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Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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4

The Economic Analysis of Standards

INTRODUCTION

This chapter reviews the models and analyses that DOE uses to justify its proposed standards economically. As is discussed in Chapter 1, the Energy Policy and Conservation Act (EPCA) requires DOE to consider seven factors in assessing the benefits and costs of a standard (42 U.S.C. 6295 (o)(2)(B)(i)), including (1) the economic impact of the standard on the manufacturers and consumers of the affected products, (2) the savings in operating costs throughout the estimated average life of the product compared to any increases in the initial cost or maintenance expense, (3) the total projected amount of energy savings likely to result directly from the imposition of the standard, (4) any lessening of the utility or the performance of the products likely to result from the imposition of the standard, and (5) the impact of any lessening of competition, as determined in writing by the Attorney General, that is likely to result from the imposition of the standard.1 Economic modeling helps address these factors.

DOE uses the screening and engineering analyses discussed in Chapter 3 as its starting point for the economic analysis. Those models estimate changes in unit energy use, performance, and production cost associated with a potential efficiency level (EL). DOE’s economic models then trace through their implications for consumer welfare and industry profits. DOE also considers whether a proposed standard will have a disproportionate impact on subgroups such as lower-income consumers and small businesses.

In addition to EPCA, DOE considers executive orders in its review and evaluation of proposed standards. Executive Order 12866 references the EPCA factors, but recognizing the difficulties in calculating costs and benefits, focuses on broader principles underlying the imposition of regulations, including why market conditions would lead to an expectation that a standard (or other form of government intervention) would improve “the well-being of the American public”—what economists call welfare. When markets function “perfectly,” market outcomes are economically efficient, meaning that market intervention is not expected to yield positive net benefits to society. Roughly, economists deem a market “perfect” when it features many buyers and sellers exchanging a good or service that does not create externalities at publicly known prices, and neither buyers nor sellers have advantageous information that the other lacks. Therefore, DOE’s economic analysis of standards proceeds against a backdrop of presumed imperfection in markets. These imperfections, often called market failures, may come from pollution externalities, manufacturers’’ market power, or consumer mistakes. Accordingly, DOE’s economic analysis includes a discussion of the market failures relevant to the product in question.

In this chapter, the committee articulates several ways in which DOE’s analysis of standards’ impact on producers and consumers deviates from a theoretical ideal suggested by economic reasoning. In some cases, the committee suggests refinements. However, attempts to tweak the DOE process to address

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1 Additional factors are (6) the need for national energy conservation and (7) other factors the Secretary considers relevant.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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individual shortcomings relative to the theoretical ideal may not always lead to a better answer. DOE often analyzes products and equipment about which there is limited information about key economic parameters. While the DOE process attends to both uncertainty and variability, it deals with complicated multi-step processes, where it is often impossible to forecast how sensitive results are to assumptions about uncertain parameters.

Thus, instead of calling for more detailed economic modeling, the committee’s overarching recommendation is for DOE to seek additional data where feasible, increase the transparency of its analyses by in many cases making explicit the sources and consequences of uncertainty, and give greater attention to ex post analysis, which can offer insight into the reliability of its judgments and the performance of its standards. Over time, DOE can fold those insights into the forward-looking standards process. In brief, given the challenges of modeling complex competition in a data-poor environment, DOE should take every effort to use past data and learn lessons ex post about firms’ behavior and the evolution of products affected by standards.

The committee organized the discussion as follows. There is first a discussion of DOE’s modeling of manufacturer behavior and the analyses of markups and markets. Then there is a look at consumers’ behavior and DOE’s characterization of consumer costs and benefits from an appliance standard. Energy savings and environmental benefits affect both individual consumers and society at large; these are discussed in subsequent sections. Finally, the committee returns to a discussion of market failures.

THE EFFECT OF STANDARDS ON PRODUCERS

From an economic perspective, appliance manufacturers come into the analysis of a proposed standard in two ways. First, DOE’s statutory authority is to set standards that are economically justified, and executive orders require it to consider whether the benefits of the standards exceed its costs, where benefits and costs can include impacts on public health and safety and the natural environment. The actions of producers (appliance manufacturers) and consumers contribute toward these benefits and costs. There may also be externalities on both the consumer and producer sides. Therefore, DOE needs to consider impacts on producers and consumers in performing the benefit-cost test and analyzing market failures. Second, EPCA requires DOE to consider the impact on manufacturers and of any competition reduction among manufacturers resulting from a regulation. That requires understanding manufacturers’ decision-making processes and the intricacies of competition among manufacturers.

This section considers DOE’s analysis of these topics on the manufacturer side. The conceptual underpinning for that analysis is the economic theory of the producer—the theory of the firm and the industry. The economic issues involved are not at all simple. The manufacturer of a regulated appliance is a multi-product producer—the manufacturer produces more than one type of that particular appliance and, often, produces other appliances. Thus, the firm operates in multiple markets. For each particular product, the manufacturer must decide its price, features, method and quantity of production, and marketing. The manufacturer typically makes these choices regarding multiple products, and there is likely interdependence among their decisions. There can be joint costs and other forms of interaction among decisions involving multiple products. It is important to note that the existing economic theory of firm behavior does not operate at the level of granularity involved in these decisions. Moreover, the empirical data required to implement theoretical models and test alternative theories at this level of granularity are generally lacking. The text makes no assumption with regard to whether material costs or labor costs are the dominant influence on the cost of manufacturing a given appliance. The point being made is that, while DOE analyzes the potential impact of an efficiency standard on the appliance’s price in isolation, without considering the pricing of other products that the manufacturers may produce, the reality is that multi-product manufacturers are free to determine their product prices simultaneously and interdependently across different products.

DOE greatly simplifies these issues in its analyses. It assesses the impact of an efficiency standard for a given class of appliances on pricing, investment decisions, manufacturer profitability, and industry

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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competitiveness through the lens of the particular appliance viewed in isolation. DOE projects the markup governing the appliance’s selling price and the impact on corporate investment requirements, financial needs, and cashflow based on that one individual product in isolation. These projections deserve significant attention because they simultaneously have substantive implications for the analysis and are based on relatively thin evidence. In the absence of better data, more sophisticated economic modeling may add little value. Thus, the committee’s overarching recommendation regarding the analysis of producers is for DOE to collect and analyze real-world data that facilitate evaluation of assumptions about markups, cashflow impacts, and other aspects of producer choice (Recommendation 4-1, below).

Below, the committee describe a conceptual framework for assessing producer behavior and profit. The committee argues for a holistic view of the appliance manufacturer that posits that manufacturers sell a product portfolio in imperfect competition. This market-setting implies that a standard that forces a firm to alter a product impacts its profits by affecting that product and through indirect effects on substitute products sold both by that firm and its competitors.

Conceptual Model of the Manufacturers

DOE wishes to characterize the likely impact of a standard on producers of the good. How should it do so? For sellers, the relevant metric is profit, and so the pertinent question is whether a standard will cause profits to rise or fall.

Most appliance markets feature many competing products that a handful of large firms produce. Products are closely related but differentiated in their performance, size, styling, and other features. Products’ design and production require substantial fixed costs (i.e., costs that do not vary with the volume of goods produced). For most but not all appliances, products reach end consumers after passing through a retail intermediary.

These conditions suggest that most producers sell a portfolio of competing products and set prices for those products considering the degree of competition they face. This implies that the pass-through of costs depends not just on a supply-and-demand elasticity but also on the degree of competition. An appliance standard can cause the exit of existing products or introduce new products, which might impact the degree of competition facing a given product. A critical feature of these markets is that each product’s price and characteristics depend on the entire portfolio of related products. The field of industrial organization in economics has well-established models that describe these conditions.2

Producers compete in price, energy efficiency, and other product features and attributes. Simplifying, the committee refers to other features as “quality.” Standards may impact quality in one of two ways. First, standards may force a change in product quality. Producers may sacrifice performance or other features to achieve efficiency while limiting cost increases. Second, standards may cause firms to compete differently on quality. This could lead to significant effects on consumer well-being and the economic efficiency of the market.

One main reason for the effects of standards on consumer welfare and market efficiency is that sellers can increase profits by segmenting a market into customers with higher and lower willingness to pay for the product by offering products with different quality levels. Sellers may use energy efficiency to achieve that product differentiation—both within and across sellers. If so, standards may limit the sellers’ ability to use energy efficiency to achieve product differentiation, as it becomes more expensive for the seller to maintain differences in efficiency when the minimum is higher. This can lead to a drop in profits and a shift in welfare toward consumers. It can also increase quality if firms add new features to achieve product differentiation when energy efficiency differences have been compressed.

The committee notes that product differentiation may benefit consumers because it tailors products to consumers’ needs and wants with different usage profiles. Where firms have substantial market power, however, which appears to be the case in many of the product and equipment markets regulated by DOE’s

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2 A standard approach, which motivates this discussion, is the Nash-Bertrand price competition model.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

efficiency standards, the product differentiation can enhance that power. Moreover, in some circumstances, incumbent producers can benefit from standards if they create barriers to entry that limit future competition.

The Determination of Markups

How manufacturers respond to an appliance efficiency standard affects consumers’ welfare in two ways: via the mix of features that the manufacturer decides to build into the appliance, as mentioned above, and by the final retail price.

One component of the consumer’s final price is the manufacturer’s price as the product leaves the factory, which in turn partially depends on the cost of manufacturing determined in the engineering analysis in the Technical Support Documents (TSDs). The consumer’s final price also depends on incremental price increases over cost added along the distribution chain. In its analysis, DOE combines the manufacturer’s markup over its production cost together with the markups over cost that occur through the distribution chain.

The overall markup is the difference between the manufacturers’ marginal cost of producing an extra unit of the appliance product and the consumer’s final price. DOE routinely uses financial statements (U.S. Securities and Exchange Commission Form 10-Ks)3 for publicly traded companies to estimate the average markup on all products sold. They then average these results across firms in an industry to arrive at an estimate of typical industry markups. In some cases, DOE may adjust these results based on stakeholders’ comments, though this process is not transparent.

Given sparse data, this is a reasonable process for assessing the order of magnitude of markups among manufacturers. However, it is far from perfect and leaves considerable uncertainty. Financial statement data do not provide breakdowns among product classes, so all calculations are blind to markup differences across appliance categories or different models. Moreover, many manufacturers are subsidiaries of larger companies, so their data is obscured even further. DOE omits manufacturers that are not publicly traded.

Even if DOE accurately measures the average markup for a class of appliances before changing the standard, markups may change because of a new standard. DOE’s process makes this distinction in the distribution and retail markup analysis, but not in consideration of manufacturer markups. Generally, the marginal markup may be greater or smaller than the average.

An alternative approach would be to model the firms competing to sell differentiated products along the lines discussed above. Methods of estimating markups as equilibria outcome of market competition are standard in academic economics. Standard economic approaches rely on detailed data on prices and market shares of alternative products (Berry et al., 1995). For some appliances, these data may not exist. Moreover, these models may not accommodate the granularity of detail required for the appliance types being DOE considers. These models have come under some criticism (Knittel, 2014), and they often require additional adaptation to model market outcomes when products are changing. The markup values derived from the current analysis are subject to considerable uncertainty, but DOE uses this single point estimate of the markup in all downstream steps. Given the imperfections highlighted above, the committee’s view is that greater attention to the markup analysis’s uncertainties is warranted.

The preceding discussion focused on the manufacturer markup. DOE also models markups in the subsequent steps of the supply chain. For each product category, DOE models the transaction chains that occur before a product reaches its final consumer. For many products, DOE considers multiple channels when goods reach final users via different routes.

DOE estimates the markups for these steps in the wholesale or retail supply chain from financial statements and sources like the U.S. Census Annual Retail Trade Survey (ARTS), which contains data on operating expenses from each economic sector. Recently, DOE has focused attention on the ‘incremental

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3 The form may be downloaded at https://www.sec.gov/files/form10-k.pdf.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

markup’ in these steps, which attempts to isolate the input components, but is sensitive to assumptions about operating costs and performance4

The incremental approach appears to be reasonable, but the committee notes the same issues as above. DOE mostly pulls the markup estimates from financial filings and surveys representing costs and revenues for firms that typically sell many product categories. The financial data DOE uses do not represent all sellers. Uncertainty is thus substantial.

The committee finds that the DOE process of evaluating markups is sensible, but the true economic context is complicated in ways that imply substantial uncertainty about how manufacturing cost will ultimately impact consumer prices. DOE currently lacks the relevant data to verify its assumptions or distinguish between alternatives, leaving significant uncertainty underrepresented in the current analysis.

The goal of the markup analysis is to estimate how an increase in the manufacturing cost translates into market outcomes. Given the complexity of the markets in question, predictions about markups will have substantial uncertainty unless much more data becomes available.

DOE is frequently evaluating technologies and products that are in the marketplace today. In such cases, DOE can validate markup analysis against existing market prices. Such data may be difficult to obtain, but DOE could put greater emphasis on systematically gathering such information.

In addition to current market data, ex post analysis could also serve an important validating role. In some cases, DOE can obtain data on final consumer prices after it regulates an increase in energy efficiency. The empirical evidence shows that, in some cases, the projected price increases did not materialize. Indeed, in some cases, the outcome appears to be some degree of price reduction. Where these outcomes occur, they have two economic implications. One implication is that the markup analysis was not reliable. Another implication is that DOE’s economic analysis may have mischaracterized manufacturers’ decision-making behavior. Moreover, if standards can lead to lower product prices and better quality, more aggressive standards might be justified.

Spurlock (2013) found that the prices of given models of clothes washers, which had been declining in real terms before the minimum energy efficiency standards in 2004 and 2007, dropped significantly at the time the standards came into effect and thereafter began trending downward more quickly. The average real price across all clothes washers did not change significantly at that time. Brucal and Roberts (2019) reported similar results for clothes washers, dryers, refrigerators, and room air conditioners.

What can explain this? One explanation is learning-by-doing (learning curve effects): as cumulative production grows, the manufacturer gains more experience in making the product and finds ways to lower production costs. Learning-by-doing has certainly occurred with some appliances (Van Buskirk et al., 2014), and DOE has incorporated learning curve phenomena in its price projections. But learning-by-doing cannot account for the entire change—otherwise, the prices of regulated models of clothes washers would not have declined so much more than the average price of all clothes washers.

Houde and Spurlock (2016) suggested standards may have spurred a reduction in market power and an incentive to increase innovation. With clothes washers, the greatest within-model price reduction occurred for the lowest efficiency washers. Moreover, there was a distinct increase in the number of models offered in the highest efficiency group. There were also within-model price declines for washers in the highest efficiency group. These changes are consistent with a model in which a firm engages in strategic pricing and product differentiation. If there is significant heterogeneity in how consumers pay attention to energy efficiency, it enables a firm to strategically segment customers and allows it to exercise its market power.

Standards can also spur investments in research and development to generate innovations in the mandated activities; indeed, there is likely to be low-hanging fruit in such areas because consumers previously undervalued the activity. Newell et al. (1999) noted, for example, that an appliance manufacturer might under-invest in energy efficiency innovation, something that government efficiency standards could correct. In a dynamic context, firms might strategically withhold or delay cost-saving

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4 See Dale et al. (2004). The article assumes cost pass-through does not affect a good’s demand, notwithstanding changes in operating costs.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

innovations from implementation to further exercise market power (e.g., Karp and Perloff, 1996; Kutsoati and Zabojnik, 2005; Loury, 1979). DOE does not consider these are factors when it assesses impacts on firm profits and consumer prices from an appliance standard.

As noted above, some economic models address these issues, but not with the granularity required to deal with the real-world complexity of the industries DOE regulates. Moreover, sufficiently detailed data to test these models may be lacking. Instead, empirical evidence, both current and after the imposition of a standard, is needed to validate models, evaluate standards, and direct subsequent policies.

RECOMMENDATION 4-1: DOE should put greater weight on ex post and market-based evidence of markups to project a more realistic range of likely effects of a standard on prices, including the possibility that prices may fall. This would improve future analyses.

Manufacturer Impact Analysis

DOE conducts a manufacturer impact analysis (MIA) to estimate the financial impact of a proposed appliance efficiency standard on manufacturers and assess the standards’ impacts on employment and manufacturing capacity. DOE conducts the analysis in three phases and uses quantitative analyses and qualitative evaluation.

Phase I, “Industry Profile,” consists of preliminary research directed at characterizing the appliance manufacturing industry, which involves collecting data on market share, sales volumes and trends, pricing, employment, and the industry financial structure.

In Phase II, “Industry Cash Flow,” DOE employs the Government Regulatory Impact Model (GRIM), an industry cash-flow model customized for this rulemaking, to model the economic impact of appliance standards on cash flow in the manufacturing industry as a whole. Appliance standards can affect manufacturer cash flows in three ways: (1) by creating a need for increased investment, (2) by raising production costs per unit, and (3) by altering revenue through higher per-unit prices or possible changes in sales volumes or both (DOE, 2014, p. 12-2). The GRIM model attempts to account for these potential cash flow impacts. GRIM draws on DOE’s engineering analysis, shipments model, census data, and financial data from corporate reports and 10-K filings. It also incorporates information obtained by DOE from interviews with manufacturers. GRIM projects annual cash flows using standard accounting principles. The key GRIM output is the industry net present value (INPV), which is the discounted present value of annual industry cash-flows over the analysis period. The analysis period is the announcement year of amended energy conservation standards until several years after the standards compliance date. DOE discounts the cash-flows by the industry weighted average cost of capital, as given in S&P credit reports. DOE assesses the financial impact of alternative appliance efficiency standards by comparing discounted cash flow under each standard. The qualitative part of the MIA addresses trends in product characteristics, manufacturer characteristics, and standards’ impact on manufacturer subgroups.

In Phase III, “Subgroup Impact Analysis,” DOE evaluates the impacts of energy efficiency standards on manufacturer cash flows, investments, and employment. Phase III also evaluates any impacts on manufacturer subgroups, specifically focusing on the potential for disproportionate impacts on small business manufacturers.

DOE’s interviews with manufacturers play an important role, providing feedback on the approaches and data used in the GRIM analysis. The interviews provide DOE information to evaluate the impacts of appliance standards on manufacturer cash flows, manufacturing capacities, and employment levels. Examples of data obtained through interviews include capital investment costs for one-time changes in plant, property, and equipment; costs for one-time investments in research, product development, testing, and marketing; product cost structure, or the portion of the manufacturing production costs related to materials, labor, overhead, and depreciation costs; and projected total shipment and shipment distribution mix.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

The interviews also provide manufacturers an opportunity to voice their concerns with potential new appliance standards. DOE invites manufacturers to identify key issues they feel DOE should explore and discuss further with them. With dishwashers, manufacturers raised issues about the impact on dishwasher performance and issues with test procedures. Manufacturers of higher-efficiency products were also concerned that they would experience increased competition as manufacturers that previously focused on low-efficiency products moved into their target segment of the dishwasher market.

The GRIM analysis is a financial analysis rather than an economic analysis. However, it raises some of the same issues as those discussed above connected to DOE’s markup analysis. Published corporate financial data typically do not provide breakdowns among product classes, meaning they are blind to differences in investment requirements and financial impacts across appliance categories or different models. Some manufacturers are subsidiaries of larger companies, so their data is obscured even further. Using average cost assumptions to develop an industry-wide cash flow estimate may not identify differential impacts of appliance standards among different manufacturer subgroups such as small companies manufacturing niche products. DOE uses the industry interviews to identify heterogeneity within the industry, and it individualizes or segments the GRIM analysis to some degree to reflect inter-firm differences. However, this process is not entirely transparent. Moreover, it is not clear how accurately the GRIM cash flow analysis can reflect corporate behavior regarding the cross-product allocation of production costs, strategic pricing, and market segmentation that could affect an efficiency standard’s cash flow consequences for one of multiple products manufactured by a company. Finally, the manufacturers impact analysis does not provide any information on how the current standard helps or hinders the manufacturers’ ability to respond to a more stringent energy efficiency standard that DOE may enact 6 and 12 years later.

RECOMMENDATION 4-2: To account properly for uncertainty and variability across manufacturers, DOE should report ranges for the input values that feed the GRIM model and run GRIM with the lower bound and upper values in the observed ranges. To make the MIA more transparent, DOE should present its estimates of financial parameters and cost of capital from publicly available sources and then report the adjusted values after the responses to interviews have been considered.

THE EFFECT OF STANDARDS ON CONSUMERS

The consumer chooses from whatever appliance products the manufacturers decide to produce at whatever final prices emerge from the distribution chain. They also base their choice on the energy and water prices set by the retail utilities. The appliance has to work alongside the configuration of other energy-using appliances, including with the existing fuel supply to the building, typically electricity or natural gas, and with its energy management system, if one exists.

DOE wishes to characterize the likely impact of a standard on consumers. How should it do so? When a household or individual uses an appliance in their home, the relevant metric is the change in consumer utility (or welfare). Changes in consumer utility encompass changes in (1) the cost of purchasing the appliance, (2) the cost of using it (and in the manner and frequency with which the consumer uses it), and (3) product performance or other attributes that are of concern to the consumer.

When a business uses an appliance, the relevant metric is the impact on the business’ profit. In this case, businesses determine profit by both cost and other product attributes that determine performance and function. Moreover, businesses may sometimes have broader concerns than short-term profit (e.g., positioning itself as a socially responsible corporate member), which would require a broader metric.

This report will primarily discuss the household consumer case, understanding that concepts generally carry over to commercial users.

This sub-section considers DOE’s analysis of consumer behavior and welfare. The committee first describes a conceptual framework to evaluate DOE’s analysis. This framework begins with consumer

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

utility as the foundational metric. It then delineates the substantive choices made by consumers throughout the life-cycle of the product: (1) ownership (e.g., does one own zero, one, or more dishwashers), (2) product choice (e.g., which model a consumer chooses), (3) fuel choice (e.g., electric versus gas-powered), (4) time, frequency, and mode of use (incorporating rebound), and (5) turnover dynamics (including repair versus replace and disposal). The committee also discusses how to account for possible consumer mistakes or lack of information.

The DOE analysis focuses on life-cycle cost (LCC) and payback period (PBP). The committee’s discussion emphasizes that LCC and PBP estimates are imprecise due to the uncertainty and variability of key inputs such as production costs, manufacturer and distributor markups, discount rates, electricity/fuel prices, equipment performance, and consumer behavior. The committee discusses these issues in detail in subsequent sections. Moreover, product attributes and behavioral responses imply that LCC and PBP are insufficient to characterize consumer utility.

The conceptual model may not be the best one for regulatory purposes when economic models require unverifiable assumptions, critical inputs are highly uncertain, or relevant data are unavailable. As a result, the committee’s recommendations do include specific ideas on how to improve DOE’s analysis, but even more, they emphasize the critical need for:

  • Retrospective analysis to confirm assumptions and characterize error/uncertainty, (Recommendation 4-1);
  • Appropriate treatment of uncertainty and variability of findings (Recommendation 4-2);
  • Analysis of how sensitive the results are to different assumptions so that DOE can prioritize data collection efforts, (Recommendation 4-5); and
  • A preference for simplicity where complexity may not improve accuracy.

Conceptual Model of Consumer Choice

Buyers of an appliance make a series of decisions. Heuristically, it is useful to outline these decisions sequentially, although they may overlap or be made jointly. First, on the “extensive margin,” buyers choose to have a number of appliances in their homes. In a given period, they decide whether to purchase a new appliance as a replacement or an addition or to stick with what they have. In the former case, but not the latter, they are “in the market” for an appliance. A consumer who decides to buy an appliance in a given period must choose which appliance to buy out of those available in the market. Next, given the consumer’s appliances, there is a choice on the “intensive margin,” which is how much to use each appliance. One example of intensive margin is whether to run the dishwasher after every meal or less frequently and in which operating mode (e.g., quick wash, full wash, etc.). The choice may depend on the amount of energy used when running the appliance in a given mode and the energy’s costliness. However, it may also depend on other features (attributes) of the appliance, like how noisy it is or how long the cycle takes.

Like all consumer choices, the choices on both the extensive and intensive margins depend on the consumer’s preferences and budget constraints. The latter reflects, in turn, how much money the consumer has available to spend on goods and services.

DOE conceptualizes the consumer’s choice as a trade-off between two costs: the up-front purchase price of an appliance (plus cost of installation) and the annual operating costs over the period the consumer uses the appliance (e.g., how long the consumer lives in that house). At the heart of this tradeoff is the consumer’s discount rate: if there is a choice between buying a more expensive model but saving on energy operating costs, how does a consumer compare those costs? If the consumer employs a higher discount rate, they will weigh the upfront purchase price more heavily than the lifetime savings in operating costs; if they use a low discount rate, they will do the reverse. The answer depends, in part, on how the consumer finances the up-front purchase. Do they use a high-interest credit card, sell stock from their portfolio, withdraw money from savings, or does the appliance store offer a deal for no payment for

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

the next 12 months? In some cases, the consumer may not be able to finance the purchase, which could generate the same outcome as a high discount rate, namely avoiding appliances with a significant upfront cost regardless of everything else.

However, the conceptualization of consumer choice as a trade-off between upfront appliance cost and ongoing operating energy costs is likely to be overly simplified for several reasons.

For one, there is empirical evidence that many consumers do not know how much energy or water they use when they run an appliance’s operating cycle. There is no meter that shows them how many kWh of electricity or gallons of water they currently use. Surveys have found that consumer estimates versus actuals of how much electricity or water they use tend to be widely off the mark.5 Moreover, many consumers have little sense of what they are paying per unit for water and electricity.6 While an appliance’s upfront purchase price is obvious, the operating cost in electricity and water is relatively shrouded for many consumers, impairing their ability to make an informed trade-off. Moreover, it is difficult to precisely predict the operating costs over the lifetime of the appliance. The challenge of calculating these costs will vary across appliances and with the availability of official labels and information from third-parties organizations or consumer reviews, all of which can influence the impact of standards by providing greater information to consumers to improve choice.

Consumer preferences may also be considerably more complex. Appliances and their services boost consumer well-being (utility); with a dishwasher, they can handle dirty dishes conveniently and effectively, avoid streaks when washing glassware, and have an appliance that looks good in the kitchen. The evidence from market research is that many attributes might affect appliance purchase decisions—both qualitative and quantitative, both subjective and objective. (See discussion in Chapter 5 based on EPRI, 1994.) Examples for dishwashers include brand loyalty, noise, reliability, repair costs, ease of operation, convenient rack layout, drawer configuration, ability to hold extra-tall items, salt container, rinse agent dispenser, etc. The attributes invoked for the purchase of a given commodity vary across consumers and, for a given consumer, can vary across choice occasions depending on the context. The presence of attributes in the utility function tends to lessen the importance of price as a driver of consumer choice.

Another source of complexity in consumer choice is the numerosity of alternative products. For example, in the case of residential dishwashers data plotted in DOE’s Technical Support Documents implies that there are more than 230 basic models of standard residential dishwashers (DOE, 2014, p. 3-36, Figure 3.14.1). Such numerosity is by no means uncommon among consumer products.7 Given the numerosity of different models and brands of a given commodity, the evidence is that consumers often choose from a limited subset of the available alternatives. In the marketing literature, this is referred to as a consideration set. If an item is not within the consideration set, the consumer will not consider or select it, regardless of price or attributes. The product set a consumer considers can be distinctive to that individual and influenced by the consumer’s past shopping experience, habit, or exposure to information or advertising.8

An appliance efficiency standard thus impacts consumer utility in several ways. First, it will alter the set of products on the market by driving out non-compliant ones. Second, it will directly impact the efficiency (hence operating cost) of some available appliances. Third, it will affect the equilibrium price through incremental cost and incremental markup. (It may further impact prices through altering the

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5 For energy, see Baird and Brier (1981), Kempton et al. (1985), Attari et al. (2010), Chen et al. (2015), Houde and Myers (2019). For water, see Hamilton (1985), Beal et al. (2013), Attari (2014), and Araya et al. (2020).

6 See Ito (2014) and Brent and Ward (2019). There is a stark contrast with automobiles and gasoline use. As you fill your car each week, you get a clear sense from the pump of both how much gasoline you have used and how much it costs per gallon.

7 Nor is it a recent phenomenon. In 1896, the H.J. Heinz company introduced the marketing slogan, “57 pickle varieties.” The slogan Heinz 57 varieties (omitting “pickles”) endured at least into the late 1970s.

8 The effect of appliance regulation may be to exclude certain products from the consideration set.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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degree of competition among sellers.) Fourth, altering one attribute for efficiency may alter the other attributes’ utility (e.g., increased energy efficiency may increase the run time of a dishwasher).

The committee finds that changes in demand due to a new appliance standard are influenced by changes in installed cost and operating cost, but also by many other factors, including the substitutes available on the market, product characteristics other than cost, and other related private and public policies and campaigns. Because of the dependence of both markups and individual demand on the complete set of alternatives available in the market, a standard can affect both characteristics and demand for substitute appliances that exceed the efficiency standard. Given data on the appliances available in a market (prices and attributes) and on choices by a sample of consumers, it has become common practice in economics and market research to estimate what is known as a discrete choice model to explain consumer choices. The model produces estimates of the utility weights placed by consumers on the various attributes included in the choice model. These weights quantify how consumers make trade-offs among alternative attributes and can be used to calculate the monetary value (willingness to pay) that consumers place on changes (improvements or reductions) in attributes. Estimating these models requires some judgment calls by the analyst, including choosing attributes, using objective data or subjective perceptions to characterize them, and modeling consumer choices as limited by a consideration set and, if so, how to compose that set.9 If DOE collected the appropriate data, it could formulate and estimate discrete choice models of consumer behavior to quantify the trade-offs that consumers face from changes in appliance attributes (performance). Alternatively, it could collect qualitative survey data about how consumers value some appliance product attributes relative to others.

The committee recognizes that acquiring data for a study of the relation between appliance attributes, consumer choice and consumer value is a challenge. The Energy Information Agency’s own surveys of residential and commercial energy use provide valuable information on some appliances, as well as private sources of information such as Nielsen; however, both the scope and timeliness of these data limit their usefulness. The committee nevertheless urges DOE to attempt such analysis where feasible.

RECOMMENDATION 4-3: DOE should collect data on consumer choices in appliance markets and estimate a discrete choice model of consumer behavior to quantify the trade-offs that consumers face from changes in appliance performance.

While a discrete choice model can significantly improve both the assessment of relative standards and the demand projections, the complexity of appliance markets means that projections of consumer behavior in response to standards necessarily will be uncertain. As is discussed in Chapter 2, rigorous evaluation of standards requires transparent treatment of uncertainty. Ex post analysis of prior standards would be useful to validate the usefulness of these tools.

DOE’s Forecast of Product Purchases (Shipments Analysis)

DOE estimates future products’ purchases (i.e., shipments) using computer models calibrated against historical data. These are stock-and-flow models representing the additions and subtractions of product from the stock of in-use units any given year of the analysis period. The models begin with an estimate of units’ stock at a reference year, broken down by age or vintage. DOE combines this estimate of the reference year’s stock with estimates of product additions and product retirements to forecast the stock for each year of the projection horizon. Depending on the product, the model may also differentiate products by class (e.g., compact or standard dishwasher), market sector (e.g., residential or commercial), and geographic region in addition to the vintage.

DOE estimates the initial stock of units from historical shipment’s data from resources like Appliances Magazine and Appliance Design (AHAM), and from its assumptions on the lifetime of the

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9 See, for example, Swait and Ben-Akiva (1987) and Shocker et al. (1991).

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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products to determine the proportion of units shipped in the past that remain in use in the present and their vintage. DOE then combines this estimate of the reference year’s stock with estimates of product additions and product retirements. Product additions are estimated as the sum of new installations and replacements. Depending on the product (e.g., residential dishwashers or residential furnaces), it may be assumed that new housing construction is the primary driver of new installations (installations for new owners of the appliance). Shipments to replace damaged equipment are projected based on estimates of the products’ lifetimes, sometimes assuming that the lifetime is affected by region. For example, Non-Weatherized Gas Furnaces are assumed to have an average lifetime of 19.5 in the North and 23.5 years in the rest of the country.

To assess the effect of standards, DOE estimates shipments both under the base-case scenario, when there are no new energy efficiency standards, and assuming each of the Trial Standard Levels (TSLs) has been adopted. To estimate the impacts of standards on consumer purchase decisions, DOE calculates the Relative Price Elasticity of Demand. The relative price is the sum of the purchasing price and present value of operating costs, divided by household income. The calculated elasticity is then combined with the TSL-induced changes in the purchase price and operating costs to estimate shipments’ impacts.

The shipments model provides DOE with point-estimates of the number of units that will be retired and added to the stock every year. The committee finds that these point-estimates fail to represent all of the uncertainty surrounding the country’s trends in appliance/product purchases. Even if DOE accurately estimated the changes in the purchase price and operating costs caused by a TSL, it would still be subject to too much uncertainty about the customers’ discount rate to estimate the operating expense’s present value. Additionally, even if DOE had perfect information about customers’ implicit discount rate, it would fail to assess the relative-price elasticity of demand; the method DOE currently uses ignores changes in appliance quality and consumer preferences. As pointed out in the above section, Conceptual Model of Consumer Choice, changes in appliance quality and consumer preferences are important determinants of consumer choice.

To consider the uncertainty on the input parameters, DOE must use the ranges (or probability distribution, if possible) of the data available, not just the averages. For example, to estimate shipments of new appliances, rather than using the estimates of new housing units for future years under the Annual Energy Outlook (AEO) reference case, DOE can consider the range given by the AEO high-economic growth and low-economic growth cases. Similarly, instead of using a point estimate of implicit customers’ discount rate (e.g., 37% for dishwashers), it can use the range reported in the literature. The upper and lower bound estimates of implicit costumers’ discount rate will propagate toward the calculation and be reflected in upper and lower bound estimates of relative-price elasticity of demand.

RECOMMENDATION 4-4: The committee recommends that DOE propagate the uncertainty in the shipments model’s input parameters and present the full range of shipment estimates.

Life-Cycle Cost and Payback Period

The primary input to DOE’s analysis of consumer benefits and cost is the life-cycle cost (LCC) and payback period (PBP) analyses. While the calculation of an LCC from a societal perspective would include the costs associated with all stages in the life cycle of a product, including manufacturing, use, repair, re-use, recycle, and final disposal, to assess direct consumer’s costs, DOE includes only the costs of acquisition, installation, and operation of an appliance. To estimate the economic impact of a standard, DOE calculates the life-cycle savings for an average household, and for specified subgroups, taking into account the appliance purchased with and without a standard (i.e., calculates the net benefit of the policy). The final calculation thus relies on cost estimates for the compliant product and other available substitutes and on purchase decisions of households. This section considers issues that arise in DOE’s estimates of installed costs, operating costs, and overall household savings. Common concerns across the three

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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components are how DOE captures variability across households from the available data and models and how it models and presents residual uncertainty.

Life-Cycle Cost Savings

To assess the effects of new or amended standards on consumers, DOE considers not just the LCC of a particular product but the LCC savings relative to a base-case. To do this, DOE estimates the difference between the LCC of the product a consumer would choose when there is a new or amended standard and the LCC of the product the consumer would choose when there is not a standard (i.e., the “base-case choice”). Therefore, DOE must assume consumers’ choices when there is and there is not a standard. The shipments analysis provides guidance, including information about which households will realize the savings by changing their purchasing decisions from a non-compliant to a compliant product. As is discussed above in the section, Conceptual Model of Consumer Choice, such estimates are subject to very significant uncertainty, compounded from uncertainty about the prices of different options and consumer behavior. Consequently, the estimates of LCC savings provide a higher-level assessment of a standard but are difficult to evaluate or interpret.

The committee finds that although DOE must consider the estimates of future shipments to calculate the average of LCC savings of an EL for all consumers in the nation, it could better communicate the results of its analysis and the variability and uncertainty of its LCC estimates by clarifying the interpretation of its estimate LCC Savings given its dependence on shipments estimates.

It is helpful to look at the probability distributions and ranges of LCC savings in Figures 8.4.2 through 8.4.5 in the residential dishwasher TSD (DOE, 2014) to see the consequence of aggregating product costs with consumer demand. They refer to the weighted LCC savings from purchasing a product with an efficiency level equal to the TSL only for the consumers for whom this TSL is higher than the efficiency level they would have chosen in the absence of the new or amended standard.

Without further explanation, presenting the LCC savings like this may make readers think that a distribution of LCC savings concentrated around $0 indicates there is not much difference between the energy efficiency of the baseline and the TSL. This is misleading because LCC savings of $0 may correspond to a case when in fact, there is a significant difference between the energy efficiency of the baseline product and the TSL, but the modeling has concluded that most consumers are already choosing products with an energy efficiency that is equal to the TSL or higher. The standard may have considerable upside risk should demand for the compliant standard be underestimated.

Considering multiple plausible base cases in the calculations of LCC savings would shed additional light on the impacts that standards have on consumers. For example, in the case of dishwashers, three base cases include (1) households without automatic dishwashers choosing between continued hand-washing or the purchase of a new appliance; (2) households without an automatic dishwasher, choosing between the baseline product and one that complies with a potential new standard; and (3) households looking to replace a currently operational dishwasher.

Additional metrics that would illuminate a standard’s potential impact include the LCC savings from a new purchase of a TSL relative to the baseline and the LCC savings from a replacement of a baseline product with a new TSL.

The first calculation presents the difference between the LCC of purchasing a baseline product (i.e., the product that meets the current standard) and the LCC of purchasing the TSL product. It represents the savings to a consumer purchasing a TSL product instead of purchasing the baseline. The committee thinks that by presenting the distribution of LCC savings for each TSL relative to the baseline, DOE can clearly convey the uncertainties on the initial and operating costs of a particular efficiency level without contaminating this estimate with an estimation of shipments during the compliance year.

Lower values of the metric of LCC savings of a new purchase, assuming projected shipments, do not necessarily indicate that savings potential of a TSL is low. DOE may want to note that low LCC estimates

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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-assuming estimates of future shipments- may signal that most consumers would be unaffected by the standard because they already choose a higher efficiency level.

The second metric of value is the probability distribution of LCC savings of a TSL relative to the replacement of a baseline product that a consumer already owns and can continue to use. By considering both, the LCC savings from purchasing a TSL product instead of a baseline product and also considering the LCC savings from replacing a currently owned baseline product with a TSL product DOE will obtain and communicate additional information on the value of setting the standard at a TSL, of interest for products where operating costs are a significant portion of life-cycle costs. Such savings are essential for estimating the benefits of appliance replacement programs that state governments, utilities, or other entities may consider to incentivize consumers to upgrade their appliances. Therefore, the calculation of LCC savings of replacements may allow DOE to improve its estimates of shipments and the final estimate of LCC savings at the national level. It is important to note that the committee’s recommendation to present two additional LCC metrics does not impose any additional burden on DOE because DOE already estimates all of the parameters necessary for these calculations.

RECOMMENDATION 4-5: DOE should make changes to the Technical Support Documents underpinning its rulemakings to clearly communicate the dependence of the life-cycle cost (LCC) calculation on shipments assumptions and thereby add clarity on the interpretation of LCC savings. In order to clarify the engineering scope of a standard, apart from consumer demand estimates, the technical support documents should include (1) LCC savings for one consumer choosing between purchasing a baseline product or purchasing a TSL and (2) LCC savings for one consumer that could continue to own a baseline product or replace it with a TSL and (3) life-cycle cost savings for products or equipment that meet a given TSL as compared to the baseline without adjusting for the assumed current and future distribution of sales (shipments).

Variability and Uncertainty in the Estimate of Installation Costs and Operating Costs for Baseline and TSL Products

DOE defines the consumer LCCs for appliances as the sum of installed costs, manufacturing cost, manufacturer markup, distributor/retailer markup, taxes, installation costs (ICs), disposal costs, and operating costs (OCs), which are a function of the life-cycle energy and water consumption, the prices of water and energy throughout the lifetime of the product, and the costs of maintenance and repair. DOE constructs an estimate of a product’s LCC to consumers by estimating each of these components. End of life costs are generally omitted by DOE; of the three case studies reviewed by the committee, these are only included as part of the installation costs of non-weatherized gas furnaces (furnaces; see DOE, 2016).

Life-cycle component costs vary significantly from consumer to consumer, depending on the country’s region where the consumer lives, their socioeconomic conditions, and the product’s usage. There is also significant uncertainty stemming from unknown values such as the future cost of materials, labor, energy, and water, and the uncertainty over decisions made by manufacturers and retailers over wholesale and retail markups.

Both variability and uncertainty raise challenges for estimating average IC and OC and subgroup analyses. DOE addresses both challenges in its analyses, using the wealth of household-level data in the Residential Energy Consumption Survey (RECS) for Monte Carlo simulations of operating cost. They integrate some regional variation in the estimated installed costs and use random variables for some parameters (such as frequency of dishwasher use) to characterize uncertainty. The committee identified several possible modifications and extensions to the analyses that would allow DOE to better simulate the spread of LCC values that result from disparate regions and user’s socioeconomic conditions and behavior. These are summarized here and detailed in the annex to the chapter.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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It is useful to start with an example. For residential dishwashers, DOE uses Monte Carlo simulation based on data from the 7,382 households that own such an appliance according to the 2009 RECS. This RECS sample represents 67.4 million households, each of the 7,382 representing a different number of households (which is accounted for in each household’s “weight”). DOE takes 10,000 random draws of the RECS sample, based on each household’s weight, and calculates the LCC savings for each efficiency level (relative to 2019 expected shipments) for each one. DOE takes information about the state or region, water heating fuel type, number of cycles the dishwasher runs per year, and household income group from each RECS household record. DOE estimates the value of some components of installation and operation costs with this information and compounds these estimates with other values previously estimated—that do not incorporate household or region characteristics—to calculate the LCC savings. DOE then generates the ranges and distributions of LCC savings from the 10,000 LCC Monte Carlo trials.

DOE could augment this process by expanding the number of variables in the trials to other variability sources, expanding the number of trials,10 and substituting point estimates with random variables for uncertain parameters. For example, additional regional sources of variation for installed costs include labor costs, retail markups, and in some cases real estate costs. Greater use can be made of RECS information, although as discussed further below, it is limited in the range of appliances included in the survey as well as its structure and timeliness (NRC, 2012).

Uncertainty and Installed Costs

Installed costs are the expenses that consumers must incur before the appliance or piece of equipment is ready to be used in their residences or businesses. DOE estimates installed costs for the baseline and TSL products, adding sales taxes and installation costs to the result of the markup analysis. See Box 4.1 for the consumer expenses DOE calculates.

DOE estimates most components of installed costs without considering the specific location of the household. For dishwashers, the exception is the sales tax, which DOE estimates based on the RECS information about the specific state or state-group where the household is located.

As is discussed above, uncertainty is likely to be important in estimating both the cost of new products and the wholesale and retail markups. Ex post evaluations are critical to assess the impact of a

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10 The committee recommends increasing the number of Monte Carlo runs to ensure that all households in RECS are sampled at least once. In the case of dishwashers, the number of Monte Carlo runs should increase by at least 70 times (i.e., from 10,000 to 700,000) to increase the chances of drawing each of 7,382 households in the RECS sample at least once in the Monte Carlo trials.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

standard, validate models, and inform policy changes. However, in addition, explicit incorporation of uncertainty can better inform the LCC. For some parameters, the uncertainty characterization can be informed by the same data sources DOE already uses. For other essential parameters, the committee recommends extending its data collection efforts in the engineering analysis.

For example, DOE estimated the incremental retailer markup for dishwashers of different efficiency levels—relative to the baseline—as 11% (i.e., the retailer price of a dishwasher is estimated by DOE to be 1.11 times the manufacturer price). DOE could instead characterize the parameter as a random variable with a minimum value of 10.3% and maximum of 11.8% based on the same data that DOE already uses, as discussed in the Annex.

An example of a parameter for which DOE can collect probabilistic information instead of a point estimate is the incremental cost of manufacturing products of different efficiency levels. For the case of residential dishwashers, the engineering analysis indicates that incremental costs for levels EL1 through EL4 are $9.52, $36.53, $74.72, and $74.72 dollars in 2013 dollars. Rather than point estimates, the engineering analysis could yield probability distribution functions representing uncertainty on the quantity and cost of the materials, labor, and specific design used to produce an appliance with the given efficiency level.

RECOMMENDATION 4-6: DOE should improve the representation of variability and uncertainty on Installed Costs by considering the variation in costs components across states and by leveraging the engineering analysis to obtain a probabilistic characterization of costs components.

Estimation of Operating Costs

Dishwashers provide a useful example of both the complexity of estimating operating costs and opportunities for further refinements. Operating costs include all of the expenses included by owners of the appliance or equipment between the moment it is installed and the end of its useful life. DOE estimates operating costs as a function of over a dozen parameters, characterizing how households use the appliance (number of cycles), household discount rates, appliance characteristics such as energy and water use, and the price of electricity, of heating water by various means, and repairs. See Box 4.2 for a list of operating costs DOE includes in its estimates.

RECS only includes household data on some of these parameters: frequency of use (i.e., cycles per year), the fuel used for water heating (i.e., electricity, gas, or oil), and the geographical variability in annual prices of water and energy. Other essential components of operating costs include water heater efficiency and water temperature-rise requirements which vary across hydroclimatic regions and across seasons, but this is not accounted for in DOE’s analysis. In this case, using RECS data to find the state of the household and select the water heater’s appropriate efficiency and required water temperature rises would be preferable.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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For some other parameters, DOE considers geographic variability but not at the level or resolution that is both possible and desirable. For example, while DOE considers energy prices for 27 states or groups of states, they only consider the resolution of water (and wastewater) price variability by looking at the values in four regions. In this case, using the water prices specific to the state of the household in the RECS sample, instead of using an average value for one of four regions to which the state belongs would be preferable.

The committee also finds that the representation of uncertainty on operational costs can significantly improve if DOE modifies how it records the responses to questions already asked by RECS. For example, in the case of dishwashers, the RECS asks users about the frequency of use of this appliance. The response is stored in one of five categories: less than once per week, once per week, 2-3 times per week, 4-6 times per week, at least once a day. Because the responses stored in these five categories cannot be directly used by DOE as an input into the LCC calculations DOE must make assumptions without any empirical support. The modeling of this parameter as random variable with a distribution and parameters “guessed” by DOE, could be avoided by changing the way RECS collects responses to a question it already asks. In this case, RECS could give respondents the opportunity to specify the number of times they use the dishwasher per day, week, month, or year.

The LCC calculations can also significantly improve if DOE changes how it documents the engineering analysis results concerning annual energy use for each efficiency level. For dishwashers, the LCC calculations rely on an estimate of each efficiency level’s yearly energy consumption. However, instead of annual energy consumption, LCC calculations require data on energy consumption per cycle, disaggregated by function to correctly differentiate the electricity consumption from energy for water heating. Similarly, DOE must make assumptions about the duration of a cycle and the number of hours a dishwasher is idle to estimate standby energy. DOE could avoid making these guesses if it documented the results from the engineering analysis at the disaggregation level needed for LCC calculations.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

Finally, the committee recommends that DOE prioritize collecting data on parameters that are essential to LCC calculations, such as the lifetime of the products analyzed. Until enough data is collected to use in its analyses, DOE must represent this crucial input’s lack of knowledge by characterizing uncertainty based on information from focus groups, expert elicitations, or educated guesses based on engineering analysis. For dishwashers, DOE notes that although products with higher efficiency may have a higher probability of failure due to the increased complexity and number of parts, there is not enough data to include this consideration, notwithstanding that the mere possibility of a reduced lifetime of higher efficiency products warrants consideration in the LCC.

RECOMMENDATION 4-7: DOE should improve the accuracy of its estimates of all of the LCC calculation components by (1) taking full advantage of disaggregated data to account for geographical and temporal variability when available, (2) specifying probability distributions instead of one-point estimates and compounding or propagating the uncertainty they represent throughout the calculation, (3) better recording the data collected by RECS to avoid losing information provided by respondents, (4) better documenting the engineering analysis to obtain disaggregated probabilistic information necessary for the LCC, (5) prioritizing the collection of information for parameters likely to have a significant impact such as the lifetime of a product (i.e., durability), and (6) validating the assumptions made in previous analyzes with data collection through the engineering analyses, focus groups with manufacturers, retailers, consumers, and other means.

As noted before, DOE, in general, omits the consideration of consumer disposal fees in the calculation of consumer LCC savings. This omission is inconsequential in calculating costs to consumers if there is no correlation between a product’s disposal fees and its energy efficiency. This is often the case, as disposal fees are generally determined by the appliance’s category and size (i.e., one large refrigerator). However, from a societal perspective, differences in the materials and components used for more efficient equipment may cause changes in the environmental consequences associated with disposal or scrappage. The committee recommends that DOE’s engineering analysis collects data on the entire life cycle of baseline and EL products, conducts a comparative environmental life-cycle assessment (LCA), and accounts for any differences in its calculation of the NPV of proposed standards.

ENERGY CONSERVED

In Situ Performance

DOE develops a rigorous test procedure to judge the efficiency of products and equipment. Standardized laboratory tests are necessary for offering consistent comparisons across standard levels and eventually for certifying products. However, in situ performance may differ significantly from laboratory tests because of usage patterns, maintenance, installation quality, or ambient conditions. At the certification stage, products may be tailored to the test procedure, or test results may even be manipulated. The committee refers to any difference between a test rating and in situ performance as the performance gap.

Currently, DOE simply assumes that the test procedure rating is accurate. For some appliances, DOE uses consumer data to look at heterogeneity in use intensity and sometimes calculates regional differences in average ambient conditions. But it does not appear that DOE attempts to determine the in-situ performance gap. This could matter when assessing technologies because some may perform worse in practice than others. A performance gap may also lead DOE to choose the wrong efficiency level if test ratings provide a biased estimate of true energy savings.

There are several reasons to suspect that a performance gap may be significant. Recent literature has demonstrated that the performance of energy efficiency retrofits is often far below ex ante expectations

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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(Fowlie et al., 2018) and that the quality of installation is an important determinant of performance (Blonz, 2019; Christensen et al., 2020). Where appliance performance depends on complementary components or regular maintenance, a performance gap can arise if users do not follow best practices.

An assessment of in situ efficiency and the possible performance gap is difficult for several reasons. The main obstacle is that appliance-level energy consumption data taken in the field is rarely available. Even then, however, DOE could develop test procedures that quantify the degree of expected variability and test the robustness of alternative technologies or designs to variance in maintenance, use, or installation. Such a modeling effort may or may not be warranted depending on costs and expected variation.

Fortunately, data on the performance of individual appliances will probably be available soon. Much data of this sort is now being collected implicitly by utilities and manufacturer, trade, professional, and consumer associations and groups. In terms of direct production cost, it would be inexpensive to greatly expand this sort of data collection among new appliances. DOE should do everything possible to foster the generation of these data, to collect them, and to analyze them to judge appliances based on their actual performance in the field, which is what matters for energy conservation and consumer welfare.

Of course, even where data are available, it may be difficult to judge in situ performance for a prospective standard or feature where the products and technologies in question are not in the market or that not many consumers use. Even in this situation, data on existing products could be useful in assessing typical performance gaps and variations in performance across space and settings and over time.

RECOMMENDATION 4-8: DOE should seek to gather and make use of in situ performance data wherever possible to account for any performance gaps. When estimates of situ performance data are unavailable, DOE should include a qualitative assessment of the potential for a performance gap. Indicators of performance include maintenance requirements and product lifetime as well as energy and water consumption.

The Rebound Effect

An improvement in the energy efficiency of an appliance lowers the operating cost of the appliance. It thus lowers the cost that end-users face to obtain services from the appliance. This may induce a rebound effect (or “take-back”): because energy services are cheaper, end users may increase their usage of the appliance and therefore their energy consumption. Whether this happens depends, in part, on the extent to which end users are aware of the amount of energy they are using when they run the appliance and how much that energy costs them. This may vary with both the type of end user (commercial versus residential) and the type of appliance.

The rebound effect has distinct implications for quantifying energy conservation and for evaluating consumer welfare. Regarding energy conservation, the rebound effect should be accounted for whenever it exists. The DOE process allows for consideration of rebound, but it does not appear to be applied uniformly. Of the three case studies considered by the committee, DOE only discusses rebound in one (furnaces; see DOE, 2016). In that instance, the DOE technical support document adjusts for the rebound effect.

The implications of the rebound effect on consumer welfare are somewhat different in nature. Whenever something occurs to lower the price of a commodity—in this case services from the operation of an appliance—the consumer experiences an increase in welfare (utility), regardless of whether her demand for the commodity (her utilization of the appliance) increases.

Furthermore, any price change has two distinct types of impact on a consumer—a substitution effect and an income effect. The substitution effect is associated with the change in the price of the given commodity relative to the prices of other commodities—this is what underlies the rebound effect. The income effect is associated with a price change that leaves the consumer with more money (in the case of a price decrease) or less money (in the case of a price increase) to spend on commodities. In the case of a

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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price decrease, such as is implied by the improved energy efficiency of an appliance, the extra income that becomes available may be spent to some degree on other goods that require the consumer to use more energy overall. This has been called an indirect rebound effect—it is driven by the consumer’s propensity to spend income on energy-using consumption activities. DOE does not currently consider this, which the committee finds appropriate for purposes of calculating consumer welfare; that is, after accounting for monetary savings, welfare analysis does not require determining how that money is spent.

The case study that considers rebound (furnaces) says that the welfare effect on consumers of rebound is approximately zero because the benefits roughly cancel out with the additional utilization costs. It is correct that these factors offset, but it is more precise to say that rebound must be a benefit, and it is feasible to approximate it and see if it is of potential significance.

An exact calculation of the welfare gain from the rebound effect requires information about the demand curve for appliance services. But it is straightforward to use an approximation of the welfare gain for consumers by assuming a linear approximation to the energy services demand curve, and then calculating the welfare gain “triangle” by multiplying the estimated rebound effect by the change in the operating cost. This requires no more information than is used to calculate the rebound effect’s implications for energy take-back.

RECOMMENDATION 4-9: For purposes of calculating changes in energy use, DOE should consider direct rebound wherever possible; if DOE believes there to be minimal rebound, they should document the reasons why. However, consumer welfare should be understood to benefit from rebound, rather than be harmed by it, notwithstanding the implied increase in energy use. Approximations of the welfare gain from rebound can be incorporated wherever sufficient information allows.

Energy Saved Versus Dollars Saved

Energy efficiency standards pursue the dual goals of conserving energy and saving money. EPCA directs DOE to design standards to “achieve the maximum improvement in energy [or water] efficiency . . . which the Secretary determines is technologically feasible and economically justified” (42 U.S.C. § 6295(o) and (42 U.S.C. § 6313(a)(6)(B)(iii)). Conserving energy and saving money are closely related, but there has always been some divergence between the two because the cost of producing electricity varies significantly at different hours of the day and at different times of the year. As such, energy saved at different hours and on different days has different economic benefits.

Until recently, this nuance could safely be ignored when evaluating the impact of a standard on residential and commercial customers because nearly all such electricity users faced flat rates. That is, they paid the same price for a kWh regardless of the time of day.

But this fact is changing. Smart meters that allow for more complex rate designs are now widespread, with more than 95 million installed in the United States (EIA, 2020). Customers increasingly face rates that vary across hours of the day, weekdays versus weekends, and seasons of the year. Currently, all of California’s investor owned utilities default their customers into time of use rates. On one extreme, some retail rates in Texas essentially pass wholesale electricity prices directly onto consumers, which creates a real-time electricity price that varies every hour. Rates will continue to change in this direction, and consumers can expect large changes to the nature of electricity prices within the lifetime of appliances sold today.

Energy conservation is not an end in itself but it stands as a proxy for the preservation of economic resources, including externalities. It has been an effective proxy, but where the two diverge, it is important to focus on the conservation of economic value rather than units of energy per se. Although the statutory law is stated in terms of energy conservation, it is also explicit that standards must be economically justified. For this reason, the committee believes that DOE should do whatever it can to ensure that it is appropriately focused on saving money and economic value.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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What does this mean in practice for DOE’s analysis?

First, for appliances with a highly seasonal component, such as air conditioners, furnaces, and dishwashers (due to the need to raise the water temperature above that of the supply), it is critical that DOE’s analysis includes appropriate adjustments of prices to reflect seasonal variation in the cost of electricity. It is also important that DOE accounts for seasonal variation in energy consumption for all appliances where such variation exists.

Second, DOE’s process should recognize and quantity consumer financial savings from features that facilitate load shifting or other so-called “smart appliance” features. Potential features and savings are discussed further in Chapter 6. The committee notes here that in addition to the reliability and environmental benefits these technologies offer today, they can facilitate and spur innovation needed for an energy transition and hence deserve credit now as economic benefits. DOE has in some cases treated internet-enabling features as an energy efficiency equivalent; they are perhaps better conceptualized as direct economic benefits. A smart dishwasher that moves a cycle from a high-price hour to a low-price hour saves no energy but delivers consumer savings. It also may deliver economic benefits by replacing electricity generation from more polluting sources with cleaner ones.

Finally, appliances that adjust energy consumption to the grid’s needs at different time-scales increase grid reliability. While reducing pollutant emissions from energy use is a clear reason to pursue end-use energy efficiency, reliability enhancement is equally important. As discussed in the next section, DOE’s analysis pays great attention to the quantification of benefits from reduced pollutant emissions, but the analysis on reliability benefits is lacking. The committee recommends heeding reliability benefits in equal fashion.

RECOMMENDATION 4-10: DOE should credit as economically valuable those features and innovations that save consumers money and enable appliances to contribute to grid efficiency and reliability.

POLLUTANT EMISSIONS FROM ENERGY USE

The clearest rationale for policies that boost energy efficiency is the pollution externalities associated with electricity generation, or the appliances’ direct use of fossil fuels. Where standards reduce energy consumption, the social benefits from reduced externalities should be monetized and included as a benefit. This is done in two steps. First, estimated reductions in energy usage are translated into changes in emissions of various pollutants using a model of the energy sector. Second, these changes in pollution are monetized using existing estimates of the damages associated with emissions.

Changes in Emissions

The DOE process takes an estimated change in energy consumption that follows from the LCC analysis and translates this into estimates of emissions reductions that include both combustion emissions for electricity generation (or direct use of the appliance) and upstream emissions due to extraction, processing, transportation, and fugitive emissions of fuel.

The direct emissions (i.e., from combustion) of methane (CH4) and nitrous oxide (N2O) are estimated from emissions intensity factors calculated by the U.S. Environmental Protection Agency (EPA).11 Direct emissions of carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxides (NOx), and mercury (Hg) are estimated using projections presented by the Energy Information Administration in the most recent Annual Energy Outlook (AEO). The AEO is generated by running the National Energy Model System

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11 U.S. Environmental Protection Agency (EPA) Center for Corporate Climate Leadership, undated, “GHG Emission Factors Hub.” https://www.epa.gov/climateleadership/ghg-emission-factors-hub.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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(NEMS) model under different scenarios that vary in assumptions about energy supply, demand, and technological change.

The DOE process backward-induces marginal emissions factors from AEO forecasts in a process described in detail in a Lawrence Berkeley National Laboratory (LBNL) report (Coughlin, 2014) and summarized in the TSDs. The method considers the results from AEO side-cases and compares them to those of the reference cases to estimate changes in supply (fuel use, emissions, generation capacity) and in demand. Then it allocates the demand changes to each of three load categories: peak, off-peak, and shoulder, and estimates the fraction of electricity demand in each load category that is met by each fuel type.

To estimate the fuel use reductions caused by lower on-peak, off-peak, or shoulder demand, DOE assumes merit-order. It assumes that all oil-fired electricity is used to meet peak demand, that natural-gas-fired electricity is used to meet the peak-demand not satisfied by oil, that nuclear and coal are used proportionally to meet demand at the three load types, and that the remaining fuels are used to serve off-peak and shoulder load. To estimate the emissions reductions caused by a reduction in fuel use at a given time, DOE applies a statistical regression analysis model that “produces coefficients that define the change in total annual emissions of a given pollutant resulting from a unit change in total annual generation for each fuel type, as a function of time.” (DOE, 2014, p. 15-3) DOE then applies these coefficients to the estimates of changes in annual generation from each fuel type to obtain estimates of emissions reductions.

Because the NEMS model is a well-studied and transparent government model, and the assumptions of the AEO reference and side cases are extensively documented and publicly scrutinized the committee finds they are the appropriate tools for bounding the uncertainty about the impacts of standards in the future.

Nevertheless, the committee finds DOE’s method has two weaknesses. First the method provides one-point estimates of emissions reductions when in fact this quantity is impossible to estimate with that precision. Second, the method uses a very coarse representation of the temporal and spatial dimension of electricity generation and consumption and hence it is unlikely to assess the true impact that energy efficiency has on emissions. By apportioning the energy savings into three load categories DOE attends to the fact that the marginal generators vary with the amount of load served at a given time, but by ignoring the seasonality of energy supply and demand DOE is likely to get the emissions of the marginal generator wrong. For example, the natural gas electricity generating unit that is called to provide power during a peak hour in the spring is likely to be less polluting than another natural gas unit required to provide electricity during the peak-hour in the summer. Similarly, whether the reduced demand occurs in Kentucky or in Texas, the reductions in emissions are likely to be different. This heterogeneity has been documented in the literature (e.g., Graff Ziven et al., 2014; Siler-Evans et al., 2012) estimated using high resolution data on emissions and demand.

In order to obtain a more precise estimate of the emissions changes that would occur in the first years of implementation of the energy efficiency standard, the committee recommends that DOE estimates the spatial and temporal specific changes in electricity demand, and then runs NEMS with these changed inputs to compare results with the reference case of no-standards. The runs should be completed under varying assumptions of fuel prices and composition of the power generation fleet as is standard in the literature (e.g., Alqahtani and Patino-Echeverri, 2019). Instead of NEMS, DOE could build a simplified electricity cost model representing the composition of the U.S. electric power system interconnects.

RECOMMENDATION 4-11: To estimate changes in emissions during the compliance year, DOE should estimate for each state, the changes in the hourly load curve that would result from the adoption of an energy efficiency standard during a full year. Using these estimates, the National Energy Modeling System can incorporate different assumptions about decarbonization of the U.S. electricity system such as the natural gas prices and the penetration of renewable energy and energy storage and estimate a range of emissions changes for each relevant region and time.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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Following an approach that uses annual hourly load curves and data-based specific emissions factors for individual regions and times will improve the estimates of emissions reductions due to a standard, taken as a given the energy mix and performance of the current U.S. electric power generation fleet. This approach will provide time and region-specific estimates useful to monetize benefits from emissions reductions of pollutants for which health impacts are time and space dependent. However, to project the impact of energy standards on future years, it is necessary to use projections of the U.S. electricity grid’s future status and performance. One source of such predictions is NEMS. If possible, the committee recommends that DOE accounts for the spatial and temporal heterogeneity of U.S. marginal emission factors by running side-cases of NEMS that considers region-specific changes in annual hourly load curve resulting from the adoption of different TSLs. The uncertainty that is inherent to NEMS as a model (i.e., is NEMS representation of power plant dispatch processes accurate?) and to the input values assumed for its parameters (i.e., prices of coal and gas to electric generators, base demand levels, costs of growing renewable generation capacity, etc.) implies that a single run of NEMS for a projected change in the load curve will be insufficient to characterize the uncertainty on future emissions.

If DOE continues to use the data of the existing AEO side cases the committee recommends that DOE estimate the uncertainty on the marginal emissions intensity factors it derives and, rather than reporting a single point estimate, it presents a range. DOE can estimate ranges of emissions changes associated to a specific region by considering the results from the two most extreme calculations obtained from AEO side-cases. Although as discussed in the Annex, the committee considers it most desirable to present results at the highest temporal and spatial resolution possible (e.g., seasonal emissions for each U.S. state) if DOE continues to present one estimate for the nation, it must compound the uncertainty derived from different assumptions with the discrepancies in emissions changes due to regional variability. One way to find the bounds of possible results for a national average of emissions reductions—after accounting for both uncertainty and variability—is to estimate emissions changes for two states in the opposite spectrum of impacts from the standards.

As discussed above, a primary source of uncertainty on the benefits of energy efficiency to consumers and society is the unknown composition of the U.S. electric power grid and the associated costs of electricity and emissions. As the energy transition unfolds there will be a great deal of variation year to year in the type of power plants that supply electricity and their marginal emissions (Holland et al., 2019). While it is not reasonable to expect DOE to try to predict that future beyond using the forecasts produced with NEMS and summarized in the AEO, the committee recommends that DOE conducts backward-looking studies to estimate the error in its past estimates of marginal emissions rates, and to use its findings in a robust analysis of the uncertainty in these parameters.

While the committee believes that estimating the benefits of reduced energy consumption from a new standard must be calculated over the course of the lifetime of the product, it recommends that DOE focuses its efforts on improving its estimates of emissions reductions with today’s fleet. DOE could estimate emissions reductions from reduced demand today and then extrapolate that estimate to account for the 15 or 30 years into the future as required by its analytic horizon. DOE can conduct retrospective analyses to determine whether the estimates of marginal emission rates estimated from AEO forecasts are better predictor than estimates derived from static forecast that takes the current grid as given or some other simple heuristic.

Given that more than 85% of the Clean Air Act’s public health benefits are attributed to reductions of particulate matter (PM) emissions (EPA, 2011), the committee recommends that DOE considers estimating the energy efficiency standards impacts on PM emissions reductions and their economic value. DOE cans use EPA’s National Emissions Inventory (NEI) is a source of PM2.5 emissions data.

As mentioned, DOE also estimates reductions in upstream emissions. The same recommendations made about direct emissions are made by the committee regarding this analysis. The uncertainty on these estimates is known to be very large, particularly when fugitive emissions are considered (Schwietzke et al., 2014).

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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Another fundamental issue in calculating emissions is to treat fugitive emissions associated with natural gas production and distribution. This is critical for natural gas appliances and electrical appliances that may draw from natural gas power plants on the margin.

Monetizing Emissions Reductions

The DOE process monetizes changes in emissions quantities using outside studies and government reports on the economic and health damages associated with emissions. The DOE process follows the presidentially mandated procedures for calculating these numbers. However, it also appears not to monetize some pollutants, nor does it represent or communicate all of the uncertainty and variability in these numbers. As with changes in energy use, emissions reductions from new appliances may significantly depend on other public policies and interventions as well as standards; in consequence absent consideration of the dependencies it is difficult to predict the impact of a standard.

In particular, DOE adopted the federal estimate of the Social Cost of Carbon (SCC) that arose following President Trump’s Executive Order 13783 (March 2017) which disbanded the Interagency Working Group (IWG) that had been convened by the Obama Administration in 2009 to develop estimates of the SCC, and withdrew the Technical Support Document and updates issued by the IWG in 2010, 2013, 2015 and 2016. In compliance with this Executive Order, the EPA put out a new estimate of the SCC which first appeared in the Regulatory Impact Analysis for the Repeal of the Clean Power Plan (EPA, 2019) and was then used by other federal agencies for the duration of the Trump administration, including DOE. The new federal estimate is based on the same analysis of integrated climate assessment models as the prior IWG, but changes in two key assumptions: (1) only domestic U.S. damages from climate change were considered, not global damages; (2) the discount rates applied to value future impacts were changed from 2.5%, 3% and 5%, with 3% being used for the central SCC estimate to 3% and 7%. Omitting non-domestic damages, while using a 3% discount rate, reduced the SCC from a prior value of $50 per ton of CO2 emissions in 2020 to $7 (GAO, 2020). Using a 7% discount rate reduced it to $1 (EPA, 2019). On his first day in office, President Biden issued Executive Order 13990 reviving the IWG and, the following month, the IWG revived the prior 2016 estimates of SCC, with a central value of $51 per ton. It also introduced values for the social cost of methane and the social cost of nitrous oxide (IWG, 2021).

The committee finds that DOE did not monetize all emissions, however, and no other monetized values feature attention to uncertainty. The default appears to monetize only NOx and carbon, and DOE calculates reductions in sulfur dioxide, nitrous oxide, and methane, but does not monetize them. The committee believes regardless of the current atmospheric concentration of different pollutants it is important to quantify and monetize the emissions of particulate matter and any precursors. The latter can now be monetized using the values promulgated in IWG (2021).

In addition, the economic damage associated with a ton of emissions for local air pollutants varies substantially across space (Muller and Mendelsohn, 2007; Muller et al., 2013) and time. Attention to geographic detail and times of emissions may be relevant to DOE’s findings, which are likely to depend on the appliance under consideration. For example, air conditioners have very different utilization rates across regions of the country, and this may lead to substantial differences in the associated benefits from emissions reductions.

Finally, the appropriate monetized value of emissions reductions may sometimes depend on the existence of other regulations. For a capped pollutant, such as SO2 from the power sector, emissions reductions can take on one of two values. If the cap is not binding, then the appropriate value is the direct health benefit taken from the epidemiology literature. However, if the cap is binding, then the social benefit of reducing emissions is not the health benefit because the total emissions do not actually change. Instead, the social benefit is the marginal cost of complying with the binding cap, which is measured by the permit price in markets with tradable permits (such as sulfur dioxide [SO2]). Thus, where a binding

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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cap exists, the appropriate value for emissions reductions is the permit price, which can be higher or lower than the health impact in practice.

RECOMMENDATION 4-12: DOE should monetize all emissions impacts for which meaningful estimates of social impacts are available, considering policy interactions when those interactions are deemed significant.

MARKET FAILURES THAT RATIONALIZE STANDARDS

Economic theory concludes that an efficient allocation of resources is provided by well-functioning markets—that, in a broad sense “welfare” can be improved by government intervention only when markets are problematic. Such problems are called market failures and provide the opportunity for regulatory action to yield benefits in excess of cost. Federal regulations require an analysis of market failures for regulatory actions, and DOE does this as part of its analysis of new standards. Given the uncertainties inherent in benefit and cost estimates of proposed standards, both a robust analysis of market failures and an analysis of how standards address the failures are critical because the case for market failure may not be universally strong across all appliance products.

Several potential market failures might justify minimum efficiency standards. The committee has sorted these possibilities into three broad categories: environmental externalities, inefficiencies due to consumer behavior, and inefficiencies due to producer behavior.

Environmental Externalities

The extraction, processing, transportation, and combustion of fossil fuels generates pollution. In the absence of comprehensive pollution pricing or other regulations, the price of energy does not reflect its full social cost because the burden of the pollution is borne by third parties outside the market. When energy prices are artificially low, market actors will underinvest in energy efficiency from the point of view of social welfare, even if consumer and producer behavior is consistent with models of full rationality and perfect competition. This provides economic justification for policy intervention.

Standards are one way of addressing this market failure. Pollution externalities are clear, compelling, and uncontroversial as a case for market failure and a rationale for policy. DOE routinely calculates estimates of environmental gains in their analysis. Environmental benefits are often substantial and should be uncontroversial as a rationale. That said, quantifying the economic costs of pollution is challenging and is subject to substantial uncertainty. This report comments further on this quantification in the section, Pollutant Emissions from energy use. Standards are often not the most efficient way to address pollution externalities. This report comments further on the importance of considering policy alternatives in Chapter 5.

Inefficiencies in Consumer Behavior

A second possible market failure is that buyers in a market are privately making inefficient choices. That is, they could save themselves money by investing more in energy efficiency. If true, then a substantial benefit of standards could come from correcting inefficiencies in consumer choice.

The energy efficiency gap, or energy efficiency paradox, refers to the phenomenon whereby a consumer makes what is considered an inefficient choice by choosing an item that does not appear to offer the lowest combination of capital cost plus operating cost (Jaffe and Stavins, 1994; Stern et al., 2016). These terms refer to analysis that suggests that the private energy savings from improved efficiency more than offsets additional product costs, yet the market reveals low take-up of the product.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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This phenomenon is ubiquitous, and associated with significant increased energy use in the consumer sector.12 The question is: why does this occur?

One reason a gap might exist is because of split incentives, or the landlord-tenant problem. Sometimes the person who decides what appliance or piece of equipment to purchase is not the person who pays the future energy bills. This can lead to fully rational self-interested behavior that is nevertheless socially inefficient. Descriptive evidence of appliance ownership from surveys is consistent with renters using less efficient appliances, supporting this view (Davis, 2010), and recent studies some provide empirical evidence that split incentives are associated with inefficiencies in energy use (Myers, 2020). Demand for rental properties has surged in the United States over the past decade, and now accounts for more than 36% of households, suggesting that the issue of split incentives may become increasingly important in the appliance sector (Harvard University, 2017). Standards can be an appealing policy solution to these sorts of inefficiencies, but the relevance of split incentives will vary across types of appliances and types of customers. There is for example a history of government-funded programs to incentivize the use of energy efficient appliances in properties that are not owner-occupied. In the 1990s the New York Power Authority implemented a $38 million program of demand side management in the New York City Housing Authority that targeted efficient residential refrigerators among other measures (Nolden and Morgan, 1996).

A second reason for the energy efficiency gap is that consumers undervalue future cost savings relative to upfront cost. The committee alluded above to factors that might cause appliance buyers not to make these “rational” choices. First, the operating cost (and, therefore, the energy savings) may be unclear to some consumers because they are not aware of the energy or water their appliance consumes in real-time when they run it. They also may have only a vague awareness of how much they are paying for electricity, natural gas, or water in their daily use. Secondly, attributes other than price usually matter to consumers and may be more important to them. For example, despite the introduction of new lighting products (compact fluorescent lamps) that were more energy-efficient than the incumbent (incandescent) technology, consumers still preferred the latter owing to its better consumer attributes such as quality of light (NRC, 2013, p. 28). After binding regulations effectively phased-out the incumbent technology, many consumers still preferred the least efficient among the compliant alternatives—itself a variant of the incumbent technology (NRC, 2013, p. 31). Finally, choice is complex in real-world markets with a numerosity of alternative products. Consumers may not make the best choices when facing complexity, particularly if sellers strategically market products to benefit from consumer fallibility.

When economists think of rational decision making, they tend to think about a decision-maker performing a global optimization; the decision-maker systematically considers all alternatives and all possible attributes associated with each alternative. In order to give due consideration to their budget constraint, the decision-maker considers every other possible use of the money that they would spend on the item in question. They make a thorough assessment of these things and choose the best course of action.13

In reality, choices—by firms and households—often fall short of this idealized conceptualization. This was first identified in economics by Herbert Simon (1955), who argued that the conventional characterization of decision making is unrealistic and lacks psychological veracity. Performing the implied analysis would be computationally demanding, if not intractable; it would require information that might not always be available; and it would demand a massive amount of time and attention for information processing and calculation. Instead, Simon argued, people display “bounded rationality.” They seek to simplify their decision making in various ways. One way is to limit their attention to a subset of the available alternatives—a consideration set—based on familiarity, habit, or other factors. Another way is to simplify their assessment of attributes—focusing mainly on some while dropping

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12 See Hirst and Brown (1990) and Stern et al. (2016). These papers review studies that conclude foregone energy savings in the consumer sector of perhaps up to 50% due to a broad set of policies, including structural barriers and pricing distortions as well as the behavioral attributes that underlie the energy efficiency gap.

13 This passage adapted from NASEM (2020).

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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others and looking to attain a good enough level of the attributes rather than an optimal level (Tversky, 1972). They employ heuristics or rules of thumb to evaluate the options and their attributes. More generally, Simon suggested that consumers “satisfice” rather than optimize—seek a reasonably good choice if not the absolute best.14

To deny that consumers may make mistakes would be unreasonable—they are, after all, humans. But to hold consumers to an idealized standard of global, all-considering optimization would also be unreasonable. There exist cases where the apparent energy savings from improved efficiency more than offsets the additional upfront cost, yet consumers do not choose those appliances—there is an energy efficiency gap on the consumer side. Note that mistakes are not necessarily only in one direction. Some consumers may overinvest in energy efficiency that does not pay back (i.e., because they have low utilization rates), which complicates the consumer benefits that derive from tighter standards.

Substantial literature has sought to examine whether consumers make systematic mistakes due to some combination of these factors. Empirical evidence remains mixed (see, e.g., Allcott and Greenstone [2012] and Gerarden et al. [2017]). Moreover, the case for underinvestment in efficiency varies substantially across types of products or equipment. For example, many products lack official energy efficiency labels, making it much more plausible that consumers make poor choices. For some products, energy consumption is a small part of the total cost of ownership. This makes it more plausible that consumers might ignore energy efficiency when making choices, perhaps even as a rational approach to minimize hassle and effort (Sallee, 2014). Likewise, the buyers’ sophistication is likely to vary across products, particularly when buyers are themselves businesses. It is hard to imagine, for example, that supermarket chains are inattentive to the operating costs of commercial refrigeration.

In sum, there is compelling evidence from many domains that consumers do not always choose the options that seem to offer the lowest overall cost. However, the evidence around the energy efficiency gap is more mixed (Allcott and Greenstone, 2012), and there are strong reasons to suspect that mistakes vary substantially across products. Small firms may not have energy managers (Janda et al., 2014), while even owners or managers of large commercial buildings where the former may be tenants often have no knowledge of the size of the energy bills of their properties (NRC, 2010, p. 101). Either might avail themselves of demand-side management (DSM) provided or encouraged by utilities, discussed in Chapter 5, or continuous commissioning discussed in Chapter 3, among other services. Considering all this, the committee’s recommendation is for the DOE process to engage more robustly with these issues and attempt a more significant context-specific quantification of any market mistakes.

RECOMMENDATION 4-13: DOE should place greater emphasis on providing an argument for the plausibility and magnitude of any market failure related to the energy efficiency gap in their analyses. For some commercial goods in particular, there should be a presumption that the market actors behave rationally unless DOE can provide evidence or argument to the contrary.

This energy efficiency gap is central to DOE’s analysis. DOE’s regulatory analyses that support tighter standards consistently premise upon an energy efficiency gap. That is, standards that have large benefit-cost ratios do so because DOE’s engineering analysis demonstrates that readily available technologies save far more in energy costs than they require in upfront cost, but the regulatory impact analysis does relatively little to interpret this fact. The question is where the burden of proof should lie.

The committee’s recommendation calls both for the gathering of more evidence, and for allowing the burden of proof to vary across types of products and equipment. One source of such relevant evidence could be additional customer interviews, for both household and business customers and for rental and owner-occupied housing, that seek to document potential biases or information gaps in choices.

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14 Note that these phenomena are less likely to occur with commercial equipment, since many commercial buyers are themselves large companies with sophisticated energy planning and management systems. However, not all are.

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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Not all interpretations of the energy efficiency gap may imply a market failure. Other explanations for the energy efficiency gap include biases in the cost estimates, a failure to properly account for performance trade-offs, a failure to account for irreversibility and option value, or a failure to account for borrowing constraints, and mismeasurement of actual energy efficiency or actual transaction prices.

Energy efficiency gap estimates are premised on engineering estimates of cost and savings that may simply be incorrect. DOE’s analysis may sometimes overstate energy savings in situ. Some recent evidence calls into question the realized performance of home energy retrofits (Christensen et al., 2020; Fowlie et al., 2018), appliance replacement programs, and automobile fuel economy ratings (Reynaert and Sallee, 2021). While this evidence does not directly comment on the in-situ performance of appliances, it suggests the value of research or data that could assess whether test procedures accurately reflect performance in the field.

Analysis will often fail to measure actual transaction prices correctly, as prices fluctuate with promotions and retailer discounts, and these data are unavailable in many settings. Moreover, an apparent energy efficiency gap may be due to some users having low utilization rates, which may not be fully characterized by the data used by analysts.

In addition, some households may face severe constraints accessing credit, which could lead to apparently inefficient trade-offs between up-front cost and future operating costs. The irreversibility of an appliance investment and uncertainty over future prices of electricity implies that consumers will rationally only buy an energy efficient product if its operational cost-savings are significantly above its additional upfront cost (Hassett and Metcalf, 1995), though these considerations do not necessarily imply slower diffusion of the high efficiency products (Baker, 2012). Given these alternative possibilities, it is important for DOE to establish evidence in favor of interpreting an energy efficiency gap as evidence of consumer mistakes that can be corrected by standards.

Inefficiencies in Producer Behavior

There also can be instances of market failure on the manufacturers’ side. This can come from monopoly power that distorts markets, or from failures to innovate efficiently. Under EPCA, DOE must consider impacts that a standard might have on the degree of competition in a market. DOE makes a qualitative assessment of any such impact in its manufacturer impact analysis, and it outsources further analysis to the U.S. Department of Justice. Both analyses appear to focus on whether a standard might shrink the number of firms competing in a particular market.

This is an important issue, but this analysis attends to only some of the potential distortions due to market power (monopoly power). This narrow view of market power simply asks whether manufacturers charge prices that are artificially high overall. A related but distinct issue pertains to what economists call second-degree price discrimination. The basic idea is that sellers attempt to segment buyers who differ in their willingness to pay by offering a higher and lower quality version of a product. Economic theory shows that when firms use energy efficiency to price discriminate, market outcomes are inefficient and standards can improve market outcomes (Fischer, 2010). Further research has placed greater emphasis on the possibility that price discrimination is an important factor in appliance markets (Spurlock, 2013). In particular, price discrimination is a possible explanation for empirical evidence showing that tightening of standards has led to price decreases (Houde and Spurlock, 2016). These findings suggest, but are not conclusive toward, the idea that standards may enhance efficiency because they counteract market inefficiencies due to price discrimination.

A separate issue regarding possible inefficiencies in producer behavior relates to innovation. Competitive forces generally spur sellers to accelerate diffusion to gain a market advantage. But advances in the development and diffusion of technology (including the marketing of a new technology to buyers) often create what economists call spillovers. This is meant to indicate situations where the technological advance of one producer benefits its competitors by demonstrating how something can be done. When spillovers exist, the free market does not lead to efficient levels of innovation. Barriers to development

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
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and diffusion of new technology also exist in oligopolistic industries, as is the case for much of the appliance sector, where firms may be in a position to eschew investing in new technology that would cut into the profits of existing products or where access to retailers by new firms is limited. In each of these cases, standards might then enhance efficiency.15 Note that such spillovers and lack of investment need not be restricted to cutting edge technologies. It can relate to the more prosaic task of educating and acclimating buyers to a new feature.

Market outcomes may also be distorted if producers are able to collude on technological rollout. To the extent that technological development and diffusion requires fixed costs that firms do not immediately recover in market equilibrium, firms would find it more profitable to roll out technological change at a slower pace. Firms would thus like to agree on a rate of innovation but face anti-trust violations if they explicitly coordinate on the roll out of product features and embedded technologies. As a recent example, a group of automakers has been accused of anti-competitive behavior surrounding a technical working group that coordinated the diffusion of features in the luxury automobile market (Beene and Mehrotra, 2017). In such circumstances, standards could protect against such distortions.

The committee notes, however, that standards might work in the opposite direction by facilitating collusion rather than mitigating it. This may be a particular problem for standards developed by industry coalitions or through negotiated rulemaking. In principle, the standards-setting process itself could constitute a vehicle through which such industry coordination is made possible and legal. Economic theory has described how quality standards can facilitate collusion, though results can go either way and are sensitive to model assumptions. More generally, standards could potentially act as barriers to entry against future competitors, thus serving the interest of incumbent producers.

In many product markets, a large fraction of all products achieves exactly the DOE minimum or exactly the Energy Star rating, which is most commonly a specific efficiency percentage beyond the DOE minimum. Moreover, the appliance industry initiates a substantial fraction of the standards that DOE develops through the Direct Final Rule process or Negotiated Rulemakings or both. These factors are not direct evidence of collusion, but they suggest the value of further scrutiny. It is the committee’s view that DOE ought to develop higher sensitivity to the possibility that this process facilitates collusive behavior, particularly via the coordination of a slower than competitive adoption of new technologies. A formal quantitative analysis may not be feasible around the possibility of collusion, but DOE can at least make it routine to report out what information they have that makes them disregard this concern for each standard.

RECOMMENDATION 4-14: The committee recommends that DOE give greater attention to a broader set of potential market failures on the supply side, including not just how standards might reduce the number of competing firms, but also how they might impact price discrimination, technological diffusion, and collusion.

The existence of market failures justify public interventions in appliance markets, including setting minimum energy efficiency standards for appliances: the social costs of energy use are generally not internalized by individual firms or consumers, while other behavioral and structural issues may result in some consumers and firms purchasing equipment that, from either a social of individual perspective, makes an uneconomic tradeoff between the purchase price and subsequent operating costs, thereby inefficiently increasing energy consumption. While DOE considers market failures, it does not include an analysis that attempts to identify and estimate the importance of specific market failures associated with appliances. (See Recommendations 4-13 and 4-14.) The review in this chapter underscores the

___________________

15 Promoting efficient innovation in energy efficiency is a complex problem that is largely outside the scope of this DOE program, which is intended to focus on existing technology, although as is discussed in Chapters 2 and 6, the program can nevertheless contribute in important ways to more ambitious energy goals. More generally, standards can play an important role in energy innovation as part of a suite of public and private actions. See Jaffe et al. (2005), Henderson and Newell (2011), and Mazzucato (2011).

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

importance of tailoring consideration of market failures to each category of appliances that are under consideration for a standard. The committee finds the existence and importance of energy-related market failures differs across appliance categories, together with the likelihood that a standard will yield improvements in economic efficiency.

FINAL OBSERVATIONS

Legislation and Executive Orders require the DOE energy efficiency standards to be “economically justified.” To do so, DOE conducts detailed analyses that attempt to calculate benefits and costs of alternative efficiency levels. However, the results are necessarily uncertain, in part because of data limitations but also because of inherent complexities in the response manufacturers, consumers and markets to a standard that changes the available types of appliances. As a result, quantifying, to the extent practical, the uncertainty of benefit and cost estimates, enhancing data collection, and focusing on market failures are critical to the economic justification of a standard. The value of the retrospective study is not primarily in estimating ultimate energy savings, which of course depend on a host of factors outside the standard itself,16 but rather in assessing the validity of assumptions made in the initial analysis and informing subsequent analyses conducted for other potential appliance standards.

This chapter’s focus on uncertainty and empirical data apply across the range of analyses that make up the economic analyses of the Appliance and Equipment Standards Program, as is summarized below.

RECOMMENDATION 4-15: In order to evaluate the economic costs and benefits of a standard, DOE should present the distribution of costs and benefits estimated in its models when (1) uncertain parameters are represented by probability distributions and (2) parameters that vary across geographic and other relevant dimensions are disaggregated. The uncertainty or variability the parameters represent should be compounded or propagated—properly accounting for any correlations—throughout the calculation. This methodology is necessary for the markup analysis and manufacturer impact analysis (Recommendation 4-2), the shipments analysis (Recommendation 4-4), and all components of the life-cycle cost analysis. (Recommendations 4-5 and 4-7). Where multiple sources of uncertainty must be combined for the final benefits result, as with net benefits depending on both the shipments analysis and the appliance unit cost and performance, the subcomponents should be reported as well. (Recommendation 4-5).

RECOMMENDATION 4-16: DOE should obtain better data for improving the economic analyses of appliance and equipment performance standards. Empirical data are needed on markups (Recommendation 4-1), consumer choices in appliance markets (Recommendation 4-3), and in situ performance (Recommendation 4-8). Some of this information can come from relatively simple changes to current surveys and studies, including engineering analyses of the Appliance and Equipment Standards Program and the Residential Energy Consumption Survey (Recommendation 4-7).

RECOMMENDATION 4-17: Ex post analyses can validate assumptions made in prior standards and evaluate the implications of prior forecasts’ inaccuracies and mistakes. DOE should use such ex post analyses routinely to improve forward-looking standards iteratively.

___________________

16 See, for example, Newell et al. (1999).

Suggested Citation:"4 The Economic Analysis of Standards." National Academies of Sciences, Engineering, and Medicine. 2021. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards. Washington, DC: The National Academies Press. doi: 10.17226/25992.
×

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The U.S. Department of Energy (DOE) issues standards regulations for energy conservation pursuant to the Energy Policy and Conservation Act of 1975, as amended, and other authorities. These standards regulations apply to certain consumer products and commercial and industrial equipment. These can include air conditioning and heating systems, washing machines, and commercial refrigeration, among numerous other examples. DOE issues standards regulations by rulemaking and includes quantitative maximum water and energy use or minimum energy conservation standards. There are currently standards regulations for more than 70 product classes (i.e., a specific type of consumer product or commercial or industrial equipment). This report reviews the assumptions, models, and methodologies that DOE uses in setting the quantitative portion of the standards regulations following the Office of Management and Budget's guidance on the use of scientific information. Review of Methods Used by the U.S. Department of Energy in Setting Appliance and Equipment Standards makes findings and recommendations on how DOE can improve its analyses and align its regulatory analyses with best practices for cost-benefit analysis.

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