US Consumers' Willingness to Pay for Green Electricity (PDF ...

Energy Policy 29 (2001) 917}925

US consumers' willingness to pay for green electricity夽 Brian Roe *, Mario F. Teisl, Alan Levy, Matthew Russell Department of Agricultural, Environmental and Development Economics, The Ohio State University, Room 225 Agricultural Administration Building, 2120 Fywe Road, Columbus, OH 43210, USA Department of Resource Economics and Policy, University of Maine, 200 Winslow Hall, Orono, Maine 04469, USA Consumer Studies Branch, U.S. Food and Drug Administration, 200 C Street, S.W., HFS-727, Washington, DC 20205, USA Received 23 October 2000

Abstract We analyze US consumers' demand for environmental attributes of deregulated residential electricity services using results from a survey designed to elicit consumers' willingness to pay for such attributes and using results from a hedonic analysis of actual price premiums charged for green electricity in several deregulated markets. Survey results suggest that many population segments are willing to pay for decreased air emissions even if there is no alteration in fuel source. Furthermore, several groups are willing to pay signi"cantly more when emissions reductions stem from increased reliance upon renewable fuels. The hedonic analysis suggests that several product features not considered in the survey help explain real price premiums, including fuel mix from newly created renewable generation capacity, Green-e certi"cation, brand name and state of o!er. While survey and hedonic results are not easily compared due to limitations of the survey, both point to similar values for key environmental attributes, though the survey results are likely to overstate actual willingness to pay. In sum, the results suggest that consumer driven purchases can, in part, support the future of renewable generation capacity in the United States, though reliance upon other policy alternatives may be needed if energy prices spike. 2001 Elsevier Science Ltd. All rights reserved. Keywords: Electricity deregulation; Green marketing; Willingness to pay

1. Introduction Spurred by the Energy Policy Act of 1992, the deregulation of the US electricity supply is happening quickly: 24 states and the District of Columbia have already enabled electricity competition (Swezey and Bird, 2000) while others are reviewing such possibilities. Already millions of retail customers and small businesses in the United States have the opportunity to choose among competing electricity suppliers. Like any other consumer service or product, consumers will evaluate product attributes and prices and choose the service most to their liking; products with attributes more pleasing to consumers will be able to charge a premium. Given the higher prices typically associated with many renewable energy sources and with technologies that 夽 This project was "nanced in part by the Regulatory Assistance Project, Gardiner, ME. Additional funding was provided through the Maine Agricultural and Forest Experiment Station and the Ohio Agricultural Research and Development Center. All errors and omissions are the sole responsibility of the authors. Maine Agricultural and Forest Experiment Station Publication XXX (pending). * Corresponding author. Tel.:#1-614-688-5777; fax:#1-614-292-4749. E-mail address: [email protected] (B. Roe).

reduce pollution from fossil fuel energy sources, there has been considerable speculation about the fate of renewable and more environmentally benign energy sources in deregulated electricity markets both in the United States (Wiser et al., 1998) and in Europe (Fouquet, 1998; Eikeland, 1998). Wiser et al. outline three policy mechanisms that can support renewable generation sources: (1) portfolio standards mandating minimum levels of renewable energy use; (2) energy support programs funded by distribution surcharges; and (3) consumers' voluntary renewable energy purchases in response to green marketing. Of these three methods, however, reliance upon green marketing is the most precarious because it relies upon the demand of a price sensitive consumer populace. Consumers' responsiveness to price in a market with undi!erentiated products is well understood. However, consumers' willingness to pay higher prices to obtain electricity service with certain environmental characteristics is not well understood, but such an understanding is essential for predicting the success of various energy generation sources in a deregulated market and for helping regulators maintain an appropriate mix of renewable support policies.

0301-4215/01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 1 - 4 2 1 5 ( 0 1 ) 0 0 0 0 6 - 4

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B. Roe et al. / Energy Policy 29 (2001) 917}925

In order for green marketing of electricity to work, in the sense of tapping consumers' willingness to pay higher prices for electricity with fewer environmental impacts, information concerning the environmental characteristics of individual electricity services must be communicated to consumers. Many state governments in the United States have already taken initial steps to develop such consumer-friendly information policies and have established requirements or are considering establishing requirements that mandate environmental disclosure by electricity generating "rms. For example, Illinois "rms must insert quarterly updates of their purchased fuel mix and projected air emissions in customer billings and post these updates on the internet (Illinois Commerce Commission, 2000). Another less informative disclosure prototype has emerged in the California markets, in which "rms are required to display only a standard fuel mix disclosure in all direct mail and internet advertising (Senate Bill 1305, California Statutes of 1997); emissions data communications are not required. Environmental disclosure provisions are required or are under consideration by about half of the states and have been included in proposed federal deregulation legislation (Regulatory Assistance Project, 1999). Given the prevalence of current and proposed green marketing programs in residential electricity service and the importance of initial renewable demand in establishing a sustainable development pattern for renewable generation capacity (Fouquet, 1998), it is crucial to understand the elements that will shape consumer demand for various types of electricity services. While consumer-driven demand for &green labeled' electricity has bloomed in countries such as Sweden (Eikeland, 1998), the potential for such change in the United States has not been fully assessed. The purpose of this paper is to provide some insight into US consumers' demand for deregulated residential electricity services that promise more favourable environmental pro"les. We present results from a survey designed to elicit consumers' willingness to pay for changes in the environmental characteristics of residential electricity service using price and environmental disclosure statements similar to those used in the emerging deregulated electricity markets in the United States. We then compare these results to the actual price premiums that have emerged for green electricity in several deregulated US markets and discuss the implications of these results.

2. Consumer survey 2.1. Survey methods and analysis The data are drawn from responses to a survey instrument featuring numerous questions concerning electric-

ity use, awareness and intentions and featuring several experimental tasks (see Winneg et al., 1998, for a complete summary of the survey results). The experiment of interest involves simultaneously presenting respondents with information disclosures sheets (Fig. 1) for two hypothetical electricity services and having the respondent choose the one they would buy. The attributes of the electricity services include monthly price, contract terms, fuel source mix and air emissions pro"le. All attributes except contract terms were randomly assigned to each information disclosure sheet so that no two respondents saw the same pair of services. This survey follows the tradition of conjoint analysis, a research technique traditionally used by marketing experts to help design and price new consumer goods (Green and Srinivasan, 1990) and, more recently, wielded by economists to value environmental attributes not priced by the market (Roe et al., 1996). One thousand and one adult respondents were recruited from shopping malls in eight di!erent US cities: Cincinnati, Ohio; Holyoke, Massachusetts; Houston, Texas; Jacksonville, Florida; Riverside, California; Philadelphia, Pennsylvania; Portland, Oregan; and Salt Lake City, Utah. A pro"le of respondent demographics is presented in Table 1. Analysis of consumers' willingness to pay for various environmental amenities of the electricity products is based upon 835 usable responses and follows techniques developed by Roe et al. (1996); complete details of the analysis are available from the authors upon request. 2.2. Survey results The estimated willingness to pay "gures (Table 2) suggest that the median consumer from several di!erent demographic segments is willing to pay more for marginal alterations in the fuel sources and pollution outputs of an electricity generation service. Annual estimates based upon usage of 1000 kilowatt-hours (kWh) per month are calculated for 20 di!erent population segments and for three alterations in a service's environmental pro"le: a 1% decrease in emissions (i.e., a joint decrease in CO , NO , and SO ); a 1% decrease in V V emissions accompanied by a 1% increase in fuel mix from renewable resources (a joint increase in hydro, solar, wind replacing a joint decrease in coal, oil and natural gas); and a 1% decrease in emissions accompanied by

One limitation of the study's experimental design is that it only considered joint changes in all types of reported emissions; hence, we cannot determine respondents' willingness to pay for, say, a change in SO alone. A similar limitation exists for the mix of renewable resources V and the mix of fossil fuels; i.e., no estimate can be made for changes in a single renewable or fossil fuel. Therefore, an increase in renewable fuel implies an increase in a "xed portfolio of renewable fuels that roughly re#ects the average US portfolio of renewables (mainly hydro).


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Fig. 1. Example of information provided in conjoint survey experiment.

Table 1 Sample demographics Variable Percent With at least a high school degree Female White Declaring membership/donation to environmental organizations Primary handler of household bills Average Annual household income Age

58 51 67 16 88

$37,300 37

a 1% increase in nuclear power in the fuel mix (replacing a joint decrease in fossil fuels). For example, interpreting the top, left "gure from Table 2, if two electricity services are identical in all ways but one uses technology that produces 1% fewer air emissions than the other, the median consumer from the Southeast with no high school degree and no environmental organization a$liation would be willing to pay $3.22 more on an annual basis (about $0.27 per monthly bill) for that service than for the higher emissions service. Drawing upon simple extrapolation of the under-

lying linear model allows us to impute a median willingness to pay estimate of $161/year for a service that decreases emissions by 50%. Interpreting this estimate in another way, it implies that the "rm would need to reduce emissions by more than 40% to support a single cent per kWh premium (see Table 3 for a schedule that translates willingness-to-pay estimates to price premiums). Median willingness to pay across all population segments for a single percent decrease in air emissions ranges from $0.38 to $5.66 per year with lower values from respondents without a high school diploma and those with no environmental organization a$liations. Note that the highest willingness to pay translates to less than a $0.50 increase in a monthly bill and that 17 of the 20 estimates presented for the lower air emissions product ("rst column) are signi"cantly di!erent from zero as measured by a con"dence interval that excludes zero. Hence, respondents' willingness to pay for marginal reductions in emissions without alteration of the fuel mix is generally small, positive and variable across demographic segments. Signi"cant di!erences across regions, as indicated by con"dence intervals that do not overlap, also emerge. Respondents from the Southwest and Northwest have signi"cantly lower willingness to pay for emissions reduction than do respondents from the Southeast; the only exceptions occur for Northwest and Southwest

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Table 2 Median willingness to pay for changed fuel mix and lowered emissions * all respondents ($/yr) For a 1% decrease in emissions

For a 1% increase in renewable fuel and a 1% decrease in emissions

For a 1% increase in nuclear fuel and a 1% decrease in emissions

No high school degree, no environmental organization azliation Southeast 3.22 [1.91, 5.06] Midwest 0.60 [0.31, 2.90] Northeast 2.01 [0.78, 3.50] Southwest 0.56 [!0.12, 1.44] Northwest 0.38 [!0.96, 1.83]

3.75 [!1.80, 10.82] 0.11 [!5.56, 6.15] 7.14 [2.50, 12.76] 1.47 [!1.99, 4.84] 7.97 [1.87, 14.88]

6.49 [1.29, 13.02] !1.21 [!6.56, 3.87] 1.03 [!3.48, 5.62] !1.03 [!4.15, 2.19] 4.77 [!0.34, 11.38]

No high school degree, azliation with an environmental organization Southeast 4.35 [2.42, 6.87] Midwest 2.46 [0.91, 4.73] Northeast 3.09 [1.52, 5.34] Southwest 1.61 [0.10, 3.78] Northwest 1.42 [!0.33, 3.82]

10.09 [0.91, 20.13] 6.50 [!1.77, 15.32] 13.38 [6.59, 23.13] 7.61 [0.45, 16.10] 14.22 [6.21, 24.52]

9.92 [2.28, 19.31] 2.07 [!5.18, 10.14] 4.28 [!2.68, 12.81] 2.38 [!4.03, 9.81] 8.32 [0.70, 17.81]

High school degree, no environmental organization azliation Southeast 4.48 [3.02, 6.44] Midwest 2.63 [1.56, 4.06] Northeast 3.25 [2.03, 4.81] Southwest 1.80 [1.02, 2.80] Northwest 1.56 [0.45, 3.11]

6.37 [0.43, 13.34] 2.55 [!2.56, 8.46] 9.79 [5.50, 14.88] 3.97 [0.56, 7.91] 10.36 [4.33, 17.97]

11.04 [5.78, 17.75] 3.25 [!1.68, 8.91] 5.62 [1.48, 9.99] 3.54 [0.47, 7.29] 9.48 [4.16, 16.24]

High school degree, azliation with an environmental organization Southeast 5.66 [3.57, 8.20] Midwest 3.73 [2.28, 5.98] Northeast 4.38 [2.75, 6.54] Southwest 2.90 [1.40, 4.92] Northwest 2.65 [1.08, 4.84]

12.80 [3.78, 22.73] 3.49 [!4.96, 11.01] 10.35 [2.70, 18.37] 4.70 [!3.36, 12.14] 10.84 [2.66, 20.42]

14.43 [7.42, 23.67] 1.05 [!5.88, 8.55] 3.42 [!3.49, 10.56] 1.45 [!5.31, 8.58] 7.37 [!0.29, 16.70]

Renewable fuels replaces fossil fuels, percent of nuclear fuels is held constant. Nuclear fuels replaces fossil fuels, percent of renewable fuels is held constant. Bolded numbers have con"dence intervals that do not include zero. Numbers in brackets are 90% con"dence intervals generated by bootstrapping from the original data.

respondents with both a high school degree and an environmental a$liation and for Northwest respondents with and high school degree but no environmental a$liation.

Accomplishing emissions reduction by substitution of renewable sources for fossil fuels generally results in higher median willingness to pay estimates than for a reduction in emissions alone. However, due to wider

B. Roe et al. / Energy Policy 29 (2001) 917}925 Table 3 Percent change in attribute needed to support a $0.01/kWh price premium Annual willingness to pay ($)

% Change in attribute(s) needed to support a one cent price premium

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 25 30 35 40 50

'100.00 60.00 40.00 30.00 24.00 20.00 17.14 15.00 13.33 12.00 10.91 10.00 9.23 8.57 8.00 7.50 7.06 6.67 6.32 6.00 4.80 4.00 3.43 3.00 2.40

con"dence intervals associated with the higher renewable product, only a few of these di!erences are statistically signi"cant. Respondents from the Northeast and Northwest are generally willing to pay more for emissions reduction created by increased reliance upon renewables than for emissions reduction created without an alteration in fuel mix. It is interesting to note that, for Northeastern and Northwestern respondents with a high school degree and an existing environmental a$liation, this di!erence is not signi"cant, perhaps signaling these groups' reluctance about some sources of renewable fuels (e.g., large-scale hydro) or these groups' focus upon air emissions. The willingness to pay estimates for products that reduce emissions via substitution of nuclear sources for fossil fuel sources vary considerably across population segment but are rarely signi"cantly di!erent than from estimates for the other two scenarios (i.e., there are few

This lack of precision in the estimates may stem from a poor choice of descriptions of the renewable components (e.g., the hydro component is not broken out into large and small hydro, which have di!erent environmental impacts in the eyes of many environmentalists, nor is the portion of the mix stemming from newly created renewable capacity revealed) and the mixed sentiments of many consumers toward the hydro components that dominate the renewable category.

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signi"cant di!erences across a given row of Table 2). Compared to the previous two scenarios, fewer population segments feature willingness-to-pay estimates that are signi"cantly di!erent than zero. Nuclear "nds its strongest support from respondents in the Southeast and Northwest and from respondents with a high school education but no environmental a$liation. In fact, respondents from the Northwest with a high school education but no environmental a$liation are willing to pay signi"cantly more for emissions reduction via increased nuclear dependence than for emissions reduction without fuel source alteration ($9.48 vs. $1.56/yr.). It is interesting to note, however, that only one nuclear facility currently operates in the Northwest region of the United States while numerous nuclear power plants populate the Southeast. Table 4 explores di!erences in willingness to pay for respondents in di!erent income brackets and, not surprisingly, reveals higher median estimates for higher income respondents. Signi"cant di!erences based solely upon a di!erence in income are not common, however. For the 10 population segments and three product scenarios examined in Table 4, there exist only "ve instances in which the con"dence intervals fail to overlap for the same product and same population segment in di!erent income brackets and all of these occurred for the product in which only emissions is lowered (i.e., "rst column of Table 4). In particular, income tended to create signi"cantly larger estimates for Southeastern and Northeastern respondents.

3. Analysis of green price premiums in US deregulated electricity markets 3.1. Existing premiums Estimates of consumers' willingness to pay for environmental improvement that are based upon surveys tend to overstate the amount consumers would actually pay if given a real opportunity in the marketplace. Simply put, pledged payments tend to overstate actual disbursements. In an e!ort to validate survey estimates we turn to the price premiums currently charged by a handful of "rms actively marketing green energy in several deregulated US residential electricity markets. Table 5 lists 21 products and their respective annual premiums and generation characteristics. This table represents a snapshot of the US green electricity market as of July 2000; changes may have occurred in the intervening months. When comparing existing price premiums to the survey results, consider several points. First, note that no information was uniformly available concerning the projected emissions characteristics of the products in Table 5, thus limiting direct comparability with the

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Table 4 Median willingness to pay for changed fuel mix and lowered emissions *by income ($/yr) For a 1% decrease in emissions

For a 1% increase in renewable fuel and a 1% decrease in emissions

For a 1% increase in nuclear fuel and a 1% decrease in emissions

($40,000/yr, no high school degree, no environmental organization azliation Southeast 1.82 3.22 [0.45, 3.56] [!3.19, 10.46] Midwest 2.07 0.65 [0.59, 4.85] [!5.97, 8.27] Northeast 0.95 1.51 [!0.13, 2.42] [!3.33, 7.32] Southwest 0.27 0.42 [!0.38, 0.98] [!2.91, 3.61] Northwest 0.22 10.77 [!1.11, 1.85] [4.39, 18.87]

4.79 [!0.81, 1.33 [!4.68, !3.02 [!7.82, !1.69 [!4.73, 2.62 [!3.67,

($40,000/yr, high school degree, azliation with an environmental organization Southeast 2.91 7.80 [0.95, 5.23] [!0.99, 16.30] Midwest 3.25 0.70 [1.28, 6.33] [!9.79, 10.45] Northeast 2.10 1.58 [0.34, 3.98] [!7.19, 10.59] Southwest 1.35 0.11 [!0.29, 3.33] [!7.95, 8.11] Northwest 1.29 10.92 [!0.30, 3.65] [1.32, 20.16]

11.39 [3.95, 20.54] 2.10 [!6.90, 10.71] !2.63 [!10.77, 5.05] !1.32 [!8.58, 5.17] 3.16 [!6.06, 12.86]

'$40,000/yr, no high school degree, no environmental organization azliation Southeast 8.57 5.10 [4.08, 23.04] [!12.25, 40.47] Midwest 1.24 0.25 [!2.28, 4.56] [!12.39, 14.19] Northeast 5.72 18.98 [2.57, 10.27] [6.06, 37.33] Southwest 3.88 11.40 [0.74, 8.28] [!0.89, 27.25] Northwest 2.20 7.48 [!1.05, 6.71] [!7.45, 27.80]

15.28 [0.70, 52.27] !1.98 [!14.27, 9.49] 9.33 [!0.01, 20.93] 5.73 [!3.35, 17.65] 11.90 [2.88, 31.60]

'$40,000/yr, high school degree, azliation with an environmental organization Southeast 13.47 22.14 [7.57, 28.75] [2.69, 64.22] Midwest 5.39 8.70 [2.30, 11.38] [!16.74, 35.47] Northeast 10.24 27.10 [5.96, 17.43] [2.63, 56.93] Southwest 8.46 19.85 [4.37, 15.03] [!6.21, 48.17] Northwest 6.74 15.77 [2.69, 13.10] [!10.71, 47.77]

27.66 [7.99, 73.27] !3.33 [!29.39, 15.82] 8.42 [!14.16, 27.69] 5.32 [!18.58, 23.68] 11.84 [!13.08, 36.26]

11.52] 8.62] 1.68] 1.19] 10.93]

One percent of the fuel mix is switched from fossil fuels to renewable fuels; nuclear is constant. One percent of the fuel mix is switched from fossil fuels to nuclear; renewable content is constant. Bolded numbers have con"dence intervals that do not include zero. Numbers in brackets are 90% con"dence intervals generated by bootstrapping from the original data.

hypothetical products used in the survey. Second, the survey, which was conducted in December 1997 before the establishment of widespread deregulated residential electricity markets, failed to communicate several subtleties concerning the renewable generation sources that

have proven to be important in marketing green power. The most important of these elements is a distinction between fuel mix from all renewable sources and fuel mix from newly created renewable generation capacity. Finally, note that a company may charge a price premium


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Table 5 US deregulated electricity products and attributes Company

Product

Annual premium

% Fuel mix from renewable sources

% Fuel mix from newly created renewable sources

Green-e certi"ed

State

Go-Green.com Commonwealth Energy GreenMountain.com GreenMountain.com GreenMountain.com PG&E Energy Services PG&E Energy Services PG&E Energy Services Utility.com CT Energy Coop Energy Atlantic Conectiv Conectiv GreenMountain.com GreenMountain.com ElectricAmerica Energy Coop of PA GreenMountain.com GreenMountain.com GreenMountain.com Mack Services Group

Eco-Save Green Smart 100% Renewable Solar for the Future Wind for the Future Clean Choice Clean choice 50 Clean choice 100 Green Planet EcoWatt PureGreen Energy Nature's Power 50 Nature's Power 100 EcoSmart Enviroblend 100% Hydro Eco Choice 100 Eco Smart Enviro Blend Nature's Choice 100% Renewable

$46.68 !18.00 59.40 83.40 263.40 20.40 130.80 210.00 !72.00 150.00 120.00 !1.20 94.80 41.40 149.40 !102.00 5.00 31.80 144.60 220.20 !33.60

100 100 100 100 100 100 100 100 100 100 100 50 100 10 50 100 100 10 50 100 100

5 5 1 5 25 5 12 25 5 0 0 0 0 1 5 0 5 1 5 5 5

Yes Yes No Yes Yes No Yes Yes Yes Yes No Yes Yes No Yes No Yes No Yes Yes Yes

CA CA CA CA CA CA CA CA CA CT ME NJ NJ NJ NJ PA PA PA PA PA PA

Derived from Swezey and Bird (Table 3). Annual premium paid by a customer purchasing 1000 kWh per month*includes applicable monthly charges and per kWh premiums. State abbreviations are CA*California, ME*Maine, CT*Connecticut, NJ*New Jersey, and PA*Pennsylvania. Includes one-time initial cost in annual premium paid. Average annual cost will be less if service extends beyond 1 yr. GreenMountain's EcoSmart product attempts to buy all electricity from either natural gas or renewable sources; their 1999 mix was about 90% natural gas and about 10% renewables (source: company web page).

for a particular renewable attribute that di!ers from the average or median consumer's willingness to pay for that attribute. This di!erence may stem from monopolistic pricing behavior that often emerges in di!erentiated consumer product markets or from "rms' misinterpretation of market demand for a given attribute. 3.2. Hedonic analysis of premiums The annual premiums for green electricity o!erings range from !$102 to $263.40; the average premium is $73.55 and the median premium is $59.40. The premium of the cheapest product is, undoubtedly, driven by its reliance upon large hydro sources while the most expensive product features signi"cant new capacity in wind generation. The following equation reports the results of

The exact generation mix for several of the products, including the most and least expensive products in Table 5, are revealed in Swezey and Bird. Detailed fuel mix data was not included in Table 5 because such details are not available for all products.

a simple hedonic regression (linear ordinary least squares) of the data from Table 5 Annual Premium"!205.8#0.81Renew#6.21New (55.8)H (0.49) (2.05)HH #60.86Cert#184.1ME#214.1C¹ (28.6)HHH (48.1)H (48.4)H #38.7PA#53.7NJ#174.2GM (26.5) (45.3) (27.3)H #117.8PGE#77.4Connectiv (37.6)HH (48.7) Adjusted-R"0.82 F-test (10 df)"10.3 (p-val"(0.001), where Renew is the percent of generation sources from any type of renewable source; New is the percent of generation sources from newly created renewable sources; Cert equals one if the product received the Green-e

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certi"cation and zero otherwise; CT, ME, PA and NJ are dummy variables representing the states of Connecticut, Maine, Pennsylvania, and New Jersey, respectively (California is the omitted state); GM is a dummy variable for Greenmountain.com products; PGE is a dummy variable for Paci"c Gas and Electric products; and Connectiv is a dummy variable for Connectiv products. Standard errors for estimated coe$cients are reported in parentheses with single, double and triple asterisks denoting statistical signi"cance at the 1, 5 and 10% levels, respectively. Despite the paucity of observations, the hedonic model "ts the data quite well. Note that the percent of fuel mix from renewable sources does not quite reach statistical signi"cance. However, the percent of generation sources from newly created renewable sources is quite signi"cant. The acquisition of Green-e certi"cation is also associated with higher annual premiums, above and beyond the percent of total and new renewable generation capacity employed. Furthermore premiums in California are signi"cantly lower than those charged in Maine and Connecticut, but not statistically di!erent from Pennsylvania or New Jersey. Note that Maine and Connecticut each feature only one green product; this fact may support the higher premiums charged in these two states. The interpretation of the signi"cant regression coe$cient on New is that a single percent increase in generation mix from new renewable sources increases a product's annual price premium by about $6.21. This compares favourably to the survey results if we can interpret changes in a hypothetical service's total renewable portfolio as changes in a real service's new renewable generation sources. The median willingness-to-pay estimates for a single percent increase in all renewables, accompanied by a single percent decrease in emissions, range from $0.11 to $14.22 for those survey results listed in Table 2. The marginal value of new generation capacity from the hedonic regression is squarely in the center of this range. Instead, suppose survey respondents interpreted a hypothetical 1% increase in renewables to principally consist of an increase in existing renewables; to be speci"c suppose the 1% increase was thought to come from a 0.95% increase in existing renewables and a 0.05% increase in new renewables. Plugging these numbers into

Products may receive Green-e certi"cation if at least half of the electricity supply is generated from renewable sources; at least 5% of this must be from newly created renewable capacity after 1 yr of deregulation. Any generation sources powered by fossil fuels must have emissions levels that are at or below average. Furthermore, no speci"c contracts may be designated with nuclear generation sources. The insigni"cance of the Renew variable may be driven by collinearity among the variables Renew, New, and Cert. Indeed, dropping both New and Cert from the above equation or dropping Cert alone yields a coe$cient on the Renew variable that is signi"cant at the 10% level. However, the overall "t of the model su!ers considerably when the either of the alternative formulations is employed.

the hedonic regression would predict an increased price premium of $1.07, which is at the lower end of the range of willingness-to-pay estimates listed in Table 2. This would reinforce the general intuition that willingness-topay estimates derived from consumer surveys are higher than actual willingness to pay. Though emissions data are missing from the variables used in the hedonic regression, the Green-e certi"cation does not allow products to exceed certain emissions levels. Hence, part of the signi"cant, positive coe$cient of the variable Cert may be driven by the fact that lower emissions products are valued by US consumers. However, the extra $60.86 charged on average by certi"ed products is unlikely to solely re#ect the marginal value of emissions reduction. Certi"cation undoubtedly carries some value merely from a &name brand' status; i.e., the quality and veracity of environmental bene"ts promised by the product have been guaranteed by a certi"cation "rm that consumers trust and this guarantee is communicated in concise fashion by the Green-e logo. The hedonic results also reveal that Greenmountain.com and Paci"c Gas and Electric charge signi"cantly higher premiums. Again the value of branding, this time in a more traditional sense, and the associated trust built via advertising surfaces as an explanatory factor in the premiums charged. 3.3. Summary and conclusions The paper presents two analyses of US consumers' willingness to pay for environmental attributes of deregulated electricity services: one based on consumerbased conjoint survey methods and one based upon hedonic analysis of price premiums charged in several deregulated US markets. The survey results suggest that a wide array of population segments are willing to pay small amounts for tangible improvements in air emissions even if no alteration of power generation sources take place. These same results suggest that, for certain population segments only, larger premiums may be obtained for emissions reductions that are accompany by increased reliance upon renewable fuels. Some willingness to pay for emissions reductions via increased reliance on nuclear power was also found, though this was generally limited to individuals in the Southeast and to those with more formal education but no a$liations with environmental organizations. Analysis of US green electricity o!erings suggests that a wide array of product features explain the price premiums charged. The percent of power supplied by newly created renewable generation capacity is one key determinate; a 1% increase in such sources increases the premium for a household using 1000 kWh per month by about $6 per annum. Certi"cation, brand name and state of o!er also help explain the premium structure. The


magnitude of the estimated environmental premiums are similar to those found in the survey analysis if survey respondents attributed di!erences in hypothetical services' renewable generation capacity to di!erences in newly created renewable generation capacity. If instead survey respondents attribute increases in total renewable sources chie#y to increased reliance upon existing renewable sources, the survey results solidly overstate predicted price premiums available for newly created generation capacity. The wide spread appeal of emissions reduction without fuel mix alteration in the survey results suggests that "rms may not need to enter the realm of renewable generation sources to garner a price premium so long as they can communicate their improved emissions record. However, in the states with active residential electricity markets featuring green alternatives used in the hedonic analysis, the standardized environmental communication vehicles currently focus on fuel mix only. Failure to require standardized communication of emissions data by deregulated "rms may dull incentives to upgrade fossil fuel facilities and to reduce emissions; the lack of such communications in the deregulated markets studied here may, in part, explain the positive e!ect of Green-e certi"cation found in the hedonic regression model. These results suggest that US consumers do value environmental bene"ts created from alterations in electricity generation methods and that US "rms have responded with several product o!erings priced to capture the value consumers hold. What remains to be seen is the success of these individual product o!erings during periods of market upheaval such as those that ravished the

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deregulated California electricity market in late 2000. If green o!erings can survive such periods in which household electricity bills rapidly spiral upward and consumers are prone to seek cheaper electricity alternatives, it would suggest that consumer-driven purchases can support the future of renewable generation capacity in the United States with less reliance upon other policy alternatives.

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