ELSEVIER 

RESOURCE 

and ENERGY 

ECONOMICS 

Resource and Energy Economics 16 ~,~994) 91-122 

The energy paradox and the diffusion 

conservation technology 

Adam B. Jaffe :~* and Robert N. Stavins b 

of 

D~Tartment ,!f Economics, ttarrard University, and National Bureau of Economic Research, 

Cambridge, MA, USA 

h John F. Kennedy School ~?¢ Government, ttarrard Unirersit)', and Resources for the Future, 

79 John F. Kenned.r Street, Cambridge, MA 02138, USA 

Final version received November 1993 

Abstract 

We develop a framework for thinking about the 'paradox' of very gradual diffusion of 

apparently cost-effective energy-conservation technologies. Our analysis provides 

some keys to understanding why this technology-diffusion process i.s gradual, and 

focuses attention on the factors that cause this to be the case, including those 

associated with potential market failures - information problems, principal/agent 

slippage, and unobserved costs - and those explanations that do not represent market 

failures - private information costs, high discount rates, and heterogeneity among 

potential adopters. Additionally, our analysis indicates how alternative policy instruments - both economic incentives and direct regulations can hasten the diffusion of 

energy-conserving technologies. 

Key words: Energy efficiency.; Cons:lvation; Technology diffusion 

JEL classification: Q48; 033 

* Corresponding author. Tel: 617-495-1820; Fax: 617-495-1635. 

Jaffe is an Associate Professor of Economics at Harvard University and Faculty Research 

Fellow of the National Bureau of Economic Research. Stavins is ar~ Associate Professor of 

Public Policy and a Senior Research Associate of the Center for Science and International 

Affairs at the John F. Kennedy School of Government, Harvard University, and a University 

Fellow of Resources for the Future. This paper is part of an ongoing research project on the 

diffusion of energy-conserving technology. We thank Harvey Brgoks, Trudy Cameron, James 

Hines. Maryellen Kelley, Sharon Oster, Ariel Pakes, Alex Pfaff, Peter Wilcoxen, Richard 

Zeckhauser, and participants at the NBER Summer Institute, the NBER-Umversities Research 

Conference on the Economics of the Environmenl, and the Kennedy School Faculty Seminar for 

0928-7655/94/$07.00 ~('i 1994 Elsevier Science B.V. All rights reserved 

SSD! 0928-7655(94)E00003-3 
92 A.B. Ja!ie and R.N. Stavm.~ Resource aml Em'rgv Ecommm'~ 16 ¢ 19941 91 122 

I. Introduction 

In the long run, the development and ",videsrread adoplion of ne~ 

technologies can greatly ameliorate what, in the short run. sometimes appear 

to be overwhelming conflicts between economic well-being and environmental quality. With existing technology, problems such as emissions of greenhouse gases and disposal of l~azardous wastes pose difficult choices between 

potentially irreversible damage to the environment and high economic costs 

of control. But if history is any guide, we know that over a period of decades 

changes in technology can alter dramatically the nature of these tradeoffs. 

Therefore, the effect of public policies on the development and spread of new' 

technologies may, in the long run, be among the most important determinants of success or failure in environmental protection tKneese and Schultz, 

1978). 

In order to achicve widespread benefits from new technology, three steps 

arc required: im'ention - the development of a new technical idea; innoration 

- the incorporation of a new idea into a marketable product or a usable 

commercial process for the first time; and dijli~sion - the typically gradual 

process of adoption of the new product or process by potential users. The 

third element - the diffusion phase - has historically been neglected both by 

research and public pc,': 'v. t 

Recently, however, technology diffusion has moved into the policy spotlight as a r,.zsult of concern over the role played by carbon dioxide (CO:) 

emissions in fo,_~tering global climate change. The largest anthropogenic 

sour~'c of CO, emissions is combustion of fossil fuels for energy generation, 

so reduction in energy use is potentially or, e of the most potent options that 

cx~st~ fo: reducing the risk of ~lobal climate change. It is widely accepted 

that energy use could be reduced significantly through more widespread 

adoption of existing technologies (Norberg-Bohm, 1990). It is almost as 

widely accepted that much un-adopted technology is cost-effective at current 

prices.-" This has led to a decade-long discussion of the 'paradox' (Shama, 

helpful comments on earlier work of the project, and we thank ,'in anonymous reviewer for 

hc!pful conmlcnls on this paper. Research assistance bv Jesse Gordon and funding from the U.S. 

l!.qxironmcntal Protcclion Agency arc gratefully acknowledged. 

t-or a recent example of an invcsligaliort of the innt~vation component, see Georg el al. ( 19921. 

A set of ca~,e ~tt,dies of the three elements in the ct~ntext of "environmental lechnologies" is 

pro,,idcd b,, Kemp cl al. 119921. 

: The constrain! on energy improvements m the short term is not primaril~ technological. The 

t~rimar.~ barrier i~, in,~ufficient implementation of existing cost-effective technologies' (Carlsmilh et 

al.. 1990. p. 251. "Our stock of housing and appliances is still far less energy efficient than would 

be ccom~mic:dl~ optimal" ~I_'.S. Department of |'~nergy 1991. p. 42L Prominent support for this 

notion came from the N;llional Academy of Science.,. 119911 in its finding that U.S. carbon 

dioxide 1('O21 cruise, ions could be significanll.~ reduced ;is parl of :In effort to address the threat 

of grce'o.housc-iilduccd c]imale change lh:ough The adoption of currently cost-effective energy 

efficient,, lechmdogies. 
A.B. Jalle and R, N. sIovm.~ Rc.~o**rcc and Enert, y t'~',m,m~i~',~ I~ ~ 19~4 p 91 122 93 

1983) of inadequate diffusion of apparently cost-effective energy-conserving 

technologies. 

If all of the costs and benefits of energy use were internalized, then the 

potential existence of such a paradox might be of academic interest, but it 

would not have obvious policy relevance. If there are significant externalities 

associated with burning fossil fuels, however, then the paradox becomes 

much more important. Indeed, the existence of such externalities could justify 

public policies tt, reduce energy use. 

The relative effectiveness and efficiency of alternative policy mechanisms to 

achieve this goal will depend on the nature of the energy-conserving 

technology diffusion process. In particular, if the diffusion process is unaffected by economic forces, then the economist's standard argument that some 

sort of market mechanism is the best way to internalize the social costs of 

CO2 emissions would presumably carry much less weight than otherwise. If 

people are not using technologies that are cost-effective at today's prices, 

should we rely on carbon taxes or other policies that would raise the cost of 

energy use? We are much more likely to achieve success, the argument goes, 

with regulatory mandates requiring the use of particular technologies. 3 

The climate-change/energy-conservation arena presents a particularly 

timely example of the broader debate about the relative merits of 'commandand-control' regulation - legal standards requiring particular levels of 

perforn .nce or particular technologies - and "economic incentives' - such as 

emission charges, tradeable permits, deposit-refund systems, and elimination 

of government subsidies. 4 

This paper provides a conceptual framework within which we can examine 

3 The technology-standard approach has been the favored approach in the past and continues to 

be favored by most politicians today. Widely discussed possibilities include uniform national 

building codes and mandatory energy efficiency standards for heating and cooling equipment 

and other major appliances. 

4 For descriptions and examples of these various categories of command-and-control and 

market-based environmental-protection mechanisms, see Hahn and Stavins {1991). There are 

two distinct dimensions along which incentive-based and conventional environmental policies 

differ. First, incentive-based policies can lead. in theory, to a cost-effective (cost-minimizing) 

allocation among firms of the overall burden of achieving any given level of environmental 

protection, in contrast with technology standards and {uniform) performance standards, which 

typically do not lead to cost-effective allocations. Second, incentive-based approaches can result 

in 'dynamic efficiency' by providing on-going ir.centives for firms to adopt new, improved 1lower 

cost} pollution-control technologies; Ihis is in contrast with command-and-control approaches, 

which tend to lock in existing technologies (Bohm and Russell, 1985). It is this latler, dynamic, 

superiority that i'~ examined in this paper. In general and on a theore!ical level, the superiority 

{in terms of inducing technological innovation and diffusion) of incentive-based approaches, 

compared with conventt,ot~al command-and-control approaches is clear {Milliman and Prince, 

1989; Downing and White, 1986). It should also be recognized, however, that under certain 

circumstances incentive-based approaches could actually reduce firms' incentives to adopt new 

technology {Malueg, 1989). 
94 A.B. Ja/]i' and R.N. Stavin.~ Re.wmrce and Energy Ectmomic.~ 16 (1994) 91- 122 

two inextricably !raked questions: what factors determine the rate of 

adoption of energy-conserving technologies; and what effects can economic 

incentives and c, mventional regulations have, in encouraging such adoption. 

By framing these questions within economic models, we hope to help clarify 

the 'paradox' of existing adoption patterns, s We proceed by developing a 

pair of theoretical models that: (1) are rooted in existing thought on the 

economics of technology diffusion: 12) are based on firm and individual 

optimizing behavior; (31 incorporate aspects c( the process that some 

observers claim explain the 'paradox:' and (4) allow for the impact of 

regulaticn on the adoption decision. 

Our two models reflec, the two important contexts in chich adoption 

decisir,ns take place. First, there are situations in which a particular activity 

is being undertaken which prompts a decision about whether or not to 

incorporate an energy-conserving technology at a specified point in time. 

Second, there are situations in which a decision must be made not only 

about whether or not to adopt an energy-efficiency technology, but also 

about when to do so, if at all. In order to analyze both situations, we 

consider the incorporation of energy-conserving technologies in new residential structures and in existing ones. The use of such technologies in buildings 

is important in overall energy use, ~' and the existence of building codes 

provides a context in ,shich to contrast the potential effects of economic 

incentives and regulations in encouraging adoption behavior. 

The next section of the paper provides some background on technology 

diffusion and cnergy-conscrva:ion investment decisions. Section 3 develops a 

thcorctical model of the decision to incorporate a given technology in a 

newly constructed home, while Section 4 focuses on decisions to retrofit a 

technology in an existing structure. We explore the policy implications of the 

analv:is in Section 5, and we provide a brief summary and conclusion in 

Section 6. 

2. Background: technology diffusion and energy conservation 

2. I. Economic mo&'L~" of technological d(/.lUsion" 

From the mcch.mical reaper of the nineteenth century {David, 1966), 

through hybrid corn seed (Griliches. 1957), chemical process innovations 

Needles.,, ~o ,,ay, approaches other lhan e¢Ollqinlic models can be used to examine these 

queqions. See, for example, Cebon (1992). 

" About 25",, of prin,:,.ry energy consumplion is used for heating, cooling, hot water and lighting 

in residential and commercial buildings IU.S. Departmenl of Energy, 199I ~. 

-Thb, section pravide,~ a brief o,,erview, of tke te~.hnologv diffusi,m lilcrature. For more 

~horo.;gh re~ic~s, see Stoneman {1983k Stt~n~lll:.tn i l9~6): David ~1986): and Thirtle and Ruttan 

{ I tJ,R.6 ~. 
A.B. Jal]~' and R,N. Sire'ins Rcsm~rcc and Fm,rgy ,'con+m+ic.~ 16 ~ /994 ~ 91 122 q5 

(Davies, 1979), steel furnaces {Oster, 1982) and optical scanners tLevin et al., 

1987) in tile twentieth century, research has consistently shown that diffusion 

of new, economically superior ~echnologies is never instantaneous. It typically follows an s-shaped or "sigmoid" curve, such that the adoption rate is 

initially slow, then faster, and then slower again as saturation is approached. 

Most models of technological diffusion are intended to pl'Oduce dynamic 

paths with these general properties 

Perhaps the simplest way to generate an s-shaped diffusion curve is with 

an 'epidemic model" (Stoneman, 1983). This approach focuses on the spread 

of information regarding the existence and profitability of the innovation. 

People cannot use a technology of which they are unaware, and they are 

unlikely to use a technology that they do not understand. If knowledge of 

existence and profitability are increasing functions of prevalence of use of a 

technology, then use of that technology can be expected to spread like a 

disease: the probability that a non-user will adopt in any time period ,~ill be 

an increasing function of the fraction of the population that has already 

adopted, If we denote the stock of users that have adopted the technolog2 r y 

time t as S,, and the universe of potential adopters as U,, then a simple 

epidemic model suggests that the technology will diffuse according to: 

ds, (s,). s,) 

di=..\u,] 

The first factor in brackets is the probability of encountering an "infected' 

agent and contracting the disease (adopting): the second fa,'tor is the 

proportion of the population that is "healthy' at d thereby candidates for 

'infection' (adoption). The multiplier, a, is the 'infectiousness' of the disease, 

and parameterizes the speed of the diffusion process. Integration of this 

equation with respect to time yields the logistic function with the characteristic shape. 

in its simplest form, the epidemic model has little economic or other 

analytical content, but the constant, a, can depend on economic forces. In 

this way, the "infectiousness' of the disease can be linked to the profitability 

of the diffusing innovation. The pioneering work of Griliches (1957) established the notion that the process of a gradually diffusing, superior technology could thus be understood in an economic framework, with the rate of 

diffusion being partly determined by the (expected) economic return to 

adoption. Subsequently, Mansfield (1968) demonstrated that ttw rate of 

diffusion can also depend on the size of adopting firms, the perceived 

riskiness of new technology, and the absolute magnitude of the required 

investment. In such models, it is possible that the new technoit~gy is 

profitable for all firms: it takes time for all to adopt only because some have 

not been "exposed." Indeed, these models generate gradual diffusion even if all 
96 .-I.B. ,tal/i' aml R.N. ,gtari*r.~ Re.~ot~rct' tltltl Etl~'r~,.t" E¢' ttlomit'.~ 16 r 1994) 91- 122 

potential adopters are identical. Economic factors explain which innovations 

diffuse fastest, or in which regions a particular innovation diffuses fastest, but 

not ~hich potential adopters actually use lhe technology first. 

As an alternative, David ~1969~ proposed heteroge~eity among potential 

adopters as an explanation (or the gradual nature of the diffusion process. 

His model -- based on inherent difference~ among adopters - is sometimes 

known as the 'probit" approach (Stonem.~n. 19831. ~ David posited that the 

population of potential adopter.', of an ~,:~,~vation differ from one another in 

v~ays that affect the desirability of tech:~ological adoption. For example. 

consider the innovation to be triple-pane c, indow glazing and the important 

dinaension that affects its desirability '.,, be local climatic conditions. An 

individual deciding whether to adopt ~iq:.~ innovation faces an investment 

decision. Hc can incur a certain cost ~,"Jav, which will reduce his home 

heating costs now and in tt~e future, or hc can wait. thus saving the cost of 

purchasing and installintz tim technolog.,,. The co!der the climate in winter 

.,,,a ,h,, '"~""'*'" the li,~ ,4 l ..... I 1 ~t ! ,L& ',- L %, m ~,um ...... ,,,. more att,,.c,,ve will this 

investment be. 

In this framework, one can think of there being a 'threshold' climatic 

inde:,;, '~ above which it is profitable to adopt the innovation and below 

which it is not. Over time, the cost of the triple-pane windows may fall and/ 

or their performance may improve, encouraging homes in more temperate 

climates to adopt the technology as tl:e climatic threshold shifts to the left. 

This movement of the tl, reshold sxveeps out the distribution of climatic 

indexes: if this distribution is smom,, "nd unimodal, the familiar sigmoid 

path of diffusion will result. 

Snell a conceptu:d model of diffusion is applicable to any situation in 

~;hich potential adopters trade off some up-front cost - cost of equipment, 

cost of learning about a new technology, cost of adapting existing processes, 

etc. - against expected future benefits of i.he technology. The improvement in 

the attractiveness of the innovation o~er time can also be very general, 

including the spread of better informatior~ on its -se, which makes it less 

costly to adopt. Finally. of course, it is not essential that the value of the 

Tile term refers to the commonly employed si:ltb, ticat model for limited dependent variables, 

which .,,hares a conceptual foundation ,a, ith David's diffusion model. See also Davies (1979); 

Sommers (19g0): and Casx~ell et al. (19901: and Cas,aell and Zilberman (1990). Another set of 

models haxe also focused on the impacts of firm size and market structure on adoption 

decis:ons: hazard~rate models are employed hv Hann,m and McDm~ell (!994) to examine the 

faclors affecting the adoption of automated teller machines (ATM's) and by Levin ct al. (1987) 

to in:estigate adoption of optical scanners at retail grocery stores. Rose and Joskow (1990) 

extend the hazard model of adoption to take advantage of a~ailable information on adopters 

mid non-adopters. 

"This index would pre~umabRy be some flmction of heating-degree days ~nd cooling-degree 
A.B. Ja//b ami R.N. Stavm.~ Re.~our~ c amt Em rgy E, ~momics 16 : 19~4 'i 91 122 97 

innovation depend on local climate. What is crucial is that potential adopters 

be heterogeneous along some dime,lsion tha! affects the value of the 

innovation, to 

The "heterogeneous adopters' and "epidemic" models each capture important aspects of the diffusion process: the modds we develop below incorporate 

both strands of thought, by allowing adoption decisions to be driven b3, 

adoption costs that have an unobscrved heterogeneous component and 

another component that depends on the prevalence of the technology among 

the stock of potential adopters. 

2.2. The workhzgs of incentive-based umt conventiomd reeulations 

From the previous discussion, we can begin to perceive how economicincentive approaches to environmental problems would affect the diffusion of 

environmentally beneficial technology. Whether through the diffusion speed 

in the epidemic model or the adoption threshold in the 'heterogeneous 

adopters" model, any policy that increased the profitability of a technology 

would speed its diffusion. It is less obvious how, in an economic context, to 

model the effects of regulation intended to foster technology adoption. 

Indeed, most of the economic literature on the effects of regulation on 

technology focuses on its inhibiting effects. ~ 

Non-economists have discussed the "technoiogy-forcing' benefits of 

command-and-control regulation.~2 It is certainly plausible that enacting and 

enforcing a law that mandates the use of triple-glazed windows (or some 

overall energy efficiency s~andard), for example, in new home construction 

would affect the prevalence of that practice. It is less clear what is the best 

way to incorporate that possibility in an internally-consistent conceptual 

model. Below, we suggest that the effects of such regulations can be 

embedded in an economic model by postulating that builders perceive that 

the (expected) cost of adopting a new technology is affected by building 

codes" treatment of that technology. Before developing that model, we return 

to the specifics of energy-conserving technologies, and the arguments that 

have been put forward to explain their observed adoption rates. 

2.3. Explanations l'or the 'parudov' 

Various explanations I.ave been put forward to explain the observed rates 

~o In the present context, the heterogeneity could likem'ise be associated with the type of home 

heating plant I furnace), the size of home, or indi'~idual preferences for indoor temperature. 

~ See, for example, Oster and Quigle.~ ( 19771. 

~2 See, for example, Ashford el a[. 11985). 
98 ,4.B. Ja//e and R.N. Stavin.s Resource and Energy Economics 16 f 1994) 91-122 

of adoption of new energy-conserving technologies. Although some facts are 

in dispute, for our purposes wc will take as given that there exist proven 

technologies that engineering calculations show to be cost-effective at current 

technology and energy prices, but that are not widely used. 13 Some energypolicy authorities (particularly non-economists) have interpreted this as 

evidence of a failure of the invisible hand that should be corrected by 

government intervention: frequently advocated interventions have included 

minimum energy-efficiency standards for particular products and construction design standards (U.S. Department of Energy, 1991). 

When most economists observe the same set of facts, their responses tend 

to fall broadly into two categories. One type of response is to seek to identify 

the specific market failure that might explain the apparent non-optimizing 

behavior. The other category of responses consists of reasons why observed 

behavior is indeed {privately) optimal, despite engineers" calculations. 

2.3.1. Market failure exphmations 

One obvious source of potential market failure affecting adoption decisions 

is lack of information about available technologies. It is costly for people to 

learn of an innovation's existence and to learn enou~,h about it to know if it 

is profitable and how to use it. Since information has public-good attributes, 

it is certainly possible that it is underprovided by the market. Further, if 

others' use of the technology is an important source of information (as in the 

'epidemic" model), then adoption creates a positive externality because it 

generates information that is valuable to others. 

Another po3sible source of market failure consists of principal/agent 

problems that can arise when energy-efficiency decisions are made by parties 

other those who pay the bills. In this case, difficulties in observability can 

make it impossible for the investing party to recover the investment from the 

party that pays the energy bills. This pk,,,blem could take several forms. If the 

builder of a new house cannot credibly represent its energy efficiency to 

potential buyers, then the sale price may not fully reflect efficiency attributes. 

Similarly, a landlord may not be able to recover all of the value of energy 

efficiency investments where renters pay fuel bills. Conversely, there may be 

sit,rations where renters would have to make the investment but the landlord 

pays for fuei.~4 

Finally, consumers may face artificially low energy prices that explain their 

disinterest in conservation (Sutherland, 1991). First, electricity and natural 

gas are typically priced on an average-cost basis that conceals from 

~3 Examples that are often cited include compact fluorescenl light-bulbs, improved insulation 

malerials, and energy-efficient appliances ~ Norberg-Bohm, lq90~. 

1.~ See Fisher and Rothkopf I lgggJ. 
,4.B, JalaP' and R.N. Stavmx R~'s~mr~'~' ~nd Em'rgl E~ommU~,~ 16 ¢ 19~4 ~ 91 ;22 q9 

customers the incremental cost of new energy supplies. Second, electricity is 

highly subsidized in some parts of the country. Third, uninternalized 

environmental externalities may be associated with the use of energy from 

particular sources ~including fossil fuels, nuclear, and hydroelectric sources). 

2.3.2. Non-marketzfaihu'e explanations 

Another set of possible "economic responses' to observed conservation 

technology adoption behavior is to conclude that there exist costs of 

adoption that engineers are ignoring or at least underestimating. Beyond the 

obvious tautological validity of such a claim, there are some reasons to give 

credence to this assertion. One aspect of cost is that of learning about the 

new technology. As noted above, the pure information-creation part of this 

cost has public-good aspects and therefore fits into the market failure 

category. But there is also a purely private part of this cost that relates to 

information acquisition and absorption. It is by no means costless to learn 

how a generic ~echnological improvement fits into one's own home or firm, 

nor is it costless to learn about reliable suppliers. ~5 Thus, even after basic 

information about a technology has been generated and disseminated, the 

"purchase price" of the new product is no more than a lower bound on its 

adoption cost; transaction costs of adoption (of various kinds) can be 

significant relative to the magnitude of the net benefits of adoption. ~6 

Another way of explaining low adoption rates is to posit that users halve 

relatively high implicit discount rates. 17 Hence, another way to make this 

behavior consistent with underlying optimizing behavior is to explain why 

discount rates relating to these investments should be unusually high. 

Sutherland (1991) notes that high discount rates may be appropriate. These 

are irreversible investments with much uncertainty about their payback, both 

because future energy prices are highly uncertain, and because actual energy 

life-cycle savings in any particular application can only be estimated. 

Finally, even if a given technology is profitable on average, there will be 

J5 Some have argued that not only costly information acquisition but also biased estimates by 

individuals of likely energy savings play a role. Consumers may not believe experts" assessments 

of the benefits of new technologies. On the other hand, the bias may go in the opposite direction 

of the energy paradox, since some studies inOicate that consumers systematically overestimate 

energy savings associated with some types of new technologies (Stern. 1986). 

1, See Joskow and Marron (1992). 

t, Hausman (1979) estimated that consumers used avzrage implicit discount rates of 20,° for 

purchasing room air conditioners (with substantial ,ariation by income class): and Dubin and 

McFadden (1984) found average implicit disc~ :nt rates of 20°/; for space-heating and waterheating investments (again, with significant variation by incomet. In a comment on Hausman 

(1979), Gately (1980) estimated discount rates of 45"0 to 3000, for refrigerators. Likewise. 

Ruderman el al. [1987) found personal (implicitt discount rates as low a t 20",, and as high as 

800", for heating and cooling eqmpment, and residential appliances. 
l(i(~ .4.B Jail(' arm R N. ,%taLtpls R('~our, c ~md E'r('rgl Economtc.s 16 ~ 1994 ~ 91 122 

some individuals or firms for whom it is not profitable, if the relewmt 

population is heterogenous with respect to the amount of energy they use, 

for example, even a technology that looks very good for the average user will 

not be attractive for a portion of the population. Referring again to the 

"heterogeneous adopters model,' we can interpret the engineer's costeffectiveness calculations to mean that the technology is profitable for the 

mean household or t2cm. Depending on the rate of movement of the 

threshold, and the shape ivariance and skewness) of the underlying distribution, it could be quite some time after the threshold cvo,"~es the mean before 

all or even most households or firms adopt (although heterogeneity does not 

explain extremely low adoption rates for "cost-effective" technologies). 

The models developed below incorporate a number of these market-failure 

and non-market-failure explanations of the 'energy paradox', including 

information problems, principal/agent slippage, incompletc pricing, unobserved costs, heterogeneity, and potentially high discount rates. Is 

3. I_Jse of energy-conserving technologies in pew construction 

We begin with the decision to incorporate a potential energy-saving 

technology in the construction of a new home. We imagine a builder at time 

7" in political jurisdiction i considering the incorporation of a new technology into the design of house j. We take the decision to build the house, and 

its design features other than the technology under consideration as given. 

We assume that the builder designs the house to maximize expected profits. 

To do this, she will need to trade off the incremental cost of the new 

technology against the expected increase in selling price associated with a 

more energy-efficient house. 

In order to allow for the considerations discussed in section 2 of the paper, 

we assume that houses are heterogeneous in their energy use and that the 

housing market may discount energy savings because builders cannot 

represent them credibly. On the cost side, we allow for the possible effect on 

incrcmcntal costs of the prevalence of the practice among builders in the area 

and of the builder's own experience with lhe technology. We allow for 

regulation to affect the decision by modifying the cost of the new 

technology. ]'~ We also allow for the possibility of a tax credit or other 

subsidy to the use of energy-conserving technologies. 

I,~ As discussed later in !he text. ~ve do nol deal explicitly with uncerlaint 3. See Howarth and 

Anderson (19921. 

~'* One interpretation is that regulation requires the use of the technology, creating an explicit or 

implicit penally for not using i:. Alternatively, regulation may merely encourage use of the 

technology by, for cx ~mpIe, selling an overall energy budget for the house. Under either 

inlerpretatBon, we Ireal the ~nagni~,ude of this perceived effecl as an unknown parameter. 
The builder's decision may thus bc modelied as an attempt to maximize 

the sum of the base selling price of the house ~tin the :~bsence of the energysaving technolog3~ and ~hc present discounted value of the expected energ3 

savings if lhe |ethnology is adopted (tl~e capitaEized ,+aluc of the ins~aEled 

technology} minus the costs of adoption {where these include costs associated 

with up-front pvrchase and instat~ation, the impmicit or explicit costs effect of 

regulation, and k rning effects due to pre~ious use by t|~is builder or current 

use by other builders in the areal. We assume that the builder recognizes 

that uncertainty is embodied in a number of the relevam variables, that the 

builder forms point estimates {expected values} of these variables, and that 

when making decisions the builder treat these expected vatues as if they were 

known with certainty, a° Thus, we treat the overall decision problem in a 

fully deterministic framework, et Formally, the builder's problem is: 

I 1 max nor= B+ I-6"[1 - w}. j" gtko,,,u,j ,}.e "dt 

{!~ 1" 

- I" [Heir. Sot, hr)-TDir- Xir] 12~ 

where upper case letters represent stocks or present values: lower case letters 

represent flows: and greek letters represent parameters {except for n and p. as 

indicated below}. The variables are: 

7~j T 

i= 

B= 

6= 

profit associatcd with adopting the technology in constructing 

house j in geographic area land political jurisdiction) i at time T: 

indicator of choice to adopt the technology t! = 1 if the technology 

is used, and 0 otherwise):" 

the base sel!ing price of the house withoet the technology; 

discount {0~<6 < 1) or premium (6 > 1) applied hy market to rah. o/ 

energy sm'ings: 23 

~o In other words, only the means of the respective probability distribution:q are taken into 

account in the optimization problem. 

21 As we indicated earlier in our discussion of possible ,~:,.f, la;,a:ions of thc energ2, paradox. 

uncertainty may play a significant role in technology diffusion. It would be pas'dble to focus on 

.~hat dimension of the energy-conservation diffusion proce.~', as do Hassett and Mel,.atf 11992~. 

For broader treatments of this perspective, see. for example. Pind3,ck <1991 I. and Di+~il t1992~. 

zz We focus on a simple discrete technology for purposes of explication. The model can be 

generalized to represent multi-valued discrete or continuous technological choices, such ;is 

installation of insulation of ~,ario.~s "R-values" in exterior walls. 

2.~ This parameter is included in our formulation to allow for principal-agent problems that may 

arise when a builder cannot credibly represent a new home's energy efficiency to potential 

buyers land hence not capture the full value of efficiency attributes in the housing price). In such 

as case, 0~<b< 1. Alternatively, if buyers of homes place excessively high valuations on energy 

efficiency attributes of new hc, n,es, we would find lhal +~ > i. 
1112 4.B Jalti' and R.N. Stm.m~ R,,.~,mr,.,, and Energy Economics 16 ¢ 19941 91 122 

w= index of average quantity of enerp_y used by the technology relative 

to energy consumption if the technology were not used 10 < w~< I); 

k,jr= vector of current and expected future valucs of observable characteristics of the home (for example, size. type of heating plant), and 

region (for example, price of fuel, climate, average income and 

educationl; 

lt,j,= an unobserved factor affecting energy use: 

g(.) = tunction that relates elements of k#, to annual fuel expenditures; 

e = base of natural logarithms; 

r = real market rate of interest; 

C~r= engineering estimate of purchase and installation cost of adoption 

of the technology: 

S,~r= the cumulative stock of houses built previously by builder j that 

incorporate the technology; 

qr= fraction of newly constructc,J 'aomes in jurisdiction i that incorporate the technology: 

L(.) = a function that generates the "effective cost' of installation from the 

engineering cost and the prevalence of use of the technology; 

D~r= dummy variable set to unity if jurisdiction i has regulation in year 

T requiring that the technology be installed; -'~ 

7= parameter that captures the average perceived monetary equivalent 

cost of ignoring regulation, presumably a function of the nature of 

the regulations, the magnitude of penalties, perceived probabilities 

of enforcement, and likely stigma: z~ and 

X~r= subsidy or tax credit in jurisdiction i for adopting technology. 

This formulation incorporates many of the features of the problem 

suggested above. The heterogeneity of potential adopters is reflected in the 

unobserved l:~j,. The 'epidemic effect' related to the prevalence of the practice 

is represented by the L(.) function. Thus, in our formulation of the problem, 

the essence of the epidemic model is that potential adopters must learn about 

the new technology before they can use it, and the probability that such 

learning will occur depends on the fraction of the population ihat has 

adopted. Implicit in our formulation is that such learning can be viewed as 

one component of the overall cost of adopting a new technology. The idea 

that information spreads by contact with previous adopters is captured by 

allowing the cost of adoption to depend on the 'regional" prevalence of use. 

Once we have allowed cost to depend on the extent to which other builders 

are using the technology (rcr), it seems natural to allow the builder's own 

experience with the technology (Sqr) to reduce effective cost as well. 

_,a Again, in this simple model, ~'e deal with a "0- I" regulation. 

:5 See Russell et al. ~[9861. 
A.B. Jaf/'e aml R N, Starms Resm~r,'e aml Energy Ec~m.mh's 16 ¢ 1994) 91 122 103 

The potential effect of regulation is captured by converting it to equivalent 

cost terms in the form of 7. Finally, we also allow for the possibility that 

some sort of tax credit or subsidy might be used to encourage adoption. 

The first-order, necessary condition for this problem compares the overall 

cost of adopting the new technology with the expected increase in the selling 

price of the house. 26 Denoting the expected discounted present value of the 

function g(.) as G{.), the condition for adoption is that the technology 

should be employed if: 

fi "( ! - w) " G(kol,,lzor) + "i'DiT > L{ CiT,Sijr,z',iT) - X iT (3) 

Thus, the builder will use the technology if the valuation placed by the 

market on the savings in expected energy costs plus the implicit or explicit 

value of complying with regulation lif any) exceeds the cost of installation. 

The "cost of installation" includes the purchase and installation cost and the 

cost of learning about the technology, and may be reduced by a government 

subsidy. 

The next steps are to specify functional forms for the G(') and L(.) 

functions and to make assumptions about the distribution of the unobservable /~, all from Eq. (2L above. Some relationships in the equations ,.re 

inherently multiplicative (such as price of fuel and quantity of fuel used), 

while others are inherently additive {such as gross installation costs and tax 

credits). Thus the choice of fimctional form is inherently somewhat arbitrary. 

For simplicity, let the function G{') be Cobb-Douglas. Recall that the 

vector k;j, includes the price of fuel and attributes of the region, house, and 

household that affect energy use. Let k j,* denote the m 'u element of the 1 x M 

vector kit,, and, in particular, let k~s, t =P;j,, the price of fuel to house j in 

region i in year t. To keep things simple, other elements of k~, {other than 

price) may be assumed either to be such that the agent holds static 

expectations about them or to be constant (that is, ks,"= k jr" for all t > T, for 

m=2 ... M). Finally, the unobservable ~ is posited to enter multiplicatively. 

The function G(-) from Eq. {3) can now be written as: 

r 1 G(')= ~ (Pij,)e-"dt " (kijT'n) #m "FtljT 

LT m=2 

(4) 

where the specific elements comprising the vector kijr might vary depending 

on the particular technology in question, but would include variables such as 

2. This ignores the possibility implied by Eq. 12~ that the builder might take into account the 

future reduction in costs (parameterized by ~a) that v, ould be brought about by using the 

technology today. 
104 .q. B, Ja//i' aml R.N. Stavins Rest,ur,r and Em,r,¢y E~ om,mir.~ 16 (1994) 91 122 

number of rooms in the house, number of heating degree-da>,s in the area, 

and income and education of the homeowncr. 

The cost/learning funttion L(.) can reasonably be ba~¢ed on proportionate 

reductions in the cost of installation as cithcr the builder's own cxpericx~ce or 

the prevalence of the technology being instalh;'J ~ncrease: 

L( . )=(C~rl', .(r~r)~'-.(Sor y ~ 

\~3/ 

(5) 

Thus, overall cost is the product of three factors tnat depend respectively on: 

the engineering cost estimate, C~r; the prevalence of installation of the 

techno!ogy within the area, fir; and the builder's own cumulative expe6ence 

with the technology, S~i r. 

Eq. (5) implies that the cngineering cost estimate, C~r, will be the actual 

cost if cveryonc is currently installing the technology (Z:/r = i), and the builder 

has "typical" experience, parameterized by :x 3. When installation is less 

prevalent (z:~r< 1), the cost is higher, with the sensitivity parameterized by :t 2 

(assumed to be less than zero). For builders with more or less experience 

than the typical level x3, costs are higher or lower, with the own-experience 

sensitivity parameterized by ~.~.2-7 

Next, we rearrange the condilion for adoption - Eq. (3) - ir~ the form of a 

benefit/cost ratio: 

6"(1 - w)" G(k:j.r,/l~jr) ] 

L( Ci i. Sij.l.,t,i.1. ) _ X il -- TDiT J >~ I 

(6) 

Substituting Eqs. (4) and (5) into Eq. (6), and taking natural logarithms of 

both sides yields the following expression: 

log(f)+log(l-w)+fftlog (i (P;j,)e "dt /" + ~ [/~r.log(k~jr)] 

T z m:2 

(7) 

Eq. (7) conveniently illustrates how a variety of factors can affect the 

diffusion of energy efficiency technologies. First of all, principal/agent 

problems associated with the builder/homeowner relationship will have an 

'- The exponent :~t. on lhe engineering cost cstimale should bc unity. 
A.B. Ja[h' and R.N. Slarms Res¢mrce and Energy Ecomunh's 16 ~ 1994) 91 122 105 

unambiguously negative effect on the rate of adoption. If principal/agent 

slack exists, the parameter 6 will be greater than zero but less than unity (see 

Eq. (2)}, and so log(61 in Eq. (7) will be negative. Likewise, individuals do not 

have perfect information about the path of future energy prices. To whatever 

degree they tend to underestimate (the present discounted value of) future 

energy prices, the qkelihood of adoption of an energy-conserving technology 

will be reduced Similarly, if energy prices, Pij,, are "a~tificially low, '28 

adoption will be slower than it would otherwise be. z'~ Furthermore, it is 

clear from Eq. (7) that if decision makers hold relatively high discount rates, 

r, the anticipated energy savings and hence the tendency to adopt the new 

technology will be less than otherwise. 

Focusing next on the term behind the summation sign in Eq. (7), we can 

see that climatic departures from temperate conditions (increases in heating 

and/or cooling degree daysj will encourage adoption, ceteris paribus. Other 

factors affecting energy use, such as income or education, could also 

matter. 3° 

Turning to the second line of Eq. (7), we can see that decreases in 

adoption costs will accelerate technology diffusion. This could be due to 

changes in the direct costs of equipment purchase and installation (Cir), or 

changes in "effective costs of adoption' associated with learning, inversely 

correlated in our model with the prevalence of installation of the technology 

within the region, t'~r; and the builder's own cumulative experience with the 

technology, S.~r. Thus, depending on the magnitude of the parameter, :~2, 

as The "artificially low" energy prices could be due to any one of a number of factors, as we 

suggesteti earlier: departures from marginal-cost pricing of electricity by utilities: subsidies for 

some fuels; and/or uninternalized environmental externalities. 

29The question of how individual expectatio:~', of future energy prices are formed is also 

relevant. If people have static expectations, only current prices matter: for adaptive expectations, 

some combination of current and past prices will be determinate; for rational expectations, all 

relevant information available at time t will matter. 

30 In order to judge the significance of particular effects, Eq. (7) could be estimated, at least in 

principle. Whether specific effects could actually be verified would depend, of course, on the 

identification of respective parameters. This is largely an empirical issue, but by examining the 

extent to ,,~hich various effects are even potentially identifiable we can shed some additional light 

on the disputes regarding the 'paradox" of slow adoption. For example, it is clear that the effects 

of household and regional factors on expected energy use Ifl2 to flu in both modelst are 

identified. Thus, it is theoretically possible to separate out the effects of these factors on 

adoption decisions. On the other hand, there is a rather convoluted relationship among: the 

discount applied by the housing ~narket to energy savings 16L the interest rate {rl. and the 

sensitivity of the decision to the price of fuel (fllJ in the new construction case. If there is a 

'paradox,' it suggests that the adoption decision is not as sensitive to fuel prices as would be 

suggested by the simple.st benefit/cost analysis. That is. principal/agent slack could be present 

{fi<lL the implicit discount rate could be relatively high. or measured fuel prices could be 

having a relatively mild impact on expected prices (fl~ < I~. Our model suggests that it may be 

difficult to separate out these factors from one another. 
106 A.B. Ja/h' and R.N. Stavin.~ Resource ,rod L)wrgy Economics 16 { 1994) 91 122 

there may be a dynamic externality in which increast, d adoption today 

fosters future adoption by increasing v,. Next, direct regulations - such as 

~'.:ii'di,,,¢ codes -.-can have a direct, positive effect on adoptio~ by, in effect. 

decreasing the expected costs of adopting {7), 3~ and government programs in 

the form of subsidies or tax credits {X~r) can directly reduce adoption cost 

and thereby spur diffusion of the technology. 

4. Retrofitting energy-conserving technologies in existing structures 

We next examine the adoption decision faced by an individual considering 

the installation of an energy-saving technology in an existing home. Thus, for 

example, we may consider a homeowner who is thinking about the possibility of injecting blown insulation into exterior walls. 32 We posit that such an 

individual will attempt to minimize expected costs, subject to various 

constraints, taking as given all relevant prices and government policies. 33 By 

formulating the problem this way, we are assuming that if the homeowner is 

not risk-neutral, her attitude toward risk is such that the riskiness of the 

investment can be captured by appropriate adjustment of the interest rate. 34 

Because of the possibility that the technology may be significantly cheaper in 

the future (either because of tcchnological change or 'epidemic' learning), this 

is not a 'yes/no' decision like that of the builder; the homeowner must decide 

at what time (if any) to perform the retrofit installation. 3s 

The costs that the homeowner wishes to minimize consist of three elements 

- the present discounted value (PV) of annual energy costs from the present 

to the time of adoption of the energy-saving technology, the PV of annual 

energy costs after the adoption, and the PV of the one-time cost of adoption 

of the energy-saving technology: 

3~ The magnitude of this impact is clearly an empirical matter. See, for example, Jaffe and 

Stavins (1993b). 

32 Whereas in the previous model we highlighted the principal/agent problem (and employed the 

parameter, 6, to allow for its effect), in the retrofit model we focus on homeowners and therefore 

do not need to consider the agency problems that may exist in the landlord-tenant relationship. 

The parameter, 6, refers instead exclusively to homeowners' possible lack of knowledge about 

the effectiveness of a given technology. 

33 It is als~ possible that enew.y conservation enters directly in some people's utility functions. 

• x'~ Hassett and Metcalf (1991) examine the effect of uncertainty on the retrofit decision. By 

focussing on utility-maximization instead of cost-minimization, we could also investigate the 

possibility that the optimal consumption of energy services (for example, the thermostat setting) 

will change if the house becomes more energy-efficient. 

3s Because retrofitting an existing building is typically much more expensive than incorporating 

a new technology at the time of construction, our analysis of new conswruction reasonably 

ignores the possibility that the retrofit option affects the initial installation optimization 

problem. 
A.B. Jaffe and R.N. Slavins Resourt'e ~:nd Energy Et'onomi(,~" 16 (1994) 91 122 I07 

T dt 

min P V( T) = S g(kij,, l%)'e -r'dt + w . ~ g(k~j,, l%) " e- r'dt 

IT) o T 

T 

+ [L (C~r, V~r) - X~r]" e-'r + ~,. ~ D~, "e-'*dt (8) 

0 

~")';'" .,,.,,,j,.,., to T >10 (9) 

where: 

T= the time of adoption (installation); 

V~r= the fraction of retrofit candidates in jurisdiction i that have adopted 

the technology by time T; 

and all other variables are as defined previously. This formulation is 

consistent with that used above for new construction, including the treatment 

of heterogeneity and the effect of regulation. We allow for the impact of 

regulations although such interventions are typically not utilized in this 

retrofitting context, because in other possible applications - such as industrial pollution control - regulations can be designed either to affect all 

sources (thus requiring retrofitting at existing sources) or can be designed to 

affect only new sources. The effect of regulations enters into the objective 

function as the final term in the second line of Eq. (8). Thus, we are viewing 

regulations as an additional cost to be minimized, where this cost is equal to 

the "effective penalty' of non-compliance from the present time to the date of 

adoption (and thus compliance). Alternatively, the limits of the integral in the 

regulation term could be the time of adoptien, T, and infinity, in which case 

the term would be subtracted instead of added. Then we would be viewing 

the effect of regulation as providing a benefit (an 'avoided cost') to the 

adopter from the time of adoption onward. Note that these two specifications of the impact of regulations are equivalent: in either case, the same 

first-order condition follows, as we will see below in Eq. (12). 

Returning to the optimization problem, note that the installation cost is 

formulated slightly differently from the new-construction case. Since the 

homeowner will not typically have previous experience with the technology, 

we take the effective cost to depend only on the engineering cost and the 

local prevalence of the technology; because of the nature of the retrofit 

situation, we take this prevalence to be represented by the fraction of the 

stock that has been retrofitted, rather than the current retrofit rate 

First-order conditions for maximizing PV(T) in Eq. (8) subject to the 

constraint of Eq. (9) are: 

(~PV(T)>_ f?,PV(T)~. 

~T ,/0 and \ ~T ,] T=O (Io) 
log ,.I.B. Jatli' and R.N. Stavins R,'~ource and Energy Econonlic.~ 16 ¢ 1994) 91 122 

The first condition indicates that retrofitting should occur at a time T such 

that waiting longer will not decrease the present value oi costs. The second 

condition indicates that retrofitting should occur either at T=0 (when 

installation is first considered) or when the derivative of the present value of 

costs with respect to time is zero. It is useful to reformulate the conditions in 

into a condensed form in which adoption should occur in year T if: 3~' 

?PV(T) >>.0 and adoption has not yet occurred. 

?T 

(11) 

Evaluating the inequality condition in Eq. (11) yields: 

-rT [(1 -6"w)g(k~ir,lqjr)-r'[L(C~r,V~r)- X ,r] + 7Dir] " e 

~L 

., -/', ,.* J-t 

(12) 

Dividing by e ,r and rearranging terms, adoption is predicted to occur at 

time T such that: 

(I -- ~ " w)g( ki jr,l: i j r) 4- T D iT > r " [ L( C i r, V,r I - X ir] 

\?C,r) \ dT J \?V,r} \ dT J + 

(13) 

The left-hand side of Eq. (I 3) indicates that higher annual energy costs can 

encourage adoption of the energy-saving technology, as can the effectiveness 

of the technology 3v and the existence of relevant regulations. The first term 

on the right-hand side of the equation indicates that higher adoption costs 

(whether direct or indirect) and higher interest rates discourage installation, 

and that government subsidies can encourage adoption. Finally, the presence 

of the last set of terms - the time derivative of adoption cost - indicates that 

adoption is discouraged by expectations of decreased (effective) costs of 

adoption in the future. Thus, even if the current savings in energy costs are 

greater than (the annual annuity of} adoption costs, it can pay to wait (f 

those adoption costs are expected to fall over time at a sufficiently rapid rate. 

Notice that Eq. (13) is a statemcnt about the current rate of energy 

savings: it does not involve present values of future streams {in contrast with 

Eq. (3) in the new construction model). This may seem counter-intuitive, but 

note that it is the standard condition for the purchase of a capital asset: the 

instantaneous r,.,.te of earnings from tile asset (tile left-haled sicie) shouid be 

3, Sufficiency depends upon the satisfaction of second-order conditions, which w,e discuss later. 

~' Recall that 1 -&v is the expected proportion of energy saved. 
A.B. Ja[]'e and R.N. Stavins Resm,r,'e and Encr,ey Ec~momi,'s I~ ; 19V4i 91-122 109 

greater than or equal to the carrying cost (the first line of the right-hand 

side) minus the instantaneous rate of capital appreciation (the last line). 

"Earnings' from the asset are the energy savings: the "cost' of the asset 

includes the installation cost and the cost of acquiring the necessary 

information, adjusted for the effecls of regulation and subsidies. The ~capital 

appreciation rate' has terms corresponding to each of the elements of the 

cost of adoption. To the extent that the overall cost of adoption is expected 

to fall (that is, the sum of the last set of terms is negative), it is as i~" the asset 

were suffering a capital loss; instantaneous earning ~,ill have to be greater to 

justify the investment. To put it concretely, to the extent that one expects 

that compact fluorescent light bulbs are getting cheaper or easier to find or 

easier to install, one might wait until next year to purchase and install them 

even if they are currently economical. 38 

If it still seems counter-intuitive that the adoption condition depends only 

on current values (and not on present values of ft.ture expectations), note 

that if the second-order condition is satisfied, the function PV(T) will have 

(at most) a single optimum, which will be just at the point when the 

instantaneous investment condition holds. It does not matter how large the 

savings will be in the future; overall costs are minimized by adopting at the 

instant when marginal costs equal marginal benefits, as represented by 

condition. 39 

Many of the issues addressed previously regarding functional forms for 

g(') and L(-) arise, of course, in the retrofit context, as well. The addition of 

the terms invelving the time derivatives of the cost components makes the 

retrofit model, on balance, 'more linear' than the new construction model, so 

we proceed with such a formulation. Consider the following form of the 

energy-cost function: 

M 

g(')=Por" ~ fl,,,k,'~T+POT (14) 

rn=2 

First of all, note that current annual prices are en, pioyed, unlike the 

new-construction case, where the present value of a future stream was 

appropriate. Relevant features of the house and region contribute additively 

38 Thus the model produces a polenlial 'non-market-failure" explanation of the 'paradox." 

beyond those suggested above. This is parallel to results derived in models with explicit 

uncertainty (Dixit, 1992). 

39 The intuition that expectatic, as of future prices should matter would be correct, however, if 

the second-order condition is violated. In this case, th-, first-order ,:,mdition of Eq. ~13) is a 

necessary but not a suJficient condition for optimal adoption. The condition could hold at a local 

maximum of discounted costs that is not a global maximum, as it could at a local minimum that 

is not globally optimal. Hence, present discounted values would matter and thus future costs 

{prices) would matter. 
I I0 A.B. Ja[li' and RN. Stavins ' Res,mrce and En('ret Economics 16 11994) 91-122 

to energy use: the fuel bill is the product of this additive function and the 

price of fuel. Since g(') is multiplied by (1-a.w) in Eq. ~13), the new 

technology reduces the overall fuel bill proportionately. 

The learning/adoption-cost function can also be specified in linear form: 

L{ " ) = ~t 3 -{-~tiCir+~t2Vil . 

(!5) 

Note that in contrast to Eel. (5), there is no term for 'own experience.' Now, 

substituting Eqs. (14) and {15) and into Eq. (13t, and evaluating the various 

derivatives yields: 

r . ] 

l (l --6"w)" P,jr" Y', IJ,,k~r +TDir 

ill ~ 2 

-r' [:~ 3 +~tCir+~zVir-Xir] 

+r ) +r 

(16) 

As explained above, this indicates that adoption decisions are made on the 

basis of current energy prices without concern for future energy price paths; 

nevertheless Eq. (16) indicates that interest rates still matter since it is the 

annuity of adoption costs that is critical. In particular, higher implicit 

discount rates, r, will tend to retard adoption. As in the new-construction 

case, adoption will be slowed by 'artificially low' energy prices (Pur); climatic 

departures from lemperate conditions will encourage adoption; and so will 

other factors that increase energy use. The existence of relevant regulations 

can likewise encourage adoption. 

The second bracketed term on the first line of Eq. (16) implies that high 

adoption costs will unambiguously discourage adoption, whether these 

adoption costs are associated with: direct cost~ of equipment purchase and 

installation (C~r); changes in effective costs of adoption associated with 

learning (inversely correlated with cumulative adoption in the area, V~r); or 

government programs in the form of subsidies or tax credits (Xir). 

Finally, note that although the future paths of energy prices turn out not 

to be relevant for adoption behavior in the retrofit case, Eq. (16) reminds us 

that the current time rate of change of adoption costs, broadly defined, does 

matter, in pamcular, if purchase and/or installation costs are falling, it can 

pay to wait, despite the fact that current net bcnefits of adoption are positive. 

Likewise, if adoption is taking place very fast and information about the 

technology is thus increasing rapidly, it can pay to wait (since ~2<0). 

Finally, if government subsidies or tax credits are increasing sufficiently 

rapidly over time, one may choose to wait Ifor the higher subsidy at a later 

datet despite the fact that the current benefit-cost picture is otherwise 

positive. 
A.B. Jal.h' and R.N. St¢svms Resource amy Energy Ecfmomh's 16 ¢ 1994 ' 91-t22 I ! 1 

5. Policy implications 

Our analysis indicates that market imperfections and other factors can 

slow diffusion and thus help explain the observed 'energy paradox.' Some of 

the factors we identify suggest a role for government intervention, but others 

should not be taken as meriting policy responses. In particular, the "nonmarket-failure causes" may help to explain the gradual diffusion of energy 

conservation technologies, but they do not argue for government intervention. Falling into this category are high di:~count rates, '*° the private costs of 

information acquisition, heterogeneity of potential adopters, and the "dynamic wait-and-see' conditions that emerge in the retrofit case. 

The other major set of factors we have examined - the market-failures - 

not only help explain the 'energy paradox" but also provide a set of potential 

justifications for government intervention. We summarize these policy implications in Table 1. Some of these implicatior~s arise from simple inspection of 

our final behavioral equations; others require investigation of respective 

partial derivatives; and some - because of the dynamic nature of the model - 

are best examined through dynamic simulations. The simulation approach 

also enables us to view the results in graphical terms. 

For illustrative purposes, we employ a simulation model of aggregate 

technological diffusion in the new home conslruction case, based upon the 

related behavioral inequality, Eq. (7). By assuming that the unobserved 

energy intensity, lt, has a legistic distribution and is independent of the other 

house-specific variables, the fraction of homes in year T that will incorporate 

the technology is simply the probability that condition (7) holds, which is 

equal to the logistic cumulative probability function evaluated at the lefthand side of Eq. (7), or: 

I 

Vr= l +e -AT (17) 

where vr is the fraction of newly constructed homes in year T that use the 

technology; and Ar is the left-hand side of Eq. (7). 4~ 

A base-case (no new policy) diffusion path is found in Fig. 1. We use the 

time oeriod 1978--1988 for the simulations because this encompasses a 

40 As noted below, to whatever degree high persenal discount rates reflect the public good 

aspect of incomplete information (uncertainty), high discount rates do pro,~ide a potential 

justification for government intervention. 

4~ Given the assumption of independence of It and the other variables, lhose variables in Eq. (7) 

that vary across i and/or j are ewtluated at their means. To keep things simple for the policy 

analysis, we drop the term with Sor from the learning function; i.e., we set ~4 in Eq. (7) equal to 

zero. Otherwise it would be nece:~sary to simulate multiple builder decisions simultaneously. 

Also, for the simulation model, w,: replace re; by the previous period's :alue, v,..; 1: and we 

adopt simple static expectations on prices, so that P#, is replaced by P~j~. 
112 A.B. JaHe and R,S. Stavms Resource and Encrgv Economics 16 ~ 19941 91 122 

Table I 

Markct ¢:nlurc and poliQ prescriptions 

Problen 3 

Incomplete information 

Public Good Aspect) 

Principal Agent Issues 

iBuilder Buyerl 

Departures from 

Marginal Cost Pricing 

by Electrical Utilities 

Federal and Stale 

Energy Subsidies 

I )nintern;dizcd 

Environmental 

Externalities 

Insufficient Research & 

Development 

Positive Adoption 

Externalities 

the diffusmn of energy conser,,ation technologies 

Policy Consequences of policy' m models 

prescription New Retrofit 

construction 

Establish standards fi~r ,Sl' 

energy audits and 

disclosure for new' 

buildings 

Public information ~'i T, ~tt, L, r,[ 

campaigns 

Product lat3elling :~t 6"[ 

rcquirements guidelines 

l-edcral home energ} 6]' 

rating s~stem 

Standards I\~r audits and fit 

disclosure 

:t3L 6"f 

Change from average to Po, T 

marginal cost electricity 

pricing 

Elirninate red.ce fuel P,j,T 

subsidies 

Pul 

t', s.l " f 

In'erna!ize externalities P,j,T Pis r T 

Regulation Dirt D,rT 

(]overnment support for ('il~.. ~]. C,1~.. w~ 

technological R&I) 

Adoption subsidies X,I T X,tl" 

Tax credils Xa T X,rT 

significant turning point in real energy prices. The resulting non-monotonic 

diffusion curve found in Fig. 1 is not atypical of the experience wilh energyefficiency technologies in new homes over this time period, in fact, our basecase parameter and variable values reflect actual data for triple-pane 

windows {Tables 2 and 3),'*-' and the simulated diffusion curve is similar to 

the observed diffusion path of that technology in the United States. ".3 

"~-" These base-case parameter values reflect all of the assumptions previously specified regarding 

the underlying behavioral relationships. For example, we use values for ~t t and ~t 2 that yield the 

appropriately shaped cost learning functkm Referring to Eq. (5}, we find that gL[ .)/Cv~_ t<0, 

?21~'}"?vT ~: >0, dlJ-l/z¢ 2 <0, and d21j • ) d~t,2> O. Thus, as I-, ~ approaches I.O {ils maximum 

value) and as zt 2 approaches 0 {from belo~L the effective cost of adoption fal!s and approaches 

the simple engineering cost. 

• ,3 Not surprisingly, the nort-monoh~nicity of Ihe diffusion path is partly a consequence of the 

related path of (expecledD real ener%' prices. See Jaffe trod Stavins [1993aj. 
A.B. Ja//c and R. '~: Stavm,~ Re.,m~rcc and Em'rg.v Ecom*mic,x /6 , I994 ~ 9t I22 I [3 

X T ~. 6 ~?. r~. C,L. ~v,~ 

/ 

m2 '; 

II '; 

I I I I I I I I I 

Iq7~ i~80 19~2 Iq84 1986 1988 

Year 

Fig. I. Base-case simulation and the effect of alternative constant policy changes. 

With the help of the simulation model, simple differential calculus, or 

simpler inspection of the behavioral relationships, we can now proceed to 

investigate the implications of potential public policies. First of all, the public 

good aspect of incomplete information can suggest a number of policy 

responses, depending upon the nature of the incomplete information. For 

situations in which there is uncertainty surrounding the potential benefits of 

energy conservation technologies in new construction, our analysis suggests 

that government could conceivably establish standards for energy audits and 

disclosure requirements for new buildings, thereby increasing 6. Graphically, 

the effect of this is to shift the diffusion path in Fig. 1 upward. Likewise, 

public information campaigns about the potential benefits and costs of 

adopting new technologies could be effective both in the new construction 

case (fiT, ~lJ,, r~) and the retrofit case (:z3,[, r,l). 4'~ Focusing on the attributes 

of the technologies themselves, product labelling requirements or guidelines 

could be effective for new construction (7~,L, f`r) and retrofitting (:z3,L, 6Tt. 

~4. As with increases in 6, so too with decreases in the Iconstant~ interest rate. r, the effect is to 

shift the diffusion path upward, while retaining its basic (non-monotonic) shape. What is 

striking, however, is lh~ dramatic effect of decreases in interest rates. Whereas increasing 6 from 

0.50 to 0.75 shifts the peak of the diffusion path (in the year 1983) from a 5.8'!,, adoption rate to 

8.50,,, decreasing real interest rates from 5'~,, tthe base case} to 1",, shifts the peak of the adoption 

curve from 11.2",, to over 40",,. On the other hand. note that 'the relath~nship between intercsl 

rates ~nd adoption is not linear; an increase in the interest rate from 5". to 10",~ has a much 

smaller effect on adoption, shifting the peak downward from 11.2,,, to 5.8",,. 
114 A.B. JaHe and R.N %tavm~ Re~ourcc and Energy E~ononm's 16 (1994) 91 ;2: 

Table 2 

B:Jse-case parameter and variable value,: 

Parameter or Description Base-case ~,duc 

variable 

t Engineering cost coefficient 1.00 

~2 Learning-effect coefficient 0.05 

fl~ Expected-price coefficient 1.00 

f12 ... JIM Observable characteristic 0.(~ 

coefficients 

,5 Market's discount of value of I.(hq 

energy savings 

;, Cost of regulatory non- 0.00 

compliance 

r Real market rate of interest 0.05 

ro Initial rate of ~echnological ~.10 

adoption 

C, Engineering purchase and $240.00 

installation cost 

D, Regulatory requirement dummy 0.00 

variable 

k .... k~ Observable characteristics of 1.00 

home and region 

w, Relative energy use of 0.87 

technology 

.\, Subsidy or tax c~edit for 0.00 

adopting technology 

P, Expected energy price in ,,'ear t 

"Time series of base-case energy prices are found in Table 3. 

Table 3 

Base-case energ~ prices 

Year Effective energy price" 

1978 9.73 

1979 10.18 

1980 I i.25 

-) -) 1 q81 1 _.0.. 

1982 12.51 

1983 13.03 

1984 12.56 

1985 12.32 

1986 11,q3 

1987 1 !.43 

1988 10.95 

"Average effectwe energy price fl~r 48 slates, in 

1988 dollars per million BTU's; a weighted 

aveage of prices of alternative fuels of newly 

built homes. 
Principal agent problems can be particularly ~-<',,~cre in the new construction case If a builder cannot credibly represent :~ ~omc's energ~ efficiency to 

potential buyers, th,.: sales price will not fully retqcc: efficiency attributes This 

concern has led in ~,, past to legislation in the Congress to require the U,S. 

Department of Energy to develop a volumary home energy lating system to 

provide consumers with better information on ~i3e efficiency of prospective 

homes (6'f}. Standards for audits and disclosure '<ould have the same basic 

result. 

We noted earlier that there are a set of reasc<s why the price of energy 

may be artificially low. Not surprisingly, the app~-opriate policy response will 

depend upon the reason for the problematic pricing. Changes from averagecost to marginal-cost pricing of electricity at utilities are one approach. The 

result in our models would be to increase energy prices (P,j,T). Similarly, 

consideration should be given ~o eliminating or at !east reducing the 

subsidies that exist for particular fuels [P~/tTL ~n this same context, the 

existence of uninternalized environmental externaiities associated with particular sources of energy clearly calls for those ex~e.cnalities to be internalized, 

such as through pollution taxes, tradeable permi~ systems, or other economic 

instruments (P,j,T}, or through conventional c~>~rmand-and-control regulations (D~rl'). 

It is frequently asserted that free-rider problm~, s will lead to less than the 

socially optimal amount of research and development by private firms. To 

the extent that this is true in the energy-efficiency technology area, government support for technological research and development may be called for. 

In our analysis, this could translate into decreases in the purchase and 

installation costs of new technologies {C~rJ,) and increases in the effectiveness 

(engineering efficiency) of those technologies (w J,). Finally, we noted at the 

outset that adoption behavior can itself result in positive externalities if 

others' use of a technology is an important source of valuable information. 

In this case, there is an argument in favor of government employing 

'adoption subsidies' or tax credits (X~rT). 45 

As indicated, some energy-efficiency technologies used in new home 

construction - such as triple-pane windows - have exhibited non-monotonic 

diffusion paths, apparently as a result of the turning point in real energy 

prices experienced in the early 1980's. From the perspective of public policy, 

it is natural to ask what policics could have been used to foster a 

monotonicly increasing diffusion path, in the fac,, of falling real energy prices. 

First of all, if adoption costs had been falling sufficiently fast over time, the 

• ,5 In the new home construction case, simulations of decreases in the purchase arid installation 

costs of new technologies, C;r, increases in those technologies" engineering efficiency 1 -w. and 

increases in adoption subsidies or tax credits. X,;. exhibit lhe same effect upwar'J shifts of the 

non-monotonic diffusion path tsee Fig. !). 
116 ,-IB ,h*lli' wld R.N. .S'Iarm,~ Rc.~<,urcc and l:'m'r.,.,.l l'~c<momics 16 ~ 1~94 1 91 122 

O~CI |IIIIC 

J 

"/I ~ II~ ~',II ", 41}til'N%~ 

211 '{ 

I~t '; 

~lqI 1 i 1 l I ! I I ! I ~eai 

t(t +~ I 

2c, ,~, 

211 '~ 

I~ ,r, 

III 'iI 

J 

{'q tallln/~ lrnm b,~xe ,.,s,e 

'..tlu¢ o',ct hlll¢ / 

II +'{ 1 I I I I 1 I l l I '~ cal 

Fig 2. The cffecl of decreasing adoplioll o.~sls on tcchmN~gical diffusioll. 

depressing incentive effects of failing energy prices would have been reversed. 

Indeed, various counterfactual time paths of falling adoption costs {C,r) 

produce diffusion paths in which the "negative effect" of failing energy prices 

after 1983 is overcome. Depending upon the rate at which adoption costs 

fall. the diffusion path of the technology can take on a constantly rising 

pa',tcrn or a classical sigmoid shape {Fig. 2}. a'' 

As noted above, government support of technological research and 

development efforts could have the effect of driving down ('~r- tiow else 

might government policy be employed to counteract the post-19g3 price 

effects and maintain a&~ption rates or even push them to continually higher 

levels'? First, government support of research and development an 

approach thal is favorably viewed by Ihe present Administration for a host 

of erwironmental and resource problems can not only have the effect of 

decreasing adoptk,n costs but can also increase the efficiency of available 

technologies t~vJ, t. As depicted in l:ig. 3, as w fat's over time from an initial 

value of t}.99 lindicating virtually no efficiency advantage) to 0.50 (indicating 

that the technology cuts energy demand by 511 percent), annual adoption 

4+, AI',~:, lhcrc i~ ;I ]c~ t'~,trcn~¢ c'OUll!Cl.f;tctli;l] palh oJ ;ltl ~plltHl C~I'~ lh:II ~lll case 3dol~lloll 
.4.B. 3a!i(' a~.l R.~V Sgarm.~ 

2u '~, 

I r, '4 

|It '*; 

I*}7£ 

f 

--'--"T~ q | I I I I I ~1 t"l! 

IL7 

Fig. 3. The cfl'ccl of increasing engineering-efficiency on technological diffusion. 

t'Fl*q 'l N1 "~,til q I[ ~ 

~C}l',dl ~1, ~ ;'l 

I-1 '; 

12 '; 

Ill '; 

I I I I I i I I I I "~¢d[ 

Fig, 4. The effect of a continually increasing subsid~ on technological diffus,on. 

increases monotonicly in an essentially sigmoid path from zero to 30 percent 

of newly constructed homes. 

Other dynamic government policies could - in theory - be employed to 

compensate for falling energy prices. The simulated diffusion path in Fig. 4 

illustrates that a co,tim.,~slv i,creasin'4 subsidy [X,r) of sufficient magnitude 

could bc used to maintain adoption ra[e.~ at their peak level (again. in the 
118 .4.B. &tile and RN. Starins Rcsm~rce attd Energy Economics 16 ~ 1994) 91 122 

",F ~, II, ~MI • Mu ~PTI~,t; 

411 (;- 

%11 ~; 

21J '~; 

IO ~'; 

I I I I I I ! 1 • 1 1 Ye,tf 

Fig. 5. The effect of a one-time increase in regulatol/slringency on technological diffusion. 

face of falling energy pricest. Subsidies of various magnitudes can be 

employed to have essentially whatever effect is desired. At one extreme, a 

constant subsidy set equal to the basic engineering cost of the technology 

results in a 100 percent rate of adoption after only eight years (the delay 

being due to the effect of learning on effective costs of adoption). 

The obvious alternative to a subsidy on the technology (or a tax on energy 

prices) is a conventional regulatory approach, such as use of building codes 

in the new-construction context. As indicated above and in Table 1, 

regulations can in theory - have the desired effect. Our dynamic 

simulations illustrate, however, that a one-time change in regulatory stringency should not be expected to lead aut,;matically to an increasing or 

even a constant level of annual adoption. Fig. 5 represents a situation in 

which a command-and-control regulation is initiated in the year 1981. As 

expected, adoption increases dramatically in that year and then continues to 

increase at a much slower rate to the peak year of 1983. Subsequent to that 

time, hov,ever, the effects of falling energy prices dominate, and we find the 

rate of diffusion falling gradually despite the constant level of regulation. 

Con,<ntional resulations, like market-based instruments, can be effective, but 

neither are likely to be a panacea in the face of strong, contrary forces in the 

eCOllO?]~ y. 

Either conventional command-and-control regulatory policies or incentivebased ecom-~mic instrqments can be used to achieve various levels and rates 

of change c,f technological diffusion desired by policy makers, whether 
A.B. Ju([~' and R.N. Sta~'ins Resourc," and Fm'rgy Economics 16 ( 1994j 91-122 | t t) 

because of (economically legitimate} market-failure reasons or otherwise. 

Which policy instruments will be best will depend in well-defined ways upon 

the relative importance of the various causes of the gradual diffusion o7 those 

technologies, in the first place. 

6. Summary and conclusions 

In this paper, we have developed a framework for thinking about the 

"paradox' of very gradual diffusion of apparently cost-effective energyconservation technologies. Our analysis provides some keys to understanding 

why this technology-diffusion process is gradual, and focuses attention on the 

factors that cause this to be the case, including those associated with 

potential market failures - information problems, principal/agent slippage, 

and unobserved costs - and those explanations that do not represent market 

failures - private information costs, high discount rates, and heterogeneity 

among potential adopters. Furthermore, our analysis indicates how alternative policy instruments - both economic incentives and direct regulations - 

can hasten the diffusion of energy-conserving technologies. 

Because there are two important contexts in which energy conservation 

adoption decisions can take place, our analysis builds upc, n two conceptual 

models: a model in which an activity is being undertaken which prompts a 

decision about whether or not to adopt an energy-conserving technology at a 

specified point in time (our new construction case); and a model in which a 

decision must be made not oniy about whether or not to adopt an energyefficiency technology, but also about when to do so (our retrofit case). Our 

analysis focused on the incorporation of energy-conserving technologies in 

new residential structures and retrofitting in existing homes. 

First of all, in the case of new residential construction, our analysis 

demonstrates how principal/agent problems thought to arise in that context 

can directly inhibit the diffusion of energy efficiency technologies. We also 

found that "artificially low' energy prices -ctue to electrical utility pricing 

practices, government fuel subsidies, or environmental externalities - can 

provide another market-failure explanation of the paradox. As has frequently 

been discussed in the empirical literature, relatively high individual discount 

rates can significantly retard adoption and diffusion. Similarly, our analysis 

illustrated how decreases in the costs of adoption will accelerate technology 

diffusion, whether due to changes in the direct costs of equipment purchase 

and installation, or changes in the 'effective costs of adoption' associated with 

learning about the technology and its application. We also saw how 

regulations - such as building codes - can have a direct, positive effect on 

adoption, as can other government programs, including subsidies and tax 

credits. Of somewhat less concern in terms of public policy perhaps, we also 
121) A.B. A!['Ii' and R.,V. Stavins Resource aml Fncrgy Economics 16 ~ 1994) 91 122 

noted how departures from temperate climatic conditions, and increases in 

income and education can accelerate diffusion. 

Second, we examined the case of retrofitting energy-efficiency technologies 

in existing residential structures. We found, somewhat counter-intuitively, 

that under certain circumstances adoption decisions are influenced by current 

energy prices, without concern for future energy price paths. Nevertheless, 

high discount rates can impede adoption by driving up the adoption-cost 

annuity. As in the new-construction case, we found that adoption will be 

slowed by 'artificially low' energy prices: and that climatic departures from 

temperate conditions will encourage adoption. Not surprisingly, low adoption costs will unambiguously encourage adoption, as may government 

programs in the form of subsidies or tax credits. 

Although the future paths of energy prices turn out not to be relevant for 

adoption behavior in the retrofit case, the current time rate of change of 

adoption costs, br,.adly &:l~m'd, does matter. In particular, if purchase and/or 

installation costs are failing, it can pay to wait, despite the fact that current 

net benefits of adoplion are positive. Likewise, if adoption is taking place 

very fast and information about the technology is thus increasing rapidly, it 

can pay to wait. Finally, if government subsidies or tax credits are increasing 

sufficiently rapidly over time, one may choose to wait (for the higher subsidy 

at a later date} despite the fact that the current benefit-cost picture is 

otherwise positive. 

In conclusion, if the 'energy paradox' of gradual diffusion of apparently 

cost-effective energy-efficiency technologies does exist - as many observers 

have claimed - it is necessary to understand the sources of the gradual 

diffusion beforc identifying appropriate policy responses. One set of causes of 

the paradox, which we have labelled the 'non-market-failure' causes, do not 

provide legitimate justifications for government intervention. On the other 

hand, a fairly large number of potential market-failure explanations of the 

paradox can provide solid arguments for government action. Which specific 

policy instruments will be appropriate, however, will depend in well-defined 

ways upon the relative importance of the various underlying explanations of 

the energy paradox. 

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