Distribution of Eco-innovators and innovators according to industry classifications 

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Drivers of and Barriers to Eco-innovation

For eco-innovation to unfold its full potential, firms and decision makers need to be able to identify the drivers of (and barriers to) eco-innovation. Policy makers need to be aware of the many drivers of eco-innovation (Kemp and Foxon, 2007a) in order to spur eco-innovativeness, while many eco-innovations fail or are abandoned because firms are unable to overcome the manifold barriers they encounter (Bleischwitz et al. 2009). Drivers are thoroughly explored in Section 2.1., while Section 2.2. deals with barriers.


The innovation literature has long been dominated by two contrasting explanations of what drives technological change: the push of the technology itself (« technology-push ») on the one hand, and the pull of the market (« demand-pull ») on the other. Empirical evidence has shown that both forces are very likely to be at play in the actual world (Pavitt, 1984). It is therefore necessary to examine drivers and barriers from both the supply side (where technology push is supposed to occur) and the demand side (which exerts the market pull). As far as eco-innovation is concerned, technology push factors include all new eco-efficient technologies, whereas market pull (or demand pull) factors include consumers’ preference for environmentally friendly products and the need for companies to maintain their environmentally-responsible reputation (Rennings, 1998). Environmental innovation and « normal » innovation notably differ in that institutional and political factors are likely to play an even more important role in the former than in the latter. The main features of eco-innovations are correlated to the determining role of regulations. Table 1 lists these three types of determinants: (1) technological and supply-side related, (2) market and demand-side related, and (3) policy related. Since environmental policy may interact with both supply and demand sides, it will be examined in the next section, and the present section focuses on the first two types of determinants.


Broadly defined, barriers to eco-innovation include every element that may hinder the development and/or diffusion of environmental innovations. Rather than thinking in terms of supply and demand, as was done for drivers, the literature usually classifies barriers and drivers into categories such as political, informational, financial etc. (Bleischwitz et al. 2009).
Among these categories, informational and financial barriers are often deemed to be the most important ones. Without being exhaustive, we can consider here some established typologies. For instance, Bleischwitz et. al. (2009) consider the following barriers to eco-innovation: (i) informational barriers arise from an asymmetric distribution of knowledge about material and resource efficiency among various actors, such as users and producers; (ii) financial barriers are generally the result of a splitting of financial incentives between actors (e.g., between user and investor) with contrasting interests as regards the introduction of eco-innovation; (iii) a gap between R&D and market launch often occurs when the risks associated with R&D expenditures are high, in which case a firm will only accept to act as a ―first mover‖ (i.e. to introduce an eco-innovation) if it can benefit from a sufficient patent protection.
Reid and Miedzinski (2008), relying on an analysis of CIS316 indicators, found that the most significant barriers for firms that are classified as eco-innovators, were (1) the high costs of innovation activity (for almost 30% of these firms), (2) the lack of an appropriate source of funding (for %23) and (3) the excessive economic risks (perceived by around 20% of these firms). This analysis thus points to financial barriers as the most important ones. However, one should be aware that any analysis of barriers conducted using CIS data is likely to be biased by the fact that the data refer to barriers encountered by innovators, and not to barriers that may have caused a firm to remain a non-innovator.
Reid and Miedzinski (2008) suggest that, whatever the data limitation, any classification of drivers and barriers to eco-innovation is difficult because it depends to a large extent on the cultural, institutional and historical context of the country. According to his conclusion, socio-cultural factors that can be considered as barriers to eco-innovation are (1) low or low-quality education at all levels, (2) low environmental awareness and lack of clear information, (3) low openness of society (e.g., ―fear of change‖, closed networks, risk aversion, etc.), (4) limited access to human resources and expert knowledge, (5) lack of organisational capacities, (6) persistent power structures within societies (―institutional inertia‖ and historical path dependency), (7) short-term decision-making, (8) weak social corporate responsibility. The risk with such an approach, however, is to be of little practical usefulness if what is really needed for the study of eco-innovation is a general model or framework. In this case, saying that the object of the study is mostly country-specific for cultural and historical reasons is hardly a step towards such a framework.
Institutional and social barriers certainly have to be taken into account, nevertheless; and they precisely seem to be absent of classifications such as the following one, proposed in European Commission (ETAP, 2004). This classification distinguishes: (i) economic barriers, ranging from market prices which do not reflect the external costs of products or service (such as health care costs due to urban air pollution) to the higher cost of investments in environmental technologies (e.g., because of the complexity of switching from traditional to ―green‖ technologies); (ii) regulations and standards can also act as barriers to innovation when they are unclear or too detailed, while a good legislation can stimulate environmental technologies; (iii) insufficient research efforts, coupled with an inappropriate working of the research system in European countries and weaknesses in information and training; (iv) inadequate availability of risk capital to move from the drawing board to the production line; (v) lack of market demand from the public sector, as well as from consumers. The weakness of this typology is that almost every barrier it lists is of an economic nature, and it fails to encompass purely technological barriers as well as social factors.
In the end, one has to go back to a work such as that of Ashford (1993) to find an extensive and complete list of barriers. Those are: (i) Technological barriers (e.g., unavailability of certain technologies for specific applications, or lack of alternative substances to substitute for the hazardous components; (ii) Financial barriers (e.g., costs of R&D, or non-comprehensive cost evaluations and cost-benefit analyses leading to a lack of funding for eco-innovation); (iii) Labour force-related barriers (e.g., lack of persons in charge of the management, control, and/or implementation of waste reduction technology); (iv) Regulatory barriers (e.g., lack of incentives to invest in reuse and recovery technologies); (v) Consumer-related barriers (e.g., risk of customer loss if output properties change slightly or if product cannot be delivered for a certain period); (vi) Supplier-related barriers (lack of supplier support in terms of product advertising, good maintenance service, expertise of process adjustments, etc.); (vii) Managerial barriers (e.g., lack of top management commitment, or reluctance on principle to initiate change in the company).

Recognition of Environmental Policy as a Driver of Eco-innovation

Empirical evidence shows that the regulatory framework and environmental policy have a strong impact on eco-innovation (Green et al. 1994; Porter and van der Linde 1995; Kemp, 1997; Hemmelskamp, 1997; Cleff and Rennings, 1998; Berman and Bui, 2001). However, according to Rennings (1998), environmental product innovation tends to be more driven by the strategic market behaviour of firms (market-pulled effect). By contrast, regulation tends to drive more environmental process innovation, because the public-good character of clean technology leads to under-investment in environmental R&D (Rennings and Rammer, 2009). Therefore, environmental regulation is especially necessary to foster environmental process innovation. When the reduction of the environmental impact of a firm‘s activity offers little operational or commercialization benefits, then regulation may become the primary driver of eco-innovation (Kemp and Foxon, 2007a). For example, regulations to protect local air quality have stimulated an innovation such as catalytic converters, which have led to dramatic reductions in the emission of pollutants from vehicles (Kemp and Foxon, 2007a).
Therefore, environmental policy is a potentially strong driving force for eco-innovation, which deserves to be studied separately. Environmental policies may fall under the ―command-and-control‖ or ―market-based‖ types. Market-based instruments such as pollution charges, subsidies, tradable 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 (Jaffe et al., 2002). By contrast, command and control regulations tend to force firms to take on similar shares of the pollution 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 (ibid).
In the wake of the Lisbon agenda, the European Council adopted in 2001 the Sustainable Development Strategy and introduced the ―Environmental Technologies Action Plan‖ (ETAP) in 2004. In the ETAP, the European Union acknowledged the strategic importance of eco-innovation. Although the actual impact of ETAP remains to be precisely assessed, it seems to have led to an increased recognition of environmental problems (and of the need for eco-innovation as an answer) in the public and political consciousness.
The results from a survey across ten OECD countries show that an increasing number of countries now perceive environmental challenges not as a barrier to economic growth but as a new opportunity (OECD, 2009). This new understanding has made environmental policy appear as an important driver of eco-innovation, thus reconciling real-world policy with the theoretical considerations of Porter and Van der Linde (1995). These authors argued in the mid-nineties that environmental progress requires companies to improve their resource productivity through dedicated innovation, so that regulation becomes not an obstacle but a driver for innovation. In Porter and Van der Linde (1995), implicitly, the more prescriptive the regulation is, the more confined will the innovation be.
More recently, Rennings (2000) also emphasized that environmental policy is becoming the main driver of eco-innovations. According to him, eco-innovations differ from normal innovations because they produce a double externality, consisting in (1) the usual knowledge externalities in the research and innovation phases and (2) externalities in the adoption and diffusion phases due to the positive impact upon the environment (Oltra, 2008). In other words, the beneficial environmental impact of environmental innovations makes their diffusion always socially desirable. However, these positive external effects lead to market failures which may hinder eco-innovation. The private return on R&D in environmental technology is less than its social return due to its public good nature, which in turn causes a lack of private incentives leading firms to under-invest in environmental R&D and innovation (Oltra, 2008). Therefore, environmental policy and/or an appropriate regulatory framework appear as a requirement for eco-innovation.

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Effectiveness of Environmental Policy as a Driver of Eco-innovation

According to Porter and van der Linde (1995), environmental standards can foster innovation under three conditions. First, they must create maximum opportunities for eco-innovation, letting the industry (and not a standard-setting agency) choose its own approach to innovation. Second, regulation should foster continuous improvement, rather than locking in any particular technology. Third, the regulatory process should leave as little room as possible for uncertainty at every stage. Therefore, the type of regulation/policy and the way it is implemented is significantly important. It should lead firms to effectively address environmental problems rather than restrict firms in a specific technology and leave the environmental problem unsolved. The stringency of the policy and the terms in which it is defined are equally important, since uncertainty depends on these factors. In spite of on-going controversies on whether environmental regulation actually has an impact on innovation and on the most efficient policy instruments (see for instance Greenstone, 2002; Jaffe et al., 2002), many empirical studies (European Commission, 2001; Rennings et al. 2006, Belin et al. 2009) find a positive correlation between innovation and regulation.
Porter and van der Linde, (1995), Kemp et al. (1998) and, Jänicke and Jacob (2002) all predict that strict environmental regulations stimulate innovation in a number of ways (e.g. first mover advantages created by the development of ―green‖ technologies). These predictions are in line with the so-called ―Porter hypothesis‖ postulates that ―there are win-win opportunities through environmental regulation, where simultaneously pollution is reduced when having an increase in productivity‖. As mentioned above, this hypothesis has fuelled controversies17, but its argument remains at the core of current research on eco-innovation.
The rationale behind this argument is that firms do not detect the potential of environmental innovations because they are ―still inexperienced in dealing creatively with environmental issues‖ (Porter and van der Linde, 1995, p. 99). Environmentally and economically benign innovations are not realized because of incomplete information, and of organizational and/or coordination problems (ibid). Firms are not able to recognize the cost saving potentials (e.g. energy or materials savings) of environmental innovation (Frondel et al. 2007). This leads many of them to believe that an environmentally-virtuous behaviour is a burden rather than an asset (Kemp and Andersen, 2004). Therefore, regulations and policies can be a catalyst and help them to understand the potential benefits of environmental innovations.
However, as Porter and Van der Linde (1995) hinted at, the types of environmental policies used matter. Popp (2009) argues that in general, market-based policies are thought to provide greater incentives for innovation, as they provide rewards for continuous improvement in environmental quality. In contrast, command-and-control policies penalize polluters who do not meet the standard, but do not reward those who do better than mandated as the command-and-control regulations direct a specific level of performance, such as pounds of sulphur dioxide (SO2) emissions per million BTUs of fuel burned, or the percentage of electricity that must be generated using renewable sources.
Popp (2009) also argues that what matters is not just the type of policy instruments used: differences within one policy type (e.g. market-based policies) may also matter. His ideas find some support in the results of Johnstone et al. (2008), who analyse the effect of different policy instruments on renewable energy innovation in 25 OECD countries. In their study, they compare price-based policies such as tax credits and feed-in tariffs18 to quantity-based policies such as renewable energy mandates. They find that quantity-based policies favour the development of wind energy while, by contrast, direct investment incentives are effective in supporting innovation in solar and waste-to-energy technologies. However, designing such instruments efficiently for different pollutants is sophisticated and costly.
Moreover, the introduction of such policies has often met with opposition from at least some industries (e.g., Arimura et al. 2008). As a result, in recent years, voluntary proactive approaches to environmental protection are considered useful supplements to traditional mandatory command and control regulations and economic incentives (e.g., Khanna and Damon, 1999; Alberini and Segerson, 2002).

Proactive approaches to environmental protection

The most important voluntary approaches to environmental protection are subsumed under ISO 14001, EMAS19 and 33/50, the first two being international and the latter being specific to the US. ISO 14001 comprises standards that must be adopted. EMAS requires facilities – besides third-party audits with independent environmental verifiers and registration bodies – to publish an environmental statement. Finally, 33/50, which was launched by the U.S. Environmental Protection Agency (EPA) in 1991, aimed at reducing aggregate emissions of 17 chemicals reported to the Toxic Release Inventory (TRI) (Ziegler and Nogareda, 2009). The main reasons for introducing these voluntary approaches are (1) that they are more flexible and cheaper than government based policies, and (2) they are likely to improve corporate environmental performance. For example, EMAS-certified facilities in Germany benefit from regulatory relief and from more (and higher) subsidies based on the EMAS privilege regulation (Ziegler and Nogareda, 2009). In line with these implications, Rehfeld et al. (2007) emphasizes that a careful design of EMAS is important for both the environmental and economic performance of a facility. It is important to note that public voluntary programs such as 33/50 do not imply any penalties for withdrawal at any time. In other words, these measures do not guarantee an improvement in environmental performance (Anton et al. 2004). For this reason, critics have arisen that consider Voluntary Environmental Programmes (VEP) as ―green washing‖. According to these critics, VEP fail to lead participants to clean their operations due to the absence of significant obligations or enforcements (e.g., Potoski and Prakash, 2005). However, some studies, such as Fischer et al. (2003), do not find a clear ranking when comparing VEP to other instruments. Fischer et al.
(2003) claim that no instrument is generally preferable, and the welfare gain of environmental policy instruments (VEP, command-and-control policies, and market-based regulation) depends on different sets of circumstances, such as the number of polluting firms, the costs of an innovation, and the costs of imitating an innovation. As a result, the debate on appropriate policy instruments for developing eco-innovation is still open and the definition of an adequate policy may change depending on the situation. This asserts that more empirical investigation needed to stress the importance of voluntary proactive approaches to stimulate eco-innovation.

Table of contents :

Chapter 2
3.1. Summary of key variables before and after merging the datasets
3.1.1. Frequency of adopters and non-adopters according observation periods
3.1.2. Summary statistics on the variables used in the econometric analysis
3.1.2. Aggregated Nace codes according to OECD‘s technology classification
4.1. Estimation Results
4.2. Average Treatment Effect on the Treated (ATT) obtained from PSM and FILM .
4.2. Summary statistics on the choice of variables before and after matching
Chapter 3
3.3. Distribution of Eco-innovators and innovators according to industry classifications
3.4. Summary statistics of the variables used in the econometric analysis
3.4. Descriptive statistics of the regressors for eco-innovators and conventional innovators
4.1. Estimates of the Generalized Tobit Model for eco-innovation
4.2. Estimates of the Generalized Tobit model by group of countries (Western Europe and Mediterranean)
4.2. Estimates of the Generalized Tobit model by group of countries (Eastern Europe and Baltic countries)
Chapter 4
3.3. Taxonomy of Firms (Terms used in this study)
3.4. Summary statistics on the variables used in the empirical estimation
3.4. Summary statistics on the variables used in the estimations by type of eco-innovator .
3.5. Descriptive statistics according to industry affiliations
3.5. Distribution of eco-innovators across industries for each type of eco-innovator .
3.5. Summary statistics for different types of eco-innovators across all manufacturing industries and service sectors
4.1. Marginal effects for the whole sample
4.2. Distribution of innovators across countries
4.2.1. Marginal effects for the Western European group
4.2.1. Marginal effects for the Baltic group
4.2.1. Marginal effects for the Mediterranean group
4.2.1. Marginal effects for the Eastern European group


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