From Fossil Fuels to Renewables: The Role of Electricity Storage

Transcription

1 From Fossil Fuels to Renewables: The Role of Electricity Storage Itziar Lazkano, Linda Nøstbakken and Martino Pelli

2 From fossil fuels to renewables: The role of electricity storage Itziar Lazkano University of Wisconsin Milwaukee Linda Nøstbakken Norwegian School of Economics Martino Pelli Université de Sherbrooke October 6, 2014 Preliminary and incomplete; please do not cite. Abstract The increased focus on renewable energy sources has sparked considerable interest in developing better energy storage solutions. Since renewable energy sources such as wind and solar are highly variable and unpredictable, significant use of renewables in the energy mix requires better storage solutions. We analyze the determinants of innovation in electricity storage, and study the role of storage in increasing the share of renewable energy. We propose a theoretical model of production and innovation in the electricity sector, where endogenous energy storage innovations affect the relative competitiveness between clean and dirty electricity sources. Next, we empirically test the predictions of our model using a unique global firm-level dataset of electricity patents. Our results suggest that electricity storage plays an important role for the direction of technological innovation in electricity generation (clean vs dirty), and should be considered separately from clean technologies to gain a complete understanding of the incentive structure. Keywords: Innovation; Renewable energy; Electricity storage; Power generation. lazkano@uwm.edu. linda.nostbakken@nhh.no. martino.pelli@usherbrooke.ca

3 1 Introduction Concerns over climate change have led experts to seek alternatives to reduce carbon emissions. To that end, many call for a shift in energy production from fossil fuels toward renewable sources. While R&D efforts have resulted in new clean technologies, one of the remaining challenges to meet renewable goals is energy storage. Large scale energy storage will enable intermittent renewable energy sources to increase their participation in the future grid mix. In this project, our goal is twofold. First, we theoretically and empirically investigate the determinants of technological innovations in electricity storage. Next, we study the role of storage in increasing the participation of renewable sources in the grid mix. The capacity to store electricity is the key component that links electricity generation to its delivery. Storage increases the flexibility to meet demand and may enable one to make use of more of the potential energy available from intermittent renewable energy sources such as wind and solar. However, when analyzing storage as a means to increase the participation of renewables in the energy mix, storage may be a double-edged sword. On the one hand, the ability to efficiently store electricity will allow us to take full advantage of intermittent energy sources; we could simply produce as much electricity as the sun and the wind offer, store it, and dispatch to the grid when needed. On the other hand, more efficient storage capabilities would create more arbitrage possibilities for existing production facilities, including fossil-fuel based power plants. The purpose of our paper is to answer the following two questions. What factors drive innovation in electricity storage at the firm level? And, does electricity storage shift the direction of innovation from nonrenewable to renewable energy? To address these questions, we first develop a stylized theoretical model of the electricity sector with innovation in electricity storage technologies as a key component. Next, we conduct an empirical analysis to test the predictions of the theoretical model using a global, firm-level electricity-patent database. To estimate the theoretical model, we build a unique, global firm-level database of patents related to electricity generation. Our database combines information on patent families from the OECD Triadic Patent Database, firm-level information from the OECD HAN database, energy prices from the IEA database, and economic data from the Penn World Tables. 1 Our empirical approach proceeds in two steps. First, we estimate how the stocks of clean, dirty, and storage knowledge accumulated by the firm and the corresponding knowledge stocks 1 A patent family is a group of patents registered in all the three major patent offices: the European, the US, and the Japanese. 2

4 available in the economy affect the propensity to obtain a new patent. Second, we investigate whether the role of innovation in storage affects the direction of technical innovations. We do so by looking at the impact of past innovations in storage on the gap between clean and dirty technologies. Our empirical results show that innovations in storage significantly affect how likely firms are to innovate in clean and dirty technologies. Hence, electricity storage can affect the direction of technological change and should thus be accounted for when evaluating environmental policies aimed at accelerating the shift toward renewable electricity sources. More specifically, our results show that both a firm s own past innovations in storage and spillover effects (storage innovations registered in the firm s country) increase the firm s propensity to patents in both clean and storage technologies. Furthermore, we find that a firm s propensity to patent in dirty energy technologies is negatively affected by the firm s past innovations in storage, while it is positively affected by past storage innovations in the firm s country (spillover effects). Hence, a firm s past innovations in storage seems to discourage innovation in dirty technologies while it encourages innovation in clean technologies. Our study contributes to the empirical literature studying energy prices, induced innovation and economic growth (Popp, 2002, 2004, 2006; Aghion et al., 2012; Acemoglu et al., 2012, 2013). Our paper has many similarities with the papers by Aghion et al. (2012), Acemoglu et al. (2012), and Acemoglu et al. (2013), who quantify the firm-level incentives to direct technological innovations toward renewable sources. However, our work differs from these studies in several ways. While Aghion et al. (2012) focus on innovation in the auto industry, we focus on innovation in electricity generation. In addition, we model not only innovation in clean and dirty technologies, but also the role of electricity storage, which in some sense affects the substitutability between clean and dirty technologies in the production process. Hence, our paper differs from the previous literature, in that we focus on the electricity sector and energy storage. The remainder of the paper unfolds as follows. In section 2, we present our theoretical model. In section 3, we explain how we build our unique database that we use in the empirical analysis, and we present descriptive statistics. Section 4 describes our empirical strategy and estimation results. Finally, section 5 concludes. 3

5 2 Theoretical framework We develop a one-period model of an economy where consumers obtain utility from consuming electricity and an aggregate outside good, which represents the rest of the economy. Electricity generators produce electricity from clean (renewable) and dirty (nonrenewable) sources and sell to electricity retailers. The retailers, in turn, sell the final electricity good to consumers. This section provides the basis for the empirical analysis and guides our identification strategy. Our framework is inspired by Aghion et al. (2012) who develop a similar model for their analysis of the auto industry. However, our basic framework differs from theirs in several respects. First, we introduce the possibility to store electricity and innovation in electricity storage, which endogenously affect the substitutability between clean and dirty inputs in our model. Second, the supply chain for electricity consists of three types of agents rather than two, as we distinguish between electricity generators and retailers. While generators can invest in R&D that yields higher efficiency in generation and thus cost savings, retailers invest in R&D in electricity storage technologies. Better storage technologies increases the substitutability between clean and dirty inputs and lowers the retailers costs of purchasing electricity inputs. The ability to store electricity means that produced electricity do not have to be immediately dispatched to the grid for consumption. Hence, the ability to store energy increases the elasticity of substitution between renewables and fossil fuels. The exception is hydropower, where we have always had the ability to store energy for later dispatch. However, given the high current utilization level of available hydropower resources, there is little room for expansion. Consequently, further growth in renewable energy must come from other sources. The economy s ability to store electricity will thus plays an important role in how this plays out. 2.1 Basic setup The economy consists of a continuum of consumers who spend their fixed income on electricity and an aggregate outside good C 0 (the numeraire) to maximize utility. The utility function is quasi-linear with respect to C 0 and takes the following form: U = C 0 + β β Y i σ 1 σ di σ σ 1 β 1 β (1) 4

6 where Y i is consumption of electricity of retailer (variety) i, β is the elasticity of substitution between electricity and the aggregate consumption good, and σ is the elasticity of substitution between electricity from different retailers. One may want to assume σ>β, which implies that the substitutability between electricity from different retailers is higher than the substitutability between electricity and other goods. Electricity generators have market power locally, which they use when selling the electricity they produce to retailers. The retailers, on the other hand, do not have market power upstream or downstream. 2 More specifically, we model the interaction between electricity generators and retailers as a continuum of local markets. A clean electricity producer, a dirty electricity producer, and a retailer operates in each local market. While the retailer takes the input prices of clean and dirty electricity, as well as the output prices as given, clean and dirty electricity generators strategically use the fact that their production affects the price they obtain. Innovation is an important feature of the model. Electricity generators can innovate in cost saving technologies, while the retailers can innovate in electricity storage technologies, which may enable them to obtain electricity for less. We can motivate this by fluctuations in electricity prices over the day, depending on current weather conditions and time of day, which affects both demand and the production from intermittent renewable energy sources such as wind and solar. Retailers can then achieve cost savings from the ability to purchase more electricity when it is relatively cheap, and then storing and dispatching to consumer when demand picks up. Both electricity generators and retailers invest in technological innovation at the beginning of the period. Subsequently, they make their production decisions to maximize profits. In the following, we briefly describe the innovation and production decisions of the electricity generators and the retailers. At the beginning of the period, generator i of electricity type j = c, d incurs a cost 1 2 ψx2 ji, measured in the aggregate consumption good, to increase its productivity as follows: A ji =(1+x ji ) A ji0, for j = c, d (2) where A ji0 is the initial efficiency of the technology. Hence, each electricity generator decides on how much to invest in R&D, x ji, given the R&D cost and the expected payoff from 2 This assumption is supported by the literature on deregulated electricity markets. According to this literature there tends to be close to perfect competition in these markets even with only two or a few competing electricity retailers. 5

7 lower production costs. At the end of the period, generator i of electricity type j chooses production level Y ji, given the new technology A ji, to maximize profits. The profit function of electricity generators differs depending on whether we consider renewable generators, who convert power from the sun and wind into electricity, or fossil-fuel based generators, who must purchase inputs such as natural gas or coal to produce electricity. We can then express electricity generator i s profits as π ji =max Y ji { p ji Y ji g ji A ji X ji },whereg ci =1andg di = f i, and f i denotes the price of the fossil fuel used by dirty generator i (e.g. coal or natural gas). The innovation and production decision process of the retailers are similar to those of the electricity generators. At the beginning of the period, retailer i can invest in storage R&D at a cost 1ψ 2 szi 2, which results in the following technical progress: B i =(1+z i ) B i0, (3) where B i0 is the initial efficiency and z i is the innovation decision variable. At the end of the period, retailer i chooses production level Y i, which involves choosing how much clean and dirty electricity to purchase from the generators (Y ic and Y id ), given { the available ( storage technology B i. The maximization problem of retailer i is: Π i =max p i Y i 1 p ci Y ci Y ci,y B i φ c + p diy di φ d )}, di ( ɛ 1 ɛ 1 ) ɛ ɛ 1 ɛ ɛ where total production is given by the production function Y i = Yci + Ydi. The parameter φ j with j = c, d, captures that the advantage of better storage technologies may not yield the same advantage for clean as for dirty inputs, while ɛ (0, + ) is the elasticity of substitution between clean and dirty inputs in the production process. If ɛ<1, the inputs are complements, while if ɛ>1, the inputs are substitutes in the production process. 2.2 Model equilibrium To solve for the model s equilibrium, we start out by solving the maximization problem of the consumers to derive their demand for electricity. Next, we use this to solve the retailer s production problem to find the demand for clean and dirty electricity. This, in turn, we use to solve each electricity generator s production problem. Having solved the model for quantities of clean and dirty electricity produced and aggregate electricity consumed, we conclude by deriving the different types of firms optimal investment in R&D. 6

8 2.2.1 Consumers Consumers maximize utility with respect to the continuum of inputs Y i. Let us define ( 1 σ 1 ) σ σ 1 σ Y Y di. Then, we can express the optimization problem of the consumer as: 0 i max {Y i } C 0 + β β 1 1 β 1 Y β 0 p i Y i di (4) The first order condition of the problem is Y 1 σ 1 β Y 1 σ i = p i. Manipulating this and using ( 1 ) 1 the definition p p 1 σ 1 σ i di along with the definition for Y allows us to derive the 0 following demand function for electricity from the consumers optimality condition: Retailers Y i = p σ β p σ i. (5) At the end of the period, when the efficiency of the storage technology is already determined, retail firm i faces the profit maximization problem: ( Π i =max Y ci,y di p i Y ci ɛ 1 ɛ + Y di ɛ 1 ɛ ) ɛ ( ɛ 1 1 pci Y ci + p ) diy di B i φ c φ d (6) After substituting in for Y i from equation (5) and some manipulation, the optimality condition for input Y ji can be expressed as: Y ji = p σ β p ɛ σ i p ɛ ji (φ jb i ) ɛ,j= c, d (7) We can now substitute the optimal input use from (7) into the retailer s profit function, to investigate how much the firm will spend on R&D in storage. This involves solving the { maximization problem max Π i 1ψ } z 2 szi 2,whereΠ i denotes the maximized profit function. i Solving this problem yields the following condition for innovation in storage, z i : z i = p σ β p ɛ σ i ( (pci φ c ) 1 ɛ + ( pdi φ d ) ) 1 ɛ (1 ) ɛ B i0 ψ s ((1 + z i ) B i0 ) 2 ɛ (8) 7

9 Equation (8) describes the impact of the current storage knowledge stock on innovations in storage technologies. Note that z i enters on both sides of equation (8). We discuss the comparative statics in section 2.3 below. First, we turn to the optimization problem of the electricity generators to derive a similar relationship between innovation in generating technologies and other model variables Electricity generators Electricity generators have local market power when selling electricity to retailers. They therefore take into account the demand function of the local retailer when determining how much to produce. From equation (7), we find that the inverse demand function for generator i of electricity type j is: p ji = Y 1 ɛ ji p σ β ɛ σ σ ɛ pi B i φ j j = c, d (9) With the local retailer s inverse demand function (9), we can set up the generator s profit maximization problem for the production stage (i.e., for a given technology, A ji ): { π ji =max Y ci Y 1 1 ɛ ji p σ β σ ɛ σ ɛ pi B i φ j g } ji Y ji A ji j = c, d (10) By combining the first-order condition of the maximization problem (10) with respect to production Y ji, with the inverse demand function of retailer i, and simplifying, we obtain the price that generator i of type j receives in equilibrium: p ji = ( ) ɛ 1 ɛ 1 A ji. Knowing the electricity generating firm s maximized profit as a function of the available technology, we can take step back to the beginning of the period and solve for the optimal investment in technological innovation. This yields the following optimality condition that investment in innovation, x ji, must satisfy: ( ) ɛ 1 x ji = p ɛ σ i p σ β (B i φ j ) ɛ A ji0 g 1 ɛ ji 2 ɛ j = c, d (11) ɛ ψ [(1 + x ji ) A ji0 ] Equation (11) describes the relationship between the current knowledge stock and innovations in efficiency improving technologies in the generating sector. Note that the parameter g ji captures the only difference in (11) between clean and dirty generators, since we have that g ci =1andg di = f i. With this in place, we discuss the drivers of innovation in storage and generating technologies in more detail by analyzing the comparative statics. 8

10 2.3 Comparative statics Both the equation characterizing the optimal innovation in storage technologies (equation 8) and the corresponding equation for optimal innovation in generating technologies (equation 11), only implicitly characterize the levels of innovation. We therefore analyze comparative statics using implicit differentiation. Let us start out by looking at the effect of a higher initial knowledge stock, B i0, on innovation in storage: dz i db i0 = dq db i0 [(1+z i )B i0 ] 2 ɛ B i0 + Q(2 ɛ) 1+z i, (12) ( ( ) 1 ɛ ( ) where Q p σ β p ɛ σ 1 ɛ p i P 1 ψ s ), and where P 1 ci φ c + p 1 ɛ. di φ d Note that Q 0for ɛ 1, and negative for ɛ>1. Note first that given the definition of Q, the second term in the denominator is positive for ɛ 1andforɛ 2, while it reaches its minimum point at ɛ =1.5. Hence, the denominator of (12) is positive when clean and dirty electricity are complements or sufficiently close substitutes, but might also be positive for ɛ (1, 2). The numerator depends critically on how more knowledge affects prices along the value chain. Given that dq innovation lowers the marginal cost of production, this price effect is likely negative, db i0 0. It follows that the effect of more knowledge on innovation is likely negative when clean and dirty electricity are complements or close substitutes, but might be positive for ɛ (1, 2). Next, we evaluate the effect of a change in the electricity price index p on innovation in storage. This yields the following: dz i dp = Q1 (σ β) + dq 1 p dp [(1+z i )B i0 ] 2 ɛ p σ β B i0 + Q 1(2 ɛ) 1+z i, (13) ( where Q 1 p ɛ σ 1 ɛ i P 1 ψ s ). It is reasonable to assume that an increase in the price consumers pay for electricity, p, has a positive impact on other electricity prices along the value chain (p i and p ji ). Hence, we have that dq 1 dp that dz i dp 0. Since by definition β<σ, equation (13) implies 0ifɛ 1orifɛ 2. If clean and dirty electricity sources are substitutes but with a low elasticity of substitution, ɛ (1, 2), a higher price might lead to more innovation. The effect of an increase in p i on innovation in storage is similar to that of an increase 9

11 in the aggregate consumer price, p: dz i dp i = Q2 (ɛ σ) p i + dq 2 dp i [(1+z i )B i0 ] 2 ɛ p ɛ σ i B i0 + Q 2(2 ɛ) 1+z i, (14) ( where Q 2 p σ β 1 ɛ P 1. This implies that dz i dp i 0ifɛ 1orifɛ 2and dq 2 dp i (σ ɛ) Q 2 p i. The second inequality depends on the difference between the elasticities of substitution between clean and dirty energy sources (ɛ) and between electricity from different retailers (σ). Hence, the closer substitutes clean and dirty electricity are, the more likely that a higher price in market i causes retailer i to invest in better storage technologies. The effect of an increase in the price of energy source j from retailer i is: ψ s ) dq dz P 3 1 i dp ij = dp ji +(1 ɛ)q 3 ( pji φ j ) ɛ [(1+z i )B i0 ] 2 ɛ B i0 + P 1Q 3 (2 ɛ) 1+z i, (15) ( where Q 3 p σ β p ɛ σ 1 ɛ i ψ s ). Given the definition of Q 2, the second term in the numerator is zero at ɛ = 1 and strictly positive for ɛ values below or above unity. We also know that the denominator is positive for ɛ 1andforɛ 2. In addition, the sign of equation (15) depends on the effect of the price in question on other prices along the value chain, dq 3 dp ij. Assuming, as above, that this effect is positive (or zero), we find that dz i dp ji 0forɛ 1or ɛ 2, but might also be positive for other elasticities of substitution. Turning to the comparative statics for innovation in electricity generating technologies, we start by considering the effect of a higher initial knowledge stock, A ij0 : dx ji da ji0 = dr da ji0, (16) [(1+x ji )A ji0 ] 2 ɛ A ji0 + R(2 ɛ) 1+x ji where R ( ) ɛ 1 ɛ p ɛ σ i p σ β (B i φ j ) ɛ g 1 ɛ ji, which is positive for ɛ 1. Given the definition of ψ R, the second term in the denominator is always negative and reaches its maximum value at ɛ = 2. As ɛ increases (or decreases) beyond this point, this term declines. The effect of the knowledge stock on innovation depends critically on whether more knowledge has a positive effect on prices along the value chain. Given that innovation lowers the marginal cost of production, it seems plausible that innovation will have a negative impact on prices. If that is the case, so that dr da ji0 0, then more knowledge increases innovation when clean 10

12 and dirty electricity are sufficiently close substitutes, while the effect is ambiguous for lower values of ɛ. Similarly, the effect of a change in the electricity price index p on innovation in generating technologies is: dx ji dp = (σ β) R1 p + dr 1 dp where R 1 ( ) ɛ 1 ɛ p ɛ σ i [(1+x ji )A ji0 ] 2 ɛ p σ β A ji0 + R 1(2 ɛ) 1+x ji, (17) (B i φ j ) ɛ g 1 ɛ ji. Given that a price increase has a positive impact on other ψ electricity prices along the value chain ( dq 1 0), equation (13) implies that an increase in dp the price p has a negative impact on innovation if the elasticity of substitution is sufficiently high (or low), but might be negative for any value of ɛ. We can express the effect of p i on innovation as: dx ji dp i = (ɛ σ) R 2 p i + dr 2 dp i [(1+x ji )A ji0 ] 2 ɛ p ɛ σ i A ji0 + R 2(2 ɛ) 1+x ji, (18) where R 2 ( ) ɛ 1 ɛ p σ β (B i φ j ) ɛ g 1 ɛ ji. As above, the effect of an increase in p ψ i on innovation is ambiguous for lower value of ɛ, while we know that the effect is negative if clean and dirty electricity are sufficiently close substitutes (high ɛ). When considering the effect on innovation of an increase in the price p ji,notethatwedo not have an explicit expression of a generating firm s optimal innovation as a function of its price, p ji. This price nonetheless affects the firm s innovation as it clearly affects the firm s investment decision. We can express the effect of an increase in the price of energy source j from retailer i as: dx ji dp ji = dr dp ji, (19) [(1+x ji )A ji0 ] 2 ɛ A ji0 + 2 ɛ 1+x ji with R as defined above. The denominator of this expression is positive for ɛ 2. Apart from this, the sign of (19) depends on dr dp ij. Assuming that this effect is positive, then a higher dx price p ji increases innovation: ji dp ij 0. For a sufficiently high elasticity of substitution between clean and dirty electricity, this sign will change, so that price has a negative effect on innovation, despite the price effect per se still being positive. Finally, we see from equation (11) that the optimal levels of innovation in generation technologies (clean and dirty) depend on the effectiveness of the storage technology. Differentiating equation (11) with respect to the initial knowledge stock of storage technologies 11

13 yields: dx ji dz i = ɛr 3 1+z i + dr 3 dz i [(1+x ji )A ji0 ] 2 ɛ A ji0 [(1+z i )B i0 φ j ] ɛ + R 3(2 ɛ) 1+x ji, (20) where R 3 ( ) ɛ 1 ɛ p ɛ σ g1 ɛ σ β ji i p. We have established that the second term in the denominator is negative, and declining as we move away from the maximum point ɛ = 2. The sign of ψ the numerator is also ambiguous, but the higher the elasticity of substitution, the more likely that the numerator is positive. Hence, while we cannot conclude about the sign of equation (20) for lower values of ɛ, we know that it is negative when clean and dirty electricity are sufficiently close substitutes. To summarize, we can conclude that an increase in electricity prices along the value chain has a positive effect on innovation in storage technologies for ɛ 1andforɛ 2, while the effect on generating technologies is ambiguous. As we let ɛ, however, the effect of higher prices is more innovation in storage and less innovation in generating technologies. Next, we have established that innovation critically depends on the effect more knowledge has on prices along the value chain. We argue that innovation will tend to lower electricity prices. In this case, a larger existing knowledge stock will induce retailers to innovate less in storage if ɛ 1orɛ 2, while the effect of more knowledge might be positive for ɛ (1, 2). In the case of electricity generators, we found that the effect of more knowledge, both in storage and generation, generally is ambiguous, but for sufficiently large ɛ, a higher knowledge stock will increase innovation. Hence, both prices and existing knowledge stocks have opposite effect on innovation in storage and innovation in generation technologies if the substitutability between clean and dirty electricity is sufficiently high. As our theoretical analysis shows, we need to empirically estimate our model to establish in which situation we are out of the many cases discussed in this section. This is needed to fully understand the role of storage in the transition toward renewable electricity. 3 Data 3.1 Selection of relevant patents Patents and patent families In this section, we explain and justify the construction of our global, firm-level, energy patent database. The data used are drawn from the OECD s Triadic Patent Families Database and 12

14 the IEA. The study of firm-level incentives for technological innovations requires the construction of a global firm-level database. We use patent data to measure research output. The main advantages of using patent data for our purposes are twofold. First, patents are available at the firm and technology level. This is important as we want to study clean, dirty and energy storage sectors at the firm level. In contrast to more aggregate measures such as R&D expenditures, which are generally only available at the industry level and for limited technology types, each individual patent contains detailed information about the inventor(s), applicant(s), and the specific type of technology (Popp, 2005). Information about the applicant is the most useful for our purposes, as it allows us to identify specific firms. In addition, thanks to the International Patent Classification (IPC) codes assigned to each patent we can identify technologies related to electricity generation, electricity storage and clean and dirty innovations. Thus, the detailed nature of patent data proves especially useful when examining firm-specific incentives to innovate in selected technologies. Second, patents provide a measure of the innovation output of firms research activities that is close to the actual time of invention. Since patent applications are normally submitted early in the research process, as indicated by the priority date they also provide a good measure of overall innovative activity of a given firm (Popp, 2005). While this is the case, there are drawbacks of patent data that must be addressed. These limitations motivate the extraction of patents from the OECD s Triadic Patent Database which contains patent family data from 1978 to Triadic patent families are useful to us because they are collections of patents that protect the same idea in different countries. For example, a particular patent application must have an equivalent application at the European Patent Office (EPO), Japanese Patent Office (JPO) and the United States Patent Office (USPTO) in order to qualify as a patent family member. Because triadic patents are applied for in three separate offices, they include only the highest valued patents and allow for a common worldwide measure of innovation that avoids the heterogeneity of individual patent office administrations (Aghion et al., 2012). Furthermore, the OECD utilizes extended families which are designed to identify any possible links between patent documents (Martinez, 2010). This is advantageous, as it provides the most comprehensive method of consolidating patents into distinct families, allowing us to include an extensive number of patented ideas. We construct our database using patent families from the Triadic Patent Database hoping to use the most valuable global patents in our study. The use of triadic patent families presents several advantages. First, triadic patent fami- 13

15 lies do not suffer from what is known as the home bias. This bias is related to the fact that, compared to their inventive activity, national firms tend to register more patents than international competitors (for instance, in 1997, firms from the United States accounted for 53% of total application to the USPTO, but only for 16% of registrations at the EPO). Second, using triadic patent families we avoid the risk of counting some patents twice, especially in a world-wide scale. Finally, this method corrects for the differential in patent values across countries. Yet, the disadvantage of triadic patent families is related to the lag associated with the USPTO. Legal delays for publishing applications is 18 months after the priority date and up to 5 years between the priority date and publication date Dernis & Khan (2004). As a consequence, US patent grants may delay the completion of data on triadic patent families. In order to mitigate this limitation, the OECD utilizes forecasts called nowcasting in order to improve the timeliness of triadic patents Dernis & Khan (2004). Despite this difficulty, triadic patents still provide the most inclusive measure of high-value, firm-level, innovative performance Electricity generation technologies We select patents related to electricity generation using IPC codes. We then categorize them into three groups: renewable energy, fossil fuel based technologies and electricity storage. Renewable energy technologies are identified from Johnstone, Hascic and Popp (2012). Specifically, we select patents whose technology classes are related to alternative energy production. This includes integrated gasification combined cycle (ICGG), fuel cells, pyrolysis, harnessing energy from man-made waste, hydro energy, wind, solar, geothermal energy, other production or use of heat, using waste heat, and devices for producing mechanical power from muscle energy. Specific descriptions of the IPC codes used in this paper are presented in the appendix. Fossil fuel technologies are selected from the general fossil fuel technology IPC codes reported in Lanzi et al. (2011). Finally, electricity storage comes from the WIPO s IPC Green Inventory Global firms We aggregate individual patent counts at the firm-level. By utilizing the OECD Harmonized Applicants Names (HAN) Database, a register that contains clean applicant names which are 3 The IPC codes listed in the IPC Green Inventory have been compiled by the IPC Committee of Experts in concordance with the United Nations Framework Convention on Climate Change (UNFCCC). For more information see 14

16 matched against company names from business register data, we are able to link patents to firms and individuals. Unfortunately, the HAN database does not contain firm information for every patent application in our sample. Names that cannot be matched using the HAN are synchronized using applicant information contained in the Triadic Patent Families Database. Although this allows us to match every patent to an applicant, it poses two difficulties. First, applicant names in the Triadic Patent Database contain a number of spelling, character, and name variations. For example, 3M INNOVATIVE PROPERTIES and 3M INNOVATIVE PROPERTIES CO would be incorrectly treated as separate firms in the absence of name harmonization. Second, the Triadic Patent Families Database does not directly link patent applications to applicant names. Instead, applicant names are linked to family identifiers. Thus, if a given family contains more than one firm name, it is impossible to tell which firm to associate with each patent. In order to minimize complications that may result from these challenges, we restrict our sample to those patents applications that can i) be matched fully from the HAN register and ii) have a single applicant and are the sole member of patent family. We conduct further harmonization using algorithms, although some name variation still remains. Our database contains 4,473 firms that claim residence in 52 countries. 3.2 Descriptive statistics Figure 1 shows the evolution of patent registrations over time. The graph shows the number of total, clean, dirty, and storage patents registered per year from 1963 to The numbers of clean and dirty patents are somewhat correlated, but when we look more closely we can identify two interesting patterns. First, while clean and dirty patent numbers tend to be close in the late 1970s and early 1980s, clean patenting surpasses dirty patenting by a large margin by Second, the sharp increase in the number of clean patents registered in the late 1990s seems to correspond with the period over which the number of storage patents initially picked up. All series exhibit a sharp decline around Although there is no clear explanation for this in the literature, it may be indicative of a strengthening of patent granting standards or, more simply, of a delay in the updating of the database. The patents in our dataset are registered by a total of 4,473 firms located in 52 different countries. 4 As can be observed in Table 1, the majority of these firms resides in the US (1,666), Japan (678), Germany (538), and France (227). Perhaps not surprisingly, the highest concentration of innovating firms is found in the United States and Japan. This is consistent with the broader literature that suggests that the majority of innovations, not 4 All firm names have been harmonized in our sample to identify unique firms. 15

17 Figure 1: Patenting over time Number of patents filed worldwide year Total Dirty Clean Storage only innovations related to the production of electricity, originate in these two countries. Table 2 reports the top-ten patent holders in our sample. The table also reports the number of clean, dirty, and storage patents registered by these firms. The table highlights several features. Interestingly, at least across the top innovators, we can observe some level of country specialization. The top-two patent holding firms are based in the United States and focus primarily on dirty technologies. The firms ranked fourth and fifth are two Japanese firms active almost exclusively in clean technologies. Moving further down in the rankings, we find more Japanese firms, and all of them seem to focus primarily on clean technologies. In contrast, the main innovators in dirty technologies seem to be primarily located in western countries, as is confirmed by Table 3. Siemens AG, a German firm which rank third in terms of innovation activity, seem to split its research resources almost evenly between clean and dirty technologies. Tables 3, 4 and 5, respectively, reports the top patent holders in dirty, clean, and storage technologies. With some notable exceptions, such as Siemens AG, we notice that firms tend to concentrate in one particular type of technology. Another interesting observation, especially given the focus of this study, emerges from table 5: firms innovating in storage 16

18 Table 1: Distribution of firms across countries Country # of firms United States 1,666 Japan 678 Germany 538 France 227 United Kingdom 200 Switzerland 148 Canada 131 Sweden 117 Table 2: tab:main patent holders Firm Country Total Clean Dirty Storage Canon JP General Electric US Toyota Jidosha JP Wobben Aloys DE Mitsubishi Heavy Ind. JP Siemens DE Matsushita Elect. Ind. JP Sony JP Hitachi JP Honda Giken Kogyo JP Table 3: Main dirty patent holders Firm Country Dirty Clean Storage Total General Electric US Mitsubishi Heavy Ind. JP Foster Wheeler Energy US Siemens DE Asea Brown Boveri CH Hitachi JP United Tech. US Alstom Tech. CH Texaco US A. Ahlstrom FI

19 technologies tend to be active also in clean innovation, but less so in dirty innovation. Table 4 confirms that the majority of innovators in clean technologies are located outside of the US or, more specifically, in Japan with the exception of Siemens AG, Air Products Chemicals, Wobben Aloys, and General Electric. Furthermore, the top dirty innovators seem to be relatively more active in clean technologies than the clean innovators are in dirty technologies. This may be related to the size of the firms that are active in the dirty sector, which tend to be relatively large. Data on GDP and GDP per capita are from the Penn World Tables. As mentioned above, the firms in our sample represent 66 countries. Data on energy prices are from the US Energy Information Agency (EIA). The natural gas price increased significantly in the early 1980s. They then declined a bit over the next decade, before increasingly sharply from the late 1990s. The price of coal, on the other hand, has been relatively stable over the period we study, but with a big difference in levels between Europe, characterized by high prices, and other regions. Table 4: Main clean patent holders Firm Country Clean Dirty Storage Total Canon JP Wobben Aloys DE Siemens DE Mitsubishi Heavy Ind. JP Kaneka JP Sharp JP Sanyo Elect. JP Energy Conversion Devices US General Electric US Matsushita Elect Ind. JP Empirical analysis 4.1 Identification From the theoretical model presented in section 2, we identified the optimality condition for investment in clean innovation,equations (8) and (11). These equations depend on a variety of factors: the aggregate price of electricity (p), the price specific to generator i (p i ), fuel price (g ji ) and the knowledge stock in clean (A ci0 ), dirty (A di0 ) and storage (B i ) technologies. We estimate this relationship using a reduced form specification capturing all this different 18

20 Table 5: Main storage patent holders Firm Country Storage Clean Dirty Total Toyota Jidosha JP Sony JP Matsushita elect. Ind. JP Black Decker US Honda Giken Kogyo JP Panasonic JP Nissan Motor JP Motorola US Sanyo Elect. JP NEC JP elements. Our identification follows closely the one used by Aghion et al. (2012) who study innovation in the automobile sector. The first step consists in estimating the three stocks of knowledge (in clean, dirty and storage technologies). We assume that the stock of relevant knowledge is composed by the knowledge stock accumulated by the firm over time, and of the spill-overs to which the inventors working for the firm may be exposed. Thus, the stock of knowledge takes the form: K ict = E ict 1 β 1 + I ict 1 β 2, (21) where K is the stock of relevant knowledge to which firm i, located in country c has access at time t, the vector E contains the stocks of knowledge in clean, dirty and storage technologies external to the firm, while the vector I consists of the stocks of knowledge in clean, dirty and storage technologies internal to the firm. The internal knowledge stocks are defined as the cumulative stocks of patents for technology type j of firm i (where j = c, d, s). The external stock of knowledge E jict is the stock of patents of technology type j = c, d, s filed in the country by the end of year t, excluding the firm s own applications. The second step consists in accounting for the impact of the aggregate price of electricity and of the price practiced by a given electricity generator. In order to control for these two prices we introduce the country specific price of electricity in the estimations, we take care of the latter effect with firm fixed effects. The firm specific fixed effects, together with time fixed effects control for other factors which may influence innovation, for example a specific policy adopted by a government. We also control for the size and the wealth of a country using gross domestic product (GDP) and GDP per capita, respectively. Finally, while for production using clean technologies fuel is free (for instance the sun 19

21 or the wind), when electricity is produced using dirty technologies the generator has to pay for fossil fuels. Therefore, their prices (g ji ) are also determinants of innovation and we need to introduce them in our specification. More specifically, we control for the country-specific price of coal and of natural gas. Therefore, our final specification takes the following form A jict = E ict 1 β 1 + I ict 1 β 2 + β 3 P ct + F ct 1 γ 1 + X ct 1 γ 2 + δ t + δ i + u jict (22) with j (clean, dirty, storage), and where A represents the number of patent applications filed by firm i in year t for technology type j, P is the country-specific price of electricity, F is a matrix of country specific fuel prices (coal and natural gas) and X is a matrix of country controls. δ t and δ i denote time and firm fixed effects, respectively. Estimating dynamic count data models, especially in the case of patent data, presents several challenges, which have been previously analyzed in the literature (see for instance Hausman et al., 1984; Blundell et al., 1995, 2002). The typical estimation strategy used for count data involves a poisson distribution, yet a poisson distribution is characterized by the equality between mean and variance. This is usually not the case when working with patent data, which are characterized by a high degree of over-dispersion (the variance is significantly larger than the mean). As we can see in Table 6 this is confirmed in our case; the large amount of zeros (95.8% of the observations) and the high number of patents held by a small number of firms have a big impact on the variance. This feature of patent data is better controlled for by a negative binomial distribution. Our estimation procedure closely follows Blundell et al. (1995). The difference in firms knowledge stocks at the beginning of the sample is one of the main sources of unobserved heterogeneity, which cannot be controlled for by a usual fixed effects specification. Therefore, we also control for the pre-sample innovation history of each firm. We take advantage of the exceptional length of our sample, , and use the first part ( ) to compute these additional fixed effects. Note, though, that this measure is bounded below by zero. To account for this, we also introduce in the estimation a dummy variable indicating the absence of pre-sample innovation activity. 4.2 Results To be written. 20

22 Table 6: Summary statistics Patent count Min Max Mean Variance Clean Dirty Storage Total Table 7: Main results firm fixed effects Dependent variable: patent count Clean Dirty Clean Dirty Storage (1) (2) (3) (4) (5) L.Knowledge Stock C (0.0004) (0.0004) (0.0015) (0.0016) (0.0005) L.Knowledge Stock D (0.0009) (0.0010) (0.0007) (0.0007) (0.0015) L.Knowledge Stock S (0.0009) (0.0014) (0.0008) L.Spillover C (0.0004) (0.0006) (0.0007) (0.0009) (0.0008) L.Spillover D (0.0007) (0.0007) (0.0010) (0.0011) (0.0010) L.Spillover S (0.0005) (0.0008) (0.0007) L.Coal (0.0007) (0.0007) (0.0010) (0.0010) (0.0013) L.Natural gas (0.0047) (0.0047) (0.0065) (0.0066) (0.0073) L.Electricity (0.0017) (0.0017) (0.0024) (0.0025) (0.0026) L.Real GDP (0.0310) (0.0309) (0.0344) (0.0343) (0.0438) L.Real GDP cap (0.1909) (0.1915) (0.2485) (0.2482) (0.2780) Year F.E. yes yes yes yes yes Firm F.E. yes yes yes yes yes Past Innovation no no no no no Observations 60,400 24,640 60,400 24,640 25,305 Notes: All estimations contain a constant. *** p<0.01, ** p<0.05, * p<

23 Table 8: Main results past innovation fixed effects Dependent variable: patent count Clean Dirty Clean Dirty Storage (1) (2) (3) (4) (5) L.Knowledge Stock C (0.0064) (0.0049) (0.0064) (0.0047) (0.0039) L.Knowledge Stock D (0.0042) (0.0125) (0.0042) (0.0124) (0.0040) L.Knowledge Stock S (0.0060) (0.0065) (0.0152) L.Spillover C (0.0007) (0.0010) (0.0009) (0.0012) (0.0012) L.Spillover D (0.0011) (0.0015) (0.0011) (0.0015) (0.0014) L.Spillover S (0.0009) (0.0011) (0.0011) L.Coal (0.0008) (0.0011) (0.0008) (0.0011) (0.0012) L.Natural gas (0.0058) (0.0073) (0.0058) (0.0073) (0.0078) L.Electricity (0.0020) (0.0024) (0.0020) (0.0024) (0.0025) L.Real GDP (0.0208) (0.0282) (0.0208) (0.0282) (0.0286) L.Real GDP cap (0.0781) (0.1202) (0.0780) (0.1199) (0.1226) Year F.E. yes yes yes yes yes Firm F.E. no no no no no Past Innovation yes yes yes yes yes Observations 111, , , , ,615 Notes: All estimations contain a constant. *** p<0.01, ** p<0.05, * p<

24 Table 9: Main results firm and past innovation fixed effects Dependent variable: patent count Clean Dirty Clean Dirty Storage (1) (2) (3) (4) (5) L.Knowledge Stock C (0.0004) (0.0004) (0.0018) (0.0020) (0.0006) L.Knowledge Stock D (0.0012) (0.0012) (0.0008) (0.0008) (0.0017) L.Knowledge Stock S (0.0009) (0.0015) (0.0009) L.Spillover C (0.0004) (0.0005) (0.0006) (0.0007) (0.0006) L.Spillover D (0.0007) (0.0007) (0.0010) (0.0010) (0.0009) L.Spillover S (0.0005) (0.0008) (0.0007) L.Coal (0.0007) (0.0007) (0.0010) (0.0010) (0.0013) L.Natural gas (0.0047) (0.0047) (0.0064) (0.0065) (0.0072) L.Electricity (0.0017) (0.0017) (0.0024) (0.0024) (0.0026) L.Real GDP (0.0316) (0.0315) (0.0354) (0.0354) (0.0447) L.Real GDP cap (0.1921) (0.1925) (0.2425) (0.2421) (0.2776) Year F.E. yes yes yes yes yes Firm F.E. yes yes yes yes yes Past Innovation yes yes yes yes yes Observations 60,400 24,640 60,400 24,640 25,305 Notes: All estimations contain a constant. *** p<0.01, ** p<0.05, * p<

25 Table 10: Robustness regional spillovers Dependent variable: patent count Clean Dirty Clean Dirty Storage (1) (2) (3) (4) (5) L.Knowledge Stock C (0.0004) (0.0004) (0.0018) (0.0020) (0.0006) L.Knowledge Stock D (0.0012) (0.0012) (0.0008) (0.0008) (0.0017) L.Knowledge Stock S (0.0009) (0.0015) (0.0009) L.Spillover C (0.0003) (0.0004) (0.0005) (0.0006) (0.0005) L.Spillover D (0.0005) (0.0005) (0.0007) (0.0008) (0.0007) L.Spillover S (0.0004) (0.0007) (0.0006) L.Coal (0.0007) (0.0007) (0.0010) (0.0010) (0.0013) L.Natural gas (0.0047) (0.0047) (0.0065) (0.0066) (0.0072) L.Electricity (0.0017) (0.0017) (0.0024) (0.0025) (0.0026) L.Real GDP (0.0304) (0.0307) (0.0334) (0.0342) (0.0440) L.Real GDP cap (0.1926) (0.1922) (0.2461) (0.2435) (0.2791) Year F.E. yes yes yes yes yes Firm F.E. yes yes yes yes yes Past Innovation yes yes yes yes yes Observations 60,400 24,640 60,400 24,640 25,305 Notes: All estimations contain a constant. *** p<0.01, ** p<0.05, * p< Robustness To be written. 5 Conclusions To be written. 24