Environmental policy and technological change.” Environmental and Resource Economics

Fondazione Eni Enrico Mattei

Environmental Policy and Technological Change

Adam B. Jaffe, Richard G. Newell and

Robert N. Stavins

NOTA DI LAVORO 26.2002

APRIL 2002

ETA – Economic Theory and Applications

Adam B. Jaffe, Department of Economics, Brandeis University

and National Bureau of Economic Research Richard G. Newell, Resources for the Future

Robert N. Stavins, John F. Kennedy School of Government, Harvard University

and Resources for the Future

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Fondazione Eni Enrico Mattei

Environmental Policy and Technological Change

Summary

The relationship between technological change and environmental policy has received increasing attention from scholars and policy makers alike over the past ten years. This is partly because the environmental impacts of social activity are significantly affected by technological change, and partly because environmental policy interventions themselves create new constraints and incentives that affect the process of technological developments. Our central purpose in this article is to provide environmental economists with a useful guide to research on technological change and the analytical tools that can be used to explore further the interaction between technology and the environment. In Part 1 of the article, we provide an overview of analytical frameworks for investigating the economics of technological change, highlighting key issues for the researcher. In Part 2, we turn our attention to theoretical analysis of the effects of environmental policy on technological change, and in Part 3, we focus on issues related to the empirical analysis of technology innovation and diffusion. Finally, we conclude in Part 4 with some additional suggestions for research.

This article draws, in part, upon: Jaffe, Newell, and Stavins (2001). We are grateful for valuable research assistance from Lori Snyder and helpful comments from Ernst Berndt, Karl-Göran Mäler, Lawrence Goulder, Nathaniel Keohane, Charles Kolstad, Ian Parry, Steven Polasky, David Popp, Vernon Ruttan, Manuel Trajtenberg, Jeffrey Vincent, and David Zilberman, but the authors alone are responsible for all remaining errors.

Prepared for

Environmental and Resource Economics

Special Issue Edited by Richard T. Carson

University of California, San Diego

Address for correspondence:

Robert N. Stavins

Albert Pratt Professor of Business and Government John F. Kennedy School of Government Harvard University

E-mail: robert_stavins@harvard.edu

ENVIRONMENTAL POLICYAND TECHNOLOGICAL CHANGE

by

Adam B. Jaffe, Richard G. Newell, and Robert N. Stavins∗

1. Economic Frameworks and Issues in Technological Change

Economists have examined a diverse set of issues associated with technological changethat go well beyond those analyses that have focused directly on implications for environmentalpolicy, including: the theory of incentives for research and development (Tirole 1988;Reinganum 19; Geroski 1995); the measurement of innovative inputs and outputs (Griliches1984 and Griliches 1998); analysis and measurement of externalities resulting from the researchprocess (Griliches 1992; Jaffe 1998); the measurement and analysis of productivity growth(Jorgenson 1990; Griliches 1998; Jorgenson and Stiroh 2000); diffusion of new technology(Karshenas and Stoneman 1995; Geroski 2000); the effect of market structure on innovation(Scherer 1986; Sutton 1998); market failures related to innovation and appropriate policyresponses (Martin and Scott 2000); the economic effects of publicly funded research (David etal. 2000); the economic effects of the patent system (Jaffe 2000); and the role of technologicalchange in endogenous macroeconomic growth (Romer 1994; Grossman and Helpman 1994). Inthis part of the article, we provide a very brief guide to some of this research. In particular, weintroduce approaches for measuring technological change, we examine critical aspects of theprocess of technological change, and we describe modeling approaches and potential marketfailures relating to technology innovation and diffusion. ∗

Jaffe is Professor of Economics, Brandeis University, and Research Associate, National Bureau of EconomicResearch; Newell is Fellow, Resources for the Future; and Stavins is Albert Pratt Professor of Business andGovernment, John F. Kennedy School of Government, Harvard University, and University Fellow, Resources forthe Future. This article draws, in part, upon: Jaffe, Newell, and Stavins (2001). We are grateful for valuableresearch assistance from Lori Snyder and helpful comments from Ernst Berndt, Karl-Göran Mäler, LawrenceGoulder, Nathaniel Keohane, Charles Kolstad, Ian Parry, Steven Polasky, David Popp, Vernon Ruttan, ManuelTrajtenberg, Jeffrey Vincent, and David Zilberman, but the authors alone are responsible for all remaining errors.

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1.1. Measurement of Technological Change

The measurement of the rate and direction of technological change rests fundamentallyon the concept of the transformation function,

T(Y,I,t)≤0,

(1)

where Y represents a vector of outputs, I represents a vector of inputs, and t is time. Equation (1)describes a production possibility frontier, that is, a set of combinations of inputs and outputsthat are technically feasible at a point in time. Technological change is represented bymovement of this frontier that makes it possible over time to use given input vectors to produceoutput vectors that were not previously feasible.

In most applications, separability and aggregation assumptions are made that make itpossible to represent the economy’s production technology with a production function,

Y=f(K,L,E;t),

(2)

where Y is now a scalar measure of aggregate output (for example, gross domestic product), andthe list of inputs on the right-hand side of the production function can be made arbitrarily long.For illustrative purposes, we conceive of output as being made from a single composite of capitalgoods, K, a single composite of labor inputs, L, and a single composite of environmental inputs,E (for example, waste assimilation). Again, technological change means that the relationshipbetween these inputs and possible output levels changes over time.

Logarithmic differentiation of the production function (Equation (2)) with respect to timeyields

yt=At+βLtlt+βKtkt+βEtet,

(3)

in which lower case letters represent the percentage growth rates of the corresponding upper casevariable; the β’s represent the corresponding logarithmic partial derivatives from Equation (2);and the t indicate that all quantities and parameters may change over time.1 The term Atcorresponds to “neutral” technological change, in the sense that it represents the rate of growth ofoutput if the growth rates of all inputs were zero. But the possibility that the β’s can changeover time allows for “biased” technological change, that is, changes over time in relativeproductivity of the various inputs.

Equations (2) and (3) are most easily interpreted in the case of process innovation, inwhich firms figure out more efficient ways to make existing products, allowing output to grow ata rate faster than inputs are growing. In principle, these equations also apply to productinnovation. Y is a composite or aggregate output measure, in which the distinct outputs of theeconomy are each weighted by their relative value, as measured by their market price. Improved 1

This formulation can be considered a first-order approximation to an arbitrary functional form for Equation (2).Higher-order approximations can also be implemented.

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products will typically sell at a price premium, relative to lower quality products, meaning thattheir introduction will increase measured output even if the physical quantity of the new goodsdoes not exceed the physical quantity of the old goods they replaced. In practice, however,product improvement will be included in measured productivity only to the extent that the priceindices used to convert nominal GDP or other nominal output measures to real output measuresare purged of the effects of product innovation. In general, official price indices and thecorresponding real output measures achieve this objective only to a limited extent.

On its face, Equation (3) says nothing about the source of the productivity improvementassociated with the neutral technological change term, At. If, however, all inputs and outputs areproperly measured, and inputs (including R&D) yield only normal investment returns, then allendogenous contributions to output should be captured by returns to inputs, and there should beno “residual” difference between the weighted growth rates of inputs and the growth rate ofoutput. The observation that the residual has been typically positive is therefore interpreted asevidence of exogenous technological change.1.2. Process of Technological Change

Economic theories of the process of technological change can be traced to the ideas ofJosef Schumpeter (1942), who distinguished three stages in the process by which a new, superiortechnology permeates the marketplace. Invention constitutes the first development of ascientifically or technically new product or process. Inventions may be patented, though manyare not. Either way, most inventions never actually develop into an innovation, which isaccomplished only when the new product or process is commercialized, that is, made availableon the market. A firm can innovate without ever inventing, if it identifies a previously existingtechnical idea that was never commercialized, and brings a product or process based on that ideato market. The invention and innovation stages are carried out primarily in private firms througha process that is broadly characterized as “research and development” (R&D).2 Finally, asuccessful innovation gradually comes to be widely available for use in relevant applicationsthrough adoption by firms or individuals, a process labeled diffusion. The cumulative economicor environmental impact of new technology results from all three of these stages,3 which we referto collectively as the process of technological change. 2

Data regarding R&D expenditures of firms are available from the financial statements of publicly traded firms, ifthe expenditure is deemed “material” by the firm’s auditors, or if the firm chooses for strategic reasons to reportthe expenditure (Bound et al. 1984). In the United States, the government carries out a “census” of R&D activity,and reports totals for broad industry groups (National Science Board 1998). Many industrialized countries nowcollect similar statistics, which are available through the Organization of Economic Cooperation and Development(OECD 2000).

Typically, for there to be environmental impacts of a new technology, a fourth step is required utilization, butthat is not part of the process of technological change per se. Thus, for example, a new type of hybrid motorvehicle engine might be invented, which emits fewer pollutants per mile; the same or another firm mightcommercialize this engine and place the innovation in new cars available for purchase on the market; individualsmight purchase (or adopt) these cars, leading to diffusion of the new technology; and finally, by driving these carsinstead of others (utilization), aggregate pollutant emissions might be reduced. Conversely, if higher efficiencyand the resulting reduced marginal cost causes users to increase utilization, then the emissions reduction associatedwith higher efficiency may be partially or totally offset by higher utilization.

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1.3. Induced Innovation and Evolutionary Approaches

If the imposition of environmental requirements can stimulate invention and innovationthat reduces the cost of complying with those requirements, this has profound implications forboth the setting of environmental policy goals and the choice of policy instruments. It is useful toidentify two major strands of thought regarding the determinants of innovative activity. We callthese two broad categories of modeling approaches the “induced innovation” approach and the“evolutionary” approach.

Induced Innovation. Within the induced innovation approach, firms undertake aninvestment activity called “R&D” with the intention of producing profitable new products andprocesses. Decisions regarding the magnitude and nature of R&D activities are governed byfirms’ efforts to maximize their value, or, equivalently, to maximize the expected discountedpresent value of cash flows. In some applications, the output of R&D is explicitly modeled as“knowledge capital,” an intangible asset that firms use together with other assets and other inputsto generate revenues (Griliches 1979; Hall et al. 2000).

When viewed as an investment activity, R&D has important characteristics thatdistinguish it from investment in equipment or other tangible assets. First, although the outcomeof any investment is uncertain to some extent, R&D investment appears to be qualitativelydifferent. Not only is the variance of the distribution of expected returns much larger than forother investments, but much or even most of the value may be associated with very low-probability but very high value outcomes (Scherer et al. 2000). This skewness in the distributionof the outcomes of the research process has important implications for modeling firms’ R&Ddecision making (Scherer and Harhoff, 2000). In addition, the asset produced by the R&Dinvestment process is specialized, sunk and intangible, so that it cannot be mortgaged or used ascollateral. The combination of great uncertainty and intangible outcomes makes financing ofresearch through capital market mechanisms much more difficult than for traditional investment.The difficulty of securing financing for research from outside sources may lead to under-investment in research, particularly for small firms that have less internally generated cash and/orless access to financial markets.

In addition to these financing difficulties, research investment differs from physicalinvestment because the asset produced by the research process — new knowledge about how tomake and do things — is difficult to exclude others from using. As first noted in the classicpaper by Arrow (1962), this means that the creator of this asset will typically fail to appropriateall or perhaps most of the social returns it generates. Much of this social return will accrue as“spillovers” to competing firms, to downstream firms that purchase the innovator’s products, orto consumers (Griliches 1979, 1992; Jaffe 1986, 1998). This “appropriability problem” is likelyto lead to significant underinvestment by private firms in R&D, relative to the social optimum(Spence 1984).

The recognition that R&D is a profit-motivated investment activity also leads to thehypothesis that the rate and direction of innovation are likely to respond to changes in relativeprices. Since environmental policy implicitly or explicitly makes environmental inputs moreexpensive, the “induced innovation” hypothesis suggests an important pathway for theinteraction of environmental policy and technology, and for the introduction of impacts on

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technological change as a criterion for evaluation of different policy instruments. We considerempirical approaches and evidence on induced innovation in section 3.1 below.

The Evolutionary Perspective. While viewing R&D as a profit-motivated investmentactivity comes naturally to most economists, the large uncertainties surrounding the outcomes ofR&D investments make it very difficult for firms to make optimizing R&D decisions.Accordingly, Nelson and Winter (1982) used Herbert Simon’s idea of boundedly rational firmsthat engage in “satisficing” rather than optimizing behavior (Simon 1947) to build an alternativemodel of the R&D process. In this “evolutionary” model, firms use “rules of thumb” and“routines” to determine how much to invest in R&D, and how to search for new technologies.The empirical predictions of this model depend on the nature of the rules of thumb that firmsactually use (Nelson and Winter 1982; Winter et al 2000).

If firms are not optimizing, a logical consequence of the evolutionary model is that itcannot be presumed that the imposition of a new external constraint (for example, a newenvironmental rule) necessarily reduces profits. There is at least the theoretical possibility thatthe imposition of such a constraint could be an event that forces a satisficing firm to rethink itsstrategy, with the possible outcome being the discovery of a new way of operating that isactually more profitable for the firm. This raises the possibility that environmental regulationcan lead to a “win-win” outcome in which pollution is reduced and profits increased.

Porter and other “win-win” theorists have argued that in a non-optimizing world,regulation may lead to “innovation offsets” that “can not only lower the net cost of meetingenvironmental regulations, but can even lead to absolute advantages over firms in foreigncountries not subject to similar regulations” (Porter and van der Linde 1995, p. 98). Of course,the fact that firms engage in non-optimizing behavior creates a possibility for profitimprovements, without suggesting that such improvements would be the norm, would besystematic, or even likely.

Porter and van der Linde (1995) provided case studies of firms which adopted newtechnology in response to regulation, and appear to have benefited, but win-win theorists do notclaim that all environmental regulations generate significant innovation offsets. Indeed, theyemphasize that regulation must be properly designed in order to maximize the chances forencouraging innovation. Quantitative evidence is limited—much of it from a large relatedliterature on the impact of environmental regulation on productivity and investment4—andresults seem to be industry and methodology dependent.

Boyd and McClelland (1999) and Boyd and Pang (2000) employ data envelopmentanalysis to evaluate the potential at paper and glass plants for “win-win” improvements thatincrease productivity and reduce energy use or pollution. They suggest that the paper industrycould reduce inputs and pollution by 2-8% without reducing productivity. Berman and Bui(2001) found significant productivity increases associated with air pollution regulation in the oilrefining industry, but Gray and Shadbegian (1998) found that pollution abatement investment“crowds out” productive investment almost entirely in the pulp and paper industry. Greenstone 4

See, for example, Gollop and Roberts (1983), Kolstad and Turnovsky (1998) and Yaisawarng and Klein (1994).

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(1998) found that air pollution regulation has a statistically significant but very small impact onoverall costs, implying a small negative productivity impact.

Generally, economists have been skeptical of the win-win theory (Palmer et al. 1995;Oates et al. 1993). From a theoretical perspective, it is possible to model apparently inefficientfirm behavior as the (second-best) efficient outcome of imperfect information and divergentincentives among managers or between owners and managers in a principal/agent framework.5From this perspective, the apparent inefficiency does not have normative implications. Sincefirms are doing the best they can given their information environment, it is unlikely that theadditional constraints represented by environmental policy interventions would be beneficial.On a more concrete level, Palmer et al. (1995) surveyed firms affected by regulation—includingthose cited by Porter and van der Linde as success stories — and found that most firms say thatthe net cost to them of regulation is, in fact, positive.

For regulation to have important informational effects, the government must have betterinformation than firms have about the nature of environmental problems and their potentialsolutions. Furthermore, while it seems likely that environmental regulation will stimulate theinnovation and diffusion of technologies that facilitate compliance, creation and adoption of newtechnology will typically require real resources, and have significant opportunity costs. Overall,the evidence on induced innovation and the win-win hypothesis seems to be a case of a “partiallyfull glass” that analysts see as mostly full or mostly empty, depending on their perspective.1.4. Microeconomics of Technology Diffusion

From the mechanical reaper of the nineteenth century (David 1966), through hybrid cornseed (Griliches 1957), steel furnaces (Oster 1982), optical scanners (Levin et al. 1987) andindustrial robots (Mansfield 19), research has consistently shown that the diffusion of new,economically superior technologies is a gradual process. Typically, the fraction of potentialusers that has adopted a new technology follows a sigmoid or “S-shaped” path over time, risingonly slowly at first, then entering a period of very rapid growth, followed by a slowdown ingrowth as the technology reaches maturity and most potential adopters have switched (Geroski2000).

The explanation for the apparent slowness of the technology diffusion process has been asubject of considerable study. Two main forces have been emphasized. First, potentialtechnology adopters are heterogeneous, so that a technology that is generally superior will not beequally superior for all potential users, and may remain inferior to existing technology for someusers for an extended period of time after its introduction. Second, adopting a new technology isa risky undertaking, requiring considerable information, both about the generic attributes of thenew technology and about the details of its use in the particular application being considered. Ittakes time for information to diffuse sufficiently, and the diffusion of the technology is limitedby this process of diffusion of information.

5

For a survey, see Holmström and Tirole (1987).

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The two main models of the diffusion process each emphasize one of these two aspects ofthe process. The probit or rank model, first articulated in an unpublished paper by David (1969),posits that potential adopters are characterized by a distribution of returns associated with thenew technology. Because adoption is costly, at any moment in time there is a threshold point onthis distribution, such that potential users with values above this threshold will want to adopt,and users for whom the value of the new technology is at or below this threshold will not want toadopt. Because the new technology will typically get cheaper and better as time passes, thisthreshold will gradually move to the left, and eventually sweep out the entire distribution. If thedistribution of underlying values is normal (or another single-peaked distribution with similarshape), this gradual movement of the threshold across the distribution will produce the typical S-shaped diffusion curve.

The other widely-used model is the epidemic model (Griliches 1957; Stoneman 1983).The epidemic model presumes that the primary factor limiting diffusion is information, and thatthe most important source of information about a new technology is people or firms who havetried it. Thus technology spreads like a disease, with the instigation of adoption being contactbetween the “infected” population (people who have already adopted) and the uninfectedpopulation. Denoting the fraction of the potential using population that has adopted as f, this

df

leads to the differential equation =βf(1−f). Solution of this equation yields a logistic

dt

function, which has the characteristic S-shape. The parameter β captures the “contagiousness”of the disease, presumably related to the cost of the new technology and the degree of itssuperiority over the technology it replaces (Griliches 1957).6

Both of the models discussed above predict that the present value of benefits fromadoption and the initial adoption cost enter into decisions affecting the diffusion rate. In theprobit model, this net present value comparison determines the location of the adoption thresholdthat determines what fraction of potential adopters will adopt at a moment in time. In theepidemic model, this net present value comparison determines the magnitude of the“contagiousness” parameter, which in turn determines the speed at which the technology spreadsfrom adopters to previous non-adopters.

While the induced innovation literature focuses on the potential for environmental policyto bring forth new technology through innovation, there is also a widely-held view thatsignificant reductions in environmental impacts could be achieved through more widespreaddiffusion of existing economically-attractive technologies, particularly ones that increase energyefficiency and thereby reduce emissions associated with fossil fuel combustion. For example, thereport of the Interlaboratory Working Group (1997) compiled an analysis of technologies thatreportedly could reduce energy use and hence CO2 emissions at low or even negative net cost tousers. The observation that energy-efficient technologies that are cost-effective at current prices 6

Both the probit and epidemic models typically focus on the fraction of the population that had adopted at a point intime. If one has individual-level data on adopters, one can take as the dependent variable the individual time untiladoption. This leads to a duration or hazard model (Rose and Joskow 1990). Kerr and Newell (2000) employed aduration model to analyze technology adoption decisions by petroleum refineries during the phasedown of lead ingasoline.

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are diffusing only slowly dates to the 1970s, having been identified as a “paradox” at least as farback as Shama (1983).

The apparent potential for emissions reductions associated with faster diffusion ofexisting technology raises two important questions. First, what is the theoretical and empiricalpotential for “induced diffusion” of lower-emissions technologies? Specifically, how doenvironmental policy instruments that implicitly or explicitly increase the economic incentive toreduce emissions affect the diffusion rate of these technologies? A second and related questionis the degree to which historical diffusion rates have been limited by market failures in theenergy and equipment markets themselves (Jaffe and Stavins 1994). To the extent that diffusionhas been and is limited by market failures, it is less clear that policies that operate by increasingthe economic incentive to adopt such technology will be effective. On the other hand, if suchmarket failures are important, then policies focused directly on correction of such market failuresprovide, at least in principle, opportunities for policy interventions that are social-welfareincreasing, even without regard to any environmental benefit. Potential sources of market failureinclude problems regarding inadequate information and uncertainty, principal-agent problems,constrained capital financing, and positive adoption spillovers.

Information plays a particularly important role in the technology diffusion process. First,information is a public good that may be expected in general to be underprovided by markets.Second, to the extent that the adoption of the technology by some users is itself an importantmode of information transfer to other parties, adoption creates a positive externality and istherefore likely to proceed at a socially suboptimal rate. As discussed further in section 3.2,Howarth et al. (2000) explored the significance of inadequate information in inhibiting thediffusion of more efficient lighting equipment. Metcalf and Hassett (1999) compared availableestimates of energy savings from new equipment to actual savings realized by users who haveinstalled the equipment. They found that actual savings, while significant, were less than thosepromised by engineers and product manufacturers.

Also related to imperfect information are a variety of agency problems that can inhibit theadoption of superior technology. An example of an external agency problem would be alandlord/tenant relationship, in which a tenant pays for utilities but the landlord makes decisionsregarding which appliances to purchase, or vice versa. Internal agency problems can arise inorganizations where the individual or department responsible for equipment purchase ormaintenance differs from the individual or department whose budget covers utility costs.7DeCanio (1998) explored the significance of organizational factors in explaining firms’perceived returns to installation of energy-efficient lighting.

Uncertainty is another factor that may limit the adoption of new technology (Geroski2000). Such uncertainty is not a market failure, merely a fact of economic life. Uncertainty canbe inherent in the technology itself, in the sense that its newness means that users are not surehow it will perform (Mansfield 1968). For resource-saving technology, there is the additionaluncertainty that the economic value of such savings depends on future resource prices, which are 7

For a discussion of the implications of the separation of environmental decision-making in major firms fromrelevant economic signals, see: Hockenstein et al., (1997). A series of related case studies are provided byReinhardt (2000).

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themselves uncertain. This uncertainty about future returns means that there is an “option value”associated with postponing the adoption of new technology (Pindyck 1991; Hassett and Metcalf1995, 1996).

Closely related to the issue of uncertainty is the issue of the discount rate or investmenthurdle rate used by purchasers in evaluating the desirability of new technology. A large body ofresearch demonstrates that purchasers appear to use relatively high discount rates in evaluatingenergy-efficiency investments (Hausman 1979; Ruderman et al. 1987; Ross 1990). The implicitor explicit use of relatively high discount rates for energy savings does not represent a marketfailure in itself; it is rather the manifestation of underlying aspects of the decision processincluding those just discussed. At least some portion of the discount rate premium is likely to berelated to uncertainty, although the extent to which the premium can be explained by uncertaintyand option value is subject to debate (Hassett and Metcalf 1995, 1996; Sanstad et al. 1995).Capital market failures that make it difficult to secure external financing for theseinvestments may also play a role (Shrestha and Karmacharya 1998). For households and smallfirms, adoption of new technologies with significant capital costs may be constrained byinadequate access to financing. And in some countries, import barriers may inhibit the adoptionof technology embodied in foreign-produced goods (Reppelin-Hill 1999). It is impossible togeneralize, however, particularly across countries.

Finally, the presence of increasing returns in the form of learning effects, networkexternalities, or other positive adoption externalities suggests that market outcomes fortechnologies exhibiting these features may be inefficient. For example, the idea that we are“locked into” a fossil-fuel-based energy system is a recurring theme in policy discussionsregarding climate change and other energy-related environmental problems. At a moreaggregate level, there has been much discussion of the question of whether it is possible fordeveloping countries to take less environmentally-damaging paths of development than haveindustrialized countries (Evenson 1995).

2. Theory of the Effects of Environmental Policy on Technological Change

The effects of environmental policies on the development and spread of new technologiesmay, in the long run, be among the most important determinants of success or failure ofenvironmental protection efforts (Kneese and Schultze 1975). It has long been recognized thatalternative types of environmental policy instruments can have significantly different effects onthe rate and direction of technological change (Orr 1976). Environmental policies, particularlythose with large economic impacts (for example, those intended to address global climatechange) can be designed to foster rather than inhibit technological invention, innovation, anddiffusion (Kempe and Soete 1990).

For purposes of examining the link between environmental policy instruments andtechnological change, policies can be characterized as either command-and-control or market-based approaches. Market-based instruments — such as pollution charges, subsidies, tradeablepermits, and some types of information programs — can encourage firms or individuals toundertake pollution control efforts that are in their own interests and that collectively meet policygoals (Stavins 2001). Command-and-control regulations tend to force firms to take on similar

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shares of the pollution-control burden, regardless of the cost. They often do this by settinguniform standards for firms, the most prevalent of which are performance- and technology-basedstandards. But holding all firms to the same target can be expensive and, in some circumstances,counterproductive, since standards typically exact relatively high costs by forcing some firms toresort to unduly expensive means of controlling pollution. Because the costs of controllingemissions may vary greatly among firms, and even among sources within the same firm, theappropriate technology in one situation may not be cost-effective in another.

All of these forms of intervention have the potential for inducing or forcing some amountof technological change, because by their very nature they induce or require firms to do thingsthey would not otherwise do. Performance and technology standards can be explicitly designedto be \"technology forcing,\" mandating performance levels that are not currently viewed astechnologically feasible or mandating technologies that are not fully developed. One problemwith these approaches, however, is that while regulators can typically assume that some amountof improvement over existing technology will always be feasible, it is impossible to know howmuch. Standards must either be made unambitious, or else run the risk of being ultimatelyunachievable, leading to political and economic disruption (Freeman and Haveman 1972).Technology standards are particularly problematic, since they tend to freeze thedevelopment of technologies that might otherwise result in greater levels of control. Underregulations that are targeted at technologies, as opposed to emissions levels, no financialincentive exists for businesses to exceed control targets, and the adoption of new technologies isdiscouraged. Under a “Best Available Control Technology” (BACT) standard, a business thatadopts a new method of pollution abatement may be “rewarded” by being held to a higherstandard of performance and thereby not benefit financially from its investment, except to theextent that its competitors have even more difficulty reaching the new standard (Hahn andStavins 1991). On the other hand, if third parties can invent and patent better equipment, theycan — in theory — have a ready market. Under such conditions, a BACT type of standard canprovide a positive incentive for technology innovation. Unfortunately, as we note below, therehas been very little theoretical or empirical analysis of such technology-forcing regulations.In contrast with such command-and-control regulations, market-based instruments canprovide powerful incentives for companies to adopt cheaper and better pollution-controltechnologies. This is because with market-based instruments, it pays firms to clean up a bit moreif a sufficiently low-cost technology or process for doing so can be identified and adopted.8

There are two principal ways in which environmental policy instruments can becompared with regard to their effects on technological change. First, one can ask— both withtheoretical models and with empirical analyses — what effects alternative instruments have onthe rate and direction of relevant technological change. Second, one can ask whetherenvironmental policies encourage an efficient rate and direction of technological change, or more 8

In theory, the relative importance of the dynamic effects of alternative policy instruments on technological change(and hence long-term compliance costs) is greater in the case of those environmental problems which are of greatmagnitude (in terms of anticipated abatement costs) and/or very long time horizon. Hence, the increased attentionbeing given by scholars and by policy makers to the problem of global climate change has greatly increased theprominence of the issues that are considered in this article.

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broadly, whether such policies result in overall economic efficiency (that is, whether the efficientdegree of environmental protection is achieved). We consider both sets of criteria.2.1. Technology Invention and Innovation

Although decisions about technology invention and commercialization are partly ademand-side function of anticipated sales (adoption), the relevant literature comparing the effectsof alternative environmental policy instruments has given greater attention to the supply side,focusing on incentives for firm-level decisions to incur R&D costs in the face of uncertainoutcomes. Such R&D can be either inventive or innovative, but the theoretical literature in thisarea typically makes no particular distinction.

The earliest work that is directly relevant was by Magat (1978), who compared effluenttaxes and CAC standards using an innovation possibilities frontier (IPF) model of inducedinnovation, where research can be used to augment capital or labor in a standard productionfunction. Subsequently, Magat (1979) compared taxes, subsidies, permits, effluent standards,and technology standards, and showed that all but technology standards would induce innovationbiased toward emissions reduction.9

Taking a somewhat broader view than most economic studies, Carraro and Siniscalco(1994) suggested that environmental policy instruments should be viewed jointly with traditionalindustrial policy instruments in determining the optimal way to attain a given degree of pollutionabatement. They showed that innovation subsidies can be used to attain the same environmentaltarget, but without the output reductions that result from pollution taxes. Laffont and Tirole(1996) examined how a tradeable permit system could — in theory — be modified to achievedesired incentive effects for technological change. They demonstrated that although spotmarkets for permits cannot induce the socially optimal degree of innovation, futures markets canimprove the situation (Laffont and Tirole 1996).10

Cadot and Sinclair-Desgagne (1996) posed the following question: if a regulated industryhas private information on the costs of technological advances in pollution control (frequently areasonable assumption), then since the industry has an incentive to claim that such technologiesare prohibitively expensive, can the government design an incentive scheme that will avoid theproblems posed by this information asymmetry? The authors developed a solution to this game-theoretic problem. Not surprisingly, the scheme involves government issued threats ofregulation (which diminish over time as the firm completes stages of technology development).It was only recently that theoretical work followed up on Magat’s attempt in the late1970’s to rank policy instruments according to their innovation-stimulating effects. Fischer et al.(1998) found that an unambiguous ranking of policy instruments was not possible. Rather, the 9

A considerable amount of theoretical work followed in the 1980’s. Although much of that work characterized itstopic as the effects of alternative policy instruments on technology innovation, the focus was in fact on effects ofpolicy on technology diffusion. Hence, we defer consideration of those studies to the next section.

In a subsequent analysis, Laffont and Tirole (1996) examined the government’s ability to influence the degree ofinnovative activity by setting the number of permits (and permit prices) in various ways in a dynamic setting.

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ranking of policy instruments depended on the innovator’s ability to appropriate spilloverbenefits of new technologies to other firms, the costs of innovation, environmental benefitfunctions, and the number of firms producing emissions.

In an analysis that is quite similar in its results to the study by Fischer et al. (1998), Ulph(1998) compared the effects of pollution taxes and command-and-control standards, and foundthat increases in the stringency of the standard or tax had ambiguous effects on the level of R&D,because environmental regulations have two competing effects: a direct effect of increasingcosts, which increases the incentives to invest in R&D in order to develop cost-saving pollution-abatement methods; and an indirect effect of reducing product output, which reduces theincentive to engage in R&D. Carraro and Soubeyran (1996) compared an emission tax and anR&D subsidy, and found that an R&D subsidy is desirable if the output contractions induced bythe tax are small or if the government finds output contractions undesirable for other reasons.Addressing the same trade-off, Katsoulacos and Xepapadeas (1996) found that a simultaneoustax on pollution emissions and subsidy to environmental R&D may be better suited toovercoming the joint market failure (negative externality from pollution and positive externalityor spillover effects of R&D).11 Finally, Montero (2000) compared instruments under non-competitive circumstances, and found that the results are less clear than when perfectcompetition is assumed. Standards and taxes yield higher incentives for R&D when the marketis characterized by Cournot competition, but the opposite holds when the market is characterizedby Bertrand competition.2.2. Technology Diffusion

The predominant theoretical framework for analyses of diffusion effects has been whatcould be called the “discrete technology choice” model: firms contemplate the use of a certaintechnology which reduces marginal costs of pollution abatement and which has a known fixedcost associated with it. While some authors have presented this approach as a model of“innovation,” it is more appropriately viewed as a model of adoption. With such models, severaltheoretical studies have found that the incentive for the adoption of new technologies is greaterunder market-based instruments than under direct regulation (Zerbe 1970; Downing and White1986; Milliman and Prince 19; Jung et al. 1996). With the exception of Downing and White(1986), all of these studies examined the gross impacts of alternative policy instruments on thequantity of technology adoption.

Theoretical comparisons among market-based instruments have produced only limitedagreement. In a frequently-cited article, Milliman and Prince (19) examined firm-levelincentives for technology diffusion provided by five instruments: command-and-control;emission taxes; abatement subsidies; freely-allocated emission permits, and auctioned emissionpermits. Firm-level incentives for adoption in this representative-firm model were pictured asthe anticipated change in producer surplus. They found that auctioned permits would provide thelargest adoption incentive of any instrument, with emissions taxes and subsidies second, andfreely allocated permits and direct controls last. The Milliman and Prince (19) study wascriticized by Marin (1991) because of its assumption of identical firms, but it was subsequently 11

See, also, Conrad (2000).

12

shown that the results remain largely unchanged with heterogeneous abatement costs (Millimanand Prince 1992).

In 1996, Jung et al. built on Milliman and Prince's basic framework for comparing theeffects of alternative policy instruments, but rather than focusing on firm-level changes inproducer surplus, they considered heterogeneous firms, and modeled the “market-levelincentive” created by various instruments. Their rankings echoed those of Milliman and Prince(19): auctioned permits provided the greatest incentive, followed by taxes and subsidies, freepermits, and performance standards.

Subsequent theoretical analyses (Parry 1998; Denicolò 1999; Keohane 1999) clarifiedseveral aspects of these rankings. First, there is the question of relative firm-level incentives toadopt a new, cost-saving technology when the price of pollution (permit price or tax level) isendogenous. Milliman and Prince (19), as well as Jung et al. (1996), argued that auctionedpermits would provide greater incentives for diffusion than freely-allocated permits, becausetechnology diffusion lowers the equilibrium permit price, bringing greater aggregate benefits ofadoption in a regime where all sources are permit buyers. But when technology diffusion lowersthe market price for tradeable permits, all firms benefit from this lower price regardless ofwhether they adopt the given technology (Keohane 1999). Thus, if firms are price takers in thepermit market, auctioned permits provide no more adoption incentive than freely-allocatedpermits.

The overall result is that both auctioned and freely-allocated permits are inferior in theirdiffusion incentives to emission tax systems (but superior to command-and-control instruments).Under tradeable permits, technology diffusion lowers the equilibrium permit price, therebyreducing the incentive for participating firms to adopt. Thus, a permit system provides a loweradoption incentive than a tax, assuming the two instruments are equivalent before diffusionoccurs (Denicolò 1999; Keohane 1999).

More broadly, it appears that an unambiguous exhaustive ranking of instruments is notpossible on the basis of theory alone. Parry (1998) found that the welfare gain induced by anemissions tax is significantly greater than that induced by tradable permits only in the case ofvery major innovations. Similarly, Requate (1998) included an explicit model of the final outputmarket, and finds that whether (auctioned) permits or taxes provide stronger incentives to adoptan improved technology depends upon empirical values of relevant parameters.

Furthermore, complete theoretical analysis of the effects of alternative policy instrumentson the rate of technological change must include modeling of the government’s response totechnological change, because the degree to which regulators respond to technologically-inducedchanges in abatement costs affects the magnitude of the adoption incentive associated withalternative policy instruments. Because technology diffusion presumably lowers the aggregatemarginal abatement cost function, it results in a change in the efficient level of control. Hence,following diffusion, the optimal agency response is to set a more ambitious target. Milliman andPrince (19) examined the incentives facing private industry, under alternative policyinstruments, to oppose such policy changes. Their conclusion was that firms would opposeoptimal agency adjustment of the policy under all instruments except taxes. Under an emissionstax, the optimal agency response to cost-reducing technological change is to lower the tax rate(assuming convex damages); under a subsidy, the optimal response is to lower the subsidy; under

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tradeable permit systems, the optimal response is to decrease the number of available permits,and thereby drive up the permit price. Thus, firms have clear incentives to support the optimalagency response only under an emissions tax regime.

In a comparison of tradeable permits and pollution taxes, Biglaiser et al. (1995) examinedthese instruments’ ability to achieve the first-best outcome in a dynamic setting. They found thateffluent taxes can do so, but permits cannot, but that this result depends on an assumption ofconstant marginal damages. If marginal damages are not constant, the optimal policy isdetermined by the interaction of marginal damages and marginal abatement costs for both taxesand permits. The result is analogous to Weitzman's (1974) rule: if the marginal damage curve isrelatively flat and there is uncertainty in marginal costs (from the regulator's perspective) due topotential innovation at the firm level, then a price instrument is more efficient.2.3. Induced Innovation and Optimal Environmental Policy

It seems logical that if environmental policy intervention induces innovation, this reducesthe social cost of environmental regulation, suggesting that the optimal policy is more stringentthan it would be if there were no induced innovation. This intuition contains an element of truth,but a number of complexities arise. First, one has to be careful what is meant by reducing thecost of regulation. If the policy intervention induces a reduction in the marginal cost ofabatement, then any given policy target (for example, a particular aggregate emission rate or aparticular ambient concentration) will be achieved at lower cost than it would without inducedinnovation. On the other hand, the lower marginal abatement cost schedule arising from inducedinnovation makes it socially optimal to achieve a greater level of pollution abatement. For a flatmarginal social benefit function evaluated at the social optimum, or for any emission tax, thisresults in greater total expenditure on abatement even as the marginal abatement cost falls.Another important issue is the general equilibrium effect of induced environmentalinnovation on innovation elsewhere in the economy (Schmalensee 1994). If inducementoperates through increased R&D expenditure, then an issue arises as to the elasticity of supply ofR&D inputs. To the extent that this supply is inelastic, then any induced innovation must comeat the expense of other forms of innovation, creating an opportunity cost that may negate theeffects observed in the regulated portion of the economy. The general equilibrium consequencesof these effects for welfare analysis depend on the extent of R&D spillovers or other marketfailures, and the magnitude of these distortions in the regulated firms or sectors relative to therest of the economy (Goulder and Schneider 1999).

In an application to global climate policy, Goulder and Mathai (2000) looked at optimalcarbon abatement in a dynamic setting, considering not only the optimal overall amount ofabatement but also its timing. In addition to R&D-induced innovation, they considered (in aseparate model) reductions in abatement costs that come about via learning-by-doing. Inducedinnovation reduces marginal abatement costs, which increases the optimal amount of abatement,but it also increases the cost of abatement today relative to the future, because of lowerabatement costs in the future, implying that with R&D-induced innovation, optimal abatement islower in early years and higher in later years than it would otherwise be. In the learning-by-doingmodel, there is a third effect: abatement today lowers the cost of abatement in the future. This

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reinforces the tendency for cumulative optimal abatement to be higher in the presence of inducedinnovation, but makes the effect on optimal near-term abatement ambiguous.123. Empirics of the Innovation and Diffusion of Green Technology3.1. Empirical Analysis of Innovation

There has been exceptionally little empirical analysis directly of the effects of alternativepolicy instruments on technology innovation in pollution abatement, principally because of thepaucity of available data. One study by Bellas (1998) carried out a statistical analysis of thecosts of flue gas desulfurization (scrubbing) installed at coal-fired power plants in the UnitedStates under the new-source performance standards of the 1970 and 1977 Clean Air Acts, butfailed to find any evidence of effects of scrubber vintage on cost, suggesting little technologicalinnovation had taken place under this regulatory regime.

Although there has been very little analysis in the context of pollution-abatementtechnologies, there is a more extensive literature on the effects of alternative policy instrumentson the innovation of energy-efficiency technologies, because data have been available. Thegreatest challenge in testing the induced innovation hypothesis specifically with respect toenvironmental inducement is the difficulty of measuring the extent or intensity of inducementacross firms or industries (Jaffe, et al. 1995). Ideally, one would like to look at the relationshipbetween innovation and the shadow price of pollution or environmental inputs, but such shadowprices are not easily observed. Instead, one must use proxies, such as expenditures on pollutionabatement, prices of polluting inputs, and characteristics of environmental regulations13. Weconsider studies that have used each of these approaches.

Lanjouw and Mody (1996) showed a strong association between pollution abatementexpenditures and the rate of patenting in related technology fields. Jaffe and Palmer (1997)examined the correlation between pollution expenditures by industry and indicators ofinnovation more broadly. They found that there is a significant correlation within industries overtime between the rate of expenditure on pollution abatement and the level of R&D spending.They did not, however, find evidence of an effect of pollution control expenditure on overallpatenting.

Evidence of inducement has also been sought by examining the response to changingenergy prices. Newell et al. (1999) examined the extent to which the energy efficiency of themenu of home appliances available for sale changed in response to energy prices between 1958and 1993, using a model of induced innovation as changing characteristics of capital goods. 12

Nordhaus (2000) introduced induced technological change into the “DICE” model of global climate change andassociated economic activities, and found in that case that the impact of induced innovation was modest.

In the literature on the relationship between environmental regulation and productivity, discussed in section 1.3, tomeasure the characteristics of environmental regulations studies have used expert judgements about relativeregulatory stringency in different states (Gray and Shadbegian 1998), number of enforcement actions (Gray andShadbegian 1995), attainment status with respect to environmental laws and regulations (Greenstone 1998), andspecific regulatory events (Berman and Bui 2001).

13

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Newell et al. (1999) generalized Hicks’ (1932) concept of induced innovation (in terms of factorprices) to include inducement by regulatory standards, such as labeling requirements that mightincrease the value of certain product characteristics by making consumers more aware of them.More generally, non-price regulatory constraints can fit within the inducement framework if theycan be modeled as changing the shadow or implicit price that firms face in emitting pollutants.In their framework, the existing technology for making a given type of equipment at a point intime is identified in terms of vectors of characteristics (including cost of manufacture) that arefeasible. The process of invention makes it possible to manufacture “models” (characteristicsvectors) that were previously infeasible. Innovation means the offering for commercial sale of amodel that was not previously offered for sale. Induced innovation is then represented asmovements in the frontier of feasible models that reduce the cost of energy efficiency in terms ofother attributes.

With this product-characteristic approach, Newell, et. al (1999) assessed the effects ofchanges in energy prices and in energy-efficiency standards in stimulating innovation, and foundthat energy price changes induced both commercialization of new models and elimination of oldmodels. Regulations, however, worked largely through energy-inefficient models beingdropped, since that is the intended effect of the energy-efficiency standards (models below acertain energy efficiency level may not be offered for sale). Through econometric estimationand a series of dynamic simulations, Newell et al. (1999) examined the effects of energy pricechanges and efficiency standards on average efficiency of the menu of products over time. Theyfound that a substantial amount of the improvement was what may be described as autonomous(that is, not otherwise explained by the model and associated with the passage of time), butsignificant amounts of innovation were also due to changes in energy prices and changes inenergy-efficiency standards. They found that technological change in air conditioners wasactually biased against energy efficiency in the 1960s (when real energy prices were falling), butthat this bias was reversed after the two energy shocks of the 1970s. In terms of the efficiency ofthe average model offered, they found that energy efficiency in 1993 would have been aboutone-quarter to one-half lower in air conditioners and gas water heaters, if energy prices hadstayed at their 1973 levels, rather than following their historical path. Most of the response toenergy price changes came within less than five years of those changes.

A closely related approach to investigating the same phenomena is that of hedonic pricefunctions. One hedonic study examined the effects of public policies in the context of homeappliances. Greening et al. (1997) estimated the impacts of the 1990 and 1993 nationalefficiency standards on the quality-adjusted price of household refrigerator/freezer units, andfound that quality-adjusted prices fell after the implementation of the energy efficiencystandards. However, such quality-adjusted price decreases are consistent with historical trends inrefrigerator/freezer prices, and hence, one cannot rule out the possibility that the imposition ofefficiency standards slowed the rate of quality-adjusted price decline.

Given the attention paid to automobile fuel economy over the past two decades, it is notsurprising that several hedonic studies of automobiles have addressed or focused on energy-efficiency, including Ohta and Griliches (1976) and Goodman (1983). Atkinson and Halvorsen(1984) found that the fuel efficiency of the new car fleet responds more than proportionally tochanges in expected fuel prices. Using an analogue to the hedonic price technique, Wilcox(1984) constructed a quality-adjusted measure of automobile fuel economy over the period1952–1980, finding that it was positively related to oil prices. Ohta and Griliches (1986) found

16

that gasoline price changes over the period 1970–1981 could alone explain much of the observedchange in related automobile characteristics.

More recently, Pakes, et. al (1993) investigated the effects of gasoline prices on the fueleconomy of motor vehicles offered for sale, and found that the observed increase in miles pergallon (mpg) from 1977 onward was largely due to the consequent change in the mix of vehicleson the market. Fewer low-mpg cars were marketed, and more high-mpg cars were marketed.Subsequently, Berry et al. (1996) combined plant-level cost data for the automobile industry andinformation on the characteristics of models that were produced at each plant to estimate ahedonic cost function — the supply-side component of the hedonic price function — finding thatquality-adjusted costs generally increased over the period 1972–1982, thus coinciding withrising gasoline prices and emission standards.

Goldberg (1998) combined a demand-side model of discrete vehicle choice andutilization with a supply-side model of oligopoly and product differentiation to estimate theeffects of CAFE standards on the fuel economy of the new car fleet. She found that automobilefuel operating costs have had a significant effect, although a gasoline tax of a magnitude thatcould match the effect of CAFE on fuel economy would have to be very large.

Finally, Popp (2001a and 2001b) looked more broadly at energy prices and energy-related innovation. In the first paper, he found that patenting in energy-related fields increases inresponse to increased energy prices, with most of the effect occurring within a few years, andthen fading over time. Popp attributed this fading to diminishing returns to R&D. In the secondpaper, he attempted to decompose the overall reduction in energy use that is associated withchanging energy prices between the substitution effect—movements along a given productionfrontier—and the induced innovation effect—movement of the production frontier itself inducedby the change in energy prices. Using energy-related patents as a proxy for energy innovation,he found that approximately one-third of the overall response of energy use to prices isassociated with induced innovation, with the remaining two-thirds associated with factorsubstitution. Because energy patents are likely to measure energy innovation only withsubstantial error, one might interpret this result as placing a lower bound on the fraction of theoverall response of energy use to changing prices that is associated with innovation.3.2. Empirical Analysis of Diffusion

One of the great successes during the modern era of environmental policy was thephasedown of lead in gasoline, which took place in the United States principally during thedecade of the 1980's. The phasedown was accomplished through a tradeable permit systemamong refineries, whereby lead rights could be exchanged and/or banked for later use. Kerr andNewell (2000) used a duration model to assess the effects of the phasedown program ontechnology diffusion. They found that increased stringency (which raised the effective price oflead) encouraged greater adoption of technology that substitutes for lead in increasing octane.They also found that larger and more technically sophisticated refineries, which had lower costsof adoption, were more likely to adopt the new technology. As theory suggests (Malueg 19),they also found that the tradeable permit system provided incentives for more efficienttechnology adoption decisions, as evidenced by a significant divergence in the adoption behaviorof refineries with low versus high compliance costs. Namely, the positive differential in the

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adoption propensity of expected permit sellers (i.e., low-cost refineries) relative to expectedpermit buyers (i.e., high-cost refineries) was significantly greater under market-based leadregulation compared to under individually binding performance standards.

Another prominent application of tradeable permit systems which has provided anopportunity for empirical analysis of the effects of policy instruments on technology diffusion isthe sulfur dioxide allowance trading program, initiated under the U.S. Clean Air Actamendments of 1990. In an econometric analysis, Keohane (2001) found evidence that theincreased flexibility of the market-based instrument provided greater incentives for technologyadoption. In particular, he found that the choice of whether or not to adopt a “scrubber” toremove sulfur dioxide — rather than purchasing (more costly) low-sulfur coal — was moresensitive to cost differences (between scrubbing and fuel-switching) under the tradeable permitsystem than under the earlier emissions rate standard.14

Turning from pollution abatement to energy efficiency, Jaffe and Stavins (1995) carriedout econometric analyses of the factors affecting the adoption of thermal insulation technologiesin new residential construction in the United States between 1979 and 1988. They examined thedynamic effects of energy prices and technology adoption costs on average residential energy-efficiency technologies in new home construction, and found that the response of mean energyefficiency to energy price changes was positive and significant, both statistically andeconomically. Interestingly, they also found that equivalent percentage adoption cost changeswere about three times as effective as energy price changes in encouraging adoption, althoughstandard financial analysis would suggest they ought to be about equal in percentage terms. Thisfinding offers confirmation for the conventional wisdom that technology adoption decisions aremore sensitive to up-front cost considerations than to longer-term operating expenses.

Hassett and Metcalf (1995) found an even larger discrepancy between the effect ofchanges in installation cost and changes in energy prices. There are three possible explanationsfor this. One possibility is a behavioral bias that causes purchasers to focus more on up-frontcost than they do on the lifetime operating costs of an investment. An alternative view is thatpurchasers focus equally on both, but uncertainty about future energy prices makes them giveless weight to energy prices than they do to capital cost, which is known. A final interpretationmight be that consumers have reasonably accurate expectations about future energy prices, andtheir decisions reflect those expectations, but our proxies for their expectations are not correct.Although empirical evidence from these two studies indicate that subsidies may be moreeffective than “equivalent” taxes in encouraging technology diffusion, it is important torecognize some disadvantages of such subsidy approaches. First, unlike energy prices, adoptionsubsidies do not provide incentives to reduce utilization. Second, technology subsidies and taxcredits can require large public expenditures per unit of effect, since consumers who would havepurchased the product even in the absence of the subsidy still receive it. In the presence of fiscal 14

In an examination of the effects of alternative policy instruments for reducing oxygen-demanding water pollutants,Kemp (1998) found that effluent charges were a significant predictor of adoption of biological treatment byfacilities. In earlier work, Purvis and Outlaw (1995) carried out a case study of EPA’s permitting process foracceptable water-pollution control technologies in the U.S. livestock production sector.

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constraints on public spending, this raises questions about the feasibility of subsidies that wouldbe sizable enough to have desired effects.

Rose and Joskow (1990) also found a positive effect of fuel price increases on theadoption of a new fuel-saving technology in the U.S. electricity-generation sector; and in a tobitanalysis of steel plant adoption of different furnace technologies, Boyd and Karlson (1993) founda significant positive effect of increases in a fuel’s price on the adoption of technology that savesthat fuel, although the magnitude of the effect was modest. For a sample of industrial plants infour heavily polluting sectors (petroleum refining, plastics, pulp and paper, and steel), Pizer et al.(2001) found that both energy prices and financial health were positively related to the adoptionof energy-saving technologies.

Greene (1990) used data on fuel prices and fuel economy of automobiles from 1978 to19 to test the relative effectiveness of Corporate Average Fuel Economy (CAFE) Standardsand gasoline prices in increasing fuel economy. He found that the big three U.S. firms faced abinding CAFE constraint, and for these firms compliance with CAFE standards had roughlytwice the impact on fuel economy as did fuel prices. Japanese firms, however, did not face abinding CAFE constraint, and fuel prices had only a small effect. Luxury European manufacturesseemed to base their fuel efficiency largely on market demand and often exceeded CAFErequirements. For these firms, neither the standards nor prices seemed to have much effect.Another body of research has examined the effects on technology diffusion of command-and-control environmental standards when they are combined with “differential environmentalregulations.” In many situations where command-and-control standards have been used, therequired level of pollution abatement has been set at a far more stringent level for new sourcesthan for existing ones. There is empirical evidence that such differential environmentalregulations have lengthened the time before plants were retired (Maloney and Brady 1988;Nelson et al. 1993). Further, this dual system can actually worsen pollution by encouragingfirms to keep older, dirtier plants in operation (Stewart 1981; Gollop and Roberts 1983;McCubbins et al. 19).

What about conventional command-and-control approaches? Jaffe and Stavins (1995)also examined the effects of more conventional regulations on technology diffusion, in the formof state building codes. They found no discernable effects. It is unclear to what extent this isdue to inability to measure the true variation across states in the effectiveness of codes, or tocodes that were in many cases not binding relative to typical practice. This is a reminder,however, that although price-based policies will always have some effect, typical command-and-control may have little effect if they are set below existing standards of practice.15

Attention has also been given to the effects on energy-efficiency technology diffusion ofvoluntary environmental programs. Howarth et al. (2000) examined two voluntary programs ofthe U.S. Environmental Protection Agency, the Green Lights and Energy Star programs, both of 15

In a separate analysis of thermal home insulation, this one in the Netherlands, Kemp (1997) found that a thresholdmodel of diffusion (based on a rational choice approach) could not explain observed diffusion patterns. Instead,epidemic models provided a better fit to the data. Kemp also found that there was no significant effect ofgovernment subsidies on the adoption of thermal insulation by households.

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which are intended to encourage greater private industry use of energy-saving technologies. Anatural question from economics is why would firms carry out additional technologyinvestments as part of a voluntary agreement? The authors respond that there are a set of agencyproblems that inhibit economically wise adoption of some technologies. For example, mostenergy-saving investments are small, and senior staff may rationally choose to restrict funds forsmall projects that cannot be perfectly monitored. The Green Lights program may be said toattempt to address this type of agency problem by providing information on savingsopportunities at the level of the firm where decisions are made.

Although the empirical literature on the effects of policy instruments on technologydiffusion by no means settles all of the issues that emerge from the related theoretical studies, aconsistent theme that runs through both the pollution-abatement and energy-efficiency empiricalanalyses is that market-based instruments are decidedly more effective than command-and-control instruments in encouraging the cost-effective adoption and diffusion of relevant newtechnologies.4. Conclusions

Virtually all research on the relationship between technological change andenvironmental policy has been linked with one of two underlying realities: first, theenvironmental impacts of social and economic activity is greatly affected by the rate anddirection of technological change; and second, environmental policy interventions themselvescreate new constraints and incentives that affect the process of technological developments.One important research need, linked with the first reality, is the frequent necessity ofdetermining the economic and environmental baseline against which to measure the impacts ofproposed policies. Forecasts based on historical experience depend on the relative magnitude ofthe effects of price-induced technological change, learning-by-doing, public sector R&D, andexogenous technical progress. Sorting out these influences with respect to environmentallyrelevant technologies and sectors poses a major challenge.

There has also been much debate surrounding the “win-win” hypothesis. Much of thisdebate has been explicitly or implicitly ideological or political. More useful would be detailedexaminations regarding the kinds of policies and the kinds of private-sector institutions that aremost likely to generate innovative, low-cost solutions to environmental problems.

More research is also needed on the second broad linkage between technology andenvironment, the effect of environmental policy interventions on the process of technologicalchange. The empirical evidence is generally consistent with theoretical findings that market-based instruments for environmental protection are likely to have significantly greater, positiveimpacts over time than command-and-control approaches on the invention, innovation, anddiffusion of desirable, environmentally-friendly technologies. But empirical studies have alsoproduced some results that appear not to be consistent with theoretical expectations, such as thefinding from two independent analyses that the diffusion of energy-efficiency technologies ismore sensitive to variation in adoption-cost than to commensurate energy price changes. Furthertheoretical and/or empirical work may resolve this apparent anomaly.

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Refutable hypotheses have emerged from theoretical models of alternative policyinstruments, but most have not been tested rigorously with empirical data. Whereas thepredictions from theory regarding the ranking of alternative environmental policy instruments isrelatively consistent, most of the empirical analysis has focused on energy-efficient technologies,rather than pollution abatement technologies per se. The increased use of market-basedinstruments and performance-based standards brings with it information with which hypothesesregarding the effects of policy instruments on technology innovation and diffusion can be tested.Finally, the long-term nature of policy challenges such as that posed by the threat ofglobal climate change makes it all the more important that we improve our understanding of theeffects of environmental policy on innovation and diffusion of new technology. What is clear isthat many relevant issues cannot be resolved at a purely theoretical level or on the basis ofaggregate empirical analysis alone. Serious investigation of induced technological change andits consequences for environmental policy requires going beyond studies that examine whetheror not such effects exist, to carry out detailed analyses in a variety of sectors in order tounderstand the circumstances under which the effects are large or small. This will inevitablyrequire research from multiple methodological viewpoints over an extended period of time.

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REFERENCES

Arrow, K.J, (1962), “Economic Welfare and the Allocation of Resources for Invention”, in: R.

Nelson, ed., The Rate and Direction of Inventive Activity (Princeton University Press,Princeton NJ).Atkinson, S.E. and R. Halvorsen (1984), “A New Hedonic Technique for Estimating Attribute

Demand: An Application to the Demand for Automobile Fuel Efficiency”, Review ofEconomics and Statistics 66:417–426.Bellas, A.S. (1998), “Empirical Evidence of Advances in Scrubber Technology”, Resource and

Energy Economics 20:327-343.Berman, E. and L. Bui (2001), “Environmental Regulation and Productivity: Evidence from Oil

Refineries”, Review of Economics and Statistics 83(3):498–510.Berry, S., S. Kortum, and A. Pakes (1996), \"Environmental Change and Hedonic Cost Functions

for Automobiles\12738.Biglaiser, G., J.K. Horowitz, and J. Quiggin (1995), \"Dynamic Pollution Regulation\

Regulatory Economics 8:33-44.Bound, J., C. Cummins, Z. Griliches, B. Hall and A. Jaffe (1984), “Who Does R&D and Who

Patents?”, in: Z. Griliches, ed., R&D, Patents and Productivity (University of ChicagoPress, Chicago).Boyd, G.A. and S.H. Karlson (1993), \"The impact of energy prices on technology choice in the

United States steel industry\Boyd, G. and J. McClelland (1999), \"The Impact of Environmental Constraints on Productivity

Improvement and Energy Efficiency an Integrated Paper and Steel Plants\of Economics and Environmental Management 38:121-146.Boyd, G. and J. Pang (2000), \"Estimating the Linkage between Energy Efficiency and

Productivity\Cadot, O. and B. Sinclair-Desgagne (1996), “Innovation Under the Threat of Stricter

Environmental Standards”, in: C. Carraro et al., eds., Environmental Policy and MarketStructure (Kluwer Academic Publishers, Dordrecht) 131-141.Carraro, C. and A. Soubeyran (1996), “Environmental Policy and the Choice of Production

Technology”, in: C. Carraro et al., eds., Environmental Policy and Market Structure(Kluwer Academic Publishers, Dordrecht) 151-180.Carraro, C. and D. Siniscalaco (1994), “Environmental Policy Reconsidered: The Role of

Technology Innovation”, European Economic Review 38: 5-555.Conrad, K (2000), “Energy Tax and Competition in Energy Efficiency: The Case of Consumer

Durables”, Environmental and Resource Economics 15: 159-177.David, P. (1966), “The mechanization of reaping in the ante-bellum Midwest”, in: H. Rosovsky,

ed., Industrialization in Two Systems (Harvard University Press, Cambridge, MA) 3-39.David, P. (1969), “A Contribution to the Theory of Diffusion”, mimeo. Forthcoming as

Chapters 1-3 of David, P., Behind the Diffusion Curve (Westview Press, Boulder)

22

David, P., B. Hall and A. Toole (2000), “Is public R&D a complement or substitute for private

R&D? A review of the econometric evidence”, Research Policy 29:497-529DeCanio, S. J. (1998), “The Efficiency Paradox: Bureaucratic and Organizational Barriers to

Profitable Energy-Saving Investments”, Energy Policy 26:441-4.Denicolo, V. (1999), \"Pollution-reducing Innovations Under Taxes or Permits\

Economic Papers 51:184-199.Downing, P.B. and L.J. White (1986), \"Innovation in Pollution Control\

Environmental Economics and Management 13:18-29.Evenson, R. (1995), “Technology change and technology strategy”, in: J. Behrman and T.N.

Srinivasan, eds., Handbook of Development Economics, Vol. 3A (North-Holland,Amsterdam).Fischer, C., I.W.H. Parry, and W.A. Pizer (1998), \"Instrument Choice for Environmental

Protection When Technological Innovation is Endogenous\Discussion Paper 99-04 (Resources for the Future, Washington, D.C.).Freeman, A.M. and R.H. Haveman (1972), \"Clean Rhetoric and Dirty Water\

28:51-65.Geroski, P.A. (1995), “Markets for Technology: Knowledge, Innovation and Appropriability”,

in: P. Stoneman, ed., Handbook of the Economics of Innovation and TechnologicalChange (Oxford: Blackwell Publishers) 90-131.Geroski, P.A. (2000), “Models of Technology Diffusion”, Research Policy 29:603-626.Goldberg, P.K. (1998), “The Effects of the Corporate Average Fuel Efficiency Standards in the

U.S.”, Journal of Industrial Economics 46:1-3.Gollop, F.M. and M.J. Roberts (1983), “Environmental Regulations and Productivity Growth:

The Case of Fossil-Fueled Electric Power Generation”, Journal of Political Economy91:6-74.Goodman, A.C. (1983), “Willingness to pay for car efficiency: A hedonic price approach”,

Journal of Transport Economics 17:247–266.Goulder, L.H. and K. Mathai (2000), \"Optimal CO2 Abatement in the Presence of Induced

Technological Change\Goulder, L.H. and S.L. Schneider (1999), \"Induced Technological Change and the Attractiveness

of CO2 Emissions Abatement Policies\Gray, W.B and R.J. Shadbegian (1995), “Pollution Abatement Costs, Regulation, and Plant-Level Productivity”, National Bureau of Economic Research Working Paper No. 4994.Gray, W.B. and R.J. Shadbegian (1998), “Environmental Regulation, Investment Timing, and

Technology Choice”, Journal of Industrial Economics 46:235-256.Greene, D.L. (1990), “CAFE or Price?: An Analysis of the Effects of Federal Fuel Economy

Regulations and Gasoline Price on New Car MPG, 1978-”, The Energy Journal 11(3):37-57.

23

Greening, Lorna A., Alan H. Sanstad, and James E. McMahon (1997) “Effects of Appliance

Standards on Product Price and Attributes: An Hedonic Pricing Model”, Journal ofRegulatory Economics 11: 181-194.Greenstone, M. (1998), “The Marginal Effects of Environmental Regulations of the

Manufacturing Sector: Evidence from the 1970 and 1977 Clean Air Act Amendments”,mimeo Princeton University.Griliches, Z. (1957), “Hybrid Corn: An exploration in the economics of technical change”,

Econometrica 48:501-522.Griliches, Z. (1979), “Issues in Assessing the Contribution of Research and Development to

Productivity Growth”, Bell Journal of Economics 10:92-116.Griliches, Z. (1984), R&D, Patents and Productivity (University of Chicago Press, Chicago).Griliches, Z. (1992), “The Search for R&D Spillovers”, Scandinavian Journal of Economics

94:S29-S47.Griliches, Z. (1998), R&D and Productivity, The Econometric Evidence (University of Chicago

Press, Chicago).Grossman, G. and E. Helpman (1994), “Endogenous Innovation in the Theory of Growth”,

Journal of Economic Perspectives 8:23-44.Hahn, R.W. and R.N. Stavins (1991), “Incentive-based Environmental Regulation: a New Era

from an Old Idea?” Ecology Law Quarterly 18:1-42.Hall, B., A. Jaffe and M. Trajtenberg (2000), “Market Value and Patent Citations: A First

Look”, National Bureau of Economic Research Working Paper No. 7741.Hassett, K.A. and G.E. Metcalf (1995), \"Energy Tax Credits and Residential Conservation

Investment: Evidence form Panel Data\Hassett, K.A. and G.E. Metcalf (1996), “Can Irreversibility Explain the Slow Diffusion of

Energy Saving Technologies?”, Energy Policy 24: 7-8.Hausman, J.A. (1979), “Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables”, Bell Journal of Economics 10:33.Hicks, J. (1932), The Theory of Wages (Macmillan, London).

Hockenstein, J.B., R.N. Stavins and B.W. Whitehead (1997), “Creating the Next Generation of

Market-Based Environmental Tools”, Environment 39(4):12-20, 30-33.Holmström, B.R. and J. Tirole (1987), “The Theory of the Firm,” in: R. Schmalensee and R.

Willig, eds., Handbook of Industrial Organization, vol. 1 (North-Holland, Amsterdam)61-133.Howarth, R.B., B.M. Haddad, and B. Paton (2000), \"The Economics of Energy Efficiency:

Insights from Voluntary Participation Programs\Interlaboratory Working Group (1997), Scenarios of Carbon Reductions: Potential Impacts of

Energy Technologies by 2010 and Beyond (Office of Energy Efficiency and RenewableTechnologies, U.S. Department of Energy, Washington)Jaffe, A.B. (1986), “Technological Opportunity and Spillovers of R&D: Evidence from Firms’

Patents, Profits and Market Value”, American Economic Review 76:984-1001.

24

Jaffe, A.B. (1998), “The Importance of ‘Spillovers’ in the Policy Mission of the Advanced

Technology Program”, Journal of Technology Transfer, Summer 1998:11-19.Jaffe, A.B. (2000), “The U.S. patent system in transition: policy innovation and the innovation

process”, Research Policy 29:531-558.Jaffe, A.B. and K. Palmer (1997), “Environmental Regulation and Innovation: A Panel Data

Study”, Review of Economics and Statistics 79:610-619.Jaffe, A.B. and R.N. Stavins (1994), “The Energy Paradox and the Diffusion of Conservation

Technology”, Resource and Energy Economics 16:91-122.Jaffe, A.B and R.N. Stavins (1995), “Dynamic Incentives of Environmental Regulations: The

Effects of Alternative Policy Instruments on Technology Diffusion”, Journal ofEnvironmental Economics & Management 29:S43-63.Jaffe, A.B., S. Peterson, P. Portney, and R.N. Stavins (1995), “Environmental Regulation and the

Competitiveness of U.S. Manufacturing: What Does the Evidence Tell Us?”, Journal ofEconomic Literature 33:132-63.Jorgenson, D. (1990), “Productivity and Economic Growth”, in: E. Berndt and J. Triplett, eds.,

Fifty Years of Economic Measurement (University of Chicago Press, Chicago) 19-118.Jorgenson, D.W. and K.J. Stiroh (2000), “Raising the Speed Limit: U.S. Economic Growth in the

Information Age”, Brookings Papers on Economic Activity 125-211.Jung, C.H., K. Krutilla and R. Boyd (1996), \"Incentives for Advanced Pollution Abatement

Technology at the Industry Level: An Evaluation of Policy Alternatives\Environmental Economics and Management 30:95-111.Karshenas, M. and P. Stoneman (1995), “Technological Diffusion”, in: P. Stoneman, ed.,

Handbook of the Economics of Innovation and Technological Change (BlackwellPublishers, Oxford).Katsoulacos, Y. and A. Xepapadeas (1996), “Environmental Innovation, Spillovers and Optimal

Policy Rules”, in: C. Carraro et al., eds., Environmental Policy and Market Structure(Kluwer Academic Publishers, Dordrecht) 143-150.Kemp, RenJ (1997), Environmental Policy and Technical Change (Cheltenham, UK: Edward

Elgar).Kemp, RenJ (1998), “The Diffusion of Biological Waste-water Treatment Plants in the Dutch

Food and Beverage Industry”, Environmental and Resource Economics 12:113-136.Kemp, R. and L. Soete. (1990), \"Inside the 'Green Box:' on the economics of technological

change and the environment,\" in: Freeman, C. and L. Soete, eds., New Explorations inthe Economics of Technological Change (Pinter, London).Keohane, N.O. (1999), \"Policy Instruments and the Diffusion of Pollution Abatement

Technology\Keohane, N.O. (2001), \"Essays in the Economics of Environmental Policy\

dissertation, Harvard University.Kerr, S. and R.G. Newell (2000), “Policy-Induced Technology Adoption: Evidence from the

U.S. Lead Phasedown”, Resources for the Future Discussion Paper 01-14, (Resources forthe Future, Washington, DC).

25

Kneese, A. and C. Schultze (1975), Pollution, Prices, and Public Policy (Brookings Institution,

Washington, D.C.).Kolstad, C.D. and M.H.L. Turnovsky (1998), “Cost Functions and Nonlinear Prices: Estimating

a Technology with Quality-Differentiated Inputs”, Review of Economics & Statistics80:444-53.Laffont, J. and J. Tirole (1996), \"Pollution Permits and Compliance Strategies\

Economics 62:85-125.Landjouw, J.O. and A. Mody (1996), \"Innovation and the International Diffusion of

Environmentally Responsive Technology\Levin, S., S. Levin and J. Meisel (1987), “A Dynamic Analysis of the Adoption of a New

Technology: The Case of Optical Scanners”, Review of Economics and Statistics 69:12-17.Magat, W.A. (1978), \"Pollution control and technological advance: A dynamic model of the

firm\Magat, W.A. (1979), \"The effects of environmental regulation on innovation\

Contemporary Problems 43:3-25.Maloney, M.T. and G.L. Brady (1988), “Capital Turnover and Marketable Pollution Rights”,

Journal of Law and Economics 31:203-226.Malueg, D.A. (19), \"Emission Credit Trading and the Incentive to Adopt New Pollution

Abatement Technology\57.Mansfield, E. (1968), Industrial Research and Technological Innovation (Norton, New York).Mansfield, E. (19), “Industrial robots in Japan and the USA”, Research Policy 18:183-192.Marin, A. (1991), \"Firm Incentives to Promote Technological Change in Pollution Control:

Comment\Martin, S. and J. Scott (2000), “The nature of innovation market failure and the design of public

support for private innovation”, Research Policy 29:437-448.McCubbins, M.D., R.G. Noll, and B.R. Weingast (19), “Structure and Process, Politics and

Policy: Administrative Arrangements and the Political Control of Agencies”, VirginiaLaw Review 75:431-482.Metcalf, G.E. and K.A. Hassett (1999), “Measuring the Energy Savings from Home

Improvement Investments: Evidence from Monthly Billing Data”, The Review ofEconomics and Statistics 81: 516-528.Milliman, S.R. and R. Prince (19), \"Firm Incentives to Promote Technological Change in

Pollution Control\Milliman, S.R. and R. Prince (1992), \"Firm Incentives to Promote Technological Change in

Pollution Control: Reply\22:292-296.Montero, J.P. (2000), \"Market Structure and Environmental Innovation\

University of Chile.

26

National Science Board (1998), Science and Engineering Indicators—1998 (National Science

Foundation, Washington; available online athttp://www.nsf.gov/sbe/srs/seind98/frames.htm).Nelson, R. and S. Winter (1982), An Evolutionary Theory of Economic Change (The Belknap

Press of Harvard University Press, Cambridge, MA).Nelson, R., T. Tietenberg, and M. Donihue (1993), “Differential Environmental Regulation:

Effects on Electric Utility Capital Turnover and Emissions”, Review of Economics andStatistics 75:368-373.Newell, R.G, A.B. Jaffe and R.N. Stavins (1999), “The Induced Innovation Hypothesis and

Energy-Saving Technological Change”, The Quarterly Journal of Economics 114:941-75.Nordhaus, W.D. (2000), \"Modeling Induced Innovation in Climate-Change Policy\

Yale University.Oates, W.E., K. Palmer, and P.R. Portney (1993), “Environmental Regulation and International

Competitiveness: Thinking About the Porter Hypothesis”, Resources for the FutureDiscussion Paper 94-02 (Resources for the Future, Washington, D.C.).Ohta, M. and Z. Griliches (1976), “Automobile prices revisited: Extensions of the hedonic

hypothesis”, in: N.E. Terleckyj, ed., Household Production and Consumption (New York:National Bureau of Economic Research).Ohta, M. and Z. Griliches (1986), “Automobile prices and quality: Did the gasoline price

increases change consumer tastes in the U.S.?”, Journal of Business & EconomicStatistics 4:187–198.Organization for Economic Cooperation and Development (2000), Basic Science and

Technology Statistics: 1999 Edition (Organization for Economic Cooperation andDevelopment, Paris).Orr, L. (1976), \"Incentive for Innovation as the Basis for Effluent Charge Strategy\

Economic Review 66:441-447.Oster, S. (1982), “The diffusion of innovation among steel firms: the basic oxygen furnace”,

The Bell Journal of Economics 13:45-56.Pakes, A., S. Berry, and J.A. Levinsohn (1993), \"Applications and Limitations of Some Recent

Advances in Empirical Industrial Organization: Prices Indexes and the Analysis ofEnvironmental Change\Palmer, K., W.E. Oates and P.R. Portney (1995), “Tightening Environmental Standards: The

Benefit-Cost or the No-Cost Paradigm?” Journal of Economic Perspectives 9:119-132.Parry, I.W.H. (1998), \"Pollution Regulation and the Efficiency Gains from Technological

Innovation\Pindyck, R. (1991), “Irreversibility, uncertainty, and investment”, Journal of Economic

Literature 29:1110-1152.Pizer, W.A., W. Harrington, R.J. Kopp, R.D. Morgenstern, J. Shih (2001), “Technology

Adoption and Aggregate Energy Efficiency”, Resources for the Future Discussion Paper01-21 (Resources for the Future, Washington, DC).

27

Popp, D. (2001a), “Induced Innovation and Energy Prices”, American Economic Review,

forthcoming.Popp, D. (2001b), \"The Effect of New Technology on Energy Consumption\

Energy Economics 23(4):215-239.Porter, M.E. and C. van der Linde (1995), \"Toward a New Conception of the Environment-Competitiveness Relationship\Purvis, A. and J. Outlaw (1995), \"What We Know About Technological Innovation to Achieve

Environmental Compliance: Policy Issues for an Industrializing Animal AgricultureSector\Reinganum, J. (19), “The Timing of Innovation: Research, Development and Diffusion”, in:

R. Schmalensee and R. Willig, eds., Handbook of Industrial Organization, vol. 1 (North-Holland, Amsterdam) 850-908.Reinhardt, F.L. (2000), Down to Earth: Applying Business Principles to Environmental

Management (Harvard Business School Press, Boston).Reppelin-Hill, V. (1999), \"Trade and Environment: An Empirical Analysis of the Technology

Effect in the Steel Industry\38:283-301.Requate, T. (1998), “Incentives to Innovate under Emission Taxes and Tradeable Permits”,

European Journal of Political Economy 14:139-65.Romer, P.M. (1994), “The Origins of Endogenous Growth”, Journal of Economic Perspectives

8:3-22.Rose, N. and P. Joskow (1990), “The Diffusion of New Technologies: Evidence from the

Electric Utility Industry”, Rand Journal of Economics 21:3-373.Ross, M. (1990), “Capital Budgeting Practices of Twelve Large Manufacturers”, in: P. Cooley,

ed., Advances in Business Financial Management (Dryden Press, Chicago) 157-170.Ruderman, H., M.D. Levine, and J.E. McMahon (1987), “The Behavior of the Market for Energy

Efficiency in Residential Appliances Including Heating and Cooling Equipment”, TheEnergy Journal 8:101-123.Sanstad, A., C. Blumstein and S. Stoft (1995), “How high are option values in energy-efficiency

investments”, Energy Policy 23:739-743.Scherer, F. (1986), Innovation and Growth, Schumpeterian Perspectives (The MIT Press,

Cambridge, MA).Scherer, F.M. and D. Harhoff (2000), “Technology Policy for a World of Skew-Distributed

Outcomes”, Research Policy 29:559-566.Scherer, F., D. Harhoff, and J. Kukies (2000), “Uncertainty and the size distribution of rewards

from technological innovation”, Journal of Evolutionary Economics 10: 175-200.Schmalensee, R. (1994), “The Costs of Environmental Protection”, in: M.B. Kotowski, ed.,

Balancing Economic Growth and Environmental Goals (American Council for CapitalFormation, Center for Policy Research, Washington, D.C.) 55-75.Schumpeter, J. (1942), Capitalism, Socialism and Democracy (Harper, New York).

28

Shama, A. (1983), “Energy Conservation in U.S. Buildings, Solving the High Potential/Low

Adoption Paradox from a Behavioral Perspective”, Energy Policy 11:148-168.Shrestha, R.M. and B.K. Karmacharya (1998), “Testing of Barriers to the Adoption of Energy-Efficient Lamps in Nepal,” The Journal of Energy and Development 23(1): 71-82.Simon, H.A. (1947), Administrative Behavior: A Study of Decision-making Processes in

Administrative Organization (Macmillan Company, New York).Spence, A.M. (1984), “Cost Reduction, Competition and Industry Performance,” Econometrica

52(1):101-121.Stavins, R.N. (2001), “Experience with Market-Based Environmental Policy Instruments.”

Handbook of Environmental Economics, eds. Karl-Göran Mäler and Jeffrey Vincent.Amsterdam: Elsevier Science, forthcoming.Stewart, R.B (1981), “Regulation, Innovation, and Administrative Law: A Conceptual

Framework”, California Law Review 69:1256-1270.Stoneman, P. (1983), The Economic Analysis of Technological Change (Oxford University

Press, Oxford, U.K.)Sutton, J. (1998), Technology and Market Structure (The MIT Press, Cambridge, MA).Tirole, J. (1988), The Theory of Industrial Organization (The MIT Press, Cambridge, MA).Ulph, D. (1998), \"Environmental Policy and Technological Innovation,\" forthcoming in: C.

Carraro and D. Siniscalaco, eds., Frontiers of Environmental Economics (Edward Elgar,Cheltenhsam, U.K.).Weitzman, M.L. (1974), “Prices vs. Quantities”, Review of Economic Studies 41:477-491.Wilcox, J. (1984), “Automobile Fuel Efficiency: Measurement and Explanation”, Economic

Inquiry 22:375–385.Winter, S.G., Y.M. Kaniovski and G. Dosi (2000), “Modeling Industrial Dynamics with

Innovative Entrants”, Structural Change and Economic Dynamics 11:255-293.Zerbe, R.O. (1970), \"Theoretical Efficiency in Pollution Control\

8:3-376.

29

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Frédéric BROCHIER, Carlo GIUPPONI and Julie SORS: Integrated Coastal Management in the Venice Area –Potentials of the Integrated Participatory Management Approach Frédéric BROCHIER and Carlo GIUPPONI: Integrated Coastal Zone Management in the Venice Area –A Methodological Framework Enrico C. PEROTTI and Luc LAEVEN: Confidence Building in Emerging Stock Markets Barbara BUCHNER, Carlo CARRARO and Igor CERSOSIMO: On the Consequences of the U.S. Withdrawal from the Kyoto/Bonn Protocol Riccardo SCARPA, Adam DRUCKER, Simon ANDERSON, Nancy FERRAES-EHUAN, Veronica GOMEZ,

Carlos R. RISOPATRON and Olga RUBIO-LEONEL: Valuing Animal Genetic Resources in Peasant Economies: The Case of the Box Keken Creole Pig in Yucatan

R. SCARPA, P. KRISTJANSON, A. DRUCKER, M. RADENY, E.S.K. RUTO, and J.E.O. REGE: Valuing Indigenous Cattle Breeds in Kenya: An Empirical Comparison of Stated and Revealed Preference Value Estimates

Clemens B.A. WOLLNY: The Need to Conserve Farm Animal Genetic Resources Through Community-Based Management in Africa: Should Policy Makers be Concerned?

J.T. KARUGIA, O.A. MWAI, R. KAITHO, Adam G. DRUCKER, C.B.A. WOLLNY and J.E.O. REGE: Economic Analysis of Crossbreeding Programmes in Sub-Saharan Africa: A Conceptual Framework and Kenyan Case Study

W. AYALEW, J.M. KING, E. BRUNS and B. RISCHKOWSKY: Economic Evaluation of Smallholder Subsistence Livestock Production: Lessons from an Ethiopian Goat Development Program SUST 108.2001 Gianni CICIA, Elisabetta D’ERCOLE and Davide MARINO: Valuing Farm Animal Genetic Resources by Means of Contingent Valuation and a Bio-Economic Model: The Case of the Pentro Horse SUST 109.2001 Clem TISDELL: Socioeconomic Causes of Loss of Animal Genetic Diversity: Analysis and Assessment SUST 110.2001 M.A. JABBAR and M.L. DIEDHOU: Does Breed Matter to Cattle Farmers and Buyers? Evidence from West Africa SUST 1.2002 K. TANO, M.D. FAMINOW, M. KAMUANGA and B. SWALLOW: Using Conjoint Analysis to Estimate Farmers’ Preferences for Cattle Traits in West Africa ETA 2.2002 Efrem CASTELNUOVO and Paolo SURICO: What Does Monetary Policy Reveal about Central Bank’s Preferences? WAT 3.2002 Duncan KNOWLER and Edward BARBIER: The Economics of a “Mixed Blessing” Effect: A Case Study of the Black Sea

CLIM 4.2002 Andreas LöSCHEL: Technological Change in Economic Models of Environmental Policy: A Survey VOL 5.2002 Carlo CARRARO and Carmen MARCHIORI: Stable Coalitions CLIM 6.2002 ETA 7.2002 KNOW 8.2002 NRM 9.2002 KNOW 10.2002 ETA 11.2002 KNOW 12.2002 NRM 13.2002 CLIM 14.2002 CLIM 15.2002 CLIM 16.2002 ETA 17.2002 Coalition Theory

Network

Coalition Theory

Network

Coalition Theory Network

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Effrosyni DIAMANTOUDI and Eftichios S. SARTZETAKIS: Stable International Environmental Agreements: An Analytical Approach

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Mordecai KURZ, Hehui JIN and Maurizio MOTOLESE: Endogenous Fluctuations and the Role of Monetary Policy

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Francesco RICCI (l): Environmental Policy Growth when Inputs are Differentiated in Pollution Intensity Alberto PETRUCCI: Devaluation (Levels versus Rates) and Balance of Payments in a Cash-in-Advance Economy

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(xlii) This paper was presented at the International Workshop on \"Climate Change and Mediterranean Coastal Systems: Regional Scenarios and Vulnerability Assessment\" organised by the Fondazione Eni Enrico Mattei in co-operation with the Istituto Veneto di Scienze, Lettere ed Arti, Venice, December 9-10, 1999.

(xliii)This paper was presented at the International Workshop on “Voluntary Approaches, Competition and Competitiveness” organised by the Fondazione Eni Enrico Mattei within the research activities of the CAVA Network, Milan, May 25-26,2000.

(xliv) This paper was presented at the International Workshop on “Green National Accounting in Europe: Comparison of Methods and Experiences” organised by the Fondazione Eni Enrico Mattei within the Concerted Action of Environmental Valuation in Europe (EVE), Milan, March 4-7, 2000 (xlv) This paper was presented at the International Workshop on “New Ports and Urban and Regional Development. The Dynamics of Sustainability” organised by the Fondazione Eni Enrico Mattei, Venice, May 5-6, 2000.

(xlvi) This paper was presented at the Sixth Meeting of the Coalition Theory Network organised by the Fondazione Eni Enrico Mattei and the CORE, Université Catholique de Louvain, Louvain-la-Neuve, Belgium, January 26-27, 2001

(xlvii) This paper was presented at the RICAMARE Workshop “Socioeconomic Assessments of Climate Change in the Mediterranean: Impact, Adaptation and Mitigation Co-benefits”, organised by the Fondazione Eni Enrico Mattei, Milan, February 9-10, 2001

(xlviii) This paper was presented at the International Workshop “Trade and the Environment in the Perspective of the EU Enlargement ”, organised by the Fondazione Eni Enrico Mattei, Milan, May 17-18, 2001

(xlix) This paper was presented at the International Conference “Knowledge as an Economic Good”, organised by Fondazione Eni Enrico Mattei and The Beijer International Institute of Environmental Economics, Palermo, April 20-21, 2001

(l) This paper was presented at the Workshop “Growth, Environmental Policies and Sustainability” organised by the Fondazione Eni Enrico Mattei, Venice, June 1, 2001

(li) This paper was presented at the Fourth Toulouse Conference on Environment and Resource Economics on “Property Rights, Institutions and Management of Environmental and Natural

Resources”, organised by Fondazione Eni Enrico Mattei, IDEI and INRA and sponsored by MATE, Toulouse, May 3-4, 2001

(lii) This paper was presented at the International Conference on “Economic Valuation of

Environmental Goods”, organised by Fondazione Eni Enrico Mattei in cooperation with CORILA, Venice, May 11, 2001

(liii) This paper was circulated at the International Conference on “Climate Policy – Do We Need a New Approach?”, jointly organised by Fondazione Eni Enrico Mattei, Stanford University and Venice International University, Isola di San Servolo, Venice, September 6-8, 2001

(liv) This paper was presented at the Seventh Meeting of the Coalition Theory Network organised by the Fondazione Eni Enrico Mattei and the CORE, Université Catholique de Louvain, Venice, Italy, January 11-12, 2002

2002 SERIES

MGMT CLIM PRIV KNOW NRM SUST VOL ETA

Corporate Sustainable Management (Editor: Andrea Marsanich)

Climate Change Modelling and Policy (Editor: Marzio Galeotti )

Privatisation, Antitrust, Regulation (Editor: Bernardo Bortolotti)

Knowledge, Technology, Human Capital (Editor: Dino Pinelli)

Natural Resources Management (Editor: Carlo Giupponi)

Sustainability Indicators and Environmental Evaluation (Editor: Carlo Carraro)

Voluntary and International Agreements (Editor: Carlo Carraro)

Economic Theory and Applications (Editor: Carlo Carraro)