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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
This paper can be downloaded without charge at:
The Fondazione Eni Enrico Mattei Note di Lavoro Series Index:
http://www.feem.it/web/activ/_activ.html
Social Science Research Network Electronic Paper Collection:
http://papers.ssrn.com/abstract_id=311023
The opinions expressed in this paper do not necessarily reflect the position of
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
1
ENVIRONMENTAL POLICY
AND 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 change
that go well beyond those analyses that have focused directly on implications for environmental
policy, including: the theory of incentives for research and development (Tirole 1988;
Reinganum 1989; Geroski 1995); the measurement of innovative inputs and outputs (Griliches
1984 and Griliches 1998); analysis and measurement of externalities resulting from the research
process (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 policy
responses (Martin and Scott 2000); the economic effects of publicly funded research (David et
al. 2000); the economic effects of the patent system (Jaffe 2000); and the role of technological
change in endogenous macroeconomic growth (Romer 1994; Grossman and Helpman 1994). In
this part of the article, we provide a very brief guide to some of this research. In particular, we
introduce approaches for measuring technological change, we examine critical aspects of the
process of technological change, and we describe modeling approaches and potential market
failures relating to technology innovation and diffusion.
∗
Jaffe is Professor of Economics, Brandeis University, and Research Associate, National Bureau of Economic
Research; Newell is Fellow, Resources for the Future; and Stavins is Albert Pratt Professor of Business and
Government, John F. Kennedy School of Government, Harvard University, and University Fellow, Resources for
the Future. 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.
2
1.1. Measurement of Technological Change
The measurement of the rate and direction of technological change rests fundamentally
on the concept of the transformation function,
(,,)0TYIt
≤
, (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 outputs
that are technically feasible at a point in time. Technological change is represented by
movement of this frontier that makes it possible over time to use given input vectors to produce
output vectors that were not previously feasible.
In most applications, separability and aggregation assumptions are made that make it
possible to represent the economy’s production technology with a production function,
(,,;)
YfKLEt=
, (2)
where Y is now a scalar measure of aggregate output (for example, gross domestic product), and
the 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 capital
goods, 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 relationship
between these inputs and possible output levels changes over time.
Logarithmic differentiation of the production function (Equation (2)) with respect to time
yields
ttLttKttEtt
yAlke
βββ
=+++
, (3)
in which lower case letters represent the percentage growth rates of the corresponding upper case
variable; 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 A
t
corresponds to “neutral” technological change, in the sense that it represents the rate of growth of
output if the growth rates of all inputs were zero. But the possibility that the
β
’s can change
over time allows for “biased” technological change, that is, changes over time in relative
productivity of the various inputs.
Equations (2) and (3) are most easily interpreted in the case of process innovation, in
which firms figure out more efficient ways to make existing products, allowing output to grow at
a rate faster than inputs are growing. In principle, these equations also apply to product
innovation. Y is a composite or aggregate output measure, in which the distinct outputs of the
economy 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.
3
products will typically sell at a price premium, relative to lower quality products, meaning that
their introduction will increase measured output even if the physical quantity of the new goods
does 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 price
indices used to convert nominal GDP or other nominal output measures to real output measures
are purged of the effects of product innovation. In general, official price indices and the
corresponding real output measures achieve this objective only to a limited extent.
On its face, Equation (3) says nothing about the source of the productivity improvement
associated with the neutral technological change term, A
t
. If, however, all inputs and outputs are
properly measured, and inputs (including R&D) yield only normal investment returns, then all
endogenous contributions to output should be captured by returns to inputs, and there should be
no “residual” difference between the weighted growth rates of inputs and the growth rate of
output. The observation that the residual has been typically positive is therefore interpreted as
evidence of exogenous technological change.
1.2. Process of Technological Change
Economic theories of the process of technological change can be traced to the ideas of
Josef Schumpeter (1942), who distinguished three stages in the process by which a new, superior
technology permeates the marketplace. Invention constitutes the first development of a
scientifically or technically new product or process. Inventions may be patented, though many
are not. Either way, most inventions never actually develop into an innovation, which is
accomplished only when the new product or process is commercialized, that is, made available
on the market. A firm can innovate without ever inventing, if it identifies a previously existing
technical idea that was never commercialized, and brings a product or process based on that idea
to market. The invention and innovation stages are carried out primarily in private firms through
a process that is broadly characterized as “research and development” (R&D).
2
Finally, a
successful innovation gradually comes to be widely available for use in relevant applications
through adoption by firms or individuals, a process labeled diffusion. The cumulative economic
or environmental impact of new technology results from all three of these stages,
3
which we refer
to 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, if
the expenditure is deemed “material” by the firm’s auditors, or if the firm chooses for strategic reasons to report
the 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 now
collect similar statistics, which are available through the Organization of Economic Cooperation and Development
(OECD 2000).
3
Typically, for there to be environmental impacts of a new technology, a fourth step is required utilization, but
that is not part of the process of technological change per se. Thus, for example, a new type of hybrid motor
vehicle engine might be invented, which emits fewer pollutants per mile; the same or another firm might
commercialize this engine and place the innovation in new cars available for purchase on the market; individuals
might purchase (or adopt) these cars, leading to diffusion of the new technology; and finally, by driving these cars
instead of others (utilization), aggregate pollutant emissions might be reduced. Conversely, if higher efficiency
and the resulting reduced marginal cost causes users to increase utilization, then the emissions reduction associated
with higher efficiency may be partially or totally offset by higher utilization.
4
1.3. Induced Innovation and Evolutionary Approaches
If the imposition of environmental requirements can stimulate invention and innovation
that reduces the cost of complying with those requirements, this has profound implications for
both the setting of environmental policy goals and the choice of policy instruments. It is useful to
identify two major strands of thought regarding the determinants of innovative activity. We call
these two broad categories of modeling approaches the “induced innovation” approach and the
“evolutionary” approach.
Induced Innovation. Within the induced innovation approach, firms undertake an
investment activity called “R&D” with the intention of producing profitable new products and
processes. Decisions regarding the magnitude and nature of R&D activities are governed by
firms’ efforts to maximize their value, or, equivalently, to maximize the expected discounted
present 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 inputs
to generate revenues (Griliches 1979; Hall et al. 2000).
When viewed as an investment activity, R&D has important characteristics that
distinguish it from investment in equipment or other tangible assets. First, although the outcome
of any investment is uncertain to some extent, R&D investment appears to be qualitatively
different. Not only is the variance of the distribution of expected returns much larger than for
other 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 distribution
of the outcomes of the research process has important implications for modeling firms’ R&D
decision making (Scherer and Harhoff, 2000). In addition, the asset produced by the R&D
investment process is specialized, sunk and intangible, so that it cannot be mortgaged or used as
collateral. The combination of great uncertainty and intangible outcomes makes financing of
research 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/or
less access to financial markets.
In addition to these financing difficulties, research investment differs from physical
investment because the asset produced by the research process — new knowledge about how to
make and do things — is difficult to exclude others from using. As first noted in the classic
paper by Arrow (1962), this means that the creator of this asset will typically fail to appropriate
all 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, or
to consumers (Griliches 1979, 1992; Jaffe 1986, 1998). This “appropriability problem” is likely
to 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 the
hypothesis that the rate and direction of innovation are likely to respond to changes in relative
prices. Since environmental policy implicitly or explicitly makes environmental inputs more
expensive, the “induced innovation” hypothesis suggests an important pathway for the
interaction of environmental policy and technology, and for the introduction of impacts on
5
technological change as a criterion for evaluation of different policy instruments. We consider
empirical approaches and evidence on induced innovation in section 3.1 below.
The Evolutionary Perspective. While viewing R&D as a profit-motivated investment
activity comes naturally to most economists, the large uncertainties surrounding the outcomes of
R&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 firms
that engage in “satisficing” rather than optimizing behavior (Simon 1947) to build an alternative
model 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 firms
actually use (Nelson and Winter 1982; Winter et al 2000).
If firms are not optimizing, a logical consequence of the evolutionary model is that it
cannot be presumed that the imposition of a new external constraint (for example, a new
environmental rule) necessarily reduces profits. There is at least the theoretical possibility that
the imposition of such a constraint could be an event that forces a satisficing firm to rethink its
strategy, with the possible outcome being the discovery of a new way of operating that is
actually more profitable for the firm. This raises the possibility that environmental regulation
can 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 meeting
environmental regulations, but can even lead to absolute advantages over firms in foreign
countries 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 profit
improvements, without suggesting that such improvements would be the norm, would be
systematic, or even likely.
Porter and van der Linde (1995) provided case studies of firms which adopted new
technology in response to regulation, and appear to have benefited, but win-win theorists do not
claim that all environmental regulations generate significant innovation offsets. Indeed, they
emphasize that regulation must be properly designed in order to maximize the chances for
encouraging innovation. Quantitative evidence is limited—much of it from a large related
literature on the impact of environmental regulation on productivity and investment
4
—and
results seem to be industry and methodology dependent.
Boyd and McClelland (1999) and Boyd and Pang (2000) employ data envelopment
analysis to evaluate the potential at paper and glass plants for “win-win” improvements that
increase productivity and reduce energy use or pollution. They suggest that the paper industry
could 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 oil
refining 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).
6
(1998) found that air pollution regulation has a statistically significant but very small impact on
overall 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 inefficient
firm behavior as the (second-best) efficient outcome of imperfect information and divergent
incentives among managers or between owners and managers in a principal/agent framework.
5
From this perspective, the apparent inefficiency does not have normative implications. Since
firms are doing the best they can given their information environment, it is unlikely that the
additional constraints represented by environmental policy interventions would be beneficial.
On a more concrete level, Palmer et al. (1995) surveyed firms affected by regulation—including
those cited by Porter and van der Linde as success stories — and found that most firms say that
the net cost to them of regulation is, in fact, positive.
For regulation to have important informational effects, the government must have better
information than firms have about the nature of environmental problems and their potential
solutions. Furthermore, while it seems likely that environmental regulation will stimulate the
innovation and diffusion of technologies that facilitate compliance, creation and adoption of new
technology 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 “partially
full 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 corn
seed (Griliches 1957), steel furnaces (Oster 1982), optical scanners (Levin et al. 1987) and
industrial robots (Mansfield 1989), research has consistently shown that the diffusion of new,
economically superior technologies is a gradual process. Typically, the fraction of potential
users that has adopted a new technology follows a sigmoid or “S-shaped” path over time, rising
only slowly at first, then entering a period of very rapid growth, followed by a slowdown in
growth as the technology reaches maturity and most potential adopters have switched (Geroski
2000).
The explanation for the apparent slowness of the technology diffusion process has been a
subject of considerable study. Two main forces have been emphasized. First, potential
technology adopters are heterogeneous, so that a technology that is generally superior will not be
equally superior for all potential users, and may remain inferior to existing technology for some
users for an extended period of time after its introduction. Second, adopting a new technology is
a risky undertaking, requiring considerable information, both about the generic attributes of the
new technology and about the details of its use in the particular application being considered. It
takes time for information to diffuse sufficiently, and the diffusion of the technology is limited
by this process of diffusion of information.
5
For a survey, see Holmström and Tirole (1987).
7
The two main models of the diffusion process each emphasize one of these two aspects of
the 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 the
new technology. Because adoption is costly, at any moment in time there is a threshold point on
this 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 to
adopt. Because the new technology will typically get cheaper and better as time passes, this
threshold will gradually move to the left, and eventually sweep out the entire distribution. If the
distribution of underlying values is normal (or another single-peaked distribution with similar
shape), 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 that
the most important source of information about a new technology is people or firms who have
tried it. Thus technology spreads like a disease, with the instigation of adoption being contact
between the “infected” population (people who have already adopted) and the uninfected
population. Denoting the fraction of the potential using population that has adopted as f, this
leads to the differential equation
(1)
df
ff
dt
β
=−
. Solution of this equation yields a logistic
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 its
superiority over the technology it replaces (Griliches 1957).
6
Both of the models discussed above predict that the present value of benefits from
adoption and the initial adoption cost enter into decisions affecting the diffusion rate. In the
probit model, this net present value comparison determines the location of the adoption threshold
that determines what fraction of potential adopters will adopt at a moment in time. In the
epidemic model, this net present value comparison determines the magnitude of the
“contagiousness” parameter, which in turn determines the speed at which the technology spreads
from adopters to previous non-adopters.
While the induced innovation literature focuses on the potential for environmental policy
to bring forth new technology through innovation, there is also a widely-held view that
significant reductions in environmental impacts could be achieved through more widespread
diffusion of existing economically-attractive technologies, particularly ones that increase energy
efficiency and thereby reduce emissions associated with fossil fuel combustion. For example, the
report of the Interlaboratory Working Group (1997) compiled an analysis of technologies that
reportedly could reduce energy use and hence CO
2
emissions at low or even negative net cost to
users. 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 in
time. If one has individual-level data on adopters, one can take as the dependent variable the individual time until
adoption. This leads to a duration or hazard model (Rose and Joskow 1990). Kerr and Newell (2000) employed a
duration model to analyze technology adoption decisions by petroleum refineries during the phasedown of lead in
gasoline.
8
are diffusing only slowly dates to the 1970s, having been identified as a “paradox” at least as far
back as Shama (1983).
The apparent potential for emissions reductions associated with faster diffusion of
existing technology raises two important questions. First, what is the theoretical and empirical
potential for “induced diffusion” of lower-emissions technologies? Specifically, how do
environmental policy instruments that implicitly or explicitly increase the economic incentive to
reduce emissions affect the diffusion rate of these technologies? A second and related question
is the degree to which historical diffusion rates have been limited by market failures in the
energy and equipment markets themselves (Jaffe and Stavins 1994). To the extent that diffusion
has been and is limited by market failures, it is less clear that policies that operate by increasing
the economic incentive to adopt such technology will be effective. On the other hand, if such
market failures are important, then policies focused directly on correction of such market failures
provide, at least in principle, opportunities for policy interventions that are social-welfare
increasing, even without regard to any environmental benefit. Potential sources of market failure
include 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 important
mode of information transfer to other parties, adoption creates a positive externality and is
therefore 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 the
diffusion of more efficient lighting equipment. Metcalf and Hassett (1999) compared available
estimates of energy savings from new equipment to actual savings realized by users who have
installed the equipment. They found that actual savings, while significant, were less than those
promised by engineers and product manufacturers.
Also related to imperfect information are a variety of agency problems that can inhibit the
adoption of superior technology. An example of an external agency problem would be a
landlord/tenant relationship, in which a tenant pays for utilities but the landlord makes decisions
regarding which appliances to purchase, or vice versa. Internal agency problems can arise in
organizations where the individual or department responsible for equipment purchase or
maintenance differs from the individual or department whose budget covers utility costs.
7
DeCanio (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 (Geroski
2000). Such uncertainty is not a market failure, merely a fact of economic life. Uncertainty can
be inherent in the technology itself, in the sense that its newness means that users are not sure
how it will perform (Mansfield 1968). For resource-saving technology, there is the additional
uncertainty 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 from
relevant economic signals, see: Hockenstein et al., (1997). A series of related case studies are provided by
Reinhardt (2000).
9
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 Metcalf
1995, 1996).
Closely related to the issue of uncertainty is the issue of the discount rate or investment
hurdle rate used by purchasers in evaluating the desirability of new technology. A large body of
research demonstrates that purchasers appear to use relatively high discount rates in evaluating
energy-efficiency investments (Hausman 1979; Ruderman et al. 1987; Ross 1990). The implicit
or explicit use of relatively high discount rates for energy savings does not represent a market
failure in itself; it is rather the manifestation of underlying aspects of the decision process
including those just discussed. At least some portion of the discount rate premium is likely to be
related to uncertainty, although the extent to which the premium can be explained by uncertainty
and 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 these
investments may also play a role (Shrestha and Karmacharya 1998). For households and small
firms, adoption of new technologies with significant capital costs may be constrained by
inadequate access to financing. And in some countries, import barriers may inhibit the adoption
of technology embodied in foreign-produced goods (Reppelin-Hill 1999). It is impossible to
generalize, however, particularly across countries.
Finally, the presence of increasing returns in the form of learning effects, network
externalities, or other positive adoption externalities suggests that market outcomes for
technologies 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 discussions
regarding climate change and other energy-related environmental problems. At a more
aggregate level, there has been much discussion of the question of whether it is possible for
developing countries to take less environmentally-damaging paths of development than have
industrialized 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 technologies
may, in the long run, be among the most important determinants of success or failure of
environmental protection efforts (Kneese and Schultze 1975). It has long been recognized that
alternative types of environmental policy instruments can have significantly different effects on
the rate and direction of technological change (Orr 1976). Environmental policies, particularly
those with large economic impacts (for example, those intended to address global climate
change) can be designed to foster rather than inhibit technological invention, innovation, and
diffusion (Kempe and Soete 1990).
For purposes of examining the link between environmental policy instruments and
technological change, policies can be characterized as either command-and-control or market-
based approaches. Market-based instruments — such as pollution charges, subsidies, tradeable
permits, and some types of information programs — can encourage firms or individuals to
undertake pollution control efforts that are in their own interests and that collectively meet policy
goals (Stavins 2001). Command-and-control regulations tend to force firms to take on similar
10
shares of the pollution-control burden, regardless of the cost. They often do this by setting
uniform standards for firms, the most prevalent of which are performance- and technology-based
standards. 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 to
resort to unduly expensive means of controlling pollution. Because the costs of controlling
emissions may vary greatly among firms, and even among sources within the same firm, the
appropriate 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 amount
of technological change, because by their very nature they induce or require firms to do things
they would not otherwise do. Performance and technology standards can be explicitly designed
to be "technology forcing," mandating performance levels that are not currently viewed as
technologically feasible or mandating technologies that are not fully developed. One problem
with these approaches, however, is that while regulators can typically assume that some amount
of improvement over existing technology will always be feasible, it is impossible to know how
much. Standards must either be made unambitious, or else run the risk of being ultimately
unachievable, leading to political and economic disruption (Freeman and Haveman 1972).
Technology standards are particularly problematic, since they tend to freeze the
development of technologies that might otherwise result in greater levels of control. Under
regulations that are targeted at technologies, as opposed to emissions levels, no financial
incentive exists for businesses to exceed control targets, and the adoption of new technologies is
discouraged. Under a “Best Available Control Technology” (BACT) standard, a business that
adopts a new method of pollution abatement may be “rewarded” by being held to a higher
standard of performance and thereby not benefit financially from its investment, except to the
extent that its competitors have even more difficulty reaching the new standard (Hahn and
Stavins 1991). On the other hand, if third parties can invent and patent better equipment, they
can — in theory — have a ready market. Under such conditions, a BACT type of standard can
provide a positive incentive for technology innovation. Unfortunately, as we note below, there
has been very little theoretical or empirical analysis of such technology-forcing regulations.
In contrast with such command-and-control regulations, market-based instruments can
provide powerful incentives for companies to adopt cheaper and better pollution-control
technologies. This is because with market-based instruments, it pays firms to clean up a bit more
if 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 be
compared with regard to their effects on technological change. First, one can ask— both with
theoretical models and with empirical analyses — what effects alternative instruments have on
the rate and direction of relevant technological change. Second, one can ask whether
environmental 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 great
magnitude (in terms of anticipated abatement costs) and/or very long time horizon. Hence, the increased attention
being given by scholars and by policy makers to the problem of global climate change has greatly increased the
prominence of the issues that are considered in this article.
11
broadly, whether such policies result in overall economic efficiency (that is, whether the efficient
degree 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 a
demand-side function of anticipated sales (adoption), the relevant literature comparing the effects
of 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 uncertain
outcomes. Such R&D can be either inventive or innovative, but the theoretical literature in this
area typically makes no particular distinction.
The earliest work that is directly relevant was by Magat (1978), who compared effluent
taxes and CAC standards using an innovation possibilities frontier (IPF) model of induced
innovation, where research can be used to augment capital or labor in a standard production
function. Subsequently, Magat (1979) compared taxes, subsidies, permits, effluent standards,
and technology standards, and showed that all but technology standards would induce innovation
biased 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 traditional
industrial policy instruments in determining the optimal way to attain a given degree of pollution
abatement. They showed that innovation subsidies can be used to attain the same environmental
target, 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 achieve
desired incentive effects for technological change. They demonstrated that although spot
markets for permits cannot induce the socially optimal degree of innovation, futures markets can
improve the situation (Laffont and Tirole 1996).
10
Cadot and Sinclair-Desgagne (1996) posed the following question: if a regulated industry
has private information on the costs of technological advances in pollution control (frequently a
reasonable assumption), then since the industry has an incentive to claim that such technologies
are prohibitively expensive, can the government design an incentive scheme that will avoid the
problems posed by this information asymmetry? The authors developed a solution to this game-
theoretic problem. Not surprisingly, the scheme involves government issued threats of
regulation (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 late
1970’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 its
topic as the effects of alternative policy instruments on technology innovation, the focus was in fact on effects of
policy on technology diffusion. Hence, we defer consideration of those studies to the next section.
10
In a subsequent analysis, Laffont and Tirole (1996) examined the government’s ability to influence the degree of
innovative activity by setting the number of permits (and permit prices) in various ways in a dynamic setting.
12
ranking of policy instruments depended on the innovator’s ability to appropriate spillover
benefits of new technologies to other firms, the costs of innovation, environmental benefit
functions, 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 found
that 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 increasing
costs, 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 the
incentive to engage in R&D. Carraro and Soubeyran (1996) compared an emission tax and an
R&D subsidy, and found that an R&D subsidy is desirable if the output contractions induced by
the 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 simultaneous
tax on pollution emissions and subsidy to environmental R&D may be better suited to
overcoming the joint market failure (negative externality from pollution and positive externality
or 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 perfect
competition is assumed. Standards and taxes yield higher incentives for R&D when the market
is characterized by Cournot competition, but the opposite holds when the market is characterized
by Bertrand competition.
2.2. Technology Diffusion
The predominant theoretical framework for analyses of diffusion effects has been what
could be called the “discrete technology choice” model: firms contemplate the use of a certain
technology which reduces marginal costs of pollution abatement and which has a known fixed
cost 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, several
theoretical studies have found that the incentive for the adoption of new technologies is greater
under market-based instruments than under direct regulation (Zerbe 1970; Downing and White
1986; Milliman and Prince 1989; 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 the
quantity of technology adoption.
Theoretical comparisons among market-based instruments have produced only limited
agreement. In a frequently-cited article, Milliman and Prince (1989) examined firm-level
incentives for technology diffusion provided by five instruments: command-and-control;
emission taxes; abatement subsidies; freely-allocated emission permits, and auctioned emission
permits. Firm-level incentives for adoption in this representative-firm model were pictured as
the anticipated change in producer surplus. They found that auctioned permits would provide the
largest adoption incentive of any instrument, with emissions taxes and subsidies second, and
freely allocated permits and direct controls last. The Milliman and Prince (1989) study was
criticized by Marin (1991) because of its assumption of identical firms, but it was subsequently
11
See, also, Conrad (2000).
13
shown that the results remain largely unchanged with heterogeneous abatement costs (Milliman
and Prince 1992).
In 1996, Jung et al. built on Milliman and Prince's basic framework for comparing the
effects of alternative policy instruments, but rather than focusing on firm-level changes in
producer surplus, they considered heterogeneous firms, and modeled the “market-level
incentive” created by various instruments. Their rankings echoed those of Milliman and Prince
(1989): auctioned permits provided the greatest incentive, followed by taxes and subsidies, free
permits, and performance standards.
Subsequent theoretical analyses (Parry 1998; Denicolò 1999; Keohane 1999) clarified
several aspects of these rankings. First, there is the question of relative firm-level incentives to
adopt a new, cost-saving technology when the price of pollution (permit price or tax level) is
endogenous. Milliman and Prince (1989), as well as Jung et al. (1996), argued that auctioned
permits would provide greater incentives for diffusion than freely-allocated permits, because
technology diffusion lowers the equilibrium permit price, bringing greater aggregate benefits of
adoption in a regime where all sources are permit buyers. But when technology diffusion lowers
the market price for tradeable permits, all firms benefit from this lower price regardless of
whether they adopt the given technology (Keohane 1999). Thus, if firms are price takers in the
permit market, auctioned permits provide no more adoption incentive than freely-allocated
permits.
The overall result is that both auctioned and freely-allocated permits are inferior in their
diffusion incentives to emission tax systems (but superior to command-and-control instruments).
Under tradeable permits, technology diffusion lowers the equilibrium permit price, thereby
reducing the incentive for participating firms to adopt. Thus, a permit system provides a lower
adoption incentive than a tax, assuming the two instruments are equivalent before diffusion
occurs (Denicolò 1999; Keohane 1999).
More broadly, it appears that an unambiguous exhaustive ranking of instruments is not
possible on the basis of theory alone. Parry (1998) found that the welfare gain induced by an
emissions tax is significantly greater than that induced by tradable permits only in the case of
very major innovations. Similarly, Requate (1998) included an explicit model of the final output
market, and finds that whether (auctioned) permits or taxes provide stronger incentives to adopt
an improved technology depends upon empirical values of relevant parameters.
Furthermore, complete theoretical analysis of the effects of alternative policy instruments
on the rate of technological change must include modeling of the government’s response to
technological change, because the degree to which regulators respond to technologically-induced
changes in abatement costs affects the magnitude of the adoption incentive associated with
alternative policy instruments. Because technology diffusion presumably lowers the aggregate
marginal 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 and
Prince (1989) examined the incentives facing private industry, under alternative policy
instruments, to oppose such policy changes. Their conclusion was that firms would oppose
optimal agency adjustment of the policy under all instruments except taxes. Under an emissions
tax, 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
14
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 optimal
agency response only under an emissions tax regime.
In a comparison of tradeable permits and pollution taxes, Biglaiser et al. (1995) examined
these instruments’ ability to achieve the first-best outcome in a dynamic setting. They found that
effluent taxes can do so, but permits cannot, but that this result depends on an assumption of
constant marginal damages. If marginal damages are not constant, the optimal policy is
determined by the interaction of marginal damages and marginal abatement costs for both taxes
and permits. The result is analogous to Weitzman's (1974) rule: if the marginal damage curve is
relatively flat and there is uncertainty in marginal costs (from the regulator's perspective) due to
potential 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 reduces
the social cost of environmental regulation, suggesting that the optimal policy is more stringent
than 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 the
cost of regulation. If the policy intervention induces a reduction in the marginal cost of
abatement, then any given policy target (for example, a particular aggregate emission rate or a
particular ambient concentration) will be achieved at lower cost than it would without induced
innovation. On the other hand, the lower marginal abatement cost schedule arising from induced
innovation makes it socially optimal to achieve a greater level of pollution abatement. For a flat
marginal social benefit function evaluated at the social optimum, or for any emission tax, this
results in greater total expenditure on abatement even as the marginal abatement cost falls.
Another important issue is the general equilibrium effect of induced environmental
innovation on innovation elsewhere in the economy (Schmalensee 1994). If inducement
operates through increased R&D expenditure, then an issue arises as to the elasticity of supply of
R&D inputs. To the extent that this supply is inelastic, then any induced innovation must come
at the expense of other forms of innovation, creating an opportunity cost that may negate the
effects observed in the regulated portion of the economy. The general equilibrium consequences
of these effects for welfare analysis depend on the extent of R&D spillovers or other market
failures, and the magnitude of these distortions in the regulated firms or sectors relative to the
rest of the economy (Goulder and Schneider 1999).
In an application to global climate policy, Goulder and Mathai (2000) looked at optimal
carbon abatement in a dynamic setting, considering not only the optimal overall amount of
abatement but also its timing. In addition to R&D-induced innovation, they considered (in a
separate model) reductions in abatement costs that come about via learning-by-doing. Induced
innovation 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 lower
abatement costs in the future, implying that with R&D-induced innovation, optimal abatement is
lower in early years and higher in later years than it would otherwise be. In the learning-by-doing
model, there is a third effect: abatement today lowers the cost of abatement in the future. This
15
reinforces the tendency for cumulative optimal abatement to be higher in the presence of induced
innovation, but makes the effect on optimal near-term abatement ambiguous.
12
3. Empirics of the Innovation and Diffusion of Green Technology
3.1. Empirical Analysis of Innovation
There has been exceptionally little empirical analysis directly of the effects of alternative
policy instruments on technology innovation in pollution abatement, principally because of the
paucity of available data. One study by Bellas (1998) carried out a statistical analysis of the
costs of flue gas desulfurization (scrubbing) installed at coal-fired power plants in the United
States under the new-source performance standards of the 1970 and 1977 Clean Air Acts, but
failed to find any evidence of effects of scrubber vintage on cost, suggesting little technological
innovation had taken place under this regulatory regime.
Although there has been very little analysis in the context of pollution-abatement
technologies, there is a more extensive literature on the effects of alternative policy instruments
on the innovation of energy-efficiency technologies, because data have been available. The
greatest challenge in testing the induced innovation hypothesis specifically with respect to
environmental inducement is the difficulty of measuring the extent or intensity of inducement
across firms or industries (Jaffe, et al. 1995). Ideally, one would like to look at the relationship
between innovation and the shadow price of pollution or environmental inputs, but such shadow
prices are not easily observed. Instead, one must use proxies, such as expenditures on pollution
abatement, prices of polluting inputs, and characteristics of environmental regulations
13
. We
consider studies that have used each of these approaches.
Lanjouw and Mody (1996) showed a strong association between pollution abatement
expenditures and the rate of patenting in related technology fields. Jaffe and Palmer (1997)
examined the correlation between pollution expenditures by industry and indicators of
innovation more broadly. They found that there is a significant correlation within industries over
time 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 overall
patenting.
Evidence of inducement has also been sought by examining the response to changing
energy prices. Newell et al. (1999) examined the extent to which the energy efficiency of the
menu of home appliances available for sale changed in response to energy prices between 1958
and 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 and
associated economic activities, and found in that case that the impact of induced innovation was modest.
13
In the literature on the relationship between environmental regulation and productivity, discussed in section 1.3, to
measure the characteristics of environmental regulations studies have used expert judgements about relative
regulatory stringency in different states (Gray and Shadbegian 1998), number of enforcement actions (Gray and
Shadbegian 1995), attainment status with respect to environmental laws and regulations (Greenstone 1998), and
specific regulatory events (Berman and Bui 2001).
16
Newell et al. (1999) generalized Hicks’ (1932) concept of induced innovation (in terms of factor
prices) to include inducement by regulatory standards, such as labeling requirements that might
increase 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 they
can 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 in
time is identified in terms of vectors of characteristics (including cost of manufacture) that are
feasible. The process of invention makes it possible to manufacture “models” (characteristics
vectors) that were previously infeasible. Innovation means the offering for commercial sale of a
model that was not previously offered for sale. Induced innovation is then represented as
movements in the frontier of feasible models that reduce the cost of energy efficiency in terms of
other attributes.
With this product-characteristic approach, Newell, et. al (1999) assessed the effects of
changes in energy prices and in energy-efficiency standards in stimulating innovation, and found
that energy price changes induced both commercialization of new models and elimination of old
models. Regulations, however, worked largely through energy-inefficient models being
dropped, since that is the intended effect of the energy-efficiency standards (models below a
certain energy efficiency level may not be offered for sale). Through econometric estimation
and a series of dynamic simulations, Newell et al. (1999) examined the effects of energy price
changes and efficiency standards on average efficiency of the menu of products over time. They
found 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), but
significant amounts of innovation were also due to changes in energy prices and changes in
energy-efficiency standards. They found that technological change in air conditioners was
actually biased against energy efficiency in the 1960s (when real energy prices were falling), but
that this bias was reversed after the two energy shocks of the 1970s. In terms of the efficiency of
the average model offered, they found that energy efficiency in 1993 would have been about
one-quarter to one-half lower in air conditioners and gas water heaters, if energy prices had
stayed at their 1973 levels, rather than following their historical path. Most of the response to
energy price changes came within less than five years of those changes.
A closely related approach to investigating the same phenomena is that of hedonic price
functions. One hedonic study examined the effects of public policies in the context of home
appliances. Greening et al. (1997) estimated the impacts of the 1990 and 1993 national
efficiency standards on the quality-adjusted price of household refrigerator/freezer units, and
found that quality-adjusted prices fell after the implementation of the energy efficiency
standards. However, such quality-adjusted price decreases are consistent with historical trends in
refrigerator/freezer prices, and hence, one cannot rule out the possibility that the imposition of
efficiency standards slowed the rate of quality-adjusted price decline.
Given the attention paid to automobile fuel economy over the past two decades, it is not
surprising 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 to
changes 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 period
1952–1980, finding that it was positively related to oil prices. Ohta and Griliches (1986) found
17
that gasoline price changes over the period 1970–1981 could alone explain much of the observed
change in related automobile characteristics.
More recently, Pakes, et. al (1993) investigated the effects of gasoline prices on the fuel
economy of motor vehicles offered for sale, and found that the observed increase in miles per
gallon (mpg) from 1977 onward was largely due to the consequent change in the mix of vehicles
on 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 and
information on the characteristics of models that were produced at each plant to estimate a
hedonic cost function — the supply-side component of the hedonic price function — finding that
quality-adjusted costs generally increased over the period 1972–1982, thus coinciding with
rising gasoline prices and emission standards.
Goldberg (1998) combined a demand-side model of discrete vehicle choice and
utilization with a supply-side model of oligopoly and product differentiation to estimate the
effects of CAFE standards on the fuel economy of the new car fleet. She found that automobile
fuel operating costs have had a significant effect, although a gasoline tax of a magnitude that
could 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 in
response to increased energy prices, with most of the effect occurring within a few years, and
then fading over time. Popp attributed this fading to diminishing returns to R&D. In the second
paper, he attempted to decompose the overall reduction in energy use that is associated with
changing energy prices between the substitution effect—movements along a given production
frontier—and the induced innovation effect—movement of the production frontier itself induced
by 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 is
associated with induced innovation, with the remaining two-thirds associated with factor
substitution. Because energy patents are likely to measure energy innovation only with
substantial error, one might interpret this result as placing a lower bound on the fraction of the
overall 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 the
phasedown of lead in gasoline, which took place in the United States principally during the
decade of the 1980's. The phasedown was accomplished through a tradeable permit system
among refineries, whereby lead rights could be exchanged and/or banked for later use. Kerr and
Newell (2000) used a duration model to assess the effects of the phasedown program on
technology diffusion. They found that increased stringency (which raised the effective price of
lead) 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 costs
of adoption, were more likely to adopt the new technology. As theory suggests (Malueg 1989),
they also found that the tradeable permit system provided incentives for more efficient
technology adoption decisions, as evidenced by a significant divergence in the adoption behavior
of refineries with low versus high compliance costs. Namely, the positive differential in the
18
adoption propensity of expected permit sellers (i.e., low-cost refineries) relative to expected
permit buyers (i.e., high-cost refineries) was significantly greater under market-based lead
regulation compared to under individually binding performance standards.
Another prominent application of tradeable permit systems which has provided an
opportunity for empirical analysis of the effects of policy instruments on technology diffusion is
the sulfur dioxide allowance trading program, initiated under the U.S. Clean Air Act
amendments of 1990. In an econometric analysis, Keohane (2001) found evidence that the
increased flexibility of the market-based instrument provided greater incentives for technology
adoption. In particular, he found that the choice of whether or not to adopt a “scrubber” to
remove sulfur dioxide — rather than purchasing (more costly) low-sulfur coal — was more
sensitive to cost differences (between scrubbing and fuel-switching) under the tradeable permit
system than under the earlier emissions rate standard.
14
Turning from pollution abatement to energy efficiency, Jaffe and Stavins (1995) carried
out econometric analyses of the factors affecting the adoption of thermal insulation technologies
in new residential construction in the United States between 1979 and 1988. They examined the
dynamic 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 energy
efficiency to energy price changes was positive and significant, both statistically and
economically. Interestingly, they also found that equivalent percentage adoption cost changes
were about three times as effective as energy price changes in encouraging adoption, although
standard financial analysis would suggest they ought to be about equal in percentage terms. This
finding offers confirmation for the conventional wisdom that technology adoption decisions are
more sensitive to up-front cost considerations than to longer-term operating expenses.
Hassett and Metcalf (1995) found an even larger discrepancy between the effect of
changes in installation cost and changes in energy prices. There are three possible explanations
for this. One possibility is a behavioral bias that causes purchasers to focus more on up-front
cost than they do on the lifetime operating costs of an investment. An alternative view is that
purchasers focus equally on both, but uncertainty about future energy prices makes them give
less weight to energy prices than they do to capital cost, which is known. A final interpretation
might be that consumers have reasonably accurate expectations about future energy prices, and
their 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 more
effective than “equivalent” taxes in encouraging technology diffusion, it is important to
recognize some disadvantages of such subsidy approaches. First, unlike energy prices, adoption
subsidies do not provide incentives to reduce utilization. Second, technology subsidies and tax
credits can require large public expenditures per unit of effect, since consumers who would have
purchased 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 by
facilities. In earlier work, Purvis and Outlaw (1995) carried out a case study of EPA’s permitting process for
acceptable water-pollution control technologies in the U.S. livestock production sector.
19
constraints on public spending, this raises questions about the feasibility of subsidies that would
be sizable enough to have desired effects.
Rose and Joskow (1990) also found a positive effect of fuel price increases on the
adoption of a new fuel-saving technology in the U.S. electricity-generation sector; and in a tobit
analysis of steel plant adoption of different furnace technologies, Boyd and Karlson (1993) found
a significant positive effect of increases in a fuel’s price on the adoption of technology that saves
that fuel, although the magnitude of the effect was modest. For a sample of industrial plants in
four 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 adoption
of energy-saving technologies.
Greene (1990) used data on fuel prices and fuel economy of automobiles from 1978 to
1989 to test the relative effectiveness of Corporate Average Fuel Economy (CAFE) Standards
and gasoline prices in increasing fuel economy. He found that the big three U.S. firms faced a
binding CAFE constraint, and for these firms compliance with CAFE standards had roughly
twice the impact on fuel economy as did fuel prices. Japanese firms, however, did not face a
binding CAFE constraint, and fuel prices had only a small effect. Luxury European manufactures
seemed to base their fuel efficiency largely on market demand and often exceeded CAFE
requirements. 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 environmental
regulations.” In many situations where command-and-control standards have been used, the
required level of pollution abatement has been set at a far more stringent level for new sources
than for existing ones. There is empirical evidence that such differential environmental
regulations 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 encouraging
firms to keep older, dirtier plants in operation (Stewart 1981; Gollop and Roberts 1983;
McCubbins et al. 1989).
What about conventional command-and-control approaches? Jaffe and Stavins (1995)
also examined the effects of more conventional regulations on technology diffusion, in the form
of state building codes. They found no discernable effects. It is unclear to what extent this is
due to inability to measure the true variation across states in the effectiveness of codes, or to
codes 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 of
voluntary environmental programs. Howarth et al. (2000) examined two voluntary programs of
the 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 threshold
model 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 of
government subsidies on the adoption of thermal insulation by households.
20
which are intended to encourage greater private industry use of energy-saving technologies. A
natural question from economics is why would firms carry out additional technology
investments as part of a voluntary agreement? The authors respond that there are a set of agency
problems that inhibit economically wise adoption of some technologies. For example, most
energy-saving investments are small, and senior staff may rationally choose to restrict funds for
small projects that cannot be perfectly monitored. The Green Lights program may be said to
attempt to address this type of agency problem by providing information on savings
opportunities at the level of the firm where decisions are made.
Although the empirical literature on the effects of policy instruments on technology
diffusion by no means settles all of the issues that emerge from the related theoretical studies, a
consistent theme that runs through both the pollution-abatement and energy-efficiency empirical
analyses is that market-based instruments are decidedly more effective than command-and-
control instruments in encouraging the cost-effective adoption and diffusion of relevant new
technologies.
4. Conclusions
Virtually all research on the relationship between technological change and
environmental policy has been linked with one of two underlying realities: first, the
environmental impacts of social and economic activity is greatly affected by the rate and
direction of technological change; and second, environmental policy interventions themselves
create new constraints and incentives that affect the process of technological developments.
One important research need, linked with the first reality, is the frequent necessity of
determining the economic and environmental baseline against which to measure the impacts of
proposed policies. Forecasts based on historical experience depend on the relative magnitude of
the effects of price-induced technological change, learning-by-doing, public sector R&D, and
exogenous technical progress. Sorting out these influences with respect to environmentally
relevant technologies and sectors poses a major challenge.
There has also been much debate surrounding the “win-win” hypothesis. Much of this
debate has been explicitly or implicitly ideological or political. More useful would be detailed
examinations regarding the kinds of policies and the kinds of private-sector institutions that are
most likely to generate innovative, low-cost solutions to environmental problems.
More research is also needed on the second broad linkage between technology and
environment, the effect of environmental policy interventions on the process of technological
change. The empirical evidence is generally consistent with theoretical findings that market-
based instruments for environmental protection are likely to have significantly greater, positive
impacts over time than command-and-control approaches on the invention, innovation, and
diffusion of desirable, environmentally-friendly technologies. But empirical studies have also
produced some results that appear not to be consistent with theoretical expectations, such as the
finding from two independent analyses that the diffusion of energy-efficiency technologies is
more sensitive to variation in adoption-cost than to commensurate energy price changes. Further
theoretical and/or empirical work may resolve this apparent anomaly.
21
Refutable hypotheses have emerged from theoretical models of alternative policy
instruments, but most have not been tested rigorously with empirical data. Whereas the
predictions from theory regarding the ranking of alternative environmental policy instruments is
relatively consistent, most of the empirical analysis has focused on energy-efficient technologies,
rather than pollution abatement technologies per se. The increased use of market-based
instruments and performance-based standards brings with it information with which hypotheses
regarding 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 of
global climate change makes it all the more important that we improve our understanding of the
effects of environmental policy on innovation and diffusion of new technology. What is clear is
that many relevant issues cannot be resolved at a purely theoretical level or on the basis of
aggregate empirical analysis alone. Serious investigation of induced technological change and
its consequences for environmental policy requires going beyond studies that examine whether
or not such effects exist, to carry out detailed analyses in a variety of sectors in order to
understand the circumstances under which the effects are large or small. This will inevitably
require research from multiple methodological viewpoints over an extended period of time.
22
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NRM 46.2001 Mutasem EL-FADEL and E. BOU-ZEID (xlvii): Climate Change and Water Resources in the Middle East:
Vulnerability, Socio-Economic Impacts and Adaptation
NRM 47.2001 Eva IGLESIAS, Alberto GARRIDO and Almudena GOMEZ (xlvii): An Economic Drought Management Index to
Evaluate Water Institutions’ Performance Under Uncertainty and Climate Change
CLIM 48.2001 Wietze LISE and Richard S.J. TOL (xlvii): Impact of Climate on Tourist Demand
CLIM 49.2001 Francesco BOSELLO, Barbara BUCHNER, Carlo CARRARO and Davide RAGGI: Can Equity Enhance
Efficiency? Lessons from the Kyoto Protocol
SUST 50.2001 Roberto ROSON (xlviii): Carbon Leakage in a Small Open Economy with Capital Mobility
SUST 51.2001 Edwin WOERDMAN (xlviii): Developing a European Carbon Trading Market: Will Permit Allocation Distort
Competition and Lead to State Aid?
SUST 52.2001 Richard N. COOPER (xlviii): The Kyoto Protocol: A Flawed Concept
SUST 53.2001 Kari KANGAS (xlviii): Trade Liberalisation, Changing Forest Management and Roundwood Trade in Europe
SUST 54.2001 Xueqin ZHU and Ekko VAN IERLAND (xlviii): Effects of the Enlargement of EU on Trade and the Environment
SUST 55.2001 M. Ozgur KAYALICA and Sajal LAHIRI (xlviii): Strategic Environmental Policies in the Presence of Foreign
Direct Investment
SUST 56.2001 Savas ALPAY (xlviii): Can Environmental Regulations be Compatible with Higher International
Competitiveness? Some New Theoretical Insights
SUST 57.2001 Roldan MURADIAN, Martin O’CONNOR, Joan MARTINEZ-ALER (xlviii): Embodied Pollution in Trade:
Estimating the “Environmental Load Displacement” of Industrialised Countries
SUST 58.2001 Matthew R. AUER and Rafael REUVENY (xlviii): Foreign Aid and Direct Investment: Key Players in the
Environmental Restoration of Central and Eastern Europe
SUST 59.2001 Onno J. KUIK and Frans H. OOSTERHUIS (xlviii): Lessons from the Southern Enlargement of the EU for the
Environmental Dimensions of Eastern Enlargement, in particular for Poland
ETA 60.2001 Carlo CARRARO, Alessandra POME and Domenico SINISCALCO (xlix): Science vs. Profit in Research:
Lessons from the Human Genome Project
CLIM 61.2001 Efrem CASTELNUOVO, Michele MORETTO and Sergio VERGALLI: Global Warming, Uncertainty and
Endogenous Technical Change: Implications for Kyoto
PRIV 62.2001 Gian Luigi ALBANO, Fabrizio GERMANO and Stefano LOVO: On Some Collusive and Signaling Equilibria in
Ascending Auctions for Multiple Objects
CLIM 63.2001 Elbert DIJKGRAAF and Herman R.J. VOLLEBERGH: A Note on Testing for Environmental Kuznets Curves
with Panel Data
CLIM 64.2001 Paolo BUONANNO, Carlo CARRARO and Marzio GALEOTTI: Endogenous Induced Technical Change and the
Costs of Kyoto
CLIM 65.2001 Guido CAZZAVILLAN and Ignazio MUSU (l): Transitional Dynamics and Uniqueness of the Balanced-Growth
Path in a Simple Model of Endogenous Growth with an Environmental Asset
CLIM 66.2001 Giovanni BAIOCCHI and Salvatore DI FALCO (l): Investigating the Shape of the EKC: A Nonparametric
Approach
CLIM 67.2001 Marzio GALEOTTI, Alessandro LANZA and Francesco PAULI (l): Desperately Seeking (Environmental)
Kuznets: A New Look at the Evidence
CLIM 68.2001 Alexey VIKHLYAEV (xlviii): The Use of Trade Measures for Environmental Purposes – Globally and in the EU
Context
NRM 69.2001 Gary D. LIBECAP and Zeynep K. HANSEN (li): U.S. Land Policy, Property Rights, and the Dust Bowl of the
1930s
NRM 70.2001 Lee J. ALSTON, Gary D. LIBECAP and Bernardo MUELLER (li): Land Reform Policies, The Sources of
Violent Conflict and Implications for Deforestation in the Brazilian Amazon
CLIM 71.2001 Claudia KEMFERT: Economy-Energy-Climate Interaction – The Model WIAGEM -
SUST 72.2001 Paulo A.L.D. NUNES and Yohanes E. RIYANTO: Policy Instruments for Creating Markets for Bodiversity:
Certification and Ecolabeling
SUST 73.2001 Paulo A.L.D. NUNES and Erik SCHOKKAERT (lii): Warm Glow and Embedding in Contingent Valuation
SUST 74.2001 Paulo A.L.D. NUNES, Jeroen C.J.M. van den BERGH and Peter NIJKAMP (lii): Ecological-Economic Analysis
and Valuation of Biodiversity
VOL 75.2001 Johan EYCKMANS and Henry TULKENS (li): Simulating Coalitionally Stable Burden Sharing Agreements for
the Climate Change Problem
PRIV 76.2001 Axel GAUTIER and Florian HEIDER: What Do Internal Capital Markets Do? Redistribution vs. Incentives
PRIV 77.2001 Bernardo BORTOLOTTI, Marcella FANTINI and Domenico SINISCALCO: Privatisation around the World:
New Evidence from Panel Data
ETA 78.2001 Toke S. AIDT and Jayasri DUTTA (li): Transitional Politics. Emerging Incentive-
b
ased Instruments in
Environmental Regulation
ETA 79.2001 Alberto PETRUCCI: Consumption Taxation and Endogenous Growth in a Model with New Generations
ETA 80.2001 Pierre LASSERRE and Antoine SOUBEYRAN (li): A Ricardian Model of the Tragedy of the Commons
ETA 81.2001 Pierre COURTOIS, Jean Christophe PÉREAU and Tarik TAZDAÏT: An Evolutionary Approach to the Climate
Change Negotiation Game
NRM 82.2001 Christophe BONTEMPS, Stéphane COUTURE and Pascal FAVARD: Is the Irrigation Water Demand Really
Convex?
NRM 83.2001 Unai PASCUAL and Edward BARBIER: A Model of Optimal Labour and Soil Use with Shifting Cultivation
CLIM 84.2001 Jesper JENSEN and Martin Hvidt THELLE: What are the Gains from a Multi-Gas Strategy?
CLIM 85.2001 Maurizio MICHELINI (liii): IPCC “Summary for Policymakers” in TAR. Do its results give a scientific support
always adequate to the urgencies of Kyoto negotiations?
CLIM 86.2001 Claudia KEMFERT (liii): Economic Impact Assessment of Alternative Climate Policy Strategies
CLIM 87.2001 Cesare DOSI and Michele MORETTO: Global Warming and Financial Umbrellas
ETA 88.2001 Elena BONTEMPI, Alessandra DEL BOCA, Alessandra FRANZOSI, Marzio GAL
E
OTTI and Paola ROTA:
Capital Heterogeneity: Does it Matter? Fundamental Q and Investment on a Panel of Italian Firms
ETA 89.2001 Efrem CASTELNUOVO and Paolo SURICO: Model Uncertainty, Optimal Monetary Policy and the Preferences
of the Fed
CLIM 90.2001 Umberto CIORBA, Alessandro LANZA and Francesco PAULI: Kyoto Protocol and Emission Trading: Does the
US Make a Difference?
CLIM 91.2001 ZhongXiang ZHANG and Lucas ASSUNCAO: Domestic Climate Policies and the WTO
SUST 92.2001 Anna ALBERINI, Alan KRUPNICK, Maureen CROPPER, Nathalie SIMON and Joseph COOK (lii): The
Willingness to Pay for Mortality Risk Reductions: A Comparison of the United States and Canada
SUST 93.2001 Riccardo SCARPA, Guy D. GARROD and Kenneth G. WILLIS (lii): Valuing Local Public Goods with Advanced
Stated Preference Models: Traffic Calming Schemes in Northern England
CLIM 94.2001 Ming CHEN and Larry KARP: Environmental Indices for the Chinese Grain Sector
CLIM 95.2001 Larry KARP and Jiangfeng ZHANG: Controlling a Stock Pollutant with Endogenous Investment and
Asymmetric Information
ETA 96.2001 Michele MORETTO and Gianpaolo ROSSINI: On the Opportunity Cost of Nontradable Stock Options
SUST 97.2001 Elisabetta STRAZZERA, Margarita GENIUS, Riccardo SCARPA and George HUTCHINSON: The Effect of
Protest Votes on the Estimates of Willingness to Pay for Use Values of Recreational Sites
NRM 98.2001 Frédéric BROCHIER, Carlo GIUPPONI and Alberto LONGO: Integrated Coastal Zone Management in the
Venice Area – Perspectives of Development for the Rural Island of Sant’Erasmo
NRM 99.2001 Frédéric BROCHIER, Carlo GIUPPONI and Julie SORS: Integrated Coastal Management in the Venice Area –
Potentials of the Integrated Participatory Management Approach
NRM 100.2001 Frédéric BROCHIER and Carlo GIUPPONI: Integrated Coastal Zone Management in the Venice Area – A
Methodological Framework
PRIV 101.2001 Enrico C. PEROTTI and Luc LAEVEN: Confidence Building in Emerging Stock Markets
CLIM 102.2001 Barbara BUCHNER, Carlo CARRARO and Igor CERSOSIMO: On the Consequences of the U.S. Withdrawal
from the Kyoto/Bonn Protocol
SUST 103.2001 Riccardo SCARPA, Adam DRUCKER, Simon ANDERSON, Nancy FERRAES-
E
HUAN, 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
SUST 104.2001 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
SUST 105.2001 Clemens B.A. WOLLNY: The Need to Conserve Farm Animal Genetic Resources Through Community-Based
Management in Africa: Should Policy Makers be Concerned?
SUST 106.2001 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
SUST 107.2001 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 L
öSCHEL: Technological Change in Economic Models of Environmental Policy: A Survey
VOL 5.2002 Carlo CARRARO and Carmen MARCHIORI: Stable Coalitions
CLIM 6.2002 Marzio GALEOTTI, Alessandro LANZA and Matteo MANERA: Rockets and Feathers Revisited: An International
Comparison on European Gasoline Markets
ETA 7.2002 Effrosyni DIAMANTOUDI and Eftichios S. SARTZETAKIS: Stable International Environmental Agreements: An
Analytical Approach
KNOW 8.2002 Alain DESDOIGTS: Neoclassical Convergence Versus Technological Catch-up: A Contribution for Reaching a
Consensus
NRM 9.2002 Giuseppe DI VITA: Renewable Resources and Waste Recycling
KNOW 10.2002 Giorgio BRUNELLO: Is Training More Frequent when Wage Compression is Higher? Evidence from 11
European Countries
ETA 11.2002 Mordecai KURZ, Hehui JIN and Maurizio MOTOLESE: Endogenous Fluctuations and the Role of Monetary
Policy
KNOW 12.2002 Reyer GERLAGH and Marjan W. HOFKES: Escaping Lock-in: The Scope for a Transition towards Sustainable
Growth?
NRM 13.2002 Michele MORETTO and Paolo ROSATO: The Use of Common Property Resources: A Dynamic Model
CLIM 14.2002 Philippe QUIRION: Macroeconomic Effects of an Energy Saving Policy in the Public Sector
CLIM 15.2002 Roberto ROSON: Dynamic and Distributional Effects of Environmental Revenue Recycling Schemes:
Simulations with a General Equilibrium Model of the Italian Economy
CLIM 16.2002 Francesco RICCI (l): Environmental Policy Growth when Inputs are Differentiated in Pollution Intensity
ETA 17.2002 Alberto PETRUCCI: Devaluation (Levels versus Rates) and Balance of Payments in a Cash-in-Advance
Economy
Coalition
Theory
Network
18.2002
László Á. KÓCZY (liv): The Core in the Presence of Externalities
Coalition
Theory
Network
19.2002
Steven J. BRAMS, Michael A. JONES and D. Marc KILGOUR (liv): Single-Peakedness and Disconnected
Coalitions
Coalition
Theory
Network
20.2002
Guillaume HAERINGER (liv): On the Stability of Cooperation Structures
NRM 21.2002 Fausto CAVALLARO and Luigi CIRAOLO: Economic and Environmental Sustainability: A Dynamic Approach
in Insular Systems
CLIM 22.2002 Barbara BUCHNER, Carlo CARRARO, Igor CERSOSIMO and Carmen MARCHIORI: Back to Kyoto? US
Participation and the Linkage between R&D and Climate Cooperation
CLIM 23.2002 Andreas LÖSCHEL and ZhongXIANG ZHANG: The Economic and Environmental Implications of the US
Repudiation of the Kyoto Protocol and the Subsequent Deals in Bonn and Marrakech
ETA 24.2002 Marzio GALEOTTI, Louis J. MACCINI and Fabio SCHIANTARELLI: Inventories, Employment and Hours
CLIM 25.2002 Hannes EGLI: Are Cross-Country Studies of the Environmental Kuznets Curve Misleading? New Evidence from
Time Series Data for Germany
ETA 26.2002 Adam B. JAFFE, Richard G. NEWELL and Robert N. STAVINS: Environmental Policy and Technological
Change
(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
Corporate Sustainable Management (Editor: Andrea Marsanich)
CLIM
Climate Change Modelling and Policy (Editor: Marzio Galeotti )
PRIV
Privatisation, Antitrust, Regulation (Editor: Bernardo Bortolotti)
KNOW
Knowledge, Technology, Human Capital (Editor: Dino Pinelli)
NRM
Natural Resources Management (Editor: Carlo Giupponi)
SUST
Sustainability Indicators and Environmental Evaluation
(Editor: Carlo Carraro)
VOL
Voluntary and International Agreements (Editor: Carlo Carraro)
ETA
Economic Theory and Applications (Editor: Carlo Carraro)
