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Missing the spark: An investigation into the low adoption paradox of combined heat and power technologies
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Energy Policy 34 (2006) 3153–3164 www.elsevier.com/locate/enpol
Missing the spark: An investigation into the low adoption paradox of combined heat and power technologies Steffen Mueller Energy Resources Center, University of Illinois at Chicago, 851 South Morgan Street, 1313 SEO, Chicago 60607, USA Available online 25 July 2005
Abstract This study tests the influence of regulatory requirements for Combined Heat and Power (CHP) technologies on its adoption in the market-place controlling for other relevant variables identified in the literature. Control variables in this study include profitability of CHP technology at the individual firm level, ownership structure of the firm, and knowledge about CHP within firms. Employing a logit model with data collected from a survey and an energy engineering software program, the study finds that firms initially search for CHP technologies in order to reduce their current energy cost. Subsequently, however, firms abandon the adoption process due to concerns about the complexity of regulatory requirements. Ownership structure and familiarity with CHP are found to be not significant in this analysis. The study recommends that information programs that promote CHP need to place stronger emphasis on the profitability of CHP. Another recommendation calls for the deployment of alternative regulatory structures that could govern CHP. This research was supported with a grant from the National Science Foundation’s Integrative Graduate Education and Research Traineeship Program (Grant DGE 9870646). r 2005 Elsevier Ltd. All rights reserved. Keywords: CHP; Distributed generation; Technology adoption
1. Introduction Recent technological advances create the potential to generate electricity more efficiently and in a more environmentally friendly manner than current power plants. Many of these advances are in the field of combined heat and power distributed generation technology (CHP). In contrast to traditional centralized power plants that distribute electricity via high-voltage transmission systems to the end-user, CHP systems generate electricity at the point of end use. Furthermore, CHP systems recover the otherwise wasted heat from the generation process and convert it into useful energy for heating, cooling, or other energy needs of a building or facility. The recovery of the waste heat allows CHP systems to utilize fuel in a much more efficient manner. In fact, CHP systems can often turn more than 80% of Tel.: +1 312 3553982; fax: +1 312 9965620.
E-mail address: [email protected]. 0301-4215/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2005.06.016
the fuel’s theoretical energy content into useful energy, compared to 33% for traditional centralized power plants (Grohnheit, 1999, p. 108). Despite the apparent efficiency advantage, widespread diffusion of this technology in the marketplace is slow. The slow adoption of cost-effective energy conservation technologies has been documented for various other energy conservation technologies and has become known as the ‘‘energy paradox.’’ Shama notes that ‘‘the apparent high potential/low adoption of conservation energy constitutes ‘‘irrational’’ behavior on the part of consumers from the viewpoint of many energy engineers and economists, who would expect speedy adoption of energy conservation, once engineering and economic calculations are favorable’’ (1983, p. 149). More recently the Intergovernmental Panel on Climate Change stated: ‘‘It has long been recognized that consumers do not act on their stated values and fail to take up measures that appear on paper to be economically worthwhile’’ (IPCC, 2001, Section 5.3.8).
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This present study attempts to provide explanations for the energy paradox associated with CHP technologies. Its main hypothesis is that regulatory requirements are partially responsible for the nonadoption of CHP technologies in the marketplace. The main regulatory requirements for CHP systems are associated with (a) controlling the air emissions (b) connecting a CHP system to the electric lines of the incumbent utility company and submitting to unreasonable backup power tariffs, and (c) complying with local electric, plumbing, and other code requirements. The influence of some of these regulations on different kinds of technology adoption has been studied before. The unique feature of this study, however, is that it employs a logit model to determine the influence of regulatory requirements on the adoption of one particular energy-efficient technology, CHP technology, controlling for other adoption relevant variables identified in the literature. Furthermore, this study applies a novel approach employed by Weiss (1994) to the study of the energy paradox. In a study on the adoption of new circuit board manufacturing technologies, Weiss not only looks at two states of technology adoption (i.e. adoption and nonadoption) but introduces a third state comprised of those firms that have sought out a new technology but not proceeded with the adoption process. The present study uses the same approach and looks at adopters, nonadopters, and searchers of CHP technologies. Data for this study are collected from a survey and an original energy engineering analysis. Data collection is geographically limited to Illinois to (a) control for regulatory variations across states, and (b) take advantage of the fact that the existence of the energy paradox for CHP has been well documented for the state (Midwest CHP Application Center, 2003). Regulators and policy makers have called for the development and deployment of regulatory instruments that may provide preferential treatment to new energy technologies that convert fuel more efficiently (Laitner, 1999; Lovins, 2002; Freedman, 2003). Understanding the effect of regulatory requirements on the adoption of a new energy-efficient technology, such as CHP technology, will serve to inform this discussion.
2. Background on the technology adoption decision Previous studies that have investigated why new technologies get adopted in the way in which they do often look at variables such as a technology’s profitability or firm-related aspects, such as a firm’s ownership structure. Edwin Mansfield (1968) in a seminal study looks at the influence of a technology’s profitability on its adoption. He finds that the length of time a firm waits to adopt a new technology is inversely related to the
technology’s profitability. Mansfield argues that if the returns from a technology are very high it is worthwhile for a firm to ‘‘gamble’’ on the new technology; if the returns are low then the firm waits till the risks are reduced. Rose and Joskow (1990) look at ownership structure as a relevant variable to technology adoption and find that this variable contributes to adoption behavior since investor-owned utilities adopted a certain coal fired electric generating technology earlier than utilities owned by government entities. They state that investor-owned utilities are more likely to behave like profit maximizing firms than government-owned utilities. While institutional economists and sociologists generally agree that firm ownership structure and a technology’s profitability are important to the adoption of new technologies, they argue that other variables, such as cognitive and regulatory ones, are equally or even more influential to the adoption behavior of many technologies. With respect to cognitive influences on technology adoption, Herbert Simon (1979) finds that economic actors (individual firms or individuals at firms) may not necessarily optimize their choices but rather look for satisfactory ones, a process that he calls ‘‘bounded rational’’ behavior. Simon states that economic actors may have to choose between two alternatives for which they have only imperfect knowledge because of limited computational power and because of uncertainty in the external world. The regulatory environment is also relevant to a technology’s adoption. Oster and Quigley (1982) show that outmoded building codes in residential construction has impeded technical progress in many regulatory jurisdictions. Joskow and Rose (1985) look at variables that influence the construction cost of coal burning generating facilities. They find a residual cost increase, i.e. a cost increase that is not easily attributable to construction cost increases. They state that some of these residual costs may reflect the costs of responding to environmental regulations or local siting requirements. Jaffe and Stavins (1994) study the adoption of energyconserving technology in residential construction and show that building codes can spur the adoption of these technologies. The authors also find that information about new technologies may be underprovided by the market in which case public information campaigns through governmental programs could effectively reduce the failure of a particular technology in the marketplace. In summary, several key variables have been identified in previous studies that influence a technology’s adoption. While this study hypothesizes that the regulatory environment contributes significantly to the nonadoption of CHP technologies, the reviewed studies necessitate the need to control for other adoption relevant
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variables such as technology profitability, firm ownership structure, and technical knowledge within firms.
3. The energy paradox for CHP technologies As discussed in the introductory section, CHP systems are able to recover the heat ejected by the power generation process for heating, cooling, or other energy needs due to their proximity to the end-user. In contrast, most centralized power plants discard this heat as waste into the environment. CHP systems can provide substantial financial savings on energy costs especially for those buildings where there is a high demand for the recovered heat. Buildings such as hospitals, office buildings, schools, museums, data centers, and hotels are generally heated or cooled during extended periods of the day and hence require large amounts of energy that can be provided by CHP systems. Furthermore, certain industrial processes such as plastic manufacturing, paper milling, and food processing have high heating or cooling needs. Valenti (2001) employs building energy analysis software and models the cost savings achieved for hypothetical CHP systems when compared to traditional utility energy provision by the incumbent utility company. Valenti indicates that while savings vary by building type and operating scenario of the CHP system, the savings amount to at least 20% of the annual energy cost for hospitals, schools, and office buildings in the Chicago area. A series of case studies also documents the costeffectiveness for CHP systems in Illinois. The Midwest CHP Application Center has conducted 10 different case studies that assess the capital investment and the yearly savings associated with a CHP installation (Midwest CHP Application Center, 2003). The cited yearly average savings with an installed CHP system over traditional energy supply from the local utility companies range from $200,000 for a small-size CHP system installed at a Chicago suburban hospital to $7,000,000 annual savings for a large university campus. Despite these relatively attractive savings for CHP technologies, the adoption of them remains slow. This nonadoption phenomenon, while evident throughout the country, has been particularly documented for Illinois. The Midwest CHP Application Center (2002) conducted an inventory assessment of current CHP installations in the commercial sector in the state. The study shows that only about 112 MW of CHP capacity are installed in the commercial sector. More specifically about 26 MW of CHP capacity are installed in hospitals and nursing homes, 74 MW in schools (including colleges and universities), and 1.2 MW in office buildings, with the bulk of the remaining commercial capacity installed at museums, conference centers, and water treatment facilities.
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In a US Department of Energy (DOE) funded study, Onsite Energy Corporation (2000) shows that, based on the thermal and electric demand characteristics of buildings in Illinois, the CHP market potential in the commercial sector is much higher than the current installation level with an estimated 2700 MW of total capacity potential: for hospitals and nursing homes alone, the Onsite study lists a market potential of 580 MW, for schools, colleges and universities a market potential of 762 MW, and for office buildings a total of 494 MW. In summary, despite the documented costeffectiveness of CHP in Illinois, the low adoption phenomenon prevails.
4. Regulatory requirements for CHP systems The US DOE and the US Environmental Protection Agency organized a workshop in 1999 that gathered representatives from CHP manufacturing companies, electric/gas utilities, state/federal governments and environmental organizations. The workshop report concludes that regulatory barriers such as interconnection and backup power tariff requirements from the local utility, air permitting, and compliance with local building codes slow the adoption of CHP technologies (Energetics Inc., 2000). In the following section each of these three regulatory requirements will be discussed in more detail. 4.1. The environmental permitting process for CHP systems The Clean Air Act from 1970 and the Clean Air Act Amendments (CAA) from 1990 set forth the environmental permitting process for air emissions sources such as CHP facilities. Depending on the prevailing air quality in geographic region different emissions thresholds apply. CHP facilities with emissions above these thresholds are required to utilize certain prescribed emission control technologies. The administrative agency responsible for the enforcement of the Clean Air Act in Illinois is the Illinois Environmental Protection Agency (IEPA). IEPA imposes yearly permitting fees for CHP facilities depending on the amount of pollutants (measured in tons per year) emitted from the facilities.1 The maximum amount chargeable by IEPA is $100,000 per year for a major source. Besides the IEPA fee structure, additional costs may be incurred with the permitting process, as many CHP developers employ permitting consultants during the initial permitting 1 Permit exemptions apply to small CHP facilities with capacity sizes less than 1118 kW in size or a fuel use of less than 10.56 GJ/h (Illinois CHP/BCHP Permitting Guidebook, Volume A, 2003).
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4.2. The electric interconnection process
electric rates that these utility companies can charge to their electricity customers are highly regulated by state utility regulatory agencies. In Illinois, the Illinois Commerce Commission (ICC) is responsible for regulating the rates of investor-owned utility companies. In most cases, facilities with CHP systems connect to the local utility company’s electric system for backup power during periods of maintenance and malfunctioning of the CHP system. The ICC also approves the rates for backup power charged by utility companies to CHP facilities in the state. CHP proponents argue that utility companies discourage CHP adoption because they (a) require costly and unreasonable study fees and upgrades to their electric system from CHP facilities trying to connect to their system, and (b) that the backup power rates are set too high and do not reflect the benefits of CHP systems to the electric system. A report by the National Renewable Energy Laboratory (2000) states that a lack of consistent interconnection standards means that the technical requirements, as well as the time and fee requirements, can vary widely for similar projects from utility to utility and even within the same utility territory. Unreasonable backup power rates that are not tied to the true costs a utility incurs to provide this service can also significantly discourage the installation of CHP systems. The report documents problems incurred by 65 distributed generation projects during the interconnection process across the United States. In 2002, The Minnesota Department of Commerce established a working group to advise the Minnesota Public Utility Commission on distributed generation interconnection standards and backup power rate setting. The working group’s final report recommends uniform interconnection standards and standardized stand-by rate making procedures. The working group also proposes that the interconnection and stand-by rate making process should take into account all of the advantages that CHP systems provide to stabilize the electricity grid (Minnesota Department of Commerce, 2003).2 Texas and Wisconsin have implemented uniform interconnection standards that specify exactly how much time utilities can spend to study the impact of a particular CHP project on their utility system as well as the fees that can be charged for these studies (Brown et al., 2002). Furthermore, California has implemented a complete exemption from stand-by power rates for projects constructed until 2005. In contrast, Chicago-based Commonwealth Edison (ComEd) does not have a fixed fee structure charged for its interconnection studies that assess the technical requirements of a CHP system trying to connect to the
Historically, investor-owned utility companies provided the majority of electricity in the United States. The
2 CHP systems connected to the electric grid can stabilize the grid by balancing certain voltage and electric current fluctuations.
phase. The time requirements for the initial permitting process may vary significantly depending on various factors of the project such as geographic location, size, and required emissions control technology and can range from at least 3 to 12 months (Illinois CHP/BCHP Permitting Guidebook, Volume A, 2003). Proponents of CHP technologies argue that by establishing one set of emissions thresholds for all emissions sources regardless of their relative efficiencies the CAA fails to promote CHP use. In fact, a summary report following a workshop hosted by the EPA in 1999 on CHP concludes that environmental permitting, and specifically the failure to recognize the efficiency of CHP during the permitting process, constitutes a major barrier to CHP adoption (Energetics Inc., 2000). Recently, permitting approaches that provide incentives for CHP systems have been developed. These include permitting approaches that constitute a shift from the currently used input-based emission assessments to output-based emission assessments. The current permitting process measures emissions based on the fuel input into a power plant or the total emissions from the plant regardless of the efficiency with which the facility converts fuel into useful energy. CHP systems, in this case, are subject to the same permitting limits despite the fact that these facilities are much more efficient, i.e. reduce the need to produce energy from less efficient plants. For that reason several groups advocate a shift to output-based standards (i.e. based on pounds per kilowatt generated) that would provide emission credits for higher generating efficiencies and a credit for the productive utilization of the waste heat. Texas and California recently introduced the use of output-based standards and provide an emissions credit for CHP facilities. Output-based standards are also supported by various groups including the Regulatory Assistance Project, the State and Territorial Air Pollution Program Administrators, the Association of Local Air Pollution Control Officials, and the Ozone Transport Commission (see Freedman, 2003). Iowa is currently considering introducing a permit-by-rule provision for CHP systems that would expedite the permitting process for these systems (Iowa Department of Natural Resources, 2003). Illinois, however, is part of a majority of states that neither provides for efficiency credits nor pre-certification of CHP equipment for environmental permitting purposes. The emissions from CHP systems in Illinois are still regulated using a command-and-control approach by setting emission standards regardless of the efficiency of these systems.
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utility’s transmission and distribution system. Furthermore, CHP systems that intend to purchase stand-by service from ComEd for backup during maintenance or unplanned outages are subject to ComEd’s stand-by service rate. A recent study shows that a 30 min long failure of a CHP system under this rate structure can erase the yearly energy savings from that system (Midwest CHP Application Center, 2002). As a result, the uncertainties associated with interconnection fees and the stand-by rate structures are viewed as major barriers by CHP proponents in the state. 4.3. Local code process In Illinois, there are approximately 1300 municipalities made up of cities, villages, and towns. Each municipality is authorized by Illinois Municipal Code 65 ILCS5 to oversee building codes, fire-codes, and zoning codes development. Chicago building codes, for example, have not been updated to include CHP technologies for backup emergency power supply. Realizing the problems created by a fragmented local code system, the US DOE implemented an educational program for code officials, termed the Distributed Energy Roadshow. As part of this program, DOE officials host meetings around the country to educate local code officials on distributed generation technologies and the code requirements of these systems. Other efforts to reduce potential barriers imposed by local codes are underway with the development of standards for CHP systems that, in turn, can be accepted into model-codes. For example, the Association of State Energy Research and Technology Transfer Institutions (ASERTTI) is currently coordinating the development of testing protocols for CHP systems that may become a standard for these systems (ASSERTI, 2003). In the meantime, however, the fragmented local code system and the unfamiliarity of local code officials with CHP technologies continue to be barriers to CHP adoption.
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choice models do not require the dependent variable to be normally distributed. Thus, a rational choice model to predict the adopter versus nonadopter decision of CHP technologies is an appropriate model for this study. Rivers et al. (2003, pp. 6–188) specifically supports the view that rational choice models, with their capability to perform an analysis between two discrete choices, is ‘‘consistent with the way most energy using technologies are actually chosen (e.g. choice between buying an electric furnace versus a natural gas furnace is a discrete, not continuous, choice).’’ Unlike OLS-based regression, which assumes a linear regression function, rational choice models are based on nonlinear functions. This is because the estimated probabilities of the dependent variables must fit within an interval of 0–1 (Kennedy, 1998, p. 234). The most commonly used function is the logistic regression function which creates the logit model (Kennedy, 1998, p. 234). The regression coefficients determined with logistic functions are called logit coefficients, which are defined as the natural log of the odds, used in the equation to estimate the log odds that the dependent variable equals 1. The logistic regression function applied to the present study takes the following form (Rivers et Al., 2003): , J X vj PðjÞ ¼ e eVj , j¼1
where P(j) is the probability that a firm will adopt CHP technology, Vj the utility function of firm j. The utility function of firm j can be represented as a linear combination of observed adoption relevant variables: V j ¼ bj þ
K X
bk ðregulatory variablesÞ
k¼1
þ
L X
b1ðcontrol variablesÞ;
l¼1
5. Methodology To test the hypothesis that regulatory complexity significantly retards the adoption of CHP technologies, a model is needed that identifies the contribution of regulatory complexity to the adoption of CHP technology, controlling for the influences of the cognitive and firm-related variables. Methodologically this does not lend itself to an analysis based on ordinary least-square regression (OLS) since one of the central assumptions of OLS regression is that the dependent variable is normally distributed; however, a dichotomous dependent variable cannot fulfill this assumption (Garson, 2004). In contrast to OLS regression models, rational
where Vj is the firm exhibiting observed CHP adoption behavior j: Vj takes the value 1 if a firm adopts CHP and 0 otherwise, bk ; bl the logit coefficients estimated from the data, bj the alternative specific constants, and K,L the sets of adoption behavior relevant variables. The significance of the regression coefficients (i.e. the test of the null hypothesis that a particular logit coefficient is zero) is commonly determined with the ‘‘Wald’’ statistic. The Wald statistic is the ratio of the logit coefficient to its standard error. The significance level (p) of the Wald statistic corresponds to the significance testing of the b coefficients in OLS regressions. Significance levels (p) will be shown in the present study.
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6. Description of data Data on regulatory, cognitive, and firm-ownership variables have been collected with a survey. CHP profitability data have been calculated with an energy engineering software program. Both survey data and energy engineering data are discussed in the following section. 6.1. Survey data The survey concentrates on those business sectors where CHP provides an attractive fit (hospitals, schools, data centers, museums, industrial facilities). Within each of the business sectors, the survey looks at three adopter-status groups: (1) facilities that have to date not adopted and not evaluated CHP technologies for their use (termed nonadopters); (2) facilities that have evaluated CHP technologies for their use but not gone forward with the adoption to date (termed searchers); and (3) companies that have evaluated CHP technologies and subsequently adopted the technology (termed adopters). As discussed in the introduction, this approach is consistent with the study by Weiss (1994), which uses a similar three-group classification in his technology adoption study. With a total of approximately 50 CHP systems installed in Illinois CHP adoption is considered low (Midwest CHP Application Center, 2002). While low adoption rates are by definition a part of the energy paradox, they also limit the sample frame for a survey. This relatively low amount of CHP adopters and searchers excluded the use of random sampling for efficiency purposes. Therefore, a stratified random sampling approach was chosen with each stratum represented by one adopter-status group. The sample frame for the adopter and searcher strata was compiled from databases obtained from the Midwest Cogeneration Association, the Midwest CHP Application Center, and websites from CHP developers active in the state. The sample frame for the nonadopter stratum was based on Dunn and Bradstreet databases and address information provided by the Metropolitan Chicago Healthcare Council (an association that covers hospitals in Illinois), the DOE Industrial Assessment Center, and the Chicago Chapter of the Association of Energy Engineers. The selection of the interviewees followed Campbell’s ‘‘technique of the informant,’’ meaning that particular effort was placed on selecting a contact person at each facility who was well informed about the subject matter and who was able to articulate the information well (1955). As a result, common titles of the interviewees were: Director of Utilities, Building Engineer, Director of Plant Operations, and Maintenance Manager.
The survey was sent out by mail to a total of 171 facilities. A total of 103 surveys were returned resulting in a response rate of 60%. Table 1 lists the returned surveys by adopter-status and business sector. Twenty survey questions asked the respondents to rate their knowledge of CHP-related technology terms and CHP regulatory processes and 12 questions to rate their perceived complexity of the individual CHPrelated regulatory processes. The employed rating scale ranged from 1 to 10 (‘‘10’’ indicating ‘‘very familiar’’ or ‘‘very complex’’ depending on the question). The mean ratings by adopter-status group for each question are shown in Table 2. Another question assessed the ownership structure of the surveyed facility. Table 2 also shows the percentage of firms owned by government/nonprofit institutions. For the majority of the questions aimed at assessing knowledge of CHP technology and the regulatory process associated with it, the adopter and searcher groups exhibit higher mean knowledge ratings than nonadopters. Searchers are generally more aligned with adopters than with nonadopters. The trend indicates that CHP evaluation studies are educational and raise the knowledge levels within firms. For the majority of the questions related to the current standard-based regulatory requirements for CHP technologies, searchers and nonadopters indicate with their higher mean complexity ratings that they perceive the regulatory processes to be more complex than adopters. Searchers align more closely with nonadopters than with adopters in these ratings. This trend may indicate that CHP evaluation studies do not alter the firms’ perception about the complexity of the regulatory processes. Finally, the survey shows that ownership (i.e whether a firm is owned by a government/nonprofit or private entity) does not vary widely among adopter-status groups. However, this may be a function of a simplified classification of the ownership variable in only two groups. Further research of this variable is warranted. 6.2. Energy engineering data As discussed earlier the profitability of a given technology likely influences its adoption. With respect to CHP technology, profitability is defined as the net Table 1 Survey response characteristics Adopter status
Adopter
Nonadopter
Searcher
Hospitals Other/industrial Schools
10 10 11
13 13 14
10 9 13
Total
31
40
32
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Table 2 Mean survey ratings Adopters
Nonadopters
Searchers
7.4 7.3 4.3 7.5 3.4 2.2 2.6 5.7 4.0 4.4 4.1 4.0 5.8 7.9 7.2 3.9 4.6 4.7 5.3 4.9
5.1 5.6 3.3 4.2 2.8 1.9 2.1 3.1 2.8 3.1 2.8 3.2 2.9 3.8 3.6 2.8 2.7 2.8 3.0 2.8
7.3 7.6 4.9 7.1 4.3 2.1 2.6 5.4 4.7 4.3 4.4 4.5 4.4 6.3 5.5 4.3 4.7 3.8 4.3 4.3
Regulatory complexity variables Rate time requirements for CHP environmental permitting Rate time requirements for CHP rezoning Rate time requirements for CHP electric interconnection Rate time requirements for CHP firecode/local code approvals Rate cost of fees for environmental permitting requirements Rate cost of fees for rezoning requirements Rate cost of fees for electric interconnection requirements Rate cost of fees for firecode/local code requirements Rate complexity of environmental permitting requirements Rate complexity of rezoning requirements Rate complexity of interconnection requirements Rate complexity of firecode/local code approval requirements
5.0 4.9 5.9 4.6 3.3 3.4 5.2 3.4 4.5 3.9 6.0 4.1
6.4 6.0 6.1 6.5 5.4 5.8 6.0 5.1 7.4 7.2 7.1 6.6
5.8 5.7 6.2 5.7 5.0 4.8 6.0 4.2 7.1 5.7 6.6 5.2
Ownership Percent of facilities government/nonprofit owned
71
65
81
Cognitive variables Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with
HRSG Technology Absorption Chilling Technology Microturbine Technology Gas Recip. Engine Technology Desiccant Wheels Technology New Source Review BACT Analysis NOx Major Source Permit Ozone Attainment Area Generator Lead Load Flow Study Interconnection Agreements Standby-Power Rates Grid Parallel Operation Zoning for Electric Generating Facilities Plumbing for Electric Generating Facilities OSHA Standards for Generating Facilities Electric Codes for Generating Facilities Noise Studies for Generating Facilities
savings achieved from energy provision from this technology relative to energy provision that does not utilize CHP technology. The profitability variable for CHP technology is approximated by the energy engineering modeling software program Building Energy Analyzer (BEA) for each of the surveyed facilities.3 This is a second-best option since CHP profitability is difficult to determine with a survey for confidentiality reasons. Certain profitability-related variables required as input variables by the energy engineering software such as facility size in square-feet, geographic location, and facility type are determined in the survey for use in the energy engineering analysis. BEA determines the profitability of a CHP system by correlating certain facility characteristics such as firm 3 BEA was developed by the Gas Technology Institute, Des Plaines, Illinois, and is distributed through InterEnergy Software Incorporated.
size, building type and construction method as well as thermal requirements and electricity demand of the facility with other relevant variables influencing CHP profitability such as the climatic conditions and the prevailing electricity and fuel prices in the geographic area of the facility. BEA is chosen for this analysis since, unlike other energy engineering software programs, it incorporates more detailed building type and construction method information into the energy engineering model (Downes, 2004). To test the accuracy of BEA, profitability estimates from the software program are compared against the documented profitability of actual, installed CHP facilities. The Midwest CHP Application Center has compiled ‘‘fact-sheets’’ for a total of 10 CHP facilities located in Illinois, which are installed at various hospitals, schools, and a nursing home (Midwest CHP Application Center, 2003). The fact sheets provide
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information on the actual profitability of the CHP systems installed at the facilities in dollars. The analysis shows that the BEA predicted savings are within 20% of the fact sheet savings. The correlation coefficient of 0.93 between the predicted savings and the fact sheet savings is relatively high indicating that 93 percent of the variation of the BEA predicted savings can be explained by variations in the fact sheet results. In summary, the BEA predicted savings provide a fairly accurate approximation of the actually incurred savings with CHP. With the ability of the BEA software model to accurately predict CHP savings confirmed, the energy savings are modeled for all 103 surveyed facilities.
7. Logit analysis 7.1. Dependent variables A logit model is applied using adopter-status as the dependent variable and selected adoption relevant variables as the independent variables. Two different model runs are performed termed ‘‘Model A’’ and ‘‘Model B.’’ Model A groups adopters and searchers together against nonadopters. Model A therefore determines the underlying differences between companies that have evaluated CHP technologies (regardless whether they have gone forward with the adoption process) versus those companies that have not at all evaluated this technology; thus, in essence Model A represent the decision to search for this technology. The dependent variable for Model A equals ‘‘0’’ for nonadopters and ‘‘1’’ for adopters as well as searchers. Model B treats the various adopter-status groups differently and determines the underlying differences between companies that have adopted CHP technologies (adopters) versus those that have not adopted CHP technologies (searchers and nonadopters) with respect to these companies’ regulatory, cognitive, and firm-related variables relevant to CHP technologies. In Model B searchers are treated as nonadopters on the premise that they have not gone forward with the adoption process to date despite their obvious interest in this technology. 7.2. Independent variables The first independent variable is selected from the survey and represents the familiarity of the respondents with CHP-related technology terms. Also selected from the survey, the second variable represents the perceived complexity of the regulatory requirements. Looking at the trends in the mean ratings across questions assessing familiarity with CHP-related technology terms (see Table 2), the variable assessing the familiarity of respondents with HRSG technology is most reflective of the trend that searchers and adopters are generally
more familiar with CHP technologies than nonadopters are: the variable exhibits a relatively high familiarity rating with CHP technologies for adopters (7.4 mean rating) that is closely aligned with the familiarity rating of searchers (7.3 mean rating), yet both ratings are distinctly higher than the ratings for the nonadopters (5.1 mean rating). In turn, looking at the trends in the mean ratings across all questions assessing regulatory complexity the variable assessing the perceived complexity of the environmental permitting process is most representative of the trend that nonadopters and searchers generally perceive all regulatory requirements to be more complex than adopters do: the variable exhibits a relatively high complexity rating for nonadopters (7.4 mean rating) that is closely aligned with a relatively high mean rating for searchers (7.1 mean rating), yet both ratings are distinctly higher than the ratings for the adopters (4.5 mean rating). A third variable derived from the survey assesses the ownership structure of the particular firm. This variable is a dummy variable that is set to ‘‘1’’ if the facility is owned by a government or a religious, nonprofit entity and set to ‘‘0’’ for all other ownership structures. A fourth independent variable used in the logit analysis controls for the profitability of CHP technologies. This variable, called ‘‘BEA Predicted Savings with CHP’’ (TSAV), is a continuous variable and represents the profitability of CHP at the various facilities as determined with the BEA software. The variable is rescaled by dividing all calculated savings by the largest predicted savings so that the variable would assume values between 0 and 1. Since the various variables are based on stratified sampling, an additional weighing variable is applied based on the sampling fraction and the population in each sampling stratum (i.e. in each adopter-status group). The population in each stratum is derived from a study by Onsite Energy Corporation (2000) that assesses existing CHP facilities and market potential for CHP systems by business sector for each state. 7.3. Estimation results Table 3 shows three different specifications for each Model A and Model B. Specification (1) shows the influence of the BEA predicted profitability variable on CHP adoption exclusive of the influence of variables such as cognitive and regulatory ones. In essence this specification represents the classic financial investment decision by firms that seek to maximize profits. Specification (2) adds one cognitive (‘‘Familiarity with HRSG Technology’’) and one regulatory variable (‘‘Complexity of Environmental Permitting Requirements’’) to the BEA predicted profitability variable. Thereby specification (2) determines the influence of
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Table 3 Binary logit model results Variables
Model A (adopters/searchers vs. nonadopters)
Model B (adopters vs. searchers/nonadopters)
Variable description
1 n ¼ 98 r2 ¼ 0:068
2 n ¼ 80 r2 ¼ 0:145
3 n ¼ 77 r2 ¼ 0:142
1 n ¼ 99 r2 ¼ 0:001
2 n ¼ 85 r2 ¼ 0:049
3 n ¼ 84 r2 ¼ 0:058
Constant
3.158 0.554 20.460 9.225
3.385 2.036 35.85 16.090 0.211 0.214 0.220 0.197
3.664 2.413 32.245 17.350 0.222 0.226 0.225 0.191 0.472 1.249
3.326 0.576 1.110 2.792
3.223 2.611 1.595 2.988 0.345 0.303 0.459 0.275
3.970 3.397 2.178 3.551 0.350 0.333 0.491 0.280 1.004 1.904
TSAV BEA predicted savings with CHP (transformed) FAMHRSG Familiarity with HRSG technology CPXPERM Complexity of environmental permitting requirements OWNSHGOV Dummy variable ¼ 1 for government/nonprofit facility
For each specification the number of observations ‘‘n’’ is shown as well as the r2 value. For each variable, the b coefficient (value in italics) is shown with the associated standard error (in the line below the b coefficient). Significance levels of po0:05 are indicated with a double star symbol next to the b coefficient; significance levels of po0:1 are indicated with a single star symbol. Significant at 10%. Significant at 5%.
regulatory requirements by controlling both for the profitability of this technology and a variable representing general familiarity with CHP technology. Specification (3), additionally, controls for ownership structure. Overall, the r2 values are higher for Model A indicating a stronger association between the adoption relevant variables and the predicted adoption behavior for this model than for Model B. Looking at the significance of adoption relevant variables for specification (1) of Model A (which groups searchers with adopters) indicates that BEA predicted profitability is a significant variable that distinguishes nonadopters from the combined group of searchers and adopters. The profitability variable exhibits a positive b coefficient indicating that higher BEA predicted profitability of CHP at a facility has a positive effect on the searching decision. Specification (2) and specification (3) for Model A show that when controlled for by BEA predicted profitability the cognitive variable, the regulatory complexity variable, and the ownership dummy variable are not significant. While profitability influences the searching decision represented by Model A, specification (1) in Model B (which groups searchers with nonadapters) indicates that BEA predicted profitability (TSAV) by itself does not significantly influence the adoption decision. Specification (2) and specification (3) for Model B reveal that, in fact, the variable representing regulatory complexity is significant to the adoption decision when controlled for by BEA predicted savings. The b coefficient for the regulatory complexity variable has a negative sign
indicating that higher time requirements have a negative effect on the CHP adoption decision. The familiarity variable is not significant in either Model A or Model B indicating that the searching and adoption decision by firms does not seem to be influenced by a firm’s knowledge about this technology. Likewise the ownership dummy variable is not significant in either Model A or Model B, indicating that ownership may not matter to the CHP adoption or searching process; in other words being owned by a government or nonprofit organization does not influence the searching or adoption decision by firms. In summary, the logit analysis looks at two different models with Model A determining the impetus of firms to evaluate or search for CHP technologies, and Model B determining the impetus of firms to finally adopt CHP technologies. High predicted CHP profitability provides a significant impetus to search for CHP technologies. Complexity of the regulatory processes does not influence the decision to search. In contrast, final adoption of CHP technology does not seem to be influenced by profitability considerations of this technology, but rather by the perceived complexity of the regulatory processes: the higher the perceived complexity associated with the regulatory processes, the lower the probability of adoption. Finally, familiarity with CHP technology and CHP regulatory processes as well as ownership was not found to be a significant to the searching or adoption decision. These findings may indicate that firms initially search for CHP technologies in order to reduce their current
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energy cost. Once the searching process confirms energy savings, regulatory compliance emerges as a subsequent concern, which leads firms to abandon the adoption process. The fact that ownership is not a significant variable may indicate that governmental entities follow similar investment considerations in new technologies as nongovernmental ones.
8. Conclusions Using selected variables from a survey and a profitability measure calculated with an energy engineering model, a logit analysis was performed that tested for the influence of regulatory complexity on CHP adoption while controlling for the influence of other adoption relevant variables. The logit analysis was designed to evaluate (a) the underlying differences between companies that have evaluated CHP technologies (searchers and adopters) versus those that have not evaluated CHP technologies in the past (nonadopters), and (b) the underlying differences between companies that have adopted CHP technologies versus those companies that have not. The results from the logit analysis showed that the potential for energy savings significantly influenced the decision by companies to search for CHP (the higher the predicted CHP profitability, the higher the predicted probability to search for CHP). The perceived complexity associated with CHP-related regulatory processes was not a significant variable in the CHP searching process. However, once the searching process confirmed energy savings, the perceived complexity associated with the CHP-related regulatory processes emerged as a subsequent concern and significantly influenced the final adoption process (the higher the perceived regulatory complexity associated with CHP, the lower the predicted adoption probability). Reports by the National Renewable Energy Laboratory (2000) and Energetics Inc. (2000) suggest that requirements associated with the current regulatory system pose a barrier to CHP adoption. While these studies rely on qualitative case studies, the present study showed that their findings can be supported using a logit model rather than reliance on qualitative case-study methodology only. While Mansfield (1968) pointed out the importance of a technology’s profitability to its adoption, the present study showed that a more detailed analysis of this variable is possible. The present study showed that there is a sequential element to the profitability variable with profitability constituting only an initial concern during the evaluation stage. Profitability, however, was shown to depend on regulatory clarity during the final adoption process where regulatory requirements emerge as an influential variable.
Rose and Joskow (1990) argue that a firm’s ownership structure contributes to adoption behavior since investor-owned utilities adopted a certain coal fired electric generating technology earlier than utilities owned by government entities. The logit analysis, however, showed that ownership structure may not be a significant variable to the adoption of this technology. Finally, this study found that the CHP adoption decision did not depend significantly on familiarity with this technology. This seems to support Simon (1979), who states that firms make decisions in an environment of imperfect information. Jaffe and Stavins (1994) stated that information campaigns through government programs can reduce the market failure of a particular technology. The finding that familiarity with CHP technology and CHP regulatory requirements is not a significant adoption relevant variable but profitability is indicates that government programs that try to alleviate a possible market failure of CHP technologies through information provision should not focus on elevating general technical knowledge about CHP; rather, these programs must focus on the profitability of this technology. For example, governmental programs that promote CHP could host CHP financing workshops for current facilities managers, or interface with current business curriculums at colleges that educate future facilities managers. Furthermore, this study shows that there is some indication that ownership structure may not be an important variable to technology adoption. Therefore, when designing a public policy program to promote the profitability of CHP, the focus should be on sectors where CHP provides a technical fit, rather than by ownership structure. For example, public policy programs should not be limited to nonprofit owned hospitals, but should be targeting hospitals in general, since they provide a good CHP fit from an energy engineering point of view. Finally, regulatory complexity associated with the current regulatory processes was found to retard adoption of CHP technologies. As a result alternative regulatory structures to the current ones should be considered. As described above for each of the current regulatory structures alternative ones have been developed and could be implemented. For the environmental permitting process, for example, output-based regulations can reduce complexity by providing expedited permitting review or permitting exemptions. For local codes, the adoption of model codes can reduce the fragmented local code system. Finally, uniform interconnection procedures can reduce the complexity associated with this process. Since this study has shown that the current regulatory instruments retard the adoption of CHP technology, it is strongly suggested
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that the deployment of these alternative regulatory structures should be considered very seriously. Changes to the electric interconnection process are within the jurisdiction of the ICC. While utilities are investor-owned, their rate structures (including backup power rates) and policies for customer access are directly approved by the ICC as part of the ICC’s mandate to ensure the citizens of Illinois safe, efficient, reliable, and uninterrupted utility service at reasonable prices. Therefore, the ICC can require revisions to the current interconnection and backuprate structures. Changes to the local codes will primarily require educating the local code authorities on the potential benefits of CHP so that they either adopt model codes that address CHP-related code issues or that these authorities directly address CHP in their existing codes. As discussed earlier, the DOE is currently sponsoring an effort called the Distributed Energy Roadshow that hosts seminars on CHP technologies for local code officials. However, the program targets only larger cities and needs to be expanded to cover smaller communities as well. Changes to the environmental permitting process that would reduce the complexity for CHP may take a long time since these changes most likely require an amendment to the Illinois Clean Air Act. This is the case because the alternatives to the current regulatory structure such as CHP pre-certification or emission credits deviate substantially from current practice. Some alterative effort has been made by the IEPA to reduce the complexity associated with the CHP environmental permitting process: the agency recently designated a single-point of contact for all CHP-related permitting questions. The above policy recommendations may not immediately solve the ‘‘energy paradox’’ associated with CHP technology. However, this study has shown that regulatory complexity significantly influences CHP adoption. Overall with this finding, well-directed policy adjustments should be able to reduce the effects of the ‘‘energy paradox’’ for CHP technologies.
References Association of State Energy Research and Technology Transfer Institutions (ASSERTI), 2003. Program Overview. Collaborative National Program for the Development and Performance Testing of Distributed Power Technologies with Emphasis on Combined Heat and Power Technologies. www.asertti.org Brown, E., et al., 2002. State Opportunities for Action: Review of States’ Combined Heat and Power Activities. American Council for an Energy-Efficient Economy, Washington, DC, White Paper Series. Campbell, D.T., 1955. The informant in quantitative research. American Journal of Sociology 60, 339–342.
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Downes, B., 2004. Evaluation of thermal and economic feasibility analysis software. Masters Thesis, University of Illinois at Chicago. Energetics, Inc. and American Council for and Energy Efficient Economy, 2000. Lake Michigan Regional CHP Roadmap Workshop November 10–11, 1999, Chicago Illinois. Workshop Report, February 23. Freedman, S., 2003. Output-based standards: a means to promote innovative technologies. Cogeneration and On-Site Power Production 4 (2), 47–52. Garson, D., 2004. Statnotes PA765: Logistic Regression. North Caroline State University, Raleigh, NC. www2.chass.ncsu.edu/ garson/pa765 Grohnheit, P.E., 1999. Energy Policy Responses to the Climate Change Challenge: The Consistency of European CHP, Renewables and Energy Efficiency Policies, RISO National Laboratory, Denmark. Illinois CHP/BCHP Permitting Guidebook, 2003. Volume A: Roadmapping the Permitting Process, Midwest CHP Application Center, www.chpcentermw.org Iowa Department of Natural Resources, 2003. Streamlining CHP Permitting: Steering Committee Recommendations. Review Draft, July 18. IPCC (Intergovernmental Panel on Climate Change), TAR 2001. WG III, Chapter 5. Jaffe, A.B., Stavins, R.N., 1994. The energy paradox and the diffusion of conservation technology. Resource and Energy Economics 16, 91–122. Joskow, P.L., Rose, N.L., 1985. The effects of technological change, experience, and environmental regulation on the construction of coal-burning generating units. Rand Journal of Economics 16 (1), 1–27. Kennedy, P., 1998. A Guide to Econometrics. The MIT Press, Cambridge, MA. Laitner, J. (Skip), et al., 1999. Federal strategies to increase the implementation of combined heat and power technologies in the United States. In: Proceedings of the 1999 ACEEE Summer Study on Energy Efficiency in Industry. American Council for an EnergyEfficient Economy, Washington, DC. Lovins, A.B., et al., 2002. Small is profitable. The Hidden Economic Benefits of Making Electrical Resources the Right Size. Rocky Mountain Institute, August. Mansfield, E., 1968. Industrial Research and Technological Innovation. An Econometric Analysis. W.W. Norton & Company, Inc., New York. Midwest CHP Application Center, 2002. BCHP Baseline Analysis for the Illinois Market. www.chpcentermw.org Midwest CHP Application Center, 2003. Searchable Database of CHP Installations.www.chpcentermw.org Minnesota Department of Commerce, 2003. Report on Distributed Generation Technical Standards and Tariffs. Docket No. E999/CI01-1023, February 3, 2003. National Renewable Energy Laboratory, 2000. Making connections. Case Studies of Interconnection Barriers and their Impact on Distributed Power Projects. NREL/SR-20028053. Onsite Energy Corporation, 2000. The Market and Technical Potential for Combined Heat and Power in the Commercial/Institutional Sector. Onsite Sycom Energy Corporation. Oster, S.M., Quigley, J.M., 1982. Regulatory barriers to the diffusion of innovation. Some evidence from building codes. The Bell Journal of Economics 57, 311–319. Rivers, N., et al., 2003. Confronting the challenge of hybrid modeling: using discrete choice models to inform the behavioural parameters of a hybrid model. In: Proceedings of the 2003 ACEEE Summer Study on Energy Efficiency in Industry.
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American Council for an Energy-Efficient Economy, Washington, DC. Rose, N., Joskow, P., 1990. The diffusion of new technologies: Evidence from the electric utility industry. Rand Journal of Economics 21 (3), 354–373. Shama, A., 1983. Energy conservation in US buildings. Solving the high potential/low adoption paradox from a behavioral perspective. Energy Policy (June), 148–167.
Simon, H., 1979. Rational decision making in business organizations. The American Economic Review 69 (4), 493–513. Valenti, G., 2001. An assessment of combined heat and power for medium-sized commercial applications. Masters Thesis, University of Illinois at Chicago. Weiss, A.M., 1994. The effects of expectations on technology adoption: some empirical evidence. The Journal of Industrial Economics XLII, 341–360.
Energy Policy 34 (2006) 3153–3164 www.elsevier.com/locate/enpol
Missing the spark: An investigation into the low adoption paradox of combined heat and power technologies Steffen Mueller Energy Resources Center, University of Illinois at Chicago, 851 South Morgan Street, 1313 SEO, Chicago 60607, USA Available online 25 July 2005
Abstract This study tests the influence of regulatory requirements for Combined Heat and Power (CHP) technologies on its adoption in the market-place controlling for other relevant variables identified in the literature. Control variables in this study include profitability of CHP technology at the individual firm level, ownership structure of the firm, and knowledge about CHP within firms. Employing a logit model with data collected from a survey and an energy engineering software program, the study finds that firms initially search for CHP technologies in order to reduce their current energy cost. Subsequently, however, firms abandon the adoption process due to concerns about the complexity of regulatory requirements. Ownership structure and familiarity with CHP are found to be not significant in this analysis. The study recommends that information programs that promote CHP need to place stronger emphasis on the profitability of CHP. Another recommendation calls for the deployment of alternative regulatory structures that could govern CHP. This research was supported with a grant from the National Science Foundation’s Integrative Graduate Education and Research Traineeship Program (Grant DGE 9870646). r 2005 Elsevier Ltd. All rights reserved. Keywords: CHP; Distributed generation; Technology adoption
1. Introduction Recent technological advances create the potential to generate electricity more efficiently and in a more environmentally friendly manner than current power plants. Many of these advances are in the field of combined heat and power distributed generation technology (CHP). In contrast to traditional centralized power plants that distribute electricity via high-voltage transmission systems to the end-user, CHP systems generate electricity at the point of end use. Furthermore, CHP systems recover the otherwise wasted heat from the generation process and convert it into useful energy for heating, cooling, or other energy needs of a building or facility. The recovery of the waste heat allows CHP systems to utilize fuel in a much more efficient manner. In fact, CHP systems can often turn more than 80% of Tel.: +1 312 3553982; fax: +1 312 9965620.
E-mail address: [email protected]. 0301-4215/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2005.06.016
the fuel’s theoretical energy content into useful energy, compared to 33% for traditional centralized power plants (Grohnheit, 1999, p. 108). Despite the apparent efficiency advantage, widespread diffusion of this technology in the marketplace is slow. The slow adoption of cost-effective energy conservation technologies has been documented for various other energy conservation technologies and has become known as the ‘‘energy paradox.’’ Shama notes that ‘‘the apparent high potential/low adoption of conservation energy constitutes ‘‘irrational’’ behavior on the part of consumers from the viewpoint of many energy engineers and economists, who would expect speedy adoption of energy conservation, once engineering and economic calculations are favorable’’ (1983, p. 149). More recently the Intergovernmental Panel on Climate Change stated: ‘‘It has long been recognized that consumers do not act on their stated values and fail to take up measures that appear on paper to be economically worthwhile’’ (IPCC, 2001, Section 5.3.8).
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This present study attempts to provide explanations for the energy paradox associated with CHP technologies. Its main hypothesis is that regulatory requirements are partially responsible for the nonadoption of CHP technologies in the marketplace. The main regulatory requirements for CHP systems are associated with (a) controlling the air emissions (b) connecting a CHP system to the electric lines of the incumbent utility company and submitting to unreasonable backup power tariffs, and (c) complying with local electric, plumbing, and other code requirements. The influence of some of these regulations on different kinds of technology adoption has been studied before. The unique feature of this study, however, is that it employs a logit model to determine the influence of regulatory requirements on the adoption of one particular energy-efficient technology, CHP technology, controlling for other adoption relevant variables identified in the literature. Furthermore, this study applies a novel approach employed by Weiss (1994) to the study of the energy paradox. In a study on the adoption of new circuit board manufacturing technologies, Weiss not only looks at two states of technology adoption (i.e. adoption and nonadoption) but introduces a third state comprised of those firms that have sought out a new technology but not proceeded with the adoption process. The present study uses the same approach and looks at adopters, nonadopters, and searchers of CHP technologies. Data for this study are collected from a survey and an original energy engineering analysis. Data collection is geographically limited to Illinois to (a) control for regulatory variations across states, and (b) take advantage of the fact that the existence of the energy paradox for CHP has been well documented for the state (Midwest CHP Application Center, 2003). Regulators and policy makers have called for the development and deployment of regulatory instruments that may provide preferential treatment to new energy technologies that convert fuel more efficiently (Laitner, 1999; Lovins, 2002; Freedman, 2003). Understanding the effect of regulatory requirements on the adoption of a new energy-efficient technology, such as CHP technology, will serve to inform this discussion.
2. Background on the technology adoption decision Previous studies that have investigated why new technologies get adopted in the way in which they do often look at variables such as a technology’s profitability or firm-related aspects, such as a firm’s ownership structure. Edwin Mansfield (1968) in a seminal study looks at the influence of a technology’s profitability on its adoption. He finds that the length of time a firm waits to adopt a new technology is inversely related to the
technology’s profitability. Mansfield argues that if the returns from a technology are very high it is worthwhile for a firm to ‘‘gamble’’ on the new technology; if the returns are low then the firm waits till the risks are reduced. Rose and Joskow (1990) look at ownership structure as a relevant variable to technology adoption and find that this variable contributes to adoption behavior since investor-owned utilities adopted a certain coal fired electric generating technology earlier than utilities owned by government entities. They state that investor-owned utilities are more likely to behave like profit maximizing firms than government-owned utilities. While institutional economists and sociologists generally agree that firm ownership structure and a technology’s profitability are important to the adoption of new technologies, they argue that other variables, such as cognitive and regulatory ones, are equally or even more influential to the adoption behavior of many technologies. With respect to cognitive influences on technology adoption, Herbert Simon (1979) finds that economic actors (individual firms or individuals at firms) may not necessarily optimize their choices but rather look for satisfactory ones, a process that he calls ‘‘bounded rational’’ behavior. Simon states that economic actors may have to choose between two alternatives for which they have only imperfect knowledge because of limited computational power and because of uncertainty in the external world. The regulatory environment is also relevant to a technology’s adoption. Oster and Quigley (1982) show that outmoded building codes in residential construction has impeded technical progress in many regulatory jurisdictions. Joskow and Rose (1985) look at variables that influence the construction cost of coal burning generating facilities. They find a residual cost increase, i.e. a cost increase that is not easily attributable to construction cost increases. They state that some of these residual costs may reflect the costs of responding to environmental regulations or local siting requirements. Jaffe and Stavins (1994) study the adoption of energyconserving technology in residential construction and show that building codes can spur the adoption of these technologies. The authors also find that information about new technologies may be underprovided by the market in which case public information campaigns through governmental programs could effectively reduce the failure of a particular technology in the marketplace. In summary, several key variables have been identified in previous studies that influence a technology’s adoption. While this study hypothesizes that the regulatory environment contributes significantly to the nonadoption of CHP technologies, the reviewed studies necessitate the need to control for other adoption relevant
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variables such as technology profitability, firm ownership structure, and technical knowledge within firms.
3. The energy paradox for CHP technologies As discussed in the introductory section, CHP systems are able to recover the heat ejected by the power generation process for heating, cooling, or other energy needs due to their proximity to the end-user. In contrast, most centralized power plants discard this heat as waste into the environment. CHP systems can provide substantial financial savings on energy costs especially for those buildings where there is a high demand for the recovered heat. Buildings such as hospitals, office buildings, schools, museums, data centers, and hotels are generally heated or cooled during extended periods of the day and hence require large amounts of energy that can be provided by CHP systems. Furthermore, certain industrial processes such as plastic manufacturing, paper milling, and food processing have high heating or cooling needs. Valenti (2001) employs building energy analysis software and models the cost savings achieved for hypothetical CHP systems when compared to traditional utility energy provision by the incumbent utility company. Valenti indicates that while savings vary by building type and operating scenario of the CHP system, the savings amount to at least 20% of the annual energy cost for hospitals, schools, and office buildings in the Chicago area. A series of case studies also documents the costeffectiveness for CHP systems in Illinois. The Midwest CHP Application Center has conducted 10 different case studies that assess the capital investment and the yearly savings associated with a CHP installation (Midwest CHP Application Center, 2003). The cited yearly average savings with an installed CHP system over traditional energy supply from the local utility companies range from $200,000 for a small-size CHP system installed at a Chicago suburban hospital to $7,000,000 annual savings for a large university campus. Despite these relatively attractive savings for CHP technologies, the adoption of them remains slow. This nonadoption phenomenon, while evident throughout the country, has been particularly documented for Illinois. The Midwest CHP Application Center (2002) conducted an inventory assessment of current CHP installations in the commercial sector in the state. The study shows that only about 112 MW of CHP capacity are installed in the commercial sector. More specifically about 26 MW of CHP capacity are installed in hospitals and nursing homes, 74 MW in schools (including colleges and universities), and 1.2 MW in office buildings, with the bulk of the remaining commercial capacity installed at museums, conference centers, and water treatment facilities.
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In a US Department of Energy (DOE) funded study, Onsite Energy Corporation (2000) shows that, based on the thermal and electric demand characteristics of buildings in Illinois, the CHP market potential in the commercial sector is much higher than the current installation level with an estimated 2700 MW of total capacity potential: for hospitals and nursing homes alone, the Onsite study lists a market potential of 580 MW, for schools, colleges and universities a market potential of 762 MW, and for office buildings a total of 494 MW. In summary, despite the documented costeffectiveness of CHP in Illinois, the low adoption phenomenon prevails.
4. Regulatory requirements for CHP systems The US DOE and the US Environmental Protection Agency organized a workshop in 1999 that gathered representatives from CHP manufacturing companies, electric/gas utilities, state/federal governments and environmental organizations. The workshop report concludes that regulatory barriers such as interconnection and backup power tariff requirements from the local utility, air permitting, and compliance with local building codes slow the adoption of CHP technologies (Energetics Inc., 2000). In the following section each of these three regulatory requirements will be discussed in more detail. 4.1. The environmental permitting process for CHP systems The Clean Air Act from 1970 and the Clean Air Act Amendments (CAA) from 1990 set forth the environmental permitting process for air emissions sources such as CHP facilities. Depending on the prevailing air quality in geographic region different emissions thresholds apply. CHP facilities with emissions above these thresholds are required to utilize certain prescribed emission control technologies. The administrative agency responsible for the enforcement of the Clean Air Act in Illinois is the Illinois Environmental Protection Agency (IEPA). IEPA imposes yearly permitting fees for CHP facilities depending on the amount of pollutants (measured in tons per year) emitted from the facilities.1 The maximum amount chargeable by IEPA is $100,000 per year for a major source. Besides the IEPA fee structure, additional costs may be incurred with the permitting process, as many CHP developers employ permitting consultants during the initial permitting 1 Permit exemptions apply to small CHP facilities with capacity sizes less than 1118 kW in size or a fuel use of less than 10.56 GJ/h (Illinois CHP/BCHP Permitting Guidebook, Volume A, 2003).
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4.2. The electric interconnection process
electric rates that these utility companies can charge to their electricity customers are highly regulated by state utility regulatory agencies. In Illinois, the Illinois Commerce Commission (ICC) is responsible for regulating the rates of investor-owned utility companies. In most cases, facilities with CHP systems connect to the local utility company’s electric system for backup power during periods of maintenance and malfunctioning of the CHP system. The ICC also approves the rates for backup power charged by utility companies to CHP facilities in the state. CHP proponents argue that utility companies discourage CHP adoption because they (a) require costly and unreasonable study fees and upgrades to their electric system from CHP facilities trying to connect to their system, and (b) that the backup power rates are set too high and do not reflect the benefits of CHP systems to the electric system. A report by the National Renewable Energy Laboratory (2000) states that a lack of consistent interconnection standards means that the technical requirements, as well as the time and fee requirements, can vary widely for similar projects from utility to utility and even within the same utility territory. Unreasonable backup power rates that are not tied to the true costs a utility incurs to provide this service can also significantly discourage the installation of CHP systems. The report documents problems incurred by 65 distributed generation projects during the interconnection process across the United States. In 2002, The Minnesota Department of Commerce established a working group to advise the Minnesota Public Utility Commission on distributed generation interconnection standards and backup power rate setting. The working group’s final report recommends uniform interconnection standards and standardized stand-by rate making procedures. The working group also proposes that the interconnection and stand-by rate making process should take into account all of the advantages that CHP systems provide to stabilize the electricity grid (Minnesota Department of Commerce, 2003).2 Texas and Wisconsin have implemented uniform interconnection standards that specify exactly how much time utilities can spend to study the impact of a particular CHP project on their utility system as well as the fees that can be charged for these studies (Brown et al., 2002). Furthermore, California has implemented a complete exemption from stand-by power rates for projects constructed until 2005. In contrast, Chicago-based Commonwealth Edison (ComEd) does not have a fixed fee structure charged for its interconnection studies that assess the technical requirements of a CHP system trying to connect to the
Historically, investor-owned utility companies provided the majority of electricity in the United States. The
2 CHP systems connected to the electric grid can stabilize the grid by balancing certain voltage and electric current fluctuations.
phase. The time requirements for the initial permitting process may vary significantly depending on various factors of the project such as geographic location, size, and required emissions control technology and can range from at least 3 to 12 months (Illinois CHP/BCHP Permitting Guidebook, Volume A, 2003). Proponents of CHP technologies argue that by establishing one set of emissions thresholds for all emissions sources regardless of their relative efficiencies the CAA fails to promote CHP use. In fact, a summary report following a workshop hosted by the EPA in 1999 on CHP concludes that environmental permitting, and specifically the failure to recognize the efficiency of CHP during the permitting process, constitutes a major barrier to CHP adoption (Energetics Inc., 2000). Recently, permitting approaches that provide incentives for CHP systems have been developed. These include permitting approaches that constitute a shift from the currently used input-based emission assessments to output-based emission assessments. The current permitting process measures emissions based on the fuel input into a power plant or the total emissions from the plant regardless of the efficiency with which the facility converts fuel into useful energy. CHP systems, in this case, are subject to the same permitting limits despite the fact that these facilities are much more efficient, i.e. reduce the need to produce energy from less efficient plants. For that reason several groups advocate a shift to output-based standards (i.e. based on pounds per kilowatt generated) that would provide emission credits for higher generating efficiencies and a credit for the productive utilization of the waste heat. Texas and California recently introduced the use of output-based standards and provide an emissions credit for CHP facilities. Output-based standards are also supported by various groups including the Regulatory Assistance Project, the State and Territorial Air Pollution Program Administrators, the Association of Local Air Pollution Control Officials, and the Ozone Transport Commission (see Freedman, 2003). Iowa is currently considering introducing a permit-by-rule provision for CHP systems that would expedite the permitting process for these systems (Iowa Department of Natural Resources, 2003). Illinois, however, is part of a majority of states that neither provides for efficiency credits nor pre-certification of CHP equipment for environmental permitting purposes. The emissions from CHP systems in Illinois are still regulated using a command-and-control approach by setting emission standards regardless of the efficiency of these systems.
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utility’s transmission and distribution system. Furthermore, CHP systems that intend to purchase stand-by service from ComEd for backup during maintenance or unplanned outages are subject to ComEd’s stand-by service rate. A recent study shows that a 30 min long failure of a CHP system under this rate structure can erase the yearly energy savings from that system (Midwest CHP Application Center, 2002). As a result, the uncertainties associated with interconnection fees and the stand-by rate structures are viewed as major barriers by CHP proponents in the state. 4.3. Local code process In Illinois, there are approximately 1300 municipalities made up of cities, villages, and towns. Each municipality is authorized by Illinois Municipal Code 65 ILCS5 to oversee building codes, fire-codes, and zoning codes development. Chicago building codes, for example, have not been updated to include CHP technologies for backup emergency power supply. Realizing the problems created by a fragmented local code system, the US DOE implemented an educational program for code officials, termed the Distributed Energy Roadshow. As part of this program, DOE officials host meetings around the country to educate local code officials on distributed generation technologies and the code requirements of these systems. Other efforts to reduce potential barriers imposed by local codes are underway with the development of standards for CHP systems that, in turn, can be accepted into model-codes. For example, the Association of State Energy Research and Technology Transfer Institutions (ASERTTI) is currently coordinating the development of testing protocols for CHP systems that may become a standard for these systems (ASSERTI, 2003). In the meantime, however, the fragmented local code system and the unfamiliarity of local code officials with CHP technologies continue to be barriers to CHP adoption.
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choice models do not require the dependent variable to be normally distributed. Thus, a rational choice model to predict the adopter versus nonadopter decision of CHP technologies is an appropriate model for this study. Rivers et al. (2003, pp. 6–188) specifically supports the view that rational choice models, with their capability to perform an analysis between two discrete choices, is ‘‘consistent with the way most energy using technologies are actually chosen (e.g. choice between buying an electric furnace versus a natural gas furnace is a discrete, not continuous, choice).’’ Unlike OLS-based regression, which assumes a linear regression function, rational choice models are based on nonlinear functions. This is because the estimated probabilities of the dependent variables must fit within an interval of 0–1 (Kennedy, 1998, p. 234). The most commonly used function is the logistic regression function which creates the logit model (Kennedy, 1998, p. 234). The regression coefficients determined with logistic functions are called logit coefficients, which are defined as the natural log of the odds, used in the equation to estimate the log odds that the dependent variable equals 1. The logistic regression function applied to the present study takes the following form (Rivers et Al., 2003): , J X vj PðjÞ ¼ e eVj , j¼1
where P(j) is the probability that a firm will adopt CHP technology, Vj the utility function of firm j. The utility function of firm j can be represented as a linear combination of observed adoption relevant variables: V j ¼ bj þ
K X
bk ðregulatory variablesÞ
k¼1
þ
L X
b1ðcontrol variablesÞ;
l¼1
5. Methodology To test the hypothesis that regulatory complexity significantly retards the adoption of CHP technologies, a model is needed that identifies the contribution of regulatory complexity to the adoption of CHP technology, controlling for the influences of the cognitive and firm-related variables. Methodologically this does not lend itself to an analysis based on ordinary least-square regression (OLS) since one of the central assumptions of OLS regression is that the dependent variable is normally distributed; however, a dichotomous dependent variable cannot fulfill this assumption (Garson, 2004). In contrast to OLS regression models, rational
where Vj is the firm exhibiting observed CHP adoption behavior j: Vj takes the value 1 if a firm adopts CHP and 0 otherwise, bk ; bl the logit coefficients estimated from the data, bj the alternative specific constants, and K,L the sets of adoption behavior relevant variables. The significance of the regression coefficients (i.e. the test of the null hypothesis that a particular logit coefficient is zero) is commonly determined with the ‘‘Wald’’ statistic. The Wald statistic is the ratio of the logit coefficient to its standard error. The significance level (p) of the Wald statistic corresponds to the significance testing of the b coefficients in OLS regressions. Significance levels (p) will be shown in the present study.
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6. Description of data Data on regulatory, cognitive, and firm-ownership variables have been collected with a survey. CHP profitability data have been calculated with an energy engineering software program. Both survey data and energy engineering data are discussed in the following section. 6.1. Survey data The survey concentrates on those business sectors where CHP provides an attractive fit (hospitals, schools, data centers, museums, industrial facilities). Within each of the business sectors, the survey looks at three adopter-status groups: (1) facilities that have to date not adopted and not evaluated CHP technologies for their use (termed nonadopters); (2) facilities that have evaluated CHP technologies for their use but not gone forward with the adoption to date (termed searchers); and (3) companies that have evaluated CHP technologies and subsequently adopted the technology (termed adopters). As discussed in the introduction, this approach is consistent with the study by Weiss (1994), which uses a similar three-group classification in his technology adoption study. With a total of approximately 50 CHP systems installed in Illinois CHP adoption is considered low (Midwest CHP Application Center, 2002). While low adoption rates are by definition a part of the energy paradox, they also limit the sample frame for a survey. This relatively low amount of CHP adopters and searchers excluded the use of random sampling for efficiency purposes. Therefore, a stratified random sampling approach was chosen with each stratum represented by one adopter-status group. The sample frame for the adopter and searcher strata was compiled from databases obtained from the Midwest Cogeneration Association, the Midwest CHP Application Center, and websites from CHP developers active in the state. The sample frame for the nonadopter stratum was based on Dunn and Bradstreet databases and address information provided by the Metropolitan Chicago Healthcare Council (an association that covers hospitals in Illinois), the DOE Industrial Assessment Center, and the Chicago Chapter of the Association of Energy Engineers. The selection of the interviewees followed Campbell’s ‘‘technique of the informant,’’ meaning that particular effort was placed on selecting a contact person at each facility who was well informed about the subject matter and who was able to articulate the information well (1955). As a result, common titles of the interviewees were: Director of Utilities, Building Engineer, Director of Plant Operations, and Maintenance Manager.
The survey was sent out by mail to a total of 171 facilities. A total of 103 surveys were returned resulting in a response rate of 60%. Table 1 lists the returned surveys by adopter-status and business sector. Twenty survey questions asked the respondents to rate their knowledge of CHP-related technology terms and CHP regulatory processes and 12 questions to rate their perceived complexity of the individual CHPrelated regulatory processes. The employed rating scale ranged from 1 to 10 (‘‘10’’ indicating ‘‘very familiar’’ or ‘‘very complex’’ depending on the question). The mean ratings by adopter-status group for each question are shown in Table 2. Another question assessed the ownership structure of the surveyed facility. Table 2 also shows the percentage of firms owned by government/nonprofit institutions. For the majority of the questions aimed at assessing knowledge of CHP technology and the regulatory process associated with it, the adopter and searcher groups exhibit higher mean knowledge ratings than nonadopters. Searchers are generally more aligned with adopters than with nonadopters. The trend indicates that CHP evaluation studies are educational and raise the knowledge levels within firms. For the majority of the questions related to the current standard-based regulatory requirements for CHP technologies, searchers and nonadopters indicate with their higher mean complexity ratings that they perceive the regulatory processes to be more complex than adopters. Searchers align more closely with nonadopters than with adopters in these ratings. This trend may indicate that CHP evaluation studies do not alter the firms’ perception about the complexity of the regulatory processes. Finally, the survey shows that ownership (i.e whether a firm is owned by a government/nonprofit or private entity) does not vary widely among adopter-status groups. However, this may be a function of a simplified classification of the ownership variable in only two groups. Further research of this variable is warranted. 6.2. Energy engineering data As discussed earlier the profitability of a given technology likely influences its adoption. With respect to CHP technology, profitability is defined as the net Table 1 Survey response characteristics Adopter status
Adopter
Nonadopter
Searcher
Hospitals Other/industrial Schools
10 10 11
13 13 14
10 9 13
Total
31
40
32
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Table 2 Mean survey ratings Adopters
Nonadopters
Searchers
7.4 7.3 4.3 7.5 3.4 2.2 2.6 5.7 4.0 4.4 4.1 4.0 5.8 7.9 7.2 3.9 4.6 4.7 5.3 4.9
5.1 5.6 3.3 4.2 2.8 1.9 2.1 3.1 2.8 3.1 2.8 3.2 2.9 3.8 3.6 2.8 2.7 2.8 3.0 2.8
7.3 7.6 4.9 7.1 4.3 2.1 2.6 5.4 4.7 4.3 4.4 4.5 4.4 6.3 5.5 4.3 4.7 3.8 4.3 4.3
Regulatory complexity variables Rate time requirements for CHP environmental permitting Rate time requirements for CHP rezoning Rate time requirements for CHP electric interconnection Rate time requirements for CHP firecode/local code approvals Rate cost of fees for environmental permitting requirements Rate cost of fees for rezoning requirements Rate cost of fees for electric interconnection requirements Rate cost of fees for firecode/local code requirements Rate complexity of environmental permitting requirements Rate complexity of rezoning requirements Rate complexity of interconnection requirements Rate complexity of firecode/local code approval requirements
5.0 4.9 5.9 4.6 3.3 3.4 5.2 3.4 4.5 3.9 6.0 4.1
6.4 6.0 6.1 6.5 5.4 5.8 6.0 5.1 7.4 7.2 7.1 6.6
5.8 5.7 6.2 5.7 5.0 4.8 6.0 4.2 7.1 5.7 6.6 5.2
Ownership Percent of facilities government/nonprofit owned
71
65
81
Cognitive variables Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with Rate familiarity with
HRSG Technology Absorption Chilling Technology Microturbine Technology Gas Recip. Engine Technology Desiccant Wheels Technology New Source Review BACT Analysis NOx Major Source Permit Ozone Attainment Area Generator Lead Load Flow Study Interconnection Agreements Standby-Power Rates Grid Parallel Operation Zoning for Electric Generating Facilities Plumbing for Electric Generating Facilities OSHA Standards for Generating Facilities Electric Codes for Generating Facilities Noise Studies for Generating Facilities
savings achieved from energy provision from this technology relative to energy provision that does not utilize CHP technology. The profitability variable for CHP technology is approximated by the energy engineering modeling software program Building Energy Analyzer (BEA) for each of the surveyed facilities.3 This is a second-best option since CHP profitability is difficult to determine with a survey for confidentiality reasons. Certain profitability-related variables required as input variables by the energy engineering software such as facility size in square-feet, geographic location, and facility type are determined in the survey for use in the energy engineering analysis. BEA determines the profitability of a CHP system by correlating certain facility characteristics such as firm 3 BEA was developed by the Gas Technology Institute, Des Plaines, Illinois, and is distributed through InterEnergy Software Incorporated.
size, building type and construction method as well as thermal requirements and electricity demand of the facility with other relevant variables influencing CHP profitability such as the climatic conditions and the prevailing electricity and fuel prices in the geographic area of the facility. BEA is chosen for this analysis since, unlike other energy engineering software programs, it incorporates more detailed building type and construction method information into the energy engineering model (Downes, 2004). To test the accuracy of BEA, profitability estimates from the software program are compared against the documented profitability of actual, installed CHP facilities. The Midwest CHP Application Center has compiled ‘‘fact-sheets’’ for a total of 10 CHP facilities located in Illinois, which are installed at various hospitals, schools, and a nursing home (Midwest CHP Application Center, 2003). The fact sheets provide
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information on the actual profitability of the CHP systems installed at the facilities in dollars. The analysis shows that the BEA predicted savings are within 20% of the fact sheet savings. The correlation coefficient of 0.93 between the predicted savings and the fact sheet savings is relatively high indicating that 93 percent of the variation of the BEA predicted savings can be explained by variations in the fact sheet results. In summary, the BEA predicted savings provide a fairly accurate approximation of the actually incurred savings with CHP. With the ability of the BEA software model to accurately predict CHP savings confirmed, the energy savings are modeled for all 103 surveyed facilities.
7. Logit analysis 7.1. Dependent variables A logit model is applied using adopter-status as the dependent variable and selected adoption relevant variables as the independent variables. Two different model runs are performed termed ‘‘Model A’’ and ‘‘Model B.’’ Model A groups adopters and searchers together against nonadopters. Model A therefore determines the underlying differences between companies that have evaluated CHP technologies (regardless whether they have gone forward with the adoption process) versus those companies that have not at all evaluated this technology; thus, in essence Model A represent the decision to search for this technology. The dependent variable for Model A equals ‘‘0’’ for nonadopters and ‘‘1’’ for adopters as well as searchers. Model B treats the various adopter-status groups differently and determines the underlying differences between companies that have adopted CHP technologies (adopters) versus those that have not adopted CHP technologies (searchers and nonadopters) with respect to these companies’ regulatory, cognitive, and firm-related variables relevant to CHP technologies. In Model B searchers are treated as nonadopters on the premise that they have not gone forward with the adoption process to date despite their obvious interest in this technology. 7.2. Independent variables The first independent variable is selected from the survey and represents the familiarity of the respondents with CHP-related technology terms. Also selected from the survey, the second variable represents the perceived complexity of the regulatory requirements. Looking at the trends in the mean ratings across questions assessing familiarity with CHP-related technology terms (see Table 2), the variable assessing the familiarity of respondents with HRSG technology is most reflective of the trend that searchers and adopters are generally
more familiar with CHP technologies than nonadopters are: the variable exhibits a relatively high familiarity rating with CHP technologies for adopters (7.4 mean rating) that is closely aligned with the familiarity rating of searchers (7.3 mean rating), yet both ratings are distinctly higher than the ratings for the nonadopters (5.1 mean rating). In turn, looking at the trends in the mean ratings across all questions assessing regulatory complexity the variable assessing the perceived complexity of the environmental permitting process is most representative of the trend that nonadopters and searchers generally perceive all regulatory requirements to be more complex than adopters do: the variable exhibits a relatively high complexity rating for nonadopters (7.4 mean rating) that is closely aligned with a relatively high mean rating for searchers (7.1 mean rating), yet both ratings are distinctly higher than the ratings for the adopters (4.5 mean rating). A third variable derived from the survey assesses the ownership structure of the particular firm. This variable is a dummy variable that is set to ‘‘1’’ if the facility is owned by a government or a religious, nonprofit entity and set to ‘‘0’’ for all other ownership structures. A fourth independent variable used in the logit analysis controls for the profitability of CHP technologies. This variable, called ‘‘BEA Predicted Savings with CHP’’ (TSAV), is a continuous variable and represents the profitability of CHP at the various facilities as determined with the BEA software. The variable is rescaled by dividing all calculated savings by the largest predicted savings so that the variable would assume values between 0 and 1. Since the various variables are based on stratified sampling, an additional weighing variable is applied based on the sampling fraction and the population in each sampling stratum (i.e. in each adopter-status group). The population in each stratum is derived from a study by Onsite Energy Corporation (2000) that assesses existing CHP facilities and market potential for CHP systems by business sector for each state. 7.3. Estimation results Table 3 shows three different specifications for each Model A and Model B. Specification (1) shows the influence of the BEA predicted profitability variable on CHP adoption exclusive of the influence of variables such as cognitive and regulatory ones. In essence this specification represents the classic financial investment decision by firms that seek to maximize profits. Specification (2) adds one cognitive (‘‘Familiarity with HRSG Technology’’) and one regulatory variable (‘‘Complexity of Environmental Permitting Requirements’’) to the BEA predicted profitability variable. Thereby specification (2) determines the influence of
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Table 3 Binary logit model results Variables
Model A (adopters/searchers vs. nonadopters)
Model B (adopters vs. searchers/nonadopters)
Variable description
1 n ¼ 98 r2 ¼ 0:068
2 n ¼ 80 r2 ¼ 0:145
3 n ¼ 77 r2 ¼ 0:142
1 n ¼ 99 r2 ¼ 0:001
2 n ¼ 85 r2 ¼ 0:049
3 n ¼ 84 r2 ¼ 0:058
Constant
3.158 0.554 20.460 9.225
3.385 2.036 35.85 16.090 0.211 0.214 0.220 0.197
3.664 2.413 32.245 17.350 0.222 0.226 0.225 0.191 0.472 1.249
3.326 0.576 1.110 2.792
3.223 2.611 1.595 2.988 0.345 0.303 0.459 0.275
3.970 3.397 2.178 3.551 0.350 0.333 0.491 0.280 1.004 1.904
TSAV BEA predicted savings with CHP (transformed) FAMHRSG Familiarity with HRSG technology CPXPERM Complexity of environmental permitting requirements OWNSHGOV Dummy variable ¼ 1 for government/nonprofit facility
For each specification the number of observations ‘‘n’’ is shown as well as the r2 value. For each variable, the b coefficient (value in italics) is shown with the associated standard error (in the line below the b coefficient). Significance levels of po0:05 are indicated with a double star symbol next to the b coefficient; significance levels of po0:1 are indicated with a single star symbol. Significant at 10%. Significant at 5%.
regulatory requirements by controlling both for the profitability of this technology and a variable representing general familiarity with CHP technology. Specification (3), additionally, controls for ownership structure. Overall, the r2 values are higher for Model A indicating a stronger association between the adoption relevant variables and the predicted adoption behavior for this model than for Model B. Looking at the significance of adoption relevant variables for specification (1) of Model A (which groups searchers with adopters) indicates that BEA predicted profitability is a significant variable that distinguishes nonadopters from the combined group of searchers and adopters. The profitability variable exhibits a positive b coefficient indicating that higher BEA predicted profitability of CHP at a facility has a positive effect on the searching decision. Specification (2) and specification (3) for Model A show that when controlled for by BEA predicted profitability the cognitive variable, the regulatory complexity variable, and the ownership dummy variable are not significant. While profitability influences the searching decision represented by Model A, specification (1) in Model B (which groups searchers with nonadapters) indicates that BEA predicted profitability (TSAV) by itself does not significantly influence the adoption decision. Specification (2) and specification (3) for Model B reveal that, in fact, the variable representing regulatory complexity is significant to the adoption decision when controlled for by BEA predicted savings. The b coefficient for the regulatory complexity variable has a negative sign
indicating that higher time requirements have a negative effect on the CHP adoption decision. The familiarity variable is not significant in either Model A or Model B indicating that the searching and adoption decision by firms does not seem to be influenced by a firm’s knowledge about this technology. Likewise the ownership dummy variable is not significant in either Model A or Model B, indicating that ownership may not matter to the CHP adoption or searching process; in other words being owned by a government or nonprofit organization does not influence the searching or adoption decision by firms. In summary, the logit analysis looks at two different models with Model A determining the impetus of firms to evaluate or search for CHP technologies, and Model B determining the impetus of firms to finally adopt CHP technologies. High predicted CHP profitability provides a significant impetus to search for CHP technologies. Complexity of the regulatory processes does not influence the decision to search. In contrast, final adoption of CHP technology does not seem to be influenced by profitability considerations of this technology, but rather by the perceived complexity of the regulatory processes: the higher the perceived complexity associated with the regulatory processes, the lower the probability of adoption. Finally, familiarity with CHP technology and CHP regulatory processes as well as ownership was not found to be a significant to the searching or adoption decision. These findings may indicate that firms initially search for CHP technologies in order to reduce their current
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energy cost. Once the searching process confirms energy savings, regulatory compliance emerges as a subsequent concern, which leads firms to abandon the adoption process. The fact that ownership is not a significant variable may indicate that governmental entities follow similar investment considerations in new technologies as nongovernmental ones.
8. Conclusions Using selected variables from a survey and a profitability measure calculated with an energy engineering model, a logit analysis was performed that tested for the influence of regulatory complexity on CHP adoption while controlling for the influence of other adoption relevant variables. The logit analysis was designed to evaluate (a) the underlying differences between companies that have evaluated CHP technologies (searchers and adopters) versus those that have not evaluated CHP technologies in the past (nonadopters), and (b) the underlying differences between companies that have adopted CHP technologies versus those companies that have not. The results from the logit analysis showed that the potential for energy savings significantly influenced the decision by companies to search for CHP (the higher the predicted CHP profitability, the higher the predicted probability to search for CHP). The perceived complexity associated with CHP-related regulatory processes was not a significant variable in the CHP searching process. However, once the searching process confirmed energy savings, the perceived complexity associated with the CHP-related regulatory processes emerged as a subsequent concern and significantly influenced the final adoption process (the higher the perceived regulatory complexity associated with CHP, the lower the predicted adoption probability). Reports by the National Renewable Energy Laboratory (2000) and Energetics Inc. (2000) suggest that requirements associated with the current regulatory system pose a barrier to CHP adoption. While these studies rely on qualitative case studies, the present study showed that their findings can be supported using a logit model rather than reliance on qualitative case-study methodology only. While Mansfield (1968) pointed out the importance of a technology’s profitability to its adoption, the present study showed that a more detailed analysis of this variable is possible. The present study showed that there is a sequential element to the profitability variable with profitability constituting only an initial concern during the evaluation stage. Profitability, however, was shown to depend on regulatory clarity during the final adoption process where regulatory requirements emerge as an influential variable.
Rose and Joskow (1990) argue that a firm’s ownership structure contributes to adoption behavior since investor-owned utilities adopted a certain coal fired electric generating technology earlier than utilities owned by government entities. The logit analysis, however, showed that ownership structure may not be a significant variable to the adoption of this technology. Finally, this study found that the CHP adoption decision did not depend significantly on familiarity with this technology. This seems to support Simon (1979), who states that firms make decisions in an environment of imperfect information. Jaffe and Stavins (1994) stated that information campaigns through government programs can reduce the market failure of a particular technology. The finding that familiarity with CHP technology and CHP regulatory requirements is not a significant adoption relevant variable but profitability is indicates that government programs that try to alleviate a possible market failure of CHP technologies through information provision should not focus on elevating general technical knowledge about CHP; rather, these programs must focus on the profitability of this technology. For example, governmental programs that promote CHP could host CHP financing workshops for current facilities managers, or interface with current business curriculums at colleges that educate future facilities managers. Furthermore, this study shows that there is some indication that ownership structure may not be an important variable to technology adoption. Therefore, when designing a public policy program to promote the profitability of CHP, the focus should be on sectors where CHP provides a technical fit, rather than by ownership structure. For example, public policy programs should not be limited to nonprofit owned hospitals, but should be targeting hospitals in general, since they provide a good CHP fit from an energy engineering point of view. Finally, regulatory complexity associated with the current regulatory processes was found to retard adoption of CHP technologies. As a result alternative regulatory structures to the current ones should be considered. As described above for each of the current regulatory structures alternative ones have been developed and could be implemented. For the environmental permitting process, for example, output-based regulations can reduce complexity by providing expedited permitting review or permitting exemptions. For local codes, the adoption of model codes can reduce the fragmented local code system. Finally, uniform interconnection procedures can reduce the complexity associated with this process. Since this study has shown that the current regulatory instruments retard the adoption of CHP technology, it is strongly suggested
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that the deployment of these alternative regulatory structures should be considered very seriously. Changes to the electric interconnection process are within the jurisdiction of the ICC. While utilities are investor-owned, their rate structures (including backup power rates) and policies for customer access are directly approved by the ICC as part of the ICC’s mandate to ensure the citizens of Illinois safe, efficient, reliable, and uninterrupted utility service at reasonable prices. Therefore, the ICC can require revisions to the current interconnection and backuprate structures. Changes to the local codes will primarily require educating the local code authorities on the potential benefits of CHP so that they either adopt model codes that address CHP-related code issues or that these authorities directly address CHP in their existing codes. As discussed earlier, the DOE is currently sponsoring an effort called the Distributed Energy Roadshow that hosts seminars on CHP technologies for local code officials. However, the program targets only larger cities and needs to be expanded to cover smaller communities as well. Changes to the environmental permitting process that would reduce the complexity for CHP may take a long time since these changes most likely require an amendment to the Illinois Clean Air Act. This is the case because the alternatives to the current regulatory structure such as CHP pre-certification or emission credits deviate substantially from current practice. Some alterative effort has been made by the IEPA to reduce the complexity associated with the CHP environmental permitting process: the agency recently designated a single-point of contact for all CHP-related permitting questions. The above policy recommendations may not immediately solve the ‘‘energy paradox’’ associated with CHP technology. However, this study has shown that regulatory complexity significantly influences CHP adoption. Overall with this finding, well-directed policy adjustments should be able to reduce the effects of the ‘‘energy paradox’’ for CHP technologies.
References Association of State Energy Research and Technology Transfer Institutions (ASSERTI), 2003. Program Overview. Collaborative National Program for the Development and Performance Testing of Distributed Power Technologies with Emphasis on Combined Heat and Power Technologies. www.asertti.org Brown, E., et al., 2002. State Opportunities for Action: Review of States’ Combined Heat and Power Activities. American Council for an Energy-Efficient Economy, Washington, DC, White Paper Series. Campbell, D.T., 1955. The informant in quantitative research. American Journal of Sociology 60, 339–342.
3163
Downes, B., 2004. Evaluation of thermal and economic feasibility analysis software. Masters Thesis, University of Illinois at Chicago. Energetics, Inc. and American Council for and Energy Efficient Economy, 2000. Lake Michigan Regional CHP Roadmap Workshop November 10–11, 1999, Chicago Illinois. Workshop Report, February 23. Freedman, S., 2003. Output-based standards: a means to promote innovative technologies. Cogeneration and On-Site Power Production 4 (2), 47–52. Garson, D., 2004. Statnotes PA765: Logistic Regression. North Caroline State University, Raleigh, NC. www2.chass.ncsu.edu/ garson/pa765 Grohnheit, P.E., 1999. Energy Policy Responses to the Climate Change Challenge: The Consistency of European CHP, Renewables and Energy Efficiency Policies, RISO National Laboratory, Denmark. Illinois CHP/BCHP Permitting Guidebook, 2003. Volume A: Roadmapping the Permitting Process, Midwest CHP Application Center, www.chpcentermw.org Iowa Department of Natural Resources, 2003. Streamlining CHP Permitting: Steering Committee Recommendations. Review Draft, July 18. IPCC (Intergovernmental Panel on Climate Change), TAR 2001. WG III, Chapter 5. Jaffe, A.B., Stavins, R.N., 1994. The energy paradox and the diffusion of conservation technology. Resource and Energy Economics 16, 91–122. Joskow, P.L., Rose, N.L., 1985. The effects of technological change, experience, and environmental regulation on the construction of coal-burning generating units. Rand Journal of Economics 16 (1), 1–27. Kennedy, P., 1998. A Guide to Econometrics. The MIT Press, Cambridge, MA. Laitner, J. (Skip), et al., 1999. Federal strategies to increase the implementation of combined heat and power technologies in the United States. In: Proceedings of the 1999 ACEEE Summer Study on Energy Efficiency in Industry. American Council for an EnergyEfficient Economy, Washington, DC. Lovins, A.B., et al., 2002. Small is profitable. The Hidden Economic Benefits of Making Electrical Resources the Right Size. Rocky Mountain Institute, August. Mansfield, E., 1968. Industrial Research and Technological Innovation. An Econometric Analysis. W.W. Norton & Company, Inc., New York. Midwest CHP Application Center, 2002. BCHP Baseline Analysis for the Illinois Market. www.chpcentermw.org Midwest CHP Application Center, 2003. Searchable Database of CHP Installations.www.chpcentermw.org Minnesota Department of Commerce, 2003. Report on Distributed Generation Technical Standards and Tariffs. Docket No. E999/CI01-1023, February 3, 2003. National Renewable Energy Laboratory, 2000. Making connections. Case Studies of Interconnection Barriers and their Impact on Distributed Power Projects. NREL/SR-20028053. Onsite Energy Corporation, 2000. The Market and Technical Potential for Combined Heat and Power in the Commercial/Institutional Sector. Onsite Sycom Energy Corporation. Oster, S.M., Quigley, J.M., 1982. Regulatory barriers to the diffusion of innovation. Some evidence from building codes. The Bell Journal of Economics 57, 311–319. Rivers, N., et al., 2003. Confronting the challenge of hybrid modeling: using discrete choice models to inform the behavioural parameters of a hybrid model. In: Proceedings of the 2003 ACEEE Summer Study on Energy Efficiency in Industry.
ARTICLE IN PRESS 3164
S. Mueller / Energy Policy 34 (2006) 3153–3164
American Council for an Energy-Efficient Economy, Washington, DC. Rose, N., Joskow, P., 1990. The diffusion of new technologies: Evidence from the electric utility industry. Rand Journal of Economics 21 (3), 354–373. Shama, A., 1983. Energy conservation in US buildings. Solving the high potential/low adoption paradox from a behavioral perspective. Energy Policy (June), 148–167.
Simon, H., 1979. Rational decision making in business organizations. The American Economic Review 69 (4), 493–513. Valenti, G., 2001. An assessment of combined heat and power for medium-sized commercial applications. Masters Thesis, University of Illinois at Chicago. Weiss, A.M., 1994. The effects of expectations on technology adoption: some empirical evidence. The Journal of Industrial Economics XLII, 341–360.