Ecological Economics 74 (2012) 161–168
Contents lists available at SciVerse ScienceDirect
Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon
Analysis
Is there a causal relation between ethanol innovation and the market characteristics of fuels in Brazil? Luciano Charlita de Freitas ⁎, Shinji Kaneko Graduate School for International Development and Cooperation, Development Policy, Hiroshima University 1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8529, Japan
a r t i c l e
i n f o
Article history: Received 27 December 2010 Received in revised form 21 October 2011 Accepted 14 December 2011 Available online 11 January 2012 Keywords: Ethanol Innovation Causality Brazil
a b s t r a c t This study examines whether a causal relation exists between ethanol related innovation and fuel market variables in Brazil. Patent counts were used as proxy for innovation and assessed market variables include ethanol consumption and price, and gasoline price. The study refers to the period 1975–2008. Empirical evidence is formulated with an Autoregressive Distributed Lag (ARDL) model for cointegration and the causality is examined with a multivariate Granger causality test. The results demonstrate a potential causal relation between ethanol innovation and ethanol consumption, evidencing a unidirectional relation from ethanol consumption to patent registers in the studied period. Such a relation indicates that increments in ethanol consumption can potentially stimulate innovation in the sector. Moreover, the ethanol price and the crosseffect of gasoline price have an indirect effect on ethanol innovation. Several questions are raised regarding the yet to be determined factors driving innovation in the sector. Further studies focused on nonmarket aspects, including policy factors, subsidies and international technology spillovers, would potentially elucidate several unanswered questions concerning ethanol innovation in Brazil. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Historically, attempts to promote fuel diversification are related to technological advances, the search for energy efficiency and national competitiveness and a general understanding that fossil fuels are not sustainable in the long term. After the 1997 Kyoto Protocol, environmental concerns were added to the factors driving fuel diversification efforts, creating new momentum for low carbon alternatives. In this context, biofuels assumed a position as an important bridge between conventional fuels and breakthrough energy sources that are currently in experimental phases. Brazil's ethanol represents a pioneer experience in the diversification of fuel mix through the introduction of biofuels. For example, taking as reference the total fuel consumption, records from 1988 put ethanol as the second most consumed liquid fuel for transport purpose in the country, ahead of gasoline and after diesel oil (EPE, 2010a). This milestone was repeated in 2008, when 21.3 million m 3 of ethanol was consumed in the country, ahead of gasoline with 18.9 million m 3 in the same period (EPE, 2010b). The current level of ethanol development in Brazil is founded on a historical process characterized by progresses and withdrawals. One major legacy of this process was the development of national technologies, which turned the country into a market leader, a supplier of technology to partner nations and one of the most competitive
⁎ Corresponding author. Tel./fax: + 81 90 2867 8995. E-mail address:
[email protected] (L.C. de Freitas). 0921-8009/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2011.12.013
producer of ethanol worldwide (Goldemberg and Lucon, 2010). The balance between policy incentives, technological progress and relative price advantage of ethanol compared to gasoline are some attributes of the ethanol program performance in Brazil over nearly four decades (Andrietta et al., 2007; Matsuoka et al., 2009; Rosillo-Calle and Cortez, 1998). The present study focuses on the technological innovation pillar. It attempts to analyze the long-term relationship between ethanolrelated innovation and selected market variables. A multivariate causality model is developed and Granger causality tests are set to examine the pattern of association between selected variables. Patent records are taken as a proxy for ethanol innovation while the selected market variables consist of key determinants of fuel demand, price and consumption. The study examines the evolution in ethanol related innovation from 1975 to 2008. This timeframe embodies all the stages of ethanol development in Brazil since the launch of ethanol program in 1975 which was when the new fuel based on sugarcane shifted from being an experimental application to being a fundamental component of the national energy policy. A secondary objective is the screening and classification of ethanol patents. It consists in classifying the nearly 2000 patents registered for ethanol and sugarcane-related innovations at the Brazil's National Institute for Industrial Property (INPI). The study of the patent records reveals a first picture of the trends in ethanol innovation in Brazil. It also unveil several unexplored themes that could play an important role in further discussion of the motivations underlying ethanol innovation in Brazil; such areas include the nationality of the patent owner, the profile of patent applicant and so on.
162
L.C. de Freitas, S. Kaneko / Ecological Economics 74 (2012) 161–168
The remainder of this article is organized into four sections. The following section presents an overview of theoretical background and highlights of the policies, technologies and evolution of ethanol production and consumption between 1975 and 2008. It provides a summary of the main events in ethanol development in Brazil, with a subsection dedicated to the technological advances and an overview of patents related to ethanol. Section 3 discusses the methodology and data sources, as well as the peculiarities of the time series considered in the study. Section 4 presents the empirical evidence on the causality tests and provides notes on policy implications. In Section 5, the remarkable conclusions are discussed. 2. Innovation, Technology Cycles and Ethanol Diffusion in Brazil 2.1. Theoretical Background Energy is a fundamental product of innovation. It is a central component of developed nations' industrial performance and economic growth, which enable nations to improve their socioeconomic conditions. Thus, it is no surprise that researchers have devoted considerable effort to comprehending the mechanisms underlying innovation on energy issues. In the attempt to develop a comprehensive framework about the mechanisms underlying innovation and to define the causal relation between innovation and its driving factors researchers have relied on robust theoretical foundations. Classical references by Cornwall (1977) and Schumpeter (1961) on the theory of economic development and by Pavitt (1979) and Pavitt (1980) on innovative activities and industrial development allowed the redesign of public policies to include innovation as an inductor for economic growth. The relevance of innovation as a fundamental component of development of nations and the inclusion of the theme as national policy priority occurred in parallel with an extensive discussion about its determinants and limiting factors. Among other approaches the induced innovation theory allowed a deeper understanding of the relation between economic factors and innovation taking as reference the correlation between market and innovation dynamics of the post war period (Ahmad, 1966; Kennedy, 1964). Original formulation by Hicks (1932) argued that changes in relative prices of factors works as fundamental inductor of new technology that ultimately allows the productivity growth. This approach was widely evaluated by researchers exerting particular influence in the design of innovation strategies adopted by public and private organizations (Jakeman et al., 2004; Liu and Shumway, 2009). Further advances in the development of the theoretical background allowed the ascension of modern approaches on innovation including the endogenous growth and evolutionary approach that currently underlie most discussion about the theme. The endogenous growth presents the technological change as the product of intentional investment decisions and stock of human capital (Grossman and Helpman, 1991; Romer, 1990). The evolutionary perspective was formulated upon the understanding that economic development is a dynamic process driven by the emergence of new technologies, product designs, routines and institutional arrangements, with an important role for trial-and error learning process (Nelson and Winter, 1982). The OECD (2008) provides one of most straightforward review of the theoretical foundations of innovation. The Organizations argues that the dominating theoretical formulations have sustained the existence of a positive correlation between, by one side the market forces and policy factors and, by the other, innovation (OECD, 2008). Sustained on the available theoretical formulations, authors have adopted energy and environmental related innovations as recurrent object of empirical analysis (Faber and Frenken, 2009; Gerlagh, 2008; Jaffe et al., 2001; Jakeman et al., 2004; OECD, 2008; Schwarz and Ernst, 2009; Van Zon and Yetkiner, 2003). Reviewed studies
about innovation in the energy sector include Lichtenberg (1986) and Popp (2002) that evaluated the effects of energy prices as inductor for innovation; Sagar and Holdren (2002) that reviewed key determinants for global energy innovation system; Pizer and Popp (2008) and Verdolini and Galeotti (2011) that addressed the domestic and international linkages between research organizations as driver for innovation of energy technologies. Several attempts to evaluate innovation in renewable energies are also available in the literature. A study by Connelly and Sekhar (2012) classifies energy from biomass, biofuel, geothermal and nuclear as high innovation energy sources and defines the fuel production as the main driver for innovation in these fuels. Innovation in the renewable energy was also object of analysis by Shum and Watanabe (2009). These authors examined the weight of market forces in the promotion of solar photovoltaic technology in Japan and the United States. Authors observed that technologies that cause minimal disruption in the routine of interested players, including consumers and producers, may have a higher chance of success than a technology that disrupts more players. Authors have eventually adopted an integrated approach addressing the relation between innovation, public policies and market factors. For example, a study developed by Becheikh et al. (2006) presents a systematic review of empirical studies published between 1993 and 2003, in which the variables related to the innovation process and the internal and contextual factors driving it are explored. Another study by the OECD (2008) confirms the relevance of policy and market forces as determinants for innovation in energy sector. To the OECD (2008) the effectiveness of each component as inductor of innovation would ultimately be determined by the characteristics of the products within the domestic market and the regulatory and policy priority of each country. A remark by Johnstone et al. (2010) encompassing several renewable energy sources and innovations in 25 OECD countries is valuable. In the study authors concluded that public policy has had a very significant influence on the development of new technologies in the area of renewable energy. This finding emphasizes the importance of a broader supporting framework, including the sustained investment in technology development and stable and consistent policy support, in the development of renewable energies (Connelly and Sekhar, 2012; Foxon et al., 2005; Johnstone et al., 2010; OECD, 2008; Shum and Watanabe, 2009). This broader approach is particularly meaningful in the introductory stage of renewable energy development when the new energy sources have to compete with established energy choices and standards (Aswathanarayana et al., 2010; Schilling and Esmundo, 2009). 2.2. Notes on Ethanol Policies and Fuel Diffusion The progress in ethanol development in Brazil is largely attributable to technological advances carried over near one century (Goldemberg et al., 2008b). Innovation enabled the conversion of the colonial sugarcane culture through the development of the most prominent example of biofuels for transportation purposes. This has turned Brazil a leading market in biofuels (Crago et al., 2010; Walter and Cortez, 1999). Ethanol was first tested as fuel in the second half of the 19th century, during the transition in transportation from animal draft to liquid fuel combustion. In Brazil, ethanol was experimentally produced using various raw materials until finally sugarcane was chosen. Benefits of sugarcane were its natural properties and the combined effect of overproduction in the context of depressed prices for sugar, then the main co-product of sugarcane (Goldemberg and Lucon, 2010). During World War I, ethanol gained attention as a commercial fuel, and was introduced into the national fuel matrix as an alternative to the fuel supply crisis and the then high prices for fossil energy (Rico et al., 2010).
L.C. de Freitas, S. Kaneko / Ecological Economics 74 (2012) 161–168
Preliminary experiences with voluntary blends were followed by national mandates, with the ethanol mostly supplied by sugarcane facilities in Brazil's northeast region. Between the first experimental blend policies, commencing in 1931 (Brazil, 1931), and the release of the National Alcohol Program (ProAlcool) in 1975, the Brazilian ethanol industry was reshaped, with the professionalized sugarcane industry in the southeast region playing an outstanding role (Macedo, 2005). The sector was institutionalized in terms of both the government structure and private organizations. For example, the National Institute for Sugar and Alcohol (IAA) was created in 1933 with the main objective of regulating and defining standards for the domestic sugarcane industry (Vieira et al., 2007). The IAA became a central component of the national policy on ethanol production and diffusion, in the face of resistance from the oil-dominated regime as well as fragile commitment from producers because of their responsiveness to variations in the price and demand of sugar (Goldemberg and Lucon, 2010). The ProAlcool program was established with the main purpose of reducing dependence on imported fuel in a context of increasing demand for liquid fuels for transportation purposes in Brazil. Technology development was related to all stages of the ethanol life cycle, from the agricultural process and productivity promotion to vehicular combustion and emission mitigation. In 1979, a record 54 new patents related to the flourishing ethanol industry were registered in the National Institute of Industrial Property (INPI, 2010). Despite the progress attained with ethanol in the years following the launch of ProAlcool, the sector experienced nearly 15 years of crises between 1985 and 2002. Determinants of the ethanol crises included decreasing international oil prices, which reduced the relative price advantage of ethanol, and the combined effect of ethanol supply deficiencies and the withdrawal of governmental support in a context marked by strong constraints on the public budget (Andrietta et al., 2007; EPE, 2010a). The result was profound destabilization in the domestic ethanol market, with reductions in supply, and a rearrangement of the automobile industry that reset manufacturing efforts to focus on the previous gasoline-based engine standards (Goldemberg and Lucon, 2010). The stagnation of the ProAlcool program triggered transformations in the production sector. During this period, the production of ethanol consolidated its change from a widespread and heterogeneous process involving familiar properties to modern multipurpose facilities managed by professional organizations, mostly concentrated in Brazil's southeast region (Macedo, 2005). The ethanol blend mandate was preserved during the period of crises, contributing ultimately to maintaining ethanol demand, given its mandatory linkage with the gasoline market (Goldemberg and Lucon, 2010). In 2003, ethanol gained new momentum, mainly because of the launch of flex-fuel technology (FFV). Flex-fuel vehicles overcame one of the main technical constraints of the previous dedicated
163
ethanol engine (Rico et al., 2010) by allowing car owners to run their vehicles with any ratio of gasoline and ethanol. The new technology allowed consumers to postpone the fuel purchase decision until the fuel station. Ultimately, the choice between which fuels to purchase is based on cost-efficiency of each fuel taking as reference the energy content of each fuel and the retail prices. In general, owners of flex-fuel vehicles will choose ethanol when the retail price of this fuel is at most 70% of the gasoline prices at the pump (BNDES, 2008). Multiple aspects are associated with ethanol development in Brazil, with several implications for policy makers, ethanol producers, consumers and related industry. Figures on the automobile industry, crop area and yield, and fuel consumption are representative of the transformation of the ethanol industry. Table 1 presents selected indicators of ethanol performance in the context of key historical moments in ethanol development in Brazil. The inclusion of the new technology in a practical context with increasing scale and broad diffusion is a major challenge for most renewable energy technologies. In the case of ethanol in Brazil the observed performance over almost 40 years is a function of several factors, remarkably the relative advantages in price and supply compared to prevailing conditions of fossil fuel alternatives. Diffusion of ethanol in Brazil is summarized in Fig. 1. Ethanol development has changed the fuel mix in Brazil, pushing down gasoline consumption so that it has the lowest growth rate of all the liquid fuels for transportation purposes (EPE, 2010b). It has also reshaped consumer behavior and leveraged the expansion of national automobile industry. In addition, ethanol has become prominent in the national emission mitigation strategy (Amaral et al., 2008; Brazil, 2008; Goldemberg et al., 2008a), although it has also given rise to several environmental concerns, with special emphasis on the homogenization of crops in certain regions and indirect impacts on land use change (Gallardo and Bond, 2010; Pereira and Ortega, 2010). Finally, the development of the sector has attracted venture investments with increasing inflow of international capital (BACEN, 2010) what have contribute to the promotion of ethanol industry and the diversification of ethanol use to other transport sub modals and applications (Macedo, 2005; Nonato et al., 2001; Santos et al., 2010).
2.3. Records on Ethanol Innovation in Brazil Ethanol is an energy co-product of sugarcane. The fuel is the product of the extraction of carbohydrates from preprocessed sugarcane juice (Luo et al., 2009; Macedo, 2005; Macedo et al., 2004; Ometto, 2005). Several co-products of sugarcane compete with or complement the ethanol production. Sugar is the main edible co-product of sugarcane and the performance of this commodity in the market is
Table 1 Evidences of ethanol development spillover on selected aspects of the Brazilian economy. Year
1970 1985 2000 2008 Unit
Energy co-products of sugarcane-productiona
Sugarcane productionb
New light vehicles — Registers share of totalc
Ethanol Anhydrousd
Ethanol Hydrouse
Bagassef
Harvested area
Production
Yield
Gasoline
Ethanol
FFV
Diesel
234 3.144 5.644 9.577 103 toe
391 8.419 5.056 17.563 103 toe
418 1.790 3.454 9.707 103 ton
1.725 3.912 4.845 8.141 103 ha
79.752 247.199 327.704 648.921 103 ton
46.23 63.19 67.62 79.71 ton/ha
99.8% 4.1% 93.4% 8.1% % of total
0% 92.2% 0.7% 0% % of total
0% 0% 0% 87.2% % of total
0.2% 3.7% 5.9% 4.7% % of total
Notes: a Source: EPE, 2010b. b Source: FAOSTAT, 2010. c Source: ANFAVEA, 2011. d Ethanol anhydrous is an additive to automotive petrol (Goldemberg and Lucon, 2010). e Ethanol hydrous is used as final fuel in adapted Otto-cycle engines or flex-fuel vehicles. Hydrous ethanol has 96% ethanol and 4% water (Goldemberg and Lucon, 2010). f Sugarcane bagasse refers to the total amount of biomass residual used as input for electricity generation (EPE, 2010a).
164
L.C. de Freitas, S. Kaneko / Ecological Economics 74 (2012) 161–168 103m3
US$/boe1
25,000
300 250
20,000
200 15,000
150 10,000
100 5,000
50 0
Hydrous Ethanol (10³m³)
Ethanol (US$ / boe)
Gasoline (US$ / boe)
2008
2006
Anhydrous Ethanol (10³m³)
2007
2005
2004
2002
2003
2001
2000
1998
1999
1997
1996
1994
1995
1993
1992
1991
1989
1990
1988
1986
1987
1984
1985
1983
1981
1982
1979
1980
0
Fig. 1. Ethanol price and consumption in Brazil. Note 1: boe refers to barrel of oil equivalent; Note 2: Prices of fuels refer to quotations of Rio de Janeiro up to 2004 and national average from 2005 on (EPE, 2010a). Source: EPE, 2010b.
engines that could operate with ethanol and gasoline–ethanol blends. The distribution of patents over the selected time frame reveals a dip in the registers of new patents from the mid-1980s to the early 2000s, coinciding with the decline of the ethanol industry in this period. Furthermore, the initial efforts in industrial production, agriculture and combustion technologies were not replicated when there was a new rise in technological innovations during the 2000s. During this period, there was a rising trend in agriculture-related patents, in contrast to the records of patents for other purposes. The third stage is observed with the remarkable increase of new patents registers during the 2000s that can be largely attributed to the new momentum experienced by the ethanol industry, triggered by the launch of flex-fuel engines and intense demand in foreign markets. Readers must note that the period before 1975, corresponding to the invention stage, is not covered in the present analysis. Agriculture-related patents correspond to all registers related to land maintenance and the improvement of inputs and processes in the field. Genetically modified seeds, crop arrangements and processes related to cutting and land cultivation were included in this category. Agriculture related innovation is in large degree resulted of several driving factors that also include the demand for sugar and other sugarcane products. Production patents correspond to innovation related to the production stage of the fuel development, which includes, to a large extent, the industrial adaptation, raw material processes and stages of production of ethanol. Combustion patents refers to innovations in the vehicular settings and fittings for ethanol
120
100
80
Patents
also a fundamental determinant of the innovation strategy in the sugarcane industry. The fundamental trigger of the ethanol innovation in Brazil was the constitution of a sectoral innovation system (Furtado et al., 2011). This framework allowed the constitution of technology intensive clusters founded on the state of Sao Paulo in a time that the leadership in sugarcane production was being transferred from the Northeast region to the Southeast (Furtado et al., 2011). The sectoral innovation system permitted a full reshaping in the production practices reaching all stages of the ethanol life cycle. Later, with the collapse of the governmental support, the market forces would gradually assume a larger role in the financing of research in ethanol sector pushed by growing demand for liquid fuels and the ethanol– gasoline blend mandate that perpetuated over the following 40 years (Furtado et al., 2011; Macedo, 2007).In this study innovation in ethanol industry is computed as the number of patents registered at the Brazil's National Institute for Industrial Property (INPI). The patent screening process conducted for this study reveals that technological innovation affects all stages of the ethanol life cycle as well as related industries, with particularly strong effects on the agriculture, industrial production of ethanol and automobile adaptation. Highlights in the history of ethanol development in Brazil include the first patent register, dating back to 1919, which refers to the adaptation of the conventional gasoline dedicated car engine to enable it to operate with ethanol (INPI, 2010). In the years following the launch of the ProAlcool program, several patents were registered in relation to sugarcane agricultural implements, indicating the professionalization of the agriculture sector resulted of increasing demand for sugarcane products. In 1986, a patent was registered to adapt exclusive ethanol vehicles to gasoline, reflecting the crisis of the ethanol industry. Several innovations regarding biotechnological development of crops were implemented after 2002. Some examples of technological advances related to ethanol development are indicated by patents regarding the usage of by-products of ethanol production, such as cogenerated electricity and the transformation of toxic vinasse residual into fertilizer, as well as innovations related to emission reductions. Fig. 2 offers a visual overview of the evolution of patents related to the ethanol industry in Brazil. Trends are categorized by total quantity of patents and by category. Three distinguishable phases stand out. First, there is a massive register of patents after 1975, coinciding with the introduction of ethanol fuel into the national energy mix. Patents in the initial phase mainly concern industrial production of ethanol, followed by vehicular adaptation under the denomination of combustion. Combustion-related patents represent efforts to adapt and develop
60
40
20
0 1970
1975 Total Emission
1980
1985 1990 Agriculture Industrial Production
2000 2010 1995 2005 By-Product Combustion Transport and Storage
Fig. 2. Patent registers by type and total (1975–2008).
L.C. de Freitas, S. Kaneko / Ecological Economics 74 (2012) 161–168
combustion, such as the register of the ethanol engine patent, innovations related to flex-fuel engines and several adaptations related to issues such as vehicular performance and fuel combustion. Patents classified as by-products include innovations related to the usage of the residuals of ethanol production, such as the usage of sugarcane bagasse for electricity generation and the production of fertilizer from toxic residual vinasse. Transport and storage are those patents related to the logistics of ethanol; this category includes several patents relating to the storage of the fuel. Finally, emission-related patents correspond to innovations related to emission treatment in several phases of the ethanol life cycle. The distribution of patents according to categories is not trivial and even a preliminary view raises several questions. For example, fewer records of innovation in industrial production could indicate maturity of production from the perspective of producers, a lack of stimulus for industrial advance or even the embedded tradeoff between land prices and industrial improvements, which would reduce pressure on efficient industrial processes. Also, innovation in sugarcane agriculture is not exclusively related to the ethanol industry but embeds driving forces from several sugarcane applications. Such questions are worthy of further investigation from a critical perspective. 3. Methodology and Data Sources 3.1. Analytical Approach: Ethanol as a Market-oriented Innovation and the Causality Hypothesis Causality is a usual method adopted in innovation, energy and environmental analysis. Recent experiences in the usage of this method include the work by Lean and Smyth (2010), which developed a multivariate Granger causality test to explore the causal relations between electricity generation, exports, price and GDP in Malaysia and Warr and Ayres (2010) that developed a multivariate causality models to assess the relationship between the quantity and quality of energy consumption and economic growth in the United States. Experiments within the multivariate framework have also included socio-environmental aspects. For example, Narayan and Smyth (2005) evaluated the relationship between electricity consumption, employment and real income in Australia, and found a causal relation between employment, income and electricity consumption. Studies on the causal relation between renewable energy innovation and market and policy variables are still incipient, and so the relation remains poorly understood, possibly because of the experimental conditions of most renewable energy implementations worldwide. In this context, the particular cases of Brazil and the United States allow some inferences to be made, given the maturity of ethanol development in those countries. Karmarkar-Deshmukh and Pray (2009), for example, focused on identifying the most significant driving motivations for innovation in biofuels in the United States. Thus, the relationship between government policies and economic factors such as the prices of oil and its alternatives is evaluated. The conclusions reveal a positive relation between high energy prices and innovation in biofuels in the United States. Causality in this study is also tested in a multivariate system that include a proxy of innovation based on numbers of ethanol-related patents and selected market determinants, i.e., ethanol consumption, the real ethanol price and the real gasoline price. Ethanol consumption and its price are direct variables of ethanol demand, whereas the gasoline price aims to capture the cross-effect of ethanol's main substitute. This study assumes that Brazilian ethanol is by nature a marketoriented product. The maintenance of ethanol as a competitive fuel alternative depends on the continued development of technologies aimed at achieving high productivity, lower costs and competitive prices.
165
3.2. Econometric Evaluation The verification of causal relation between variables was preceded by several tests on the variable data series. The order of integration was examined by applying the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests. The lag structure for the ADF test was defined according to Schwarz Bayesian criteria, and the bandwidth for the PP test was selected with the Newey–West Bartlett kernel. For reference, following the lessons by Zivot and Andrews (1992) the exam of unit root was also tested in the presence of structural break resulted of possible breaks in the trend function caused by possible shocks on variables trend along the assessed period. Tests confirmed for all variables the rejection of null hypothesis of unit root. Once the level of integration is verified, the existence of a long-run equilibrium relationship between selected variables is tested with an autoregressive distributed lag (ARDL) model (Pesaran and Shin, 1999; Pesaran et al., 2001). This approach is regarded as a preferential method for cointegration analysis in datasets with limited number of observations as the case of the present study (Lean and Smyth, 2010; Narayan and Singh, 2007; Narayan and Smyth, 2005). The ARDL model is formulated with the lags of the dependent variable and the lagged and contemporaneous values of the independent variables. The ARDL model specification for the present study is defined according to the following unrestricted error correction model. Δ lnPat t ¼ α 0Pat þ
k k k X X X β iPat Δ lnPat t−i þ φiPat Δ lnEconst−i þ δi Pat Δ ln Epricet−i i¼1
i¼1
i¼1
k X þ τ iPat Δ lnGpricet−i þ θ1Pat ln Pat t−1 þ θ2Pat ln Econst−1
ð1Þ
i¼1
þθ3Pat ln Epricet−1 þ θ4Pat lnGpricet−1 þ ε iPat
Δ lnEconst ¼ α 0 Econs þ
k k k X X X βi Econs Δ lnPat t−i þ φi Econs Δ ln Econst−i þ δi Econs Δ lnEpricet−i i¼1
i¼1
i¼1
k X þ τ i Econs Δ lnGpricet−i þ θ1 Econs ln Pat t−1 þ θ2 Econs ln Econst−1
ð2Þ
i¼1
þθ3 Econs lnEpricet−1 þ θ4Econs lnGpricet−1 þ ε iEcons
Δ ln Epricet ¼ α 0Eprice þ
k k k X X X βiEprice Δ lnPat t−i þ φiEprice Δ lnEconst−i þ δiEprice Δ lnEpricet−i i¼1
i¼1
i¼1
k X þ τ iEprice Δ lnGpricet−i þ θ1Eprice lnPat t−1 þ θ2Eprice lnEconst−1 i¼1
þθ3Eprice lnEpricet−1 þ θ4Eprice lnGpricet−1 þ ε iEprice
Δ lnGpricet ¼ α 0Gprice þ
ð3Þ
k k k X X X βiGprice Δ lnPat t−i þ φiGprice Δ lnEconst−i þ δiGprice Δ lnEpricet−i i¼1
i¼1
i¼1
k X þ τ iGprice Δ lnGpricet−i þ θ1Gprice lnPat t−1 þ θ2Gprice lnEconst−1 i¼1
þθ3Gprice lnEpricet−1 þ θ4Gprice lnGpricet−1 þ ε iGprice
ð4Þ
where Δ corresponds to the first difference. Variables Pat, Econs, Eprice and Gprice stand for the number of ethanol related patents, quantity of ethanol consumption in m 3, real (US$ 2008) ethanol price and real (US$ 2008) gasoline price, respectively. Variables were computed in natural logarithm form represented by the expression ln. Coefficients α, β, φ, δ, τ and θ are parameters to be estimated. Given the small sample size of the present study, critical values provided by Narayan (2005) were used instead of references estimated by Pesaran et al. (2001). Cointegration was tested according to the relative position of the computed F-statistics and the critical values provided by Narayan (2005). The F-statistics was used to test the significance of the lagged levels of the variables. In this approach, the
166
L.C. de Freitas, S. Kaneko / Ecological Economics 74 (2012) 161–168
cointegration is confirmed when the computed F-statistics exceeds the upper critical bound values Pesaran et al. (2001). The presence of long-run equilibrium is a precondition to the existence of causality between variables. Once the cointegration is confirmed, the causality pattern is examined within a Vector Error Correction Model (VECM) according to the Granger representation theorem (Engle and Granger, 1987) specified as follows. 2
3 2 3 c1 lnPat 6 lnEcons 7 6 c2 7 6 7 6 7 ¼ ð1−LÞ4 lnEprice 5 4 c3 5 c4 lnGprice
2
32 3 lnPat t−i d11i d12i d13i d14i p X 6 d21i d22i d23i d24i 76 lnEconst−i 7 76 7 ð1−LÞ6 þ 4 d d d d 54 lnEprice 5 31i 32i 33i 34i t−i i¼1 d41i d42i lnGpricet−i 2 3 2 d43i3d44i λ1 ε1t 6 λ2 7 6 ε2t 7 7 6 7 ð5Þ þ6 4 λ3 5½ECT t−1 þ 4 ε3t 5 λ4 ε4t
where L is the lagshift operator and coefficients ds are parameters to be estimated. The parameters λs are the coefficients of the Error Correction Term (ECT). The ECTt-1 is the lagged error-correction term derived from the long-run cointegration. It captures the adjustment time of each variable towards the equilibrium after disturbances provoked by the interaction between variables. The null hypothesis of no long-run relationship is established when the estimated coefficients of the ECT have zero values. The ECT is generated after the cointegration regression of variables is verified; in the case when it is stationary, it is incorporated into the model. The terms ε represent the serially uncorrelated error. Finally, the pattern of the causality between variables is examined with Granger causality test (Engle and Granger, 1987; Granger, 1980; Granger, 1989). Evidences of causality and direction are determined whether the coefficients of the equation are significant. 3.3. Data Sources and Patent Classification This study relies on two main sources of data. For patent registers, it was adopted the dataset provided by the INPI. The INPI is a public autarchy responsible for keeping and managing the registers of trademarks and patents in Brazil. Patents were initially selected after multiple keyword searches of the entire INPI database, with the initial search involving a total of 1906 patent registries. Given the multiple usage of ethanol it is expected that the boundaries of patent usages are narrow and, in many cases, patents span several applications. For example, considering the registered patents for sugarcane, it is reasonable to assume that not all technological advances in the field have the production of ethanol as their final target because sugarcane is the basis for several other by-products. To ensure classification matched the purpose of the present study as closely as possible, patents were carefully reviewed, taking into account their purpose and applicability. The screening of patents and classification were conducted by three different researchers who analyzed the patents in the full database individually before getting a consensus on the final classification of patents related to ethanol fuel. Identified patents were finally consolidated into six categories, namely: agriculture, industrial production, combustion, by-product, transport and storage and emissions. Patents whose main purpose was not related to ethanol life cycle were excluded from the sample. The decision for each category of patent was arbitrarily decided based on authors' consensus. The screening resulted in 1426 patents related to ethanol. Data on fuel prices and consumption were taken entirely from the Brazilian Energy Balance, published yearly by the Energy Research Company linked to the Brazilian Ministry of Mining and Energy (EPE, 2010b). This database provides aggregate values on ethanol
Table 2 Augmented Dickey–Fuller (ADF) and Phillips–Perron unit root tests. Variables
ADF
PP
ln Pat Δ ln Pat ln Econs Δ ln Econs ln Eprice Δ ln Eprice ln Gprice Δ ln Gprice
− 1.97 − 6.36*** − 1.95 − 3.18** − 1.65 − 6.62*** − 1.10 − 5.81***
− 1.89 − 6.36*** − 2.31 − 3.25** − 1.59 − 6.62*** − 1.13 − 5.92***
Notes: ** and *** indicate the rejection of null hypothesis of unit root at 5% and 1%, respectively.
production, consumption and prices and includes a compilation of secondary data collected from different official sources. Price references were adjusted from nominal to real values by employing consumer price index estimated by the Brazilian Institute of Geography and Statistics (IBGE, 2011). 4. Empirical Evidences Causality was examined after evaluating the conditions of the selected dataset, described in the methodology section. Table 2 presents the results of the unit root tests, estimated according to augmented ADF and PP statistics on the natural logarithms of the levels and the first differences (Δ) of the variables. Results of both tests demonstrate similar conclusions, evidencing that variables are nonstationary in level and become stationary after the first difference. The estimated F-statistics confirms the existence of a long-run equilibrium relationship between selected variables rejecting the null hypothesis of no cointegration. Lag was tested for one to three years, with the optimal lag length being identified as one year. The results of the bounds tests of cointegration are reported in Table 3. Results indicate that cointegration is only present when ethanol consumption is the dependent variable. In this case the computed F-statistic is higher than the upper bound critical value at the 5% critical value. The presence of cointegration implies the existence of causal relation between variables in at least one direction. Therefore, the exam of the direction of the causality was estimated according to the VECM specified previously. Results of the analysis are displayed in terms of pair-by-pair relations, as summarized in Table 4. Several implications can be inferred from the results of the causality. The unidirectional causality running from ethanol consumption to patent registers reveals that an increase in ethanol consumption in Brazil potentially stimulates innovation in the sector. This result is consistent with the trend in the number of patents registers since the launch of the ProAlcool program. Therefore, the robust growth in innovation in the sector coincides with the intensification of ethanol demand in the end of the 1970s and first half of the 1980s while the drop in the register of new patents in the period between mid1980s and the second half of 1990s follows the reduction in the ethanol demand following the crisis in the sector. Finally, the recent growth in the demand for ethanol and the fuel usage diversification
Table 3 Bounds tests for cointegration. Equation
F-statistics
Eq. Eq. Eq. Eq.
1.80221 5.30527 1.10389 1.19739
(1): FPat (Pat|Econs, Eprice, Gprice) (2): FEcons (Econs|Pat, Eprice, Gprice) (3): FEprice (Eprice|Pat, Econs, Gprice) (4): FGprice (Gprice|Pat, Econs, Eprice)
99% critical value
95% critical value
I (0)
I (1)
I (0)
I (1)
4.28
5.84
3.058
4.223
L.C. de Freitas, S. Kaneko / Ecological Economics 74 (2012) 161–168 Table 4 Granger causality test. Effect/cause
Δ ln Pat
Δ ln Econs
Δ ln Eprice
Δ ln Gprice
Δ Δ Δ Δ
– 0.192 (0.83) 0.444 (0.65) 0.231 (0.79)
3.217* (0.04) – 0.108 (0.898) 0.244 (0.78)
0.443 (0.65) 2.99** (0.08) – 0.801 (0.46)
0.938 (0.4078) 3.244** (0.06) 0.475 (0.63) –
ln Pat ln Econs ln Eprice ln Gprice
Notes: The symbols * and ** denote statistical significance at the 5% and 10% levels, respectively; values in parenthesis refer to p-value of estimations.
may justify the growth in the number of patents after the first half of the 2000s. It is worth to mention that while the ethanol demand experienced robust drop during the ProAlcool crises there was an opposite trend in the sugar production and demand that undergone growing rates pushed by the liberalization of national sugar exports, the repeal of sugar price controls and the increasing domestic and international demand for this commodity (Borrell, 1991). However, this event was not manifested in the number of patents registered in the period; see for example records of patents for agriculture in Fig. 2. This event demonstrates the relative advantage of the technology intensive ethanol compared to lower value-added sugarcane products as inductor of innovation in the sector. Others observed relations with particular interest to this research are the observed causality between the ethanol and gasoline prices and ethanol consumption. Although this relationship has demonstrated a relatively weaker causality, i.e., it is statistically significant at the 10% level, the finding are relevant given the role of the competing fuel prices as a direct determinant of ethanol consumption, which ultimately affects ethanol innovation. The results of causal tests demonstrated relations between other pairs of variables to be statistically insignificant. Results offer relevant input for policy considerations. Remarkably, it implies that policies addressing ethanol consumption would potentially induce innovation in the sector. In this sense, the current trends on ethanol usage diversification would be expected to drive new technological advances in the sector. By the other hand, the reliance on fuel demand as inductor of innovation imposes risks to the innovation progress due to natural ethanol market fluctuations. It must also be noticed that the present study embodies a simplified set of market forces and a broader review of innovation determinants is necessary to fully understand the driving determinants of innovation in the ethanol sector in Brazil. 5. Conclusions Ethanol is a technology intensive co-product of sugarcane, mainly produced from mechanical and chemical treatment of sugarcane juice, and residually from other sugarcane by-products. The fuel was introduced in Brazil as a regular fuel in 1975 as an alternative to the increasing costs of oil imports and to overcome the crisis faced by sugar producers against cyclic variations in international prices. The successful implementation of the new fuel in the country is partially attributed to innovation in the sector. The present study examines the causal relation between ethanol-related innovation and fuel market variables in Brazil. Causality was tested in a multivariable framework and several relations were examined. Findings reveal the presence of a demandpull effect between ethanol consumption and innovation. This relation is consistent with the trends in the registers of new patents in the sector which followed a curve similar to the performance of the consumption of ethanol in Brazil since the implementation of the ethanol program in the 1970s. Reviewers of this study reminded that the increment in demand of energy and non-energy co-products of sugarcane must also be an important determinant for innovation in the ethanol sector. Such supposition is subsidized by the data available
167
at the Brazilian energy balance 2010 (EPE, 2010b) that attributes to the final non-energy consumption of ethanol a share of 6% of final consumption in 2008. An accurate evaluation of this relation is recommended for future studies. Additionally, the study reveals an as yet unexamined picture of ethanol innovation in Brazil. Patents were selected and classified in a systematic manner, resulting in a dataset of 1426 patents, distributed across several stages of the ethanol life cycle. Although further critical analyses of the patents database are expected, the examination of the patents conducted in this article provides evidence of an uneven distribution of registered patents, with remarkable weight on patents related to production and agriculture productivity; by contrast, innovations related to emissions mitigation, fuel transportation and storage and combustion exhibit a stable or decreasing pattern over the data series. A possible reason for the observed pattern is the spillover effect of other sugarcane products demand, e.g. sugar, on the counts of patents. This is particularly reasonable in the innovation intensity related to the agriculture stage. A full comprehension of innovation in biofuels and the response of this technology to economic incentives may contribute to define energy and environmental policies. Besides, understanding the importance of biofuels innovation inductors could improve the effectiveness of public policies aimed at encouraging research and innovation in the sector.
References Ahmad, S., 1966. On the theory of induced invention. The Economic Journal 76 (302), 344–357. Amaral, W.A.N., Marinho, J.P., Tarasantchi, R., Beber, A., Giuliani, E., 2008. Environmental sustainability of sugarcane ethanol in Brazil. In: Zuurbier, P., Van de Vooren, J. (Eds.), Sugarcane Ethanol: Contribution to Climate Change Mitigation and the Environment. Wageningen Academic, pp. 113–138. Andrietta, M.G.S., Andrietta, S.R., Steckelberg, C., Stupiello, E.N.A., 2007. Bioethanol— Brazil, 30 years of Proalcool. International Sugar Journal 109, 195–200. ANFAVEA—Brazilian Automotive Industry Association, 2011. Brazilian Automotive Yearbook 2010. Centro de Documentação da Indústria Automobilística—CEDOC, São Paulo. Aswathanarayana, U., Harikrishnan, T., Sahini, K.M.T. (Eds.), 2010. Green Energy: Technology, Economics, and Policy. Taylor & Francis Group, London. BACEN — Central Bank of Brazil, 2010. Foreign Direct Investment in Brazil. Website: http://www.bcb.gov.br/rex/Censo2000/ingl/CENSUS2000ListofTables.asp. Retrieved August 29, 2010. Becheikh, N., Landry, R., Amara, N., 2006. Lessons from innovation empirical studies in the manufacturing sector: a systematic review of the literature from 1993–2003. Technovation 26, 644–664. BNDES — The Brazilian Development Bank, 2008. Sugarcane-Based Bioethanol: Energy for Sustainable Development. BNDES/ CGEE, Rio de Janeiro. Borrell, B., 1991. How a Change in Brazil's Sugar Policies Would Affect the World Sugar Market. WPS 642. The World Bank, Washington. Brazil — Government of Brazil, 1931. Decree no. 20.356, 1st September 1931. Brasilia. Brazil — Government of Brazil, 2008. National Plan on Climate Change. Interministerial Committee on Climate Change, Brasilia. Connelly, M.C., Sekhar, J.A., 2012. U. S. energy production activity and innovation. Technological Forecasting and Social Change 79 (1), 30–46. Cornwall, J., 1977. Modern Capitalism: Its Growth and Transformation. Martin Robertson, London. Crago, C.L., Khanna, M., Barton, J., Giuliani, E., Amaral, W., 2010. Competitiveness of Brazilian sugarcane ethanol compared to U.S. corn ethanol. Agricultural & Applied Economics Association 2010, AAEA,CAES, & WAEA Joint Annual Meeting, Denver. Engle, R.F., Granger, C.W.J., 1987. Cointegration and error correction representation: estimation and testing. Econometrica 55, 251–276. EPE—Energy Research Company, 2010a. Brazilian Energy Balance 2010: Base Year 2009. EPE, Rio de Janeiro. EPE — Energy Research Company, 2010b. Brazilian Energy Balance 2010 — Full Data Series. Website: https://ben.epe.gov.br/BENSeriesCompletas.aspx. Retrieved December 22, 2010. Faber, A., Frenken, K., 2009. Models in evolutionary economics and environmental policy: towards an evolutionary environmental economics. Technological Forecasting and Social Change 76, 462–470. FAOSTAT—FAO Statistical Database, 2010. Crops. Website: http://faostat.fao.org/site/ 567/default.aspx#ancor. Retrieved July 28, 2010. Foxon, T.J., Gross, R., Chase, A., Howes, J., Arnall, A., Anderson, D., 2005. UK innovation systems for new and renewable energy technologies: drivers, barriers and systems failures. Energy Policy 33, 2123–2137. Furtado, A.T., Scandiffio, M.I.G., Cortez, L.A.B., 2011. The Brazilian sugarcane innovation system. Energy Policy 156–166.
168
L.C. de Freitas, S. Kaneko / Ecological Economics 74 (2012) 161–168
Gallardo, A.L.C.F., Bond, A., 2010. Capturing the implications of land use change in Brazil through environmental assessment: time for a strategic approach? Environmental Impact Assessment Review 31 (3), 261–270. Gerlagh, R., 2008. A climate-change policy induced shift from innovations in carbonenergy production to carbon-energy savings. Energy Economics 30, 425–448. Goldemberg, J., Lucon, O., 2010. Energy, Environment and Development. Earthscan, London. Goldemberg, J., Coelho, S.T., Guardabassi, P., 2008a. The sustainability of ethanol production from sugarcane. Energy Policy 36, 2086–2097. Goldemberg, J., Nigro, F.E.B., Coelho, S.T., 2008b. Bioenergy in the State of São Paulo: Present Situation, Perspectives, Barriers and Proposals. Imprensa Oficial do Estado de São Paulo, São Paulo. Granger, C.W.J., 1980. Testing for causality. Journal of Economic Dynamics and Control 4, 229–252. Granger, C.W.J., 1989. Some recent developments in the concept of causality. Journal of Econometrics 39, 199–221. Grossman, G.M., Helpman, E., 1991. Innovation and Growth in the Global Economy. MIT Press, Cambridge. Hicks, J.R., 1932. The Theory of Wages. Macmillan, London. IBGE — The Brazilian Institute of Geography and Statistics, 2010. Índice nacional de preços ao consumidor Amplo. Website: http://www.ibge.gov.br/home/estatistica/ indicadores/precos/inpc_ipca. Retrieved July 10, 2010. INPI—National Institute for Industrial Property, 2010. Database on patents. Website: http://pesquisa.inpi.gov.br/MarcaPatente/jsp/servimg/servimg.jsp?BasePesquisa= Patentes, retrieved July 2, 2010. Jaffe, A.B., Newell, R.G., Stavins, R.N., 2001. Technological change and the environment. In: Mäler, K.G., Vincent, J. (Eds.), The Handbook of Environmental Economics. North-Holland/Elsevier Science, Amsterdam. Jakeman, G., Hanslow, K., Hinchy, M., Fisher, B.S., Woffenden, K., 2004. Induced innovations and climate change policy. Energy Economics 26, 937–960. Johnstone, N., Hascic, I., Popp, D., 2010. Renewable energy policies and technological innovation: evidence based on patent counts. Environmental and Resource Economics 45, 133–155. Karmarkar-Deshmukh, R., Pray, C.E., 2009. Private sector innovation in biofuels in the United States: induced by prices or policies? AgBioforum 12 (1), 141–148. Kennedy, C., 1964. Induced bias in innovation and the theory of distribution. The Economic Journal 74 (295), 541–547. Lean, H.H., Smyth, R., 2010. Multivariate Granger causality between electricity generation, exports, prices and GDP in Malaysia. Energy 35 (9), 3640–3648. Lichtenberg, F.R., 1986. Energy prices and induced innovation. Research Policy 15, 67–75. Liu, Y., Shumway, C.R., 2009. Induced innovation and marginal cost of new technology. Economics Letters 105, 106–109. Luo, L., van der Voet, E., Huppes, G., 2009. Life cycle assessment and life cycle costing of bioethanol from sugarcane in Brazil. Renewable and Sustainable Energy Reviews 13, 1613–1619. Macedo, I.C., 2005. Sugar Cane's Energy: Twelve Studies on Brazilian Sugar Cane Agribusiness and its Sustainability. UNICA, São Paulo. Macedo, I.C., 2007. Situação atual e perspectivas do etanol. Estudos Avançados 21 (59), 157–165. Macedo, I.C., Leal, M.R.L.V., Silva, J.E.A.R., 2004. Assessment of Greenhouse Gas Emissions in the Production and Use of Fuel Ethanol in Brazil. State Government of Sao Paulo, São Paulo. Matsuoka, S., Ferro, J., Arruda, P., 2009. The Brazilian experience of sugarcane ethanol industry. Vitro Cell. Developmental Biology Plant 45, 372–381. Narayan, P.K., 2005. The saving and investment nexus for China: evidence from cointegration tests. Applied Economics 37, 1979–1990. Narayan, P.K., Singh, B., 2007. The electricity consumption and GDP nexus for the Fiji Islands. Energy Economics 29, 1141–1150. Narayan, P.K., Smyth, R., 2005. Electricity consumption, employment and real income in Australia: evidence from multivariate Granger causality tests. Energy Policy 33, 1109–1116. Nelson, R.R., Winter, S.G., 1982. An Evolutionary Theory of Economic Change. The Belknap press of Harvard University Press, Cambridge.
Nonato, R.V., Mantelatto, P.E., Rossell, C.E., 2001. Integrated production of biodegradable plastic, sugar and ethanol. Applied Microbiology and Biotechnology 57, 1–5. OECD — Organization for Economic Co-operation and Development, 2008. Environmental Policy, Technological Innovation and Patents. OECD Studies on Environmental Innovation, Paris. Ometto, A.R., 2005. Life Cycle Assessment of Hydrated Ethylic Alcohol Fuel by EDIP, Exergy and Emergy Methods. Tese de Doutorado, Escola de Engenharia de São Carlos. USP, São Carlos. Pavitt, K., 1979. Technical innovation and industrial development: the new causality. Futures 11 (6), 458–470. Pavitt, K., 1980. Technical innovation and industrial development: the dangers of divergence. Futures 12 (1), 35–44. Pereira, C.L.F., Ortega, E., 2010. Sustainability assessment of large-scale ethanol production from sugarcane. Journal of Cleaner Production 18, 77–82. Pesaran, M.H., Shin, Y., 1999. An autoregressive distributed lag modeling approach to cointegration analysis. In: Storm, S. (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium. Cambridge University Press, Cambridge, pp. 1–31. Pesaran, M.H., Shin, Y., Smith, R., 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16, 289–326. Pizer, W., Popp, D., 2008. Endogenizing technological change: matching empirical evidence to modeling needs. Energy Economics 30 (6), 2754–2770. Popp, D., 2002. Induced innovation and energy prices. The American Economic Review 92 (1), 160–180. Rico, J.A.P., Mercedes, S.S.P., Sauer, I.L., 2010. Genesis and consolidation of the Brazilian bioethanol: a review of policies and incentive mechanisms. Renewable and Sustainable Energy Reviews 14, 1874–1887. Romer, P., 1990. Endogenous technological change. Journal of Political Economy 98, 71–102. Rosillo-Calle, F., Cortez, L.A., 1998. Towards ProAlcool II—a review of the Brazilian bioethanol program. Biomass and Bioenergy 14, 115–124. Sagar, A.D., Holdren, J.P., 2002. Assessing the global energy innovation system: some key issues. Energy Policy 30, 465–469. Santos, M.F.R.F., Borschiver, S., Couto, M.A.P.G., 2010. Biorefinery in Brazil: new technological trends adopted by domestic companies. Journal of Technology Management & Innovation 5 (2), 84–90. Schilling, M.A., Esmundo, M., 2009. Technology S-curves in renewable energy alternatives: analysis and implications for industry and government. Energy Policy 37, 1767–1781. Schumpeter, J., 1961. The Theory of Economic Development. Oxford University Press, Oxford. Schwarz, N., Ernst, A., 2009. Agent-based modeling of the diffusion of environmental innovations — an empirical approach. Technological Forecasting and Social Change 76, 497–511. Shum, K.L., Watanabe, C., 2009. An innovation management approach for renewable energy deployment—the case of solar photovoltaic (PV) technology. Energy Policy 37, 3535–3544. Van Zon, A., Yetkiner, I.H., 2003. An endogenous growth model with embodied energysaving technical change. Resource and Energy Economics 25, 81–103. Verdolini, E., Galeotti, M., 2011. At home and abroad: an empirical analysis of innovation and diffusion in energy technologies. Journal of Environmental Economics and Management 61, 119–134. Vieira, M.C.A., Lima, J.F., Braga, N.M., 2007. Setor sucroalcooleiro Brasileiro: evolução e perspectivas. BNDES, Rio de Janeiro. Walter, A., Cortez, L., 1999. An historical overview of the Brazilian bioethanol program. Renewable Energy for Development 11 (1), 1–4. Warr, B.S., Ayres, R.U., 2010. Evidence of causality between the quantity and quality of energy consumption and economic growth. Energy 35, 1688–1693. Zivot, E., Andrews, D.W.K., 1992. Further evidence on the Great Crash, the oil shock and the unit root hypothesis. Journal of Business & Economic Statistics 10 (3), 251–270.
