What Determines the Adoption of
Technologies for Renewable Energy
Generation Across Countries?
Julián D. Gómez
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CEDE
ISSN 1657-7191 Edición electrónica.
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63
Serie Documentos Cede, 2018-63 ISSN 1657-7191 Edición electrónica. Noviembre 2018
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Universidad de los Andes | Vigilada Mineducación
1
What Determines the Adoption of Technologies for Renewable Energy
Generation Across Countries?
Julián D. Gómez1
Abstract
I provide evidence about the effects of induced biased technical change as a main determinant of solar and wind energies adoption. This study presents an empirical framework based on an input demand function for electricity production. Implementing this model helps us to understand what is driving the decision of electricity generators to adopt solar and wind energies. The data used for this study was a cross country panel dataset on 29 OECD countries between 1990 and 2015. I test three mechanisms that may be driving the adoption of these technologies: 1) a pure substitution effect between renewable energies and fuel; 2) induced technological change and innovation; 3) fuel taxes. The results show that induced technical change is the main driver to increase the rate of adoption of solar and wind energies.2
JEL Classification: O31, O33, O38, Q42, Q55, Q58
Key Words: Adoption, renewable energies, technological change & innovation, fossil fuel energy, fuel taxes.
1 Research assistance, University of los Andes (jd.gomez1400@uniandes.edu.co).
2 I would like to thank Xavier Duran for his guidance and for providing relevant multiple enthusiastic advisements. I would also
like to thank Andrea Romero, Angélica Olaya, David Ayala, Gabrielle Penrose, Hernando Zuleta, Jorge García, Juan Camilo Cárdenas, Paula Jaramillo, Santiago Gómez and Javier Mejía for many helpful comments. Participants at the Thesis Seminar also helped to improve the paper. All errors remain my own.
2
¿Qué determina la adopción de tecnologías para la generación de energías
renovables entre países?
Julián D. Gómez3
Resumen
Este trabajo provee evidencia de que el efecto del cambio tecnológico inducido es un determinante principal de la adopción de las energías solar y eólica. Este estudio hace uso de una estrategia empírica basada en una función de demanda de inputs derivada del proceso de maximización de una función de producción de electricidad. La implementación de este modelo permite entender los principales mecanismos que afectan la decisión de adoptar energías solar y eólica por parte de los productores de electricidad. Para esto, se utiliza una base de datos tipo panel de 29 países de la OECD entre 1990 y 2015. Con ello, se comprueban tres hipótesis: 1) efecto de sustitución puro entre renovables y combustibles fósiles; 2) cambio tecnológico inducido e innovación; 3) Impuestos al combustible. Los resultados muestran que el efecto de cambio tecnológico e innovación es uno de los principales mecanismos que afecta la adopción de las energías solar y eólica.4
Códigos JEL: O31, O33, O38, Q42, Q55, Q58
Palabras Clave: Adopción, energías renovables, cambio tecnológico e innovación, combustibles fósiles, impuestos al combustible.
3 Asistente de investigación, Universidad de los Andes (jd.gomez1400@uniandes.edu.co).
4 Quiero agradecer a Xavier Durán por su guía y los múltiples comentarios que ayudaron a mejorar esta tesis. También me
gustaría agradecer a Andrea Romero, Angélica Olaya, David Ayala, Gabrielle Penrose, Hernando Zuleta, Jorge García, Juan Camilo Cárdenas, Paula Jaramillo, Santiago Gómez y Javier Mejía por todos sus valiosos comentarios. Los participantes del seminario de tesis 2017-2 también ayudaron a mejorar este trabajo. Todos los errores son responsabilidad del autor.
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1.
Introduction
Understanding the determinants of the rate of adoption of technologies to produce electricity from renewable sources is fundamental to mitigate climate change, pollution and
natural resources exploitation.5 In the last twenty years the energy challenge has become
increasingly stark. The need to reduce and substitute fossil fuel energy production has become increasingly urgent, while at the same time electricity demand is constantly increasing.6
However, the main source of energy production is still derived from fossil fuels (see Figure 1).7 Although countries like Denmark have reached a 60% share of renewable energies over its total electricity production, the average share of these energy sources is close to 15% the OECD countries, whereas the average share of fossil fuels is almost 50% (see Figure 1).8 Nonetheless, the adoption rate of renewable energies have increased in most of the OECD countries during the last years (Rubio-Vargas, 2017; Baudry & Bonnet, 2017).
¨Particularly, two patterns of renewable energy production can be identified before and after year 2000 in the OECD countries. The first one, was a forty-year period where renewable energies accounted for less than 3% of the energy supply.9 The second one, was a fifteen-year period where a significant increase of the production of renewable energies took place. The production rate of renewable energies grew from 3%, during the year 2000, to approximately 15% in 201510 (Figure 1).
5 I understand adoption as the rate of production of renewable energies over the total country’s energy production. In this case, I only consider the cases of solar and wind energy.
6 Several international climate agreements have taken place in the last 20 years to reduce the fossil fuel energy consumption. As a consequence, the adoption of renewable energies has increased. Some of the agreements are the follow: 1) the United Nations Framework Convention on Climate Change (UNFCCC), 2) the Kyoto Protocol and 3) the Paris Climate Agreement. For further information visit: http://www.consilium.europa.eu/en/policies/climate-change/international-agreements-climate-action/ 7 Energy consumption average Growth rate was 42% for the OECD countries between 1990-2014. Own Calculations from IEA.
8 IEA Electricity and heat generation database (see section 3.1.1). 9 OECD Average.
4
Figure 1.
Source: IEA (2017)
The adoption of technologies for renewable energy production may be driven by a pure substitution effect as: (i) fossil fuel prices increase, (ii) renewable energy prices decline and (iii) policies disincentivize fossil fuels and promote renewable energies consumption (World Bank,
2017).11 Between 2000 and 2010, commodity prices experienced a boom super cycle that
probably incentivized the adoption of these technologies by increasing fuel prices. Therefore, it is not clear yet that environmental policy instruments and the development of technological innovations are enough to keep on inducing the adoption of renewable energies when the commodity price super-cycle finishes, and the fossil fuel prices start to fall.12 Consequently, the focus of this study is to comprehend which mechanisms are driving the process of adoption of solar and wind technologies for energy generation, between 1990 and 2015 across the OECD countries.
In particular, I examine three hypotheses. First, a pure substitution effect between renewable energies and fuel energy due to an increase of fossil fuel prices. Second, substitution
11 After the 2000 the commodities market experienced a commodities super-cycle that increased the prices of fossil fuels (Oil, natural gas, coal and related products). http://blogs.worldbank.org/developmenttalk/where-commodity-prices-are-going-explained-nine-charts
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may be also driven by a decline in the cost of production of renewable energies. The decline in the prices of the capital goods used to produce renewable energies, due to innovation, reduce the cost of production and may induce the adoption of these sources of energy. (Aghion et al, 2016; Hicks, 1932; Acemoglu, 2002; Zuleta, 2008). Similarly, knowledge stocks and spillovers determine the innovation success of renewable energy capital goods and their rate of diffusion
(Rosenberg, 1972; Roper & Hewitt-Dundas, 2015). Third, energy and environmental policy
instruments may generate a higher adoption of renewable energies by increasing the relative cost of other sources of energy, like fossil fuel, and by subsidizing the process of innovation in renewables (Comin and Hobijn, 2010; Heiniz and Zelner, 2006; Pargal & Wheeler, 1996; Calel and Dechezleprêtre, 2016; Acemoglu et al, 2012; Peretto, 2009).
In order to test the previous three hypotheses, this study presents an empirical framework based on an input demand function for electricity production. I use a cross country panel dataset, on 29 OECD countries between 1990 and 2015, and a two-stage regression analysis. In the first stage, I estimate the effects of (i) knowledge stocks and (ii) the prices for metals and minerals on the capital good efficiency prices for solar and wind energy generation.13 Solar and wind energy capital goods prices declined driven by technological change, but after 2005 wind energy capital goods prices increased when the metal and mineral prices rose and dominated the effect of technical change until 2010 (see Figure 9). In the second stage, I estimate an energy input demand to examine the effects of (i) the technological predictions of solar and wind energy efficiency prices, (ii) fossil fuel prices, (iii) fuel taxes and (iv) electricity output prices on the adoption of wind and solar energy. In sum, results are striking. Induced technological change produced a decline in the prices of solar and wind capital goods, ceteris paribus metal inputs prices. This effect is the key driver for the adoption of solar and wind technologies for energy generation. In contrast, fuel taxes and electricity output prices are not significant to induce adoption of these technologies. Therefore, we should expect the adoption of solar and wind energies to be faster if the prices of their capital goods continue to decline as a consequence of innovation, higher R&D investments and technological opportunity.
The paper is organized as follows. In Section 2, I discuss the theoretical framework. Secondly, I introduce and describe data in Section 3. In section 4, I present the empirical strategy. Section 5 provides the results. And finally, conclusions in Section 6.
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2.
Theoretical Framework
Previous literature like Aghion et al (2012) have found that past productivity of innovation in renewable energies and environmental policy instruments, are significant to induce biased technological change in the direction of renewable energies (Acemoglu, 2012; Peretto, 2007; Newell et al, 1999; Calel & Dechezleprêtre, 2014; Johnstone et al, 2009). I seek to extent the previous analysis to comprehend if the process of innovation in renewable energies is leading to higher rates of adoption.
2.1 The reduced form model: Input demand function
Energy generators and electric utilities are the major electricity providers in most of the countries. Utilities choose from a mix of energy sources the quantity of electricity to be produced and sell to final consumers at a price given by the local electricity market. Therefore, different energy sources are inputs for electricity production. More specifically, in the OECD, the liberalization of electricity markets has allowed end users to freely choose their supplier, the source of energy and to negotiate their contracts (IEA & OECD, 2001). Hence, the rate of adoption of different energy sources is determined by the demand of electricity generators for each type of energy.
Following this thought, we can think that the adoption of renewable energies is the result of an input demand function for electricity production, where multiple energy sources are inputs. 14 Thus, if we suppose that the decision to adopt renewable energies of an electric producer is
the result of a profit maximization, then we can model this decision using the classic production theory to derive an input demand function for renewable energies (Uri, 1978; Bopp & Costello, 1990). As a consequence, the derived demand function for renewable energies would be a function of their prices, the substitute energy prices and the final electricity price.15 I consider solar energy, wind energy and oil products as the energy inputs for a country’s electricity production.16
14 Fossil fuels, renewable energies, nuclear energy and hydroelectric energy.
15 Prices of renewables and substitute energy sources are endogenous while the electricity output prices are exogenous. 16This simplification correspond to the lack of information related with other sources of energy like hydroelectric (see Section 3).
7
Specifically, I use a Cobb-Douglas function to model the production of energy, where
is the electricity output price, and are the cost of renewable and fossil fuel energies,
and are the renewable and fossil fuel inputs for electricity production, and and are the
output elasticities of and . Thus, the maximization problem that electric utilities face is:
П , 1
Deriving the input demand for renewable energies from (1) and applying logs we obtain:
∗ , , 2
And finally, the reduced form model equation is:
∗ , , 3
Under this specification we would expect that: (i) higher electricity output prices increase the adoption for renewable energies; (ii) increasing renewable energy costs decrease their adoption; (iii) higher fossil fuels energy costs incentivize the adoption of renewable energies. Nonetheless, the reader should notice that the accuracy of this input demand function for electricity generation is restricted by the omission of other important sources of energy like nuclear and hydroelectric. As a consequence, results derived from this equation may not be appropriated for countries like Mexico where water is an abundant resource and, therefore, hydroelectric energy is the principal input source for electricity generation.
2.2Factor endowments, biased technical change and innovation:
Solar photovoltaics (PV) industry has been characterized by a large market expansion, rising demand, excess inventory, declining government support, significant industry consolidation, vertical integration and a great price reduction during the past decade. A highly competitive market and a crowed industry produced an accelerated process of technological innovation that increased the production capacities, reduced the cost of production of modules and ended up into an oversupply that declined the prices of PV modules faster than the
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production cost.17 As a consequence, some of the largest PV companies, like Solyndra (US) and Q-Cells (Germany), became insolvent between 2010 and 2011 because were unable to achieve full-scale operations quickly enough to compete with larger foreign manufacturers or companies located in countries with greater support on renewables. The leadership in modules production shifted from the United States, Europe and Japan to China who currently dominates the use and the manufacturing of solar PV.18 In 2011, China was leading the market with six companies in the top 10 manufacturers while the US, Canada and Japan were competing with two and one companies in this top, respectively. Moreover, in 2015 the PV module market was leaded by Chinese and Canadian companies, whereas US companies lost position and Japan companies got out of the top 10 manufacturers (Sawind, 2012; Sawin, Sverrisson, & Rickerson, 2015; Sawin, Seyboth, & Sverrisson, 2017; Mints, 2016).19
As with solar PV, the cost of electricity from wind power has fallen measurably. Wind prices rose between 2005 and 2009 due to rising global demand and the increasing price of steel.20 However, recent price declines have resulted from over-capacity among manufacturers, increased competition, increasing scale, and greater efficiency, which have combined to drive down turbine costs, increase capacity factors, and reduce operations and maintenance costs. In 2011, the world’s top 10 turbine manufacturers captured nearly 80% of the market whereas in 2016 they accounted for 75% of the global production. The top turbine producers hailed from Europe, China, India, and the United States (Sawind, 2012; Sawin, Sverrisson, & Rickerson,
2015; Sawin, Seyboth, & Sverrisson, 2017).21 Nonetheless, the turbine market is not as
competitive and diversify as the PV modules market.22 Hence, the innovation process has been slower, and the prices of turbines have not fallen as the PV module prices (see Figure 9).
Basic economic theory suggests that, if the prices of renewable energies rise, the input
17 The top 15 solar PV module manufacturers accounted for 49% of the global production and module prices fell more than 40% during 2011. PV module price reductions continued in 2011, due to economies of scale associated with rising production capacities, technological innovations, competition among manufacturers, and a large drop in the price of silicon—and they outpaced cost reductions (REN21, 2012).
18 China accounted for 65% of global PV modules production during 2016 (REN21, 2017).
19 Suntech Power (China; 5,8%) and First solar (US, 5,7%) had the larger market shares in 2011. In 2015 Trina Solar (China), JA solar (China) and Hanwha Q-Cells (Germany/South Korea) were the top three PV manufacturers (7% all). In the same year First Solar had the larger market share (5%) among the US module companies.
20 Most of turbines manufacturers companies are vertically integrated (Rogowsky & Laney-Cummings, 2009).
21 In 2016 the top 10 producers were: Vestas (Denmark, 16%), GE Wind (US, 12%), Goldwind (China, 12%), Gamesa (Spain, 8%), United Power Enercon (Germany, 7%), Siemens (Germany, 6%), Mordex Acciona (Germany, 5%), (China, 4%), Envision (China, 4%) and Mingyang (China, 4%) (REN21, 2017).
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demand of this energy will fall as a result of a price effect (pure substitution effect). In other words, this implies that electricity producers and investors will look for cheaper substitutes of renewable energies. Over the long run, the pace and direction of technological change will be affected so that, the renewable capital goods available to adopt would contain more energy-efficient and low-cost choices (Newell et al, 1999). In this sense, the induced innovation hypothesis implies that, when electricity prices rise, efforts to use cheaper inputs to produce electricity, like solar irradiance and wind speed, will increase. Therefore, we would expect higher technological efforts to improve renewables than for fossil fuel capital goods (Hicks, 1932; Schumpeter, 1939). Particularly, if path dependency is important, it would be costly to switch from on source to another for capital goods producers. Moreover, competition between producers of both, renewables and fossil fuels, capital goods increase the speed of technical change. Thus, inputs scarcity and high capital good prices for renewable energy generation incentivize the process of biased technological change to produce more efficient and cheaper innovations. This may allow countries that have low quantities of solar irradiance and wind speed to adopt and produce more renewable energies (Hicks, 1932; Habakkuk, 1962; Acemoglu, 2002, 2012; Zuleta, 2008; Popp, 2002; Newell et al, 1999).
As a result, differences in factor endowments and in capital good prices may be drivers that induce biased technological change, which could be determining the rate of adoption of renewable energies. In fact, theory predicts that in the short run biased technological change theory predicts that a scarce factor of production with high relative prices will induce firms to adopt factor saving technologies for this factor (Hicks, 1932; Habakkuk, 1962; Acemoglu, 2002, 2012; Zuleta, 2008). Hence, we would expect that if solar and wind energies are relatively expensive than other sources of energy, this will incentivize the development of factor saving innovations in the renewables industry. Furthermore, in the long run the direction of technical change will be determined by the relative profitability of each kind of energy source, which will also depend on the price and the market size effects (Acemoglu, 2002, 2012).23
In the case of solar and wind energy production, the distribution of solar irradiance, wind speed and technological knowledge endowments is heterogeneous across countries.24 In fact,
23 The price effect creates incentives to develop technologies favoring scarce factors used in the production of more expensive goods, whereas the market size effect encourages the development of innovation that complement abundant factors and have a larger market. The elasticity of substitution between the factors determines the relative strengths of these two effects (Acemoglu, 2002).
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inputs for renewable energy generation are free and non-tradable goods, which limits the available quantity of these inputs for each country, and therefore, the possibility to produce solar and wind energies. As result, a country that has a large supply of solar irradiance and/or wind speed will face lower cost of energy production that may be equivalent to the cost of the capital goods, the installation cost and the maintenance cost (Wiser and Bolinger, 2016; Galen and Darghouth, 2016). Moreover, solar and wind energies are young technologies that need to become more efficient and have a wider diffusion to compete with mature energy technologies like fossil fuels. Endowments knowledge stocks and spillovers positively affect the innovation process and diffusion of a technology so that, these technologies become more efficient, cheaper and known across countries (Rosenberg, 1972; Rogers, 1962; Griliches, 1980; Hall & Khan, 2003; Popp et al, 2010). Thus, to measure the effect of success of innovation within a country and technological spillovers on the prices of solar panels and wind turbines, I use individual-country patents and overall patents as proxies of knowledge stocks and spillovers. Both types of knowledge are expected to be significant in decreasing the prices of these technologies. Nonetheless, this result depends on the development history of each technology (Garrone et al, 2014; Roper & Hewitt-Dundas, 2010; Noailly & Smeets, 2014; Haskel et al. 2009; Rogers, 1962; Popp et al, 2010; Cai et al, 2017).25
2.3 Policy instruments and lobby:
The rate of innovation and adoption of a technology can be positively or negatively affected by political decisions.26 The structure of political institutions and the level of organization of interest groups will determine the probability of success of lobbying. In turn, the interaction between political institutions and interest groups will produce a political equilibrium that will accelerate or slow the adoption of a technology. This equilibrium will generate the use of market and non-market policy instruments to affect the prices of an existing technology (Hall & Khan, 2003; Comin and Hobijn, 2010; Heiniz & Zelner, 2006; Pargal & Wheeler, 1996).27
As I mentioned in section 2.2, the increase in prices of a technology would affect the
fundamental for the development of more efficient innovations in solar panels and wind turbines.
25 Because of potential core-rigidities or negative path dependencies knowledge stocks can have a minor negative effect on innovation (Roper and Hewitt-Dundas, 2015; Leonard-Barton 1992; Thrane et al. 2010).
26 (Comin and Hobijn, 2010; Acemoglu et al, 2012; Peretto, 2009; Pargal & Wheeler, 1996; Aghion et al, 2012; Heiniz & Zelner, 2006; Duran, 2013; Duran and Bucheli, 2017; Newell et al, 1999; Bointner, 2014; Calel & Dechezleprêtre, 2014).
27Market policy instruments: Taxes, trading schemes and feed-in tariffs. Non-market policy instruments: Standards and R&D
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direction of technological change and the rate of adoption of new technologies; this, by incentivizing the development of cheaper and more efficient models that will substitute the prior technology (Acemoglu, 2012; Peretto, 2007; Newell et al, 1999; Aghion et al 2012; Calel & Dechezleprêtre, 2014). Empirical evidence in the energy market shows a significant relation between environmental policy instruments and biased technological change. Authors like Aghion et al (2012) show that policy instruments can induce technological change (Newell et al, 1999; Calel & Dechezleprêtre, 2014). Indeed, their findings suggest that environmental taxes and R&D subsidies have a strong impact on inducing clean energy technological change and innovation. In contrast, fuel taxes affect the input demand for renewable energies by increasing the relative cost of fossil fuels and, consequently, triggering substitution effect of fossils for renewable energies (see Section 2.1). Put differently, policy instruments can induce technological change and also modify the demand for different energy sources (Newell et al, 1999; Aghion et al 2012; Calel & Dechezleprêtre, 2014).
I assume that environmental policy instruments reflect the bargaining equilibrium between interest groups (communities, industrial producers and fossil fuel companies) and the political institutions of a country.28 Depending on this equilibrium, we would expect that fuel taxes will increase the adoption of solar and wind energies by raising the cost of electricity production by using fossil fuels (see Sections 2.1 and 4).
3.
Data
3.1 Main Data
I use a panel dataset for 29 OECD countries between 1990 and 2015. The principal variables used in this research are classified in: (i) renewable energy generation source; (ii) resource factor endowments; (iii) capital goods and energy prices; (iv) fuel taxes; (v) private and public knowledge related to solar and wind energies. The majority of these variables are drawn from the World Bank Development Indicators (WBDI, 2017), the Global Solar Atlas (2017), the IRENA Global Atlas for Renewable Energy (2017), the International Energy Agency (IEA,
2017), the Berkley Electricity Markets & Policy Group (EMP Group, 2017), the Bureau of
Economic Analysis (2017), the IMF Primary Commodity Prices (2017), and the World
12 Intellectual Property Organization (Patentscope, 2017).
3.1.1 Energy generation
To measure the adoption of wind and solar energies, I use the gross electricity production (GWh) of solar photovoltaics and wind turbines, which is available in the electricity and heat generation database from the IEA.29 With this data I calculate the country’s share of these energy sources over the total electricity production.30 This measure reflects the level of adoption for solar and wind energies made by electric generators.
As shown in Table 1, the average adoption rate is 2% for wind energy, 0.34% for solar energy and 55% for fossil fuels. However, the average adoption at the beginning of the period is 0.075% for wind, 0.0003% for solar and 56% for fossil fuels. In contrast, in 2015 the average adoption rate grew to 7.07% for wind, 1.88% for solar and decreased to 48% for fossil fuels. In this dataset Germany, Italy and the UK are leading countries in solar and wind energy production while Mexico and the US are laggards in both cases. Leaders have shares of production between 6% and 8% for solar energy, and above 10% for wind energy. Laggards present shares of production below 4% and 6% for solar and wind energies. Put differently, data on electricity production shares show that between 1990 and 2015 a substantial process of adoption for solar and wind energies is observed (see Figures 2 and 3).
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Table 1. Renewable and fossil fuels Energy shares
Variable Obs Mean Std. Dev. Min Max
Full sample
Wind Energy as % of total 918 2.00 4.76 0.00 48.8231
Solar Energy as % of total 893 0.34 1.05 0.00 8.11
Fossil Fuels Energy as % of total 909 55.07 30.74 0.01 100.00
1990
Wind Energy as % of total 34 0.08 0.40 0.00 2.35
Solar Energy as % of total 34 0.00 0.00 0.00 0.00
Fossil Fuels Energy as % of total 35 56.02 32.24 0.07 100.00
2015
Wind Energy as % of total 34 7.07 9.44 0.01 48.82
Solar Energy as % of total 33 1.88 2.10 0.00 8.11
Fossil Fuels Energy as % of total 34 48.33 29.19 0.02 97.85
Data from the International Energy Agency (2017)
14
Figure 2.
Source: IEA (2017) Figure 3.
Source: IEA (2017) 3.1.2 Resource Factor Endowment
15 irradiance, and 20% for wind speed (See Table 2).
Differences in factor endowments of solar irradiance and wind speed are fundamental to understand the adoption of solar and wind energies. These inputs are invariant free non-tradeable goods that represent a comparative advantage for countries with higher endowments. For example, solar electricity production is more expensive in a country like Iceland than in Mexico, because solar irradiance in Mexico is abundant (see Figure 4).32
Figure 4.
Source: Global Solar Atlas (2017) and the IRENA Atlas for Renewable Energy (2017)
To address this heterogeneity, I compute the average of solar irradiance (Kw/m^2 yearly
sum) and wind speed (1 km at 100m height DTU avg(m/s)) using the maximum and minimum
endowment of these inputs in each country. Next, I calculate a distribution indicator of each
energy source dividing the country’s endowment by the maximum endowment of the sample
(i.e. Germany solar irradiance/maximum solar irradiance). As a result, each distribution takes
values between cero and one where values close to cero means a relatively small endowment of
16
solar irradiance (wind speed), whereas values close to one represent a relatively high endowment
of the resource within the sample.33 Evidence presented on Figure 5 seem to suggest that the
distribution of solar irradiance and wind speed are substitutes within each country. However,
understanding the relation between these energy sources is complex because of technological
complementarities (Wu et all, 2015; NREL, 2016). I use these distributions to proxy for the
production cost of solar and wind energies implied by the resource availability of solar irradiance
and wind speed of each country (see Section 3.1.2).
Figure 5.
Source: Global Solar Atlas (2017) and the IRENA Atlas for Renewable Energy (2017)
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Table 2. Independent variables
Variable Obs Mean Std. Dev. Min Max
Full sample
Wind Energy as % of total 918 2.00 4.76 0.00 48.8234
Solar Energy as % of total 893 0.34 1.05 0.00 8.11
Fossil Fuels Energy as % of total 909 55.07 30.74 0.01 100.00
1990
Wind Energy as % of total 34 0.08 0.40 0.00 2.35
Solar Energy as % of total 34 0.00 0.00 0.00 0.00
Fossil Fuels Energy as % of total 35 56.02 32.24 0.07 100.00
2015
Wind Energy as % of total 34 7.07 9.44 0.01 48.82
Solar Energy as % of total 33 1.88 2.10 0.00 8.11
Fossil Fuels Energy as % of total 34 48.33 29.19 0.02 97.85
Data from: the International Energy Agency(2017), the World Bank (2017), the Global Solar Atlas (2017), the Global Atlas for Renewable Energy (2017), the Electricity Market & Policy Group (2017) and the World Intellectual Property Rights (2017).
3.1.3 Energy prices
In a context of profit maximization, input and output prices determine the demand of factors of production (see Section 2.1). These factors can be used as complementary as well as a substitute of each other. Therefore, to understand what induces electric generators to adopt (demand) solar and wind energies, it is necessary to consider the energy prices, the prices of substitute energy sources and the electricity output prices. However, due to lack of data, this analysis is limited to electricity output prices, fossil fuel end-user prices, and solar panels and wind turbines efficiency prices.35
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Electricity output prices are drawn from the Energy Prices and Taxes database of the IEA. I calculate the electricity price index for 1990 using the household and industries index price for 2010. This index indicates the market price of electricity in each country and, thus, we would expect that electric generators will demand more energy inputs when this price increases. Consequently, the level of adoption of all energy inputs rises when the electricity output prices increase. In particular, the country heterogeneity of these prices is less than the observed in fuel prices. The coefficient of variation is around 27% and the increase in prices is not significant (Table 2).
Fossil fuel prices are taken from the Energy Prices and Taxes database of the IEA.36 These prices correspond to the total USD PPP/liter paid at the power plant for electricity generation and can be decomposed in fuel prices and fuel taxes.37 Country heterogeneity in prices is a consequence of differentials in transformation and distribution costs, import costs, non-internationally tradable energy sources, market structure, and pricing policies like taxes and subsidies (IEA, 2017). I compute the average fuel prices and fuel taxes and then the price indexes for 1990 (see Figures 6 and 7). Data shows that fuel prices significantly increased between 2000 and 2013 overall countries (see Figure 6). This is a consequence of the commodity boom super-cycle that augmented the crude oil commodity price index between 2000 and 2010 (World Bank, 2017; see Figure 8).38
36 Diesel, light fuel oil, RON 95, RON 98 and unleaded gasoline
37 Total price corresponds to the tax-adjusted fuel price= ex taxes fuel prices + fuel taxes.
19
Figure 6.
Source IEA (2017)
Figure 7.
20
Figure 8.
Solar and wind energy prices are drawn from the BerkleyElectricity Markets & Policy Group (EMP Group, 2017). Ideally, data on input prices for solar and wind energies across countries would be expected. Data for renewable energies are, however, not easily available; hence, I consider the United States (US) total installation efficiency prices of residential solar panels and wind turbines projects as a proxy of these prices for all the countries of the sample.39
These prices are the sum of solar module panel prices and their installation costs, and the sum of turbines prices and their installation and maintaining costs (Galen and Darghouth, 2016; Wiser and Bolinger, 2016).40 Prices are measured as USD/Kw which allows to control for quality improvements. This represents an advantage to understand the impact of biased technological change on the adoption of renewable energies.
I reconstruct the solar photovoltaic (PV) installation prices between 1990 and 1998 due to incomplete data. Using the wind and solar capital goods index from the BEA (2017), I predict the solar prices.41 Then, I compute the prediction growth rate and project only the missing
39 Data on utility scale solar panels is available but is too short. Moreover, trends between residential and utility scale solar installation prices are similar, their correlation is 0.99 (see, Wiser and Bolinger, 2017; Galen and Darghouth, 2016) 40 Total installation price=Capital good price + other costs (marketing and customer acquisition, system design, installation labor, permitting and inspection costs, and installer margins)
21
years.42 Data shows a significant reduction in solar installation prices. These prices fall more than 60% between 1990 and 2015 (see Table 2a and Figure 9). Consequently, we can think that the reduction of solar panel efficiency prices is incentivizing the adoption of this source of energy.
Table 2a. US Descriptive Statistics
Variables Obs Mean Std.
Dev Min Max p25 p50 p75
Electricity Output Prices USD Index
1990=100 780 109.20 29.65 56.01 271.99 91.10 102.32 120.39
Average solar irradiance (Kw/m^2) 910 1259.39 397.99 225.00 2154.50 1022.00 1168.00 1533.50
Average Wind speed (m/s) 910 6.44 1.33 3.75 9.00 5.50 7.00 7.50
Average solar irradiance Distribution 910 0.58 0.18 0.10 1.00 0.47 0.54 0.71
Average Wind speed Distribution 910 0.72 0.15 0.42 1.00 0.61 0.78 0.83 Average fossil fuels USD PPP/liter
1990=100 702 170.57 89.35 45.41 445.03 99.68 136.32 232.88
Average fuels taxes USD PPP/liter
1990=100 702 193.49 107.18 49.45 713.93 119.03 160.21 235.89
Private Solar Knowledge Stock 875 8.28 55.03 0.00 777.65 0.00 0.00 0.00
Private Wind Knowledge Stock 875 7.04 25.10 0.00 236.25 0.00 0.00 1.85
Public Solar Spillover Stock 875 281.58 407.24 0.00 1262.25 9.65 44.45 430.55
Public Wind Spillover Stock 875 239.40 306.72 0.00 952.25 14.45 82.05 374.35
Data from: the Electricity Market & Policy Group (2017) and the World Intellectual Property Rights (2017).
In contrast, wind turbine installation efficiency prices do not constantly decline over time. After decreasing 14 years, prices present a humpback shape between 2005 and 2015 (see Figure 9). This pattern is the result of changes in endogenous drivers of wind installation prices. Specifically, the metals and minerals commodity prices, the industry wages and the warranty provisions increased during 2005 and 2010, which made turbine prices almost as expensive as they were at the beginning of the period (see Moné et al, 2015).43
42 ∗ 1
22
Figure 9.
Source: the Berkley Electricity & Policy Group (2017)
3.1.4 Fuel taxes
Fuel taxes are obtained from the Energy Prices and Taxes database of the IEA.44 These taxes are paid for fuels consumption and are measured as USD PPP/liter. I calculate the average fuel taxes between diesel, light fuel oil, RON 95, RON 98 and unleaded gasoline to produce a tax index for 1990 (see Figure 7).
These taxes can be interpreted as a country’s stringency (political equilibrium) to disincentivize the consumption of oil products and incentivize the demand for other sources of energy (Aghion et al; 2012).45 Actually, taxes directly affect the demand of electric generators by making fossil fuels relatively more expensive than solar and wind energies (see Section 2.3). However, this effect is heterogeneous across the countries of the sample. While the Czech Republic and the US have some of the lower fuel taxes of the sample, the UK and Germany are leading countries with taxes almost 100% higher than the average rate (see Figure 7).46 Put differently, the coefficient of variation of fuel taxes is 55% (see Table 2). Consequently, differences in fuel taxes across the countries should generate different incentives for electric
44 Diesel, light fuel oil, RON 95, RON 98 and unleaded gasoline
23
generators to adopt solar and wind energies (see Section 2).
3.1.5 Knowledge stocks and spillovers
Patent stocks and spillovers are key mechanisms to explain improvements in innovation performance among countries (Garrone et al, 2014; Roper & Hewitt-Dundas, 2015; Noailly & Smeets, 2015; Haskel et al. 2009). I use cumulative patent counts to construct country knowledge and spillover stocks. Solar and wind energy patents are selected based on the OECD IPC classification for renewable energy technologies (Johnstone et al, 2009).47 Patents are available at the Patentscope (2017) and I only consider those that were submitted to the World Intellectual Property Organization to avoid duplications.48
In addition, I account for the fact that knowledge becomes obsolete as time progresses.49 Thus, I assume that knowledge stocks depreciate annually by 15% as it is commonly assumed in the literature (Aghion et al, 2012; Hall and Mairesse, 1995;Park and Park 2006; Bointner; 2014). Next, knowledge stocks and spillovers are computed using the perpetual inventory method
(equations 4 and 5) where and are the country’s knowledge stocks and spillovers, is
the depreciation rate of knowledge and are the current period patents:
1 ∗ (4)
1 ∗ , , (5)
As is expected, innovation activity is diverse among countries. The coefficients of variation are 664% for solar energy patents and 356% for wind energy patents overall countries (See Table 2). By comparison, knowledge spillovers are similar across countries and are almost two orders of magnitude higher than knowledge stocks (See Table 2).50 The US and Japan have the lion’s share innovative activity for solar panels while Germany and the US are the major knowledge producers for wind turbines (see Figures 10 and 11).51 Specifically, the US knowledge stocks of solar PV modules and wind turbines increased incredibly fast after 2005. The maximum values of these stocks at the end of the period are around 4 times the average stock in both technologies (see Table 2). As a result, we would expect that knowledge stocks and spillovers will have a large
47 See https://www.oecd.org/env/consumption-innovation/44387191.pdf and the Table 8 of Johnstone et al (2009). 48 A same patent can be submitted to different intellectual property organizations. Therefore, I only consider innovations that granted a WIPO patent to avoid duplicates.
49 For example, when new knowledge is created.
50 Only the major producers of knowledge stocks face spillovers below the mean.
24
impact on the innovation performance of solar PV modules and wind turbines (Roper & Hewitt-Dundas, 2010).
Figures 10 and 11
.
Data from the World Intellectual Property Rights (2017)
3.1.6 Data Issues
The analysis presented in this research is mainly restricted to the few data available in some of the variables. Ideally, to measure the effects of biased technological change, policy instruments and fuel prices over the adoption of wind and solar energies, we would need to have in hand a wider country-level database. Additionally, the lack of country-level information for wind and solar energy prices produce a limitation to exploit heterogeneity across countries. This limitation also applies for the metals and minerals commodity price and for the prices of coal and gas that could not be include in this research.52 Similarly, the availability of a more diverse sample of policy instruments, would help to improve the predictions of the two-regression analysis.
4.
Empirical Strategy
To examine the extent to which biased technological change, environmental policies and fuel prices are determining the adoption of solar and wind energies, I use a two-stage regression analysis. In the first stage, I use a OLS time series regression to test if the total installation
25
efficiency prices of solar panels and wind turbines are affected by knowledge stocks and the
metals and minerals commodity prices.53 Strategic competition among firms is, however,
captured by the error term because of lack of information.54 As a result, this regression allows to understand if trends in the efficiency prices of both technologies are being driven by: 1) biased technological change; 2) solar panels and wind turbines input prices (see Section 2).55
Throughout the second stage, I use the predictions of the first stage to examine if technical change and factor endowments are determining the increasing adoption of solar and wind energies, or if fuel prices and fuel taxes are the main mechanisms that induce electric generators to adopt these energies in the late years (see Sections 2.3 and 3.1.1). More specifically, I use a fixed effect methodology to test if, as theory suggests, the adoption of solar and wind energies across the OECD countries is being affected by: (i) predictions of solar panels and wind turbines total installation efficiency prices, (ii) factor endowments of solar irradiance and wind speed, (iii) fossil fuel prices, (iii) fuel taxes and (iv) electricity output prices (see Section 2.1).56
4.1 First Stage
As mentioned in Section 3.1.3, data on solar and wind capital good prices is not available at country level. I assume the US total installation efficiency prices of solar panels and wind turbines to be the same overall the countries of the sample. Then, I model these prices as to be determined by knowledge stocks and the prices of their inputs of production.57 Next, I predict the installation efficiency prices of each technology using the knowledge stock coefficient to avoid the effect of the commodity price boom super-cycle, over the inputs of production for solar and wind capital goods.
Particularly, I use an OLS time series regression analysis to estimate the elasticity of knowledge stocks and the metals and minerals commodity prices.58 In equation (6) and (7), is
53 Both solar panels and wind turbines extensively require metals like copper and aluminum to be produced. Moreover, the cost of wind turbines can make up 70% or more of the entire cost of a land-based wind project, while solar panels are up to 30% of the cost of a solar project (Wiser and Bolinger, 2016; Galen and Darghouth, 2016).
54 This because of the lack of detail information about how PV and turbines firms compete.
55 Data on wages was not available for both industries. Similarly, data on silicon prices was not available for solar panels. Therefore, I used the metals and minerals commodity prices as a proxy of silicon prices due to the commodity super-cycle (See section 3.1.3 and Figure 8).
56 Electricity prices are derived from the IEA (2017) and correspond to the Electricity Price Index 1990=100.
26
the time index, and are the logs of solar and wind total installation efficiency
prices, and are the logs of the US knowledge stocks, is the metals and
minerals commodity prices index, is the error term, is the constant of the regression and
, , are the variable coefficients.59
, , 6
, , 7
Finally, I calculate a proxy of the cost per Kw for solar and wind energies by dividing the predictions of the US efficiency prices by the endowment distribution of solar irradiance and wind speed (see Section 3.1.1).60; this, to account for the production cost heterogeneity implied by differences in the endowment of those resources in the second stage (see Figure 12 and Figure 13).61
Figure 12 and 13
.
Source: the Berkley Electricity & Policy Group (2017), Global Solar Atlas (2017) and the IRENA Atlas for Renewable Energy (2017)
different rates (Figure 10 and Figure 11). Consequently, trends of innovation and patenting across the OECD are positively correlated although growth rates are different.
59 All prices are price indexes 1990=100 (see Section 3).
60 Country’s cost per Kw: US predicted efficiency pt / Resource endowment distribution.
61 Figure 12 shows that countries like UK, the Netherlands and Germany with low levels of solar irradiance face a higher production cost than countries like the US and Mexico that have higher levels of this resource. Similarly, countries with low endowments of wind speed, such as Italy and the Czech Republic, have higher production cost while countries like the UK and the US with higher endowments of this resource have a lower production cost (see Figure 13).
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4.2 Second Stage
Following the intuition of the reduced form equation presented in Section 2.1, I use a panel fixed effects methodology to study the determinants of the adoption of solar and wind energies, among the countries of the OECD. Here, I assume the fixed effects will capture idiosyncratic country effects including the electricity generators market structure and regulation. Thus, parameters implied in the input demand function derived from a classic profit maximization model should help us to understand which of the mechanisms studied in this paper is triggering the adoption of these energies (see Section 2.1).
More specifically, I estimate the log of a Cobb-Douglas electricity production function for solar and wind energies (see Section 2.1 and Equation (3)). I use a fixed effects methodology to (i) estimate the elasticity of the electricity output prices, biased technological change, fuel taxes and fuel prices over the production share of solar and wind energies in each country; (ii) control
for unobserved time-invariant individual characteristics. In Equations (7) and (8), , are the
country-time indexes, and are the solar and wind electricity measure of adoption
(electricity production shares), is the electricity market price, and are the cost of solar
and wind and energies, and are the fuel prices and taxes, is the individual effect, ,..,
are the variable coefficients and , is the error term.62
, , , , , 7
, , , , , 8
We would expect the cost per Kw of solar and wind energies to have a negative coefficient, fuel prices to behave like substitutes (negative coefficient), fuel taxes to act as incentives to decrease fuels demand (positive coefficient) and electricity market prices to increase the demand of all sources of energy (positive coefficient).
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5. Results
The main results of the regressions are presented in Tables 3, 4 and 5. Table 3 contains the coefficient of the first stage regressions, while Tables 4 and 5 present the coefficients of the second stage regression for solar and wind energies, respectively.
5.1 First stage
In Table 3, columns 1 and 2 show the coefficients for the US solar and wind knowledge stocks and the metals and mineral commodity prices over the US solar PV modules and wind turbines efficiency prices. As expected, these results imply that biased technological change has been significant to reduce the efficiency prices of both technologies (see Section 2.2). The elasticity of solar and wind knowledge stocks predicts that an increase of 1% in these variables will reduce solar PV and wind turbines efficiency prices in 0.19% and 0.14%, respectively. Put differently, evidence suggests that the effect of induced technological change has been more important to reduce PV module prices than wind turbines prices.
Similarly, the coefficient of the metals and minerals commodity prices present an expected positive sign for both technologies, which is consistent with what theory predicts. However, the parameter is only significant for wind turbine prices, and it is significantly larger than the one implied by knowledge stocks.63 These results are coherent with evidence presented in Figures 8 and 9: wind turbine prices are significantly affected by changes in the prices of the inputs of production while than solar panels are not.64
63 The elasticity of the metals and minerals commodity prices predicts that an increase of 1% in this variable rises the wind turbines efficiency prices in 0.47% (Table 3).
29
Table 3. First Stage Regression
5.2 Second stage
The regression coefficients of solar energy adoption are presented in Table 4. In Column 1, I include the prediction of the cost per Kw of solar energy described in Section 4.1. The elasticity is negative and significant, which is consistent with the prediction of the input demand
function derived in Section 2.1. In other words, the input demand of a normal factor of
production negatively depends on its cost. Column 2 includes the electricity output prices. The price elasticity of cost per Kw of solar energy remains negative, significant and with stable magnitude, whereas the electricity prices coefficient is positive but non-significant. Column 3 adds fuel taxes to the specification of column 2.65 The elasticity coefficients present expected signs, but only the cost per Kw of solar energy is significant. This means that, although fuel taxes disincentivize fuel energies demand and electricity output prices incentivize the demand of all energy sources, only technological change is determining the adoption of solar energy by declining the cost per Kw of solar energy. In Column 4, I change the specification of the model to test the effect of the fuel prices over the adoption of solar energy excluding the cost per Kw of solar energy. Results show a strong positive and significant effect of fuel prices over the adoption of solar energy.66 Nonetheless, as shown in Column 5, once the cost per Kw of solar energy is included the magnitude of the elasticity of fuel prices decrease one order of magnitude,
65 Pedroni cointegration test indicates that variables specified in Columns 3 and 4 are cointegrated. All the tests are significant at the 5% level.
66 Fuel prices elasticity predicts that a rise of 1% in these prices increases the adoption of solar energy in 0.29%. Electricity output prices and Fuel taxes are significant at 10% level under this specification (Table 4).
(1) (2)
VARIABLES Solar Total Installation
Efficiency Pt
Wind Total Installation Efficiency Pt
Lag ln(US Solar Knowledge Stocks) -0.1930***
(0.039)
Lag ln(Metals and Minerals Commodity pt) 0.1197 0.4765***
(0.145) (0.053)
Lag ln(US Wind Knowledge Stocks) -0.1395***
(0.020)
Constant 4.1205*** 2.2121***
(0.618) (0.217)
Observations 24 25
R-squared 0.871 0.635
Country US US
30
the sign turns out to be counterintuitive (negative) and it becomes non-significant.67 In contrast, the elasticity of the cost per Kw of solar energy is significant (at 5% level ) and similar to columns 1, 2 and 3.68
This is a striking result that suggest that: (i) declining prices of technologies for solar energy generation produced by technological change are driving the effect of solar energy adoption and (ii) changes in fuel energies prices do not significantly affect the adoption of this energy. In other words, evidence provided in columns 3 and 5 may indicate that if solar PV modules prices continue to fall due to technological innovations, the adoption of solar energy will continue to rise although fuel prices decline.
To examine robustness of these results, I include all the variables specified in Column 3 with one-year lag to improve the exogeneity of the model in Column 6. All variable elasticities present similar results to Column 3 and only the elasticity of the cost per Kw of solar energy is significant.69 Additionally, in Columns 7 and 8 I test the same model specification of Columns 3 and 5 but using the cost per Kw of solar energy calculated with the original solar PV module prices. This, to understand if the original solar and wind capital good prices do provide different results than the predictions of the first stage.70 As expected, Column 7 presents different results to Column 3. The magnitude of the elasticity of the cost per Kw of solar energy augments 84% in comparison with the first stage predictions. In addition, the coefficient of fuel taxes becomes negative which is counterintuitive. In a similar way, results in Column 8 are different to Column 5. The fuel prices coefficient becomes negative and significant, while fuel taxes elasticity turns out to be negative. Furthermore, the elasticity of the cost per Kw of solar energy increases more than 100% in comparison to the elasticity presented in Column 5. Coefficients in Columns 7 and 8 are biased and counterintuitive because the specification of the cost per Kw of solar energy in these equations do not exclude the effect of the commodity boom super-cycle. Hence, evidence seems to indicate that the cost per Kw of solar energy obtained from the first stage is more accurate to measure the effect of technological change on the adoption of solar energy.
67 The correlation between solar PV module prices and fuel prices is significantly high (-0.76). This may be causing the counterintuitive sign of fuel prices in Column 5. Therefore, considering this correlation and results presented in Columns 3, 4 and 5, I consider the specification of Column 3 as the most accurate.
68 The elasticities for the prediction of solar PV module prices predicts that an increase in 1% of these prices generates a decrease of 0.51%, 0.50%, 0.44% and 0.45% over the adoption of solar energy, respectively (Table 4).
31
On the other hand, Table 5 presents the regression coefficients of wind energy adoption. Column 1 presents the elasticity of the cost per Kw of wind energy described in Section 4.1. This elasticity is negative and significant which is consistent with the theory (Section 2.1). Column 2 includes the electricity output prices. This elasticity is positive but non-significant while the price elasticity of wind turbines prediction remains negative and significant. Column 3 includes the fuel taxes to the specification of Column 2. All variables elasticities present expected signs, but only the cost per Kw of wind energy has a significant coefficient. Particularly, this coefficient predicts that an increase in 1% in the cost per Kw of wind energy will reduce the adoption of wind energy in 2.39%.71 Put differently, although fuel taxes disincentivize fuel energies demand and electricity output prices incentivize the demand of all energy sources, only the effect of technological change is determining the adoption of wind energy by declining the cost per Kw of this energy. Column 4 excludes the effect of the cost per Kw of wind energy and includes fuel prices.72 Results present evidence of a strong positive and significant effect of fuel prices and fuel taxes on the adoption of wind energy.73 However, as shown in Column 5, once the cost per Kw of wind energy is included the elasticity of fuel prices falls 20% and continues to be significant, while fuel taxes elasticity decreases one order of magnitude and becomes non-significant. Moreover, the coefficient of the cost per Kw of wind energy is significant but declines more than 100% in comparison with the elasticity presented in Column 3.74
As a consequence, the adoption of wind energy seems to be determined not only by biased technological change and factor endowment, but also by changes in fuel prices. The magnitude of both elasticities indicates that technological change is more important to determine the adoption of wind energy. More specifically, the elasticity of the prediction of wind turbines prices is almost 50% bigger than the fuel prices elasticity.75 This means that: (i) declining prices of technologies for wind energy generation due to technological change are more important than fuel prices to trigger the adoption of wind energy; (ii) the adoption of wind energy is sensitive to changes in fuel energies prices. In other words, although the cost per Kw of wind energy fall as
71 In Columns 1 and 2 the elasticities are -2.26% and -2.39% respectively (Table 5).
72 Pedroni cointegration test indicates that variables specified in Columns 3 and 4 are cointegrated. All the tests are significant at the 5% level (except the v test in Column 3).
73 Fuel prices and fuel taxes elasticities predicts that a rise of 1% in these variables rise the adoption of wind energy in 0.96% and 0.31% respectively (Table 5).
74 The correlation between the prediction of wind turbines prices and fuel prices is significantly high (-0.68). This may be biasing the magnitude of the coefficients and, therefore, causing the reduction of the elasticity of prediction of wind turbines prices from 2.3%, in Column 3, to 1.03% in Column 5 (Table 5).
32
a result of technological innovations, the pace of the adoption of wind energy will slow down if fuel energy prices decline.
I examine the robustness of the result of Table 5 in Columns 6, 7 and 8. First, I include all the variables specified in Column 3 with one-year lag to improve the exogeneity of the model in Column 6. All variable elasticities present expected signs and similar magnitudes to Column 3. Furthermore, only the elasticity of the cost per Kw of wind energy is significant.76 Next, in Columns 7 and 8 I test the same model specification of Columns 3 and 5 using the cost per Kw of wind energy calculated with the original wind turbine prices.77 This specification allows to understand if considering only the effect of biased technological change over the wind turbines prices leads to different results than when the metals and minerals commodity prices are not excluded from the cost per Kw of wind energy (see Section 5.1 Figure 9 and Figure 13).78 As expected, variables coefficients in Column 7 and Column 8 are different to coefficients estimated in Columns 3 and 5. In the first case, the elasticity of the cost per Kw of wind energy is positive and significant (counterintuitive), whereas the fuel taxes coefficient turns out to be significant and increases two orders of magnitude.79 In the second case, all elasticities have expected signs but only fuel taxes and fuel prices are significant.80 As discussed before, the increase of the metals and minerals commodity prices, due to the commodity boom super-cycle, is large and significant to explain the humpback behavior presented in the wind turbines prices between 2005 and 2010 (see Figures 8 and 9). Consequently, if the effect of the metal and mineral commodity prices is included in the cost per Kw of wind energy, the effect of biased technological change will be underestimated (see Section 5.1).
76 The elasticity of these prices predicts that an increase of 1% in this variable reduces the adoption of wind energy in 2.54%. 77 Different than the solar energy case, the correlation between the original wind turbines prices and fuel prices (-0.02) is smaller than the correlation between the wind prices prediction and the fuel prices (-0.68).
78 I expect the coefficient of these prices to be bias upwards due to the effect of the commodity super-cycle.
79 The fuel taxes elasticity indicates that an increase of 1% in fuel taxes produces a rise of 1.3% in the adoption of wind energy (Table 5).
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Table 4. Solar Second Stage Regression81
81 Lnspt: prediction of solar PV modules/Endowment distribution; lneindex: electricity output prices; lnavg33: fuel taxes; lnavg11: fuel prices; lnspt2: original solar PV module prices/Endowment distribution; l.variables: 1 lag of the variable.
(1) (2) (3) (4) (5) (6) (7) (8)
VARIABLES
Solar Electricity % of Total
Solar Electricity % of Total
Solar Electricity % of Total
Solar Electricity % of Total
Solar Electricity % of Total
Solar Electricity % of Total
Solar Electricity % of Total
Solar Electricity % of Total
lnspt -0.5101*** -0.5026*** -0.4446*** -0.4595**
(0.084) (0.072) (0.091) (0.173)
lneindex22 0.2379 0.3285 0.4389* 0.3261 0.1223 0.1325
(0.229) (0.250) (0.225) (0.259) (0.188) (0.183)
lnavg33 0.1302 0.1658* 0.1308 -0.0930 -0.0353
(0.131) (0.092) (0.128) (0.091) (0.079)
lnavg11 0.2988*** -0.0126 -0.1687**
(0.055) (0.108) (0.062)
L.lnspt -0.4968*** (0.090) L.lneindex22 0.2966 (0.242) L.lnavg33 0.1370 (0.132)
lnspt2 -0.8109*** -0.9713***
(0.127) (0.137)
Constant 2.3279*** 1.1758 -0.1447 -4.2328*** -0.0097 0.2131 4.0146*** 5.2957***
(0.362) (1.053) (1.857) (1.341) (2.622) (1.748) (1.303) (1.289)
Observations 826 726 651 678 651 624 678 678
R-squared 0.317 0.347 0.367 0.344 0.367 0.391 0.524 0.535
Number of unit_id 34 30 27 27 27 26 27 27
FE YES YES YES YES YES YES YES YES
34
Table 5. Wind Second Stage Regression82
5.3 Estimation issues
The first stage regression has important limitations and disadvantages. In one hand, measure innovation and biased technological change is not easy. Although there are different ways to measure innovation rates and technological change, this study is limited to patents knowledge stocks due to lack of information (Kuznets, 1962). In fact, useful data on these topics is generally scarce, and it is even more for young industries such as solar panels and wind turbines. Therefore, it is not possible to perform robustness checks for the effect of innovation implied by knowledge stocks.
On the other hand, solar and wind energies knowledge stocks are positively correlated
82lnwpt: prediction of wind turbines/Endowment distribution; lneindex: electricity output prices; lnavg33: fuel taxes; lnavg11: fuel prices; lnspt2: original wind turbines prices/Endowment distribution; l.variables: 1 lag of the variable.
(1) (2) (3) (4) (5) (6) (7) (8)
VARIABLES
Wind Electricity % of Total
Wind Electricity % of Total
Wind Electricity % of Total
Wind Electricity % of Total
Wind Electricity % of Total
Wind Electricity % of Total
Wind Electricity % of Total
Wind Electricity % of Total
lnwpt -2.2656*** -2.3011*** -2.3906*** -1.0330***
(0.276) (0.320) (0.363) (0.333)
lneindex22 0.4005 0.3279 0.2070 0.1572 0.6908 0.2343
(0.331) (0.362) (0.382) (0.377) (0.469) (0.398)
lnavg33 0.0952 0.3112*** 0.0638 1.3564*** 0.2704**
(0.146) (0.084) (0.126) (0.280) (0.098)
lnavg11 0.9689*** 0.7066*** 0.9864***
(0.129) (0.118) (0.127)
L.lnwpt -2.5463*** (0.392) L.lneindex22 0.2456 (0.378) L.lnavg33 0.0534 (0.145)
lnwpt2 0.3755** -0.0981
(0.136) (0.111)
Constant 5.6691*** 3.9502* 4.0216* -6.7635*** -1.6189 5.0253** -11.2236*** -6.3279***
(0.624) (1.945) (2.089) (1.453) (1.454) (2.297) (1.507) (1.260)
Observations 884 780 702 702 702 675 702 702
R-squared 0.541 0.586 0.623 0.664 0.680 0.635 0.467 0.664
Number of unit_id 34 30 27 27 27 27 27 27
FE YES YES YES YES YES YES YES YES
35
with the fuel energy prices.83 In other words, higher fuel prices could be triggering the process of innovation of solar panels and wind turbines.84 This effect is complex and difficult to identify because of the structure of both industries (see Section 2.2). As a consequence, further research on this topic should consider to include a three-stage regression analysis in order to address for the determinants of innovation in solar and wind energy industries. I consider the next step of this analysis should be to incorporate a tree stage regression analysis where we can explore the role of technological opportunity (articles, scientific publications, citations, other technologies patents and older patents) and fuel prices over the production of new patents for solar and wind
technologies. In equations (9), (10) and (11) , , , , , , , mean articles, citations,
other patents and past patents, respectively, while are the US knowledge stocks calculated with the patents prediction estimated in the first stage.
First stage: , , , , , , , , 9
Second stage: , , 10
Third stage: , , , , , 11
Besides, a dynamic panel estimation should be considered to con control for possible endogeneity. Least squares-based inference methods, like fixed effects or random effects estimator, are biased and inconsistent because of endogenous covariates. The inclusion of lags of the dependent variable seems to provide an adequate characterization of many economic dynamic adjustment processes (Bun & Sarafidis, 2013).
In addition, the substitutability between solar and wind energy production should to be better explored. Although evidence may indicate that both energy sources are substitutes (see Figure 4), some authors have pointed out that because seasonal, location, diurnal and infrastructure complementarities wind and solar energies can behave like complements (Wu et all, 2015; NREL, 2016).
Finally, as mentioned in Section 3.1.3, data on solar and wind capital good prices is not available at country level. As a result, this study does not exploit the country heterogeneity of the solar and wind capital good prices and thus, results in Tables 4 and 5 may be biased.
