- Email: [email protected]
The Emergence of Organic Conservation
Ryan Hledik is a Principal in The Brattle Group’s San Francisco office. His expertise is in the economics of policies and technologies that are focused on the energy consumer. He assists clients confronting complex issues related to the recent slowdown in electricity sales growth and the evolution of utility customers from passive consumers to active managers of their energy needs. Mr. Hledik holds a master’s degree in Management Science and Engineering from Stanford University and bachelor’s degree in Applied Science, with minor in Economics and Mathematics, from the University of Pennsylvania. Ahmad Faruqui, a principal with The Brattle Group, leads the firm’s practice in understanding and managing the changing needs of energy consumers. This work encompasses rate design, distributed generation, energy efficiency, demand response, demand forecasting and cost-benefit analysis of emerging technologies. The author, co-author, editor or co-editor of four books and more than 150 articles dealing with energy issues, he holds bachelor’s and master’s degrees from the University of Karachi and master’s and doctoral degrees from the University of California, Davis where he served as a Regents Fellow and was the recipient of a grant from the Kellogg Foundation. Wade Davis is a research analyst at The Brattle Group. He has developed economic, financial, and mathematical models to support expert witnesses on matters related to dynamic pricing, utility rate design, utility conservation programs, environmental damages, contract disputes, and antitrust. He is a graduate of Williams College where he studied Economics and Environmental Science. This article is based on research conducted with Xcel Energy. The authors express their gratitude to the Xcel Energy staff for comments on earlier drafts. All results and any errors are the responsibility of the authors and do not represent the opinion of The Brattle Group, Inc. nor its clients.
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The Emergence of Organic Conservation Recent improvements in electric efficiency have been driven in part by demand-side management programs and state and federal codes and standards for electric efficiency. However, some of these improvements have happened naturally, a process we call ‘organic conservation.’ This article surveys expert opinion on organic conservation and estimates the likely impact that organic conservation has had on energy consumption for three specific end uses. Ryan Hledik, Ahmad Faruqui and Wade Davis
I. Introduction U.S. electricity sales growth has slowed down, even several years after the Great Recession of 2008– 2009.1 A survey of two dozen utility load forecasters carried out by The Brattle Group suggests that future utility sales growth will be less than 1 percent annually on average.2 Some utilities have observed a complete flattening of their sales growth. On a per-capita basis, sales growth has been negative in some regions and could remain that
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way into the foreseeable future.3 Part of this reduction in sales growth can be attributed to utility demand-side management (DSM) programs and state and federal codes and standards for electric efficiency.4 here is a prevalent belief among many electricity industry experts, however, that some improvements in energy efficiency happen naturally and are not directly attributable to codes and standards or DSM programs. These improvements are driven by factors such as the
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‘‘greening’’ of consumer attitudes toward energy, scientific discoveries in universities and labs, competition among manufacturers to differentiate product offerings and add value by incorporating new features in their products (i.e., technical innovation), and consumer response to rising energy prices. In this article, we refer to these naturally occurring improvements in energy efficiency as ‘‘organic conservation.’’ f the impact of organic conservation on sales growth is significant and persists into the future, there are important implications for state and federal energy policy. For example, it will be necessary to account for the combined impact of organic conservation and increasingly stringent codes and standards when establishing utility energy savings targets. But while there are detailed studies on the impacts of codes and standards and utility DSM programs, organic conservation remains a relatively under-researched area. To explore this issue, we developed a series of case studies establishing an order-ofmagnitude estimate of the likely impact that organic conservation has had on energy consumption for three specific end uses.5 We begin by discussing the findings of a survey of expert opinion on organic conservation. We then describe our estimates of organic conservation for three case studies in Xcel Energy’s Northern States Power (NSP) Minnesota
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service territory: residential lighting, commercial lighting, and residential displays. We conclude with a summary of key findings and recommendations for further research.
II. The Survey of Expert Opinion Given organic conservation’s evolving nature, we began by
It may be necessary to account for the combined impact of organic conservation and increasingly stringent codes and standards when establishing utility energy savings targets.
reaching out to over 100 energy efficiency experts and sought their opinion on the likely impact of organic conservation on future electricity sales. We received over 60 responses from utilities, state regulators, environmental advocacy groups, energy policy think tanks, appliance/ equipment manufacturers, government energy research labs, consultants, academics, and large national customers. The responses provided us with a variety of perspectives and opinions. We found that most respondents were familiar with
the concept of organic conservation, but knew it by a different name. The concept is alternatively known to others in the industry as: naturally occurring conservation, natural energy efficiency, naturally occurring market adoption of efficiency, autonomous technological change, nonprogrammatic energy efficiency, normally occurring market adoption (NOMAD), and autonomous rate of energy efficiency improvement (AEEI). ost experts acknowledged that organic conservation exists but there was a divergence of views on its magnitude and persistence. Some opined that it has already been quantified when utilities reported their estimates of free-ridership in their DSM programs. Free-ridership measures that fraction of customers who would have taken the actions that are incentivized through a DSM program even if the incentives had not been offered. Others felt that the impact of organic conservation extends beyond DSM free-ridership, and to confine its impact only to that of free-ridership would define it too narrowly. These respondents stated that evolving customer attitudes toward energy consumption – and toward efficiency and sustainability in particular – are driving an additional natural increase in the adoption of energy efficient appliances. Some respondents believed that market
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competitiveness among equipment and technology manufacturers is leading to the introduction of energy efficient features as a way to differentiate product lines. Others believed that such efficiency improvements are occurring generally as a byproduct of overall technological improvements. For example, a large semiconductor manufacturer pointed to Moore’s Law as evidence of improvements in computer processing that are not driven by any programs or standards.6 espondents from utilities tended to share the view that organic conservation is large in magnitude. One respondent from a Midwestern utility felt that organic conservation has had a larger impact in their service territory than either codes and standards or the utility’s DSM programs. Some of those who felt that the impacts of organic conservation were very large suggested that targets and mandates for utility DSM are no longer needed, because conservation had now become a natural occurrence. In other words, they felt that the market would adopt energy efficient appliances in the absence of intervention through new programs or standards, suggesting that rebates for more efficient technologies were unnecessary subsidies. A minority of respondents did not believe that organic conservation is significant in
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magnitude. In these instances, the respondents felt that ‘‘naturally occurring’’ efficiency improvements can ultimately be traced to either utility or governmental initiatives. For example, some indicated that the cumulative impacts of utility DSM programs persist long after the programs have ended since DSM programs transform the energy marketplace. While a utility may only be given credit
Some suggested that targets and mandates for utility DSM are no longer needed, because conservation had now become a natural occurrence. for the efficient appliance purchases that are formally made through its DSM program, the customers purchasing the appliances may permanently change their preferences as a result and continue to purchase the more efficient appliances long after the program has ended. Respondents indicated that these impacts are often attributed to organic conservation, but should instead be attributed to the utility DSM programs. This is commonly referred to as the ‘‘spillover effect.’’ Others felt that efficiency improvements are occurring
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outside of DSM programs and codes and standards, but that these improvements are attributable to other types of ‘‘market intervention’’ and that they would not have occurred on their own. For example, some felt that the development of many efficient technologies should be attributed to federal funding for research and development. Others felt that lobbying efforts by trade associations and ‘‘soft’’ programs like Energy Star labels are driving efficiency improvements. In all of these cases, regardless of whether or not the impacts are attributed to organic conservation or some form of market intervention, they still generally fall under the rubric of initiatives whose impacts should be accounted for when developing new utility energy efficiency policies. inally, a few skeptics of organic conservation believed that any naturally occurring efficiency improvement that happens ‘‘coincidentally’’ in one technology is likely offset by a coincidental reduction in efficiency in another technology (due to the addition of energyintensive new features). They felt that these naturally occurring impacts would occur in roughly equal proportions in both directions, yielding a negligible impact in the end. Respondents all agreed that it will be very challenging to isolate and quantify the impact of organic conservation. Very little literature exists on the topic, and
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we did not identify any studies that comprehensively establish quantitative estimates of the impact of organic conservation (akin to those that exist for utility DSM programs and governmental codes and standards). This suggests the need for a new and original approach to the topic. Therefore, we designed our approach to address the four key challenges identified through our survey: Challenge #1: Utility sales forecasting models do not typically include end-use granularity. While some utilities claim to implicitly account for organic conservation in their sales forecasting processes, its impact is difficult to isolate. To address this challenge, we have used a bottom-up case study approach to quantifying organic conservation for specific end-uses, rather than relying on a top-down econometric modeling approach.7 Challenge #2: It is difficult to account for the indirect impact of codes and standards and DSM programs (e.g., the spillover effect) on efficiency improvements. For the purpose of our analysis, we have defined organic conservation to include any efficiency improvements that are not directly attributable to codes and standards or DSM. Any indirect impacts, such as the spillover effect described earlier, are accounted for in our estimate of organic conservation. Challenge #3: It is difficult to account for substitution across technologies. Naturally occurring June 2015,
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energy savings can occur in the form of switching from one technology (e.g. a desktop computer) to a different technology (e.g. an iPad). In our analysis, we consider this a secondary effect and focus specifically on the primary effect, i.e., efficiency improvements in individual technologies. Inclusion of this secondary effect would possibly lead to larger estimates of organic conservation (although
While some utilities claim to implicitly account for organic conservation in their sales forecasting processes, its impact is difficult to isolate.
a scenario can also be envisioned in which the opposite occurs and consumption increases). Challenge #4: There is uncertainty in the future impact of any standard or DSM program. There is undoubtedly uncertainty in any forecast of future technology adoption and conservation-related behavior. In recognition of this uncertainty, and to better understand the key drivers of our estimates of organic conservation, we have included sensitivity cases in our analysis. n summary, all experts were familiar with the concept of organic conservation, although
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virtually everyone knew it by a different name. Most experts felt that it exists and many believe its impacts are significant. A few argued that efficiency improvements are driven largely by market intervention (i.e., policy initiatives, DSM programs, lobbying, etc.). In all cases, it was difficult to disentangle sentiments about organic conservation from the respondents’ own professional agendas. However, all of the experts agreed that the impact of organic conservation is difficult to isolate and quantify, that little research exists on the topic, and that it is necessary to better understand its potential future impact – whether large or small – on electricity consumption.
III. The Residential Lighting Case Study Our quantitative assessment of organic conservation focused on three different end uses for a single utility service territory: Xcel Energy’s Northern States Power (NSP) Minnesota service territory. Focusing on individual end-uses for a single utility allowed us to isolate the impact of organic conservation from other drivers of changes in energy consumption. The first case study is residential lighting. Our first step was to establish the efficiency level of the average household light bulb in the Xcel Energy’s Minnesota service territory. This
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average bulb is a composite of incandescents, halogens, compact fluorescents (CFLs), and lightemitting diodes (LEDs).8 Based on data provided by Xcel Energy and other publicly available sources (e.g. data from the U.S. Energy Information Administration, or EIA), we estimated that the average household light bulb consumes 34 kWh of electricity per year. We then propound a frozen efficiency case in which this value continues into the indefinite future. The frozen efficiency case assumes no change in light bulb efficiency or in consumer behavior and forms an important analytical baseline against which the future impact of DSM programs, codes and standards, or organic conservation can be envisioned. The frozen efficiency case is illustrated by the horizontal line in Figure 1. uture deviations from this frozen efficiency case will be the result of two factors. The first factor is change in consumer behavior. Evolving customer attitudes and increasing energy awareness could lead to
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reductions in lighting use. For example, customers may become more likely to turn off lights in empty rooms as their energy awareness increases. The second factor is technological change. Over time, customers will purchase more efficient light bulbs and the overall existing bulb stock will shift toward these more efficient options. Commercially available options which consumers can purchase today include halogens, which use 28 percent less energy than incandescents (a 40 percent improvement in efficiency, as measured in lumens per watt), and CFLs and LEDs, which use 75 percent to 80 percent less energy (a 300 to 400 percent improvement in efficiency). odes and standards will be a key driver of the adoption of these more efficient bulbs. Specifically, the Energy Independence and Security Act (EISA) of 2007 mandates that minimum bulb energy consumption be reduced by 28 percent relative to that of an incandescent (beginning in 2012). This effectively establishes
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Figure 1: Annual Energy Consumption per Average Bulb (Frozen Efficiency)
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halogens as the least efficient residential lighting option in the market. And beginning in 2020, the standard requires roughly 65 percent energy savings per bulb relative to an incandescent. This will establish CFLs as the least efficient residential lighting option among existing technologies. EISA will gradually lead to the phasing out of incandescents in Xcel Energy’s Minnesota service territory, except in specialty applications. The switch to more efficient bulbs is expected to occur over a relatively long time horizon, as incandescents that are currently in use will eventually burn out and be replaced. As a starting point for quantifying the impact of EISA, we have adopted a relatively conservative methodology that was developed by Xcel Energy. This projected impact of EISA is illustrated in Figure 2.9 It produces a 2 percent reduction in per-bulb energy consumption by 2015. Xcel Energy’s approved DSM programs will lead to incremental lighting improvements above and beyond those resulting from EISA. Xcel Energy’s residential lighting program has been approved through 2015 and provides rebates that are between 30 percent and 40 percent of the incremental cost for CFL and LED purchases. This will accelerate the purchase of light bulbs that not only meet but also exceed the minimum efficiency requirements established in EISA. Based on Xcel Energy’s projections, roughly 1.4 million CFLs are expected to be The Electricity Journal
[(Figure_2)TD$IG]
Figure 2: Annual Energy Consumption per Average Bulb (After Codes & Standards)
[(Figure_3)TD$IG]
Figure 3: Annual Energy Consumption per Average Bulb (After Utility DSM)
sold per year through the program, to roughly 225,000 participants per year. Annual LED sales through the program will average around 78,000 units per year, to roughly 75,000 participants per year. The result, when combined with the impact of EISA, is an average reduction in per-bulb energy consumption of about 11 percent by the end of 2015. This is illustrated in Figure 3. portion of Xcel Energy’s projected DSM program impacts includes free riders. As discussed above, free-ridership is considered a form of organic conservation, because it represents the adoption of energy efficient light bulbs that would have happened even if the incentive payments had not been offered. A 2012 consultant study
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for Xcel Energy found that 46 percent of its residential lighting DSM impacts were attributable to free-ridership.10 This estimate was based on customer surveys, corporate interviews, and an econometric model with sales tracking data. In the corporate interviews, retailers were asked to estimate sales in the absence of the utility program. The customer surveys focused on consumer
[(Figure_4)TD$IG]
purchasing habits. Figure 4 reflects the impact of freeridership on lighting efficiency.11 As we have defined it for this study, organic conservation includes all expected efficiency improvements not directly driven by DSM programs or codes and standards. Therefore, it is possible that there is additional organic conservation that is not accounted for in the free-ridership measure. To capture this additional organic conservation, we established an all-inclusive forecast of residential lighting efficiency improvements, with the incremental difference between this forecast and the one in Figure 4 being implicitly attributable to organic conservation. We relied on projections in the EIA’s 2013 Annual Energy Outlook (AEO) to establish our all-inclusive lighting efficiency case.12 The AEO provides a reasonable all-inclusive forecast of lighting efficiency improvements, because it explicitly accounts for the impact of codes and standards and – based on our review of the EIA’s methodology – implicitly accounts for the impact of utility
Figure 4: Annual Energy Consumption per Average Bulb (After Free-Ridership)
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[(Figure_5)TD$IG] DSM programs.13 It also accounts for organic conservation in several different ways: Technology efficiency improvements: The efficiency of new technology options is projected based primarily on interviews with manufacturers. This accounts for market-driven changes to product features. Technology cost reductions: Consultant forecasts are used to develop projections of technology cost reductions over time. As the relative cost of efficient technologies drops, projected customer purchases increase. Changing electricity prices: The EIA’s electricity price projections affect the payback period for new technologies; as electricity prices rise, so does the financial attractiveness of more efficient equipment. Customer choice: The EIA’s technology choice module accounts for observed customer preferences for efficient equipment based on historical data. Consumer behavior: The EIA’s demand module can account for changes in customer behavior such as reducing the number of hours per year that a given piece of equipment (e.g., a light bulb) is used. rganic conservation is calculated as the difference between the AEO forecast (scaled to the characteristics of Xcel Energy’s Minnesota service territory) and NSP’s projected impact of codes and standards and DSM programs. This is
Figure 5: Annual Energy Consumption per Average Bulb (With Organic Conservation)
illustrated in Figure 5. Including the impact of free-ridership, organic conservation will account for roughly 65 percent of total household lighting efficiency improvement between 2012 and 2015.14
IV. The Commercial Lighting Case Study We used a very similar approach to estimate the impact of organic conservation in commercial lighting as we had used for residential lighting. The impact of codes and standards was derived from a projection by Xcel Energy and accounts for the impact of both EISA and the Energy Policy Act (EPACT) of 2005.15 Utility DSM impacts were
also provided by Xcel Energy based on its basic commercial lighting program, and assume a very small number of participants (roughly 37 per year) and rebates of roughly 10 percent to 30 percent of the incremental cost of various efficient lighting packages. Free-ridership was assumed to account for 17 percent of the utility DSM impacts, based on a meta-analysis conducted by Lawrence Berkeley National Laboratory.16 The incremental impact of additional organic conservation was derived using the commercial lighting forecast in the 2013 AEO. The results are illustrated in Figure 6.17 nlike the large gains seen in residential lighting, commercial lighting efficiency is only expected to improve by 6.7
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Figure 6: Annual Commercial Lighting Energy Consumption per Square Foot
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percent between 2012 and 2015. This is likely because the most stringent codes and standards for commercial lighting were introduced back in the 2008–2009 timeframe and have already had a significant impact. Presumably, large commercial customers have a more sophisticated approach to energy management than individual households and therefore require less market intervention to encourage adoption of efficient technologies. Organic conservation represents 77 percent of the total efficiency improvement in this case.18 Its share of the total efficiency gain is larger than that of residential lighting, but it is smaller in overall magnitude of efficiency improvement.
V. The Residential Displays Case Study
increasing on a per-unit basis. This could possibly be attributed to monitors that were increasing in size and in output, or to an increase in the amount of time that owners were spending using their computers. However, as monitors and computer processors became more efficient over time, overall energy consumption per computer decreased significantly. Between 2008 and 2012, energy use per PC dropped by 8 percent. By 2020, the AEO projects that it will decrease by 24 percent relative to the 2008 peak. This is all due to organic conservation. The trend in energy consumption per PC is illustrated in Figure 7. Energy consumption per TV has exhibited a similar trend. Prior to 2009, TV size increased as plasma TVs and LCDs replaced cathode ray tube TVs. The associated increase in average TV screen size more than offset improvements in TV efficiency, and the result was an overall increase in TV energy consumption.19 However, a transition toward even more efficient TVs like LED-backlit LCDs has helped reverse this
Residential displays (i.e., personal computers, TVs) are an interesting case study because there are no codes and standards and few successful utility DSM programs to drive the market toward more efficient products. Therefore, all observed efficiency gains can be attributed to organic conservation. The residential displays case study is based entirely on historical and projected stock efficiency as derived from region-specific data reported in the 2013 AEO. rior to 2008, the amount of electricity consumed by personal computers (PCs) was
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trend beginning in 2009.20 By 2020, TVs are projected to consume 16 percent less energy than at the peak in 2008. This is illustrated in Figure 8. New standards for residential displays may be on the horizon. A 2012 study by the American Council for an Energy-Efficient Economy (ACEEE) posited that an efficiency standard for personal computers could come into being as early as 2019.21 ACEEE’s analysis assessed the impact of a standard that is consistent with the Energy Star version 5.0 requirements (computers meeting this standard use 65 percent less energy than the least efficient new products). Such a standard would produce national annual energy savings of 11.8 TWh by 2035 at a net present value of $8.6 billion, according to ACEEE. Similarly, ACEEE envisioned a potential efficiency standard for TVs. By 2016, ACEEE estimates that TVs could meet the Energy Star 5.3 efficiency requirements, which would lead to 10 TWh of annual energy savings at a present value of $8.3 billion nationally.22
Figure 7: Annual Energy Consumption per Personal Computer
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[(Figure_8)TD$IG]
Figure 8: Annual Energy Consumption per TV
VI. Conclusions and Policy Implications The findings of our study support the existence of organic conservation. We have identified three case studies in which energy efficiency improvements are expected to occur above and beyond any impacts of DSM programs or codes and standards. This conclusion is further supported by our survey of expert opinion. Most industry experts, based on firsthand experience and general intuition, agree that some improvements in energy efficiency occur naturally. he magnitude of the impact of organic conservation varies widely across our three case studies. It depends not only on the characteristics of the technology or appliance that is being evaluated, but also on timing in that technology’s development cycle. As observed historically in the case of residential displays, there are points where technology can naturally become less energy efficient due to customer preferences for other energy intensive features (e.g., larger TV
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screen sizes). Organic conservation may be cyclical in this sense for some technologies. But as technologies mature, there appears to be a trend toward improving efficiency. There is debate about what causes organic conservation. Some attribute it to evolving customer attitudes. Others feel it is driven naturally by the demands of the market. Others argue that it is the byproduct of policy initiatives that are not strictly considered DSM programs or codes and standards, but are still forms of ‘‘market intervention’’ nonetheless. However, from an energy efficiency policy perspective, the exact cause of organic conservation may not matter. The simple conclusion that efficiency gains are happening outside of both utility DSM programs and codes and standards have significant implications for energy efficiency policies. onsider energy savings targets – also known as energy efficiency resource standards – which exist for utilities in many states, including Minnesota. These targets are
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based on an assumption that, through DSM programs, utilities can achieve incremental sales reductions relative to a baseline forecast of electricity sales. If that baseline does not fully account for the impact of organic conservation (or, for that matter, codes and standards), the utilities may have to pursue unexpectedly expensive DSM programs in order to achieve the stated targets. Whether these more expensive DSM programs are cost-effective will depend on the specific system conditions of the utility. Decoupling is another energy efficiency related policy mechanism for which organic conservation has implications. Decoupling mechanisms can be structured many different ways. In most cases, utilities are made ‘‘whole’’ for sales reductions due to efficiency improvements. If the estimate of these sales reductions does not include the impact of organic conservation, the utilities could under-recover their costs. Utility DSM programs should also be designed with organic conservation impacts in mind. Certain end uses are naturally experiencing significant improvements in efficiency. It will be important to account for this effect when assessing the costeffectiveness of the programs. Some utilities already do this by accounting for free-ridership when establishing the impacts that are attributable to the DSM program. Similar considerations exist for codes and standards. The costs associated with establishing The Electricity Journal
a new standard should be weighed against the rate at which the intended efficiency improvement is likely to happen naturally in the absence of the standard.
VII. Recommendations for Further Research The organic conservation impact projections presented in this study are order-of-magnitude estimates. They illustrate the general degree of efficiency improvement that is happening outside of DSM programs and codes and standards. As the first study of its kind, the findings could be strengthened significantly through further research in a number of key areas. We have identified six research activities that would be particularly valuable in further extending the industry’s understanding of organic conservation: 1. Estimate organic conservation using a Delphi approach. As a follow-up to the survey of expert opinion, manufacturers could be interviewed to assess the degree to which appliances are being manufactured and sold above and beyond required efficiency levels. The manufacturers and other experts would be asked to quantify the magnitude of organic conservation’s likely impacts, and the collection of estimates would be used to derive a meaningful conclusion about the likely magnitude of impacts. June 2015,
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2. Back out the impact of organic conservation from utility sales forecasts using a regressionbased approach.It might be possible to establish a sales forecasting model, which, based on historical data, controls for the effects of the electricity price, weather, the economy, DSM programs, codes and standards, and other important factors. If the
model is designed well, the remaining energy savings trend observed in the model’s forecast can be attributed to organic conservation. Alternatively, rather than building a model from scratch with publicly available data, this activity could also be implemented using an existing utility sales forecasting model, controlling for any of the above described factors that are not already accounted for, and adding a time trend to the model. In either case, this would be a nice complement to the bottom-up case study approach, because it would provide an estimate of organic conservation at the class or system level.
3. Expand the sensitivity analysis. More robust sensitivity analysis could be conducted as an enhancement of the case studies. It would be possible to establish a plausible distribution of values for each uncertain variable in the analysis, and then run Monte Carlo simulations to create a measure of the overall uncertainty in the results. This would also help to identify the key drivers of the findings. 4. Develop additional case studies.It would be valuable to include additional appliance and end-use case studies, and develop an estimate of their impacts using a methodology similar to that described above. Industrial motors are one example of a potentially interesting new case study. 5. Incorporate historical assessments into the case studies. It may be possible to expand the case studies in our assessment to include a historical timeframe. This would require additional data gathering and may or may not be feasible given available data. 6. Conduct a pre-DSM era assessment of efficiency improvement.Prior to the origin of DSM programs and efficiency codes and standards in the 1970s, all improvements in per-capita energy efficiency could be considered organic conservation (or vice versa). It should be possible to quantify this trend using historical energy data.&
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Endnotes: 1. Faruqui, A., Shultz, E., 2012, December. Demand Growth and The New Normal. Public Utilities Fortnightly. 2. Faruqui, A., 2013, June. surviving sub one percent sales growth. Electr. Policy. 3. Derived from U.S. EIA data in the 2013 Annual Energy Outlook and 2012 Annual Energy Review.
26 percent of residential lighting energy consumption that is currently from CFLs is attributed to EISA. This impact is fully reached in 2020, with a linear ramp-up in prior years. An alternative and more aggressive assumption about EISA-driven efficient lighting adoption was analyzed through sensitivity analysis. 10. The Cadmus Group, 2012, November. Minnesota Home Lighting Program Evaluation. Prepared for Xcel Energy. p. 48.
4. Utility DSM programs provide a financial incentive for customers to consume electricity more efficiently. Codes and standards establish minimum efficiency levels for certain end uses.
7. Such an approach, however, would be a valuable research activity and is included in our recommendations for further analysis.
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15. EISA includes a maximum allowable wattage for incandescent and halogen lamps (2012), and certain metal halide lamp fixtures must meet minimum ballast efficiency requirement (2009). EPACT includes standards for medium base CFLs (2006), for ballasts for Energy Saver fluorescent lamps (2009 and 2010), and bans mercury vapor lamp ballasts (2008). 16. Vine, E., Eto, J., Shown, L., Sonnenblick, R., Payne, C., 1994, Evaluation of Commercial Lighting Programs: A DEEP Assessment. Lawrence Berkeley National Laboratory. p. 8.243. http://emp.lbl. gov/sites/all/files/lbnl-36522.pdf.
5. We use the terms ‘‘energy consumption,’’ ‘‘sales,’’ and ‘‘usage’’ interchangeably throughout the paper. 6. Moore’s Law, named after Gordon Moore, the co-founder of Intel, is the observation that computing efficiency doubles approximately every two years. See www.mooreslaw.org. For a review of the improvements in digital electronics that have taken place during the past five decades, consistent with the tenets of the law, see this article: http://www. mercurynews.com/business/ ci_27934824/silicon-valleymarks-50-years-moores-law. It says that if semiconductor prices had stayed at their 1971 levels, a personal computer today would cost $195 million. An iPhone 6 has some 3 billion transistors on it, up from the 1 billion transistors on an iPhone 5S, which came out just a year prior.
organic conservation could represent 42 percent to 65 percent of total efficiency improvement.
11. This estimate of free-ridership does not include any ‘‘spillover effect,’’ described earlier as customers changing their preferences as a result of the DSM program. For example, customers may continue purchasing energy efficient products after the program has ended, or they may purchase even more efficient products than are covered by the program. 12. U.S. EIA, 2013, April. Annual Energy Outlook 2013.
8. Slightly over half of the bulbs in the average home are incandescents, roughly a quarter are CFLs, around 1 percent are LEDs, and the rest are other types of bulbs.
13. The AEO forecast does not explicitly account for the impact of new utility DSM programs. However, it is calibrated to historical trends in lighting technology adoption. To the extent that utility DSM programs have helped to drive these trends, their impacts should be embedded in the forecast.
9. Under this methodology, since EISA only mandates a roughly 30 percent improvement in lighting efficiency, only 30 percent of the
14. We also conducted sensitivity analysis on the key assumptions in our analysis. Under different assumptions and methodologies, we find that the
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17. The chart is only representative of customers participating in the basic commercial lighting program, which is limited to medium and large businesses that apply for a single technology change. Utility DSM impacts in this graph do not include any bundle approaches to energy efficiency or other lighting-focused programs. 18. Based on an alternative case, we found that organic conservation could account for as much as 83 percent of the total efficiency improvement. 19. Herter, K., 2012. Get Smart Guide: Energy Innovation for the Consumer Electronic Industry. Smart Electronics Initiative. p. 6. http://greentech leadership.org/documents/2013/07/ get-smart-guide.pdf. 20. Park, W.Y., Phadke, A., Shah, N., Letschert, V. TV Energy Consumption Trends Energy-Efficiency Improvement Options. Lawrence Berkeley National Laboratory. p. xv. https://isswprod.lbl.gov/library/ view-docs/public/output/rpt81012. PDF. 21. Lowenberger, A., Mauer, J., et al., 2012, March. ASAP/ACEEE. The Efficiency Boom: Cashing in on Savings from Appliance Standards. p. 27. 22. Ibid, p. 31.
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The Emergence of Organic Conservation Recent improvements in electric efficiency have been driven in part by demand-side management programs and state and federal codes and standards for electric efficiency. However, some of these improvements have happened naturally, a process we call ‘organic conservation.’ This article surveys expert opinion on organic conservation and estimates the likely impact that organic conservation has had on energy consumption for three specific end uses. Ryan Hledik, Ahmad Faruqui and Wade Davis
I. Introduction U.S. electricity sales growth has slowed down, even several years after the Great Recession of 2008– 2009.1 A survey of two dozen utility load forecasters carried out by The Brattle Group suggests that future utility sales growth will be less than 1 percent annually on average.2 Some utilities have observed a complete flattening of their sales growth. On a per-capita basis, sales growth has been negative in some regions and could remain that
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way into the foreseeable future.3 Part of this reduction in sales growth can be attributed to utility demand-side management (DSM) programs and state and federal codes and standards for electric efficiency.4 here is a prevalent belief among many electricity industry experts, however, that some improvements in energy efficiency happen naturally and are not directly attributable to codes and standards or DSM programs. These improvements are driven by factors such as the
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‘‘greening’’ of consumer attitudes toward energy, scientific discoveries in universities and labs, competition among manufacturers to differentiate product offerings and add value by incorporating new features in their products (i.e., technical innovation), and consumer response to rising energy prices. In this article, we refer to these naturally occurring improvements in energy efficiency as ‘‘organic conservation.’’ f the impact of organic conservation on sales growth is significant and persists into the future, there are important implications for state and federal energy policy. For example, it will be necessary to account for the combined impact of organic conservation and increasingly stringent codes and standards when establishing utility energy savings targets. But while there are detailed studies on the impacts of codes and standards and utility DSM programs, organic conservation remains a relatively under-researched area. To explore this issue, we developed a series of case studies establishing an order-ofmagnitude estimate of the likely impact that organic conservation has had on energy consumption for three specific end uses.5 We begin by discussing the findings of a survey of expert opinion on organic conservation. We then describe our estimates of organic conservation for three case studies in Xcel Energy’s Northern States Power (NSP) Minnesota
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service territory: residential lighting, commercial lighting, and residential displays. We conclude with a summary of key findings and recommendations for further research.
II. The Survey of Expert Opinion Given organic conservation’s evolving nature, we began by
It may be necessary to account for the combined impact of organic conservation and increasingly stringent codes and standards when establishing utility energy savings targets.
reaching out to over 100 energy efficiency experts and sought their opinion on the likely impact of organic conservation on future electricity sales. We received over 60 responses from utilities, state regulators, environmental advocacy groups, energy policy think tanks, appliance/ equipment manufacturers, government energy research labs, consultants, academics, and large national customers. The responses provided us with a variety of perspectives and opinions. We found that most respondents were familiar with
the concept of organic conservation, but knew it by a different name. The concept is alternatively known to others in the industry as: naturally occurring conservation, natural energy efficiency, naturally occurring market adoption of efficiency, autonomous technological change, nonprogrammatic energy efficiency, normally occurring market adoption (NOMAD), and autonomous rate of energy efficiency improvement (AEEI). ost experts acknowledged that organic conservation exists but there was a divergence of views on its magnitude and persistence. Some opined that it has already been quantified when utilities reported their estimates of free-ridership in their DSM programs. Free-ridership measures that fraction of customers who would have taken the actions that are incentivized through a DSM program even if the incentives had not been offered. Others felt that the impact of organic conservation extends beyond DSM free-ridership, and to confine its impact only to that of free-ridership would define it too narrowly. These respondents stated that evolving customer attitudes toward energy consumption – and toward efficiency and sustainability in particular – are driving an additional natural increase in the adoption of energy efficient appliances. Some respondents believed that market
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competitiveness among equipment and technology manufacturers is leading to the introduction of energy efficient features as a way to differentiate product lines. Others believed that such efficiency improvements are occurring generally as a byproduct of overall technological improvements. For example, a large semiconductor manufacturer pointed to Moore’s Law as evidence of improvements in computer processing that are not driven by any programs or standards.6 espondents from utilities tended to share the view that organic conservation is large in magnitude. One respondent from a Midwestern utility felt that organic conservation has had a larger impact in their service territory than either codes and standards or the utility’s DSM programs. Some of those who felt that the impacts of organic conservation were very large suggested that targets and mandates for utility DSM are no longer needed, because conservation had now become a natural occurrence. In other words, they felt that the market would adopt energy efficient appliances in the absence of intervention through new programs or standards, suggesting that rebates for more efficient technologies were unnecessary subsidies. A minority of respondents did not believe that organic conservation is significant in
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magnitude. In these instances, the respondents felt that ‘‘naturally occurring’’ efficiency improvements can ultimately be traced to either utility or governmental initiatives. For example, some indicated that the cumulative impacts of utility DSM programs persist long after the programs have ended since DSM programs transform the energy marketplace. While a utility may only be given credit
Some suggested that targets and mandates for utility DSM are no longer needed, because conservation had now become a natural occurrence. for the efficient appliance purchases that are formally made through its DSM program, the customers purchasing the appliances may permanently change their preferences as a result and continue to purchase the more efficient appliances long after the program has ended. Respondents indicated that these impacts are often attributed to organic conservation, but should instead be attributed to the utility DSM programs. This is commonly referred to as the ‘‘spillover effect.’’ Others felt that efficiency improvements are occurring
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outside of DSM programs and codes and standards, but that these improvements are attributable to other types of ‘‘market intervention’’ and that they would not have occurred on their own. For example, some felt that the development of many efficient technologies should be attributed to federal funding for research and development. Others felt that lobbying efforts by trade associations and ‘‘soft’’ programs like Energy Star labels are driving efficiency improvements. In all of these cases, regardless of whether or not the impacts are attributed to organic conservation or some form of market intervention, they still generally fall under the rubric of initiatives whose impacts should be accounted for when developing new utility energy efficiency policies. inally, a few skeptics of organic conservation believed that any naturally occurring efficiency improvement that happens ‘‘coincidentally’’ in one technology is likely offset by a coincidental reduction in efficiency in another technology (due to the addition of energyintensive new features). They felt that these naturally occurring impacts would occur in roughly equal proportions in both directions, yielding a negligible impact in the end. Respondents all agreed that it will be very challenging to isolate and quantify the impact of organic conservation. Very little literature exists on the topic, and
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we did not identify any studies that comprehensively establish quantitative estimates of the impact of organic conservation (akin to those that exist for utility DSM programs and governmental codes and standards). This suggests the need for a new and original approach to the topic. Therefore, we designed our approach to address the four key challenges identified through our survey: Challenge #1: Utility sales forecasting models do not typically include end-use granularity. While some utilities claim to implicitly account for organic conservation in their sales forecasting processes, its impact is difficult to isolate. To address this challenge, we have used a bottom-up case study approach to quantifying organic conservation for specific end-uses, rather than relying on a top-down econometric modeling approach.7 Challenge #2: It is difficult to account for the indirect impact of codes and standards and DSM programs (e.g., the spillover effect) on efficiency improvements. For the purpose of our analysis, we have defined organic conservation to include any efficiency improvements that are not directly attributable to codes and standards or DSM. Any indirect impacts, such as the spillover effect described earlier, are accounted for in our estimate of organic conservation. Challenge #3: It is difficult to account for substitution across technologies. Naturally occurring June 2015,
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energy savings can occur in the form of switching from one technology (e.g. a desktop computer) to a different technology (e.g. an iPad). In our analysis, we consider this a secondary effect and focus specifically on the primary effect, i.e., efficiency improvements in individual technologies. Inclusion of this secondary effect would possibly lead to larger estimates of organic conservation (although
While some utilities claim to implicitly account for organic conservation in their sales forecasting processes, its impact is difficult to isolate.
a scenario can also be envisioned in which the opposite occurs and consumption increases). Challenge #4: There is uncertainty in the future impact of any standard or DSM program. There is undoubtedly uncertainty in any forecast of future technology adoption and conservation-related behavior. In recognition of this uncertainty, and to better understand the key drivers of our estimates of organic conservation, we have included sensitivity cases in our analysis. n summary, all experts were familiar with the concept of organic conservation, although
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virtually everyone knew it by a different name. Most experts felt that it exists and many believe its impacts are significant. A few argued that efficiency improvements are driven largely by market intervention (i.e., policy initiatives, DSM programs, lobbying, etc.). In all cases, it was difficult to disentangle sentiments about organic conservation from the respondents’ own professional agendas. However, all of the experts agreed that the impact of organic conservation is difficult to isolate and quantify, that little research exists on the topic, and that it is necessary to better understand its potential future impact – whether large or small – on electricity consumption.
III. The Residential Lighting Case Study Our quantitative assessment of organic conservation focused on three different end uses for a single utility service territory: Xcel Energy’s Northern States Power (NSP) Minnesota service territory. Focusing on individual end-uses for a single utility allowed us to isolate the impact of organic conservation from other drivers of changes in energy consumption. The first case study is residential lighting. Our first step was to establish the efficiency level of the average household light bulb in the Xcel Energy’s Minnesota service territory. This
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average bulb is a composite of incandescents, halogens, compact fluorescents (CFLs), and lightemitting diodes (LEDs).8 Based on data provided by Xcel Energy and other publicly available sources (e.g. data from the U.S. Energy Information Administration, or EIA), we estimated that the average household light bulb consumes 34 kWh of electricity per year. We then propound a frozen efficiency case in which this value continues into the indefinite future. The frozen efficiency case assumes no change in light bulb efficiency or in consumer behavior and forms an important analytical baseline against which the future impact of DSM programs, codes and standards, or organic conservation can be envisioned. The frozen efficiency case is illustrated by the horizontal line in Figure 1. uture deviations from this frozen efficiency case will be the result of two factors. The first factor is change in consumer behavior. Evolving customer attitudes and increasing energy awareness could lead to
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reductions in lighting use. For example, customers may become more likely to turn off lights in empty rooms as their energy awareness increases. The second factor is technological change. Over time, customers will purchase more efficient light bulbs and the overall existing bulb stock will shift toward these more efficient options. Commercially available options which consumers can purchase today include halogens, which use 28 percent less energy than incandescents (a 40 percent improvement in efficiency, as measured in lumens per watt), and CFLs and LEDs, which use 75 percent to 80 percent less energy (a 300 to 400 percent improvement in efficiency). odes and standards will be a key driver of the adoption of these more efficient bulbs. Specifically, the Energy Independence and Security Act (EISA) of 2007 mandates that minimum bulb energy consumption be reduced by 28 percent relative to that of an incandescent (beginning in 2012). This effectively establishes
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Figure 1: Annual Energy Consumption per Average Bulb (Frozen Efficiency)
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halogens as the least efficient residential lighting option in the market. And beginning in 2020, the standard requires roughly 65 percent energy savings per bulb relative to an incandescent. This will establish CFLs as the least efficient residential lighting option among existing technologies. EISA will gradually lead to the phasing out of incandescents in Xcel Energy’s Minnesota service territory, except in specialty applications. The switch to more efficient bulbs is expected to occur over a relatively long time horizon, as incandescents that are currently in use will eventually burn out and be replaced. As a starting point for quantifying the impact of EISA, we have adopted a relatively conservative methodology that was developed by Xcel Energy. This projected impact of EISA is illustrated in Figure 2.9 It produces a 2 percent reduction in per-bulb energy consumption by 2015. Xcel Energy’s approved DSM programs will lead to incremental lighting improvements above and beyond those resulting from EISA. Xcel Energy’s residential lighting program has been approved through 2015 and provides rebates that are between 30 percent and 40 percent of the incremental cost for CFL and LED purchases. This will accelerate the purchase of light bulbs that not only meet but also exceed the minimum efficiency requirements established in EISA. Based on Xcel Energy’s projections, roughly 1.4 million CFLs are expected to be The Electricity Journal
[(Figure_2)TD$IG]
Figure 2: Annual Energy Consumption per Average Bulb (After Codes & Standards)
[(Figure_3)TD$IG]
Figure 3: Annual Energy Consumption per Average Bulb (After Utility DSM)
sold per year through the program, to roughly 225,000 participants per year. Annual LED sales through the program will average around 78,000 units per year, to roughly 75,000 participants per year. The result, when combined with the impact of EISA, is an average reduction in per-bulb energy consumption of about 11 percent by the end of 2015. This is illustrated in Figure 3. portion of Xcel Energy’s projected DSM program impacts includes free riders. As discussed above, free-ridership is considered a form of organic conservation, because it represents the adoption of energy efficient light bulbs that would have happened even if the incentive payments had not been offered. A 2012 consultant study
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for Xcel Energy found that 46 percent of its residential lighting DSM impacts were attributable to free-ridership.10 This estimate was based on customer surveys, corporate interviews, and an econometric model with sales tracking data. In the corporate interviews, retailers were asked to estimate sales in the absence of the utility program. The customer surveys focused on consumer
[(Figure_4)TD$IG]
purchasing habits. Figure 4 reflects the impact of freeridership on lighting efficiency.11 As we have defined it for this study, organic conservation includes all expected efficiency improvements not directly driven by DSM programs or codes and standards. Therefore, it is possible that there is additional organic conservation that is not accounted for in the free-ridership measure. To capture this additional organic conservation, we established an all-inclusive forecast of residential lighting efficiency improvements, with the incremental difference between this forecast and the one in Figure 4 being implicitly attributable to organic conservation. We relied on projections in the EIA’s 2013 Annual Energy Outlook (AEO) to establish our all-inclusive lighting efficiency case.12 The AEO provides a reasonable all-inclusive forecast of lighting efficiency improvements, because it explicitly accounts for the impact of codes and standards and – based on our review of the EIA’s methodology – implicitly accounts for the impact of utility
Figure 4: Annual Energy Consumption per Average Bulb (After Free-Ridership)
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[(Figure_5)TD$IG] DSM programs.13 It also accounts for organic conservation in several different ways: Technology efficiency improvements: The efficiency of new technology options is projected based primarily on interviews with manufacturers. This accounts for market-driven changes to product features. Technology cost reductions: Consultant forecasts are used to develop projections of technology cost reductions over time. As the relative cost of efficient technologies drops, projected customer purchases increase. Changing electricity prices: The EIA’s electricity price projections affect the payback period for new technologies; as electricity prices rise, so does the financial attractiveness of more efficient equipment. Customer choice: The EIA’s technology choice module accounts for observed customer preferences for efficient equipment based on historical data. Consumer behavior: The EIA’s demand module can account for changes in customer behavior such as reducing the number of hours per year that a given piece of equipment (e.g., a light bulb) is used. rganic conservation is calculated as the difference between the AEO forecast (scaled to the characteristics of Xcel Energy’s Minnesota service territory) and NSP’s projected impact of codes and standards and DSM programs. This is
Figure 5: Annual Energy Consumption per Average Bulb (With Organic Conservation)
illustrated in Figure 5. Including the impact of free-ridership, organic conservation will account for roughly 65 percent of total household lighting efficiency improvement between 2012 and 2015.14
IV. The Commercial Lighting Case Study We used a very similar approach to estimate the impact of organic conservation in commercial lighting as we had used for residential lighting. The impact of codes and standards was derived from a projection by Xcel Energy and accounts for the impact of both EISA and the Energy Policy Act (EPACT) of 2005.15 Utility DSM impacts were
also provided by Xcel Energy based on its basic commercial lighting program, and assume a very small number of participants (roughly 37 per year) and rebates of roughly 10 percent to 30 percent of the incremental cost of various efficient lighting packages. Free-ridership was assumed to account for 17 percent of the utility DSM impacts, based on a meta-analysis conducted by Lawrence Berkeley National Laboratory.16 The incremental impact of additional organic conservation was derived using the commercial lighting forecast in the 2013 AEO. The results are illustrated in Figure 6.17 nlike the large gains seen in residential lighting, commercial lighting efficiency is only expected to improve by 6.7
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Figure 6: Annual Commercial Lighting Energy Consumption per Square Foot
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percent between 2012 and 2015. This is likely because the most stringent codes and standards for commercial lighting were introduced back in the 2008–2009 timeframe and have already had a significant impact. Presumably, large commercial customers have a more sophisticated approach to energy management than individual households and therefore require less market intervention to encourage adoption of efficient technologies. Organic conservation represents 77 percent of the total efficiency improvement in this case.18 Its share of the total efficiency gain is larger than that of residential lighting, but it is smaller in overall magnitude of efficiency improvement.
V. The Residential Displays Case Study
increasing on a per-unit basis. This could possibly be attributed to monitors that were increasing in size and in output, or to an increase in the amount of time that owners were spending using their computers. However, as monitors and computer processors became more efficient over time, overall energy consumption per computer decreased significantly. Between 2008 and 2012, energy use per PC dropped by 8 percent. By 2020, the AEO projects that it will decrease by 24 percent relative to the 2008 peak. This is all due to organic conservation. The trend in energy consumption per PC is illustrated in Figure 7. Energy consumption per TV has exhibited a similar trend. Prior to 2009, TV size increased as plasma TVs and LCDs replaced cathode ray tube TVs. The associated increase in average TV screen size more than offset improvements in TV efficiency, and the result was an overall increase in TV energy consumption.19 However, a transition toward even more efficient TVs like LED-backlit LCDs has helped reverse this
Residential displays (i.e., personal computers, TVs) are an interesting case study because there are no codes and standards and few successful utility DSM programs to drive the market toward more efficient products. Therefore, all observed efficiency gains can be attributed to organic conservation. The residential displays case study is based entirely on historical and projected stock efficiency as derived from region-specific data reported in the 2013 AEO. rior to 2008, the amount of electricity consumed by personal computers (PCs) was
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trend beginning in 2009.20 By 2020, TVs are projected to consume 16 percent less energy than at the peak in 2008. This is illustrated in Figure 8. New standards for residential displays may be on the horizon. A 2012 study by the American Council for an Energy-Efficient Economy (ACEEE) posited that an efficiency standard for personal computers could come into being as early as 2019.21 ACEEE’s analysis assessed the impact of a standard that is consistent with the Energy Star version 5.0 requirements (computers meeting this standard use 65 percent less energy than the least efficient new products). Such a standard would produce national annual energy savings of 11.8 TWh by 2035 at a net present value of $8.6 billion, according to ACEEE. Similarly, ACEEE envisioned a potential efficiency standard for TVs. By 2016, ACEEE estimates that TVs could meet the Energy Star 5.3 efficiency requirements, which would lead to 10 TWh of annual energy savings at a present value of $8.3 billion nationally.22
Figure 7: Annual Energy Consumption per Personal Computer
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[(Figure_8)TD$IG]
Figure 8: Annual Energy Consumption per TV
VI. Conclusions and Policy Implications The findings of our study support the existence of organic conservation. We have identified three case studies in which energy efficiency improvements are expected to occur above and beyond any impacts of DSM programs or codes and standards. This conclusion is further supported by our survey of expert opinion. Most industry experts, based on firsthand experience and general intuition, agree that some improvements in energy efficiency occur naturally. he magnitude of the impact of organic conservation varies widely across our three case studies. It depends not only on the characteristics of the technology or appliance that is being evaluated, but also on timing in that technology’s development cycle. As observed historically in the case of residential displays, there are points where technology can naturally become less energy efficient due to customer preferences for other energy intensive features (e.g., larger TV
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screen sizes). Organic conservation may be cyclical in this sense for some technologies. But as technologies mature, there appears to be a trend toward improving efficiency. There is debate about what causes organic conservation. Some attribute it to evolving customer attitudes. Others feel it is driven naturally by the demands of the market. Others argue that it is the byproduct of policy initiatives that are not strictly considered DSM programs or codes and standards, but are still forms of ‘‘market intervention’’ nonetheless. However, from an energy efficiency policy perspective, the exact cause of organic conservation may not matter. The simple conclusion that efficiency gains are happening outside of both utility DSM programs and codes and standards have significant implications for energy efficiency policies. onsider energy savings targets – also known as energy efficiency resource standards – which exist for utilities in many states, including Minnesota. These targets are
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based on an assumption that, through DSM programs, utilities can achieve incremental sales reductions relative to a baseline forecast of electricity sales. If that baseline does not fully account for the impact of organic conservation (or, for that matter, codes and standards), the utilities may have to pursue unexpectedly expensive DSM programs in order to achieve the stated targets. Whether these more expensive DSM programs are cost-effective will depend on the specific system conditions of the utility. Decoupling is another energy efficiency related policy mechanism for which organic conservation has implications. Decoupling mechanisms can be structured many different ways. In most cases, utilities are made ‘‘whole’’ for sales reductions due to efficiency improvements. If the estimate of these sales reductions does not include the impact of organic conservation, the utilities could under-recover their costs. Utility DSM programs should also be designed with organic conservation impacts in mind. Certain end uses are naturally experiencing significant improvements in efficiency. It will be important to account for this effect when assessing the costeffectiveness of the programs. Some utilities already do this by accounting for free-ridership when establishing the impacts that are attributable to the DSM program. Similar considerations exist for codes and standards. The costs associated with establishing The Electricity Journal
a new standard should be weighed against the rate at which the intended efficiency improvement is likely to happen naturally in the absence of the standard.
VII. Recommendations for Further Research The organic conservation impact projections presented in this study are order-of-magnitude estimates. They illustrate the general degree of efficiency improvement that is happening outside of DSM programs and codes and standards. As the first study of its kind, the findings could be strengthened significantly through further research in a number of key areas. We have identified six research activities that would be particularly valuable in further extending the industry’s understanding of organic conservation: 1. Estimate organic conservation using a Delphi approach. As a follow-up to the survey of expert opinion, manufacturers could be interviewed to assess the degree to which appliances are being manufactured and sold above and beyond required efficiency levels. The manufacturers and other experts would be asked to quantify the magnitude of organic conservation’s likely impacts, and the collection of estimates would be used to derive a meaningful conclusion about the likely magnitude of impacts. June 2015,
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2. Back out the impact of organic conservation from utility sales forecasts using a regressionbased approach.It might be possible to establish a sales forecasting model, which, based on historical data, controls for the effects of the electricity price, weather, the economy, DSM programs, codes and standards, and other important factors. If the
model is designed well, the remaining energy savings trend observed in the model’s forecast can be attributed to organic conservation. Alternatively, rather than building a model from scratch with publicly available data, this activity could also be implemented using an existing utility sales forecasting model, controlling for any of the above described factors that are not already accounted for, and adding a time trend to the model. In either case, this would be a nice complement to the bottom-up case study approach, because it would provide an estimate of organic conservation at the class or system level.
3. Expand the sensitivity analysis. More robust sensitivity analysis could be conducted as an enhancement of the case studies. It would be possible to establish a plausible distribution of values for each uncertain variable in the analysis, and then run Monte Carlo simulations to create a measure of the overall uncertainty in the results. This would also help to identify the key drivers of the findings. 4. Develop additional case studies.It would be valuable to include additional appliance and end-use case studies, and develop an estimate of their impacts using a methodology similar to that described above. Industrial motors are one example of a potentially interesting new case study. 5. Incorporate historical assessments into the case studies. It may be possible to expand the case studies in our assessment to include a historical timeframe. This would require additional data gathering and may or may not be feasible given available data. 6. Conduct a pre-DSM era assessment of efficiency improvement.Prior to the origin of DSM programs and efficiency codes and standards in the 1970s, all improvements in per-capita energy efficiency could be considered organic conservation (or vice versa). It should be possible to quantify this trend using historical energy data.&
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Endnotes: 1. Faruqui, A., Shultz, E., 2012, December. Demand Growth and The New Normal. Public Utilities Fortnightly. 2. Faruqui, A., 2013, June. surviving sub one percent sales growth. Electr. Policy. 3. Derived from U.S. EIA data in the 2013 Annual Energy Outlook and 2012 Annual Energy Review.
26 percent of residential lighting energy consumption that is currently from CFLs is attributed to EISA. This impact is fully reached in 2020, with a linear ramp-up in prior years. An alternative and more aggressive assumption about EISA-driven efficient lighting adoption was analyzed through sensitivity analysis. 10. The Cadmus Group, 2012, November. Minnesota Home Lighting Program Evaluation. Prepared for Xcel Energy. p. 48.
4. Utility DSM programs provide a financial incentive for customers to consume electricity more efficiently. Codes and standards establish minimum efficiency levels for certain end uses.
7. Such an approach, however, would be a valuable research activity and is included in our recommendations for further analysis.
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15. EISA includes a maximum allowable wattage for incandescent and halogen lamps (2012), and certain metal halide lamp fixtures must meet minimum ballast efficiency requirement (2009). EPACT includes standards for medium base CFLs (2006), for ballasts for Energy Saver fluorescent lamps (2009 and 2010), and bans mercury vapor lamp ballasts (2008). 16. Vine, E., Eto, J., Shown, L., Sonnenblick, R., Payne, C., 1994, Evaluation of Commercial Lighting Programs: A DEEP Assessment. Lawrence Berkeley National Laboratory. p. 8.243. http://emp.lbl. gov/sites/all/files/lbnl-36522.pdf.
5. We use the terms ‘‘energy consumption,’’ ‘‘sales,’’ and ‘‘usage’’ interchangeably throughout the paper. 6. Moore’s Law, named after Gordon Moore, the co-founder of Intel, is the observation that computing efficiency doubles approximately every two years. See www.mooreslaw.org. For a review of the improvements in digital electronics that have taken place during the past five decades, consistent with the tenets of the law, see this article: http://www. mercurynews.com/business/ ci_27934824/silicon-valleymarks-50-years-moores-law. It says that if semiconductor prices had stayed at their 1971 levels, a personal computer today would cost $195 million. An iPhone 6 has some 3 billion transistors on it, up from the 1 billion transistors on an iPhone 5S, which came out just a year prior.
organic conservation could represent 42 percent to 65 percent of total efficiency improvement.
11. This estimate of free-ridership does not include any ‘‘spillover effect,’’ described earlier as customers changing their preferences as a result of the DSM program. For example, customers may continue purchasing energy efficient products after the program has ended, or they may purchase even more efficient products than are covered by the program. 12. U.S. EIA, 2013, April. Annual Energy Outlook 2013.
8. Slightly over half of the bulbs in the average home are incandescents, roughly a quarter are CFLs, around 1 percent are LEDs, and the rest are other types of bulbs.
13. The AEO forecast does not explicitly account for the impact of new utility DSM programs. However, it is calibrated to historical trends in lighting technology adoption. To the extent that utility DSM programs have helped to drive these trends, their impacts should be embedded in the forecast.
9. Under this methodology, since EISA only mandates a roughly 30 percent improvement in lighting efficiency, only 30 percent of the
14. We also conducted sensitivity analysis on the key assumptions in our analysis. Under different assumptions and methodologies, we find that the
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17. The chart is only representative of customers participating in the basic commercial lighting program, which is limited to medium and large businesses that apply for a single technology change. Utility DSM impacts in this graph do not include any bundle approaches to energy efficiency or other lighting-focused programs. 18. Based on an alternative case, we found that organic conservation could account for as much as 83 percent of the total efficiency improvement. 19. Herter, K., 2012. Get Smart Guide: Energy Innovation for the Consumer Electronic Industry. Smart Electronics Initiative. p. 6. http://greentech leadership.org/documents/2013/07/ get-smart-guide.pdf. 20. Park, W.Y., Phadke, A., Shah, N., Letschert, V. TV Energy Consumption Trends Energy-Efficiency Improvement Options. Lawrence Berkeley National Laboratory. p. xv. https://isswprod.lbl.gov/library/ view-docs/public/output/rpt81012. PDF. 21. Lowenberger, A., Mauer, J., et al., 2012, March. ASAP/ACEEE. The Efficiency Boom: Cashing in on Savings from Appliance Standards. p. 27. 22. Ibid, p. 31.
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