Vol.9, No.1, 2013 ISSN 1822-3346
Economics and Rural Development
Engagement in Biofuel Production: Underlying Factors in Oil-Rich Countries
Bernardas Kniuksta1
Aleksandras Stulginskis University
Scientific discussions on the factors determining engagement in biofuel production by different countries are rather scarce, with
a slightly larger numbers of studies into the drivers of biofuel production or other related activities in individual regions or districts.
The identification of determinants of engagement in biofuel production helps to reveal the dominant national goals, which, presumably, depend on the mix of natural resources in the country. That is to say, the country's willingness to become a biofuel producer may
be triggered by the shortage of energy resources, the abundance of agricultural production resources or a combination of both factors.
In engagement in biofuel production, the environmental protection motive may also play a major role. When the development of biofuel production in oil-lacking countries is considered, the argument of the shortage of energy resources sounds plausible. However,
this argument seems to be less convincing with respect of the participation of oil-rich countries in biofuel production. The work conceptualizes engagement in biofuel production based on the location theory. The aim of the article is to offer a theoretical definition of
the determinants of engagement in biofuel production and to empirically test the manifestation of those factors in oil-rich nations. In
order to circumvent the impact of the biofuel production heterogeneity, the research object is limited to bioethanol production. The
methods of the research include analysis and synthesis of scientific literature, generalization, logical abstractions, the methods of descriptive statistics and probit regression. In this work, countries that specialise in oil extraction are attributed to oil-rich countries.
Such oil-extracting countries are further divided into two groups to filter out countries that specialize in the production of petroleum
products. The Balassa Specialization Index was used to determine the specialization of the countries. The results of the research revealed that the key factors of engagement in biofuel production in countries specializing in oil extraction and production of petroleum products are larger areas of available agricultural land and a greater focus on scientific research and experimental development.
Meanwhile, among the countries which specialise in oil extraction only, bioethanol producing countries are characterised by larger
quantities of available agricultural land, higher GDP levels and sufficiency of bioethanol production feedstock.
Key words: bioethanol production, oil-rich countries, comparative advantage, specialization, probit regression
JEL Classification: E23, F52, F10, Q16.
Introduction 1
Possibilities offered by biofuel production make it attractive in many countries worldwide. While in some
countries biofuel production is seen as an alternative to
imports of crude oil or its products, in other countries it is
considered as a possibility to exploit abundant agricultural production resources, and/or to reduce greenhouse gas
emissions. Those inherent possibilities of biofuel production, or, in other words, the complexity of biofuel effects,
may disguise the real motives for its production. Therefore it is difficult to define what impulses determine a
country's engagement in biofuel production. The first
thought professed by economic logic is that poor sufficiency of fossil fuel energy resources should encourage
exploitation of alternative energy sources, including biofuels. It is worth noting, that this trend is clearly apparent
in the practice of different countries. For example, most
bioethanol producers are countries that lack crude oil resources, such as Australia, Spain, Jamaica, China, Costa
1
PhD student Bernardas Kniuksta
Fields of scientific interest: alternative use of agricultural
products, bioenergy, economic self-reliance.
Mailing address: Institute of Economics, Accounting and
Finance, Aleksandras Stulginskis University, Universiteto street
10, LT-53361, Akademija, Kaunas district, Lithuania.
E-mail: [email protected]
Rica, Cuba, France, Hungary, and others. Here comes the
question how to explain the engagement of oil-rich countries, such as Argentina, Columbia, Canada, Kazakhstan
and others, in bioethanol production. It should emphasised that, for example, the energy return on investment
(EROI) of bioethanol is between 0.8:1 and 10:1 depending on the feedstock, while the energy return on investment of oil and gas energy in the last decades has ranged
between 10:1 and 18:1 (Murphy, Hall, 2010). Although
that is quite a rough comparison, it shows that the energy
return on investment of bioethanol is usually lower to a
lesser or greater degree. Therefore, it is difficult to get an
intuitive understand why oil-rich countries produce bioethanol or other biofuels. This calls for a more thorough
analysis of biofuel production factors, which has to be
coupled with the issue of preferences: why countries with
similar sufficiency of fossil fuels demonstrate different
preferences for biofuel production. Economic logic leads
to an assumption that in the countries with sufficient oil
resources and biofuel production specific factors come into play, the manifestation whereof is not usually observed
in most other oil-rich countries. It can be argued that the
impact of such specific factors can be explained by the
location theory or, to be more precise, the industrial location theory. According to F. X. Aguilar (2009b), the location of a resource-based industry, such as wood product,
7
Economics and Rural Development
food or biofuel production, can be explained by both the
location theory and the new economic geography. As far
back as 1929, A. Weber related the factors of location of
economic activities to the advantages that unfold when
the development of an economic activity starts in a certain location (Weber, 1929). Advantages can be understood in many different ways, although usually they are
associated with a favourable environment for production
and realization of the produced products. As D. M. Lambert et al. (2008) pointed out in their analysis of the location of bioethanol plants, the choice of the production location is affected by the access to the product and production resource markets, business services and industrial
agglomeration. This research identifies the factors of engagement in biofuel production with the characteristics of
the country, which can have a positive or negative impact
on the formation of preferences for biofuel production.
Although the research embraces four types of economies
that reflect the specialization of the oil industry, the results presented in this article were obtained through the
analysis of oil-rich countries only and it does not present
the results obtained in the analysis of oil-lacking countries. Aim of the research: to theoretically define the
factors of engagement in biofuel production and to empirically test the manifestation of those factors in oil-rich
countries.
Objectives of the research:
1) to discuss the peculiarities of engagement in
biofuel production in economies with different
specialization in oil industry, and to identify factors that
may stimulate countries to engage in biofuel production;
2) on the basis of bioethanol production, to
empirically test the manifestation of identified factors in
oil-rich countries.
Object of the research: the determinants of engagement in biofuel production.
The article excludes energy policy measures, such as
biofuel blending or use targets, subsidies, fuel tax exemptions for biofuel producers, regulation measures for trade
in energy resources (import tariffs) and other measures
that promote greater efficiency at all the stages of the biofuel supply chain. Although those energy policy measures
have a direct impact on the development of biofuel production, they fall outside the scope of the article, because
even before taking those energy policy measures, countries already have certain motives that drive to opt for the
field of bio-energy, i.e. to start biofuel production. In this
respect, policy measures are an accompanying effect, rather than a factor of starting biofuel production. Nevertheless, the research fails to dissociate from the impact of
agricultural policy, which indirectly manifests itself in the
bioenergy sub-sector through the supply of biofuel production feedstock. In order to circumvent the impact of
biofuel production heterogeneity, the research object is
limited to bioethanol production.
8
Vol.9, No.1, 2013 ISSN 1822-3346
Research methods: the article is based on the methods of analysis and synthesis of scientific literature, generalization, logical abstractions, the methods of descriptive statistics and probit regression.
Drivers of Engagement in Biofuel Production
A country's engagement in biofuel production can be
triggered by various factors. The scientific literature,
which is often not only of economic character, presents
quite a wide spectrum of factors: technological, economic, political, etc. In some countries engagement in biofuel
production can be determined by political initiatives,
whereas in others it is the initiative of the market players
aimed at satisfying the demand for energy or unfolding
the potential of the available agricultural resources.
P. North (2010), referring to G. Bridge (op. cit.), calls the
development of economic activities or other economic
transformations, which occur under the influence of political processes, intentional. And on the contrary, the scientist calls economic transformations that result from the
decisions of individual market players and market laws
immanent. Such classification could be also applied to
explain the case of countries' engagement in biofuel production. It is worthwhile to note that in practice usually
there is a combination of the above, i.e. biofuel production is determined by both political initiatives and decisions of the market players. As stated above, this article
strives to dissociate from political factors, while engagement in biofuel production is explained following economic regularities, such as an abundance of agricultural
resources, a favourable economic environment, etc.
The peculiarities and factors of location of economic
activities, or, as it can be said in this case, the attraction
of economic activities to a location, have been analysed
for nearly two hundred years. New economic activities
keep actualising one or another aspect of this broad economic issue. Alongside with the analysis of various biofuel production issues, there has been a new shift in the
subject of economic activities. Until then, various issues
of the location of heavy and light industries as well as agricultural and food industries had been discussed.
F. X. Aguilar (2009b) points out that as far back as 1826
von Thunen (op. cit.) raised the questions of agricultural
production location and offered a respective model. In his
main work The Isolated State, Von Thunen tries to explain the principles that determine the prices of agricultural products and the ways of land use. In his later works
Von Thunen (op. cit.) came to a conclusion that the structure of land use represents concentric rings, which correspond to different types of land use and which move
away from the central urban market. Such use of land in
the agricultural sector forms under the conditions of isolation (for example, no trade with other localities). In his
analysis, Von Thunen (op. cit.) also included components
of the transport system, which are used to carry goods to
Vol.9, No.1, 2013 ISSN 1822-3346
the central market. That emphasized the links of the use
of agriculture with the unit transportation costs to the
place of consumption. If the transportation costs are low,
the production can be located further away from the central market, and vice versa (ibid).
As noted by F. X. Aguilar (2009b), more than fifty
years later, in 1896, E. Ross (op. cit.) suggested the general
theory of location of industries which emphasized the role
of specific economic advantages that the geographic location can offer for entire industries including specific advantages in costs. F. X. Aguilar (2009b) also points out
that later A. Marshall (op. cit.) expanded E. Ross's (op. cit.)
ideas by identifying three factors which may determine decisions on the location of industries: availability of sufficient labour resources, local supply with commodities and
services (raw materials, resources, consultation and cooperation), the potential for exchange of knowledge and
spillovers between neighbouring firms and institutions. All
those factors are also, to a lesser or greater extent, significant in the case of biofuel production.
G. T. Renner (1947) summarised earlier theoretical
insights in his work Principle of Industrial Location and
stated that each industry tends to locate in such areas that
provide with an optimal access to all constituent elements
required by the industry (resource markets, technological
environment, etc.). E. M. Rawstron (1958) suggested
three principles that determine industrial location:
• physical restriction (when production is dependent
on natural resources and thus restricted by the availability
of those resources);
• economic restriction (when the costs of inputs in
different locations may vary significantly and thus make
some areas more attractive than others);
• technical/technological restrictions (when technological changes may make decisions on the location of
economic activities less significant in certain industries,
with the exception when such changes require to build
new factories).
In this case, the location of biofuel production is no
exception and theoretically the same three principles apply. Some differences may occur where biofuel production is artificially furthered by political mechanisms rather than develop in an integrated manner. For instance,
some countries, such as the USA, stimulate the development of biofuel production by applying various intervention measures, even though, as D.J. Murphy and C.A.S.
Hall (2010) point out, biofuels, such as corn ethanol, are
often non-competitive either energetically or economically and their production is often unviable. The aforementioned authors also claim that the most basic analysis reveals that even given the highest possible EROI of corn
ethanol the effect of corn ethanol is particularly low and
does not have a more significant influence on the energy
supply at the national level. The authors refer to the fact
that the energy return on investment in corn bioethanol
Economics and Rural Development
production is close to negative and ranges between 0.8:1
and 1.6:1, while the return in sugar cane bioethanol production is close to that in the oil industry (10:1). In this
case, a physical restriction is crucial, when only feedstock
that is less suitable for biofuel production is available in
the country.
F.X. Aguilar (2009b) states that the said theories of
industrial location and location of other economic activities provided the basis for the theory which is known as
the classical location theory. According to the said author, it was difficult to apply the classical location theory
in empirical analysis. In view of this weakness,
P. Krugman (1995) suggested a model which comprehensively embraced various forces that attract or turn industries away from the city centre. This triggers the separation of the urban core industry and the peripheral resource-based industry (Krugman, 1991). This new approach towards the site selection issue became known as
the "new economic geography". The biofuel production
industry should be attributed to the peripheral industries,
which particularly depend on the production resources
available in the location.
The numbers of purely economic scientific works
that specifically analyse the case of biofuel production
are scarce. However, a rather comprehensive research in
this field was conducted by A. Doku and S. Di Falco
(2012). The authors analysed factors which could stimulate the formation of biofuel policies in eleven countries.
The analysis used the methods of ordinary least squares
(OLS) and probit regression. The analysis included both
OECD countries, considered to be representative of developed countries, and non-OECD countries, which were
seen as developing countries. The said research found out
that different countries have different drivers of creating
biofuel policies. It is stated that the GDP proves to be
more significant in framing the biofuel policy in OECD
countries, while the amount of arable land and the feedstock prices are more important for non-OECD countries.
A. Doku and S. Di Falco (2012) came to a conclusion
that a naturally endowed comparative advantage may not
necessarily equate to a successful biofuel industry. Slightly more studies into the factors of engagement in biofuel
production are conducted with a focus on the US counties. Those studies endeavour to identify factors that determine attraction of investment to biofuel production in a
specific location. For example, D. M. Lambert et al.
(2008) studied the location factors of bioethanol production and the competitive advantages of counties. The empirical analysis applied methods of probit regression and
spatial clustering. The analysis included counties in 48
US states in the period from 2000 to 2007. The authors
established that the dominant motive of the site selection
for bioethanol production was the availability of feedstock. Other significant factors, with respect to attracting
bioethanol production to a specific location, were infrastructure (navigable rivers and railroads), product and in9
Economics and Rural Development
put markets (including that of the labour force) and producer credit conditions. D. M. Lambert et al. (2008) also
point out that state incentives have a positive impact on
attracting potential investors to bioethanol production and
positively correlate with the location of the existing
plants. The results of the research by the said authors
were presented in the publication Spatial Heterogeneity
of Factors Determining Ethanol Production Site Selection
in the U.S., 2000-2007 (Stewart, Lambert, 2011). Furthermore, the publication emphasizes the manifestation of
factors, which depends on heterogeneity of counties. The
key characteristic which distinguishes locations is the rurality of areas measured by the rurality index. The said
authors suggest that areas characterized by rurality are
more likely to attract the bioethanol production industry.
Similar conclusions were made by K. C. Dhuyvetter
et al. (2005), who proposed that the bioethanol, including
all other biofuels, industry is a commodity-based industry
and therefore low-cost producers will be the most competitive in the long term providing there are no factors
distorting competitive conditions. The said authors consider the main factors that determined the distribution of
bioethanol plants in the US counties to be the feedstock
prices and supply, the prices of fossil energy resources,
and the numbers of fed livestock in the counties and the
neighbouring counties. The numbers of livestock as a
significant factor at the county level is pointed out because bioethanol production by-products can be used as
fodder. Hence, the greater the number of livestock, the
larger the potential of the fodder market. In this subject,
references can be made to the contributions of scientists
who analysed industries related to the use of various renewable resources. For instance, in literature there are
parallels drawn between the location of the biofuel industry and the food industry. D. M. Lambert et al. (2008)
emphasize that there are many similarities between food
manufacturing and grain-based bioethanol production.
Earlier food manufacturing location studies provide certain insights into the factors determining the location of
bioethanol plants. As far back as 1929, A. Weber suggested that resource-based industry with a high material
index, such as wood industry, should be brought nearer to
locations with concentrated feedstock supply rather than
to the end-use product market (Weber, 1929). This argument is supported by F. X. Aguilar and R. P. Vlosky
(2006), who claim that there is no evidence of geographical correlation between the industry of primary wood
products and the end-markets characterized by a greater
population density. It should be noted that Weber's theory
of industrial location was not completely original: it was
introduced as the continuation of von Thunen's theory of
agricultural production location (Aguilar, 2009b).
In general, food manufacturing location studies show
that crucial factors of production agglomeration in certain
locations are the proximity of product and input markets,
the infrastructure and the characteristics of labour re10
Vol.9, No.1, 2013 ISSN 1822-3346
sources (Leistritz, 1992; Lopez, Henderson, 1989). Agglomeration of food manufacturers in certain locations
was studied by S. Goetz (1997), J. R. Henderson and
K. McNamara (2000), D. M. Lambert, K. McNamara and
M. Garrett (2006). The authors came to conclusions that
the distribution of food manufacturers was influenced by
the same factors that affect decisions on investment in the
industry: the access to product and input markets, agglomeration economics and infrastructure.
F. X. Aguilar (2009a) studied preferences for develop
wood-based energy in the USA. The author demonstrated
that the determinant that drives investment in wood-based
energy is the return on investment in renewable energy,
which, to a large extent, is determined by high energy
prices and federal mandates in the area of renewable energy. The insights of the research can be helpful in the
analysis of preferences in other energy sub-sectors. In another study F. X. Aguilar (2009b) used spatial econometric analysis to identify factors of spatial location of industry based on renewable resources. The empirical research
encompasses wood industry companies in the southern
US counties. The said author argues that the most decisive factors determining the attraction of wood industry
to a location are the price of wood, availability of labour
force, highways and their condition, access to forest resources, energy costs, and value of the land.
It should be noted that the coverage of research of
factors determining the engagement in biofuel production
at the level of counties and other regions of a country is
different than that at the national level. The analysis of
determinants of engagement in biofuel production at the
country's or national level can exclude economic, natural
environmental or infrastructural disparities in the country
(e.g., value of the land, road network, navigable rivers,
numbers of fed livestock, etc.). This research focuses on
the following important national-level determinants that
can foster the engagement in biofuel production at the
country level:
• amount of land resources;
• agricultural population;
• gross domestic product;
• scientific research and experimental development;
• import of crude oil;
• feedstock self-sufficiency;
• export of agricultural feedstock.
Land resources. One of the expected effects is an increase in the biofuel supply in those countries that have
least restrictions on increasing the total volumes of agricultural production received from the arable land. It is
obvious that compromises on the use of production factors for biofuel production or food/fodder differ in different countries (Peskett et al., 2007). I. Sheldon and
M. Roberts (2008) notice that despite the increased economic viability of corn-based bioethanol in the USA the
increased prices of crude oil allow Brazil to maintain a
Vol.9, No.1, 2013 ISSN 1822-3346
considerable comparative advantage in the production of
sugar cane bioethanol. According to the said authors, the
need for land resources, which is necessary to satisfy targeted proportions of biofuel use in transport, is a proof
that Brazil again has a clear comparative advantage. For
instance, Brazil needs only 3 per cent of agricultural land
in order to replace one tenth of fuel used in the domestic
transport by biofuels. Whereas, the need for land resources for the same purposes in the USA, Canada and
the EU amounts to 30 per cent, 36 per cent and 72 per
cent respectively (OECD, 2006). It should be noticed that
these facts confirm the significance of the natural productive capacity and suggest that the countries characterized
by relatively greater sufficiency of land resources are
more likely to be apt to engage in bioethanol production.
It can be assumed that in the case of oil-rich countries,
sufficiency of land resources is the most decisive factor
of engagement in bioethanol production.
Agricultural population. In FAOSTAT methodologies this population is defined as all persons whose living
depends on agriculture, hunting and forestry. It comprises
all persons economically active in agriculture as well as
their non-working dependents. The agricultural population does not have to be exclusively equalled to the rural
population. The agricultural population is a potential labour resource, similarly to the aforementioned land resources. The essential difference regarding labour resources is that the emphasis is placed on the employment
of resources rather than the issue of competition. Most
authors (Lambert et al. (2008); Fonseca et al. (2010);
Doku, Di Falco (2012)) agree that biofuel production can
create new jobs in the country, increase the existing employment, the income of the rural population and reduce
poverty. The manifestation of those positive effects may
be hindered by biofuel production based on industrial agriculture, which often eliminates local communities from
the production (Franco et al., 2010). Despite the uncertainty about the said positive effects, A. Doku and S. Di
Falco (2012) are of the opinion that a large agricultural
population is a fundamental factor of engagement in biofuel production. Thus it is likely that in the countries with
a relatively large agricultural population, the drivers of
engagement in biofuel production will be stronger. It is
also likely that this factor will be more significant in oilrich countries.
Gross domestic product. As already indicated, various political measures can be a fundamental factor for the
development of biofuel production. Usually the most effective political measures are expensive and therefore only economically strong countries are able to implement
them. The GDP represents a country's welfare and financial capabilities to take expensive measures to support
biofuel production. According to A. Doku and S. Di Falco (2012), if the level of the national GDP is higher, it is
more likely that the country will financially support the
creation and promotion of the biofuel policy. Such coun-
Economics and Rural Development
tries also have greater opportunities to implement protectionist policies and thus to support biofuel production.
The said authors also point out those countries with higher GDP levels usually are among the greatest greenhouse
gas emitters. A higher GDP can be related to the availability of „good“ institutions and appropriate infrastructure, which enables to develop a successful biofuel industry in the country (Doku, Di Falco, 2012).
Research and development. A. Doku and S. Di Falco
(2012) notice that if a country wants to successfully compete in biofuel production, its capability to develop new
and innovative technologies or to afford importing them
(e.g., conversion technologies that convert feedstock into
biofuel) becomes of paramount importance. Developing
countries that lack financial resources for research and
development can import technologies and thus acquire a
leapfrogging effect. Research and development are undoubtedly important for agriculture which essentially
provides the biofuel industry with feedstock. R&D represents the factor of technological innovations. Countries
which spend a larger part of GDP on research and development are expected to be more apt to engage in biofuel
production because of more appropriate technologies.
Import of crude oil. Dependence on import of crude
oil manifests itself as a driver of engagement in biofuel
production only provided that most produced biofuels are
used to satisfy domestic needs. As A. Doku and
S. Di Falco (2012) point out, a country which imports
large amounts of crude oil can be more predisposed to
engage in biofuel production in order to reduce the dependence on imported oil. Presumably, a non-crude-oilimporting country can be a net importer of petroleum
products (petrol, diesel, etc.). Based on back of the envelope calculations, the data of the U.S. Energy Information
Administration lead to an assumption that noninvolvement in crude oil trade and reliance on the import
of petroleum products is characteristic of small and/or
developing countries. Such countries are likely to lack resources and/or competences necessary for the development of the oil industry and therefore they will not be
likely to start biofuel production. The said back of the envelope calculations show that merely 7 per cent of such
countries are engaged in bioethanol production.
Biofuel feedstock self-sufficiency. Raw material/biomass push can be seen as one of the market drivers.
This factor is identified in many countries with surplus
biomass resources (Faaij, Domac, 2006). Export of ethanol from Brazil and export of wood pellets from Canada
are examples of a successful push strategy. Raw material/biomass push can be related to the natural endowment
theory rooted in the economic theory (Sheldon, Roberts,
2008). The case study analysed in the USA by K.
C. Dhuyvetter et al. (2005) shows that ethanol production
is heavily concentrated in the Middle West. Such concentration stems from the fact that most of the feedstock
(corn) is produced in those regions and that is done at the
11
Economics and Rural Development
lowest costs. In this area, both the feedstock costs and the
bioethanol by-product transportation costs are declining.
The said authors also point out that capacities of plants
located elsewhere than the Middle West are usually lower
and such plants use other cultures as the main feedstock
rather than corn. The planned ethanol plants are also concentrated in the Middle West, Iowa and Minnesota. The
latter example again demonstrates the significance of raw
material/biomass push.
Export of agricultural feedstocks. A. Doku and S. Di
Falco (2012) say that one of the factors that can influence
a country's engagement in biofuel production is the country's position in the international agricultural trade. The
said authors maintain that a country, which is a net exporter of agricultural products, can be more predisposed
to engage in biofuel production. D. M. Lambert et al.
(2008), referring to R. Shaffer, S. Deller and D. W. Marcouiller (op. cit.), claim that communities, which are
highly specialized in the given sector, are likely to export
the production of that sector. Export is considered to be
one of the alternatives to local consumption of agricultural products for food and therefore it is often compared to
an alternative use of agricultural products. It should be
noticed, however, that a great share of agricultural feedstock in the export structure may indicate not only a possibly greater self-sufficiency of agricultural feedstock in
the country but also its leaning towards export of low
value added products, which is characteristic of less developed economies. Since biofuels are a product of a considerably high added value, it is less certain whether exporters of agricultural feedstock will be predisposed to
engage in biofuel production. For this reason, it is not
completely clear what impact the share of agricultural
feedstock in export levels makes on the country's engagement in biofuel production.
The factors selected for this research do not include
such factors as oil prices, feedstock prices or environmental drivers, which can cause the effect of omitted variables bias. The variable of feedstock prices is partially reflected by the variable of feedstock self-sufficiency. In
this case, a recourse is made to economic logic that in
countries with feedstock deficit, feedstock are relatively
more expensive than those in the countries with their surplus. Besides, as C. Almirall et al. (2010) discovered, declining land resources cause increase in cereal prices and
thus it can be concluded that the variable of feedstock
prices is also partially reflected by the variable of land resources. The omitted variables do not hinder the identification of the significance of the selected variables in
characterizing biofuel producing countries.
Research Methodology
As indicated above, the research of location of economic activities can use various cross sections, for example, regions can be grouped into rural and urban, located
far from city/industrial centres and in close proximity to
12
Vol.9, No.1, 2013 ISSN 1822-3346
them, etc. D. M. Lambert et al. (2008) maintain that, in
the case of biofuel production, countries/regions can be
characterized according to the prevailing agricultural
production, soil, climate and demographic conditions.
This article analyses several types of economies,
which are described with regard to self-sufficiency of
widely used fossil fuel energy source, i.e. crude oil. Selfsufficiency of fossil energy sources can be identified in
different ways. One of the simplest ways, which can be
used to distinguish fossil energy resource rich countries,
is the use of country ratings based on the abundance of oil
reserves. It should be noted that in a country with a potential of physical oil resources such resources may be
economically unfeasible. In order to avoid uncertainty related to economically unfeasible resources, Balassa index
is used to measure specialization of economies (Svaleryda, Vlachos, 2005). The index is calculated as the net
export to total volume of trade ratio (1). It is assumed that
a high level of net crude oil export reflects rather large
and economically feasible oil resources.
1+(Xij − Mij )/(Xij + Mij )
(1)
where: Xij – export of i commodity of j country;
Mij – import of i commodity of j country.
In establishing countries' specialization in the production of petroleum products, there can be two values of
the indicator: less than one (no specialization) or more
than one (there is specialization). If the Balassa specialization index is greater than 1, the country is a net crude
oil exporter; if the index is less than 1, the country is a net
crude oil importer. Petroleum products, which for the
purpose of this work include gasoline, jet fuel, kerosene,
distillate fuel oil (diesel), fuel oil, liquefied petroleum gas
(LPG), belong to commodities that satisfy the basic
needs; therefore basically there are no countries that are
neutral in the trade in petroleum products. This is proved
by statistical data of the U.S. Energy Information Administration. A slightly different situation is in the case of
crude oil. Some economies are not interested in the import of crude oil since its refining requires a developed oil
industry, neither does crude oil satisfy the basic needs.
Nevertheless, it should be pointed out that in their empirical research of tendencies towards introduction of biofuel production policy, A. Doku and S. Di Falco (2012) use
the import of crude oil and natural gas as a variable defining the shortage of fossil fuel. It should be noted that specialization in oil extraction is a proxy variable to define
countries' abundance of oil reserves. Since specialization
in oil extraction is not equivalent to specialization in petroleum products, the types of economies are described
on the basis of the specialization in oil extraction and the
specialization in petroleum products. On that basis, 6
types of economies are distinguished (Fig.1). The axis
scale used in the scheme to describe the types of econo-
Vol.9, No.1, 2013 ISSN 1822-3346
Economics and Rural Development
Balassa specialization index (petroleum products)
mies corresponds to the potential values of the Balassa
index. It should be noted that an exclusive unit value of
the Balassa index is assigned to the countries that do not
participate in the crude oil trade (Types 2 and 5). Although this value is not mathematically possible, its use
allows to distinguish a specific segment of countries.
2
Specialization in oil
extraction and
petroleum products
Specialization in
petroleum products
1
1
2
Specialization in oil
extraction
No specialization
4
3
5
6
0
1
Balassa specialization index (crude oil)
2
Figure 1. Types of Economies According to the Specialization
of the Oil Industry (Compiled by the Author)
Type One economies are not reasonably selfsufficient in crude oil but they specialize in the production of petroleum products (e.g., according to the average
in 2008 – 2010, such economies are in Lithuania, Sweden, the United Kingdom). It should be noted that Type
Two economies are theoretically possible, however in
practice they are unlikely since under conditions of an intensive trade in crude oil usually there are no countries
with sufficient crude oil reserves to specialize only in the
production of petroleum products. Countries of Type
Three economy are self-sufficient in crude oil and specialize in the production of petroleum products (e.g., according to the average in 2008 – 2010, such economies
are in Argentina, Kazakhstan, Norway). Countries of
Type Four economy are not reasonably self-sufficient in
crude oil and do not specialize in the production of petroleum products (they are net importers of crude oil and petroleum products) (e.g., according to the average in 2008
– 2010, such economies are in France, China, Japan).
Countries of Type Five economy are not reasonably selfsufficient in crude oil and do not have sufficient facilities
for crude oil refinery. Those countries receive petroleum
products through external sources (e.g., according to the
average in 2008 – 2010, such economies are in Cambodia, Paraguay, Zimbabwe). Type Six economies are reasonably self-sufficient in crude oil, but do not specialize
in the production of petroleum products (e. g., according
to the average in 2008 – 2010, such economies are in
Brazil, Ecuador, Mexico). This article analyses economies assigned to the economies of Type 3 and 6.
To decide on the research structure it is worth focusing attention on the aspects of similar research. In this
case, the research of the location of lumber industry in the
U.S. regions conducted by F. X. Aguilar (2009b) should
be brought to notice. The research aspects of the renewable resource-based lumber industry are, undoubtedly, also
significant for the research of the biofuel industry. According to F. X. Aguilar (2009b), the analysis of the location of economic activities encounters constraints at various stages of research. Some constraints involve a correct
identification of factors in the factor analysis, heterogeneity of sawmills in the wood industry, involvement of both
urban and rural counties. Similar constraints arise in analysing the biofuel production industry, since, as was already indicated, it is not always possible to include desired factors due to insufficient data, while the size of
biofuel plants and the variety of used feedstock make
them rather heterogeneous. F. X. Aguilar (2009b) also argues that in the long run the factors of industrial location
change and the most significant factors at a given moment can be different from those at the time when industrial facilities are launched. Such factors as the value of
land, feedstock prices, energy costs, etc. change over
time. The research aims to exclude such factors. Furthermore, consideration should be given to the use of proxy
variables instead of the data received by direct observations. As the said F. X. Aguilar (2009b) points out, very
often geo-statistical analysis forces to use proxy variables
instead of direct observations since noteworthy data either are not available or they are not in a workable format. Since most direct observations do not exist in an appropriate workable format, it naturally requires to rely on
research methods where proxy variables are possible.
The research employs the probit regression which is
widely applied in research of a similar character (Doku,
Di Falco, (2012); Pancholy et al. (2011); Aguilar
(2009ab)). The general model of the probit regression is
expressed by the equation (2) below. The mathematical
model, which is recorded not for the dependent variable
itself but for its probability P (Y=0). Φ-1(∙), denotes a regression function which is called the probit function.
P (Y=0) = Φ(C+b1X+b2Z+b3W)
Φ-1(P (Y=0)) = C+b1X+b2Z+b3W
(2)
The initial research model is described by the equation (3) below:
P (Y=0)= P (produces bioethanol) = Φ(z)
z =C+b1UAA+b2GDP+b3AGRPOP+
+b4R&D+b5OIL+b5FSSR+b7EXRM
(3)
The variables presented in the initial research model
are explained in Table 1. The dependent variables are divided into three formal groups. According to F. X. Agui13
Economics and Rural Development
Vol.9, No.1, 2013 ISSN 1822-3346
lar (2009b), the initial research model, which is commonly based on theoretical insights or conducted surveys,
usually includes a considerable number of factors (variables). By applying the probit regression for the set of
identified variables, some variables are rejected and thus
the resulting probit regression model has a smaller number of statistically significant variables.
Table 1. Abbreviations and Explanations of the Variables Used
in the Model
Variable
Explanation
Independent variable
Categorical variable denoting bioethanol production
(0: the country produces biPROD
oethanol, 1: the country
does not produce bioethanol)
Agricultural production resource variables
Utilized agricultural area
UAA
(ha per 1 inhabitant)
Agricultural
population
AGRPOP
(1000 inhabitants)
Self-sufficiency variables
Import of crude oil (1000
OIL
barrels per day)
Self-sufficiency of biofuel
FSSR
production feedstock (per
cent)
Economic environment variables
GDP per capita (in U.S.
GDP
dollars)
Expenses for research and
R&D
development (percentage
on GDP)
Agricultural
feedstock
EXRM
share in the total export of
the country (per cent)
Expected
sign in the
regression
model
N/A
+
+
+
+
+
Results
+
+/-
Some aspects related to the variables used in the
model should be highlighted. This research, unlike the research by A. Doku and S. Di Falco (2012), uses utilised
agricultural area rather than arable land as a factor of land
resources. Such choice is based on the fact that the utilised agricultural area more suitably reflects a country's
potential to provide the population of the country with
food and/or bio-feedstock. The utilised agricultural area
has a potential to be converted into arable land. Besides,
the feedstock for the new 2nd generation biofuels can be
derived from non-arable land. F. X. Aguilar (2009b) analysed the case of the wood industry and demonstrated that
the availability of the total woodlands is a proxy variable
to the availability of feedstock for the wood industry. On
this basis, a parallel situation can also be envisaged in the
biofuel production. A proxy variable for the availability
14
of feedstock for biofuel production is the amount of utilised agricultural area. However, it should be born in
mind that products derived from the utilised agricultural
area are characterized by a wide variety, unlike the marketable products derived from woodlands. Therefore, this
research relies not only on the variable of the utilised agricultural area and introduces a further variable for selfsufficiency in biofuel production feedstock. In this work,
bioethanol production feedstocks are considered to be agricultural products that can potentially be used in bioethanol production. According to the FAOSTAT system,
those products fall into three large groups: grain (wheat,
barley, corn, rye, oats, millet, sorghum, rice), starchy root
vegetables (potatoes, cassava, sweet potatoes, batata,
etc.), and sugar crops (sugar cane and sugar beets). A
country's self-sufficiency in bioethanol production feedstock is equated to a maximum self-sufficiency in one of
the product groups.
Data used in the empirical research. The research
covers the period of 2000 through 2010. The data on
crude oil and petroleum product trade and the data on
biofuel production and consumption are taken from the
U.S. Energy Information Administration database. The
data on feedstock production and consumption, and the
data on land resources and agricultural population are
taken from the FAOSTAT database. The data on GDP
and its ratio to the research and development expenditure,
and the data on the share of agricultural feedstock in the
total export of the country are taken from the World Bank
database. It should be noted that the compiled array of data has certain constraints. Some data lines include single
lines and therefore mean values should be relied on.
Although the research covers four types of economies defined according to different specialization of the
oil industry, the results presented in this article cover only
two types of economies which oil-rich countries are assigned to (Types 3 and 6). As noted in the beginning of
the article, it analyses the case of bioethanol production
in those countries rather than biofuel production in general. The Balassa specialization index and the assessment
of the countries' specialization in oil extraction and petroleum products enabled to identify 12 countries, which can
be considered to be rich in oil and to be engaged in bioethanol production (Fig. 2). In the probit regression model those countries were assigned to Group Zero, which
denotes biofuel-producing countries.
Vol.9, No.1, 2013 ISSN 1822-3346
Economics and Rural Development
son and a larger share of GDP assigned to research and
development are more predisposed to produce bioethanol.
This conforms to the previously mentioned economic
logic those countries which are self-sufficient in most
common fossil fuel energy resources can be driven towards bioethanol production by a significantly larger
amount of land resources.
P(Y=0) = P(produces bioethanol) = Φ(z)
z = -1.7068 + 0.1821× UAA + 1.4010 × R&D (4)
Figure 2. Bioethanol Producing Countries Specializing in Oil
Extraction and Production of Petroleum Products
(Average for 2008–2010)
Source: calculations made by the author based on the data from the
U.S. Energy Information Administration, 2008 – 2010
Economies of the countries, where the Balassa specialization index for petroleum products is more than 1,
are assigned to Type 3, i.e. economies specializing in oil
extraction and production of petroleum products. Figure 2
shows that such economies are found in 5 countries: Argentina, Canada, Kazakhstan, Colombia and Norway. After the same selection criteria were used for countries that
do not produce bioethanol, 14 countries were found to
have the same specialization in the oil industry. Due to
insufficient data, 2 countries out of the said 14 were excluded from the further stages of analysis.
Economies of the countries, where the Balassa specialization index for petroleum products is less than 1, are
assigned to Type 6, i.e. economies specializing in oil extraction only. 7 countries were attributed to this type of
economy: Brazil, Denmark, Ecuador, Guatemala, Mexico, Sudan and Vietnam. 22 countries were found not to
produce bioethanol, but to have the same specialization in
the oil industry. Due to insufficient data, one country
which produces bioethanol and 6 countries which do not
produce bioethanol were eliminated from the further
analysis. As expected, the number of countries that produce bioethanol and possess the said specialization in the
oil industry was rather small because countries lacking oil
resources prevail among bioethanol producing countries.
A further analysis of the countries' intention to produce bioethanol is conducted in the two groups of countries: countries with Type 3 economies and countries with
Type 6 economies. The previously defined probit regression model in countries with Type 3 economies produced
a model with two significant regressors: utilised agricultural area (UAA) and research and development (R&D)
(4). As expected, both regressors are marked with a positive sign. This proves that among countries specializing
in oil extraction and petroleum products, the countries
with a larger amount of utilised agricultural area per per-
In this model, the maximum likelihood χ2 equals
78.321 (p=0.000... <0.01). Both regressors in the model
are statistically significant (all Wald p values < 0.01).
There are no outliers: the maximum value of Cook’s distance is 0.029, and the deviation to the number of degrees
of freedom ratio is approximate to one: 0.806. With the
help of the said two regressors, the model classifies 84.2
per cent of countries correctly. The countries that produce
bioethanol are classified at 60.0 per cent of accuracy, and
the countries that do not produce bioethanol are classified
at 91.7 per cent accuracy. It should be noticed that in
blind guessing only 70.6 per cent of countries are classified correctly, which proves again the significance of the
selected regressors in determining countries’ intention to
produce bioethanol.
The same regression model was applied for the countries with Type 6 economies and a pattern with three regressors was produced: the utilised agricultural area
(UAA), the gross domestic product (GDP) and the feedstock self-sufficiency ratio (FSSR) (5). As expected, all
three regressors are positive. Here, like in the previous
case, the countries with a larger amount of utilised agricultural area per person are more predisposed to produce
bioethanol. Unlike in the case of Type 3 group, the country's GDP per person and the country's self-sufficiency in
first generation bioethanol feedstock (FSSR) here become
significant regressors. Again, this conforms to the assumption that in more economically developed countries
biofuel production is easier to integrate. This is likely to
arise from a greater purchasing power of the consumers
and/or a greater potential of the country to finance various bio-energy projects. The long-term self-sufficiency in
bioethanol feedstock can obviously drive the country towards bioethanol production.
P(Y=0) = P(produces bioethanol) = Φ(z)
z = -13.0941 + 2.5136× UAA + 0.00006 × GDP +
0.1040 × FSSR
(5)
In this model, the maximum likelihood χ2 equals
155.016 (p=0.000... <0.01). All three regressors in the
model are statistically significant (all Wald p values
< 0.01). There are no outliers: the maximum value of
Cook’s distance is 0.302, and although the deviation to
the number of degrees of freedom ratio is only 0.540,
15
Economics and Rural Development
Vol.9, No.1, 2013 ISSN 1822-3346
Pearson χ2 equals 0.807. With the help of the said three
regressors, the model classifies 92.6 per cent of countries
correctly. The countries that produce bioethanol are classified at 78.8 per cent accuracy, and the countries that do
not produce bioethanol are classified at 97.7 per cent accuracy. It should be noticed that in blind guessing only
72.7 per cent of countries are classified correctly, which
again proves that the selected regressors describe countries’ intention to produce bioethanol rather well.
Table 2 summarizes factors behind the engagement
in bioethanol production in countries with both types of
economies. It shows that the utilised agricultural area is a
decisive factor for engagement in bioethanol production
in both types of economies.
Table 2. Factors of Engagement in Bioethanol Production in
Type 3 and 6 Economies
Factor
(model
variable)
UAA
FSSR
GDP
R&D
Type 3
economies
Factor
p-value
value
0.1821
0.000...
1.4010
0.000...
Type 6
economies
Factor
p-value
value
2.5136
0.000...
0.1040
0.000...
0.00006
0.001...
-
Since the said groups of the countries were characterized as the countries specializing in oil extraction, the factor of oil import, as it may be expected, was not significant
in those countries. Neither the share of agricultural feedstock in exports and the size of agricultural population was
important in engagement in bioethanol production.
Conclusions
1. The industry of biofuel production is attributed to
those industries which find important the availability of
the production resource base in the location. Consequently, most of the variables in the produced research model
were linked specifically to the supply of production resources. The production resource base was reflected
through land resources, economically active population,
and self-sufficiency in feedstock. The variable which expresses the importance of research and development in
the country also has a close relationship with the production resources, since in many cases technological innovations can reduce the related constraints.
2. The research of the engagement in biofuel production at the county level or at the level of other regions of
the country differ in its particularity from that focused on
the country level. In the analysis of determinants of engagement in biofuel production at the national level, it is
possible to exclude the variables that express economic,
natural environment or infrastructure irregularities inside
16
a country, such as the land value, the road network, navigable rivers, the number of fed livestock, etc.
3. The following determinants have a greater theoretical significance in countries’ engagement in bioethanol
production: amount of land resources, agricultural population, GDP, research and development, import of crude
oil, self-sufficiency of feedstock and the net export of agricultural feedstock. Although the feedstock price can be
to a certain degree reflected through other variables, such
as self-sufficiency of land resources, many authors consider the price to be a separate factor.
4. In the analysis of the localization of economic activities, regions are usually divided according to their rurality or the distance from city/industrial centres. A research
at the national level can group countries depending on the
characteristics of their specialization. In the analysis of biofuel production localization it is convenient to classify
countries based on the specialization of the oil industry,
since it is likely that in countries with different oil specialization engagement in biofuel production is determined by
different factors.
5. Among the countries which specialize in oil extraction and petroleum products, the countries with a
greater amount of utilised agricultural area per person and
also with a greater share of GDP on research and development, are more predisposed to produce bioethanol.
This supports the assumption that countries which are
self-sufficient in oil resources can be driven towards bioethanol production by a significantly larger amount of
land resources.
6. Among the countries which specialize in oil extraction only, the countries with a larger amount of utilised agricultural area are again more predisposed to produce bioethanol. Unlike in the case of the countries specializing in petroleum products, the country's GDP per
person and the country's self-sufficiency in the first generation bioethanol feedstock (FSSR) become significant
regressors. Again, this supports the assumption that in
more economically developed countries biofuel production is easier to integrate. This is likely to be triggered by
a greater purchasing power of the consumers and/or a
greater potential of the country to finance various bioenergy projects.
7. The probit regression model was applied to oilrich countries, some of which also specialize in petroleum
products, and the factor of the oil import, as it can be expected, was not significant in those countries. The share
of agricultural feedstock in exports and the size of agricultural population cannot be considered to be significant
regressors either.
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