GGSD 2015 - Issue Note 2: Emerging Technologies & Firm Dynamics: The implications of Green Growth

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ISSUE NOTE Session 2

Emerging Technologies & Firm Dynamics: The Implications of Green Growth




The Green Growth and Sustainable Development (GGSD) Forum is an OECD initiative aimed at providing a dedicated space for multi-disciplinary dialogue on green growth and sustainable development. It brings together experts from different policy fields and disciplines and provides them with an interactive platform to encourage discussion, facilitate the exchange of knowledge and ease the exploitation of potential synergies. By specifically addressing the horizontal, multi-disciplinary aspects of green growth and sustainable development, the GGSD Forum constitutes a valuable supplement to the work undertaken in individual government ministries. The GGSD Forum also enables knowledge gaps to be identified and facilitates the design of new works streams in order to address them.

Authorship & Acknowledgements

This issue note was prepared for the 2015 GGSD Forum to steer discussion around the theme of Session 2, “Emerging Technologies & Firm Dynamics: Implications for Green Growth�. The authors are Florian Egli (Mercator Fellow on International Affairs) and Nick Johnstone (OECD Directorate for Science, Technology and Innovation). This issue note benefited from OECD staff comments including comments from Kumi Kitamori and Ryan Parmenter, as well as from Elena Verdolini (Fondazione Eni Enrico Mattei). The note benefited from financial support of German Development Cooperation. Thanks also to Benjamin Simmons, and the support of the Green Growth Knowledge Platform (, in particular, the Technology and Innovation Research Committee. The opinions expressed herein do not necessarily reflect the official views of the OECD member countries.













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Business as usual Climate Change Mitigation Technology Carbon Capture and Storage Expert Elicitation Fiscal Year Global Apollo Program General Purpose Technology Integrated Assessment Model Internet and Communication Technologies International Patent Classification Initial Public Offering Levelized Cost of Electricity Merger and Acquisitions Nomenclature Statistique des Activités Economiques dans la Communauté Européen (Statistical Classification of Economic Activities in the European Community) Research and Development (and Demonstation) Science Citation Index Science, Technology and Industry Directorate Venture Capital Patents related to Climate Change Mitigation


EXECUTIVE SUMMARY To direct our economies onto a low-carbon development path while sustaining growth and prosperity is one of the greatest challenges of our society. Many authors have analysed climate change and its economic costs and most of them have concluded that inaction will be extremely costly. At the same time, growth forms the backbone of our political and social systems and is a prerequisite for development. Innovation is a central element in squaring this circle. In the case of the environment, this means investment in green technologies is below the social optimum because private investors struggle to capture positive effects from developing radically new technologies due to spillovers and because negative effects of dirty technologies (e.g. pollution) are seldom priced. Therefore, in order to induce such innovation, pricing of carbon and other greenhouse gases is essential. However, this may not suffice. Firms, scientists and public policy makers are bound by path dependencies and likely stick to what they have been doing in the past. As such, temporary public support for green technologies is necessary in the short- and medium-term. The core of such innovation policy frameworks should be technology neutral. Also, in order to avoid regulatory capture by subsidy beneficiaries the support needs to be tied to a well-defined exit strategy. In the first instance, this paper provides an overview of the methods used to identify promising green technologies for targeted public R&D support. This is a hazardous exercise and rather than seeking to identify promising technologies, the paper tries to make the reader aware of the opportunities and pitfalls of different methods. Ex-post analysis of patent data shows that there is an important crossfertilization of innovations in the clusters of energy storage and distribution, lighting, and battery. In addition, it also shows that technologies developed by firms with a wide industry portfolio are more likely to be successful later on. Together with energy storage and bioenergy, photovoltaics also emerges as a very promising technology in an analysis of R&D portfolios. Applying a different methodology, the results of ex-ante expert elicitation surveys on future technology costs point out that renewables will most likely not become cost-competitive by 2030 without public support. For high R&D spending scenarios however, they are likely to be cheaper than fossil technologies by 2030: higher levels of R&D are associated with lower future expected costs. The paper also points out important methodological issues such as the selection of experts and the need to control for their backgrounds in order to get robust future cost estimates. Both ex-ante and ex-post deliver first clues that current R&D portfolios are seldom optimal and much too low: experts recommend 3 to 10-fold increases across all technologies. Such analysis is only useful though if budgetary constraints and equity effects are fully taken into account, which is presently not the case. Because of their exploratory nature, both ex-ante and ex-post technology identification are useful tools, but need to be nested in a carefully drafted evaluation and reform process.


Moreover, and given the points made above concerning path dependency, any policy interventions introduced must fall on fertile ground if the technologies supported are to have far-reaching consequences in the economy. As such, the paper investigates the importance of firm dynamics on the green transition. It finds evidence that new firms are more likely to develop radical innovations. Moreover, the average age of firms innovating in climate mitigation technologies is lower than for other technologies. For public policy, this means a special emphasis on young firms may be justified and a dynamic startup environment is more likely to "disrupt" existing high-carbon means of production. Paradoxically environmental policies themselves may discourage entry and exit. Unless carefully designed both "carrots" (e.g. R&D support measures) and "sticks" (e.g. regulations) are far too often biased in favour of the incumbent firms. Besides the positive findings, this paper also identifies important unknowns, which should be addressed in the design of a successful green innovation policy. Although some counties have embarked on the realization of green growth, there is yet no systematic policy evaluation. Pioneering countries should start doing this and share lessons learned. Policies also have to become more predictable, mirroring central banks, which set expectations for at least 2-3 year time horizons. The timing of public intervention is crucial too: what is the right time to push a technology and when should the public withdraw? Because international R&D cooperation promises very large efficiency gains – international negotiations could increasingly target exchange and cooperation mechanisms. Finally, governments should find means to cash in on successful products they have supported in the beginning. Equity participation or payback schemes and loans could be effective ways to do so.


1. GENERAL STRUCTURE OF THE PAPER As defined by the OECD (2011), "green growth is about fostering economic growth and development while ensuring that natural assets continue to provide the resources and environmental services on which our well-being relies. It is also about fostering investment and innovation which will underpin sustained growth and give rise to new economic opportunities." As a case in point mitigation and adaptation to climate change is a major global challenge to our society, and innovation will be a key contributor to addressing the challenge. This paper focuses on the specific case of climate change, but the lessons are of wider relevance to green growth in general. Given the potentially catastrophic and irreversible nature of climate change, this focus leads inevitably to paying attention to the case of breakthrough (or radical) innovations. Much has been written on the attributes of environmental (and innovation) policy which are likely to lead to green growth.1 The literature strongly supports the use of technology-neutral environmental (e.g. taxes or tradable permits) and innovation (e.g. support for basic research, intellectual property rights) policy instruments because they provide incentives for search to inventors (and adopters), and reduce the information requirements of public authorities. The premise of this paper is that technology-neutral measures are necessary conditions for green growth, but that complementary measures of a more discretionary nature may be required. On the one hand, this means that methods to identify potentially promising measures for targeted (or discretionary) support may be required. But how should this be done in a world of technological, commercial and policy uncertainty? On the other hand, an improved understanding of the nature of the markets in which "green" innovations are developed and adopted is required. How can opportunities be maximised for the "winners" that generate potentially valuable breakthrough innovations that can lead to green growth? This paper reports on recent work undertaken at (or commissioned by) the OECD in these two areas. The first section of the paper reviews some of the relevant literature, which motivates the premise, identifying factors present in green innovation, which underscore the need for more targeted support. The second part highlights methodologies, which can be used to provide more targeted public support, and their relative success or failure at identifying promising technologies. The third part of the paper then takes a closer look at the micro level, firm characteristics, which are supportive of breakthrough innovations, and the role of public policy to enable their development. A conclusion sums up the main insights and a box highlighting the key findings (A), knowledge gaps (B) and points for discussion (C) summarizes each chapter.


See Section 7.4 of OECD (2015) for a review, and a set of recent references.


2. THE ROLE OF (INNOVATION) TECHNOLOGY IN GREEN GROWTH The economic cost of inaction with respect to climate change is likely to be high and increasing over time because risks are a function of cumulative emissions and emissions (CO2) stay in the atmosphere for around 100 years (e.g. IPCC 2014). Many policy-makers, eager to combat climate change, are looking for solutions, which do not negatively impact growth prospects (see Box 1 for an example) and green growth has emerged from this need to find emission-reducing but growth enhancing regulatory frameworks for the economy. Box 1. Low-Carbon Green Growth in the Republic of Korea Former President Lee Myung-bak declared “Low-Carbon, Green Growth” as the new national development paradigm in 2008 (Han 2013). The same year Korea launched its first five-year plan for green growth with the aim to “become a world top-7 country in seven key science and technology fields by increasing national investment in R&D to 5% of GDP” (GTCK 2015). The green growth plan included mid- and long-term GHG reduction targets, a cap and trade system and GHG emission standards for automobiles (GTCK 2013). In the following years, 27 green technologies were selected (e.g. solar cell, high-efficiency low-pollution vehicles, LED lighting, green IT) for benefits (tax deduction, financial assistance). As a result, R&D investment in green technology has been growing significantly faster than overall R&D investment for the past 5 years and the Republic of Korea now ranks first in government R&D in environment and energy-related R&D projects among OECD countries (see e.g. OECD Economic Survey 2012 Germany, p. 31). The future policy evaluation body, the Green Technology Center (GTC), was founded in 2013. It is therefore too early for a systematic evaluation, but some key facts since the start of the program can be highlighted. 1.



The technology gap narrowed significantly compared to international competitors. Compared to advanced economies, the technology sophistication increased from 51% in 2009 to 79% in the 27 selected technologies. In the domain of high-efficiency and low-pollution vehicles the gap practically vanished and Korea launched its first mass produced electric vehicle. The SCI (Science Citation Index) demonstrates that papers in the field of green technology now account for 26% of all scientific publications and patents account for a staggering 75% of all registered patents. Furthermore, the number of SCI papers and patents per $ of R&D investment is significantly higher for green technologies than for other national R&D projects (GTCK 2013). The implementation of the “Green New Deal” (GND) has coincided with both an increase in CO2 emissions and GDP growth (Sonnenschein et al. 2015). According to these preliminary findings, the GND may have spurred economic growth but it may not contribute to abating CO2 emissions or moving towards a low-carbon economy.

To achieve a significant emission reduction in a timely manner, the steady (incremental) improvement of existing technologies and their wider diffusion will not suffice. The development of radically new low-emission technologies will be necessary. However, these "breakthrough technologies" are hard to find - they are classic low-probability, high-payoff investments - and most companies are unwilling to invest in them due to their long time horizon and the uncertainty of the payoffs. 2 There have been few attempts at modelling the impact of climate-related innovation on emissions and temperature in a more sophisticated manner. Bosetti et al. (2012) use a model with endogenous technical change in the energy sector and two possible solutions: a global cooperative solution and a 2

See figure A1 in the annex for examples of (capital intensive) high risk technologies.


solution where every state seeks to maximize its own benefits. Their findings indicate that all innovation policies - when applied alone - fall short of achieving a carbon concentration stabilization. In the presence of a carbon tax, they find that the best option is to invest in “advanced technologies” R&D, whereas investment in “energy efficiency” R&D leads to almost no change with respect to the BAU scenario. Further, their findings indicate very large gains to international cooperation on R&D, which internalizes knowledge spillovers. For policy this means, removing barriers to knowledge diffusion and shortening the ‘patent protection’ 3 of inventions may be efficient. Box 2. Pushing for breakthrough innovation: The Global Apollo Programme (GAP) Inspired by the Apollo Programme, which placed the first man on the moon, the GAP is a UK-based initiative chaired by leading experts and practitioners that urges Heads of Governments to commit to an ambitious international R&D agenda in order to push breakthrough innovation in climate change technologies. The global public community currently spends $6 billion on renewables RD&D, $101 billion on renewables production subsidies and a stunning $550 billion on fossil fuel energy subsidies. GAP wants to change this by committing participating governments to spend 0.02% of GDP each year from 2016 to 2025 on RD&D. Governments can spend the money at their discretion, but a Roadmap Committee will lay out key challenges and technological bottlenecks and prioritize areas (much like the expert elicitation discussed in chapter 3.2). Currently there are three selected focus areas: renewables, storage and transmission. The target is that “new-built base-load energy from renewable sources becomes cheaper than new-build coal in sunny parts of the world by 2020, and worldwide from 2025”. The programme is expected to start early 2016 with a secretariat at the IEA in Paris. Source: King et al. (2015).

Acemoglu et al. (2012) show that public R&D support can trigger a restructuring of the economy that would otherwise not take place and Aghion et al. (2014) show that innovative activity depends on expectations about future technology costs: If governments manage to commit to stringent environmental regulation in the future, the relative cost of clean technologies declines today. 4 A classic dilemma: It is an illusion that a government agency can predict the development path of an economy and invest accordingly. But while discretionary support of this kind will inevitably lead to the government picking some losers, it is also an illusion to hope for the private sector to come up with disruptive (breakthrough) technologies without targeted support. 5 Why is this so? Experience shapes firms' innovative activity. This is partly because firms are reluctant to destroy wellfunctioning business models and partly because their internal knowledge systems are structured in a way that makes disruptive changes less likely. This is "path dependence". Aghion et al. (2014) describes three such path dependencies in innovation processes. First, scientists go into well-funded and crowded areas, second, existing infrastructure makes deployment heavily path dependent (e.g. it is much harder to bring an electric car to the market than it is for a classic car because the gas station infrastructure already exists) and third, technology adoption is less costly for incremental innovation 3

Time until a patented technology can be used without paying licencing fees.


In order to shift the technological pathway, both the initial conditions (e.g. green R&D and the infrastructure for green technologies) and the expectations (e.g. committing to a path of carbon price and climate policy) have to be aligned (see Aghion et al. 2014). Major CO2 emitting companies like Exxon Mobile, BP, Shell and Total already start using an internal carbon price for investment decisions, which is much higher than the EU ETS or the California cap and trade price (The Economist 2013). 5 The private energy sector spends particularly little on R&D. Major international wind and solar equipment manufacturer spend under 2% of their sales revenue on R&D, whereas this figure is up at 5% in consumer electrics and 15% in pharmaceuticals (King et al. 2015).


due to network externalities. This is why public subsidies should not go to R&D but should also support deployment. 6 A well-known example of the difficulties to change pathways in the automobile industry is Tesla Motors Inc. The company has strategically chosen to make all its patents publicly available for free, voluntarily forfeiting potential licence revenues. The motivation for this strategy was most likely the insight that producing the best electric car only leads to significant revenues once more adopters join and infrastructure is developed on a large scale. In effect, the logic behind the move clearly illustrates the power of system path dependencies and the struggle of new firms to break them. Initial public investment suffices in order to break these path dependencies. Acemoglu et al. (2012) demonstrate that public support has to be temporary to allow for the development of green technologies until the switching costs are low enough for market forces to become the driving force. 7 Because governments face budget constraints, support mechanisms usually aim at sectors or technologies. If the choice is not be completely random, this requires prior analysis to estimate the probabilities of success for different support plans, a point to which we now turn.


For a study related to motor vehicles see Dechezlepretre and Calel (2015).


A set of questions, that will be omitted in this paper is centred around the issue of return on investment (ROI). If governments act as “innovation-enablers�, then the question is to what extent they reap potential benefits. Future arrangements should make sure that governments participate more strategically (beyond tax returns) in the return of innovation. An example would be systematic public equity participation (see also Mazzucato 2013, TED Talk).


• • • •

• • •

• • • •

A – KEY FINDINGS Climate change risks are a function of cumulative risks – early action pays off - but many of the benefits accrue in the longer term. Path dependence is a structural phenomenon present in various fields and can lead to a lock-in of current technologies despite unfavourable cost-benefit ratios. Policy mixes are required, with pricing being a necessary but not sufficient condition for green innovation. Targeted/differentiated public technology support must be temporary and exit strategies well defined. B - KNOWLEDGE GAPS Except with respect to specific policy instruments (e.g. tradable permit systems), systematic evaluation of GG policies is missing even in leading countries. The characteristics of firms (i.e. age, size, sector) which initiate green innovations (relative to innovation in general) are unknown. There are few examples of credible policy exit strategies in cases where a policy is either not meeting its objectives or support is no longer required. C - POINTS FOR DISCUSSION International R&D cooperation promises large benefits, but rarely enters international negotiations. A new framework for green patents with shorter protection periods may enable faster diffusion, thereby realizing more of the potential innovation gains. GG policy should move to a “central bank system”, where expectations are set into the future and the policy horizon is at least 2-3 years. Governments should put in place mechanisms to reap the benefits of successful ventures initiated with public capital.


3. WHICH HORSE(S) TO PUT YOUR MONEY ON - THE MOST PROMISING TECHNOLOGIES Under-investment in research due to positive externalities, which are not captured by the original investor, is well documented. In the case of environmental research, this problem is compounded because pollution is too often a (free) negative externality and this in turn makes the social benefit of any action exceed the private return. Policies such as R&D subsidies and public research on the one hand; and, taxation of environmental bads and regulatory measures on the other hand, seek to address these respective failures. However, even in the presence of measures, which seek to internalise both the knowledge and environmental market failures, there is likely to be under-investment in the kinds of technologies, which bring about far-reaching structural transformation of the economy for at least three reasons: i)

“general-purpose technologies” are likely to have particularly high levels of positive spillovers on other technologies, inducing innovation across the economy;


“radical” innovations, which are of particularly low probability but potentially very high return investments are notoriously under-financed; and


“public good technologies” in which public policy plays an important role in shaping the market, are subject to a level of regulatory risk, which compounds commercial and technological risk.

Indeed, governments have often paved the way for new technologies where markets did not yet exist. Disruptive technologies that have originated in public research include nuclear power, personal computers, semiconductors, the development of the internet (CERN) 8, the touch screen, GPS, Apple’s Siri and an estimated 75% of breakthrough drug developments. 9 Even when governments were not directly part of the research that led to a new general purpose technologies (GPTs), often private enterprises benefited from grants and early stage government funding (e.g. Nokia in Finland, Google, Apple, Intel or Compaq in the US). Hence, it matters, which technological domain receives public support. The table below tries to summarise possible vectors of differentiation.


Keen (2015) documents the development of the internet from an academic public non-profit network to a privately owned global web. Using anecdotal evidence, he argues that the milestones in hard- and software developments (computer-tocomputer communication or TCP/IP protocol) are the results of public research. 9

See Mazzucato 2011, The Entrepreneurial State in Aghion et al. 2014, Mazzucato 2013 (TED Talk: and Rutan 2006.


Table 1. Vectors of differentiation for public innovation support • • •

Private enterprises Private or private-public research institutes Public universities

• •

Dirty technologies (e.g. coal, oil) Clean technologies (e.g. renewable energy, energy efficiency in buildings, etc.)

Technological (narrow)

• • • • • •

Photovoltaics Hydro Biofuels Nanotechs IT Etc.

Firm structure

• • • •

Multinationals Large national companies SMEs Start Ups


Technological (broad)

Policy makers have different assessment methods at hand to direct public technology support. 10 In this section, we focus on two methods, which are exploratory in nature, so the conclusions should be interpreted with caution. The first approach can be summarized by the term "early ex post studies". It uses past data on innovation and knowledge to estimate future probabilities of success. Such methods assume linear independence among different technology support schemes (see Akram 2012) and decreasing marginal returns to innovation. At the same time, many studies have found significant (non-linear) spillovers and cluster effects in innovation. Therefore, it is very likely that supporting one technology has an effect on the innovative activity and the probability of breakthrough innovations in other (unfunded) areas, which cannot be captured with these methods. The second strand of literature, expert elicitation (EE) studies, tries to estimate expected future technology costs by analysing expert opinions. Their great advantage is the ability to make discontinuous and non-linear guesses about future developments, to quantify uncertainty and to draw upon knowledge that may be concentrated among experts and unavailable to the public (Verdolini et al. 2015). 11 They can also incorporate so-called “option values”, the utility of having a certain option available in the future, and extreme events. Models and simulations usually struggle to incorporate extreme events (tails of distributions) and take averages. As noted by Bosetti et al. (2015) “the best alternative under uncertainty is not necessarily the average of the best alternatives under the range of inputs”. However, while structured, they are nonetheless "educated guesses", and not surprisingly Arts et al. (2013) show that ex-post indicators are more reliable than ex-ante indicators.


See OECD (2015) for a general discussion on the governance of innovation.


The capacity to depict discontinuous change in EE is unclear: Often EE studies suffer from path dependency bias too (see e.g. Rai 2013 or Krueger et al. 2012 on experts’ ignorance). The bias results from the fact that participating experts have passed their career in the existing industries using existing business models. Intentionally (to defend prior decisions) or unintentionally they are biased towards the status quo.


Early identification of potentially promising technologies can reduce the "window" of potential recipients and define a technology portfolio. As noted above, this is a hazardous exercise. In fact, predictions have a rather poor track record in many fields (see e.g. Armstrong and Sotala 2015), therefore rather than talking about predictions this paper should be seen as a guide to estimate relative probabilities of different pathways (or technologies). It should be emphasised that besides mechanisms to identify specific technologies warranting public support, it is an imperative to have in place processes whereby such support mechanisms are evaluated and reformed on a regular basis.

3.1 EARLY EX-POST TECHNOLOGY IDENTIFICATION As one important recent contribution, Dernis et al. (2015) uses data mining to identify technological areas in both scientific publications and patents, which grow and accelerate faster than others.12 Using the data from 1996-2011 they identify technological and geographical areas, where what they call “burst-growth” has happened. The use of patent and bibliometric data allows for the identification of emerging technologies at a relatively early stage of development. It is telling that they find that environmentally relevant technologies, such as wind energy, lighting, heating, electricity distribution saw a persistent burst, as well as technologies related to electric and hybrid car systems. Nanotechnologies - which can also have environmental applications - also burst widely in the late 2000s (see Table A1 in the annex). The paper also highlights where patent bursts happen simultaneously, indicating fields where there may be spillover and cross-fertilisation effects. As noted in the OECD (2010) this can be particularly important in the environmental sphere since many relevant technologies "build on the shoulders" of a very diffuse set of fields. Figure 1 shows the top 50 co-occurrences of patent classes by burst intensity (size of bubble) for patents filed at at least two of the five most important patent offices. A dark blue ring on the outside points out an increase in co-occurrences, whereas a light blue indicates a decrease. Digital data, communication and wireless is the largest cross-fertilisation cluster, but from an environmental point of view the energy storage and distribution cluster, the battery cluster and the lighting cluster are more relevant. For the others is it difficult to say to what extent they include energy saving technologies (e.g. energy efficient computing, communication and data storage).


See Haščič and Migotto (2015) for a discussion of use of patent data to develop measures of environmental innovation.


Figure 1. Cross-fertilisation of patented technologies 2001-03


IPC code H05K

Digital data acquisition

H05B H04W H04N H04M H04L

IPC Classes

Energy storage and distribution for electric and hybrid vehicles



H02J H01M

Wireless and digital communication


H01L H01J H01B G08B G07F G06T

Computers and data processing

G06Q G06G


G06F G03F G02F

IP5 patent families

F21Y F21V C40B

100 families Nanotechnology

Digital devices and cameras

C12P B82Y

1 000 families

5 000 families


A01 - Agriculture A61 - Medical science A63 - Sports, games, etc. B05 - Spraying, atomising, etc. B32 - Layered products B60 - Vehicles B82 - Nanotechnology C07 - Organic chemistry C09 - Dyes, paints, etc. C11 - Oils, fats and waxes C12 - Biochemistry C40 - Combinatorial Technology D06 - Treatment of Textiles F21 - Lighting F24 - Heating, ventilating, etc. G01 - Measuring, testing G02 - Optics G03 - Photography, etc. G05 - Controlling, regulating G06 - Computing G07 - Checking-devices G08 - Signalling, transmission G09 - Educating, display, etc. G11 - Information storage H01 - Basic electric elements H02 - Electric power H04 - Electric communication H05 - Electric techniques n.e.c.


IPC code

Source: Dernis et al. (2015)

Building on work by Squicciarini et al. (2013), Egli et al. (2015) have taken a first step at defining indicators for climate change innovation in order to explore the underlying characteristics of technologically and commercially successful green inventions. More specifically, the attributes used for the analysis are: •

Originality – (Tratjenberg et al., 1997) – an indication of the “breadth” of the technology fields on which a patent relies.

Radicalness – (Shane, 2001) – an indication of the extent to which a patent relies on previous inventions from fields other than its own.

Industrial Generality – (modified from Bresnahan and Tratjenberg, 1995) – an indication of the range of sectors of firms who subsequently cite a given patent. 13

Family size – (Lanjouw et al., 1998; Guellec and van Pottelsberghe de la Potterie, 2000) – an indication of the number of markets in which a patent is protected.

Closeness to Science – (Tratjenberg, Henderson and Jaffe, 1997) – an indication of the extent to which a patent draws upon the scientific literature rather than patents.

They assess these indicators according to different measures of "success" including: growth in subsequent patenting rates (1 and 2); commercial applicability (3); 14 and attractiveness to risk finance


The industrial generality measure corresponds to the Hirschman-Herfindahl Index measuring the distribution of patent shares across industry sectors of the patent applicant (firm). E.g. if all patents in technology X were filed by firms in the same 3-digit NACE industry, the index would equal zero for technology X. 14

Defined as the % of assignees of patents in a given field which are private firms.


(4) 15. The results (Table 2) indicate that radical inventions are not generally more successful later on in terms of the different measures of technological and commercial success, except insofar as they attract more risk finance (4). 16 Perhaps, success of a radical technology reveals itself through spillovers to other fields, which are not captured by this empirical approach. However, other quality indicators, such as industrial generality, seem to be better predictors of the future success of a technology. This means that the broader the range of sectors of innovative firms in a given technology, the more successful this technology is expected to become. This could be taken as measure of diffusion; general or broad technologies are more successful. Table 2. Predictors of technological diffusion 17

Industrial generality Radicalness Closeness to science Family size Observations R-squared

(1) Patents granted 2001-2010 + *** - *** + -

(2) Patent applications 2001-2010 + *** - ** + ** +

(3) Applicability 2001-2010 + ** - *** - **

(4) Risk finance 2001-2010 + *** + *** + *** + ***

266 0.80

266 0.80

266 0.42

266 0.45

***, **, * indicates statistical significance at the 1%, 5% and 10% level respectively. Source: Egli et al. (2015)

Taking a closer look at single technologies, photovoltaic energy generation emerges as the most promising one, whereas hydrogen technology appears to be a risky but high potential bet for the future, which is likely to attract a lot of risk finance. Biofuels show similar attributes as hydrogen technologies. Focusing only on the indicator of industrial generality, fuel cells, energy storage (e.g. for electro-mobility), solar thermal energy and fuel from waste are other promising technologies. Three main conclusions emerge from these observations. First, at least some ‘climate mitigation relevant technologies’ look more promising than the technology-neutral counterfactual subsample (see figure A3 in annex). Second, most of the promising technologies are located in the subcategory ‘energy’ (other subcategories are buildings, carbon capture and storage, and transport). This is also the field responsible for the largest share of the rapid growth of environmental patent applications starting around 2000 (see figure A2 in annex), which is an encouraging observation. And third, many of the most heavily subsidised green technologies (e.g. hydro energy, wind energy or nuclear fission) rank lower than the counterfactual subsample for all indicators. This is an indication that current policy may be well intentioned but aimed at the wrong technologies.

3.2 EX-ANTE TECHNOLOGY IDENTIFICATION As an alternative to reliance upon early indicators derived from patent and bibliometric data, ex ante expert elicitation methods have been applied commonly in the energy field, and in this section the


Defined as risk finance investments in projects using given technologies, using data drawn from the CleanTech data base.


The significant negative effect of the indicator radicalness should be read with caution. When replacing radicalness with originality, this effect disappears. The effects of the other indicators are robust. 17

Controls and several other variables omitted.


work of Bosetti et al. (2015) 18 is highlighted, which is referred to as “the study� for this section. The study focuses on low-carbon energy technology expert elicitation studies undertaken since 2006, with the intention to inform decision making in energy (R&D) technology portfolio. It analyses, standardizes and compares more than 20 different studies in the following: technological domains: nuclear, solar, bioelectricity, biofuel, CCS, vehicles and others. It also critically analyses metaanalysis studies, which control for methodological differences to get robust estimates. This section discusses first the cost estimates for different technologies, second the key bottlenecks for the diffusion of some of these technologies, and third the issues identified in the meta studies analysed in Bosetti et al. (2015). The study collects and shows the best-case, termed "breakthrough", median and worst-case estimates of the levelized cost of electricity (LCOE) 19 for 2030 by study for a standardized low, medium and high-R&D scenario. Two key results emerge. First, experts estimate lower future costs for higher R&D scenarios. This result is not trivial, it forms the backbone of any argument for public R&D; if R&D programs were not contributing to lower future technology costs, the rationale for it would vanish. Second, it is fair to say that most estimates are not very optimistic. Compared to current LCOE, the improvements are not very large, even in high R&D scenarios. Currently only hydro and onshore wind are competitive compared to fossil technologies. According to optimistic experts, all renewables will be cheaper than today’s fossil fuels by 2030 (assuming fossil fuels do not become cheaper with time); pessimistic scenarios question this finding however. This underlines, how difficult predictions are and how uncertain the development of renewables is, even more so in low R&D scenarios. Although the prediction of cost levels is difficult, an analysis of meta studies in Bosetti et al. (2015) shows that, with the exception of solar PV, R&D scenarios do not significantly influence the uncertainty (range of cost estimates) confirming some robustness with respect to uncertainty. However, looking at Figure 2, which reports the LCOE declines in solar PV over the past 5 years, the picture is brighter than predicted by experts. We will see much lower LCOE by 2030 if such rates of decline are to persist. This could be because some elicitation studies date from as early as 2006/07 and the renewable energy market is changing faster than ever, making forecasts difficult. It is certainly food for thought for optimists - as early as after 2-3 years, market developments may outpace expert guesses for a 25-year horizon.


Bosetti et al. (2015) summarizes expert elicitation works on (green) technologies of research groups at UMass, Harvard, FEEM, Duke, CMU, NRC, NearZero, Stanford, GHG MI, and UCL. Most of these studies compile new data by asking experts in various ways to estimate future expected technology costs depending on a hypothetical R&D spending scenario. Bosetti et al. (2015) provides an excellent overview across these studies and makes their future expected cost estimates comparable. It also includes the most recent meta-studies in the field. 19 The LCOE attempts to compare average total costs across different generation methods. The metric reports building and operating costs divided by total power output over the assets lifetime. A simple method of calculation (excluding financial and discount rate aspects) can be found here:


Figure 2. LCOE developments for solar and wind 2009-2015 (left) and current cost comparisons (right)

Source: BNEF (2015), BNEF (2014)

By summarizing experts’ opinions on key bottlenecks for the development and diffusion of breakthrough technologies Bosetti et al. (2015) offer another angle to the question where public money should be directed to. Table 3 summarizes the top three bottlenecks that experts mentioned. The experts were also asked whether they see the solution to the identified bottleneck in terms of policy intervention, additional investment, education or marketing. Usually they identify policy intervention as the most important solution; if additional investment is seen as an important solution too, this is marked () in the table below. Investments are seen as helpful to avoid intermittency when moving to solar PV (grid infrastructure), to provide the infrastructure for batteries (e.g. the electric fuel stations mentioned in the introduction) and to ease the land use competition in biofuels (e.g. increasing crop yield efficiency). To the extent that there is fundamental uncertainty about the importance of different barriers, such findings from EE studies can be useful guides in designing government support schemes alongside more traditional forms. Table 3. Bottlenecks to breakthrough technologies Bioenergy Solar PV Batteries Biofuels

1st Food vs. energy competition for water Intermittency ()

2nd Environmental externalities Long lived capital stock

Behavioural changes are difficult Food and land use competition ()

Infrastructure is lacking () Environmental externalities

3rd Food vs. energy competition for land Unfavourable power pricing rule Critical mass of users needed for adoption Geographical constraints

Source: Bosetti et al. (2015)

More specifically, it is possible to formulate policy recommendations for biofuels, batteries for electric vehicles and solar PV drawing on EEs carried out as part of the ICARUS project: 20 • •

Demonstration and application should become a key area of funding; Without carbon policy (or significant R&D increase in the case of batteries) none of the technologies will become cost-competitive by 2030; and

20 Fiorese et al. 2013 on biofuels, Catenacci et al. 2013 on batteries for electric vehicles and Bosetti et al. 2012 on solar PV are part of the ICARUS (Innovation for Climate chAnge mitigation: a study of energy R&D, its Uncertain effectiveness and Spillovers; project.



The majority of experts believe that by 2050, no more than 20% of vehicles will run on biofuels and they estimate the likelihood of 21%-30% solar PV penetration in electricity supply at less than 50%. 21

As this is an emerging field of research, some caveats are in order. As noted by Bosetti et al. (2015) there is little research on quantifying the effect of different elicitation modes. There are, for example, documented differences between US and EU experts (see also Box 3) and between paper and online surveys. Another unresolved issue is the optimal number and sampling of experts. The composition and size of the pool of experts obviously matters (e.g. public servants vs. private entrepreneurs or national background). Further, the technology definition matters. When descriptions are broad, experts think of certain sub-fields and depending on what they have in mind, there may be a large bias. Finally, estimates can differ between residential, commercial, and utility for every technology. Failing to account for these differences may lead to uninformed decisions.22 In order to better account for such biases Bosetti et al. (2015) take a closer look at three meta studies. 23 Overall, these studies point to two broad conclusions: 1) Higher R&D levels are associated with lower future cost estimates, confirming previous findings. 2) Depending on the technology, expert background (e.g. academia or business), survey mode and geographic location (e.g. US or EU) may influence future expected costs. This means controlling for such characteristics and selecting a balanced pool of experts for surveys is crucial. Of the three meta studies, Verdolini et al. 2015 is particularly interesting because of its focus on photovoltaics, which is one of the most widespread and promising renewable energy sources. It standardizes 65 EEs from 5 studies for the 2030 cost of photovoltaics. For solar PV, unlike nuclear, expert affiliation and nationality do not matter. This indicates a greater consensus among experts on the impact of R&D on future solar PV costs. Survey modes still matter though. In person studies tend to produce more optimistic cost estimates with lower confidence (higher range) than online surveys. Interestingly, high R&D scenarios decrease the low cost (breakthrough) estimates more than the high cost (worst case), leading to the larger uncertainty. 24


For OECD and developing countries it is 40% and 39% respectively; for fast growing countries it is only 27%.

22 For an in-depth review of the effects of survey characteristics, expert selection and proposed R&D budgets for different technologies, see the discussion of three such meta studies in chapter 4.4, Bosetti et al. (2015). 23

Anadon, Nemet and Verdolini (2013), (2015) and Verdolini et al. (2015).


For a more general and theoretic discussion of uncertainty quantification in EEs and technology forecasts and definitions of knowledge, see Akram (2012).


Box 3. The importance of the country focus Liu et al. 2011 use a Delphi survey to rank 55 alternative automobile technologies in five sub fields (alternative fuel, hybrid power, fuel cell, industry-specific, clean diesel) in China. The ranking uses a weighted “integrated importance indicator”, which takes into account elicitations on a technology’s “degree of importance to China”, “China’s capabilities today” and “technology sustainability”. This is an interesting approach because the weighting makes assumptions with respect to the relative importance of the areas explicit. The weight distribution for the final composite indicator is 60-20-20, making the importance of the technology for China the prime ranking indicator. The top 10 were surprisingly concentrated in two subfields. 60% of clean diesel and 40% of hybrid power technologies made it into the top 10, whereas only 25% of industry-specific, 10% of all alternative fuel and 0% of fuel cell did so. This indicates that the priority for the Chinese automobile market are not uncertain future breakthrough technologies (fuel cell, alternative technologies like biodiesel, alcohol fuel etc.) but rather mature technologies like hybrid and clean diesel. This is somewhat different to other findings and could point to the issue that existing expert elicitation studies, which rely largely on experts from the US and the EU may not be applicable to a wider range of countries.

Finally, Bosetti et al. (2015) stress that policy needs to take into account two sides of the equation, when analysing technology potential. First, the theoretical potential, which is the subject of this paper, but second also the importance of that particular technology in the economy and in climate change dynamics using integrated assessment models (IAM) and simulations. Combining technology experts’ recommendations with energy-economic modelling Anadon et al. (2011) recommend that: the U.S. federal government increase its annual investment in ERD&D to $10.0 billion, a 92% increase over the FY 2009 appropriations. This increase should proceed at the maximum rate consistent with efficient expenditure of the funds. Experts advise to increase RD&D budgets by a factor of 3 to 10 depending on the technology. The largest increase (in % terms) is recommended for energy storage, which is also one of the promising technologies identified in Egli et al. (2015) and Dernis et al. (2015) with regards to cross-fertilization (see Figure 1). Fossil energy, solar PV and bioenergy receive a recommended three-fold increase. It is revealing that experts support increases for all technology classes.25 The validity of the results of such studies is crucial for policy, because they indicate large efficiency gains if the current R&D spending allocation were to be reformed. The recent launch of “Mission Innovation” by President Obama and other world leaders at the COP-21 in Paris shows that governments are starting to see the necessity and the benefits of large-scale green innovation programs. The initiative brings together 20 countries (incl. Brazil, China, India, Indonesia and the US) covering 80% of global clean energy R&D in a commitment to double their clean energy R&D over five years. It also puts emphasis on the publicprivate R&D cooperation by working together with the Breakthrough Energy Coalition, a group of 28 large private investors. However, systematic understanding of the full implications and the trade-offs (i.e. crowding out/in, fiscal implications) of such large increases in public R&D expenditures is a task for future research. Instead of relying on experts’ knowledge, the use of information technology to tap the knowledge pool of the many is an alternative option. One option to do so are prediction markets, which have 25

Anadon et al. (2011). This reaffirms the observation that current R&D expenditure is very low (see box 2) and the private energy sector is particularly inactive (see footnote 5).


widely been used in political science. 26 In theory, (political) prediction markets should outperform polls (Kou and Sobel, 2004), which is one of the reasons why many policy experts call for a more widespread use of markets to improve (public) decision making (Arrow et al. 2008). However, two observations diminish the appeal of such markets. Erikson and Wlezien (2012) use historical data back to 1880 and find that (political) markets did remarkably well before polls existed, but are influenced and even outperformed by them once polls become more sophisticated. 27 Moreover, the potential gains in accuracy may be very small compared to simple statistical predictions and come at a large cost as Goel et al. (2010). Hence, while there may be important theoretical and anecdotal advantages of market mechanisms, it is unclear whether this is the case for technology cost predictions depending on R&D scenarios. First, the time horizon may be too long. If the payoff lies too far in the future, a market may not develop. Second, it is unclear why an artificial market should outpace real markets for different technologies, which should incorporate expectations about future policy environments too when taking long-term investment decisions. Third, the set of alternatives may be too large for markets to function. This said, it is still crucial for policy decisions to rely more on the broad public to ensure that the public supports and gradually adopts technologies (and policies). An example for such interactions in policy design is the redrafting of the Icelandic Constitution after the financial crisis. A national forum provided the guidelines, and the committee of 25 non-political drafters interacted with the public through social media to receive more than 3600 comments. 28 Another example is the European Union’s recently completed Digital Futures project on imagining future trends and policy making using an online platform dubbed “Futurium” (DG CONNECT 2015). Through this platform, it was possible to engage with more than 3’500 participants in more than 100 brainstorming events and 30 webinars. An online community of around 1’800 members helped identifying trends in 11 themes in the dimension people and systems. There is a large potential to use such crowd intelligence methods to identify focus areas for discretionary green technology support – especially because they are able to involve the young generation, which will be most affected by these decisions.

26 In one of the seldom (and infamous) cases, where prediction markets were designed to inform public policy, DAPRA set up a prediction market project on the “future of the middle east”. This policy analysis market was quickly withdrawn due to widespread criticism on the speculation on events related to violence. Another widely accepted project (not for policy information though) is the Iowa Election Market, which is mainly used to predict Presidential Elections in the U.S. 27 A careful comparison accounts for the fact that a market system displays the participants’ expectation about the outcome on day X, whereas polls display the participants intention to do something on day X at the day of the poll. 28

The Constitution was accepted in a nation-wide vote in October 2012 but the bill was subsequently stalled by Parliament, which put an end to the bottom-up project.


• •

• •

• •

• •

A – KEY FINDINGS Green technologies suffer from a two-fold under-investment bias. First, private investors struggle to capture positive effects from their investment. Second, negative effects (e.g. pollution) are not priced and therefore often unconsidered in investment decisions. This bias is potentially worse for green GPT or green breakthrough technologies. According to experts, without a carbon policy, green technologies will not become costcompetitive by 2030. Expansion and efficient allocation of public R&D could realize large gains. Technologies developed by firms with a wide industry portfolio (“general”) are more likely to be successful and to achieve broader commercial application and attractiveness to investors in the future. Green (energy) technologies seem to be more “general” than the average technology. Some of the most successful breakthrough technologies (e.g. the internet, GPS, breakthrough drugs) initially received public support. Given the high degree of technological and market uncertainty both ex ante and early ex post methods for the identification of promising technologies are useful exercises, but need to be housed inside a dynamic evaluation and reform process. B - KNOWLEDGE GAPS There is a need for improved understanding of the methods governments use to allocate support in a differentiated manner across technology fields. A shift in public R&D portfolio may be needed, but budget constraints and equity considerations are too seldom taken into account (i.e. trade-offs in public money allocation). The combination of integrated climate change policy modelling with experts based estimation of future technology costs remains relatively rare. Improved understanding of the relevance of cross-fertilisation in the environmental domain relative to other fields is required. A first step is the mapping of technology cluster evolution. The role of societal (consumer) pull factors and the optimal inclusion of the public in decision-making processes should be analysed further. C - POINTS FOR DISCUSSION How should policy react to a technology burst? Should policies support trending technologies or withdraw support from them? What is the optimal time of exit? Suggested optimal increases in public R&D are at least 3-fold (up to around 10-fold). What are the trade-offs in increasing R&D budgets? How can positive (long-term) impacts be communicated? How can expert knowledge best be combined with the “wisdom of the crowd” to optimize decision-making?


4. FIRM DYNAMICS AND EMERGING TECHNOLOGIES 4.1 YOUNG FIRMS AS VEHICLES OF RADICAL INNOVATION The previous section has focused on methods for the identification of promising technologies. In this section, the links between firm dynamics (entry, growth and exit) and the generation and diffusion of breakthrough innovations will be discussed. Intuitively these issues lie at the heart of the "greening" of economic growth trajectories. This is because "new" firms are often the vehicle through which radical innovations enter the market, and because older incumbent firms may have an incentive to focus on incremental innovations to established technologies. Moreover, the link between firm dynamics and the emergence of breakthrough technologies is a concern because start-up rates have been declining across OECD countries in recent years (see Figure 3). Figure 3. Start-Up Rates for a Selection of Countries

Source: Criscuolo, Gal and Menon (2014)

The importance of firm dynamics for innovation arises from the common observation that firms that have been responsible for one technological wave are not generally pioneers in subsequent waves (Benner and Tushman 2002, Henderson 1993, Tushman and Anderson 1986). To a certain extent, this is borne out in Table 4, which reports on recent research indicating that there is a negative relationship between firm age and two indicators of the degree of "radicalness" or "novelty" of patented inventions. 29 For example, the estimates in column 2 imply that the number of citations to the nonpatent literature (which as noted in Section 3 above proxies for the closeness of patents to science) could be expected to be about 16% higher for a relatively young firm aged 5 years compared to a firm aged 30 years. With the average age of firms increasing as start-up (and exit) rates fall the "flow" of new radical inventions may well slow. 30


For a discussion of the measures of radicalness see Squicciarini, Criscuolo and Dernis (2013).


Although it must be emphasised that other structural or technological changes may well be changing the relationship between firm age and propensity to innovate in terms of breakthroughs. Further work is required on this issue.


Table 4. Young Firms Patent Stock is More Radical and Closer to Science

Constant Firm Age (log) Firm Fixed Effects Sectoral (NACE) Fixed Effects Observations

(1) Median "Radicalness" 0.381*** -0.00318** Yes Yes 33,544

(2) Median Citations to NPL 0.667*** -0.106*** Yes Yes 33,544

***, **, * indicates statistical significance at the 1%, 5% and 10% level respectively. Radicalness refers to the number of different IPC classes which are different from the one to which the patent belongs NPL - refers to citations to non-patent literature. Source: Andrews et al. (2014).

Moreover, in the environmental context the links between firm dynamics and the emergence of radical technologies may be particularly important for at least two reasons: A) Political (or regulatory) uncertainty co-exists with market uncertainty, which can have important disincentives for investment in relevant technologies; and, B) Many of the environment-intensive sectors (e.g. energy, transport) are sectors in which other market attributes (e.g. network effects, long-lived capital) can provide additional barriers to firm entry. Indeed, there is supporting qualitative evidence indicating that in the environmental sphere, "young" firms are a greater source of patented inventions than for inventions in general. Figure 4 presents data on the distribution of firm "ages" (years since incorporation) of the assignee of patents related to climate change mitigation (Y02) and for a sample of firm assignees in a randomly selected set of classes. The results indicate that mean age of Y02 patenting firms is 1.5 years less than for the counterfactual, with a significant leftward shift of the distribution at very young ages. This difference appears to be even more relevant for the electricity sector. Figure 4. The Importance of “Start Ups� for Innovation in Climate Mitigation (Firm Age of Patenting Firms for Climate Mitigation Technologies (Y02) and Technologies in General)

Source: authors' calculations. 31


Thanks to Carlo Menon for generating these figures based on extractions from EPO World Patent Statistical Database, linked with firm-level data from Bureau van Dijk's Orbis database.


4.2 THE ROLE OF PUBLIC POLICY The role of policy is central. As discussed in Andrews, Criscuolo and Menon (2014), insofar as younger firms have a comparative advantage in more radical innovations, evidence that policies have a disproportionate effect on younger firms is a concern. This may be an incidental outcome of the fact that regulations generally impose a fixed cost on firms, which disproportionately affects firm entry and the capacity of young firms to grow. 32 As discussed by Heyes (2009) this arises from two factors: •

Most environmental regulations generally increase the fixed capital required for production; and,

The nature of the capital investment is a function of the characteristics of the regulations, and these can be very different across different policy regimes.

Together these two factors indicate that more stringent regulations lead to more of the fixed costs of entry being irrecoverable at exit. 33 Moreover, a third factor can be added to these two, namely: •

If the regulation changes over time within a given jurisdiction, then demand from other firms for the capital within that jurisdiction will fall correspondingly.

In summary, environmental regulations can result in higher fixed costs, which the investor is not able to recover if market or policy conditions changes. This results in lower contestability, and thus less market discipline for incumbents and less penetration of market entrants. For example, Helland and Matsuno (2003) estimate the effect of industry-level environmental compliance costs (as measured by the EPA's survey on Pollution Abatement Costs and Expenditures) on financial performance for over 1000 U.S. firms over the period 1983-1994. The authors find evidence that environmental regulation allows firms in the top quartile to earn above normal profits, reflecting reduced market competitiveness and barriers to entry. Thus far, the argumentation presented has assumed that the regulation is – at least statutorily – neutral with respect to firm age, even though the implications can be very different for entrants and incumbents. The bias set out above arises out of the effect of an age-neutral regulation on entry and exit. In this case, there is not policy failure per se. However, in the environmental-innovation domain, the problem can be one of explicit commission, i.e. a function of intentional policy design, which treats entrants and incumbents in a differentiated manner. Indeed, in practice it is very common for environmental policies to treat production units of different ages in a differentiated manner, with older plants facing relatively less stringent regulation. In some cases, this may even take the form of ‘grandfathering’ whereby units, which have been in operation before some threshold date, are exempted entirely from some new regulation. While there may be a "static" economic logic to such differentiation, this generates a cost advantage of established incumbents over potential entrants, with important negative "dynamic" consequences. It can further slow capital turnover and exit rates for existing sources, and reduce new investment and entry of new 32

Bain (1956) defines barriers to entry as “… the advantages of established sellers in an industry over potential entrant sellers”. Stigler (1968) refers to “… a cost of producing which must be borne by a firm which seeks to enter an industry but is not borne by firms already in that industry.” 33

For a discussion of the importance of the latter see Johnstone and Haščič (2010).


firms. The extent to which this is materially important is largely a function of policy instrument choice and design. •

Technology-based standards are often differentiated by age with older plants explicitly facing less onerous abatement requirements. In some cases this is explicit (i.e. exemptions for plants constructed before a base year), and in other cases implicit whereby interpretation by permitting authorities may be different for new and existing facilities for example the application of the principle BATNEEC ("best available technology not entailing excessive costs"). In addition, technology-based standards generally have important implications for sunk costs since they are by nature prescriptive – mandating specific technologies, and thus constraining possibilities for cost recovery.

Performance-based standards often differentiate between new and existing sources, with the same implications as those noted above for technology-based standards. However, they may have less important sunk cost implications depending upon the degree of flexibility in the capital investment to meet varying abatement standards. In the face of policy change investors are likely to be able to recover a greater share of investment costs.

Tradable permits - the key distinction for tradable permits in terms of entry barriers is that between grandfathered and auctioned permits. However, contrary to usual assumptions the "grandfathering" of tradable permits to incumbents might not have as pernicious effects as technology-based standards since even though a rent is granted to them which is not available to entrants, the "opportunity cost" of not exiting the market remain the same as would be the case under auctioned tradable permits. However, there can be implications for financially constrained new and young firms if they have to purchase auctioned permits, which are granted gratis to incumbents.

Environmental taxes do not generally differentiate between sources by firm age and should therefore not be biased between facilities of a different age, except insofar as new and young firms may face more binding financial constraints.

The grandfathering of regulations is particularly well documented in the case of power plants (see e.g., Ellerman 1998; Levinson 1999; Bushnell et al. 2007), although most of the existing studies relate to the United States. One exception is Johnstone et al. (2015), who collected data for a broad crosssection of countries. They develop a set of country-level measures of stringency for different pollutants for “old” and “new” power plants of different sizes. These vectors are computed as the mean emission standards that apply to power plants constructed prior (old) or posterior (new) to the effective date of the regulation. Figure 5 plots the ratio of regulatory stringency for new plants over regulatory stringency for old plants. A ratio of 1 implies no bias, while a ratio higher than 1 characterises regulations setting higher standards for new plants. For a representative plant of 60 MW of thermal input, new plants have 1.6 times more stringent regulations of particulate matter emissions than old plants (for the countries in the sample).34

34 For each country, the figures on the Y-axis are the average across all the policies implemented through time. Each of the selected pollutants is plotted for a different representative plant size. The graphic shows only cases when it is possible to


Figure 5. New source bias in emission standards, by pollutant

Source: Johnstone et al. (2015)

The study then assesses the implications on productive efficiency. Indeed, based on the results of data envelopment analysis (see Johnstone et al. 2015), it is found that regulations that are differentiated by plant age have a negative effect on the efficiency of both SOX and NOX abatement (significant at the 5% level). This indicates that giving preferential treatment to existing plants over new entrants has negative consequences for the efficiency of abatement for the plant stock as a whole (See Table 5). Table 5. Regression estimates of efficiency of electricity generation with age-differentiated regulations (only coefficients of variable for age-differentiated regulations reported)

Dependent Variable




SOX Abatement Efficiency Score




NOX Abatement Efficiency Score







Coal Input Efficiency Score

***, **, * indicates statistical significance at the 1%, 5% and 10% level respectively. Source: Johnstone et al. (2015)

It is not only the “sticks” which may be biased against entrants, but also the “carrots”. For example, Figure 6 shows a variety of country situations in terms of the generosity of R&D tax incentives. Depending on design, the benefits can be restricted. Most obviously, the benefits depend not only on R&D expenditures, but also on profitability (e.g. in case of a credit on corporate income tax). This practice may be an impediment for young companies that may need a few years before they become compare standards for old vs new plants. Cases are excluded when countries regulate only new plants – i.e. complete grandfathering.


profitable. Perhaps more importantly, it provides a valued source of revenue for less productive incumbents. As such, markets in sectors where firms are subject to significant regulatory oversight and/or support mechanisms, the design of the policy may have pernicious consequence for the dynamics of firm development, and ultimately for the environment itself. Figure 6. Tax subsidy rates on R&D expenditures, 2015 1-B-Index, by firm size and profit scenario Large, profitable firm

SME, profitable firm

Large, loss-making firm

SME, loss-making firm

1-B index 0.5 0.4 0.3 0.2 0.1 0.0 - 0.1

Source: OECD, R&D Tax Incentive Indicators,, July 2015. See OECD STI Scoreboard 2015.

In addition to the regulatory and policy support environment, access to finance plays a role. And for young firms seeking to introduce more radical innovations to the market, access to venture capital is particularly important. For example, in the software and biotech sectors, venture capitalists have been key providers of finance, particularly in cases where the investment is based on an uncertain valuation, due to a lack of a clear performance history and a high technology risk. In order to balance their portfolio, venture capitalists have simultaneously tended to finance projects with low capital intensity that can show rapid commercial viability, and be sold within the life of a fund, which is usually about 10 years or so. This issue has become increasingly important for the firms involved in the environment-related sectors. Indeed, some projects in the green sector have attributes which are similar to those areas traditionally backed by venture capital (VC), such as energy efficiency software, fuel cells etc. Not surprisingly, the funding of “green” projects from such sources is relatively more important in countries where venture capital markets are already well developed, such as the United States and to a lesser extent the United Kingdom, but they are also growing in importance in emerging economies (See Criscuolo and Menon 2014). However, while it had shown robust growth prior to the crisis, risk finance in all areas fell after 2008. Lerner (2011) points out that the post-2008 decline in green risk finance is due to three additional reasons. First, fluctuations in (and ultimately falling) energy prices; second, the more conservative investment preferences following the crisis; and third, the penalization of ventures with large capital needs due to liquidity constraints. More generally, risks related to some “green” sector investment project may be qualitatively different than in more traditional target sectors. For example, in new and promising fields venture capitalists


may lack the skills needed to identify promising companies. This can be exacerbated if the project has a long horizon, since in general the longer the time horizon, the higher the financing risk. These issues can be compounded for the development and the commercialisation of “radical technologies”, including in the environmental domain. Based on these criteria, examples of projects at risk would include deepwater offshore wind farms, advanced biofuel refineries and first commercial plants for unproven solar cell technologies. As noted by Criscuolo et al (2015), successful “exit”, whether through mergers and acquisitions (M&As) or initial public offerings (IPOs), is particularly important as means of inducing VC financing. For seed and early stage VCs, the prospect of being unable to ensure a successful exit, or to raise follow-up funding before the end of the life of the VC fund is a significant barrier (Nanda and Rhodes-Kropf, 2010). There are still very few IPOs in the green sector, as shown in Figure 7. In addition, there was a big drop during the crisis. Figure 7. IPOs and M&As in the green sector, 2005-2010

Note: dollar values in real terms. Source: OECD elaboration based on the Cleantech Market Insight Database. See Criscuolo and Menon (2014).

However, there are encouraging signs due to the increasing role of major corporations as acquirers of successful start-ups (Criscuolo and Menon 2014). In line with the experience of biotech, ICT and semiconductors, this might lead to a take-off of innovation from the seed stage to large-scale deployment and commercialisation. With greater prospects for successful exits via M&As, this might generate increased supply of financing for innovative ventures in the green sectors. Policy makers can play an important role in nurturing this. Obviously, the most essential element is the implementation of policies, which internalise externalities, directly increasing returns on investment in "green" technologies. Cardenas-Rodriguez et al. (2015) provide a rich body of empirical evidence on the importance of policy conditions on private finance for green investments in technologies of different maturity. In summary, they find that a pricebased instrument, which provides greater predictability on returns, is more effective in terms of


inducing private finance for less mature technologies and sectors. However, this result only holds in cases where financial markets are well developed and efficient. In the absence of well-developed capital markets, the "environmental" policy instrument is not sufficient to induce private finance for less mature technologies. (See also HaĹĄÄ?iÄ? et al. 2015). Table 6. The effect on private finance flows; feed-in-tariffs (FIT) vs. renewable energy certificates (REQ) Support mechanism Mature tech (e.g. onshore wind) Immature tech (e.g. solar PV)


High credit information (good market) 0 + + 0

Low credit information (bad market) + 0 0 0

Source: Cardenas-Rodriguez et al. (2015)

However, above and beyond the details of policy design, and the mix of policies implemented, the role of policy consistency and predictability is paramount. Opportunities for successful "exit" are undermined if investors do not have a clear planning horizon. Therefore, early stage financing difficulties are exacerbated because of the inherent regulatory risk in the green sector, which might arise because private investors are unsure that established policies and regulation will remain in place over the longer term. (See Kalamova et al. 2013 for a discussion.) Ensuring that the new firms, which are responsible for the kinds of breakthrough technologies, discussed in Section 3 of this report requires a policy environment, which gives potential inventors and investors a degree of clarity in the policy framework over the medium-term.


• • •

• • • •

• • •

A – KEY FINDINGS New firms are generally responsible for the generation of more radical and transformative breakthrough innovations. This may be particularly true in at least some environmental fields, and evidence is presented for the case of climate mitigation technologies. There are a number of policy settings which can discourage the "entry" (and "exit") of firms and this can have negative environmental implications, as well as economic implications. Paradoxically, environmental policy measures themselves may play a role in discouraging entry and exit, slowing firm dynamics. B - KNOWLEDGE GAPS A clear understanding of how different policy settings can influence the entry of new firms with potentially radical environmental innovations is required. Improved understanding of the characteristics of firms (sector, size, age, etc…) which have been responsible for breakthrough innovations. Better understanding of the barriers faced by innovative firms in the environmental field in getting access to finance. The role that policy predictability and credibility plays relative to more general problems of market and technological uncertainty. C - POINTS FOR DISCUSSION Is it really the case that firms generating radical environmental innovations are more likely to be new firms than incumbents? Are environmental policies which are biased against entrants a consequence of political economy decisions, with governments favouring incumbents with "voice"? Is it true to say that the difficulties that firms face in obtaining financing for the development of radical innovations just reflect market valuation of the risks involved?


5. CONCLUSIONS While the adoption of existing technologies and incremental innovations are a key part of the green growth story, if climate change is to be addressed in a sufficiently ambitious and timely manner to avoid potentially catastrophic and irreversible damages, breakthrough innovations will be required. There are, however, many unanswered questions related to the policy and market conditions which are likely to lead to breakthrough innovations. This paper has sought to cast light on two important policy questions. Firstly, if discretionary (or targeted) support is required for specific technologies what guidance can researchers provide which reduces the window of potential recipients - beyond simply emphasising the importance of adopting a portfolio approach? Two exploratory methods have been presented. However, it must be emphasised that above and beyond techniques, which can be used to guide the allocation of support, it is more important to ensure that, they are housed inside a governance structure, which involves continuous evaluation, and ultimately exit. Secondly, in order for such innovations to take root, it is important that there be a business and policy environment, which allows the young firms to enter the market and flourish. They are likely to be the vehicles from which transformative inventions emerge. With falling start-up rates and evidence of policy settings which discourage entry (and exit) this is particularly important, not least since in the environmental field the role of the "new" may be particularly important.


6. REFERENCES Acemoglu, Daron, Philippe Aghion, Leonardo Bursztyn, and David Hemous. 2012. “The Environment and Directed Technical Change.” American Economic Review 102 (1): 131–66. doi:10.1257/aer.102.1.131. Aghion, Philippe, Cameron Hepburn, Alexander Teytelboym, and Dimitri Zenghelis. 2014. “Path Dependence, Innovation and the Economics of Climate Change.” Policy Paper. November. Akram, Muhammad Farooq Bin. 2012. “A Methodology for Uncertainty Quantification in Quantitative Technology Valuation Based on Expert Elicitation.” Ph.D. Thesis, 269. Anadón, Laura D., Matthew G. Bunn, Melissa Chan, Charles A. Jones, Ruud Kempener, Gabriel Angelo Chan, Audrey Lee, Nathaniel James Logar, and Venkatesh Narayanamurti. 2011. “Transforming US Energy Innovation.” Anadón, Laura Díaz, Gregory Nemet, and Elena Verdolini. 2013. “The Future Costs of Nuclear Power Using Multiple Expert Elicitations: Effects of RD&D and Elicitation Design.” Environmental Research Letters 8 (3): 034020. doi:10.1088/1748-9326/8/3/034020. Anadón, Laura Díaz, Gregory Nemet, and Elena Verdolin. 2015. “Expert Selection and Elicitation Design Affect Confidence in Probabilistic Judgments about Future Technologies”. Mimeo Andrews, D., C. Criscuolo et C. Menon (2014), « Do Resources Flow to Patenting Firms? : Cross-Country Evidence from Firm Level Data », OECD Economics Department Working Papers, No. 1127, Éditions OCDE, Paris. DOI : Armstrong, Stuart, and Kaj Sotala. 2015. “How We’re Predicting AI–or Failing to.” In Beyond Artificial Intelligence, 11–29. Springer. Arts, Sam, Francesco Paolo Appio, and Bart Van Looy. 2013. “Inventions Shaping Technological Trajectories: Do Existing Patent Indicators Provide a Comprehensive Picture?” Scientometrics 97 (2): 397–419. doi:10.1007/s11192-013-1045-1. BNEF, Bloomberg New Energy Finance. 2014. “H1 2014 Levelised Cost of Electricity – PV”. BNEF, Bloomberg New Energy Finance. 2015. “Global Trends in Renewable Energy Investment 2015.” Borup, Mads. 2003. “Green Technology Foresight as Instrument in Governance for Sustainability.” In 2003 Berlin Conference on the Human Dimensions of Global Environmental Change, 376–98. Bosetti, Valentina, Laura Diaz Anadon, Lara Laeluia Reis, and Elena Verdolini. 2015. “The Future of Clean Energy Technologies An Overview of Expert Elicitations.” Bosetti, Valentina, Carlo Carraro, Romain Duval, and Massimo Tavoni. 2011. “What Should We Expect from Innovation? A Model-Based Assessment of the Environmental and Mitigation Cost Implications of Climate-Related R&D.” Energy Economics 33 (6): 1313–20. doi:10.1016/j.eneco.2011.02.010. Bosetti, Valentina, Michela Catenacci, Giulia Fiorese, and Elena Verdolini. 2012. “The Future Prospect of PV and CSP Solar Technologies: An Expert Elicitation Survey.” Energy Policy, Special Section: Fuel Poverty Comes of Age: Commemorating 21 Years of Research and Policy, 49 (October): 308–17. doi:10.1016/j.enpol.2012.06.024. Bushnell, J.B. and C.D. Wolfram (2007), The Economic Effects of Vintage Differentiated Regulations: The Case of New Source Review, Center for the Study of Energy Markets. UC Berkeley: Center for the Study of Energy Markets.


Cárdenas Rodríguez, M., et al. (2014), « Inducing Private Finance for Renewable Energy Projects : Evidence from Micro-Data », OECD Environment Working Papers, No. 67, Éditions OCDE, Paris. DOI : Catenacci, Michela, Elena Verdolini, Valentina Bosetti, and Giulia Fiorese. 2013. “Going Electric: Expert Survey on the Future of Battery Technologies for Electric Vehicles.” Energy Policy 61 (October): 403–13. doi:10.1016/j.enpol.2013.06.078. Criscuolo, C. and C. Menon (2014), "Environmental Policies and Risk Finance in the Green Sector: Crosscountry Evidence", OECD Science, Technology and Industry Working Papers, No. 2014/01, OECD Publishing, Paris. DOI: Criscuolo, C., P. N. Gal and C. Menon (2014), "The Dynamics of Employment Growth: New Evidence from 18 Countries", OECD Science, Technology and Industry Policy Papers, No. 14, OECD Publishing, Paris. DOI: Criscuolo, C., et al. (2014), "Renewable Energy Policies and Cross-border Investment: Evidence from Mergers and Acquisitions in Solar and Wind Energy", OECD Science, Technology and Industry Working Papers, No. 2014/03, OECD Publishing, Paris. DOI: Dechezleprêtre, Antoine (2013) Fast-tracking “green” patent applications: an empirical analysis" Dechezleprêtre, A. and R. Calel (2015) Environmental Policy and Directed Technological Change: Evidence from the European carbon market" in Review of Economics and Statistics (forthcoming Dernis, Hélène, Roberto de Pinho, and Mariagrazia Squicciarini. 2015. “Detecting the Emergency of Technologies and the Evolution and Co-Development Trajectories in Science (DETECS): A ‘Burst’ Analysis-Based Approach” Egli, F., N. Johnstone and C. Menon (2015), "Identifying and inducing breakthrough inventions: An application related to climate change mitigation", OECD Science, Technology and Industry Working Papers, No. 2015/04, OECD Publishing, Paris. DOI: Ellerman, D. (1998), “Note on the Seemingly Indefinite Extension of Power Plant Lives, A Panel Contribution”, The Energy Journal, Vol. 19/2, pp. 129-132. Fiorese, Giulia, Michela Catenacci, Elena Verdolini, and Valentina Bosetti. 2013. “Advanced Biofuels: Future Perspectives from an Expert Elicitation Survey.” Energy Policy 56 (May): 293–311. doi:10.1016/j.enpol.2012.12.061. Ghosh, Shikhar, and Ramana Nanda. 2010. “Venture Capital Investment in the Clean Energy Sector.” Harvard Business School Working Paper 11-020. Harvard Business School. GTCK, Green Technology Center. 2013. „Green Tech Review“. GTCK, Green Technology Center. 2015. “Status of Green Policy.” Habegger, Beat. 200. Horizon Scanning in Government. Concept, Country Experiences, and Models in Switzerland. Center for Security Studies, ETH Zurich. Han, SuHyeon. 2013. “Green Tech.”


Haščič, I. and M. Migotto (2015), "Measuring environmental innovation using patent data", OECD Environment Working Papers, No. 89, OECD Publishing, Paris. Haščič, I., et al. (2015), « Public Interventions and Private Climate Finance Flows: Empirical Evidence from Renewable Energy Financing », OECD Environment Working Papers, No. 80, , OECD Publishing, Paris. DOI : IPCC. 2014. Climate Change 2014: Synthesis Report. Johnstone, Nick and Haščič I. (2010) “Environmental Policy Design and the Fragmentation of International Markets for Innovation” (with Ivan Hascic) in V. Ghosal (Editor), Reforming Rules and Regulations, MIT Press, 2010, forthcoming. Johnstone, Nick, Shunsuke Managi, Miguel Cárdenas Rodríguez, Ivan Haščič, Hidemichi Fujii and Martin Souchier (2015). "Environmental policy design, innovation and efficiency gains in electrical supply" (forthcoming). Kalamova, M., I Haščič and N. Johnstone (2013) 'Implications of Policy Uncertainty for Innovation in Environmental Technologies: The Case of Public R&D Budgets' in V. Constantini and M. Mazzanti (eds.) The Dynamics of Environmental and Economic Systems (Berlin, Springer) Keen, Andrew. 2015. The Internet Is Not the Answer. Atlantic Monthly Press. King, David, John Browne, Richard Layard, Gus O’Donnell, Martin Rees, Nicholas Stern, and Adair Turner. 2015. “A Global Apollo Programme to Combat Climate Change.” London School of Economics, London. Krueger, Tobias, Trevor Page, Klaus Hubacek, Laurence Smith, and Kevin Hiscock. 2012. “The Role of Expert Opinion in Environmental Modelling.” Environmental Modelling & Software, Thematic issue on Expert Opinion in Environmental Modelling and Management, 36 (October): 4–18. doi:10.1016/j.envsoft.2012.01.011. Lerner, Josh. 2011. Venture Capital and Innovation in Energy. In: Accelerating Energy Innovation: Insights from Multiple Sectors. Chicago; London: University of Chicago Press. Levinson, A. (1999), “Grandfather Regulations, New Source Bias, and State Air Toxics Regulations”, Ecological Economics, Vol. 28/2, pp. 299-311. Liu, Guang-fu, Xiao-li Chen, Ralph Riedel, and Egon Müller. 2011. “Green Technology Foresight on Automobile Technology in China.” Technology Analysis & Strategic Management 23 (6): 683–96. doi:10.1080/09537325.2011.585038. Mazzucato, Mariana. 2013. “State Investments in Innovation: Fixing vs. Creating Markets.” Mazzucato, Mariana. 2013. TED Global 2013 Talk in Edinburgh: Government - Investor, Risk-Taker, Innovator. Nanda, Ramana, and Matthew Rhodes-Kropf. 2014. “Financing Risk and Innovation.” Harvard Business School Entrepreneurial Management Working Paper, no. 11-013. OECD. 2010. The OECD Innovation Strategy: Getting a Head Start on Tomorrow. OECD Publishing, Paris. OECD. 2011. Towards Green Growth. OECD Green Growth Studies. OECD Publishing, Paris. OECD. 2015. The Innovation Imperative: Contributing to Productivity, Growth and Well-Being. OECD Publishing, Paris. Rai, Varun. 2013. “Expert Elicitation Methods for Studying Technological Change under Uncertainty.” Environmental Research Letters 8 (4): 041003. doi:10.1088/1748-9326/8/4/041003.


Sonnenschein, Jonas, and Mundaca, Luis. 2015. „Fiscal expansion as decarbonization policy. South Korea's Green New Deal 2009-2013“. Green Growth Knowledge Platform (GGKP), Third Annual Conference, 29-30 January 2015. Squicciarini, M., H. Dernis and C. Criscuolo (2013), "Measuring Patent Quality: Indicators of Technological and Economic Value", OECD Science, Technology and Industry Working Papers, No. 2013/03, OECD Publishing.(doi: 10.1787/5k4522wkw1r8-en) The Economist. 2013. “Companies and Emissions: Carbon Copy,” December. U.S. Energy Information Administration. 2015. “Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2015.” Verdolini, Elena, Laura Diaz Anadon, Jiaqi Lu, and Gregory F. Nemet. 2015. “The Effects of Expert Selection, Elicitation Design, and R&D Assumptions on Experts’ Estimates of the Future Costs of Photovoltaics.” Energy Policy 80 (May): 233–43. doi:10.1016/j.enpol.2015.01.006.


ANNEX: SUPPLEMENTARY FIGURES Figure A1: Technology examples

Source: Ghosh and Nanda 2011 in Mazzucato 2013

Figure A2. Growth in patent applications and granted patents (normalized in 1991)

Source: Egli et al. 2015

Figure A3. Environmental technologies vs. counterfactual for all indicators


Source: Egli et al. 2015

Table A1. IPC subclasses with the largest burst intensity observed in 2000s (IPC subclasses among the top 10 burst intensities measured in IP5 patent families and PCT patent applications)

Technology area

Electric elements & techniques Engines, pumps & turbines

Burst period and intensity IP5 Families

IPC subclass IPC description

H05K H01M F03D

Printed circuits; casings or constructional details of electric apparatus; manufacture of assemblages of electrical components Processes or means, e.g. batteries, for the direct conversion of chemical energy into electrical energy Wind Motors

Burst period and intensity PCT patents
















Data processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes; systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes, not otherwise provided for Wireless communication networks Functional features or details of lighting devices or systems thereof; structural combinations of lighting devices with other articles, not otherwise provided for Indexing scheme associated with subclasses F21L, F21S and F21V, relating to the form of the light sources Processes for applying liquids or other fluent materials to surfaces, in general




Other working of metal; combined operations; universal machine tools



Motor vehicles






Pharmaceutical Production & distribution of electricity & heat


Propulsion of electrically-propelled vehicles Specific uses or applications of nano-structures; measurement or analysis of nanostructures; manufacture or treatment of nano-structures Specific therapeutic activity of chemical compounds or medicinal preparations Production or use of heat not otherwise provided for Circuit arrangements or systems for supplying or distributing electric power; systems for storing electric energy



H04W F21V Lighting F21Y Machine tools

F24J H02J











Note: The thickness of the lines in the 5 and 7 columns reflects the intensity of the burst observed in a given IPC subclass. IPC = International Patent Classification; IP5 = five largest patent offices (US, Europe, Japan, Korea, China); PCT = Patent Cooperation Treaty (permits to seek protection for an invention in 148 countries simultaneously).

Source: Authors’ calculations based on the Worldwide Patent Statistical Database, EPO, Spring 2014 edition; October 2014 and International Patent Classification (IPC) Official Publication, version 2014.01 (, last accessed in October 2014).


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