LBNL-54437 

NREL/TP-620-35609 

ERNEST ORLANDO LAWRENCE

BERKELEY NATIONAL LABORATORY

NATIONAL RENEWABLE ENERGY LABORATORY

Utility Green Pricing Programs: 

A Statistical Analysis of Program 

Effectiveness

Ryan Wiser and Scott Olson

Lawrence Berkeley National Laboratory 

1 Cyclotron Rd., MS 90-4000 

Berkeley, California 94720 

Lori Bird and Blair Swezey 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, Colorado 80401 

Environmental Energy 

Technologies Division

February 2004 

Download from: http://eetd.lbl.gov/EA/EMP/

The work described in this study was funded by the Assistant Secretary of 

Energy Efficiency and Renewable Energy of the U.S. Department of Energy 

under Contract No. DE-ACO3-76SF00098 (LBNL), and DE-AC36-99-

GO10337 (NREL). 
Disclaimer 

This document was prepared as an account of work sponsored by the United States 

Government. While this document is believed to contain correct information, neither 

the United States Government nor any agency thereof, nor The Regents of the 

University of California, nor any of their employees, makes any warranty, express or 

implied, or assumes any legal responsibility for the accuracy, completeness, or 

usefulness of any information, apparatus, product, or process disclosed, or represents 

that its use would not infringe privately owned rights. Reference herein to any specific 

commercial product, process, or service by its trade name, trademark, manufacturer, or 

otherwise, does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States Government or any agency thereof, or The Regents of 

the University of California. The views and opinions of authors expressed herein do 

not necessarily state or reflect those of the United States Government or any agency 

thereof, or The Regents of the University of California. 
1

Table of Contents 

Acknowledgements......................................................................................................................... 2 

Executive Summary........................................................................................................................ 3 

1. Introduction............................................................................................................................... 4 

1.1 Background...................................................................................................................... 4 

1.2 Report Objectives and Structure ...................................................................................... 5 

2. Data Collection and Methods.................................................................................................... 6 

2.1 Data Collection ................................................................................................................ 6 

2.2 Variables and Hypotheses................................................................................................ 6 

2.3 Analysis Techniques ...................................................................................................... 11

2.4 Study Limitations........................................................................................................... 11 

3. Measures of Program Effectiveness........................................................................................ 13 

4. Bivariate Analysis Results ..................................................................................................... 16 

4.1 Residential Customer Analysis...................................................................................... 16 

4.2 Non-Residential Customer Analysis.............................................................................. 18 

5. Multivariate Analysis Results................................................................................................. 20 

5.1 Residential Customer Analysis...................................................................................... 20 

5.2 Non-Residential Customer Analysis.............................................................................. 22 

6 Conclusions............................................................................................................................. 24 

References..................................................................................................................................... 27 

Appendix A: Green Pricing Program Questionnaire .................................................................... 29 

Appendix B: Other Independent Variables Considered................................................................ 31 

Appendix C: Further Detail on Bivariate Results......................................................................... 32 

Residential Customer Analysis............................................................................................... 32 

Non-Residential Customer Analysis....................................................................................... 33 
2

Acknowledgements 

This work was funded by the Assistant Secretary of Energy Efficiency and Renewable Energy of 

the U.S. Department of Energy under Contract No. DE-ACO3-76SF00098 (to LBNL) and 

Contract No. DE-AC36-99-GO10337 (to NREL). We particularly appreciate the support and 

encouragement of Larry Mansueti, Susan Holte, Mary Beth Zimmerman, David McAndrew, and 

Jack Cadogan, all of the U.S. Department of Energy (U.S. DOE). Useful review comments were 

provided by Mark Bolinger (LBNL), Mary Beth Zimmerman (U.S. DOE), Ed Holt (Ed Holt & 

Associates), Adam Capage (Platts), and Leo Levenson (U.C. Berkeley). A special thanks also 

goes out to the utilities that returned the questionnaires, the results of which are analyzed in this 

report. 
3

Executive Summary 

Utility green pricing programs represent one way in which consumers can voluntarily support the 

development of renewable energy. Such programs have grown in number in recent years. The 

design features and effectiveness of these programs varies considerably, however, leading a 

variety of stakeholders to suggest specific marketing and program design features that might 

improve customer response and renewable energy sales. 

This report analyzes actual utility green pricing program data to provide further insight into 

which program features might help maximize both customer participation in green pricing 

programs and the amount of renewable energy purchased by customers in those programs. 

Statistical analysis is performed on both the residential and non-residential customer segments. 

Data comes from information gathered through a questionnaire completed for 66 utility green 

pricing programs in early 2003. The questionnaire specifically gathered data on residential and 

non-residential participation, amount of renewable energy sold, program length, the type of 

renewable supply used, program price/cost premiums, types of consumer research and program 

evaluation performed, different sign-up options available, program marketing efforts, and 

ancillary benefits offered to participants. 

While there are significant limitations to the approach used in this study, and our analysis yields 

fewer statistically significant relationships than had been hoped, this assessment does yield 

several interesting results. 

 

• First, we find that program length has a substantial impact on customer participation and 

purchases – to achieve higher levels of success, utilities will need to remain persistent and 

committed to their product offering for some time (e.g., on average, we find that each year of 

program operation leads to an increase in residential participation rates of ~0.25%). 

• Second, our findings suggest that utilities should consider higher purchase thresholds for 

residential customers – those utilities with larger renewable energy block sizes (>200 kWh) 

or percent-of-use products (>25%) also tend to have higher levels of residential renewable 

energy sales, with no obvious negative impact on the overall level of customer participation. 

• Third, neither the volumetric price premium (for non-residential customers) nor the minimum 

monthly cost (for residential customers) are found to be primary determinants of program 

success, at least over the relatively narrow range of premiums embedded in our data set. 

• Fourth, while larger utilities benefit from a greater number of potential customers, smaller 

utilities appear able to achieve higher levels of residential customer participation on a 

percentage basis. Interestingly, once size is controlled for, we find little evidence that utility 

ownership (private vs. public) is a principal determinant of green pricing success. 

• Fifth, we find some evidence that providing private benefits to non-residential participants 

(e.g., business recognition) can enhance success. 

While the results presented in this paper are suggestive, we urge readers to not place undue 

emphasis on them. Green pricing programs in the United States are still relatively new and have 

yet to gain a foothold in many markets. As these programs continue to grow and mature, more 

data will be available from which to assess the key aspects to program success. 
4

1. Introduction 

1.1 Background 

Consumer choice in electricity markets is a relatively new occurrence in the United States and 

throughout the world. As consumers have been given greater freedom in selecting the electricity 

supplier and products of their choosing, a small but growing number have voluntarily opted to 

support renewable energy sources. 

Green pricing programs represent one way in which consumers can voluntarily support 

renewable energy: wind, solar, biomass, landfill gas, geothermal, or hydropower. Green pricing 

programs are offered by electric utilities as a voluntary “add-on” product to the standard utility 

electric service in noncompetitive electricity markets.1

 Through these programs, participating 

customers typically agree to pay a premium on their electric bills to cover the incremental cost of 

renewable energy. The renewable generation supported by green pricing is generally intended to 

be additional to what the utility would otherwise have purchased due to either the favorable 

economics of certain renewable sources or regulatory mandates to purchase renewable energy. 

What started as three green pricing programs in 1993 had evolved into more than 90 programs 

offered by more than 300 utilities throughout the United States by the end of 2002. Utilities in at 

least 32 states offered green pricing programs, reaching roughly 20% of the nation’s electricity 

consumers. Approximately 270,000 customers (including 3,900 non-residential customers) had 

elected to participate in a green pricing program by the end of 2002. Green pricing programs 

were responsible for the development of 290 MW of renewable generation capacity by the end of 

2002, with another 140 MW expected by the end of 2003. The median premium among these 

programs is 2.5 cents/kWh, equating to a monthly cost of $5 for a residential household that 

purchases 200 kWh/month of renewable energy (Bird and Swezey 2003). Outside the United 

States, green pricing programs have also been established in Canada, Japan, Australia, and at 

least 12 European countries (Bird, Wüstenhagen, and Aabakken 2002). 

While green pricing programs have been successful in offering consumers a growing array of 

electricity choices, and in stimulating some incremental demand for renewable energy, the 

effectiveness of these programs varies considerably. Residential participation in these programs, 

for example, ranged from under 0.1% to nearly 6.5% at the end of 2002. With a median value of 

1%, typical program response is well below the 40-80% of customers who express a willingness 

to pay for renewable energy in opinion surveys.2

 Of the 290 MW of new renewable energy 

capacity brought on line by these programs as of the end of 2002, 90% can be attributed to just 

17 programs; some 75 additional programs added under 0.5 MW each on average. 

 

1

 Alternatively, where retail electricity competition is allowed, consumers can sometimes select a competitive retail 

electricity provider that offers a “green” power product. Regardless of electricity market structure, consumers can 

also purchase renewable energy certificates to help support renewable energy separate from their standard electricity 

service (see Bird and Swezey 2003 and Wiser, Bolinger and Holt 2000 for more information on these markets). 2

 For more information on stated willingness to pay for renewable energy and related survey research, see, e.g., 

Farhar (1999), Wiser (2003), Zarnikau (2003), and Roe et al. (2001). 
5

1.2 Report Objectives and Structure 

A variety of authors have suggested marketing and program design tactics to improve customer 

response rates and increase the amount of renewable energy supported by green pricing 

programs (see, e.g., Lieberman 2002, Holt and Holt 2004, Mayer et al. 1999, Swezey and Bird 

2001, Wiser 1998). A large number of electric utilities have also learned important practical 

lessons on how to enhance customer response, and how to do so cost effectively.3

The present study uses statistical analysis of actual utility green pricing program data to provide 

further insight into what program features might help maximize the effectiveness of utility green 

pricing programs. We define “effectiveness” to include residential and non-residential 

participation rates in utility green pricing programs (in percentage terms), as well as the percent 

of residential and non-residential load that is being served with renewable energy. We are aware 

of no previous studies that have performed similar statistical analysis of the possible drivers to 

the relative success of green pricing programs. 

Data for our analysis was obtained from a questionnaire sent to utility green pricing program 

managers, which covered program features and customer participation rates as of the end of 

2002. (For additional results from that survey, see Bird et al. 2004). The questionnaire gathered 

data not only on basic program components such as price/cost premiums, amount of green power 

sold, and number of participants, but also on marketing tactics, program features, sign-up 

options, and other aspects of program design. Rigorous statistical analysis then provided insight 

into how these program components affect success. 

The remainder of this report is organized as follows: 

• Section 2 highlights the data sources and methods used in our evaluation, the variables 

included in the statistical analysis, and the key limitations to our approach. 

• Section 3 provides quantitative information on the dependent variables used in our analysis. 

• Section 4 offers the results of the bivariate statistical analysis. 

• Section 5 describes the results of the more rigorous multivariate data analysis, and identifies 

program features with statistically significant relationships with program success variables. 

• Section 6 concludes by reviewing the key implications of the analysis. 

The questionnaire used to gather the data analyzed in this report is reproduced in Appendix A. 

Appendix B highlights explanatory variables that were considered, but not included in the final 

statistical analyses provided in this report. Appendix C provides graphs and tables of some of the 

interesting bivariate analysis results. 

 

3

 Perhaps the best source of information on these “case studies” comes from utility presentations to the annual 

national green power marketing conference (see http://www.eere.energy.gov/greenpower/conference/). 
6

2. Data Collection and Methods 

2.1 Data Collection 

Data for our analysis comes from a questionnaire sent to U.S. utility green pricing program 

managers. The questionnaire was distributed by the National Renewable Energy Laboratory 

(NREL) to the program managers of 90 known green pricing programs. Of these 90 programs, 

four were subsequently found to be no longer active, reducing the potential data pool to 86. 

After multiple contacts, including phone reminders, we received completed questionnaires for 66 

green pricing programs in January 2003, for a response rate of 77%.4

 

The questionnaire contained a range of questions on program design, marketing approaches, and 

customer response. The questions are included in Appendix A. The questionnaire specifically 

gathered data on program length, residential and non-residential participation, amount of 

renewable energy sold, the type of renewable supply used, program cost premiums, types of 

consumer research and program evaluation performed, different sign-up options available, 

program marketing efforts, and ancillary benefits offered to customers. The data provided in 

response to the questionnaire were current as of the beginning of 2003. 

2.2 Variables and Hypotheses 

The principal purpose of the statistical analysis is to evaluate the relationship of various program 

design and marketing features (independent/explanatory variables) to program effectiveness 

(dependent variables). For example, do programs that embody lower premiums or more 

aggressive marketing result in higher customer participation rates or support more renewable 

energy? Does the size of the utility, or its ownership structure, affect program success? Do 

programs that have been operating for a longer time period have systematically higher levels of 

customer participation? 

The questionnaire was designed such that a number of independent (program design and 

marketing features) and dependent (program effectiveness) variables could be constructed based 

on the data collected.5

 Below we identify and explain the variables that are used in our analysis. 

(Additional independent variables that were considered, but that are not included in our final 

analysis, are highlighted in Appendix B). 

Dependent Variables: Measures of Program Effectiveness 

There are a variety of ways to gauge the success of individual green pricing programs (Swezey 

and Bird 2001). Some groups have looked at the total number of customers subscribed or the 

revenue generated, for example, while others have focused on the amount of new renewable 

generation capacity built as a result of program participation. 

 

4

 With some exceptions, the majority of the respondents completed the entire questionnaire. Where possible, gaps in 

individual utility responses were filled with information from previous NREL surveys and with data from the 

Renewable Northwest Project, the U.S. Energy Information Administration, and utility Web sites. 5

 Based on the data type, both continuous and dummy variables were chosen. 
7

The four measures of program effectiveness or success that we chose to include as our dependent 

variables are listed and summarized in Table 1, and described below: 

TABLE 1: DEPENDENT VARIABLES: MEASURES OF PROGRAM EFFECTIVENESS

Dependent Variables Explanation6

Residential Participation 

(%) 

Total number of residential program participants divided by 

the total number of eligible residential program participants. 

Non-Residential 

Participation (%) 

Total number of non-residential program participants divided 

by the total number of eligible non-residential program 

participants. 

Residential Renewable 

Energy Purchases (%) 

Total renewable energy purchases of residential program 

participants in megawatt-hours divided by total eligible 

residential electrical usage. 

Non-Residential 

Renewable Energy 

Purchases (%) 

Total renewable energy purchases of non-residential program 

participants in megawatt-hours divided by total eligible nonresidential electrical usage.7

 

• Customer Segments: The four dependent variables cover the two major customer segments 

targeted to varying degrees by utility green pricing programs: residential customers and nonresidential customers. Many programs have historically focused only on the residential 

sector, but non-residential customers have received increased attention in recent years. 

• Participation and Renewable Energy Purchase Percentages: The variables also reflect 

two basic measures of program effectiveness: customer participation and renewable energy 

purchase amounts, expressed on a percentage basis. Reporting of participation rate data – the 

number of residential or non-residential green pricing participants divided by the number of 

eligible participants – is common among utility green pricing programs. Renewable energy 

purchases percentages – the amount of renewable energy purchased by residential or nonresidential green pricing participants divided by the total amount of eligible utility residential 

and non-residential load – are less commonly reported. Nonetheless, this variable is arguably 

of more value than participation rates because it considers not only the number of customer 

participants, but also the amount of renewable energy being supported by those participants.8

 

 

6

 Note that in most – but not all – cases, the number of eligible residential or non-residential customers equals the 

total number of such customers in the utilities’ service territory. 7

 Six utility survey respondents were not able to provide data on non-residential usage, due to limited data tracking 

capabilities. For these six programs, an approximation was made for this variable based either on information about 

large renewable energy purchasers, average non-residential usage for other programs, or other specific program 

information. 

8

 A program that has a high level of customer participation, but that sells a green pricing product that contains only a 

small amount of renewable energy, may not be doing much to support new renewable generation. 
8

• Percentage Representation: All four of the dependent variables used in the study are 

presented in percentage terms, in order to take into account utilities of varying size.9

 

Independent/Explanatory Variables: Program Design and Marketing Features 

The independent variables are those program design or marketing features that are hypothesized 

to potentially impact the four program effectiveness measures identified earlier. Based on the 

data collected, as well as a review of the broader green pricing literature,10 the independent 

variables included in our analysis are listed and summarized in Table 2. Additional potentially 

important independent variables that were considered, but that are not included in our final 

analysis for a variety of reasons, are highlighted in Appendix B.11

Note that the independent variables used in our analysis of residential participation and 

renewable energy purchases are in some cases different from those used in our analysis of nonresidential participation and renewable energy purchases. This reflects our view of possible 

differences in the nature of customer response between these two segments, as well as data 

limitations. These differences are highlighted in Table 2. 

 

Each of these independent variables was included to test a particular hypothesis. 

• Price Premium/Minimum Monthly Cost: One might expect that green power products that 

have higher volumetric price premiums (cents/kWh; used for non-residential customers) or 

that have a higher minimum monthly cost ($/month; used for residential customers) would 

also receive lower levels of customer participation and purchases. 

• Program Length: Those programs that have been operating for a longer period of time are 

expected to have a higher level of customer response. 

• Research and Evaluation: Utilities that have conducted consumer research and evaluated 

the success of their programs might be expected to reach higher levels of program success. 

• Sign-Up Options: As with most everything in life, most customers want to avoid hassle—

especially when signing-up for a program that will cost them more and provide few tangible 

benefits in return. Green pricing programs that make it easy for customers to join through 

multiple sign-up options (e.g., Web-site, electric bill check-off, special events, mail-in card, 

utility call center) may achieve greater levels of success. 

• Size of Utility: Some analysts have hypothesized that smaller utilities may be able to 

generate a level of community interest in a green pricing product that larger utilities may be 

hard pressed to achieve, due in part to the ease of communication in a smaller community. 

• Investor-Owned Utility: Similarly, some believe that investor-owned utilities may have a 

harder time achieving success with a green power program than publicly owned electric 

 

9

 If, instead, we simply used the number of customers or amount of renewable energy supported as the dependent 

variables, then utilities with a larger base of eligible customers would (on average) naturally be ranked higher in 

terms of program effectiveness. 

10 For example, Lieberman (2002), Holt and Holt (2004), Mayer et al. (1999), Swezey and Bird (2001), and Wiser 

(1998). 

11 These variables include, for example, whether a program is verified/certified by a third party, whether a utility 

offers multiple green pricing programs, and whether a program is mandated by state law. Exploring whether some of 

these variables have a significant influence on green pricing success is an important area of future research, though it 

would require additional data that were not available for the present study (see Appendix B). 
9

utilities, due perhaps to a greater level of trust in publicly owned utilities, or alternatively a 

higher level of motivation among publicly owned utilities in achieving program success 

(publicly owned utilities may arguably be more interested in the public benefits that can be 

derived from these programs, while investor-owned utilities may be driven by shareholders 

interests). 

• Contribution Program: Green pricing programs are often designed around different product 

concepts. The most common designs include fixed-quantity block products (e.g., 100 

kWh/month of renewable energy at a cost of $3/block) and percent-of-use products (e.g., 

renewable energy to cover 25% of a consumer’s energy demand, at a premium of 2 

cents/kWh). Less common are contribution programs, which seek voluntary payments but 

with no specific promise of energy delivery. These latter programs sometimes have unclear 

goals and impacts, and contribution programs may therefore not fare as well as other green 

pricing product types. 

• High Purchase Threshold: High renewable energy purchase thresholds for customers may 

reduce customer participation rates by requiring higher levels of financial commitment. On 

the other hand, such thresholds may increase the amount of renewable energy purchased by 

any individual consumer, thereby improving renewable energy purchase percentages. 

• Contract Length: Those utilities that require long-term contracts for green pricing program 

participants might be expected to achieve lower levels of customer interest in their programs. 

• Private Benefits: Green power is an intangible product that benefits everyone. As such, 

utilities that provide consumers with direct private incentives (e.g., recognition, discounts at 

participating stores, or compact fluorescent light bulbs) may benefit from higher levels of 

customer interest. 

• Biomass: Solar, wind, and hydropower are generally ranked highest among the renewable 

energy sources in terms of consumer preferences, while biomass is not rated as highly. All 

else equal (e.g., assuming equivalent pricing), those programs that offer biomass as a 

component of their product offerings might therefore be expected to garner less participation 

than those that emphasize solar and wind. 

• Marketing Repetition: Programs that are competitively priced and have attractive benefits 

are worthless if no one knows about them. All else equal, greater levels of marketing would 

therefore be expected to lead to greater levels of program success. 
10

TABLE 2: INDEPENDENT VARIABLES: PROGRAM DESIGN AND MARKETING FEATURES

Variable Explanation 

Price Premium/ 

Minimum 

Monthly Cost 

Residential Customers: Minimum monthly incremental cost ($/month) for participating in the 

program. If a block product is offered, this variable represents the cost of a single block. (The 

model does not account for programs that require customers to purchase multiple blocks). For 

percent-of-use product (e.g., 25%, 50%, or 100%), the variable represents the incremental cost of 

the smallest percentage product offered for a customer with average electricity use. For 

contribution programs, the variable represents the smallest contribution amount allowed. We 

hypothesized that minimum monthly cost would be a more important cost variable than the 

actual premium charged (cents/kWh), and this variable construction allowed all green pricing 

program types to be included in the analysis. 

Non-Residential Customers: Price premium charged for renewable energy on a cents/kWh basis. 

With varying but much larger levels of electricity usage than residential customers, the minimum 

monthly incremental cost in $/month is less relevant (e.g., large non-residential customers are 

not likely to purchase just one small block of renewable energy). Because contribution programs 

do not have a defined cents/kWh premium, contribution programs were eliminated from our 

multivariate analysis of non-residential customers (also note that contribution programs do not 

generally target non-residential customers). 

Program Length The number of years a utility green pricing program has been in existence. 

Research and 

Evaluation 

A dummy variable indicating if a utility claims that is has both (1) performed consumer research 

to aid in the design of the green product, and (2) evaluated the performance of its program. 

Sign-Up 

Options 

The number of ways a customer can sign-up for the program, among a list of five options given 

in the questionnaire (Web-site, electric bill check-off, special events, mail-in card, utility call 

center). 

Size of Utility The total number of (residential and non-residential) customers in a utility service area. 

Investor-Owned 

Utility 

Dummy variable indicating if a program is offered by an investor-owned utility as opposed to a 

publicly owned utility or co-op. 

Contribution 

Program 

Dummy variable for programs structured as “contributions.” This variable is not included in the 

non-residential analysis, as discussed above. 

High Purchase 

Threshold 

Dummy variable that indicates if a program has a larger-than-average minimum renewable 

energy purchase requirement for participants. Residential programs with block sizes of 200 kWh 

or more, or a minimum percent-of-use product of 25%, are qualified for this category. From an 

analysis of the data, this breakpoint represents a significant increase in minimum consumer 

purchases when compared to other programs. This variable is not included for non-residential 

customers due to data limitations and difficultly in defining an appropriate threshold level. 

Contract Length Minimum number of months customers are obligated to remain in the program. Programs that 

allow customers to come and go on a monthly basis as they choose were assigned a contract 

length of zero. 

Private Benefits Residential Customers: The total number of different types of “private” benefits offered to 

residential customers, among a list of eight specific possibilities listed in the questionnaire: 

compact fluorescent light-bulb giveaways, discounts at participating stores, newsletters and other 

literature, tours, school programs, individual recognition, fuel surcharge exemptions, 

environmental cost exemptions, and any other private benefits. 

Non-Residential Customers: The total number of different types of “private” benefits offered to 

non-residential customers, among a list of four specific possibilities listed in the questionnaire: 

specific recognition of the business by the utility, decals for use at the business, fuel surcharge 

exemptions, environmental cost exemptions, and any other business benefits. 

Biomass Dummy variable for if biomass generation was used for part of the green pricing program. 

Marketing 

Repetition 

Total number of rounds of bill inserts, direct mailings, and newsletters used to market the green 

pricing program in the last year, according to the questionnaire (See Appendix B for alternative 

marketing variables that were attempted). 
11

2.3 Analysis Techniques 

Both bivariate and multivariate statistical analyses are used. Bivariate analysis simply shows the 

direct relationships between individual independent variables and the dependent variables of 

choice. Multivariate analysis is more rigorous, and can more completely account for the impact 

of multiple independent variables on the selected dependent variable. Only complete data sets 

that encompass all the explanatory variables were used for the multivariate regressions; this 

reduced the number of possible observations. 

2.4 Study Limitations 

There are significant limitations to our analysis that should be acknowledged in advance: 

• Sample Size: Perhaps most importantly, green pricing programs are still relatively new in the 

United States. As a result, the maximum number of programs available to analyze is small 

relative to the number of independent variables that are explored. A number of independent 

variables that might be expected to have an effect on program success may therefore not be 

found to be statistically significant. Moreover, non-residential customers have not been 

strongly targeted by many of the green pricing programs; as such, statistical results from the 

non-residential analysis that follows are not as robust as those for the residential sector. 

• Outliers: Program outliers were not found to be a significant problem in much of our 

analysis. In both the non-residential participation rate and renewable energy purchase rate 

analyses, however, a single (top-performing) utility program has the possibility of greatly 

affecting the results of the analysis. Accordingly, in the analysis that follows, we exclude the 

top-performing program in each of the non-residential regressions. 

• Missing or Blunt Variables: While the independent/explanatory variables used in our 

analysis encompass a number of items previously identified to explain program success, 

some omitted variables may still remain. Similarly, some of the independent variables used 

in the present analysis are bluntly defined, for example, (1) the marketing repetition variable 

does not capture the full range of marketing efforts attempted by the various utilities, nor 

does it distinguish between the relative efficacy of different marketing approaches (2) the 

research and evaluation variable does not effectively distinguish among the wide scope of 

possible research and evaluation efforts, and (3) the biomass variable is triggered if any

biomass is included in the product offering.12 As such, those independent variables that are 

not found to be statistically significant in our analysis should not then be viewed as 

unimportant; sharper tools and a larger sample of data in the future may help to refine this 

analysis. 

• Time-Lag Issues: Our analysis was performed based on aggregate customer participation 

and renewable purchases as of the end of 2002 (not year-to-year changes). The cost premium 

and marketing repetition variables, however, represent data for 2002 (i.e., the cost premium 

 

12 The marketing variable might best be defined as the amount of funds spent in aggregate on marketing, though 

utilities are often reluctant to provide such data. The research and evaluation variable could be more tightly defined 

if information was obtained on (1) when the research or evaluation was performed (2) the overall 

comprehensiveness of the efforts, and (3) whether action had been taken to react to research findings. The biomass 

dummy variable might best be triggered if biomass is the primary source of renewable energy supply. 
12

in 2002 and the amount of marketing in 2002), not for the full program life. To the extent that 

these variables were very different in 2002 than in previous years, they may not be optimally 

specified. Similarly, the questionnaire did not specify whether market research or program 

evaluations were conducted in 2002, or earlier, and may have resulted in different 

interpretations based on the respondent. 

• Multicolinearity: Some of the independent variables display correlation with other 

independent variables, raising the concern of statistical multicolinearity. While we sought to 

define a model that minimized this concern (see Appendix B for variables that were removed 

due to concerns over multicolinearity), any remaining multicolinearity may reduce the 

significance of some potentially important variables. 

Because of these limitations, one must view the results of our analysis with some caution. While 

our results do provide some insight into the success factors of green pricing programs, the results 

are only as good as the data that are analyzed. 
13

3. Measures of Program Effectiveness 

Before proceeding with the statistical analysis, it is first useful to review the range of results for 

each of the four dependent variables. As shown in Figure 1, at the end of 2002, residential 

participation rates ranged from a high of 6.45% to a low of 0.02%. The average participation rate 

was 1.39%, with and a median value of 1.06% (n=63). 

0

1

2

3

4

5

6

7

Programs

Percent Participation

Non-residential participation rates – shown in Figure 2 – were lower, ranging from a high of 

1.96% to a low of 0.00%. The average was 0.28%, with a median of 0.11% (n=56).13

0

0.5

1

1.5

2

2.5

Programs

Percent Participation

 

13 Note that Figure 2 does not include data on a single outlier, whose non-residential participation rate was 

reportedly 30%. If this program is included, the average becomes 0.80%, with a high of 30%, a low of 0.00%, and a 

median of 0.11% (n=57). 

FIGURE 2: NON-RESIDENTIAL PARTICIPATION (%)

FIGURE 1: RESIDENTIAL PARTICIPATION (%) 
14

As noted earlier, renewable energy purchases (in percentage terms) are arguably a more valuable 

indicator of program success than are customer participation rates. As shown in Figure 3, 

residential renewable energy purchases through green pricing programs (as a percentage of total 

eligible residential customer load) ranged from a high of 3.38% to a low of 0.00%. The average 

was 0.52%, and a median of 0.20% (n=60).14 Many programs require customers to place only a 

portion of their entire load on the green pricing option, which explains why renewable energy 

purchase percentages are lower than customer participation rates. 

The distinction between participation and purchase percentages is also stark when it comes to the 

non-residential customer segment. This is because those few non-residential consumers that have 

purchased renewable energy through a green pricing program have generally purchased far less 

than their total load. As shown in Figure 4, non-residential green pricing purchases at the end of 

2002 ranged from a high of 1.92% to a low of 0.00%. The average was just 0.11%, with a 

median of 0.01% (n=55).15 

 

 

14 Note that contribution programs are included in these figures, calculated as the total amount of renewable energy 

supported through the contribution program divided by the total eligible load of the customer class. 

15 Note that in this case, the single program outlier is included in the Figure and the data. If that outlier is excluded, 

the average becomes 0.08%, with a high of 0.75%, a low of 0.00%, and a median of 0.11% (n=56). 

0

0.5

1

1.5

2

2.5

3

3.5

4

Programs

Percent of Eligible Residential Load

FIGURE 3: RESIDENTIAL RENEWABLE ENERGY PURCHASES (%)
15

0

0.5

1

1.5

2

2.5

Programs

Percent of Eligible Non-Residential Load

FIGURE 4: NON-RESIDENTIAL RENEWABLE ENERGY PURCHASES (%)
16

4. Bivariate Analysis Results

Bivariate analysis was performed between the independent and dependent variables, which helps 

in developing an understanding of the correlation between these variables.16 While bivariate 

results are briefly presented here, we note that the multivariate results presented later provide a 

more defensible assessment of the impact of various program design factors on the metrics of 

green pricing success. This is because the multivariate results can account for interrelationships 

between the various independent variables. 

4.1 Residential Customer Analysis 

Focusing initially on the residential customer segment, Table 3 shows the relationship between 

the independent variables listed in Table 2 and the dependent variables of: (1) residential 

participation (in percentage terms), and (2) residential renewable energy purchases (in 

percentage terms). Pearson correlation coefficients are used to define this relationship, and 

represent the strength and direction of any linear relationship that exists between the two 

variables. Also shown are the number of observations (n) and the statistical significance of the 

relationships (p-value). Variables with 90% statistical significance or better have been bolded. 

Appendix C presents the results of some of these relationships in more detail. 

TABLE 3: PEARSON CORRELATION COEFFICIENTS, RESIDENTIAL DATA

 Participation (%) Purchases (%) 

 

 Coefficient n P-value Coefficient n P-value 

Minimum Monthly Cost 0.002 63 0.986 0.210 60 0.108 

Program Length 0.411** 63 0.001 0.315* 60 0.014 

Research and Evaluation -0.052 61 0.689 -0.024 59 0.859 

Sign-Up Options -0.186 60 0.154 -0.112 58 0.407 

Size of Utility -0.255* 63 0.044 -0.212 60 0.103 

Investor-Owned Utility -0.219† 63 0.084 -0.243† 60 0.062 

Contribution Program -0.084 63 0.513 -0.023 60 0.860 

High Purchase Threshold 0.240† 63 0.058 0.451** 60 0.000 

Contract Length 0.105 63 0.412 0.232† 60 0.074 

Private Benefits 0.112 60 0.396 0.114 57 0.400 

Biomass 0.073 63 0.571 0.111 60 0.400 

Marketing Repetition 0.038 60 0.771 0.119 58 0.374 

† Correlation is significant at the 10% level 

* Correlation is significant at the 5% level 

** Correlation is significant at the 1% level 

 

16 We also analyzed the correlation among the various independent variables, in order to diagnose and remedy any 

multicolinearity problems that might exist. That analysis is not presented here. 
17

Though the bivariate results must be viewed with some caution, some of the hypotheses 

presented in Section 2 are confirmed statistically. 

• Those programs that have been operating for a longer period of time have garnered a greater 

percentage of residential participants, and have generated higher levels of residential 

renewable energy sales. For example, based on the statistical analysis, both residential 

participation and purchase rates appear to (on average) double between the first and fourth 

year of program operation. 

• Investor-owned utilities (IOUs) have, on average, been less successful in generating high 

participation and purchase rates relative to publicly owned utilities. For example, the average 

residential participation rate for IOUs is 1% compared to 1.6% for publicly owned utilities, 

while the average residential renewable energy purchase rate is 0.3% for IOUs compared to 

0.7% for public utilities.17

• Smaller utilities have, on average, been more successful than larger utilities, though this 

relationship is statistically significant at the 10% level only for the residential participation 

rate variable. 

• Finally, a high renewable energy purchase threshold was found to have a positive 

relationship with both residential participation and renewable energy purchases. Programs 

offering green power in at least 200 kWh blocks or for 25% of a customer’s usage averaged a 

2% residential participation rate and a 1.2% residential purchase percentage, while programs 

with lower thresholds averaged a 1.2% participation rate and 0.3% purchase rate. 

Oddly, a longer contract length initially appears to increase residential purchases of renewable 

energy, rather than the expected negative relationship between these variables; this finding, 

however, is greatly influenced by a single successful program with a long contract length, and 

removing that program makes the correlation insignificant. 

A number of additional hypothesized relationships are not found to be statistically significant. 

The minimum monthly cost variable, for example, does not appear to have an obvious effect on 

residential program effectiveness. Those utilities that offer a greater number of green pricing 

sign-up options (e.g., Web-site, electric bill check-off, special events, mail-in card, utility call 

center) appear to fare more poorly, but the relationship is not statistically significant. Marketing 

repetition has the expected positive relationship with the two residential program effectiveness 

variables, but that relationship is also far from statistically significant. The private benefits and 

contribution program variables also have the expected signs (greater private benefits lead to 

greater levels of program success, while contribution programs result in lower levels of success), 

but neither relationship is statistically significant. While the average residential participation rate 

for contribution programs was lower than for other program types (1.10% for contribution 

programs versus 1.44% for block and percent-of-use products), the small number of contribution 

programs in the sample (n=9) prevented a finding of statistical significance. The research and 

evaluation and biomass variables, meanwhile, are also not statistically significant. 

 

17 Note that once utility size is controlled for in the multivariate results presented later, utility ownership is no longer 

found to be a significant determinant of green pricing success. Accordingly, the bivariate results presented here 

should be viewed with caution. 
18

4.2 Non-Residential Customer Analysis 

Table 4 provides corollary bivariate results for the non-residential customer segment. Appendix 

C again presents the results of some of these relationships in more detail. Our analysis of nonresidential renewable energy participation and purchases (in percentage terms) was performed 

without the top-performing green pricing program. This is because the top-performing program 

is significantly different from the rest of the programs analyzed (see Section 3), and a better 

picture of “typical” programs is gained by excluding this single outlier. 

TABLE 4: PEARSON CORRELATION COEFFICIENTS, NON-RESIDENTIAL DATA

Participation (%) Purchases (%)

 

Coefficient n P-Value Coefficient n P-Value 

Price Premium -0.207 49 0.155 -0.111 48 0.452 

Program Length 0.246† 49 0.088 0.403** 48 0.005

Research and Evaluation -0.192 48 0.191 -0.211 48 0.151 

Sign-Up Options -0.305* 48 0.035 -0.124 48 0.400 

Size of Utility -0.300* 49 0.036 -0.199 48 0.175 

Investor-Owned Utility -0.295* 49 0.048 -0.253† 48 0.082

Contract Length 0.163 49 0.264 0.291* 48 0.045 

Private Benefits 0.114 48 0.439 0.234* 48 0.025

Biomass 0.014 49 0.924 -0.044 48 0.769 

Marketing Repetition -0.041 48 0.783 -0.023 48 0.876 

† Correlation is significant at the 10% level 

* Correlation is significant at the 5% level 

** Correlation is significant at the 1% level 

These results provide some indication of the success factors of green pricing programs that target 

the non-residential sector. Unfortunately, some of the relationships vary among the participation 

and purchase comparisons, making interpretation challenging. Perhaps this is due to the fact that 

few utilities have heavily marketed their green pricing programs to the non-residential sector. 

Nonetheless, many of the variables found to be significant in our residential customer analysis 

are also significant here. Program length again appears to be related to participation and purchase 

decisions: programs that have operated for a longer duration have garnered a greater percentage 

of non-residential participants and non-residential renewable energy sales. Investor-owned 

utilities have been less successful in generating high participation and renewable purchase 

percentages than publicly owned utilities, and smaller utilities appear to have higher success 

rates. 

The sign-up options variable has a sign that is the opposite of what would otherwise be expected, 

and is significant with respect to participation rates. The contract length variable also has an 

unexpected sign, but is significant only with respect to renewable energy purchases. The price 
19

premium variable (in cents/kWh) is not found to be statistically significant; however, it is at least 

of the expected sign – higher cost green pricing products result in lower levels of participation 

and purchases. The biomass, research and evaluation, and marketing repetition variables do not 

have statistically significant impacts on non-residential program effectiveness, as is the case for 

the residential sector. 

In contrast to the earlier residential customer analysis, the private benefits variable rises to 

significance in the purchase percentage correlations shown in Table 4, suggesting that programs 

that provide non-residential customers with private benefits have resulted in greater renewable 

energy sales. 
20

5. Multivariate Analysis Results 

Many of the results of the bivariate analysis could be dismissed for not controlling for the 

impacts of other independent/explanatory variables. For this reason, multivariate regression 

analysis was performed against the four dependent variables. 

5.1 Residential Customer Analysis 

The results of the residential regressions are shown in Table 5. Regression coefficients, t-scores, 

and p-values (again representing statistical significance) are displayed. Variables that are 

significant with 90% confidence or better have been bolded. The significance of the overall 

model, indicated by the R2

 and F scores, is shown at the bottom of the table. 

TABLE 5: RESIDENTIAL MULTIVARIATE REGRESSION RESULTS

 Participation (%) Purchases (%) 

 

 Coefficient T-Score P-Value Coefficient T-Score P-Value 

Constant 0.899 1.09 0.280 -0.077 -0.20 0.844 

Minimum Monthly Cost 0.021 0.18 0.854 0.020 0.36 0.717 

Program Length 0.264** 2.83 0.007 0.094* 2.18 0.034 

Research and Evaluation 0.374 0.78 0.441 0.156 0.72 0.475 

Sign-Up Options -0.261 -1.31 0.197 -0.066 -0.73 0.472 

Size of Utility -7.98x10-7* -2.20 0.033 -3.04x10-7† -1.88 0.067 

Investor-Owned Utility -0.148 -0.34 0.739 -0.254 -1.29 0.204 

Contribution Program -0.418 -0.77 0.445 -0.233 -0.88 0.382 

High Purchase Threshold 0.562 1.13 0.264 0.764** 3.40 0.001 

Contract Length 0.046 1.66 0.103 0.025† 1.99 0.053 

Private Benefits 0.082 0.62 0.539 0.054 0.89 0.379 

Biomass 0.253 0.66 0.511 0.185 1.09 0.284 

Marketing Repetition 0.091 1.38 0.173 0.051† 1.72 0.092 

 

N 59 57 

R2 0.42 0.58 

Adjusted R2 0.27 0.46 

F 2.8 5.0 

† Correlation is significant at the 10% level 

* Correlation is significant at the 5% level 

** Correlation is significant at the 1% level 

The F scores for both regressions show that each model has some predictive capability, but the 

model of residential renewable energy purchases has significantly higher F and R2

 values due to 
21

strong correlations from several specific independent variables. The small number and relative 

youth of green pricing programs may bias the models against finding statistical significance in 

the explanatory variables. 

The multivariate regression results confirm some of our earlier hypotheses, as well as results 

from the bivariate analysis, but also lead to a few surprises: 

• Program length has a significant impact on residential participation and purchases. 

Confirming the bivariate analysis results, the regression results suggest that, on average, each 

year of program operation leads to an increase in residential participation rates of ~0.25% 

and an increase in residential renewable energy sales of ~0.1%. Data quality is not high 

enough to determine whether there is a tapering off of this increase over time, or whether the 

increase actually accelerates over some limited period. 

• Smaller utilities have been able to generate greater levels of green pricing success. This 

confirms the bivariate analysis results presented earlier. 

• Higher purchase thresholds appear to maximize residential renewable energy sales 

while not negatively influencing customer participation rates. This finding suggests that a 

sizable number of residential participants are willing to participate at whatever minimum 

level is allowed. In other words, initial customer participants in green pricing programs may 

not be highly sensitive to cost, and may be willing to purchase higher quantities of renewable 

energy if that is what is required to participate. 

• Marketing levels impact residential customer purchases of renewable energy. While this 

was not found in the bivariate analysis, we find some statistical evidence that marketing 

levels do impact purchase rates. 

• A longer contract length appears, initially, to increase residential purchases. As with the 

bivariate analysis, a longer contract length appears to increase residential purchases of 

renewable energy; this finding, however, is greatly influenced by a single successful program 

with a long contract length.

Additional variables are not found to be statistically significant. For example, the minimum 

monthly cost variable shows no effect on residential participation or sales. While common sense 

suggests that cost should be a factor in participation decisions, over the limited cost range 

embedded in our data and with the low levels of current participation in green pricing programs, 

we find no such statistical evidence. Similarly, unlike the bivariate results, once utility size is 

controlled for, the investor-owned utility variable is not statistically significant. Accordingly, 

utility size appears to be a better determinant of green pricing success than the form of utility 

ownership. We also find weak evidence that contribution programs garner lower levels of 

customer response, and that providing private benefits increases program success; these effects, 

however, are not found to be statistically significant. Finally, while it may be a result of 

inadequate data or variable definition, the following additional independent variables are found 

to have no statistically significant impacts on residential green pricing effectiveness: biomass, 

research and evaluation, and sign-up options. 
22

5.2 Non-Residential Customer Analysis 

Results for the regressions performed on the non-residential data sets are presented in Table 6. 

As we did in the bivariate analysis, we excluded the top-performing green pricing program. 

TABLE 6: NON-RESIDENTIAL MULTIVARIATE REGRESSION RESULTS

Participation (%) Purchases (%) 

 

Coefficient T-Score P-Value Coefficient T-Score P-Value 

Constant 0.699* 2.28 0.029 -0.033 -0.32 0.752 

Price Premium -0.026 -1.47 0.149 -0.004 -0.62 0.538 

Program Length 0.058 1.53 0.135 0.033* 2.64 0.012 

Research and Evaluation -0.021 -0.12 0.902 -0.067 -1.16 0.252 

Sign-Up Options -0.139† -1.85 0.073 -0.004 -0.18 0.861 

Size of Utility -2.33x10-7 -1.15 0.259 1.08x10-10 0.00 0.998 

Investor-Owned Utility 0.031 0.14 0.887 -0.051 -0.90 0.375 

Contract Length -0.001 -0.21 0.834 6.63x104 -0.25 0.801 

Private Benefits 0.091 1.40 0.171 0.052* 2.56 0.015 

Biomass -0.039 -0.28 0.782 -0.037 -0.83 0.409 

Marketing Repetition -7.00x10-5 -0.00 0.998 0.002 0.31 0.762 

 

N 48 48 

R2 0.30 0.40 

Adjusted R2 0.11 0.24 

F 1.55 2.49 

† Correlation is significant at the 10% level 

* Correlation is significant at the 5% level 

** Correlation is significant at the 1% level 

Overall, model fit is reasonably poor, as documented by the F and R2

 statistics. Accordingly, few 

conclusions can be definitively reached based on this analysis. In the regression with 

participation rate as the dependent variable, for example, only the sign-up options variable 

(negative coefficient) shows some correlation with participation rates. The low F score for this 

data set limits the evaluative capacity of this model, however. Even in the purchase rate 

regression – which does yield a better model fit – only two independent variables are shown to 

be statistically significant at the 10% level. 

Clearly, with limited utility focus on this customer segment historically and with widely different 

program offerings and results, it is difficult to come up with an adequate model of nonresidential renewable energy participation and purchases. Taking the purchase percentage 

regression as the best model of the two, only two independent variables rise to the level of 

statistical significance. 
23

• First, program length again influences green pricing success. This confirms our bivariate 

analysis results, and is consistent with the residential analysis as well. 

• Second, the provision of a greater number of private benefits (recognition of the business by 

the utility, decals for use at the business, fuel surcharge exemptions, environmental cost 

exemptions, and any other business benefits) is found to increase purchase rates. 

Other variables that were found to be significant in the bivariate non-residential analysis, such as 

utility size, IOU, and contract length, have become less important once other independent 

variables are considered in the multivariate regression results. The price premium of the product 

is again not found to be a significant explanatory variable in these results, though it nearly rises 

to significance (at 10%) in the participation rate regression. 

 
24

6 Conclusions 

Utility green pricing programs currently in operation in the United States vary in their product 

design, features, and marketing tactics, and by the type and size of the utilities offering them. 

With a growing number of green pricing programs, and experience that varies substantially 

across utilities, a variety of analysts and market participants have proposed program design 

elements that may enhance chances of success. 

In this report we have statistically analyzed actual utility green pricing program data to provide 

further insight into what program features might help maximize the effectiveness of these efforts. 

Data for our analysis was obtained from utility green pricing program managers in early 2003. 

Our analysis yields fewer statistically significant relationships than had been hoped, especially 

among the non-residential customer segment. A relatively small data set, and limited experience 

with green pricing programs more generally, may be contributing to the weak predictive capacity 

of the regression models. Lack of utility focus on and marketing to the non-residential customer 

segment is likely a key contributor to the weak statistical results. Additionally, some of the 

independent variables used in the present analysis are bluntly defined. As such, those 

independent variables that are not found to be statistically significant should not necessarily be 

viewed as unimportant; sharper tools and a larger sample of data in the future may help to refine 

this analysis. 

Despite the limitations, our analysis does yield several interesting results: 

• Program duration impacts customer response. The longer a program has been operating, 

the more likely its message has spread and the higher the probability of strong program 

success. This is perhaps the most consistent of our statistical findings – both for residential 

and non-residential customers – and provides support for the idea that new products 

generally follow a “diffusion curve” of increased penetration over time.18 To achieve higher 

levels of success, utilities need to remain persistent and committed to their product offering 

for some period of time. An initially low level of customer participation can be remedied 

over time. Further data would be necessary to better understand the actual shape of the green 

power diffusion curve. 

• Higher purchase thresholds for residential customers should be considered. Those 

utilities with larger renewable energy block sizes (>200 kWh) or percent-of-use products 

(>25%) also tend to have higher levels of residential renewable energy sales, and with no 

obvious negative impact on the overall level of customer participation. In other words, initial 

customer participants in green pricing programs may not be highly sensitive to cost, and may 

be willing to purchase higher quantities of renewable energy if that is what is required to 

participate. If purchase thresholds are low, on the other hand, those same customers may only 

choose to purchase that minimum quantity of renewable energy. Within reason, utilities 

should therefore consider slightly higher minimum purchase thresholds, in order to maximize 

residential renewable energy sales. This is especially the case for those utilities focused on 

maximizing renewable energy sales, not customer participation rates. 

 

18See Wiser et al. (2001) for further discussion of the diffusion curve as applied to green power markets. 
25

• Price premiums and minimum monthly costs are not the primary determinants of 

program success. There can be little doubt that price does matter: common sense and 

economic theory tell us that higher priced products can and should result in lower levels of 

customer participation and renewable energy purchases. Nonetheless, using minimum 

monthly cost as the independent variable for residential customers, and volumetric price 

premiums for non-residential customers, we find little statistical evidence of this effect. This 

suggests that over the relatively narrow range of volumetric price premiums and minimum 

monthly costs embedded in our data set (for virtually every residential program, the 

minimum monthly cost is under $10) and given that most green pricing programs are only 

capturing the first wave of customer participants, the premium is not a primary determinant 

of success. Price may become a more important determinant as green pricing programs 

expand to target more than the early innovator customers; in the meantime, other factors 

appear to play more important roles in shaping program effectiveness. 

• Smaller utilities appear to have a greater likelihood of achieving success. While larger 

utilities will always benefit from a greater number of potential customers, smaller utilities 

appear able to achieve higher residential customer participation and renewable purchases on 

a percentage basis (the same effect is not as strongly observed among non-residential 

customers). This may be due to stronger community ties and favorable standing among the 

customer base. Interestingly, once size is controlled for, we find little evidence that utility 

ownership (IOU vs. public) is a principal determinant of green pricing success. 

• Providing private benefits to non-residential customers may enhance success. Especially 

among non-residential customers, we find some evidence that offering a greater number of 

private benefits – including business recognition – can increase renewable energy sales. 

Additional data would be needed to refine our analysis of this issue. 

• Higher levels of marketing may positively impact sales and participation: We were 

unable to develop a robust metric for the level of marketing used by each program, instead 

resorting to a marketing repetition variable that is, as discussed earlier, not ideal for our 

purpose. This variable was positively related to the level of residential renewable energy 

purchases, but additional analysis of the impact of marketing levels on customer response is 

merited. 

A number of other variables are found to have little statistical effect on program success, or to 

have an effect opposite of what might be expected. For example, we find little empirical 

evidence that the type of renewable supply plays a major role. In addition, consumer research 

and program evaluation is not found to be statistically significant. Oddly, if anything, our data 

suggest that providing more sign-up options (e.g., Web-site, electric bill check-off, special 

events, mail-in card, utility call center) might even reduce customer participation. Future 

research should explore these relationships in more depth, ideally with a greater quantity of data 

and tighter and more detailed definitions of independent variables. 

While the results presented in this paper are suggestive, we urge readers to not place undue 

emphasis on them. Green pricing programs in the United States are still relatively new and have 

yet to gain a foothold in many markets. The limited data that do exist do not allow as 

comprehensive a statistical analysis as might be hoped. As green pricing programs continue to 

grow and mature, more data will be available from which to assess the key aspects of program 

success. These additional data will allow a more rigorous and thorough evaluation of the 
26

determinants of program effectiveness. In the meantime, readers interested in gleaning additional 

design lessons for utility green pricing programs would be well served to review some of the 

documents referenced earlier that offer qualitative insights into the determinants of green pricing 

success.19

 

 

19 See, e.g., Lieberman (2002), Holt and Holt (2004), Swezey and Bird (2001), and 

http://www.eere.energy.gov/greenpower/conference/. 
27

References 

Bird, L., Swezey, B., Aabakken, J. 2004. Utility Green Pricing Programs: Design, 

Implementation, and Consumer Response. NREL/TP-620-35618. Golden, Colorado: National 

Renewable Energy Laboratory. 

Bird, L., Swezey, B. 2003. Green Power Marketing in the United States: A Status Report. Sixth 

Edition. NREL/TP-620-35119. Golden, Colorado: National Renewable Energy Laboratory. 

Bird, L., Wüstenhagen, R., Aabakken, J. 2002. Green Power Marketing Abroad: Recent 

Experience and Trends. NREL/TP-620-32155. Golden, Colorado: National Renewable Energy 

Laboratory. 

Farhar, B. 1999. Willingness to Pay for Electricity from Renewable Resources: A Review of 

Utility Market Research. NREL/TP-550-26148. Golden, Colorado: National Renewable Energy 

Laboratory. 

Holt, E., Holt, M. 2004. Green Pricing Resource Guide. Second edition, review draft. 

Washington, D.C.: American Wind Energy Association. 

Lieberman, D. 2002. Green Pricing at Public Utilities: A How-To-Guide Based on Lessons 

Learned to Date. San Francisco, California: Center for Resource Solutions. 

Mayer, R., Blank, B., Swezey, B. 1999. The Grassroots are Greener: A Community-Based 

Approach to Marketing Green Power. Research Report No. 8. Washington, D.C.: Renewable 

Energy Policy Project. 

Roe, B., Teisl, M., Levy, A., Russell, M. 2001. “US Consumers’ Willingness to Pay for Green 

Electricity.” Energy Policy, 29: 917-925. 

Swezey, B., Bird, L. 2001. Utility Green Pricing Programs: What Defines Success?

NREL/TP.620.29831. Golden, Colorado: National Renewable Energy Laboratory. 

Wiser, R. 1998. “Green Power Marketing: Increasing Customer Demand for Renewable 

Energy.” Utilities Policy, 7 (2): 107-119. 

Wiser, R. 2003. Using Contingent Valuation to Explore Willingness to Pay for Renewable 

Energy: A Comparison of Collective and Voluntary Payment Vehicles. LBNL-53239. Berkeley, 

California: Lawrence Berkeley National Laboratory. 

Wiser, R., Bolinger, M., Holt, E. 2000. “Customer Choice and Green Power in the United States: 

How Far Can It Take Us?” Energy and Environment, 11(4): 461-477. 

Wiser, R., Bolinger, M., Holt, E., Swezey, B. 2001. Forecasting the Growth of Green Power 

Markets in the United States. LBNL-48611. Berkeley, California: Lawrence Berkeley National 

Laboratory. 
28

Zarnikau, J. 2003. “Consumer Demand for ‘Green Power’ and Energy Efficiency.” Energy 

Policy, 31: 1661-1672. 
29

Appendix A: Green Pricing Program Questionnaire 

1. Utility name _______________ 

2. Name of green power program ___________________ (if you have multiple programs, please fill out a separate 

form for each program) 

3. Year program was launched ________________ 

4. Which states is the program offered in? ____________________________ 

Participation 

5. In the table below, please provide participation data as of December 31, 2002. If data is provided for a different 

month (e.g., November) please specify _______________ 

Question Response 

Number of current residential green power participants 

Number of current non-residential green power participants 

Number of residential customers (or members) eligible to participate 

Number of non-residential customers (or members) eligible to participate 

Is the program open to new customers? Yes/no 

Number of customers on waiting list 

Number of participants who have dropped out of the program this year 

Minimum period of time residential customers must participate in program (e.g., 1 year) 

Minimum period of time non-residential customers must participate (e.g., 2 years) 

6. For programs that are jointly offered to multiple distribution cooperatives or municipal utilities, please indicate 

the highest overall participation rate achieved by a utility participating in the program. _______________ 

Sales for the last year 

7. In the table below, please indicate the sales of green power to customers during the previous 12-months. Please 

also indicate the top 3 non-residential purchasers and the amount purchased during the last 12 months. 

Green power sales for most recent 12 months Blocks Block size KWh/year of 

green power 

Green power sales to residential customers 

Green power sales to non-residential customers 

Top 3 non-residential purchasers: 

1. 

2. 

3. 

Renewable Energy Supplies 

8. Of the renewable energy used to supply your program, what percentage comes from the following? 

 ____Renewable projects owned or partially-owned by your utility 

___ Renewable energy purchases from others 

____Renewable certificate purchases 

9. In the table below, please indicate the type and amount of renewable resources used to supply your green pricing 

program during 2002. 

Nameplate 

Capacity 

Installed (kW) 

Energy Purchases 

in 2002 (kWh/yr) 

Nameplate Capacity 

Planned (kW) 

Wind 

PV 

Solar Thermal 

Landfill methane 

Other Biomass 

Hydro 

Geothermal 
30

Premium 

10. Please indicate the price premium charged for this green power product _______________________________ 

11. Was there a change in the premium in 2002? Yes/no _____________ 

 If so, why? ___________________________________________________________________________ 

12. Are green power customers protected, by virtue of their green power purchase, from increases in fuel costs (i.e., 

natural gas) or increases in the price of conventional electricity? Yes/no If so, how? 

___________________________________________________________________________________________ 

___________________________________________________________________________________________ 

Program Design and Implementation 

13. Have you done your own customer research to aid in the design of your green power product or development of 

your marketing plan? Yes/no 

14. Have you performed an evaluation of the program? Yes/no 

15. In which of the following ways can customers sign up for your green power program? (check all that apply) 

Utility web site By returning a mail-in card 

Checking a box on their electric bill Over the phone through the utility call center 

Sign up at special events Other? 

 

Marketing 

16. What percentage of the green power program budget was spent on marketing in 2002? _______________ 

17. On average, how much does it cost to sign up each residential customer ($/customer)? ________________ 

18. In the table below, please indicate how many times, if any, you have used the following marketing strategies for 

your green power program in the past year. For example, if bill inserts, direct mail or newsletters were sent to 

30,000 customers during the year, please indicate 30,000. Also, please rank the cost-effectiveness of the strategy on 

a scale of 1 to 5 with 5 being the most cost-effective strategy for obtaining customers. 

 

Strategy Frequency Cost-effectiveness 

(1-5, 5=Best) 

Bill inserts # of inserts/year 

Direct mail # of mail pieces/year 

Utility newsletter that mentions green 

power 

 # of newsletters/year 

Television # of commercials 

aired/year 

Radio # of announcements/year 

Newspapers # of ads/year 

Telemarketing # of calls/year 

Billboards # of billboards 

Events # events/year 

Publicity/feature stories (non-paid) # of articles/year 

Other? 

Value for Consumers 

19. What other value-added products or services do you provide to your green power customers? Please check as 

many as are applicable. 

Compact fluorescents or efficiency products Decals for display in store windows 

Recognition of business customers in program 

ads or local media 

 Installations on schools/renewable energy 

education programs 

Discounts or promotions at local businesses Plaques or other items for recognition 

Newsletters that provide program updates Protection from fuel cost increases 

Tours to renewable energy project sites Exemption from environmental fees 

Other (please list) 
31

Appendix B: Other Independent Variables Considered 

A variety of independent variables were considered for inclusion in the regression models. The 

following text lists variables that were at one point used in the multivariate regressions, and 

describes the reasons for the removal of these variables in the final models. 

• Research and Evaluation – While we included a combined “Research and Evaluation” 

variable in the final model, we also tested for the separate influence of each variable; neither 

individual variable was found to be significant. We chose to combine the two to ensure a 

higher standard for determining whether a utility had performed critical analysis of its 

program. 

• Renewable Portfolio Standard – We considered including a variable representing whether a 

utility was required by state law to purchase renewable energy. Some stakeholders have 

suggested that such a mandate may affect the success of the green market. Only six utilities 

that responded to the questionnaire had to abide by such a policy, however, ensuring that this 

variable would not be found to be statistically significant. 

• Marketing Activity – We considered multiple variables to try to represent marketing 

activity, ultimately selecting the “Marketing Repetition” variable included in the final model. 

One approach that we considered was a dummy variable that signified if a utility had used 

bill stuffers, direct mail, and newsletters to market their program. This variable could not 

distinguish between different quantities of these mailings, however, and could not 

compensate for small utilities that do not have the capability to undertake all three activities. 

Additional measures were also considered, but ultimately rejected as being inferior. 

• Multiple Green Pricing Programs – Some utilities offer more than one green pricing 

program, and the provision of multiple programs may impact customer response. Only a few 

utilities responding to the questionnaire could be classified as having multiple programs, 

however, making the inclusion of this variable unnecessary. 

• Solar and Wind – As an alternative to the “Biomass” dummy, we also considered dummies 

for wind or solar. The vast majority of green pricing programs include wind power, so 

including this variable had little value. A solar dummy was found to be statistically 

insignificant, and experienced colinearity with the “Cost Premium” variable. 

• No Fuel Surcharge – This dummy signified if customers are exempt from fuel surcharges 

that non-green pricing customers are required to pay. Very few green pricing programs offer 

this benefit, so its significance as an independent explanatory variable was small. 

• Independent Certification – We considered including a variable indicating whether a 

program was certified by the Green-e program, operated by the Center for Resource 

Solutions.20 Few programs were certified by Green-e at the time, however, so this variable 

was excluded from the final analysis. 

• Mandated Green Pricing Programs – Some green pricing programs are mandated by state 

law, and a dummy was considered to assess whether such requirements have an effect on 

participation. There was very strong correlation between this and the “Program Length” 

variable, so to remove colinearity problems this variable was removed. 

 

20 http://www.green-e.org/ 
32

Appendix C: Further Detail on Bivariate Results 

Included below are select additional results from the bivariate analysis presented in the body of 

the report. 

Residential Customer Analysis 

Average Residential 

Participation Rate 

Average Residential 

Renewable Energy 

Purchase Percentage 

 

IOUs 0.97% 0.26% 

non-IOUs 1.62% 0.66% 

High Purchase Threshold 

(>200kWh or 25%) 

2.02% 1.17% 

Low Purchase Threshold 

(<200kWh or 25%) 

1.21% 0.32% 

 

Contribution Programs 1.10% 0.47% 

Energy-Based Programs 1.44% 0.53% 

 

Residential Participation vs. Minimum Monthly Cost

0

1

2

3

4

5

6

7

0 2 4 6 8 10 12

Minimum Monthly Cost ($)

Participation Rate (%)
33

Residential Participation vs. Program Length

0

1

2

3

4

5

6

7

0 2 4 6 8 10 12

Program Length (years)

Participation Rate (%)

Residential Participation vs. Utility Size

0

1

2

3

4

5

6

7

0 500000 1000000 1500000 2000000 2500000 3000000

Utility Size (# customers)

Participation Rate (%)
34

Non-Residential Customer Analysis 

Non-Residential Participation vs. Price Premium

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25

Price Premium (cents/kWh)

Participation Rate (%)

Non-Residential Participation vs. Program Length

0

0.5

1

1.5

2

2.5

012345678

Program Length (years)

Participation Rate (%)

Non-Residential Participation vs. Utility Size

0

0.5

1

1.5

2

2.5

0 500000 1000000 1500000 2000000

Utility Size (# customers)

Participation Rate (%)
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Utility Green Pricing Programs: A Statistical Analysis of Program Effectiveness 

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Ryan Wiser and Scott Olson (LBNL) 

Lori Bird and Blair Swezey (NREL) 

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This report analyzes actual utility green pricing program data to provide further insight into which program features might help 

maximize both customer participation in green pricing programs and the amount of renewable energy purchased by customers in 

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analysis; green pricing; consumer response; green pricing programs; renewable energy 

supplies; green energy; customer participation rates; contribution program; bivariate 

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green power markets; utility green pricing programs; Lori Bird; Blair Swezey; Ryan Wiser; 

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