prices led vendors to lower their prices by roughly 7% between fourth quarter 1991 and first quarter 1992... Similarly, a design innovation marketed by a leader in refrigeration systems prompted the company to lower prices in mid 1993, causing its competitors to follow suit" ( Vatavuk, 1995). These changes represent significant evidence of progress effects in the pollution control industry. 34 Conclusions Exhibits 4 and 5 indicate that the progress effect may be present in the Vatavuk cost indices. However, the VAPCCI by itself does not contain enough information to allow this effect to be isolated or quantified. The VAPCCI traces trends in cost over time, not cumulative production ( which is the actual driver of the log linear progress effect). 35 To identify actual progress curves, it would be necessary to determine cumulative production of each of the VAPCCI technologies. 36 While traditional labor costs are not quantified in the Vatavuk indices, engineering costs are factored in, and labor does factor into the cost of input production. The potential unquantified progress benefit specifically applies to improved assembly of the product over time.

23 The VAPCCI also would likely reflect only a portion of the progress curve associated with end user compliance costs, because it does not account for changes in pollution technology production labor costs. 36 The index also does not record the pollution control technology customer cost reductions that should develop as personnel learn to use the products more effectively. Since the progress effect is linked closely to the impacts of labor learning, the VAPCCI data may not fully record the cost reductions due to cumulative production.

There are clear limitations on the use of the Vatavuk indices to quantify the progress effect, but the sinking real costs of pollution technology do indicate that learning and progress are present on an industry wide level. The Vatavuk indices offer further evidence that the progress curve applies to VOC control technologies, PM control technologies, and the pollution control industry as a whole. 24 Exhibit 4: Rate of VAPCCI Growth Relative to CPI  30  25  20  15  10  5 0 5 1988 1990 1992 1994 1996 1998 2000 2002 Year Growth of Index relative to CPI Catalytic Incinerators Flares Gas Absorbers Refrigeration Systems Regenerative Themal Oxidizers Thermal Incinerators Wet Scrubbers Exhibit 5: Rate of VAPCCI Relative to GDP  25  20  15  10  5 0 5 10 1988 1990 1992 1994 1996 1998 2000 2002 Year Index Growth Relative to GDP Catalytic Incinerators Flares Gas Absorbers Refrigeration Systems Regenerative Themal Oxidizers Thermal Incinerators Wet Scrubbers 25 Exhibit 6: CPI and GDP Deflator Growth Rate, 1989 to 2001 100 105 110 115 120 125 130 135 140 145 150 1988 1990 1992 1994 1996 1998 2000 2002 Year Index value CPI GDP Deflator IMPLICATIONS OF PROGRESS CURVES FOR EPA COST ESTIMATION Our review of the existing theoretical and empirical evidence on the impacts of learning and progress on costs over time, as well as our analysis of available data on cost trends in pollution control devices, leads us to two major conclusions:

 The learning curve concept has a sound theoretical and empirical basis; and  There is evidence that learning and progress do occur in the production of air pollution control devices, so that costs for these devices can be expected to decline over time, in real terms.

For these reasons, we find there is a strong basis for adjusting regulatory cost estimates to reflect the effects of progress. There remains some uncertainty about the precise magnitude of the adjustment that ought to be applied. While the available evidence suggests that in the vast majority of cases this factor will have the effect of reducing estimates of regulatory compliance costs, there are some limited and likely rare situations where an active and consistent effort to invest in and maintain learning may either be very costly or may fail altogether, suggesting that overall costs might not decrease or could even increase over time. Data reported by Dutton and Thomas ( 1984), for example, indicate that only one of the 108 industries that they studied experienced a cost increase over their study period. 37 An alternative is to use time as a proxy for production volume, provided reasonable projections of production rates can be made. This approach was used in the recent Tier II tailpipe standards RIA ( USEPA 1999).

26 The presence of these uncertainties in the quantitative effect of the application of progress curves suggests that analysts should consider whether an explicit quantitative uncertainty analysis can be applied. The use of a single progress ratio may in some cases suggest an unrealistic sense of certainty in the anticipated pace of progress, so a range or, better, a distribution of progress ratios could be applied to characterize uncertainty. Some of the data presented above provide a basis for quantifying within industry and cross industry variation in the pace of progress ( see McDonald and Schrattenholzer ( 2001) for an example of the former, Dutton and Thomas ( 1984) for the latter). However, this type of quantitative treatment of uncertainty may not always be practical or appropriate for this particular parameter. Available data present a relatively tight distribution of estimates around the most commonly cited 80 percent progress ratio. Analysts may therefore be justified in applying a single adjustment factor, particularly in those cases where it is clearly applied conservatively to avoid overstating the effect of progress on costs. This type of approach was applied in the recent mobile source RIAs, as described above.

Beyond this general recommendation to acknowledge uncertainty, we also suggest the following guidelines for application of progress curves to regulatory cost estimates:

 Pay careful attention to the production cycle. Rapid cost decreases as a result of progress curves and new regulatory requirements are likely to be more appropriate for the production of relatively new or small production technologies. A slower decrease in costs would be expected for established technologies with a long history of production, because the increment of increased production relative to cumulative production is smaller, implying a longer time period for additional cost decreases. For this reason, proper application of the 80 percent rule requires reasonable data on production volume and projected production volume, as described above. 37  Apply progress adjustments only to cost elements where a realistic possibility of learning exists. Progress adjustments are generally most applicable to production of specific devices, and are less applicable to direct materials or energy costs. An argument can be made that energy and materials extraction and refinement technologies are also likely to grow more efficient over time, but the markets for these commodities are complex and clearly affected by forces other than learning. Sensitivity analyses are likely to be a more appropriate way of addressing potential cost estimation inaccuracies for materials and energy cost elements.

 Pay attention to the timing of rule implementation, and whether it allows for learning. For some rules, particularly those that prescribe the installation of a specific technology within a short period of time ( a year or less), it may not be realistic to assume that there is enough time for learning by doing or learning by using to be meaningful. In those cases, learning by doing and/ or learning by using may be more applicable for new establishments that purchase 27 the technology after the initial installation cycle, assuming that the cumulative production volume is sufficient for significant learning.

 Rely on the 80 percent rule, and attempt to refine as data permit. The case studies presented above illustrate the 80 percent rule is a good central estimate of a progress curve for many types of pollution control and, more generally, for manufacturing industries. In some cases, however ( e. g, scrubbers), specific industry data may be available and analysts should consider applying an industry specific progress curve. Use of the 80 percent rule as a default value, however, is consistent with the conclusion that the general literature on progress is sound and has proven to be broadly applicable.

 Consider making use of data from similar air pollution devices ( described above) to evaluate the validity of assumptions about the applicability of the 80 percent rule. In general, the available data sources to estimate progress curves for pollution control devices suffer from limitations that make the data imperfect representations of a true learning. Even in the case of sulfur scrubbers, where there are relatively comprehensive data on costs over time, analysts make slightly different assumptions in calculating a progress curve that cause the estimates to vary, in some cases over a considerable range. An encouraging finding of our work, however, is that despite specific concerns over our ability to isolate learning effects from other factors that influence costs over time, the historic pollution control cost data appears broadly consistent with the results of applying an 80 percent progress ratio. 28 SOURCES Argote, L. and D. Epple, February 23, 1990. " Learning Curves in Manufacturing," Science, Vol. 247, p. 920 924.

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Dutton, J. and A. Thomas, 1984. " Treating Progress Functions as a Managerial Opportunity," Academy of Management Review, Vol. 9, No. 2, p. 237 247.

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Greening, L., J. Laitner, and S. Bernow, September 2000. " Capturing the Role of ` Technology Learning' in Transforming Energy Consumption Patterns within the U. S. Manufacturing Sector," Transforming Energy: Proceedings of the USAEE/ IAEE North American Conference, Philadelphia, Pennsylvania.

Harrington, W., R. D. Morgenstern, and P. Nelson, January 1999. " On the Accuracy of Regulatory Cost Estimates," Resources for the Future Discussion Paper 99 18.

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[Federal Register Volume 85, Number 135 (Tuesday, July 14, 2020)]
[Notices]
[Pages 42380-42381]
[FR Doc No: 2020-15164]

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DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Project No. 405-125]

Exelon Generating Company, LLC; Notice of Application Accepted
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list for the project. Further, if an intervenor files comments or
documents with the Commission relating to the merits of an issue that
may affect the responsibilities of a particular resource agency, they
must also serve a copy of the document on that resource agency.
    k. Description of Request: Exelon Generating Company, LLC is
requesting Commission approval to authorize the Maryland Environmental
Service (applicant) to implement a dredging pilot project approximately
5 miles upstream from the Conowingo Dam. The applicant proposes to
mobilize the dredging barge at Dorsey Park Boat Launch, mechanically
dredge 1,000 cubic yards of sediment from project waters, stockpile the
stored sediment at Peach Bottom Road Boat Launch, then transport dried
material outside the project boundary for beneficial use within the
state of Maryland. The dredging pilot project would result in limited
availability of the Dorsey Park Boat Launch to half capacity for 3
weeks and closure of the Peach Bottom Road Boat Launch for up to two
months. The proposed dredging pilot project would occur outside the
recreational boating season.
    l. Locations of the Application: In addition to publishing the full
text of this document in the Federal Register, the Commission provides
all interested persons an opportunity to view and/or print the contents
of this document via the internet through the Commission's Home Page
(http://www.ferc.gov) using

the ``elibrary'' link. Enter the docket number excluding the last three
digits in the document field to access the document. At this time, the
Commission has suspended access to the Commission's Public Reference
Room, due to the proclamation declaring a National Emergency concerning
the Novel Coronavirus Disease (COVID-19), issued by the President on
March 13, 2020. For assistance, contact FERC at
<EMAIL_ADDRESS>or call toll-free,<PHONE_NUMBER> or TYY,
(202) 502-8659. Agencies may obtain copies of the application directly
from the applicant.
    m. Individuals desiring to be included on the Commission's mailing
list should so indicate by writing to the Secretary of the Commission.
    n. Comments, Protests, or Motions to Intervene: Anyone may submit
comments, a protest, or a motion to intervene in accordance with the
requirements of Rules of Practice and Procedure, 18 CFR 385.210, .211,
.214, respectively. In determining the appropriate action to take, the
Commission will consider all protests or other comments filed, but only
those who file a motion to intervene in accordance with the
Commission's Rules may become a party to the proceeding. Any comments,
protests, or motions to intervene must be received on or before the
specified comment date for the particular application.
    o. Filing and Service of Documents: Any filing must (1) bear in all
capital letters the title ``COMMENTS'', ``PROTEST'', or ``MOTION TO
INTERVENE'' as applicable; (2) set forth in the heading the name of the
applicant and the project number of the application to which the filing
responds; (3) furnish the name, address, and telephone number of the
person commenting, protesting or intervening; and (4) otherwise comply
with the requirements of 18 CFR 385.2001 through 385.2005. All
comments, motions to intervene, or protests must set forth their
evidentiary basis. Any filing made by an intervenor must be accompanied
by proof of service on all persons listed in the service list prepared
by the Commission in this proceeding, in accordance with 18 CFR
385.2010.

    Dated: July 8, 2020.
Kimberly D. Bose,
Secretary.
[FR Doc. 2020-15164 Filed 7-13-20; 8:45 am]
BILLING CODE 6717-01-P


[Federal Register Volume 77, Number 85 (Wednesday, May 2, 2012)]
[Notices]
[Pages 26062-26063]
[FR Doc No: 2012-10544]

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SECURITIES AND EXCHANGE COMMISSION

[Release No. 34-66867; File No. SR-CME-2012-13]

Self-Regulatory Organizations; Chicago Mercantile Exchange Inc.;
Notice of Filing and Order Granting Accelerated Approval of Proposed
Rule Change To Add Reporting Requirements for FCM Clearing Members

April 26, 2012.
    Pursuant to Section 19(b)(1) of the Securities Exchange Act of 1934
(``Act'') \1\ and Rule 19b-4 thereunder,\2\ notice is hereby given that
on April 23, 2012, the Chicago Mercantile Exchange Inc. (``CME'') filed
with the Securities and Exchange Commission (``Commission'') the
proposed rule change described in Items I and II below, which items
have been prepared primarily by CME. The Commission is publishing this
Notice and Order to solicit comments on the proposed rule change from
interested persons and to approve the proposed rule change on an
accelerated basis.
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    \1\ 15 U.S.C. 78s(b)(1).
    \2\ 17 CFR 240.19b-4.
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I. Self-Regulatory Organization's Statement of Terms of Substance of
the Proposed Rule Change

    CME proposes including certain additional reporting requirements
for futures commission merchant (``FCM'') clearing members. The
enhanced reporting requirements are designed to further safeguard
customer funds held at the FCM level. The text of the proposed rule
change is available at the CME's Web site at http://www.cmegroup.com/market-regulation/rule-filings.html.

II. Self-Regulatory Organization's Statement of Purpose of, and
Statutory Basis for, the Proposed Rule Change

    In its filing with the Commission, CME included statements
concerning the purpose and basis for the proposed rule change and
discussed any comments it received on the proposed rule change. The
text of these statements may be examined at the places specified in
Item III below. CME has prepared summaries, set forth in sections A, B,
and C below, of the most significant aspects of such statements.

A. Self-Regulatory Organization's Statement of Purpose of, and
Statutory Basis for, the Proposed Rule Change

    CME is registered as a derivatives clearing organization with the
Commodity Futures Trading Commission (''CFTC'') and operates a
substantial business clearing futures and swaps contracts subject to
the jurisdiction of the CFTC. CME wants to impose additional reporting
requirements for FCM clearing members pursuant to CME Rule 970.D. The
new reporting requirements will require all FCM clearing members to
file daily, segregated, secured 30.7 and ``sequestered'' (or customer
cleared swaps) statements, as applicable, on a daily basis. The
proposed effective date for this new requirement is May 1, 2012.
    As further described in CME-issued Audit Information Bulletin 12-
04, the enhanced reporting requirements are designed to further
safeguard customer funds held at the FCM level. The proposed daily
reporting requirements comport with the CFTC's DCO Core Principle F
(Treatment of Funds), which requires each DCO to ``have standards and
procedures designed to ensure the safety of member and participant
funds.''
    CME Audit Information Bulletin 12-04 constitutes the CME's proposed
changes. CME also made a filing, CME Submission 12-112, with its
primary regulator, the CFTC, with respect to the proposed changes.
    CME believes the proposed changes are consistent with the
requirements of the Act. First, CME, a derivatives clearing
organization, is implementing the proposed changes in furtherance with
applicable CFTC regulations and Commodity Exchange Act (``CEA''), which
contains a number of provisions that are comparable to the policies
underlying the Act, including, for example, promoting market
transparency for derivatives markets, promoting the prompt and accurate
clearance of transactions and protecting investors and the public
interest. Second, CME believes the proposed changes are specifically
designed to protect investors and the public interest because the
requirements help safeguard customer funds held at the FCM level.

B. Self-Regulatory Organization's Statement on Burden on Competition

    CME does not believe that the proposed rule change will have any
impact or impose any burden on competition.

C. Self-Regulatory Organization's Statement on Comments on the Proposed
Rule Change Received From Members, Participants, or Others

    CME has not solicited and does not intend to solicit comments
regarding this proposed rule change. CME has not received any
unsolicited written comments from interested parties.

III. Solicitation of Comments

    Interested persons are invited to submit written data, views and
arguments concerning the foregoing, including whether the proposed rule
change is consistent with the Act. Comments may be submitted by any of
the following methods:
     Electronic comments may be submitted by using the
Commission's Internet comment form (http://www.sec.gov/rules/sro.shtml), or send an email to<EMAIL_ADDRESS>Please include
File No. SR-CME-2012-13 on the subject line.
     Paper comments should be sent in triplicate to Elizabeth
M. Murphy, Secretary, Securities and Exchange Commission, 100 F Street
NE., Washington, DC 20549-1090.
    All submissions should refer to File Number SR-CME-2012-13. This
file number should be included on the subject line if email is used. To
help the Commission process and review your comments more efficiently,
please use only one method. The Commission will post all comments on
the Commission's Internet Web site (http://www.sec.gov/rules/sro.shtml). Copies of the submission, all subsequent amendments, all
written statements with respect to the proposed rule

change that are filed with the Commission, and all written
communications relating to the proposed rule change between the
Commission and any person, other than those that may be withheld from
the public in accordance with the provisions of 5 U.S.C. 552, will be
available for Web site viewing and printing in the Commission's Public
Reference Room, 100 F Street NE., Washington, DC 20549 on official
business days between the hours of 10 a.m. and 3 p.m. Copies of such
filing also will be available for inspection and copying at the
principal office of CME. All comments received will be posted without
change; the Commission does not edit personal identifying information
from submissions. You should submit only information that you wish to
make available publicly. All submissions should refer to File Number
SR-CME-2012-13 and should be submitted on or before May 23, 2012.

IV. Commission's Findings and Order Granting Accelerated Approval of
Proposed Rule Change

    Section 19(b) of the Act \3\ directs the Commission to approve a
proposed rule change of a self-regulatory organization if it finds that
such proposed rule change is consistent with the requirements of the
Act and the rules and regulations thereunder applicable to such
organization. The Commission finds that the proposed rule change is
consistent with the requirements of the Act, in particular the
requirements of Section 17A of the Act, and the rules and regulations
thereunder applicable to CME.\4\ Specifically, the Commission finds
that the proposed rule change is consistent with Section 17A(b)(3)(F)
of the Act which requires, among other things, that the rules of a
clearing agency be designed to protect investors and the public
interest because the proposed rule change should allow CME to better
monitor the financial status and risk management procedures of its
clearing members.\5\
---------------------------------------------------------------------------

    \3\ 15 U.S.C. 78s(b).
    \4\ 15 U.S.C. 78q-1. In approving this proposed rule change, the
Commission has considered the proposed rule's impact on efficiency,
competition, and capital formation. 15 U.S.C. 78c(f).
    \5\ 15 U.S.C. 78q-1(b)(3)(F).
---------------------------------------------------------------------------

    In its filing, CME requested that the Commission approve this
proposed rule change on an accelerated basis for good cause shown. CME
cites as the reason for this request CME's operation as a DCO, which is
subject to regulation by the CFTC under the CEA. This rule change is
being made to enhance CME's efforts to protect investors who utilize
its clearinghouse services through its FCM clearing members.
    The Commission finds good cause for approving the proposed rule
change prior to the 30th day after the date of publication of notice in
the Federal Register because the proposed rule change allows CME to
implement the additional clearing member surveillance designed
specifically to protect investors and the public interest.

V. Conclusion

    It is therefore ordered, pursuant to Section 19(b)(2) of the Act,
that the proposed rule change (SR-CME-2012-13) is approved on an
accelerated basis.
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    \6\ 17 CFR 200.30-3(a)(12).

    For the Commission, by the Division of Trading and Markets,
pursuant to delegated authority.\6\
Kevin M. O'Neill,
Deputy Secretary .
[FR Doc. 2012-10544 Filed 5-1-12; 8:45 am]
BILLING CODE 8011-01-P


[Federal Register: September 28, 2005 (Volume 70, Number 187)]
[Notices]
[Page 56668-56669]
[DOCID:fr28se05-97]

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DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Docket No. RP05-658-000]

Transcontinental Gas Pipe Line Corporation; Notice of Refund
Report

September 20, 2005.
    Take notice that on August 31, 2005 Transcontinental Gas Pipe Line
Corporation (Transco) filed a report reflecting the overcollection of
the Storage Amortization Adder of flow through refunds received from
Dominion Transmission, Inc. (Dominion) in Docket No. RP05-632-018.
Transco states that on August 31, 2005, in accordance with section 4 of
its Rate Schedules LSS and section 3 of its Rate Schedule GSS, flowed
through the amount of $13,280.93 refund by Dominion to its LSS and GSS
customers.
    Transco states that copies of this filing are available for public
inspection, during regular business hours in a convenient form and
place at Transco's main offices at 2800 Post Oak Boulevard in Houston,
Texas.
    Any person desiring to intervene or to protest this filing must
file in accordance with Rules 211 and 214 of the Commission's Rules of
Practice and Procedure (18 CFR 385.211 and 385.214). Protests will be
considered by the Commission in determining the appropriate action to
be taken, but will not serve to make protestants parties to the
proceeding. Any person wishing to become a party must file a notice of
intervention or motion to intervene, as

appropriate. Such notices, motions, or protests must be filed in
accordance with the provisions of Section 154.210 of the Commission's
regulations (18 CFR 154.210). Anyone filing an intervention or protest
must serve a copy of that document on the Applicant. Anyone filing an
intervention or protest on or before the intervention or protest date
need not serve motions to intervene or protests on persons other than
the Applicant.
    The Commission encourages electronic submission of protests and
interventions in lieu of paper using the ``eFiling'' link at http://www.ferc.gov.
 Persons unable to file electronically should submit an

original and 14 copies of the protest or intervention to the Federal
Energy Regulatory Commission, 888 First Street, NE., Washington, DC
20426.
    This filing is accessible online at http://www.ferc.gov, using the

``eLibrary'' link and is available for review in the Commission's
Public Reference Room in Washington, DC. There is an ``eSubscription''
link on the Web site that enables subscribers to receive e-mail
notification when a document is added to a subscribed docket(s). For
assistance with any FERC Online service, please e-mail
<EMAIL_ADDRESS>or call<PHONE_NUMBER> (toll free). For

TTY, call<PHONE_NUMBER>.

Magalie R. Salas,
Secretary.
 [FR Doc. E5-5262 Filed 9-27-05; 8:45 am]

BILLING CODE 6717-01-P