Lionel Nesta
email: lionel.nesta@unice.fr

Elena Verdolini
email: elena.verdolini@feem.it

Francesco Vona
email: francesco.vona@sciencespo.fr

Threshold Policy Effects and Directed Technical Change in Energy Innovation *

Keywords: Directed technical change, threshold models, environmental policies, policy mix. JEL classification: Q58, Q55, Q42, Q48, O34

This paper analyzes the effect of environmental policies on the direction of energy innovation across countries over the period 1990-2012. Our novelty is to use threshold regression models to allow for discontinuities in policy effectiveness depending on a country's relative competencies in renewable and fossil fuel technologies. We show that the dynamic incentives of environmental policies become effective just above the median level of relative competencies. In this critical second regime, market-based policies are moderately effective in promoting renewable innovation, while commandand-control policies depress fossil based innovation. Finally, market-based policies are more effective to consolidate a green comparative advantage in the last regime. We illustrate how our approach can be used for policy design in laggard countries.

Introduction

The Paris Agreement, signed in December 2015 by 195 parties, represents a global action plan to address climate change by limiting global warming to well below two degrees. One key feature of the Agreement, compared to the Kyoto Protocol, is that all countries committed to undertake an active role to reduce greenhouse gas (GHG) emissions, even if recognizing the principle of "common but differentiated responsibilities and respective 1 capabilities". This important political turnaround reflects a more general shift in policy making, whereby fast-developing and developing countries are taking a more active role in addressing the challenges linked with sustainable development. With respect to climate change, stronger commitment arose from the fast growth in global share of GHG emissions from BRIICS countries in last two decades1 and from growing concerns in developing and fast-developing countries over the consequences of climate change.

A crucial step in successfully curbing emissions is to promote innovation in green technologies. This opens up opportunities as well as burdens for both developing and fast-developing countries, and raises some key questions related to the design of effective climate policies. What is the optimal policy portfolio to redirect innovation towards low-carbon technologies? Are policy instruments equally effective in developed and developing countries? And, given the strong path dependency in innovation, how can emerging economies move from being laggards in low-carbon innovations to becoming leaders in this promising domain?

This paper proposes a new empirical methodology to provide a preliminary and necessarily incomplete answer to these questions, shedding light on the choice of the appropriate policy instrument to promote green innovation in countries at different stages of technological development. Using information on patent production and environmental policies, we test whether the dynamic incentives of different policy instruments exhibit discontinuities depending on the country's technological know-how. In this context, the factor mediating policy effectiveness is the level of relative technological specialization of a given country, which is measured by the ratio of the stock of patents in green versus brown energy technologies. This variable captures the path-dependency in the direction of energy innovation of a given country.

Detecting discontinuities in policy effectiveness is essential for identifying the environmental policies that are relevant at specific stages of technological development. To the best of our knowledge, the current empirical literature on the determinants of energy innovation and on directed technical change has ignored this issue. 2 The starting point of our paper is to see the inducement effect of environmental policies on the direction and the rate of innovation as a particular case within the more general debate on the theoretical relationship between optimal policy and the stage of technological development [START_REF] Acemoglu | Distance to frontier, selection, and economic growth[END_REF][START_REF] Aghion | Joseph schumpeter lecture appropriate growth policy: A unifying framework[END_REF]. As suggested by [START_REF] Acemoglu | Distance to frontier, selection, and economic growth[END_REF], policies should be changed at the right moment to switch from an investmentbased strategy for countries far from the technological frontier to an innovation-based strategy for countries near the frontier. Understanding whether and to what extent policy effectiveness depends on the stage of technological development would provide a much-needed foundation for clearer policy insights to emerging economies such as China and India. For these countries, which also face other development challenges, the choice of the most effective policy instrument is indeed a crucial concern. 3 Our research addresses this general issue using energy innovations as an important case study, given the potential opportunities and burdens opened by the Paris Agreement

To identify different regimes of policy effectiveness, we adapt Hansen's threshold effect model [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF], which is particularly suited to empirically detect regime switches in the effect of a certain (policy) variable contingent on specific contextual factors, i.e. green vs. brown technological specialization. We amend the model in two ways. First, we use the pre-sample mean of the dependent variable to model unobservable individual effects in a flexible way [START_REF] Blundell | Individual effects and dynamics in count data models[END_REF]. This method is particularly useful in retrieving consistent estimates of variables that have minimal variation over time, such as environmental policies. Second, we test the presence of discontinuities in the effect of two different policy instruments. As discussed at length below, countries can adopt either market-based (i.e. subsidies and taxes) or command-and-control policies (i.e. emission standards) to address climate change mitigation. These policy instruments differ in effectiveness and in political acceptability, but also, and more importantly in our context, in dynamic incentives to innovate. Furthermore, we address the issue of environmental policies endogeneity through an IV strategy. The main concern in addressing endogeneity is not to retrieve a precise estimate of the effect of each policy, but rather to validate the thresholds of technological specialization around which the policy effect significantly changes using arguably exogenous variations in policy stringency.

Our main finding is that there exist two discontinuities in policy effectiveness depending on the in-house competencies in renewable relative to fossil fuel technologies. This suggests the presence of three policy regimes. In countries whose level of relative competencies is below the median, neither market-based nor command-and-control policies being effective in promoting greener technology options (first regime). As countries increase their specialization towards renewable innovation, the dynamic incentives of environmental policies become powerful. In this critical second regime, market-based policies are moderately effective in promoting green innnovation, while command-andcontrol policies play an important role in depressing brown innovation. Finally, only market-based policies are effective in the third policy regime, allowing to consolidate a comparative advantage in renewable energy technologies. These findings lead to a key policy implication: countries need to strengthen their level of green relative to brown technological competencies before they can fully benefit from the dynamic incentives of environmental policies.

The remainder of the paper is organized as follows. Section 2 contains a conceptual discussion on the importance of discontinuities in the effects of policies as contingent on technological competencies. Section 3 presents our empirical approach by detailing the modifications to Hansen's threshold model, describing our data and sources, and discussing the instrumental variables chosen to address the concerns related to the endogeneity of the policy variables. Sections 4 and 5 present and discuss our results, respectively. Section 6 concludes, highlighting the key policy implications of our analysis.

Discontinuous Policy Effects on Innovation

The idea that appropriate policy choice is contingent on the stage of technological development of a country is certainly not new in the literature on economic growth. [START_REF] Rodrik | Growth strategies[END_REF] provides a thorough discussion of the appropriate growth strategy, defined as mix of policies and institutions, for countries at different stages of economic development. Several papers relate the emergence of multiple equilibria and poverty traps to factors, and thus implicitly to policies, affecting the accumulation of physical (see, e.g., [START_REF] Murphy | Industrialization and the big push[END_REF]) and human capital (see, e.g., [START_REF] Galor | Income distribution and macroeconomics[END_REF] as well as technological development (see, e.g., [START_REF] Howitt | R&D, Implementation, and Stagnation: A Schumpeterian Theory of Convergence Clubs[END_REF]. This notwithstanding, only [START_REF] Acemoglu | Distance to frontier, selection, and economic growth[END_REF] develop an endogeneous growth model that explicitly examines how the choice of the appropriate policy depends on the distance to the technological frontier. In this model, laggard countries do not have enough competencies to innovate, but invest in order to enhance their capacity to absorb foreign innovations. At an early stage of development, entry barriers and anticompetitive strategies can favour technological catching-up because they magnify the appropriability of these investments in absorptive capacity. However, as countries approach the technological frontier, a switch in policy is required to jump to an innovation-based growth path, which entails greater competition and the continuous entry of newcomers to boost the emergence of new ideas. Importantly, the model exhibits discontinuities: countries that reach a certain threshold of competencies but do not switch to innovation-based policies remain stack into a non catching-up trap.

These policy threshold effects are extremely important when two technologies compete with one another, as in the case of green vs. brown innovations. Indeed, what matters in the context of competing technologies is not the overall level of competencies, but the existing comparative advantages in one technology versus the alternative. As pointed out by the seminal work of [START_REF] Arthur | Competing technologies, increasing returns, and lock-in by historical events[END_REF], pure market forces may not ensure that the most socially desirable technology prevails. In the presence of increasing returns in technological adoption, small random variations in the initial number of adopters may lock the economic system into an inefficient, or less socially desirable, technology. The paradox is that although technological selection can occur through small random events, escaping the lock-in may become very costly because increasing returns in adoption are further reinforced by complementary investments in the dominant technology. [START_REF] Acemoglu | The environment and directed technical change[END_REF] study the path-dependency in green vs. brown innovation as function of environmental policies and of the ratio of the accumulated competencies. Their model shows that, provided that the degree of substitution between green and brown technologies is not too low, there always exists a policy stimulus that allows to switch on a greener innovation path and, at the same time, sustain long-term economic growth. Notably, the magnitude of the policy response required to redirect energy innovation depends on the relative technological maturity of brown and green technologies. As a result, the system can exhibit sharp nonlinearities in policy effectiveness as dependent on the relative degree of specialization in green versus brown technologies.

In practice, redirecting energy innovation is an enormous endeavor. The energy system is locked in fossil fuel technologies both because of accumulated learning and because of the support of an extensive infrastructure of fuel supply, distribution, maintenance and ancillary services [START_REF] Kline | Positive feedback, lock-in, and environmental policy[END_REF]. Improving the efficiency of well-established fossil fuel technologies through more incremental changes (i.e. brown innovation) is presumably easier than investing in radical, yet uncertain innovation in renewable energy, which entails large fixed costs to learn and experiment (i.e. green innovation). In this respect, the transition from fossil fuel to renewable technologies can be seen as a case of lockingout. The energy system should gradually replace a well-established dominant technology with a new competing technology triggering a virtuous cycle whereby increased adoption gives rise to further technological developments. In this context, the possibility that a policy stimulus may be too small to trigger a virtuous circle of clean innovations is not a remote concern, but rather constitutes a central problem for policy design.

Surprisingly, while the issue of switching from a dominant technology to a new technology has been addressed from different theoretical perspectives, empirical research is scant on the role of policy in making a technological lock-out feasible. The main contribution of our paper is to present an empirical case study of how policies may contribute to lock out from a mature technology (fossil fuel) to a new technology (renewables). In doing so, our second contribution is to use a methodology, the threshold effect model developed by [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF], that allows to empirically identify discontuinities in policy effectiveness. We differ from earlier empirical tests based on the model of [START_REF] Acemoglu | The environment and directed technical change[END_REF], which assume the effect of environmental policies as independent from the level of competencies in clean relative to dirty technologies [START_REF] Aghion | Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry[END_REF][START_REF] Noailly | Directing technical change from fossil-fuel to renewable energy innovation: An application using firm-level patent data[END_REF]. By explicitly allowing for the policy effectiveness to depend on the past competencies in green and dirty technologies, we relax and empirically test this assumption.

Our final contribution concerns the choice of the appropriate policy instrument in environmental economics. To spur environmental innovations, the choice of the appropriate policy is complicated by multidimensionality. We distinguish between policies that affect innovation directly and indirectly.

On the one hand, policies such as green public research and development (R&D) investments affect innovation directly and purposively aim at building technological competencies in a given domain. These investments represent an essential policy in building a critical level of competencies [START_REF] Freeman | The Economics of Industrial Innovation[END_REF]), but usually their effects emerge with a long time delay. In the case of energy innovation, [START_REF] Popp | Economic analysis of scientific publications and implications for energy research and development[END_REF] shows that research outputs from basic R&D projects appear after a time span of up to ten years. This is due to the fact that building competencies necessarily involved a wide variety of public and private institutions active in upstream research, namely public research organizations and private firms. But equating technological competencies with upstream research exclusively would downplay the role of downstream applications as a source of new ideas [START_REF] Rosenberg | Inside the Black Box: Technology and Economics[END_REF]. Technological trajectories are the product of technology developers and final adopters, due to the multiple linkages that bond end-users with inventors [START_REF] Kline | The positive sum strategy: harnessing technology for economic growth[END_REF]. Whether technology-pushed or demand-pulled [START_REF] Dosi | Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change[END_REF], innovations always involve the commitment of a great variety of actors. In these complex networks, government intervention is essential [START_REF] Mazzucato | The entrepreneurial state: Debunking public vs. private sector myths[END_REF] not only in reducing the level of uncertainty born in research activities but also in providing markets with clear signals on where, when and how to invest. 4On the other hand, all those instruments which directly address environmental externalities also indirectly affect the incentives to invest in green innovations. Among these, economists generally distinguish between command-and-control (CC) and market-based (MB) approaches [START_REF] Requate | Dynamic incentives by environmental policy instruments -a survey[END_REF]. The most commonly used command-and-control instruments are standards regulating either the type of technology adopted or the level of emissions. Commonly adopted market-based instruments include subsidies on abatement of emissions, tradable permits, and to a lesser extent, emission taxes.

Economists' preference for M B instruments is by virtue of their static efficiency: M B instruments incentivize pollution reductions through either direct or indirect prices, leaving firms free to decide how much they want to emit or to abate. In a competitive market, such instruments lead to the equalization of marginal abatement costs for all actors in the market, meaning that a given emission target is reached at the lowest possible cost. However, as shown by the seminal paper by [START_REF] Weitzman | Prices vs. Quantities[END_REF], this conclusion is sensitive to the way of abatement costs and benefits are designed: price-based policies outperform quantity-based policies only when uncertainty regarding abatement costs is relatively low and when technological alternatives are relatively abundant.5 Further, the superior performance of M B instruments over CC instruments is not warranted if one either relaxes the assumption of perfectly competitive markets or considers the dynamic incentives to innovation associated with each policy [START_REF] Requate | Dynamic incentives by environmental policy instruments -a survey[END_REF]. Empirically, a growing literature on the determinants of renewable energy innovations did not reach a firm conclusion on the superiority of one instrument over the other [START_REF] Johnstone | Renewable energy policies and technological innovation: Evidence based on patent counts[END_REF][START_REF] Nicolli | Heterogeneous policies, heterogeneous technologies: The case of renewable energy[END_REF]. Using the long history on environmental policies of the USA, two papers examined the effect of a change in policy strategy. [START_REF] Popp | Pollution control innovations and the clean air act of 1990[END_REF] shows that after the passage of the 1990 CAAAs, which instituted permit trading, innovation activity decreased in intensity. However, he also shows that the inventions developed under the market-based policy were of higher efficiency than those developed under the command-and-control regulation. [START_REF] Taylor | Innovation under cap-and-trade programs[END_REF] shows that for both the US cap-andtrade program for SO 2 emissions and the Ozone Transport Commission N O x Budget Program, patenting activity collapsed when traditional CC regulation was replaced by a cap-and-trade program (M B). A likely explanation put forward in the analysis is that planned investments in clean technologies, which are spurred by the expectation of high permit prices, are reduced once the economic actors realize that the regulation may not be as stringent as expected. Another explanation is that either M B or CC policies can be appropriate at a different stage of technological development.

To deal with policy multidimensionality, our working assumption is that any policy indirectly affecting innovation rests upon a critical accumulation of scientific and technical knowledge. To paraphrase [START_REF] Patel | The technological competencies of the world's largest firms: complex and path-dependent, but not much variety[END_REF], if a country wants to innovate in solar energy, it must know about photovoltaic science; if a country wants to specialize into wind turbines, it must know about wind gust modeling and aerodynamics. Hence, the very step for a country is to invest in knowledge accumulation in order to build its critical mass, thereby increasing its absorptive capacity [START_REF] Cohen | Innovation and learning: the two faces of r&d[END_REF] and develop its own expertise in particular fields of applications. Only then will the dynamic incentives of CC and M B instruments come into play. Our empirical strategy allows to directly explore the possibility that the appropriate choices between CC and M B depends on a country's stage of technological development. Indeed, lacking a clear theoretical ranking of instruments as explained above, testing this hypothesis ultimately remains an empirical issue.

3 Empirical Framework

Econometric Strategy

The starting point of our analysis is the standard empirical specification to test the effect of policy on the direction of technical change in the energy sector [START_REF] Aghion | Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry[END_REF][START_REF] Noailly | Directing technical change from fossil-fuel to renewable energy innovation: An application using firm-level patent data[END_REF]. This model reads as:

y i,t = β mb M B it + β cc CC it + β K K R/F,it + BX + µ i + λ t + e it , (1)
where innovation y and knowledge stock K R/F are the log-transformed ratio of innovation (resp. knowledge stock) in green innovation over the innovation (resp. knowledge stock) in brownl technologies. This is consistent with the literature on directed technical change. Variables M B and CC stand for market-based and command-and-control policies, respectively, while X is a vector of control variables that will be specified in the next Section; µ i and λ t are country i and time t effects; and e it is the error term. The parameters of interest are β mb and β cc , which are the effects of the two types of policies on renewable energy innovation, which we expect to be significant and positive.

Our key novelty is that policy effectiveness depends upon the degree of specialization in renewable energy. The most immediate econometric counterpart is to extend specification 1 by interacting the two policy variables M B and CC with the ratio of the two knowledge stocks ratio K R/F . The basic model then becomes:

y i,t =β mb M B it + β cc CC it + β K K R/F,it + β mbK K R/F,it × M B it + β ccK K R/F,it × CC it + BX + µ i + λ t + e it .
(2) A limit of Equation 2 is that it assumes that the change in the policy effectiveness on innovation is linear in K. That is: ∂y it /∂pol = β pol + β polK × K R/F,it , where pol stands for either M B or CC policies. One alternative is to investigate whether there are discontinuities in this interaction, so that there may be threshold values of K R/F below which a given policy is ineffective and above which the policy becomes both effective and stable.

To detect sharp nonlinearities in the effect of policies on green innovation, we rely on the estimation and inference methodology developed by [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF]. We amend Hansen's approach in two ways. First, we model the country fixed effect µ i using the presample mean of the dependent variable rather the relying on within-country variations. Our motivation lies in the fact that although there is a time variation in the policy variables, such measures change only slowly over time. As is well known in the literature [START_REF] Blundell | Individual effects and dynamics in count data models[END_REF], the use of within differences would withdraw a large share of the identifying variation, possibly leading to inconsistent estimates of the parameters of interest β mb and β cc . 6 Second, whereas in his example on firm financial constraints [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF] interacts the threshold variable with only one variable of interest -namely cash flow -we interact the threshold variable -namely the ratio between the renewable and the fossil fuel knowledge stock K-with two variables of interest, M B and CC policies. This approach allows us to test which policy approach is more effective at a given level of technological maturity and thus to track how the appropriate policy mix changes with the level of technological competencies.

Hansen's method also determines empirically the number of thresholds underlying the relationship between the dependent variable and the variable(s) of interest. For the case of two policy instruments and one threshold, the model reads:

y i,t =β mb 1 (γ)M B it I(K R/F,it ≤ γ) + β p 2 (γ)M B it I(K R/F,it > γ)+ β cc 1 (γ)CC it I(K R/F,it ≤ γ) + β cc 2 (γ)CC it I(K R/F,it > γ)+ β K K R/F,it + BX + µ i + λ t + e it , (3)
6 One objection against the use of the pre-sample mean is that the presence of less developed countries in our sample may substantially decrease its variance. Should this be the case, one would not be able to properly account for unobserved heterogeneity among less developed countries. We test this by first assuming that the pre-sample mean follows a normal distribution P SM ∼ N (µ, σ). We then condition parameters µ and σ on the log of GDP per capita using maximum likelihood estimation methods. Results show that whereas µ is positively associated with GDP per capita, the variance of the distribution captured by parameter σ is independent from the level of development of the country, implying that the country fixed effects can be accounted for using the pre-sample mean. Results are available upon request.

where I is an indicator variable set to unity, whether the threshold variable K R/F,it is below (K R/F,it ≤ γ) or exceeds (K R/F,it > γ) a given threshold value γ. An important requirement is that the panel is balanced with n countries and T time periods, so that N = n × T . Notice that this model allows for the empirical detection of discontinuities in the relationship between the threshold variable K R/F and our variables of interest. It is thus important to compare the results of this model with those of the interactions model in (2), as we do extensively in the next Section.

To retrieve an estimate of γ, we should first define Y, the vector stacking all observations of the dependent variable; Ŷ(γ), the corresponding vector of predicted values by estimating equation 3; and the vector of residuals ê(γ) = Y -Ŷ(γ). The algorithm proposed by [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF] chooses γ so as to minimizes the sum of squared errors S 1 (γ), where S 1 (γ) = ê(γ) ê(γ). More precisely, the estimator of γ reads:

γ = argmin γ S 1 (γ). ( 4
)
The computation of the least squares estimate of the threshold γ involves the minimization exercise 4. To do so, we first sort the threshold variable K R/F in ascending order and exclude the bottom and top 5% of observations. This step is to rule out regimes which would include too few observations below or above an obtained threshold. The remaining N .95

.05 observations represent the set of values over which the optimal γ is determined. We obtain the sum of squared errors ê(γ) and its associated S 1 (γ) using equation in (3). The smallest value for S 1 (γ) is γ.

The first step regards the significance of the threshold and tests whether the identified regimes are significantly different from one another, the null hypothesis being H 0 : β 1 = β 2 , where 1 and 2 refer to the estimated coefficients in the first and second regime, respectively. The second step concerns efficiency in order to determine the 95% confidence interval of the threshold likely values, with the null hypothesis being H 0 : γ = γ * , the true value of the threshold. Appendix A provides more details on the inference procedure.

Model (3) implicitly assumes that there are only two regimes of policy effectiveness in the direction of technical change. Yet, there may very well be several thresholds. For instance, in the case of two policy instruments and two thresholds, with γ 1 < γ 2 , the equation becomes:

y i,t =β mb 1 (γ)M B it I(K R/F,it ≤ γ 1 ) + β mb 2 (γ)M B it I(γ 1 < K R/F,it ≤ γ 2 )+ β mb 3 (γ)M B it I(K R/F,it > γ 2 )+ β cc 1 (γ)CC it I(K R/F,it ≤ γ 1 ) + β cc 2 (γ)CC it I(γ 1 < K R/F,it ≤ γ 2 )+ β cc 3 (γ)CC it I(K R/F,it > γ 2 )+ β K K R/F,it + BX + µ i + λ t + e it .
(5)

In such cases, one possibility is to search simultaneously for (γ 1 , γ 2 ) by minimizing S 2 (γ 1 , γ 2 ). While this seems to be a reasonable path to take, the scope of search over the entire grid may be computationally cumbersome.7 Rather, [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF] suggests proceeding sequentially by taking threshold γ1 as given and searching for γ 2 over the threshold variable K R/F by minimizing S 2 ( γ1 , γ 2 ).8 Fixing γ 1 to γ1 , the minimization program to identify the second threshold can be written as:

γ2 = argmin γ 2 S 2 γ 2 γ 1 = γ1 . (6)
The inference of γ 2 and the determination of its 95% confidence interval are determined as detailed in Appendix A based on bootstrapped samples to test [START_REF] Bai | Estimating Multiple Breaks One at a Time[END_REF] shows that the estimation of γ1 must be refined by a third stage estimation, taking γ2 as given, and the refinement minimization program becomes:

H 0 : β 1 (γ) = β 2 (γ) and H 0 : γ = γ 0 . If γ2 proves significant,
γ1 = argmin γ 1 S 1 γ 1 γ 2 = γ2 . (7)
The algorithm described above can be generalized to any higher order of thresholds. In this paper, we fix the maximum number of possible thresholds to three, implying potentially four types of policy regimes. The advantage of this measure is that it avoids arbitrarily setting the number of regimes. Instead, this number is determined endogenously, giving rise to significant structural breaks that are not time-dependent but dependent upon the threshold variable K R/F representing competencies in renewable energy innovation.9

Endogeneity

A key requirement for a causal interpretation of the coefficients of interest (β mb and β cc ) is the exogeneity of the environmental policies variables. This requirement is likely to be violated in our context for four reasons. First, policy choices depend upon the expected effectiveness of the policy in terms of both economic and environmental outcomes. For instance, a policy maker of a technologically laggard (resp. leading) country can correctly forecast that a given environmental policy will have little (resp. large) effect on the country's capacity to produce renewable energy innovations. As a result, the policy response will positively depend on both the current and the future innovative capacity of the country, leading to an upward bias in the coefficients of interest.

Second, a well-known argument postulates that policy interventions should be temporary and support renewable energy only during an initial phase of technological development when these technologies are significantly more costly than fossil fuel-based technologies. In more mature stages, technological development in renewable energy can proceed independently from the existence of a policy support [START_REF] Acemoglu | The environment and directed technical change[END_REF], which would give rise to a downward bias in the coefficients of interest. Indeed, there is some evidence in our data that the stringency of M B policies has decreased for certain periods in leading countries such as Germany, Denmark and Spain. These two sources of reverse causality go in opposite directions and may counterbalance each other. Hence, assessing the direction (if any) of the overall reverse causality bias in the estimates of the policy effects remains an empirical issue.

Third, another source of endogeneity arises from errors in the measurement of M B and CC policies. As discussed in detail in Section 3.3, our policy stringency measures assign a time-varying categorical score to each country. This score is in turn based on underlying continuous data on the stringency of several policy instruments, such as taxes and feed-in tariffs. This approach mechanically creates a source of measurement error. Typically, if the measurement error is only on the explanatory variable and normally distributed, it is expected to give rise to a downward bias in the estimates.

Finally, it is worth noting that due to data constraints, we cannot observe the presence and size of subsidies to fossil fuel production, which implies the existence of an omitted variable bias. If, as plausible, fossil fuel subsidies are negatively correlated with both renewable energy policies and with our main dependent variable (i.e., the ratio between renewable and fossil fuel patents), the estimated coefficients of environmental policies should be biased downward.

To address the above concerns on the endogeneity of the M B and CC variables, we use an instrumental variable approach, as discussed in Section 5.1. An important caveat is that our IV strategy should be seen more as a robustness exercise used to corroborate the identification of the thresholds levels of K R/F rather than a way to retrieve the exact effect of M B and CC policies on those thresholds. A precise identification of the effect of each of the policy instruments in each of the different regimes would require a validation using separate regressions for observations belonging to each regime. This is unfeasible given the relatively small number of country-year observations used to estimate our effects of interest. While this is beyond the scope of this paper, such an endeavor should be the focus of future research once data availability constraints are relaxed.

Data

Our analysis is based on a sample of 34 countries over the years 1990-2012.10 Below, we discuss in turn the use of patent statistics in our context, the definition of our dependent variable, our proxies for technological competencies and for environmental policies, and the control variables included in our estimation. Descriptive statistics for the sample and for each of the 34 countries are presented in Tables 1 and2.

Patent Statistics

We use patent data as a proxy for innovation, i.e. the dependent variable, and for accumulated competencies, i.e. the threshold variable. The pros and cons of this proxy with respect to other available innovation proxies, and particularly R&D investments, have been widely discussed [START_REF] Griliches | Patent Statistics as Economic Indicator: A Survey[END_REF], Popp et al. 2010). We argue that patent statistics are well suited to capture the level of technological activity of a country in our context for two reasons. First, as discussed above, patents are the result of several polices and institutional settings that span well-beyond mere R&D investments. Second, unlike R&D investments, patent databases are much more detailed than most other sources of information on renewable and fossil investments. Indeed, patent statistics are readily available and exhaustive, and cover a great variety of public and private institutions, essentially business firms, universities, other education and training institutions, and government. Third, patent data allow quantifying each country in terms of innovation performance (the dependent variable) and accumulated competencies (the threshold variable) in renewable energy relative to fossil fuel technologies. Overall, in the absence of systematic information on the private and public funding and on the technological content of R&D investments, the measurement error embodied in patent data is likely to be substantially lower than in R&D data.

Our patent data come from the OECD Green Growth Indicators Database, which includes patent counts for several energy technologies (Haščič et al. 2015[START_REF] Haščič | Measuring environmental innovation using patent data[END_REF]. Renewable energy generation patents include solar, wind, geothermal, marine, and hydro as well as technologies for energy generation from biomass and waste. Fossil-based generation technologies include technologies for improved output efficiency (such as combined heat and power and combined cycles) and technologies for improved input efficiency (such as efficient combustion or heat usage).11

Innovation

The share of renewable over fossil fuel patents with a family of two or larger measures the innovation relative performance of countries in these technologies. The first and second rows of Figure 1 show the evolution of renewable energy and fossil-based patents, respectively, for selected OECD countries, for the BRIICS countries 12 and for the overall sample average. Four facts emerge. First, innovation in renewable energy technologies has been larger than in fossil-based technologies over the sample period. This should be expected because the fossil technologies included in the OECD Green Growth Indicators Database are only those relative to improving the efficiency of fossil fuel energy production, rather than the overall number of fossil technologies. Moreover, fossil innovations are quantitatively fewer than renewables innovations due to the fact that fossil technologies are more mature, while renewables are more likely to be protected by intellectual property rights because they are novel and less incremental. Second, there is a global upward trend in patenting in both renewable and fossil fuel technologies, but the latter grew at a significantly slower pace than the former. Third, OECD countries produce far more patents that BRIICS countries during the period of investigation. But among the BRIICS countries, China seems to take a significant leap forward and invest massively in renewable energy technologies. Fourth, and related to this, while all countries in the sample show a re-direction of innovation in favor of renewable technologies, the phenomenon is more pronounced, but also more heterogenous, in the BRIICS countries than in the largest OECD economies. Among the BRIICS, the dynamics of patenting in China and India clearly stand out.

[Figure 1 about here.]

Threshold Variable and Environmental Policy

Patent data are used to compute the threshold variable, which measures each country's level of technical competencies in renewables relative to fossil-fuels. Specifically, we compute the knowledge stocks for both renewable and fossil energy technologies for each country and each year. The threshold variable K R/F is then defined as the ratio between the former and the latter. We rely on the perpetual inventory method to compute knowledge stocks in renewables and fossil fuel technologies as follows. Details are presented in the Appendix B. The first row of Figure 2 shows the evolution of our threshold variable over time. We observe that K R/F increased significantly in the BRIICS over the sample period, while in large OECD countries, this variable displays more of a U-shape with a turning point concomitant with the Kyoto agreement.

[Figure 2

about here.]

To proxy for environmental policy, which is our other main variable of interest, we extract data from the OECD Environmental Policy Stringency (EPS) Indicator [START_REF] Botta | Measuring Environmental Policy Stringency in OECD Countries: A Composite Index Approach[END_REF], which allows to distinguish between market-based M B policies and command-and-control CC regulations (see Appendix B for details on the computation of the indexes). The evolution of these variables is presented in the second and third rows of Figure 2. Note that command-and-control policies are vastly adopted in developed countries, while the score is lower and displays less variation in the BRIICS. Marketbased policies have a much lower score overall, but their use/stringency increases steadily throughout the sample period, reflecting a gradual replacement of CC policies with M B policies in several countries.

Control variables

To estimate the impact of technological competencies and policy instruments on innovation ceteris paribus, we include in the estimation a vector X of control variables 13 , which are quite standard in the literature and discussed in the Appendix B. Two controls deserve to be discussed. First, the pre-sample mean of the dependent variable is computed over the years 1980 and 1989 and is used to control for heterogeneity across countries, as explained in Section 3.1. Second, the (log of the) overall stock of knowledge is built using information on all patents applied for by inventors of each country each country i at time t following the formula used for the renewable and fossil patent stocks presented in Appendix B. This variable conditions the estimates on overall trends in patenting activities, which are country-specific and time-varying. Indeed, countries with a larger total patent stock (e.g. USA, Japan, Germany) may be less specialized in a particular energy technology than countries with a small stocks because they are more likely to be actively engaged in both types of technologies. Tables 1 and2 present descriptive statistics of all the variables included in subsequent econometric analyses.

[Table 1 4 Econometric Results

Baseline Results

Table 3 shows a first set of results. Model 1 is a parsimonious specification that includes only time fixed effects, the log-transformed ratio of renewable and fossil knowledge stocks K R/F (the threshold variable) and the pre-sample mean of the dependent variable. In line with related papers of [START_REF] Aghion | Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry[END_REF] and [START_REF] Noailly | Directing technical change from fossil-fuel to renewable energy innovation: An application using firm-level patent data[END_REF], the coefficients of both K R/F and the pre-sample mean are positive and statistically significant at 1% level. This confirms our hypothesis of persistence in direction of innovation in energy technologies: countries with experience in renewables -relative to fossil fuel -have a comparative advantage in producing further renewable energy innovation -relative to fossil fuel innovation. It also reinforces the idea that in the realm of energy innovation, there is a first-mover advantage for early innovators in a particular field, i.e. brown vs. green. This, however, also implies that laggard countries can be locked in a fossil fuel technological paradigm.

Model 2 includes environmental policies, namely M B and CC. Both variables take positive values. However, the coefficient associated with M B policies fails to reach acceptable levels of significance (p-value=0.147). This surprising result is however not robust. In Model 3, which includes additional control variables, the coefficient associated with the M B variable is similar in magnitude to that estimated in Model 2, but is now statistically significant. Conversely, the coefficient associated with CC, which becomes smaller in size, is now imprecisely estimated (p-value=0.145). On the whole, our results corroborate the well-accepted fact that environmental policies have a positive effect in redirecting innovation from brown to green technologies [START_REF] Aghion | Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry[END_REF]). However, the lack of precision in the point estimates may conceal heterogeneity in the effects of M B and CC policies on innovation.

Two of the control variables included in Model 3 emerge as particularly strong. Electricity exports and the total stock of knowledge positively influence our dependent variable, implying that they are associated with a redirection of innovation from fossil to renewable technologies. Concerning the latter variable, this suggests that countries located at the overall technological frontier tend to innovate more in renewables. Indeed, innovation in renewables is still a highly exploratory path to take, and technologically advanced countries are better equipped to follow this course. Electricity exports behave as expected. Exports act as a buffer to handle the intermittency of renewable electricity generation, making innovation in renewable relatively more attractive. Finally, the inclusion of the control variables yields non-significance of the pre-sample mean. This should be expected, since the pre-sample mean catches the countries' unobserved heterogeneity, which is essentially captured in variables such as GDP per capita, human capital, exports and total knowledge stock. This implies that the series of control are successful in grasping the specific -regulatory and institutional -features of countries in our sample.

[Table 3 about here.]

The discussion in Section 2 suggests that environmental policies play a role in redirecting energy innovation. Models 2 and 3 provide preliminary evidence in this respect, but do so by averaging away the heterogeneity in the policy effectiveness for countries with different level of relative competencies. Model 4 tackles this issue by interacting K R/F with the two policy variables. Two interesting results deserve to be discussed. First of all, the Log-Likelihood Ratio (LR) test confirms that Model 4 brings significant explanatory power when compared with Model 3 (LR-test of 54.05 with a p-value of zero). In other words, the specification with the interaction terms outperforms the specification without them in explaining the direction of innovation in energy technologies. Second, the coefficients of the interaction terms indicate that both CC and M B are more effective in redirecting innovation towards renewable energy technologies when the relative stock of competencies in these technologies is larger.

These results conform to our claim that the indirect effect of environmental policies on the direction of energy innovation is proportional to the degree of specialization in renewable as opposed to fossil-fuel technologies. One possible explanation is that countries with more competencies in fossil-fuel than in renewable technologies will try to meet the environmental requirements of such policies by making fossil fuel energy more efficient. These results implicitly suggests that direct innovation policies may be required to elicit the indirect innovation effect of M B and CC policies. As argued in previous studies [START_REF] Popp | They don't invent them like they used to: an examination of energy patent citations over time[END_REF], 2016[START_REF] Mazzucato | The entrepreneurial state: Debunking public vs. private sector myths[END_REF], targeted public R&D plans represent a good example of chief policy to boost knowledge accumulation in specific technological fields.

Threshold Specification Results

The interaction model confirms that the effect of environmental policies is contextual, but assumes away the possibility of discontinuities in policy effectiveness. Specifically, the model does not allow for a discontinuous switch in the selection of appropriate policies, but only a difference in the two policy effects associated with the relative level of competencies. As mentioned in Section 3.1, we use Hansen's threshold model to determine whether such discontinuities exist and, if so, what are the values of the threshold(s) that delimit different policy regimes as dependent on K R/F . For the sake of exposition, we first discuss the number and levels of the thresholds which emerge from the model, and then present the results of the estimation of the threshold model, although both steps are simultaneous.

Determination of the thresholds

Table 4 presents the estimated thresholds and their associated F-statistics: F 1 , where the null hypothesis H 0 posits the absence of threshold and the alternative hypothesis H 1 states that there is at least one threshold; F 2 as a test of the existence of one threshold (H 0 ) against the alternative hypothesis of at least two thresholds (H 1 ); F 3 as a test of the existence of two threshold (H 0 ) against the alternative hypothesis of at least three thresholds (H 1 ). The main message of Table 4 is that policy effectiveness exhibits threshold effects: both the F 1 and the F 2 tests for the single threshold and double threshold models are highly significant, with bootstrapped p-values of 0.000, and 0.094, respectively. Conversely, the F 3 test for the three-threshold model is not significant, with a bootstrap p-value of 0.736. Overall, the Hansen's procedure provides strong evidence that there are two thresholds, and hence three regimes, linking M B and CC policies with directed energy innovation.

Table 4 also displays the point estimates of the two thresholds, 2.856 and 1.706, which correspond to respectively the 88 th and 47 th percentiles of the distribution of the K R/F threshold variable. The confidence intervals around the estimated thresholds are small, indicating little uncertainty about the location of the level of K R/F needed to switch from one regime to another. The plots of the concentrated likelihood ratio function, which are shown in Figure 3, provide further information about the threshold estimates. In particular, the graph for the one-threshold model indicates a first threshold, which is where the LR hits zero at the 88 th percentile of the threshold variable, and a second major fall in the LR at the 47 th percentile. The Hansen's procedure uses the values of the estimated thresholds to create dummy variables for the different regimes, which we are then interacted with the policy variable(s). This means including three variables for each of the policy instruments (CC and M B), indicating whether a given observation belongs to the first, second or third regime, as detailed below.

[Table 4 about here.]

[Figure 3

about here.]

A legitimate question is whether the threshold specification outperforms the interaction specification of Model 4. The answer lies in the last two rows of Model 5 displayed in Table 5. We use the LR-test to compare the two-threshold model with 15 explanatory variables with the interaction model which has 11 explanatory variables. Note that the difference between the two models is not the use of more information coming from an additional explanatory variable; rather, the difference lies in the way we exploit how the level of expertise K R/F interacts with the two policy variables. In the LR-test, the null hypothesis H 0 is that any change in the level of expertise in renewable energy relative to fossil fuel energy modifies policy effectiveness linearly (i.e. the interaction model is corroborated), whereas the alternative H 1 is that there are several regimes between which policy effectiveness differs significantly and within which policy effectiveness is stable (i.e. the threshold model is corroborated). The key result here is that with a critical probability value of 0.060, we can reject the null hypothesis H 0 and accept the alternative hypothesis H 1 : there are regimes between which policy effectiveness differs significantly and within which policy effectiveness is stable.

Estimation Results

The results of the estimation of the two-threshold model are presented in Table 5, which strongly supports our conjecture that the selection of the appropriate policy instrument switches discontinously across the different regimes, which are defined by the country's degree of specialization in renewable technologies as compared to fossil-based technologies.

In the first regime where K R/F is low, only the threshold variable is positive and significant, while the CC and M B policy indexes are not statistically significant. This implies that in the first regime, policy effectiveness of either CC or M B is simply nil, which includes almost half of our country-year observations (47%). Importantly, these results contradict the paradoxical results stemming from the interaction model which suggest a counter-productive effect of environmental policies on the direction of technical change when the level of expertise is particularly low. The second regime, which is characterized by values of K R/F between the median and the 87 th percentile, gives similar results, with the important difference that the coefficient associated with CC policies is positive and almost significant (p-value of 0.146). While this coefficient is not precisely estimated, this result suggests that CC policies may be somewhat more effective than M B policies in redirecting energy innovation towards renewables for countries that lack specialization in any of the two technological domains. Lastly, in the third regime, the positive effect of the threshold variable is compounded by a positive and statistically significant effect of the M B index. Therefore, a country that has accumulated a considerable amount of experience in renewables vis-a-vis fossil technologies (the top 12% of the country-year observations in our sample) can avoid the use of command-and-control policies and rely exclusively on market-based policies.

Figure 4 provides a graphical representation of the size of the policy effects in different regimes. In particular, we quantify the effect of a one standard deviation of each policy index around the mean in each regimes. Market-based policies have a large impact on the direction of technical change in the third regime, with a one-standard deviation increasing the ratio of renewable to fossil patent by 0.55, which represents 40% of the average value of the dependent variable P R/F . Notice that the insignificant effect of M B policies in the second regime is less than a fifth of that in the third regime. CC policies appear more useful in redirecting energy innovation in the crucial second regime, although their effect is only barely significant. The estimated impact is moderately large with one-standard deviation increase in CC policies accounting for an 8% increase in the expected number of renewable patents relative to fossil fuels.

Given the nature of our dependent variable, which is defined as the ratio of renewable over fossil fuel innovation, a legitimate question raised by these results is the exact mechanism through which M B and CC policies affect the direction of energy innovation.

A key issue to understand the mechanisms is whether (and how) M B and CC affect the numerator and/or the denominator of our ratio. Table 5 displays two additional models, which explore separately the effect of the policy instruments with respect to the level of renewable (Model 6) and fossil fuel innovation (Model 7). As in Model 5, the M B and CC variables are interacted with dummy variables representing each of the three regimes, where the previously estimated threshold values are taken as given. This accounts for the fact that the level of innovation in green or brown technologies still depends on the relative degree of technological specialization, which captures the opportunity cost of not investing in the alternative technology. The two models provide important insights on the mechanisms at work. Specifically, they indicate that M B policies in the second and third regimes positively impact renewable energy innovation. Conversely, CC policies impact the direction of innovation by depressing fossil-based innovation in the second regime (see also Figure 4).

The focus on the effectiveness of the two policy instruments on the level of clean vs. brown innovation points to the second regime as the crucial period where an appropriate policy mix must be implemented. This policy mix is characterized by a positive impact of M B policies on renewable innovation and a negative impact of CC policies on fossilbased innovation. Clearly, command-and-control policies, by setting up emissions quotas, send a clear orientation to private actors on the future of fossil fuel within a country and deter brown innovation. However, CC policies do not spur innovation in renewables, and only the support of M B policies comes into play and provides actors with the necessary incentives to actually undertake invention activities in new carbon-free energy solutions. 14In sum, command-and-control policies act as a stick to properly orientate actors in a regime characterized by a not yet well-defined specialization in energy technologies (first threshold is at 47%), but must be combined with the M B policies, which act as a carrot in providing direct monetary incentives to the green innovators. Beyond a certain level of comparative advantage in renewables (88%), only the carrot is effective in reinforcing green technological specialization.

[Table 5 about here.]

[Figure 4 about here.]

Discussion

Robustness Checks

In this section, we show that our results are robust to a series of alternative specifications, which are presented in Table 6 alongside our favourite specification (Model 5) for the sake of comparison. Before commenting each robustness exercize, we note the high persistence of the estimated threshold percentiles throughout Models 5 to 10. In all cases, our model estimates two thresholds, located near the median and near the 9 th decile.

First and foremost, to reinforce a causal interpretation of the policy effects, we begin by estimating our favourite specification using an IV strategy. As Subsection 3.2, our least square estimates could be biased due to three different reasons: reverse causality, omitted variable bias and measurement errors. The direction of bias in the case of reverse causality cannot be determined a priori, while in the case of both omitted variable and measurement errors, one would expect a downward bias. To perform an IV estimation, we select two external instruments, which are arguably correlated with our policy stringency scores but not with the direction of technical change. Our first instrument is a shift-share variable constructed as an interaction between population growth and the pre-sample mean of PM2.5 concentration, i.e. the concentration of particles with an aerodynamic diameter of less that 2.5 µm. 15 The instrument P M 2.5 i,t is built as follows: P M 2.5 i,t = (1 + g P OP i,t )P M 2.5 i,t-1 , where P M 2.5 i,t=0 = P M 2.5 i,1990 represents emission concentrations in 1990, and g P OPt is the growth of the population in country i at time t. This instrument captures the counterfactual level of PM2.5 concentration that would be uniquely attributed to population dynamics. The exclusion restriction here is that, conditional on a set of controls for the country's size and wealth (such as total stock of patents and GDP per capita), this counterfactual level of emissions does not alter the relative incentives of pursuing renewable energy rather than fossil fuel innovation. We are confident that this restriction is satisfied because fossil-fuel plants have a limited impact on PM2.5 emissions, and hence concentrations, which are primarily caused by the transport sector. Therefore, the effort of the fossil-fuel lobbies against this regulation should be limited. Further, the concentration of PM2.5 emission is also affected by geographical and atmospheric factors that are partially unrelated with effective PM2.5 emissions. This does not affect the strength of the instrument that is corroborated by the first-stage results, which are presented in Appendix C. 16 That the instrument is strong and positively correlated with both CC and M B policies is expected as policy 15 The source of this data is [START_REF] Brauer | Ambient air pollution exposure estimation for the global burden of disease 2013[END_REF], as reported by [START_REF] Wdi | World development indicators database[END_REF]. Satellite measurements of concentrations (ug/m3) measure the concentration levels in a given country in a given year. These in turn are determined by a wealth of anthropogenic and non-anthropogenic factors, namely: anthropogenic emissions, background concentrations, natural emissions (dust, vegetation, natural aerosols, etc..), secondary pollution (particulates formed in the atmosphere), transboundary pollution (i.e., pollution originating in other countries).

16 Appendix C presents the results of the first stage estimation where M B and CC are instrumented with the series of exogenous variables X in equation 3 and the two additional instruments. Note that the first column of Table A1 presents the second stage of the IV procedure estimating equation 1.

makers adopt environmental policies to respond to emissions above the norm, especially for diffused sources like PM2.5 to which citizens are more sensitive.

The second instrument we use is the length of time for which a country has had consolidated and durable democratic institutions.17 This instrument simply captures the fact that democratic countries tend to approve stricter environmental policies and has been used by the closely related paper of [START_REF] Nesta | Environmental policies, competition and innovation in renewable energy[END_REF], to which we refer for a more detailed discussion. The first-stage results in Appendix C show that this second instrument is strong and correlated with both CC and M B policies. However, while the estimated coefficient is positive in the CC fist stage regression, it is negative in the M B specification. Note however that this last result can be totally attributed to the inclusion of the control variables, as a reduced model with regresses M B only on TENSYS and time dummies shows a positive and significant effect of the latter18 . Overall, the F-test of excluded instruments corroborates our choice regarding both instruments.

The second stage results, presented in Model 8, are consistent with those presented so far, and indicate a downward bias in the least square estimates.Not only is the coefficient associated with M B policies in the third regime more than double that presented in the preferred model, but the coefficient associated with CC policies in the second regime also becomes significant and considerably larger.

We are cautious in giving full credit to these estimates as capturing the true magnitudes of the policy effects in different regimes because our exclusion restrictions are neither theory-driven nor extensively validated in previous literature, with the exception of the paper of [START_REF] Nesta | Environmental policies, competition and innovation in renewable energy[END_REF]. However, the policy variation captured by our instruments is arguably more exogenous than that of the original policy indexes. We are thus confident that, at least, this robustness exercise corroborates our main findings regarding the location of the regime switches and the qualitative importance of CC and M B policies in different regimes.

Second, Model 9 presents the results of the estimation when restricting attention to patents of higher economic value. This is obtained by considering only innovations that are protected in at least four, not two, countries. The results differ slightly from those of the base model 5 because the estimated thresholds are higher, indicating that switching from the first to the second regime and from the second to the third regime occur at higher levels of the threshold variable. The estimated thresholds are indeed associated with the 94th and the 65th percentiles. This is in line with the expectation that innovation of higher quality is more difficult to achieve. 19 Another important finding of Model 9 is that command-and-control policies do not seem to matter for directing technical change at the frontier. These remarks are consistent with the third regime of Model 5 where only market-based policy instruments matter.

Third, Models 10 shows the results using a broader definition of our dependent variable, and specifically including in the computation of the numerator of the threshold variable also patents relative to "supporting" technologies that ease the diffusion of renewable energy sources in the electricity system. These are energy storage and smart grids. The former contributes to smoothing the variable of reneweable energy supply, which is one of the main difficulties linked with integration high shares of renewables in the energy system (Verdolini et al. 2016, Carrara & Marangoni in press), while the latter allows distributed generation as opposed to centralized generation of energy. We observe that the results are robust.

Whether M B and CC promote the diffusion of renewable technologies, rather than mere innovation, is a key issue because laggard countries can use M B and CC policies first to favour the diffusion of renewable energy technology and then, through learning effects, to become green innovators. Taking the estimates regimes as given, Model 11 extends the focus of our analysis from the dynamic incentives of policy instruments to their impact on technology diffusion. In details, the dependent variable is now calculated as the percentage of electricity generation coming from renewable sources over total electricity produced in a given country i in a given year t. 20 As in Models ( 6) and ( 7) in Table 5, we take the estimates thresholds as given. To control for the overall dynamics of energy generation in a given country, we substitute the Total Stock of Knowledge variable with a variable measuring Total Primary Energy Supply in the country (from the IEA). Model 5 in Table 5 shows that policy effectiveness in different regimes is qualitatively similar for diffusion and innovation. Specifically, CC policies are associated with higher shares of renewable energy generation in the second regime, while M B policies are most effectively in promoting renewable penetration in the third regimes. We can hence conclude that a demand-pull strategy based on diffusion first and then innovation does not allow laggard countries to lock out from brown technologies. 21

[Table 6 about here.]

Country Diagnostics

Our results allow establishing a set of diagnostics to measure the adequacy of each country's policy mix with its level of expertise in renewable relative to fossil energy 20 The results are similar if one includes also statistics relative to heat generation, and they are available upon request.

21 As a final comment, note that our contribution is specifically designed to detect discontinuities in the dynamic incentives of domestic policies. These are by far the most important policy driver identified in the literature. Recent empirical evidence points to a considerably smaller, yet significant, effect of foreign policies in promoting domestic innovation [START_REF] Peters | The impact of technology-push and demand-pull policies on technical change: does the locus of policies matter?[END_REF][START_REF] Dechezleprêtre | Does foreign environmental policy influence domestic innovation? evidence from the wind industry[END_REF]). In our model, the role played by foreign policies is accounted for in a general way with the inclusion of time fixed effects. A more thorough investigation would require, on the one hand, the computation of a country-specific, time varying index of exposure to foreign policies and, on the other hand, a significant complication of the empirical set up of the threshold model. In unreported analyses available upon request, we built simple proxies of foreign policies, as the average values of M B and CC policies in all countries i = j, and introduced them in the vector of control variables. The results are very similar to the ones presented above, thereby corroborating our findings.

technologies, which help highlighting successes and failures. This is especially important to draft policy recommendation for countries which lack technological specialization in either fossil or renewable energy technologies, but need to implement environmental policies to comply with international agreement. Specifically, our results can be used to identify the policy mix which is most effective in promoting dynamic incentives to redirect innovation towards more sustainable, green technologies. To this end, Table 7 displays the position of a country in a percentile within the threshold variable K R/F and market-based and command-and-control policy indexes for the entire period 1990-2012. It also displays each of these variables for three subperiods, where the first period gathers the pre-Kyoto years (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997), the second period concerns the post-Kyoto pre-great recession years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) and the last period ranges from 2008 to 2012. Looking at the mean of the threshold variable by subperiod, the redirection of innovation towards renewables in the third period is striking, with the average country located in the 72 th percentile of K R/F compared with the 42 th and the 45 th in the first and second period, respectively. 22Table 7 highlights a fundamental difference between countries which adopt environmental policies resulting in a clear technological direction and countries that do not. We discuss these differences illustrating a successful case, two failure cases, and then concentrate on Eastern European countries and the BRIICS countries more specifically.

The typical successful example comes from Denmark. In the pre-Kyoto period, Denmark had not yet reached the required level of expertise in renewable energy relative to fossil fuel energy (41 st percentile, below the 47 th ). With the new millennium, Denmark chooses to increase both M B and CC policies (reaching .45 and .42, respectively). This is in tune with our findings that both types of policies are useful in providing private investors with a clear orientation of the technological direction where the country wants to embark. Yet, in the post-crisis era, Denmark has reached a level of expertise that allows the country to switch to the third regime K R/F,DEN = 96.6. Meanwhile, Denmark is the only country where M B policies moderately outperform CC policies in the last period, and increasingly so in the most recent -unreported -years. Now focusing on failures, two examples are France and Sweden. At a first glance, France does not seem to fit in our story. The lack of an adequate M B support in the nineties has led to the full dissipation of the French early advantage in renewable technologies. With accumulated competencies in the 94 th percentile of K R/F in the first period, France is the only country that is in the third regime in the first period. France was then in an ideal position to implement ambitious M B policies before other countries and keep its relative technological advantage. Instead, its choice to fully specialize in the nuclear energy has led to a reduction of resources devoted to the development of renewable energy sources. In the same vein, Sweden represents a case of failure predicted by our model. A revealed specialization in fossil fuel technologies (on average 27 th percentile) undermined the dynamic effect of ambitious environmental policies. Correctly predicting this structural disadvantage, Swedish policy-makers did not actively invest in wind and solar R&D compared with other OECD countries (see, e.g., Figures 2-3 in Popp 2015).

Transition economies joined the EU (Poland, Hungary, the Slovak Republic, the Czech Republic and Slovenia) with a clear agenda to gradually comply with EU environmental regulations upon their entry in 2004. This represented a big policy push that is also evident in our data. For all these countries, both the M B and the CC indexes move from nearly zero initially to values in line with the sample mean in the second period. Looking at the last two subperiods characterized by this policy push, countries in the position to adopt ambitious policies (Czech Republic, Hungary and the Slovak Republic) succeeded in redirecting innovation towards renewable energy technologies. Of these three countries, Slovenia has been more reluctant to adopt ambitious CC policies compared to Hungary and the Czech Republic. In line with our findings, this policy gap has been translated into a less pronounced redirection of innovation in Slovenia compared with the other two countries.

The final case is that of the BRIICS countries. These also started regulating emissions from fossil fuel generation only recently, but, absent from the policy push of the EU membership, at a significantly lower level of stringency than transition economies. China already started a virtuous circle reaching the 97 th percentile of knowledge accumulation and specialization K R/F in the last period, sharply contrasting with the 49 th percentile of the second period, making it possible for China to implement the third type of policy mix. The reasons underlying the Chinese green turnaround are not fully clear. Green policies remain rather timid, and lower wages may have attracted foreign investors to develop given segments of the renewable sector. Other BRIICS countries still suffer from a general lack of total knowledge, including energy-related technological expertise. 23 As highlighted by the examples of India and Indonesia, which are at the bottom percentiles of K R/F , policies directly targeting innovation, i.e. to develop the country's absorptive capacity, are of paramount importance in these cases.

[Table 7 about here.]

Conclusions

This paper has analyzed the effectiveness of different environmental policies instruments in terms of dynamic incentives to redirect technical change towards clean technologies. The question is admittedly not new, but our approach to providing an answer is innovative for at least two reasons. First, we use threshold econometric models [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF] to test whether policy effectiveness displays discontinuities depending on the relative specialization of a country in renewables vis-á-vis fossil fuel technologies. This choice is corroborated by the fact that the threshold model explains the largest share of the variance in innovation compared to alternative specifications. Second, in the context of thresholds models, we compare the effectiveness of the dynamic incentives of two types of environmental policies, market-based and command-and-control policies.

Our results can be summarized as follows. We provide evidence on the existence of three regimes, with thresholds located at the 47 th and the 88 th percentiles of relative technological specialization. In the first regime, neither M B nor CC policies are effective in promoting a switch towards renewable energy innovation. In the second critical regime, command-and-control policies act as a stick by depressing brown innovation and properly orientating actors in countries without a yet well-defined specialization in clean technologies. In these countries, market-based policies compound this effect by acting as a carrot which provides incentives to the green innovators. However, beyond a certain level of comparative advantage in renewables, a third regime emerges where only the carrot is effective in reinforcing green technological specialization. Finally, our results are robust to several robustness checks and alternative specifications, which include an IV approach and different definitions of (renewable) energy innovation.

These results imply that implementing policies in support of innovation in renewable energy is highly contextual. When knowledge accumulation and specialization is low, countries need to strengthen their level of green relative to brown technological competencies before they can fully benefit from the dynamic incentives of environmental policies. Therefore, our conclusion is that policies aiming at the accumulation of scientific and technical expertise appear to be a critical. We mainly think of basic R&D investments and education to build and expand national capabilities. By developing their absorptive capacity, laggard countries will be able to satisfactorily identify, assimilate and eventually exploit green competencies invented elsewhere. Moreover, without such capabilities, a laggard country will simply not be able to fully exploit the dynamic incentives of environmental policy instruments and will not catch up with frontier countries. Finally, we illustrate cases where environmental policies have been effective in redirecting energy innovations in laggard countries. The case of transition economies that joined the EU is encouraging in that such external push for environmental policies can become an opportunities to redirect energy innovation also for countries without a clear green specialization. The Paris agreement can represent a similar opportunity for other developing and emerging economies, provided that the right enforcement mechanisms are put in place.

increases steadily throughout the sample period, reflecting a gradual replacement of CC policies with M B policies in several countries. Note that in the empirical estimation, we transform the two policy variables so as to make them vary between 0 and 1 by using the following transformation: Pit = P it -Min P Max P -Min P , where P stands for either M B or CC policies. We do this in order to facilitate the comparison of the parameter estimates for the two policy instruments.24

B.3 Controls

Our estimates are conditional on a vector X of control variables which are rather standard and include GDP per capita, average years of schooling, the share of electricity and heat exports over total electricity production and Coal Dependence. GDP per capita, defined as output-side real GDP at chained PPPs in million 2011 USD, is obtained from the Penn Tables [START_REF] Feenstra | The next generation of the penn world table[END_REF] and controls for time-varying, country-specific, macro-economic shocks that could likely affect our dependent variable. We also extract from the Penn Tables a human capital index, defined as the average years of schooling in the population. 25 We claim that a higher educational attainment should make the population of the country more inclined to renewable energy and more generally to environmental issues. The share of electricity and heat exports over total electricity production in a given country and Coal dependence are control variables specific to the energy sector. The former is built using data on exports and production from IEA (2016) and controls for the ability of countries to export electricity to neighbours. This is an important aspect of the energy system because, as discussed more in detail in [START_REF] Verdolini | Bridging the gap: Do fast reacting fossil technologies facilitate renewable energy diffusion[END_REF], it provides a buffer to handle the intermittency of renewable electricity generation. Such flexibility would affect our dependent variable because it makes innovation in renewables relatively more attractive. The latter is defined as the sum of coal, gas and oil rents measured as share of GDP, as provided by the World Development Indicators, and controls for the strength and relevance of the fossil fuel sector in a given economy, which can affect both innovation dynamics and the propensity to adopt environmental and energy policies.

Appendix C. Instrumental Variable Method. Results on the first and second stages with no threshold effect. Columns 2 and 3 of Table C1 display the results of the first stage of the IV procedure where both M B and CCg policies are being instrumented by the series of exogenous variables X in equation 3 and the two additional instruments, length of democracy and P M 2.5 counterfactual, as exclusion restrictions. The first column of Table C1 presents the second stage of the IV procedure estimating equation 1.

Figure 1 :Figure 2 :Figure 3 :

 123 Figure1: Evolution of renewable patent counts P REN , of fossil fuel patents counts P F F S , and of the ratio of the two patent counts (P R/F ) for selected OECD and BRIICS countries

  about here.] [Table 2 about here.]

Table C1 :

 C1 Results on the second and first stages of the instrumental variables

		(C1)	(C2)	(C3)
		OLS IV	OLS IV	OLS IV
		2 nd stage 1 st stage	1 st stage
	VARIABLES		MB	CC
	Pre-sample mean	-0.028	0.093***	-0.033**
		(0.084)	(0.016)	(0.017)
	K R/F	0.288***	-0.008	0.003
		(0.0418)	(0.008)	(0.010)
	P M 2.5 Counterfactual		0.005***	0.004***
			(0.000)	(0.000)
	Length of Democracy		-0.001**	0.002***
			(0.000)	(0.000)
	MB policies	1.346**
		(0.577)
	CC policies	1.494***
		(0.564)
	GDP per capita	-0.038	0.114***	0.082***
		(0.079)	(0.016)	(0.019)
	Coal Dependence	0.048	-0.038**	-0.108***
		(0.104)	(0.015)	(0.0141)
	Electricity Exports	1.638***	0.110	-0.209***
		(0.338)	(0.067)	(0.0751)
	Human Capital	0.057	-0.019	-0.046**
		(0.089)	(0.018)	(0.022)
	Total Knowledge Stock	0.092*** -0.019***	0.016**
		(0.030)	(0.006)	(0.006)
	Observations	782	782	782
	R-squared	0.535	0.570	0.451
	F-Test on excluded Instruments		37.790	29.698

Table 1 :

 1 Vertical axis: LR-test; horizontal axis: percentiles of the threshold variable K R/F . The dashed horizontal line represents the 5% critical value of the test.Figure4: Marginal effect of market-based policies (left panel) and command-and-control policies (right panel) on the ratio of renewable over fossil fuel patents (P R/F , top panel), the number of renewable patents (P REN , middle panel) and the number of fossil fuel patents (P F F S , bottom panel). Marginal effects have been computed using a onestandard-deviation change in the policy variable. Vertical axis: Marginal effect from models 5 (top panel), 6 (middle panel) and 7 displayed in Table5; horizontal axis: percentiles of the threshold variables. Dashed lines denote non-significance at 10% level Summary statistics

		40
		30
	LR 1 (γ)	20
		10
		0
		0	.2	.4	.6	.8	1
		40
		30
	LR 2 (γ)	20
		10
		0
		0	.2	.4	.6	.8	1
		40
		30
	LR 3 (γ)	20
		10
		0
		0	.2	.4	.6	.8	1

a Electricity Exports is the log of the ratio of electricity exports over production of electricity.

Table 2 :

 2 Summary statistics by country (Average over the period 1990-2015) Number of patents in renewables; P F F S : Number of patents in fossil fuels; PSM: Pre-sample mean of the dependent variable P R/F ; K R/F : Renewable over fossil fuels patent stock ratio; MB: Markat based policies; CC: command-and-control policies; GDP: GDP per capita, in thousands of 2011 USD PPP; HC: Human capital index; TPS: total patent stock (in thousands); CD: Coal dependence (×10 2 ); EE: Electricity export; INS1: particular matter P M 2.5

	Country P R/F PREN PF F S PSM K R/F MB CC GDP HC TKS CD EE IN ST 1 IN ST 2	ALL 1.32 42.72 4.75 0.92 1.69 0.76 1.94 9.96 2.971 31.26 13.60 -0.065 23.3 37	AUS 1.66 9.46 0.69 1.08 2.31 0.70 1.85 10.50 3.474 5.83 30.50 0.000 8.9 69	AUT 1.86 22.28 1.97 1.39 2.01 1.04 3.67 10.43 3.15 10.92 0.00 -0.170 27.2 45	BEL 1.79 11.81 0.67 1.77 2.44 0.34 2.70 10.32 2.986 7.19 0.00 -0.086 30.1 69	BRA 1.13 5.25 0.33 0.53 2.34 0.00 0.67 9.15 2.159 1.53 0.15 -0.001 11.6 10	CAN 1.82 23.94 2.15 1.61 1.99 1.15 2.64 10.50 3.517 16.82 3.15 -0.070 13.1 69	CHE 1.23 20.91 5.04 1.82 1.46 0.65 2.55 10.68 3.557 16.16 0.00 -0.426 25.2 69	CHN 1.69 44.17 1.87 0.25 1.61 0.57 1.53 8.55 2.192 14.30 80.70 -0.003 43.5 7	CZE 1.15 3.43 0.07 0.00 2.22 0.92 1.91 10.00 3.506 1.07 7.19 -0.147 32.9 11	DEU 2.01 313.00 28.48 1.48 1.95 1.29 3.74 10.44 3.57 178.82 0.80 -0.065 30.5 51	DNK 2.21 37.39 1.13 1.30 2.15 1.47 2.26 10.45 3.317 4.61 0.00 -0.130 19.2 69	ESP 2.36 37.11 0.96 1.44 2.75 1.57 2.15 10.15 2.665 6.54 0.34 -0.036 19.4 22	FIN 1.12 14.81 3.71 0.59 0.74 0.28 3.20 10.35 3.178 10.10 0.00 -0.016 10.0 69	FRA 1.54 69.73 8.85 2.68 2.24 1.45 2.77 10.34 2.942 63.92 0.00 -0.118 24.2 69	GBR 1.94 72.41 6.62 1.65 1.82 1.02 2.73 10.38 3.516 52.25 0.73 -0.003 20.7 69	GRC 1.12 3.64 0.17 0.56 1.84 1.22 1.45 10.04 2.775 0.52 0.63 -0.030 24.7 25	HUN 0.99 2.41 0.07 0.65 1.71 0.91 1.87 9.69 3.051 1.79 0.30 -0.094 33.4 11	IDN 0.14 0.21 0.00 0.00 0.30 0.29 0.94 8.46 2.202 0.09 58.60 0.000 25.2 14	IND 0.44 7.37 3.05 0.42 0.68 0.36 0.44 7.81 1.786 2.54 54.00 0.000 37.8 10	IRL 1.02 4.37 0.13 0.50 1.95 0.26 2.04 10.47 2.901 1.40 0.03 -0.004 13.8 69	ITA 1.61 53.98 5.88 1.54 1.82 1.32 2.39 10.35 2.816 32.10 0.00 -0.004 31.2 23	JPN 2.16 274.40 28.35 2.84 2.26 0.98 1.65 10.42 3.37 316.79 0.00 0.000 20.0 23	KOR 1.99 119.50 5.11 0.59 2.05 1.06 2.51 10.06 3.231 48.74 0.30 0.000 38.7 12	MEX 0.63 1.41 0.00 0.05 0.84 0.01 0.85 9.41 2.444 0.59 1.35 -0.007 17.9 6	NLD 2.16 27.92 1.93 1.73 2.26 0.82 2.28 10.50 3.165 15.13 0.00 -0.031 30.2 69	NOR 1.77 15.09 1.34 0.47 1.73 0.42 2.35 10.83 3.406 3.67 0.66 -0.081 10.2 69	POL 0.79 4.34 0.59 0.55 1.62 0.96 1.88 9.55 3.049 0.87 23.50 -0.041 31.2 10	PRT 1.12 3.65 0.10 0.41 2.17 1.13 2.17 9.93 2.21 0.36 0.00 -0.061 15.4 23	RUS 1.18 6.49 0.71 0.10 1.46 0.13 0.73 9.56 3.113 2.70 60.50 -0.009 19.4 8	SVK 0.65 1.43 0.09 0.05 1.39 0.67 0.57 9.71 3.386 0.34 0.16 -0.162 32.0 10	SVN 0.39 0.91 0.11 0.10 0.94 0.38 1.87 9.97 3.282 0.46 2.19 -0.308 25.5 10	SWE 1.02 15.97 3.94 1.28 1.26 1.20 2.06 10.45 3.238 18.81 0.00 -0.072 12.3 69	TUR 0.60 1.74 0.11 0.00 0.89 0.39 1.15 9.45 2.04 0.44 0.88 -0.007 24.0 15	USA 1.28 221.10 47.34 1.65 6.24 -0.004 18.7 69 1.53 0.78 2.64 10.71 3.596 225.09	9.17 2.221 14.6 12 0.45 125.10 -0.033 ZAF 0.29 0.62 0.06 0.18 0.83 0.23 0.23	P R/F : Renewable over fossil fuels patent count ratio; P	i,t ; INS2 democracy longevity (in	years).

REN :

Table 3 :

 3 Sequential Regressions. Dependent Variable: (Log of the) Ratio of renewable to fossil fuel patents (P R/F,it )

		(1)	(2)	(3)	(4)
	Pre-Sample Mean	0.386*** 0.315***	0.026	-0.001
		(0.090)	(0.095)	(0.144)	(0.131)
	K R/F	0.353*** 0.338*** 0.294***	0.106
		(0.076)	(0.070)	(0.081)	(0.065)
	MB Policies		0.388	0.456*	-1.497***
			(0.264)	(0.258)	(0.462)
	MB ×K R/F				0.951***
					(0.255)
	CC Policies		0.464*	0.266	-0.350
			(0.252)	(0.183)	(0.313)
	CC ×K R/F				0.224
					(0.149)
	GDP per capita			0.100	0.151
				(0.099)	(0.094)
	Coal Dependence			-0.130	-0.100
				(0.108)	(0.100)
	Electricity Exports			1.265**	0.910**
				(0.465)	(0.403)
	Human Capital			0.026	-0.005
				(0.143)	(0.120)
	Total Knowledge Stock			0.110*	0.147**
				(0.063)	(0.058)

Table 6 :

 6 Robustness tests using various specifications for the threshold regressions.Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All regressions include a full vector of unreported year fixed effects. In Models 5 through 10 the dependent variable is the (log of the) ratio of renewable over fossil fuel patents (P R/F ). Model 5 is displayed in Table3. Model 8 uses instrumental variables (IV) to control for the endogeneity of the two policy variables. The results for the first stage are displayed in Appendix C. Model 9 only counts patents protected in at least 4 countries. Model 10 uses a broader definition of renewable patents. Finally, Model 11 uses the share of renewable energy generation in country as a depedent variable.

		(5)	(8)	(9)	(10)	(11)
	Pre-Sample Mean	0.006	-0.047	-0.079	-0.031	0.505***
		(0.128)	(0.139)	(0.128)	(0.117)	(0.110)
	K R/F	0.134*	0.115*	0.204***	0.131**	-0.037
		(0.066)	(0.063)	(0.063)	(0.057)	(0.144)
	MB 1 st regime	0.052	0.623	-0.126	0.007	-0.408
		(0.293)	(0.784)	(0.231)	(0.344)	(0.608)
	MB 2 nd regime	0.376	0.780	0.909***	0.463	-0.079
		(0.300)	(0.921)	(0.316)	(0.286)	(0.476)
	MB 3 rd regime	2.276*** 7.015*** 2.478***	1.550*	1.895**
		(0.591)	(1.742)	(0.377)	(0.766)	(0.914)
	CC 1 st regime	-0.204	0.966	0.005	-0.008	0.305
		(0.211)	(0.898)	(0.211)	(0.259)	(0.402)
	CC 2 nd regime	0.362	1.801*	-0.036	0.401	0.629**
		(0.247)	(1.022)	(0.233)	(0.265)	(0.278)
	CC 3 rd regime	-0.199	-2.360	-0.168	0.568	-0.481
		(0.384)	(1.400)	(0.407)	(0.557)	(0.643)
	GDP per capita	0.141	0.049	0.093	0.226***	0.348
		(0.094)	(0.115)	(0.095)	(0.079)	(0.237)
	Coal Dependence	-0.120	0.029	-0.138*	-0.108	-0.230**
		(0.093)	(0.123)	(0.069)	(0.098)	(0.100)
	Electricity Exports	0.949**	1.150**	0.858**	0.839*	-1.494
		(0.395)	(0.431)	(0.396)	(0.433)	(1.082)
	Human Capital	0.016	0.044	0.092	-0.046	-0.466
		(0.124)	(0.127)	(0.075)	(0.106)	(0.338)
	Total Knowledge Stock	0.139**	0.117**	0.134***	0.124**
		(0.058)	(0.054)	(0.049)	(0.050)
	Total Primary Energy Supply					-0.006
						(0.074)
	First Threshold	88	89	94	87	88
	P-value first threshold	0.000	0.002	0.008	0.006	given
	Second Threshold	47	48	65	57	47
	P-value second threshold	0.094	0.022	0.052	0.076	given
	Third Threshold	64	11	13	10	-
	P-value third threshold	0.736	0.448	0.446	0.440	-
	Observations	782	782	782	782	782
	R-squared	0.631	0.634	0.650	0.633	0.693
	LL	-752.1	-748.9	-731.2	-750.5	-788.7
	RSS	313.4	310.8	297.1	312.1	344.1

As of today,

2/5 of global GHG emissions comes from BRIICS: Brazil 2.3%; Russia 5%, but second after the USA in per-capita terms; China 26.8%, reflecting the boom of Chinese industrial production after the entry in the WTO in 1999; India 6.7% and South Africa 1.1%2 See for instance[START_REF] Popp | Induced innovation and energy prices[END_REF],[START_REF] Johnstone | Renewable energy policies and technological innovation: Evidence based on patent counts[END_REF],[START_REF] Verdolini | At home and abroad: An empirical analysis of innovation and diffusion in energy technologies[END_REF][START_REF] Nesta | Environmental policies, competition and innovation in renewable energy[END_REF],[START_REF] Noailly | Directing technical change from fossil-fuel to renewable energy innovation: An application using firm-level patent data[END_REF],[START_REF] Aghion | Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry[END_REF][START_REF] Calel | Environmental Policy and Directed Technological Change: Evidence from the European Carbon Market[END_REF].

Our contribution specifically focuses on the dynamic incentives of domestic policies. Recent contributions in this literature have pointed to the role played by foreign (demand-pull) policies in promoting domestic innovation[START_REF] Peters | The impact of technology-push and demand-pull policies on technical change: does the locus of policies matter?[END_REF][START_REF] Dechezleprêtre | Does foreign environmental policy influence domestic innovation? evidence from the wind industry[END_REF] or foreign patenting[START_REF] Verdolini | Environmental policy and the international diffusion of cleaner energy technologies[END_REF]. However, available evidence confirms that the role of foreign demand is significantly less strong that that of domestic demand and policies. In the case of wind, for instance,[START_REF] Dechezleprêtre | Does foreign environmental policy influence domestic innovation? evidence from the wind industry[END_REF] shows that the marginal impact of foreign demand is considerably lower -by a factor of 12 -that of domestic demand in a sample of OECD countries. In Section 5.1 we briefly return on this issue, describing the robustness of our results to the inclusion of proxies for foreign demand pull.

For the case of green technologies,[START_REF] Mazzucato | The entrepreneurial state: Debunking public vs. private sector myths[END_REF] provides evidence on the crucial role of federally funded R&D research labs for the development of green start-ups.[START_REF] Vona | Measures, Drivers and Effects of Green Employment: Evidence from US Local Labor Markets[END_REF] show that the presence of these public research laboratories and of green inventors favour the emergence of green industrial clusters in US regions.

Extensions of the Weitzman's model that incorporate technology choices are carried out in a static framework and therefore did not allow for path dependency in innovation, i.e., the influence of past competencies on the probability of innovating (e.g.,[START_REF] Krysiak | Prices vs. quantities: The effects on technology choice[END_REF].

A search grid over (γ1, γ2) requires (N .95 .05 ) 2 regressions. The search grid for a higher order number of thresholds rapidly becomes prohibitive.

Because it is important to have a minimum number of observations in each regime, we restrict the search over K R/F so that the distance between γ1 and γ2 amounts to at least a decile.

In the remainder of the paper, we will indifferently call variable K R/F competencies in, knowledge stock of, or technological expertise in renewable energy.

The countries included in the analysis are listed in 2.

For a more detailed discussion of the different technologies, please refer to[START_REF] Lanzi | Efficiency-improving fossil fuel technologies for electricity generation: Data selection and trends[END_REF],[START_REF] Haščič | Measuring environmental innovation using patent data[END_REF] and the OECD ENV-TECH classification, available at http://www.oecd.org/environment/indicators-modelling-outlooks/green-patents.htm.

These countries are Brazil, Russia, India, Indonesia, China and South Africa

These variables are: the presample mean of the dependent variable, the total stock of knowledge in a given country, GDP per capita, average years of schooling, the share of electricity and heat exports over total electricity production and Coal Dependence

Note that the impact of M B policies in the second regime is very heterogeneous, and the combined effect on the direction of technical change is not precisely estimated.

The data on TENSYS are sourced from the Database on Political Institutions[START_REF] Thorsten | New tools in comparative political economy: The database of political institutions[END_REF][START_REF] Cruz | Database of political institutions: Changes and variable definitions[END_REF]).

Results are available upon request.

The fact that the required level of technological specialization increases with patent quality also seems intuitive. Although the order of detection of the threshold may swap from one specification to the other, this altogether is a source of confidence in the results stemming from Hansen's threshold specification.

Unreported analyses of variance (ANOVA) show that both cross-country and time variations explain a substantial share of the variance in the sample. For the threshold variable K R/F , ANOVA results show that cross-country variations explain 50% of the variance and that time variations explain 21%. For M B (resp. CC policies), the same decomposition shows that cross-country variations explain 28% (resp. 27%) and that time variations explain 43% (resp. 45%). It is thus important to consider the dynamics of countries individually.

The country diagnostic Table highlights the importance of controlling for the total patent stock in our regressions. Indeed, countries with a large total patent stock (e.g. USA, Japan, Germany) are less specialized than countries with a smaller knowledge stocks.

Importantly, this transformation does not affect the covariance of either the dependent variable or the set of control variables with the two policy variables.

We refer the reader to the above-mentioned paper[START_REF] Feenstra | The next generation of the penn world table[END_REF] and to the documentation attached to the new Penn World Tables proposed by the Groningen Growth and Development Centre (http://www.rug.nl/ggdc/productivity/pwt/).

* Lionel Nesta and Francesco Vona gratefully acknowledge the funding received from the H2020 project: 'Innovation-fuelled, Sustainable, Inclusive Growth (ISI-GROWTH)' under grant agreement 649186. Elena Verdolini gratefully acknowledges funding received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 730403, Project INNOPATHS. The authors are grateful to David Popp and the participants of the CONCORDi 2017 Conference for their valuable comments. The usual disclaimer applies.

Appendix A. Determination of the 95% confidence interval for the estimated thresholds

Once threshold γ has been identified, two additional steps are needed. The first one regards the significance of the threshold and tests whether the two identified regimes are significantly different from one another, the null hypothesis being H 0 : β 1 = β 2 , where 1 and 2 refer to the first and second regimes, respectively. The second step concerns efficiency in order to determine the 95% confidence interval of the threshold likely values, with the null hypothesis being H 0 : γ = γ * . Concerning the first step, inference on γ is achieved by generating bootstrapped samples and comparing model 1 with model 3. First, observe that specification 1 is nested in 3, which is a more general representation of the effect of policies on green innovation. Hence we can rely on the use of a likelihood ratio test to determine whether specification 3 conveys more information than specification 1. Second, given the panel nature of the data, we randomly draw (with replacement) countries in order to produce a bootstrapped sample of size N . Using the bootstrapped sample, we then estimate specifications 1 and 3 and perform the likelihood ratio test. We repeat this procedure a sufficiently large number of times and count the number of times for which the likelihood ratio test fails to reject the null hypothesis that the specification 1 brings as much information as specification 3. By way of example, for the case of M B policies the null hypothesis is H 0 :

, which implies the absence of two distinct regimes. The share of samples failing to reject the null hypothesis of no threshold is used as the critical probability value.

The second step is concerned with efficiency, with the null hypothesis being H 0 : γ = γ * . We follow [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF] and use the likelihood ratio statistics LR 1 (γ * ) as follows:

where σ = 1 n(T -1) S 1 (γ). [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF] shows that this statistics follows the distribution function Pr(LR

, where α is the chosen critical probability value at which one fails to reject the null H 0 . For example, the null hypothesis is rejected at the 5% level when the LR statistics exceeds c(α = .05) = 7.35. To form a confidence interval for γ, the no-rejection region of the (1 -α) confidence level is the set of values for which LR 1 (γ) ≤ c(α = 0.05). This is done by plotting the LR 1 (γ) and drawing a flat line at c(α = 0.05) (see Hansen 1999, pages 351-352).

In the two-and three-threshold models, the likelihood ratio statistics reads, respectively:

and 29

Appendix B. Threshold variable, policy variables and controls

B.1 Threshold variable: computation of the knowledge stocks

The threshold variable is then defined as the ratio between the knowledge stocks for renewable over the knowledge stock for fossil energy innovations for each country and each year. To compute the stock variables, we rely on the perpetual inventory method to compute knowledge stocks in renewables and fossil fuel technologies as follows:

where s corresponds to either renewable energy or fossil-based technologies and δ = 0.1 is the depreciation rate set at a level in line with the literature on innovation [START_REF] Peri | Determinants of knowledge flows and their effect on innovation[END_REF].

The initial value of the knowledge stock is defined as K 0 = P AT i,s,t 0

(ḡs+δ) , with ḡs being the average rate of growth of patenting in the technology s for the period between t 0 and t 0 -4. We use t 0 = 1984 as the initial year to compute knowledge stock. The first row of Figure 2 shows the evolution of our threshold variable over time. We observe that the threshold variable increased significantly in the BRIICS over the sample period, while in the top OECD countries, this variable displays more of a U-shape. For the latter, note that the upward trends initiate between 2000 and 2005, which is when the Kyoto agreement was to be implemented.

B.2 Policy Variables

Our policy proxies are sourced from the OECD EPS databse, which is the largest country-specific and internationally-comparable database, including information on 14 environmental policy instruments primarily related to climate and air pollution and covering the years 1990-2012 for the countries in our sample. The databases covers both market-based and non-market based instruments. Within the former, it reports information about Taxes (CO 2 , Diesel, N O x and SO 2 ), Traditing Schemes (Green Certificates, CO 2 and White Certificates), Feed-in Tariffs (Wind and Solar). Within the latter, it reports information on Standards (emission limites for N O x , SO 2 , P M s and diesel sulphur content), and R&D subsidies (Renewable energy public RD&D budget). Stringency is defined as the degree to which environmental policies put an explicit or implicit price on polluting or environmentally harmful behavior. For each policy instruments, countries are scored on a scale from 0 (not stringent) to 6 (highest degree of stringency). For the purpose of our analysis, we create and indicator for CC and one for M B policies, measuring the stringency of market-based and command-and-control policies, respectively. M B is calcuated as the weighted average of (a) taxes on CO 2 , N Ox and SO x ; (b) trading schemes (Green Certificates, White Certificates and CO 2 ); and (c) feed-in tariffs (for wind and solar power generation). CC is calculated as the weighted average of the scores for emission limits of N Ox, SO x and P M . These variables are presented in the second and third row of Figure 2. Note that command-and-control policies are vastly adopted in developed countries, while the score is lower and displays less variation in the BRIICS. Market-based policies have a much lower score overall, but their use/stringency The obtained thresholds are estimated from model 5 of Table 3 with 500 bootstrapped samples. See Appendix A about inference of the estimated thresholds and on the determination of their confidence intervals. 4). a The LR-test compares model 4 from Table 3 with model 5.

Table 7: Percentile values of the threshold variable of renewable over fossil fuel patent stocks (K R/F ), market-based policy index, and command-and-control policy index by country and by subperiods