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Page 1: Which policy instruments to induce clean innovating?

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Research Policy 41 (2012) 1770– 1778

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Research Policy

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hich policy instruments to induce clean innovating?

einhilde VeugelersULeuven, MSI, Naamsestraat 69, B-3000 Leuven, Belgium

r t i c l e i n f o

rticle history:eceived 16 May 2011eceived in revised form 3 June 2012ccepted 11 June 2012vailable online 22 October 2012

eywords:lean innovations

a b s t r a c t

In view of the sizeable climate change challenge, we need a clean innovation machine operating atfull speed. Beyond the supply of public clean R&D infrastructure and clean public procurement, thedevelopment and adoption of new clean technologies by the private sector needs to be assured to reduceGreen House Gas (GHG) emissions. The private clean innovation machine, left on its own, is not up to thischallenge. It needs government intervention to address the combination of environmental and knowledgeexternalities and overcome path dependencies. The firm level evidence presented in this contributionon the motives of private sector firms for introducing clean innovations from the latest Flemish CIS eco-

rivateevelopmentiffusionolicy mixemand-inducing instruments

innovation survey confirms that firms are responsive to eco-policy demand interventions. At the sametime, the high importance of demand pull from customers and voluntary codes of conduct or voluntarysector agreements as drivers for introducing clean innovations, is a reminder of the internal strength ofthe private innovation machine, which governments need to leverage. Policy interventions are shown tobe more powerful to induce the adoption and development of new clean technologies when designed inpolicy mix and time consistently, affecting future expectations.

© 2012 Elsevier B.V. All rights reserved.

. Why we need the private innovation machine for climatehange and how to turn it on

How to limit climate change is one of the grand policy chal-enges facing the world today (Stern, 2007). Simulation exercisese.g. Bosetti et al., 2009) confirm that to keep the costs of mitiga-ion and adaptation “manageable”, a wide portfolio of technologieseeds to be available and used by polluters soon. Radically newlean technologies which are not yet available or still far from com-ercialization will be needed to tackle climate change, but these

equire a longer-term perspective. In the shorter term, we also needhe deployment of already available cleaner technologies and theevelopment of near to market cleaner technologies.

For clean technologies to be developed and diffused suffi-iently fast and at the appropriate scale, policy intervention wille needed. In view of the pervasive combination of environmentalnd knowledge externalities characterizing clean innovations, therivate clean innovation machine cannot be expected to be sociallyffective on its own. In addition, new clean technologies face com-etition from the existing more dirty technologies, who enjoy an

nitial installed base advantage. Private actors need to be providedncentives to switch from existing dirty technologies to new cleanechnologies (Acemoglu et al., 2009).

E-mail address: [email protected]

048-7333/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.respol.2012.06.012

The issue is not just whether we need government interven-tion, but also how this government intervention should be designedto lower emissions at the lowest possible cost for economicgrowth.

Recently developed economic models of directed technologicalchange (e.g. Acemoglu et al., 2009; Bosetti et al., 2009) stronglysupport the case for a portfolio of instruments including carbonprices, R&D subsidies and regulation (see Aghion et al., 2009a).Carbon prices, obtained through a carbon tax or a cap-and-tradesystem, will not only reduce the production/consumption of dirtytechnologies, they will also be important as incentive for the pri-vate sector to develop new clean technologies and accelerate theadoption of existing cleaner technologies. Expectations of futurecarbon prices and regulations are an especially important lever forprivate sector research, development and adoption of clean tech-nologies. In tandem with a sufficiently high and long-term, timeconsistent carbon price as well as performance based regulation,public support for the development and adoption by the privatesector of clean technologies is needed. Public R&D support is espe-cially crucial for clean technologies which are still in the early stagesof development, neutralizing the installed base advantage of theolder, dirtier technologies. It is important that policy instrumentsare deployed simultaneously, as there are important complemen-

tarities to exploit. Acemoglu et al. (2009) show that, while a carbonprice alone could deal with both the environmental and the knowl-edge externalities at the same time, using the carbon price alonewould be a more “costly” policy scenario, in terms of resulting in
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Belgium, one of the countries that included the “environmentalmodule”.

Information on patent applications can be used to measureinventive activities related to environmental protection.5 To be

2 There is only information available by the economic sector in which the R&Dexpending firms are active. In most sectors important for greenhouse gas emissions(like cars, chemicals, petroleum), overall innovative activities cannot be used as areliable measure for clean innovations, as the innovations in these sectors are also(and even mostly) related to other motives.

3 In 2009, among the 1000 largest R&D spenders in the EU, 12 dedicated compa-nies from the alternative energy sector managed to get into the list, another 2 in the1000 non-EU spenders. They hold an R&D intensity of 3.5% (EC-JRC-IPTS Scoreboard,

R. Veugelers / Research

ower economic growth. Similarly, when using only the subsidynstrument, keeping the carbon price instrument inactive, wouldmply excessively high levels of subsidies compared to their level

hen used in combination.1

Are governments deploying the right effective policies for stim-lating clean innovations? Aghion et al. (2009b) examined theecord of government policies for clean innovation. With low,olatile and fragmented levels of carbon pricing and subsidies, theirverall conclusion is that we are still far off from an effective policyramework capable of leveraging the power of the private sector toesearch, develop and deploy cleaner technologies.

In this contribution we look at private clean innovation in moreetail and how this process can be stimulated by a green missionriented government policy. Climate change has unique featureshat differentiate this challenge from other mission oriented gov-rnment policies (Jaffe, 2011). Unlike in the Manhattan or Apollorojects, the mission is not focused on a specific isolated technolog-

cal objective. What is needed is a pervasive transformation of thehole energy-economic system, mobilizing polluters to switch toew low-emission systems. It shares with health related missionshe need to include support for development of new technologiess well as policies on the demand side to accelerate the adoptionf new technologies. The ICT area lacks the negative externali-ies associated with pollution, but it provides perhaps the nearestxample of a similarly scaled transformation of the socio-economicystem that is needed to fully leverage the power of new technolo-ies. In ICT, many OECD governments played a major role beyondupporting (mission-oriented) research through purchases of com-onents and systems, particularly in the early phases of technologyevelopment. In energy, one could similarly envisage public pur-hases of clean solutions in areas where the government has atrong procurement interest (e.g. electric military vehicles). Thatnergy is a highly specific and challenging case for designing a mis-ion oriented technology policy is demonstrated by its history. Jaffe2011) reports on the 1970s large scale policy initiatives in the US toemonstrate the commercial feasibility of technologies to replaceetroleum. The large scale synfuels and related projects failed toesult in significant commercial outcomes and because of theiroor design and implementation crowded out rather than com-lemented private investments (see also Yang and Oppenheimer,007).

This contribution will not discuss the public research infrastruc-ure, large scale public projects or public procurement for cleannnovations (several of these topics are covered in the paper inhis special issue by Davis). Our focus is on the part of a climatehange technology policy that to leverage the private sector intoeveloping and adopting clean technologies, to induce the neededransformation of the energy-economic system. We look both athe development and the deployment of new clean technologiesy the private sector. To this end, we first provide a quick lookt available data on clean innovations (Section 2). The major partf this contribution is dedicated to a micro-economic analysis ofhich firms have been creating and/or adopting clean innova-

ions, and how strong which types of government interventions

ave been in affecting these decisions (Section 3). To this end weresent new evidence from the Flemish CIS eco-innovation mod-le (Section 4). We close with some suggestions for a mission

1 A way of showing the higher costs when using only 1 instrument (i.e. the carbonrice or R&D subsidies), rather than a combination of carbon pricing and subsidies,

s to express how high the optimal carbon price or subsidies would have to be whensed as a singleton instrument relative to its optimal level when used in combi-ation. Calibrating this scenario in the Acemoglu et al. (2009) model, Aghion et al.2009a) show that the carbon price would have to be about 15 times bigger duringhe first 5 years, while subsidies would have to be on average 115% higher in therst 10 years.

41 (2012) 1770– 1778 1771

oriented green innovation policy on how to leverage the privatesector.

2. Data on clean innovations

In this section, we take a look at data on clean innova-tions. A first important observation to note is the poor qualityof standard data on clean innovations. No common definitionsare used, with terminology ranging from clean, sustainable pro-duction to eco-technologies, antipollution technologies and theirinnovations. While each source uses its own definitions, a com-mon denominator of clean or eco-innovations is that they areaimed at generating substantial improvements for the environ-ment. These environmental effects include CO2 emission reductionfor climate change, but also involve other types of pollution reduc-tion, waste treatment as well as the environmental gains from abetter use of resources. Clean energy and energy efficiency area major subsector of eco-innovation. In reporting data, our focusis on private firms’ activities in innovations for climate change,but depending on data availability, we also report broader cate-gories.

Data on private R&D expenditures are not reported bytechnology.2 There is only information available by the economicsector in which the R&D expending firms are active (NACE clas-sification). The EC-JRC-IPTS Scoreboard on large R&D spendersclassifies firms and their R&D expenditures on the basis of theirmajor sector of activity. Although in this classification, alternativeenergy is one of the sectors considered, it fails to capture the cleanR&D investments of firms whose major sector of activity is in othersectors (like GE or Siemens).3

A commonly used data source for measuring private sector inno-vations is the Community Innovation Survey, organized bi-annuallyby EUROSTAT/OECD. The survey collects evidence on the innovativebehavior of companies, not only innovations developed in-house orin collaboration with others, but also innovations developed else-where that are adopted by firms. Unfortunately, the CommunityInnovation Survey offers few insights into eco-innovation.4 Onlythe last wave, CIS-VI (2006–2008), partly overcomes this problem,as it includes an optional one-page set of questions on environ-mental innovation. In Section 4 we will use these results from

2010). An assessment of R&D for low-carbon energy innovations, using websites andfield survey interviews, was performed for an EU funded project (SRS (2008)) andconfirmed that innovation in the energy sector may not predominantly be carriedout by energy companies. Industries with elevated research activities in low-carbonenergy technologies include companies active in industrial machinery, chemicals,and energy.

4 The previous CIS waves include evidence on motives for innovation, whichprovides some links to environmental innovations, although very imperfect. Thequestion on “improving energy efficiency” as an innovation motive relates to envi-ronmental benefits, but does not necessarily reflect an explicit green motive. Themore direct question on “reducing environmental impact” as an innovation motive,unfortunately is merged with health and safety motives.

5 There are a number of limitations in using patents to measure eco-innovations.First, patents measure inventive activity, not innovations. Second, eco-patentsmainly measure identifiable inventions that underlie clean product innovations and

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1772 R. Veugelers / Research Policy 41 (2012) 1770– 1778

Graph 1. Growth rates of patents (applications) for selected CET. Notes: Patents arecounted on the basis of claimed priorities (patent applications filed in other countriesbased on the first filed patent for a particular invention).

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Table 1Who’s who in CET patenting?

Size Specialization ConcentrationShare of country inworld CET patents

RTA in CET patents Herfindahl acrossCET technologies

TOP 6Japan 29.7% 0.99 0.72US 15.9% 0.87 0.33Germany 15.2% 1.05 0.28Korea 5.6% 1.21 0.82France 3.9% 0.70 0.26UK 3.6% 0.98 0.28EU 32.0% 1.01 0.25

Source: Own calculations on the basis of UNEP/EPO/ICTSD (2010). Patents and cleanenergy: bridging the gap between evidence and policy; a Top 6 country has at least2% of world CET; together the Top 6 represent 74% of world CET patents; RTA = shareof the country in world CET patents relative to the share of the country in total worldpatents; RTA > 1 measures specialization in CET patents; Herfindahl is the weightedsum of the share of each CET technology in total country’s CET patents, with the

ource: On the basis of UNEP/EPO/ICTSD (2010). Patents and clean energy: bridginghe gap between evidence and policy.

icked up as an eco-patent, the environmental effects should bessociated with a patent class linked to clean technologies or envi-onmental effects should be described in the patent application.

hile the OECD and WIPO use the first approach, EPO also useshe second approach. Applying this approach to clean energy, EPO’slassification of Clean Energy Technologies (CET patents) includes 6ain categories: solar (both thermal and PV), wind, carbon capture

nd storage, hydro, geothermal, biofuels and integrated gasificationombined cycle (IGCC) (UNEP/EPO/ICTSD, 2010) (Graph 1).

Until the mid-1990s, CET patents stagnated and even declined,ertainly in relative terms as overall patenting activities contin-ed to grow. But since the late nineties, CET patents have trendedpwards. One cannot ignore the correlation between political deci-ions and the take-off of CET technologies, as the upward trendtarted around 1997, when the Kyoto protocol was signed. Thispward trend holds particularly when compared to the traditionalnergy fields (fossil fuels and nuclear) which have trended downince 2000. When looking at individual CET technologies, patent-ng rates in solar PV, wind and carbon capture have shown the mostctivity. Biofuels is a more recent growth story. Solar- and geother-al are growing more slowly if at all, reflecting their still premature

tage of development.If we look at which countries are active in clean energy

atenting,6 Japan is the clearest positive outlier (Table 1). Japanolds about 30% of all CET patents, but it is not particularly special-

zed in Clean Energy Technologies, and it is heavily concentratedn a particular CET technology, namely solar PV. Korea is anothermportant player in CET patenting, specialized in solar PV. The US,espite its 16% share of world “clean patents”, is not specialized in

ny single class of Clean Energy Technologies. It is more dispersedcross various CET technologies. If the EU were to be counted as aomogeneous block, it would be the block with the largest share

nd of pipe technologies, whose environmental impacts are specific aims and moti-ations of the inventions. For other types of innovation, such as process changes,atent analysis is less useful because many of these innovations are not patented.6 Patents are assigned to countries on the basis of the location of the assignee,hich in most cases is a corporation.

weights being the share. The Herfindahl ratio varies between 0 (maximal dispersion)and 1 (perfect concentration).

of CET patents. In Europe, Germany is by far the largest country forCET patents.7

This quick glimpse at the data is suggestive of a positive asso-ciation with active government policy, particularly when lookingat when clean energy patents have taken off, i.e. post-Kyoto, andwhich countries are most active, i.e. with the relatively weak posi-tion of the US and the relatively strong position of the EU andAsia in individual classes of CET technologies. But how impor-tant has government policy been in explaining patterns in CETpatenting? In the next sections we examine in more detail, usingmicro-economic evidence, the effectiveness of government poli-cies in motivating private clean innovations. Before we presentnew analysis using the Flemish eco-innovation module results fromthe latest CIS-VI survey Section 3 presents a review of existinganalyses.

3. Evidence on the impact of government policies to induceprivate clean innovations

As this paper focuses on the role of climate change technologypolicy in inducing the private sector into developing and adoptingclean innovations, this section accordingly does not look at mis-sion oriented government policies to supply clean technologies.The evidence on the impact of public research infrastructure, largescale public projects or public procurement for clean innovations isanyway scant and not very encouraging (e.g. Jaffe, 2011; Yang andOppenheimer, 2007).

The results of an extensive empirical literature support the con-tention that environmental policies do succeed in incentivizingthe private sector to develop and adopt new clean technolo-gies. Jaffe et al. (2002) and Johnstone (2008) provide reviews ofthe empirical literature, on which the following paragraphs drawheavily.

Most of the existing empirical studies concentrate on the impactof environmental policies on the creation of new clean tech-nologies, using patents as an empirical proxy. An early paper

is Lanjouw and Mody (1996), who examined the relationshipbetween the number of environmental patents granted and envi-ronmental policy for a series of countries for the period 1971–1988.

7 Also some other EU countries are specialized in environmental technologies(RTA > 1), but are nevertheless small players (<2% of CET patent share) (in orderof size): Netherlands 1.19; Denmark 13.46; Spain 1.14; Austria 1.05, Portugal 4.93,Hungary 1.11.

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hey use expenditures for pollution abatement as a very indi-ect and imperfect measure of environmental policy stringency,nd find that pollution abatement costs affect the number ofatents successfully granted. Unfortunately, their study failed toontrol for other determining factors. Using US industry-level data,affe and Palmer (1997) extended Lanjouwand Mody’s study, byncorporating various factors that potentially affect environmen-al innovation. Focusing on US manufacturing industries during977–1989, Jaffe and Palmer examined the relationship betweennvironmental stringency(as measured by higher level of Pollu-ion Abatement Costs and Expenditures (PACE)) and innovation.hey measured innovations in terms of both R&D expenditures andatents, without restricting the investment or patent data to covernly clean innovations. They found that increased environmentaltringency does increase R&D expenditures in US manufacturingectors, but not the number of patents.

Brunnermeier and Cohen (2003) built on Jaffe and Palmer’sork, narrowing innovation to purely “environmental” patents.s policy indicators, they used pollution abatement costs (PACE)nd the number of inspections undertaken by regulatory insti-utions. Contrary to Jaffe and Palmer, they found that the PACEariable has a statistically significant (and positive) effect on envi-onmental patents, where as subsequent monitoring does not.aylor et al. (2003) studied the time path of patents in sul-ur dioxide (SO2) control, especially activities related to fluidas desulfurization. Analyzing a100 year time span (1887–1995),hey found that more patent applications were filed after SO2egulation was introduced in the 1970s. In addition to SO2 regu-ation, Popp (2006) also examined NOX regulation in the US, as

ell as in the German and Japanese electricity sectors, exploringhether these regulations affected (inter) national innovation andiffusion.

More recent work by Aghion et al. (2010) studied clean versusirty innovations in the auto industry, using (taxes on) fuel pricess instrument for government policies, patents for electric vehi-les to measure clean innovations and patents for combustionechnologies to measure dirty innovations. Their results confirmhat fuel prices drive clean patents more than dirty patents, butave a greater effect on firms with a larger stock of dirty patents.his is consistent with the need for government interventiono break firm-level path dependency in the old dirty technolo-ies.

Very few studies have compared the effects on firms’ innova-ive behavior of different policy instruments. Using patent data,opp (2003) examined the effects of the introduction of the trad-ble permit system for SO2 emissions as part of US Clean Air Actmendments on the technological efficiency of fluid–gas desul-

urization. Comparing patent applications after the introductionf the tradable permit scheme with those submitted under therevious technology-based regulatory system, Popp (2003) foundvidence the while early regulations did little to improve the envi-onmental effectiveness of the technologies developed, innovationsntroduced after the introduction of the tradable permit scheme didead to increased environmental effectiveness.

The empirical evidence with respect to the use of other policyeasures, particularly subsidies for environmental R&D, solely

nd preferably in combination with market based instruments andegulation, is even more limited. Johnstone (2008) reports on threeifferent case studies: abatement technologies for wastewaterffluent from pulp production; abatement of motor vehicle emis-ions; and development of renewable energy technologies. Overall,he case study evidence is supportive of the argument that envi-

onmental policy affects technological innovation, but whether aolicy instrument is effective or not varies across the clean tech-ologies considered and at which phase of the technology life cyclehe instruments was used. For instance, in the study on renewable

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energy, the implementation of different policy measures had ameasurable impact on innovation, with tax measures and quotaobligations being statistically significant determinants of patentactivity. However, the effect of the different policies varied bythe type of renewable energy involved. Public expenditures ontargeted R&D were statistically significant for every type of renew-able energy. Relative prices are found to induce particular kindsof innovation. In the case of motor vehicle emissions abatement,fuel prices encouraged investment in “integrated” innovation, butnot in “post-combustion” technologies. In the case of renewableenergy, the role of electricity prices was rarely significant, exceptfor solar energy. Other market factors can also be important spursto innovation. In the case of bleaching technologies in the pulpingprocess, public concerns about the environment appeared to stim-ulate the development of new cleaner technologies, pre-dating theintroduction of regulatory standards. Eco-labeling did not appearto have an influence on innovation in this case. The case studiesdid not report on any possible complementarity between R&Dgrants and demand-side policies such as carbon pricing and/orregulation.

In a follow-up econometric analysis, Johnstone et al. (2010) con-firm that overall environmental policies significantly affect privateinnovators, although the strength of the effects varies over tech-nologies. Quantity-based policy instruments such as obligationsand tradable certificates were found to be most effective in inducinginnovations in wind power technology. Price-based instrumentssuch as investment incentives, tax measures and tariffs provedmost effective in encouraging innovation in solar. Unfortunately,due to too high correlation with the intercept, the effectiveness ofR&D support programs could not be isolated.

Government policy is not only important to induce the creationof new cleaner technologies, as measured by patents. It is alsoimportant to drive the adoption of already developed clean tech-nologies by firms. Far fewer studies exist on the adoption of cleantechnologies, although it is an important part of the transformationof the energy-economic system that is needed to tackle climatechange. A survey of firms in eight sectors in five European countrieson motives for adopting eco-innovations confirms the importanceof complying with regulations (Arundel et al., 2009). But the sur-vey found that there are many more important reasons – besidescomplying with regulations – for introducing an eco-innovation.These are: improving the firm’s image, reducing costs, and, forproduct and service innovations, demand pressure (measured bysecuring existing markets and increasing market share as motivefor adopting clean technologies). Compliance with environmentalregulations was more important for adoption of pollution controlinnovations than for the other types of eco-innovation. Processinnovations and recycling were often introduced in responseto the need to comply with regulations, but many of themwere also introduced to obtain cost savings (not environment-related) or to improve the environmental image of thefirm.

Overall, the econometric evidence from the economics litera-ture is not unfavorable for the impact of clean policies on cleaninnovations. But it also highlights that policies are no panacea forstimulating clean innovations. Although the evidence suggest thatthe type of policy instrument (e.g. tariffs versus grants), the typeof clean technology targeted (e.g. motor vehicle emissions versussolar) and the nature of the environmental effect (e.g. reducing CO2emissions versus enhancing energy efficiency) all seem to matterfor effectiveness, we still have a very incomplete view on whichcombination of policy instruments is most effective in stimulating

clean innovation creation and diffusion. This comes on top of a lackof evidence on the effectiveness of public R&D infrastructure build-ing and public procurement as parts of the technology policy mixfor climate change.
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. Micro evidence from Belgian firm level CIS-VI data on thempact of government policies to induce clean innovations

This section discusses new evidence, drawn from the FlemishIS eco-innovation module, on which firms have been developingnd adopting clean innovations, and which types of governmentnterventions have been most effective in influencing these pri-ate decisions. EUROSTAT/OECD introduced in the 6the Communitynnovation Survey (CIS), covering the period 2006–2008, an eco-nnovation module. This module surveyed firms on what factors,ncluding government policy, affected the introduction of eco-nnovations. These data thus allow us to examine the impact oflean government policy on the likelihood of firms introducinglean innovations. As the module is part of the larger CIS-survey, itlso allows us to control for other innovation related characteristicsf the firms surveyed. The disadvantage of the CIS data is that it is

cross section for a single period of time rather than a time seriesr panel.

Belgium was one of the countries that included the eco-nnovation module in its CIS, and we use the Flemish data tonvestigate the impact of government policies on firms’ clean inno-ation behavior. Flanders is one of the three Belgian regions. Ints Innovation Communication for 2009–2013 Flanders expressedts ambition to be part of the top 5 regions in European innova-ion. To this end it has developed a “New Industrial Policy” typef strategic framework (“Flanders in Action”) coordinating existingovernment budgets, instruments and stakeholders around “spear-eads”/clusters and large projects. One of these “spearheads” isnergy and Environment, with large scale innovation projects inmart grids and electric vehicles. In addition, Flanders providesncentives for adoption of clean technologies as well as subsi-ies for R&D generally. Environmental regulations have also been

ntroduced, many of which originate at EU level. In addition tohe policy instruments at the Flanders regional level, there are taxncentives and financial support for the adoption of clean or energyfficient solutions at the national (Belgian) level. The EU also has alean technology policy that includes a mixture of support for coop-rative R&D, energy efficiency regulations, carbon pricing (ETS) andenewables targets (see Veugelers, 2011).

We will not be evaluating the individual instruments of the cleannergy technology policy framework of these three levels of gov-rnment. We instead look only at the extent to which the packagef instruments has affected the private sector’s incentives to creatend adopt clean innovations. The data only allow us to separate theffects on firms’ behavior of subsidies, regulations and taxes, andoluntary sectoral agreements, many of which are government-nduced.8 We first briefly discuss the data (Section 4.1), the researchypotheses (Section 4.2), descriptive statistics on who introducesco-innovations (Section 4.3) and why (Section 4.4), before pre-enting the econometric results on the importance of governmentntervention for clean innovation (Section 4.5).

.1. The CIS-VI eco-innovation data

In the CIS-VI eco-innovation module, a first set of questions asksespondents if they have introduced an innovation with one orore environmental benefits (ECO). Nine types of environmental

enefits are being distinguished:

Six types of environmental benefits that can occur during the useof the innovation by the enterprise (ECOOWN):

8 Croci (2005) present an analysis of examples of environmental voluntary agree-ents (such as benchmarking convenants on energy efficiency) through a series of

ase studies. Benchmarking convenants are commonly used in Flanders.

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◦ lower use of materials (ECOMAT), lower energy use (ECOEN),lower CO2 emissions (ECOCO), less use of pollutants (ECOPOL),less pollution of soil, water, air or noise control (ECOSUB),recycling (ECOREC).

• Three types of environmental benefits that can occur during theuse of the innovation by the end user (ECOUSER):

◦ lower energy use (ECOENU), less pollution (ECOPOLU), recycling(ECORECU).

While the first six types of benefits flow from the adoptionof eco-innovations (ECOOWN), the other three types of benefitare associated with the firm’s development of eco-innovations(ECOUSER).

A second set of question asks about different motives forintroducing environmental innovations. It asks whether the firmintroduced environmental innovations as reaction to the followingset of factors:

• current environmental regulations or environmental taxes(ENREG),

• expected environmental regulations or environmental taxes(ENREGF),

• grants, including R&D subsidies, or other public financial incen-tives for environmental innovations (ENGRA),

• existing or expected demand from customers for environmentalinnovations (ENDEM), and

• voluntary codes of practice used in the sector or sectoral agree-ments to stimulate eco-friendly practices (ENAGREE).

This second set of questions covers a wide range of governmentpolicies, including regulations, taxes and public financial incen-tives. The latter can include R&D subsidies, but these could alsoinclude subsidies for the adoption of clean technology and tax cred-its for clean innovations. The list of policies does not include the useof public research infrastructure or public procurement to stimu-late private eco-innovations, although these also may be importantinfluences on firm-level innovation.

Both sets of questions are asked on a simple ‘yes or no’ basis, withno information on the relative size of the environmental innova-tion efforts, nor on the relative importance of specific policies. Thesimple format of the questions resulted from two rounds of cog-nitive testing with the managers of 20 enterprises, representingsmall, medium and large firms in eight EU countries and six sectors(Arundel et al., 2009).

With a response rate of 44%, the Flemish CIS-VI data covering theperiod 2006–2008, holds 2963 observations. Of these firms, 42%claim to be active in innovation, 33% are engaged in intra-muralR&D activities and 10% have applied for at least 1 patent. 88% of thesample firms are SMEs, and 45% are in the service sector.

We linked the Flemish firms in the CIS-data to data on mem-bership in the EU Emission Trading System to test for the impactof the EU-ETS scheme on firms’ environmental innovative behav-ior. Only 6 companies in the CIS also are in the ETS scheme, whichmay reflect the fact that a large number of Flemish firms in the ETSscheme are not in the CIS-sample because they are not innovationactive (e.g. firms in building material) or did not respond to the CISsurvey. It is also a reflection of the highly targeted nature of the ETSsystem with respect to sectors and firms, concentrating as it doeson large polluting sectors and firms. Due to the small number ofobservations, we are unable to evaluate the ETS scheme’s influenceon firms’ innovative behavior.

4.2. Research questions

With all the caveats in mind, the Flemish CIS-VI eco-moduledata allow us to test the impact of clean government policy on

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Table 2Which sectors introduce clean innovations?

ALL FOOD CHEM ELEC AUTO NUTS OTHER MANUF TRANS PORT OTHER SERV

ECOCO 20 23 35 19 17 48 17 34 1368 46 46 3046 25 27 2472 49 48 33

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Table 3Which type of firms introduce clean innovations?

ALL SMEs Large YOUNG INNOV PROCESS INNOV

ECOCO 20 17 43 20 30 36ECOOWN 43 40 71 38 60 69ECOUSER 28 26 46 28 42 45ECO 46 44 76 38 66 72

Table 4Which motives for clean innovations?

ECO ECOCO ECOOWN ECOUSER ECOEN

ENREG 32 42 33 34 38ENREGF 25 37 37 29 32ENGRANT 15 22 15 18 21

to focus on larger firms. Financial grants also are more influential inthe involvement by large companies in eco-innovations. One thirdof all large enterprises listed grants as a factor having induced theireco-innovation activities.

ECOOWN 43 47 65 52 59

ECOUSER 28 26 41 48 41

ECO 46 48 67 59 64

he likelihood of firms introducing clean innovations, either ownr elsewhere developed. More specifically, these data allow us tonvestigate and compare the effects of different clean policy instru-

ents.

We are particularly interested in analyzing which types of gov-ernment interventions, if any, are most influential: R&D subsidies(ENGRA) or environmental regulations and taxes (ENREG).We will also investigate whether there is any complementaryeffect from both types of instruments.Comparing the effect of current and expected regulations (ENREGversus ENREGF) allows us to analyze the impact on firm behaviorof managers’ perceptions of the long-term consistency of policy.

As the CIS data are not structured as panel data, it is not possibleo test for any path dependency or other dynamic effects.

When testing the importance of clean government instrumentsor the introduction of clean innovations, we control for otheretermining factors. First we include other potential influences onlean innovations that are not related to government policies: themportance of demand from customers (ENDEM) and voluntarygreements (ENAGREE). Secondly, we control for firm character-stics that may influence a firm’s decision to introduce innovationsn general, like firm size, firm age and technology/sector of the firm.

e also control for the innovative profile of the firm, i.e. whetherhe firm has introduced other product, process, organizational or

arketing innovations. The main modules of CIS-VI provide thenformation for these controls.

.3. Who introduces clean innovations?

Overall 46% of the total 2894 firms in the sample respond thathey introduced a clean innovation in the period 2006–2008 (ECO).3% have introduced a clean innovation in their own operationsECOOWN). These are the clean adopters. 8% report having devel-ped clean innovations for their users (ECOUSER). These are thelean developers. Note that the clean developers are also signif-cantly more likely to introduce clean innovations in their ownperations: of all ECOUSER firms, 85% have also introduced a cleannnovation in their own operations.

If we look at the different types of clean innovations, we seeimilar penetration rates. Reducing CO2 (ECOCO) holds 20%, energyaving (ECOEN) 22%, reducing pollutants (ECOPOL) 21%, reducingollution (ECOSUB) 22%.

Companies also report a mix of different types of motives for orenefits from environmental innovations. For instance, those firmshat reduce CO2 are also significantly more likely to reduce theirnergy consumption: 72% of ECOCO firms also are ECOEN and viceersa 67% of ECOEN firms also report ECOCO; 63% of all ECOCO firmslso develop clean innovations for their users (ECOUSER).

For all these reasons, although our prime area of interest is CO2eductions (ECOCO), we will also look at clean process innovatorsore generally (ECOOWN) as well as clean developers (ECOUSER)

nd clean innovations overall (ECO) (Table 2).Not surprisingly, the chemicals and the utilities sectors score

ighest on clean innovations, particularly for CO2 reductions. Theervices sectors are less likely to be involved in clean innovation.

ENDEMAND 21 29 22 29 28ENAGREE 39 51 41 45 50

The transport sector is over represented in terms of clean innova-tions (Table 3).9

SMEs are less likely to introduce clean innovations compared tolarge enterprises. This difference in firm size is most pronouncedfor CO2 reductions. This is perhaps related to the supra reportedsectoral distribution, with CO2 sensitive sectors being larger scale.Firms that are active in introducing other innovations, be it prod-uct, process, organizational or marketing innovation are also morelikely to introduce clean innovations. With the exception of cleandevelopers (ECOUSER), the introduction of process innovationsshows the strongest complementarity with clean innovations.

4.4. Motives for clean innovations

The motive most frequently identified by firms as leading totheir introduction of eco-innovations is participation in sectoralvoluntary agreements between regulators and polluters Govern-ment regulations and taxes are mentioned by almost one thirdof all eco-innovators as motive. Financial incentives (grants) arementioned least frequently, in only 15% of the cases, although theyare somewhat more influential for CO2 and energy saving innova-tions. Current and future regulations and taxes are more influentialfor CO2 reductions than for energy saving. This and other findingsmay reflect the fact that CO2 reduction innovations are relativelycapital-intensive, increasing the influence on adoption decisions offinancial factors (Table 4).

For large enterprises, regulations are significantly more impor-tant drivers in development or adoption of environmentaltechnologies and appear to be almost as important as voluntaryagreements, perhaps reflecting the tendency of government policy

9 The paper industry also is over represented in eco-innovations (54%), butbecause of the low number of observations, this sector is included in other man-ufacturing.

Page 7: Which policy instruments to induce clean innovating?

1776 R. Veugelers / Research Policy 41 (2012) 1770– 1778

Table 5Which motives for whom?

ALL ECO SMEs LE FOOD CHEM ELEC AUTO NUTS OTHER MANU TRANS PORT OTHER SERV

ENREG 32 28 61 52 45 43 25 28 28 32 22ENREGF 25 22 49 30 41 28 21 33 21 28 20ENGRANT 15 14 33 20 21 12 19 17 13 23 10ENDEMAND 21 19 38 15 28 31 23 28 17 13 27ENAGREE 39 35 67 44 51 47 50 39 35 34 35

24

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• both current and future regulations and taxes are drivers,• only current regulations and taxes are drivers, and• only future regulations and taxes are drivers.

Table 6Probit results on the drivers for introducing eco-innovations.

ECO ECOUSER ECOOWN ECOCO ECOEN(1) (2) (3) (4) (5)

ENREG .302 .079 .306 .082 .058.036 .034 .038 .029 .028*** ** *** *** **

ENREGF .266 .130 .217 .146 .075.050 .039 .051 .035 .032*** *** *** *** ***

ENGRA .218 .095 .098 .087 .115.061 .043 .060 .034 .036*** ** + *** ***

ENDEM .315 .279 .186 .119 .126.043 .038 .049 .032 .032*** *** *** *** ***

ENAGR .380 .237 .421 .211 .225.026 .029 .027 .027 .027*** *** *** *** ***

Firm size (Emp06), firm age (Young), innovative inputs (InnovIn) and sectordummies included:A full report of all coefficients is reported in Appendix A to this paper.A bivariate probit for estimating (ecouser, ecoown) and (ecoco, ecoen) to take intoaccount the correlation between the dependent variables, cf supra, yields a signifi-

CURR & FUTURE 19 16 43 25 34

REG & GRANT 10 9 27 16 17

Government policies are significantly correlated10 with onenother in affecting firms’ behavior. Companies which rate regu-ations and taxes as decisive motive are significantly more likelyo rate grants as decisive (29% compared to 9%) and vice versa,rms that rate grants as important are more likely to rate regu-

ations as important (60% compared to 27%).11 This is consistentith complementarity between government instruments.

The time-consistency of government interventions can benalyzed by comparing the impact of current and/or future inter-entions. While 12% of eco-innovators list only current regulationsnd taxes as influential and no more than 6% cite as an impor-ant influence only future regulations, 19% of eco-innovators listoth current and future regulations as decisive. Among the cross-otive correlations, the correlation between current and future

egulations is the strongest.12 This is consistent with the idea thategulations and taxes will be more influential if they are consistentver time, rather than subject to instability.

The sector that appears to be most sensitive to regulation andaxes is the food sector, with more than half of the companieseporting this factor (ENREG) as influential. Also the chemicalsector has a high sensitivity to regulation and taxes, includingnticipated regulations and taxes. In the car manufacturing sec-or, voluntary agreements are the most frequently reported drivers.rants are in no sector among the most important drivers, although

hey are somewhat more decisive in transport services, chemicals,ood and car manufacturing (Table 5).

.5. Econometric analysis of the impact of government policies onlean innovations

In the last part of the analysis, we examine the impact of gov-rnment policy on clean innovations in a multivariate analysis. Were particularly interested in which of the direct government pol-cy instruments are more important drivers for clean innovations,ontrolling for other motives: demand push (ENDEM) and volun-ary agreements (ENAGR) and controlling for firm and industryharacteristics (firm size and age, sector dummies and innova-ive inputs of the firm). We look at eco-innovations in general, butlso separately consider eco-innovations introduced in own opera-ions (ECOOWN) and the development of eco-innovations for usersECOUSER). For innovations introduced in own operations, we arearticularly interested in those aimed at reducing CO2 emissionsECOCO) and reducing energy consumption (ECOEN).

The results reported in Table 6 indicate that demand-pullENDEM) and voluntary agreements (ENAGR) are the most impor-

ant motives for ECO innovations. Voluntary agreements betweenegulators and polluters are the most influential driver for allypes of eco-innovations. Government policy instruments are in

10 All cross-motive correlations are statistically significant at the 1% level.11 The Chi-2 test, testing independence between both variables, rejects indepen-ence at the 1% level.12 The Pearson’s correlation coefficient between REG and REGF is 0.555 and signif-cant at the 1% level.

17 19 16 21 134 11 8 14 7

general less important drivers for eco-innovations compared todemand-pull and voluntary agreements. Regulations and taxes(ENREG) are more important to incite the adoption of clean tech-nologies (ECOOWN) than for the development of clean technologies(ECOUSER). Government subsidies (ENGRA) are less importantinfluences.

When we examine the adoption of eco-innovations, we com-pare those aimed at reducing CO2 emissions (ECOCO) and thoseto reduce energy consumption (ECOEN). Voluntary agreements arestill the most important factor for inducing ECOCO and ECOEN. Forinnovations that reduce energy consumption, financial incentivesseem to be the most potent government lever. For innovationsaimed at reducing CO2, anticipated future regulations and taxesare the strongest policy influence, corroborating the importance oflong-term policies in the implementation of CO2 innovations.

In Table 7 we test the importance of consistency in governmentpolicy. We do this by identifying 3 cases:

cant correlation among error terms, but gives very similar results.We also include the ETS dummy although it is highly skewed, covering only 7 obser-vations. It shows a high responsiveness for innovations to reduce CO2 emissions.Inclusion of the ETS dummy does not affect the other coefficients.N = 2893.Our interest is in comparing the coefficient size across government policies.Note: Marginal effects reported (discrete change of dummy variables from 0 to 1)(Dprobit (robust) command in STATA). Standard errors reported in the second line,significance levels in the third line.

*** Represents 1% significance level.** Represents 5% significance level.+ Represents 15% significance level.

Page 8: Which policy instruments to induce clean innovating?

R. Veugelers / Research Policy

Table 7Bivariate probit results on the importance of time consistency of clean policies.

ECO ECOUSER ECOOWN ECOCO ECOEN(1) (2) (3) (4) (5)

ENREG and ENREGF .357 .160 .360 .226 .112.041 .040 .043 .038 .035

ONLYENREG

.386 .181 .411 .139 .121

.032 .044 .038 .038 .037

ONLYENREGF

.439 .343 .426 .264 .202

.030 .061 .048 .060 .058

Firm size (Emp06), firm age (Young), innovative inputs (InnovIn), sector dummies,ENDEM, ENAGR included. N = 2893.Marginal effects reported (discrete change of dummy variables from 0 to 1) (Dprobit(robust) command in STATA). Standard errors in the second row. All coefficientsare significant at 1% level. Our interest is in comparing the coefficient size acrossgovernment policies.

Table 8Bivariate probit results on policy mixing regulations and taxes with subsidies.

ECO ECOUSER ECOOWN ECOCO ECOEN(1) (2) (3) (4) (5)

REG/TAX andSUBSIDIES

.371 .196 .340 .283 .253

.046 .053 .056 .051 .051

ONLY SUBSIDIES .343 .312 .265 .183 .158.045 .065 .058 .054 .055

ONLY REG/TAX .445 .249 .448 .214 .138.023 .031 .028 .023 .029

Firm size (Emp06), firm age (Young), innovative inputs (InnovIn), sector dummies,ENDEM, ENAGR included. N = 2893.Marginal effects reported (discrete change of dummy variables from 0 to 1) (Dprobit(ag

r

scuaditmt

rsoa

fced

5p

ctmte

robust) command in STATA). Standard errors in the second row. All coefficientsre significant at 1% level. Our interest is in comparing the coefficient size acrossovernment policies.

The default being no impact from neither current nor futureegulations nor taxes.

For innovations aimed at reducing CO2, we find the highest sen-itivity to “only future regulations and taxes”, closely followed by aombination of current and future regulations. “Only current reg-lations and taxes” has the lowest impact. For innovations aimedt reducing energy consumption, future regulations are a strongerriver than current ones. The same holds for development of eco-

nnovations for users. We interpret this as evidence in support ofhe argument that the long-term nature of regulations and taxes

akes them especially influential in the adoption of clean innova-ions.

In Table 8 we test the combined influence of subsidies andegulations and taxes, again by constructing 3 cases: one whereubsidies and regulations and taxes are jointly present, one wherenly subsidies are present and one where only regulations and taxesre present.

For the adoption of eco-innovations aimed at reducing CO2 andor reducing energy consumption, the results clearly suggest that aombination of regulations and taxes with subsidies is most influ-ntial, in contrast to the limited effects of this combination on theevelopment of eco-innovations.

. Conclusions: linking clean government policies torivate clean innovations: does it work?

The climate change challenge can be met effectively only by alean innovation machine that is operating at full speed. In addition

o publicly support for R&D infrastructure and public procure-

ent, the development and adoption of new clean technologies byhe private sector is essential to the needed transformation in thenergy-economic system for reductions in Green House Gas (GHG)

41 (2012) 1770– 1778 1777

emissions. The private clean innovation machine, left on its own,is not up to this challenge. Government intervention is needed toaddress the combination of environmental and knowledge exter-nalities and to overcome path dependencies. The evidence oncurrent clean innovation performance hints at the failure so far ofgovernment intervention to fully activate the private clean innova-tion machine. If governments want to leverage the needed privateinnovations for clean energy technologies, they will have to providea well-designed, time consistent policy, by a combination of con-sistent carbon pricing, performance based regulations and publicfunding.

The evidence from earlier micro-econometric studies, on patentdata and from selected environmental technologies confirms thatgovernment intervention can affect private sector innovations,albeit with substantial variation among policy instruments andtechnologies.

The firm-level evidence presented in this contribution on themotives for introducing clean innovations using the latest FlemishCIS eco-innovation survey confirms that firms are responsive toeco-policy interventions. The evidence is also suggestive of howimportant the details of the policy design are.

The evidence supports the increased leverage of policies whencombining regulations and taxes with subsidies, particularly for theadoption of innovations to reduce CO2 emissions. This complemen-tarity between policy instruments for accelerating the adoptionof CO2-reducing innovations is particularly important to take intoaccount for the design of public clean subsidy policies, as the cur-rent evidence provides little support for the efficacy of subsidies forCO2 reducing innovations, when used in isolation. It is a reminderfor those governments contemplating a public clean R&D supportprogram that the lack of a strong carbon price expected to prevailin future will seriously reduce the effectiveness of subsidies as pol-icy instrument to leverage private innovative incentives for climatechange.

The intertemporal consistency of policy is relevant to all typesof eco-innovations, but especially important for climate changeinnovations and more so for developers than for adopters. Pol-icy interventions will have greater influence on the adoption anddevelopment of new clean technologies when designed to be cred-ible and consistent over time, affecting future expectations morethan current incentives.

Finally, the high importance of demand pull from customers andvoluntary sectoral codes of conduct or voluntary sector agreementsas drivers for firms introducing clean innovations, is a reminder ofthe internal strength of the private innovation machine. Govern-ments should leverage this power, by a time consistent clean-techpolicy design affecting the expectations of the market.

This paper has focused on the design of a clean tech policyfor activating the private sector in creating and adopting eco-innovations, and accordingly has fewer implications for the designof a mission-oriented public R&D infrastructure policy in this field.Nonetheless, this discussion underscores the need for any mission-oriented clean public R&D infrastructure policy to be designed tobe compatible with and supportive of a clean tech demand policy,if the results of this public R&D are to be further developed andadopted by the market to ensure the needed transformation of theenergy-economic system.

Acknowledgements

The author would like to thank ECOOM for providing access to

the CIS-VI data. Financial support from FWO/KUL and SEEK is grate-fully acknowledged. Comments from A. Jaffe, R. Nelson, D. Mowery,D. Foray and the participants in the Lausanne workshop were veryhelpful.
Page 9: Which policy instruments to induce clean innovating?

1 Policy

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Change 80, 199–204.Veugelers, R., 2011. Europe’s clean technology investment challenge,

778 R. Veugelers / Research

ppendix A.

See Table A.1.

able A.1robit results on the drivers for introducing eco-innovations.

ECO ECOUSER ECOOWN ECOCO ECOEN(1) (2) (3) (4) (5)

Firm size .00025** .00004 .00023 .00014 .00011(Empl06) .00012 .00003 .00010** .00004*** .00004***

Firm age −.033 .010 −.042 .010 −.023(DYoung) .029 .023 .029 .020 .019Innov inputs .236*** .164 .198 .057 .135(InnovIN) .022 .020*** .024 .017*** .018ENREG .302 .079 .306 .082 .058

.036*** .034** .038*** .029*** .028**

ENREGF .266 .130 .217 .146 .075.050*** .039*** .051*** .035*** .032***

ENGRA .218 .095 .098 .087 .115.061*** .043** .060+ .034*** .036***

ENDEM .315 .279 .186 .119 .126.043*** .038*** .049*** .032*** .032***

ENAGR .380 .237 .421 .211 .225.026*** .029*** .027*** .027*** .027***

FOOD .047 −.032 .116 .049 .193.042 .036 .041*** .032* .038***

CHEM .179 .024 .243 .085 .174.039*** .036 .038*** .032*** .039***

ELEC .059 .129 .076 −.037 .053.062 .056** .062 .039 .047

CARS .188 .089 .203 −.067 .094.057*** .061+ .065*** .040 .064*

NUTS .337 .184 .377 .322 .390.045*** .081** .052*** .084*** .076***

OTH MANUF .129 −.009 .164 .022 .093.026*** .022 .026*** .020 .022***

TRANSP .137 .049 .191 .235 .106.036*** .034 .024*** .035*** .035***

Pseudo-R2 .303 .210 .289 .247 .234N = 2893

ote: Marginal effects reported (discrete change of dummy variables from 0 to 1)Dprobit (robust) command in STATA).*** Represents 1% significance level.** Represents 5% significance level.* Represents 10% significance level.+ Represents 15% significance level.

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