antagonistic bioenergies: technological divergence of the ethanol industry in brazil

Energy Policy 39 (2011) 6951–6961

Contents lists available at ScienceDirect

Energy Policy

0301-42

doi:10.1

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Antagonistic bioenergies: Technological divergence of the ethanolindustry in Brazil

Roberto Guerrero Compean n, Karen R. Polenske

Massachusetts Institute of Technology. 77 Massachusetts Ave., Room 9-549, Cambridge, MA 02139, USA

a r t i c l e i n f o

Article history:

Received 1 April 2010

Accepted 5 November 2010Available online 17 December 2010

Keywords:

Technology

Sugarcane ethanol

Brazil

15/$ - see front matter & 2010 Elsevier Ltd. A

016/j.enpol.2010.11.017

esponding author. Tel.: +1 617 258 7706.

ail address: [email protected] (R.G. Compean).

a b s t r a c t

We present evidence for the coexistence of two antagonistic sugarcane ethanol production technologies

in Brazil, with the Southeast region of the country having relatively mechanized production processes,

and the Northeast area using labor-intensive ones. We highlight the main differences between the hand-

production and fully automated mechanical manufacturing in the Brazilian ethanol industry and examine

the historical, political, and economic factors that induced this regional technology gap that is currently

observed. We then construct an environmental model based on a 375-industry interregional input–

output system for the Brazilian regions, in order to determine the extent to which the primitive ethanol

production of Northern Brazil differs from the automated manufacture technologies of the South in terms

of greenhouse gas emissions. We show that ethanol produced with modern technologies generates lower

carbon dioxide (CO2) emissions than ethanol produced with traditional production processes. We also

demonstrate that ethanol, regardless of the technology with which it was produced, is more carbon-

efficient than petrochemical products.

& 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Energy insecurity, global climate change, and environmentaldeprivation are at the heart of many of the world’s most pressingissues. These threats are largely explained by the ever-growingglobal dependence on oil. An indisputable fact, however, is that thesupreme reign of fossil fuels is condemned to imminent abdication,with oil production declining by several percentage points annuallyduring the decades to come (Deffeyes, 2002; Heinberg, 2006;Roberts, 2004; Zittel and Schindler, 2007). The economic andenvironmental impacts of a reduced oil supply will depend heavilyon the development and adoption of effective fuel alternatives.Hirsch et al. (2005) argue that if alternatives are not forthcoming,the products produced with oil will become scarce and expensive.At the very least, this could lower living standards in developed anddeveloping countries alike, and, in the worst case, lead to socialunrest and worldwide economic collapse.

The economic boom experienced by countries that only threedecades ago were trapped in poverty, from Africa to East Asia to LatinAmerica, has increased oil demand significantly. As the prospect of oilsupplies falling further and faster materialize, this rapid industrializa-tion might pose international economic threats and political tensionsthat exacerbate regional inequities and lower the standard of living(Pareto and Pareto, 2008). In the face of these challenges, the search for

ll rights reserved.

secure and sustainable forms of transport fuel has intensified. Ethanol,among other biofuels, has emerged as a decisive ‘‘multi-purpose’’alternative able to reduce oil dependence, limit the environmentalimpact and footprint of transportation, and sustain growth of eco-nomic activity simultaneously (Goldemberg, 2008b; Goldemberget al., 2008; Guerrero Compean, 2008; Hilgemberg, 2004).

In this paper, we show how the coexistence of two ethanolproduction technologies in Brazil – the largest global ethanolproducer – affects the greenhouse gas (GHG) emissions. Althoughwithin the scientific community of Brazil this coexistence isrelatively well known, international industrial analysts, and inparticular, non-Brazilian analysts do not recognize this apparenttechnological gap and its regional ramifications for GHG emissions.Rather than being a monolithic manufacturing system devoid ofspatial variations in technologies, the ethanol industry in Brazilexhibits major technological differences throughout the ethanol-making process, with the Southeast region of the country havingrelatively mechanized technologies, and the Northeast area usinglabor-intensive production technologies. This, however, was notalways the case. Once spatially uniform, the ethanol productionprocess began in the 1970s to show this Northeast–Southeastbifurcation pattern due to particular economic, political, technical,and geographical factors, which we discuss in detail.

We analytically link this technological divergence to differentlevels of performance in terms of regional GHG emissions. FollowingGay and Proops (1993), Hilgemberg (2004), Labandeira and Labeaga(2002), Lenzen (1998), Machado et al. (2001), Proops et al. (1993), andTarancon and Del Rıo (2004), we construct a model that examines

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Table 1Main inputs used in the ethanol industry, by regions, Brazil, 2002 (%).

Source: The authors, based on data provided by Centro de Estudos Avanc-ados em

Economia Aplicada.

Input North South

Sugarcane 37.9 41.5

Manufacturing 8.5 9.3

Fertilizers and chemicals 2.3 2.5

Machines, equipment, and construction 0.4 0.4

Energy and water 3.4 3.8

Commerce 1.4 1.5

Transportation 1.0 1.0

Services 1.6 1.8

Labor 5.8 3.8

Taxes 6.4 6.4

Imports 1.2 1.8

Gross operating profit 30.1 26.1

Total output 100.0 100.00

Table 2Ethanol industry, selected indicators by region, Brazil, 2006.

Source: The authors, based on data provided by Instituto de Pesquisa Economica

Aplicada; Ministerio de Agricultura, Pecuaria e Abastecimiento; Ministerio do

Trabalho e Emprego; Agencia Nacional do Petroleo, Gas Natural e Biocombustıveis.

North South

Sugarcane harvested area (in 1000 km2) 11.4 50.0

Sugarcane production (million tonnes) 64.5 392.8

Ethanol production (millions m3) 1.7 16.2

Sugarcane yields (tonnes/km2) 5,647.7 7851.2

R.G. Compean, K.R. Polenske / Energy Policy 39 (2011) 6951–69616952

industrial direct-plus-indirect carbon-intensities in order to studywhich technology is more carbon-efficient, as a means to evaluate theregional environmental impact of ethanol production technologiesand emission-control policies. In particular, we analyze how and towhat extent different ethanol-production processes in Brazil have animpact on emissions of carbon dioxide (CO2) on a sector-by-sectorbasis, and determine whether or not ethanol is a less-polluting fuelthan gasoline.1 We show that, in the case of ethanol, the regions withmore advanced technologies tend to be cleaner from the productionpoint of view. We also show that, regardless of the technology withwhich the biofuel is produced, the ethanol industry is more carbon-efficient than the oil refining industry. In the last section, wesummarize our findings and present a series of policy recommenda-tions and potential new research avenues.

This study is a contribution to the growing literature on thespatial and environmental implications of technology in threerespects. Naturally, the first contribution of this work is thetreatment of ethanol as a case of technology choice, where differentregions have differentiated production methods. Second, despite aburgeoning literature on the ethanol sector in Brazil and its impactson the economy, empirical analyses that characterize the indirectenvironmental effects associated with the sector’s backward andforward linkages are comparatively rare, an exception being theworks by Guilhoto and his colleagues (Burnquist et al., 2004;Guilhoto and Sesso Filho, 2005; Hilgemberg et al., 2006, amongothers). Third, due to its regional nature, we take account ofimportant differences in technology at the subnational scale, ratherthan evaluating the environmental effect of an expansion of theethanol sector without accounting for spatial differences.

Ethanol production costs (in 12/2005 R$/l) 1.5 1.1

Average monthly wage (in 12/2005 R$) 599.1 985.7

Total employment (in thousands) 20.0 60.3

Productivity (in m3 of ethanol per worker) 87.4 268.1

Employees per 1000 R$ of output 9.6 6.4

Note: km2¼square kilometer; m3

¼cubic meter; R$¼Brazilian real; l¼ liter.

2. Different ethanol production technologies in Brazil

Although analysts have recognized the crucial role of technol-ogy for sustainable development (Todd and Simpson, 1983), thereis no consensus on which technology is best or, at least, mostsuitable for the purposes of economic development and environ-mental sustainability. The debate comparing whether traditionalor modern technologies are the best means to achieve economicdevelopment and environmental sustainability is still ongoing.

Different technologies to produce ethanol and harvest sugarcaneexist in Brazil, with the South and Southeast regions of the countryhaving relatively mechanized production processes, while the Northand Northeast areas use labor-intensive technologies, as shown inthe composition of output (Tables 1 and 2). Yet, most analysts havenot discussed what caused these technological disparities in the firstplace, and how these differences imply divergent economic andenvironmental outcomes for regions (Perosa, 2008).

The empirical evidence, moreover, is contradictory: using asocial accounting matrix, Nolan (1997) finds that labor-intensiveethanol production technologies generate more environmental andenergy benefits for Brazil’s economy than capital-intensive tech-nologies. Recently, Hilgemberg et al. (2006) show that capital-intensive ethanol production technologies release fewer CO2

emissions than their labor-intensive counterparts. Using input–output multipliers, Guerrero Compean et al. (2009) also findempirical support that capital-intensive ethanol technologies aremore carbon-efficient.

For Brazil, most analysts have not examined the regionalimplication of the dual ethanol production technologies, nor the

1 For the case of Brazil, we improve upon the work done by Hilgemberg (2004),

as we make use of more recent energy datasets and adhere to sectoral classifications

more homogeneous with national accounting international conventions, based on

the methodological contributions by Pederneiras (2007), thus making the research

results appropriate for international comparisons and analyses.

use of alternative forms of technology in disadvantaged regions(Perosa, 2008). We investigate the underlying factors causing thedivergences between traditional (labor-intensive) and modern(capital-intensive) technologies, used in the ethanol sector, under-scoring their regional economic and environmental repercussions.

The disparities in ethanol production in Brazil cannot beunderstood based on pure economic, spatial, or political sciencetheories. Rather, we show that technology differences areexplained by a complex interaction of complementary harvestingmethods, different research and development practices, public–private collaboration, political biases, and geographical location.

2.1. Increased competitiveness of ethanol in the fuel market,

economies of scale, and infrastructure

Goldemberg et al. (2004) demonstrate, using the case ofBrazilian sugarcane ethanol, that economies of scale and marketexperience led to increased competitiveness of ethanol. This effect,they say, is often referred to as learning-by-doing, a progress curve,an experience curve, or a learning curve. Production costs decreaseas a function of technological progress and final demand, which arealso a function of the accumulation of experience in a particularmarket. As shown in Table 2, the cost of ethanol production was36% higher in the North than in the South in 2006. As output in theSouth increased to fulfill increased demand, the cost of productiondecreased due to economies of scale. Agricultural modernizationand other technology changes in the South occurred in the largeindustrial centers of the region, namely S~ao Paulo and BeloHorizonte. Manifestations of this new pattern are the increasingly

R.G. Compean, K.R. Polenske / Energy Policy 39 (2011) 6951–6961 6953

integrated cane production stages, a higher level of mechanization,chemical inputs, transport capacity, sugarcane irrigation, and thesubstitution of permanent forms of employment with temporarylabor arrangements. In addition, rising labor costs combined withlow real prices for machinery and industrial inputs owing tomassive infusions of subsidized credit from state governmentsforced producers to modernize (da Silva and Kohl, 1994).

Transportation infrastructure is another factor that createsregional discrepancies in terms of yields and productivity in theethanol industry. In Brazil, cane is predominantly transported to thefactory by road. Cane is carried complete (manual gathering) orchopped into 20–25 cm sections (mechanical harvest). Quick load-ing and transportation of sugarcane are essential to avoid significantlosses of the amount of sucrose per tonne. Hence, the poor roadinfrastructure in the North and Northeast regions, where damagedroads are common and distances are large, reduces sugarcane yieldsas it takes more time than in the South to take sugarcane to theethanol plants. Losses of 6–10 kg in the amount of total reduciblesugars per tonne of cane (TRS) are common in the North. In contrast,highways in S~ao Paulo State and its surrounding areas are consideredto be the best of the country, and land transportation is exceptionallyefficient by Brazilian standards, in part because of the well-estab-lished network of the so-called rodotrems canavieiros (trailers forsugarcane) (van den Wall Bake, 2006).

2.2. Technological change, innovation, and research and development

investments

The divergence in production processes in the ethanol industry isin part a consequence of the financial capacity of the Southernregions to invest in new technologies and, conversely, the inability ofthe less-affluent Northern regions to finance their industrial growth.Compared to the modest investment activities and limited resourcesof the North, the private sector of the richer states of the South wasable to fund costly technological innovations, infrastructure updates,and agricultural research conducted by universities and researchinstitutes (Guerrero Compean, 2008, 2009; Rosillo Calle et al., 2000).

After the termination in 1988 of permanent subsidies offered bythe federal government, the ethanol industry in the Southeastregion reduced its costs of production, replaced labor-intensivewith capital-intensive technologies, and improved managementskills, among other organizational changes. In addition, severaltechnological innovations took place in the South, particularly infermentation technology to yield liquor with higher alcohol con-tent, which resulted in substantial reductions in energy require-ments for ethanol production, because it could reduce energyconsumption in distillation, and, at the same time, decrease stillagevolume at low capital costs (Guerrero Compean, 2008).

Moreover, research on energy-efficiency improvements of ethanolproduction through efficient distillation and heat-recovery design tookplace. Similarly, ethanol concentration was increased through absorp-tion, vapor recompression, and multiple-effect evaporators. Innova-tions on crystallization, use of molecular sieves and reverse osmosis,which would reduce energy requirements as well, are also beingconducted more prominently in the South than in the North of Brazil.Other chemical technologies being implemented by the ethanol sectorinclude anaerobic biodigestion to treat effluents, non-toxic hydroalco-holic solutions, substitution of azeotropic distillation for improvedmolecular sieves and bioscanning for pollution and sugar loss identi-fication (Abarca, 1999). In addition, digitalization of ethanol productionis in progress in the most competitive ethanol plants in the South.Today, harvest planning using optimization software with satellitesystems and remote sensors (geographical information systems andglobal positioning systems) has been put into operation by a number ofethanol plants in the South. Similarly, digital-monitoring systems that

automate ethanol production industrial processes have been adoptedby modern ethanol distilleries in this region. Integration of manage-ment procedures using internal electronic-data-interchange networks,among other enterprise resources planning, has also been implemen-ted in several industrial units, particularly in the state of S~ao Paulo(Ministerio da Agricultura and Pecuaria e Abastecimiento, 2007).

Moreover, to improve the efficiency of the ethanol industry,universities, research centers and foundations have developed andtested new sugarcane varieties with improved yields continuously(Badaloo et al., 1999). In the 1970s, there were only 10 varieties ofsugarcane available; presently, more than 500 cane varieties are beingbred (Ueki, 2007). Yet, this leadership in research and development onsugarcane varieties focused mostly on the Southern regions of thecountry during the early years of the ethanol program. For instance,the Escola Superior de Agricultura Luiz de Quieroz, the InstitutoAgronomico de Campinas, the Centro de Tecnologia Canavieira (CTC),the Rede Interuniversitaria para o Desenvolvimento do Setor Sucroal-cooleiro, the Fundac- ~ao de Amparo a Pesquisa do Estado de S~ao Paulo(S~ao Paulo Research Foundation), and other important public researchprograms began focusing their research on improved cane varietiesand agricultural processes as a means of obtaining financial resources.Ethanol producers from the North have not been able to finance thistype of research. Therefore, research for new cane varieties suitablefor the soil type of the North has taken a lower priority than in theSouth (Guerrero Compean, 2008).

As a recent example, in 2007, CTC launched its third generation ofsugarcane varieties, which yield around 20% more biomass thanprevious varieties and contain higher levels of saccharose, the sugarthat ends up as ethanol. It is expected that these new varieties willincrease profits per hectare up to 38% for these regions. All of thesenew varieties are tailored to be exclusively planted in the South andSoutheast regions, because the North and Northeast exhibit differentregional climatic conditions, soil type, planting and harvestingseasons, and technology (Centro de Tecnologia Canavieira, 2007).

2.3. Political regimes and the environmental legal framework

The prevailing circumstances facing the Brazilian sugar industryin the early 1970s largely contributed to the development of thealternative fuel program being based on sugarcane. The govern-ment planned to capitalize on the expectations of expanded sugarexport markets following the United States’ embargo of sugar fromCuba, and powerful sugarcane growers and processors neededalternative markets to counteract the volatility of sugar prices.Thus, both the national pressures and the 1973 oil shock, combinedwith the national strategy to become more self-sufficient, led to thecreation of the Brazilian National Program of Ethanol (ProgramaNacional de Alcool in Portuguese, or Proalcool, as it is widelyknown) on November 14, 1974. The ethanol program was theresponse by the military government to the economic challengesthat threatened its power base, and it basically consisted of massivesubsidies and investments in the ethanol sector to produce analternative fuel to decrease the dependence of external energysources, reduce regional income inequality, and reach nationaleconomic growth levels of 12% by 1979 (Rubio, 2006).

The politics of Proalcool had a major regional impact and shapedthe evolution of the ethanol industry, creating institutionalmechanisms that hindered the development of the Northeasternstates. The subsidies to sugarcane cultivation and alcohol produc-tion in the Northeast derived from Proalcool served to consolidateexisting production systems, increase monoculture, and economicdependency on one single crop, allowing inefficient producers tostay in business (Lehtonen, 2007).

Most notably, unlike the entrepreneurial South, where thefinancial efforts were seen as the means to make the industry


more competitive, the subsidies to the producers in the Northeastwere seen as a disincentive to innovate, devote resources toresearch and development, or take entrepreneurial risks. Ratherthan being driven by profit and efficiency, the landowning aristocracyof the Northeast was said to be driven by the desire to retain controland power in the hands of the family. As Lehtonen (2007, p. 18)claims, ‘‘the main reason for the stagnation in the Northeast is thevirtually complete lack of investment in R&D (research anddevelopment),’’ while in contrast, ‘‘the success of the S~ao Pauloregion [is the result of] their own entrepreneurial skills [as well as]the overwhelming dominance of the region in the production ofintellectual know-how in the form of research institutes anduniversities.’’

With falling oil prices during the 1990s, the subsidies to ethanolbecame an increasing burden to the already hard-squeezed statebudget. The National Sugar and Ethanol Institute (Instituto deAc-ucar e Alcool, IAA), perceived as one of the main supporters of theNorth sugar producers, was abolished in 1990, which – togetherwith the removal of subsidies – meant mounting problemsespecially for the producers in the North, because IAA had beenresponsible for stabilizing price fluctuations by transferring fundsbetween good and bad-harvest years (Cavalcanti et al., 2002).Likewise, with the disappearance of IAA, the North was left with nopolitical support to continue research on new crops and methodsspecifically adapted to the region’s conditions. These institutionalprocesses accelerated the decline of sugarcane in the North. As anillustration, between the harvest years 1982–1983 and 1994–1995,the share of the North in Brazil’s sugarcane production fell from 30%to 18% (Lima and Sicsu, 2001).

More recently, technological change in the ethanol sector hasalso been driven by the Brazilian national and regional environ-mental legal framework. Aiming at environmental conservation,state regulations that require mechanized harvesting, particularlyin S~ao Paulo state and other Southern regions, have been enacted.Such regulations have rarely been passed in the Northeasternstates, in part due to the difficulty of mechanizing sugarcaneharvesting because of topographical factors, as we discuss later. Forexample, the Agriculture and Environmental Protocol for theSugarcane and Ethanol Industries signed by the Government ofS~ao Paulo in 2007 focuses on legal deadlines for ending sugarcaneburning and instead implementing mechanized harvesting. Asimilar initiative is happening in Minas Gerais with the Protocolo

de Intenc- ~oes de Eliminac- ~ao da Queima no Setor Sucroalcooleiro de

Minas Gerais from 2008 (Neves do Amaral et al., 2008).

2 For the reader interested in the most detailed structure of the input–output

framework, as well as the foundations of the input–output methodology, Miller and

Blair (2009) thoroughly discuss the assumptions as well as constraints of this

economy-wide model, along with a detailed introduction to the notations and

economic fundamentals.

2.4. The influence of topography and the spatial dimension

Brazil has two main sugar-cane-growing and sugar-producingregions. The larger of the two is located adjacent to and in the S~aoPaulo state region, which lies in the Southeast of Brazil. This fertileand flat region is perfectly suited for growing sugarcane, as thereare ample nutrients in the soil to nourish the cane through itsgrowing stages, and when it is time to harvest the sugarcane, thelarge flat fields of sugarcane are easily harvested mechanically.According to the Ministerio da Agricultura, Pecuaria eAbastecimiento (2007), over 60% of all sugarcane grown in Brazilis grown in the S~ao Paulo region.

The second major sugar-producing region in Brazil is in theNortheast, and lies in the Pernambuco and Alagoas states. Theterrain here is much less suited to growing sugarcane, as it is hilly(about 60% of the sugarcane in the Northeastern regions is on slopesbetween 121 and 251), and the soil quality is relatively poor becauseof erosion (James, 1953). As previously mentioned, harvesting isalso much more labor intensive as mechanical harvesters arerelatively ineffective, and manual labor is needed to make the

most of the sugarcane crop in the Northeast. The reason for theprevalence of sugarcane plantations in the less productive North-eastern region is largely historical. The northern states of Brazilwere the original locations of the first Brazilian sugarcane planta-tions and sugar mills. Yet, once Brazilians realized the profits to bemade from sugar, a new sugarcane production region emerged inthe more productive large flat expanses of central Brazil, inparticular S~ao Paulo, Minas Gerais, and Rio de Janeiro. In fact,some of the traditional Northeast sugar elite had been diversifyingtheir activity by investing in S~ao Paulo, precisely because of morefavorable natural conditions for sugarcane (Lehtonen, 2008).

Sugarcane harvesting is done mainly by hand in Northern Brazil(with an extremely limited number of exceptions in the states ofAlagoas, Amazonas, Pernambuco, Rio Grande do Norte, and Tocan-tins), whereas the Southern Brazilian states carry out partiallymechanized harvesting. Mechanized harvesting is difficult toimplement in Northern Brazil because of topographical factors:large machinery is extremely difficult to operate in hilly areas.Hand-cut cane is a less productive process than mechanizedharvesting given that cane cutting is a very time-consumingactivity, and an enormous amount of labor is required. Additionally,the mechanical harvester leaves a lush layer of chopped greenleaves over the harvested field, which ‘‘coats’’ the soil with aprotective layer that conserves water, protects the soil fromerosion, contributes organic matter, and recycles nutrients, whichmay secure good yields for the next harvest (Maciel, 2008).

3. Linking cause and effect: an input–output approach tomodel technical change

In Section 2, we discussed the causes of the technology gapobserved in the ethanol industry. Economic, institutional, andpolitical, and even spatial phenomena, all intertwined, are ableto explain the differentiated regional performance of the Brazilianethanol sector. But what are the effects, particularly in terms ofenvironmental sustainability, of such divergent performances?

Due to the growing economic and environmental importance ofthe ethanol industry and the inherent environmental effects of itsexpansion on the Brazilian economy, analysts need to quantify thecarbon-dioxide emissions from energy use of ethanol, and comparethem to those of natural gas and oil by-products, and evaluateregional impacts of eventual policies for emissions control. Giventhe genuine and tangible interactions of industrial production andpollution, the need of studying environmental and economicproblems simultaneously becomes apparent (Forssell andPolenske, 1998). An extension of the input–output model to linkeconomic activity and ecological processes at the regional level is auseful way to analyze such a necessity.2

Another important purpose in developing an interregionalinput–output model is to account for the direct and indirectimpacts on GHG emissions of different ethanol production tech-nologies. Calculations derived from the interregional system allowus to disentangle the regional effects of ethanol productiontechnologies in order to understand the linkages between eco-nomic activity and ecological processes. Even though the input–output framework is static in nature and embodies relatively rigidtechnology assumptions, namely no substitution of inputs, and noprice effects or constraints on resources, as well as no changes intechnology or economies of scale, these are offset by many

Table 3Regional aggregation of the 23-Region 115-Sector Input–Output System,

Brazil, 2002.

Source: Guilhoto, 2008.

Original regions Aggregatedregions

Acre, Amazonas, Amapa, Para, Rondonia, Roraima, Tocantins North

Alagoas, Bahia, Ceara, Maranh~ao, Paraıba, Pernambuco, Piauı,

Rio Grande do Norte, Sergipe

Northeast

Distrito Federal, Goias, Mato Grosso, Mato Grosso do Sul Center-West

Parana, Santa Catarina, Rio Grande do Sul South

Espırito Santo, Minas Gerais, Rio de Janeiro, S~ao Paulo Southeast


compensating advantages, such as considerable interindustrydetail; most important, input–output models pass the critical testthat for short-term purposes they predict extremely well (Isard andKuenne, 1953; Leontief and Strout, 1963; Moses, 1960; Polenske,1980).3

Given that ethanol produced in the Northern and Northeasternstates of Brazil represents a different mix of inputs from ethanolproduced in S~ao Paulo, and the rest of the Southeast, we determinedthat an interregional input–output model is the appropriate tool tostudy the socioeconomic and environmental effects of differen-tiated ethanol production techniques, as well as the extent to whichsuch differences have an impact on the economic system.

Two types of data are required for this model: (1) interindustryflows of goods and (2) sectoral energy use. We used the Brazilianinterregional system, which includes five regions and 75 economicactivities, aggregated from the 23-region 115-sector input–outputsystem for Brazil developed at the University of S~ao Paulo Centro deEstudos Avanc-ados em Economia Aplicada by Guilhoto (2008). Thisinterregional input–output system is an expansion of the national42-sector input–output table constructed by the Instituto Brasileirode Geografia e Estatıstica (IBGE).4 Tables 3 and 4 present the regionaland sectoral aggregation. In addition to the Brazilian interregionalinput–output system developed at the Centro de Estudos Avanc-adosem Economia Aplicada by Guilhoto (2008), we need sectoralinformation on energy flows to calculate the energy-and carbon-intensities. We obtained energy transactions from the BrazilianBalanc-o Energetico Nacional (National Energy Balance, or BEN, inPortuguese) (Ministerio de Minas e Energia, 2007). Industries,however, are not based on the same industrial classification system.Whereas the interregional input–output system is based on theInternational Standard Industrial Classification, BEN is based onthe Codigo Nacional de Atividades Economicas Revis~ao I (FirstRevision of the Brazilian National Code of Economic Activities)(Pederneiras, 2007). In order to reconcile differences in classifica-tions, we aggregated the interregional system for each of the fiveregions to 18 sectors (Table 4), thus obtaining a 90-sector inter-regional system.

We derived an environmentally extended input–output modelfrom the traditional input–output framework (Guerrero Compean,2008). Given that economic activities are interrelated, not only interms of pollution production but also in terms of pollution emissions,we could not estimate the actual emitters by considering a singlesector, making the input–output approach justifiable (Labandeira andLabeaga, 2002). More specifically, in order to assess impacts of GHGabatement policies on total emissions, analysts need to model howgreenhouse gases are embodied in industrial production (Gay andProops, 1993; Lenzen, 1998). In other words, to inform policy analysis,a measure of direct as well as indirect industrial pollution emissions isrequired (Casler and Blair, 1997).

3 In addition, a particular strength of regional input–output models is that

analysts can use them to estimate whether the structure of production in a

particular region may differ markedly from that recorded in the national input–

output table (Miller and Blair, 2009). Analysts conduct a wide range of applications

uncommon to other regional models, particularly studies of shifts in the location of

industrial activity and employment, regional impact studies, and for this case,

estimation of regional and industrial differences in production techniques

(Richardson, 1972).4 The last benchmark input–output table constructed by IBGE was for 1996.

Given the need for more timely and expanded information, in 2005 Guilhoto and

Sesso Filho developed a methodology to estimate input–output matrices using

preliminary data from the Brazilian National Accounts. They tested this methodol-

ogy for years 1994 and 1996, and compared their results to IBGE’s input–output

matrices. The Pearson correlation of type I production multipliers, Rasmussen–

Hirschman backward and forward linkages, and pure backward and forward

linkages between both sets of matrices are high for both years (Guilhoto and

Sesso Filho, 2005). This is the same methodology used by Guilhoto (2008) to derive

his 23-region 115-sector input–output system.

We develop the expanded input–output model based on theconstruction of a transactions table in the so-called ‘‘hybrid units’’,that is, energy flows in the economy are traced in energy units,whereas we trace non-energy flows in monetary values (Miller andBlair, 2009). For the Brazilian five-region input–output system usedin this paper, energy flows are measured in tonnes of oil equivalent(toe), and non-energy flows in Brazilian reais. This type of modelhas its foundations in the process analysis: ‘‘a target product isidentified either as a good or service, then a list is compiled of thegoods and services directly required to deliver the product. Theseinputs to the target production process include fuels (direct energy)and non-energy goods and services’’ (Miller and Blair, 2009, p. 401).The process traces inputs back to primary resources; the first roundof energy inputs is the direct energy requirement; subsequentrounds of energy inputs comprise the indirect energy requirement.The sum of these two is the total energy requirement of industries,which is the energy intensity of industries (Hilgemberg, 2004).5

The environmentally extended input–output model in hybridunits is based on a set of matrices analogous to the conventionalinput–output framework, that is, an energy transactions matrix(now measured in tonnes of oil equivalent (toe)), a direct energyrequirements matrix, and a total energy requirements matrix. ForBrazil, datasets are available for energy flows of natural gas,ethanol, electricity, and a number of oil by-products, namely dieseloil, fuel oil, gasoline, liquefied petroleum (LP) gas, kerosene, andother oil by-products, such as jet fuel and naphtha. We assume thatthe four industries that produce these energy sources (sectors 3, 8, 9,and 13 in Table 4) are energy sectors. This means that, for eachregion, we consider 4 out of 18 sectors as energy sectors, a total of 20out of 90 industries. We define the 20�90 energy-transactionsmatrix as E. Also, we assume that the energy consumed by finaldemand (in physical units) is given by the 20-term column vector Ey

and that the total energy consumption in the economy is given bythe 20-term column vector F. The energy flows accounting identityis thus given by:

EþEy ¼ F ð1Þ

The sum of energy consumed by interindustry sectors plus thatconsumed by final demand is the total amount of energy consumedby the economy.

By taking the original interindustry-transactions matrix Z, andreplacing the energy rows by the corresponding rows in the energy-flows matrix E, we construct an interindustry transactions matrixin hybrid units. After the substitution, the new interindustrytransactions matrix Z* represents the energy interindustry flowsin physical units and the non-energy interindustry flows in

5 In calculating the energy intensity of a product, analysts should make a

distinction between primary energy sectors (such as crude oil or coal mining) and

secondary energy sectors (such as refined petroleum or electricity). Secondary

energy sectors receive primary energy as an input and convert it into secondary

energy forms (Miller and Blair, 2009).

Table 4Sectoral Aggregation of the 23-Region 115-Sector Input–Output System, Brazil, 2002.

Source: The authors, based on Guilhoto, 2008; Pederneiras, 2007; United Nations Statistics Division, 2008.

18 sectors 75 sectors

1. Agriculture Sugarcane crops (1); agriculture (2)

2. Mineral mining Mineral mining (3)

3. Oil and natural gas Extraction of crude petroleum (4); extraction of natural gas (5); service activities incidental to oil and gas extraction (6)

4. Non-metallic minerals Coal mining (7); manufacture of non-metallic mineral products (8)

5. Iron and steel Manufacture of iron and steel (9); manufacture of non-ferrous metals (10); other metallurgic industries (11)

6. Pulp and paper Manufacture of pulp and paper (22)

7. Chemicals Manufacture of coke (25); manufacture of basic petrochemical products (27); manufacture of resins, elastomers, and man-made

fibres (28); manufacture of fertilizers (29); manufacture of paints and varnishes (30); manufacture of pesticides (31);

manufacture of miscellaneous chemical products (32); manufacture of pharmaceuticals, soaps, and detergents (33)

8. Ethanol Manufacture of ethyl alcohol (24)

9. Oil refinery Manufacture of refined petroleum products (26)

10. Textiles Manufacture of textiles (35)

11. Food and beverages Manufacture of coffee (37); manufacture of vegetable products (38); mroduction, processing, and preserving of meat, poultry and

fish (39); manufacture of dairy products (40); manufacture of sugar (41); manufacture of vegetable oils and fats (42);

manufacture of other food products (43)

12. Machinery and other industries Manufacture of agricultural machinery (12); manufacture of oil extraction equipment (13); manufacture of other machinery (14);

manufacture of electrical materials (15); manufacture of electronic equipments (16); manufacture of automobiles (17);

manufacture of trucks and buses (18); manufacture of parts and accessories for motor vehicles and their engines (19);

manufacture of wood (20); manufacture of furniture (21); manufacture of rubber (23); manufacture of plastics products (34);

manufacture of wearing apparel and footwear (36); manufacture of miscellaneous products (44); construction (55)

13. Electricity and public utilities Production of electricity (hydroelectricity) (45); distribution of electricity (51); water supply (53); garbage collection (54)

14. Non-hydro energy Production of electricity (fuel oil) (46); production of electricity (coal) (47); production of electricity (diesel) (48); production of

electricity (gas natural) (49); production of electricity (other sources of energy) (50); production and distribution of gas (52)

15. Commerce and services Wholesale of gasoline (56); wholesale of other automotive fuels (57); wholesale of ethanol (58); other wholesale (59); retail sale of

other automotive fuels (60); retail sale of ethanol (61); other retail sale (62); communications (69); financial intermediation (70);

private household service activities (71); business service activities (72); real estate and renting activities (73); activities within

households, where the same household is the consumer of the products produced (75)

16. Land transport Land transport (63)

17. Other transport Air transport (64); transport via railways (65); water transport (66); transport via pipelines (67); supporting and auxiliary

transport activities (68)

18. Public administration Public administration and defense (74)

6 In order for this energy model and its extension as a pollution–emission model

to depict accurately the energy flows in the economy, it should satisfy the energy-

conservation condition that total primary energy intensity of a product equals the

total secondary energy intensity of the product plus the amount of energy lost in

energy conversion. Technically, akjXj ¼ ðSni ¼ 1akizijÞþ f �kj . In this case, akj is the

amount of energy required to produce a Brazilian real’s worth of sector j’s output.

Again, Xj is the total output of sector j in reais; and zij is the value of sector i’s product

consumed by sector j, also measured in reais. f �kj is the total energy output of only

primary energy sectors, that is, the energy embodied in any sector output Xj equals

the amount of energy embodied in all that sector’s inputs zij plus the primary energy

input, f �kj , which is nonzero only for primary energy sectors (Miller and Blair, 2009).

In matrix terms, this is aX ¼ aZþ F� , and since Z ¼ AX, it is straightforward that

aX ¼ aAXþ F� ,and thus, a¼ F�ðXÞ�1ðI�AÞ�1.


monetary values. A similar process takes place for the vectors,Y*, X*, and F* so that,

Z�i ¼zj for non-energy rows

toek for energy rows

(ð2Þ

Y�i ¼yj for non-energy rows

toeky for energy rows

(ð3Þ

X�i ¼xj for non-energy rows

fk for energy rows

(ð4Þ

F�i ¼0 for non-energy rows

fk for energy rows

(ð5Þ

In other words, just like in the traditional input–output methodol-ogy, Z* represents the interindustry transactions matrix; X* is theoutput vector, and Y* is the final demand vector. The asterisk indicatesthat energy sectors are in physical units. In addition, F* is also a hybridtotal output vector, but in contrast to X*, where non-energy sectors arein monetary units, in F*, they are equal to zero.

If energy sectors are in tonnes of oil equivalent (toe) and non-energy sectors are in Brazilian reais (R$), A� ¼ Z�ðX�Þ�1, where thehat indicates that the vector is diagonalized, we will have units interms of the requirement (toe or R$) per unit (toe or R$) of totaloutput. The matrix (I�A*)�1 will have the same units as A*, exceptthat they are in terms of the requirement per unit of final demand

(total requirements) instead of per unit of total output (directrequirement) (Miller and Blair, 2009).

Both the direct energy requirements matrix and the totalenergy-requirements matrix are obtained by extracting the energyrows from A*and (I�A*)�1, respectively. This is done by construct-ing the matrix product F�ðX�Þ�1. Note that the elements of F*, F�i ,

were defined in Eq. (5). Since the nonzero elements of F* areidentical to the corresponding values in X*, the result of this productis a vector of ones and zeros, the ones denoting the locations ofenergy sectors. By postmultiplying the direct and total require-ments matrices by F�ðX�Þ�1, we obtain the energy coefficients, thatis, the energy intensity (Hilgemberg, 2004).

Assuming that CO2 emissions have a linear relation to theenergy requirements, we can estimate the CO2 direct, indirect, andtotal emissions by converting energy flows in tonnes of oilequivalent to tonnes of CO2 equivalent.6 The conversion coeffi-cients to obtain energy consumption in Terajoules, carbon contentin tonnes, and total carbon dioxide emissions from fuel combustionare shown in Table 5.

4. The environmental effects of the technological divergence inthe ethanol industry

A surprising finding in the technology-choice literature is thatlittle analytical work has been done at the macroeconomic level intrying to incorporate technology within a comprehensive

Table 5Carbon emission factors by fuel.

Source: Energy Information Administration (2007), Intergovernmental Panel on

Climate Change (1996), Ministerio de Minas e Energia (2007).

Conversionfrom:

ktoe into energyconsumption(TJ)

TJ into tonnes ofcarbon content(tC)

TJ into tonnesof CO2 emissions(tCO2)

Natural gas 41.87 17.20 63.07

Diesel oil 41.87 20.20 74.07

Fuel oil 41.87 21.10 77.37

Gasoline 41.87 18.90 69.30

LP gas 41.87 17.20 63.07

Kerosene 41.87 19.60 71.87

Naphthaa 41.87 20.00 73.33

Ethanol 41.87 14.80 54.27

Electricity 41.87 7.04 25.83

Note: TJ¼terajoule; ktoe¼metric kilotonnes of oil equivalent; LP¼ liquid petroleum,

t¼tonnes.

a This category also includes jet fuel and other oil by-products.


intersectoral framework to estimate the effects of alternativetechnologies on such development objectives as energy conserva-tion and sustainability, for Latin America. Our model, based on aninterregional input–output methodology appears to be one of thefirst attempts at exploring the macroeconomic and environmentaleffects of dualistic technological choice and examining thetechnology–production interactions in the ethanol sector of Brazil.This section provides an empirical assessment of the ethanolindustry based on the environmentally extended input–outputmethodology introduced in the previous section.

A crucial component that policymakers should consider whenassessing whether labor-intensive or capital-intensive ethanol-pro-duction processes are preferred is that of environmental sustain-ability. On the one hand, energy intensity, measured as energyconsumed per unit of gross domestic product, is slowly increasingin Brazil (Polenske et al., 2007). On the other, Brazil is home to some ofthe greatest, yet extremely fragile, ecosystems of the planet, such asthe Amazon, making the country highly vulnerable to climate change.At the global scale, Brazil is one of the top ten greenhouse gas emittersworldwide and the third largest CO2 emitter in the developing world,after China and India (Guerrero Compean, 2007). It signed in 1998 andratified in 2002 the Kyoto Protocol to the United Nations FrameworkConvention on Climate Change, aimed at combating global warming(United Nations Framework Convention on Climate Change, 2008).Brazil and other developing countries were not included in anynumerical limitation of the Kyoto Protocol because they were not themain contributors to the greenhouse gas emissions during the pre-treaty industrialization period. However, even without the commit-ment to reduce according to the Kyoto target, developing countries doshare the common responsibility that all countries have in reducingemissions.

Therefore, given that increasing energy consumption and green-house gases emissions are mainly explained by rapid economicdevelopment, we examine the structure of the Brazilian economyin order to establish the relationship between industrial perfor-mance and pollution emissions, taking into account the technicalstructure of the energy sectors. Our environmentally extendedinput–output model helps determine whether modern technolo-gies are more carbon-intensive than their traditional counterparts.

Table 6 presents total carbon-intensities for five regions and 18economic activities. This table also provides disaggregated coeffi-cients for four types of energy sources, namely natural gas, ethanol,oil by-products, and electricity. Carbon-intensity is expressed ingrams of CO2 per real (g CO2/R$). Notice that the total carbon-intensity is the sum of the intensities from the different energysources. We did not consider CO2 emissions associated with

land-use changes in the calculation of carbon-intensities. Emis-sions from land-use changes resulting from massive deforestationwould, of course, release large amounts of CO2, but the expansion ofthe sugarcane plantations in Brazil is taking place over degradedpastures very far from the Amazonia (Guerrero Compean, 2008;Goldemberg, 2008a). Emissions from such land-use change havebeen shown to be small (Cerri et al., 2007). Owing to datalimitations and methodological problems in disaggregatingmineral coal, coal gas, and coke data, we were unable to studythe environmental implications of coal use.

In terms of the technology-choice implications on GHG emis-sions, we find that traditional ethanol production processes aremore carbon-intensive than modern ethanol production techni-ques. The highest carbon-intensity is found in the Northeast region(64.3 g CO2/R$), while the lowest is that of the Southeast region(61.1 g CO2/R$). Notice that oil by-products, are, by far, the majorsources that explain high carbon-intensity coefficients. In contrast,ethanol is the least significant determinant of carbon-intensity,explaining, on average, only 3% of the coefficient values.

In any case, the carbon-intensity of the petroleum refiningindustry, whose main product in terms of volume is gasoline, issignificantly higher than that of the ethanol sector by at least 44%(in the case of the North region) and by up to 59% (in the case of theCenter-West region).

For the five regions analyzed, the land-transport sector registersone of the highest total carbon-intensities, followed by the non-landtransport sector. Other carbon-intensive sectors are the chemical,non-hydro energy, and mineral mining industries. Conversely, thelowest carbon-intensities amongst non-energy sectors are usuallyfound in the public administration sector. The reader interested inmetallurgy may find it noticeable that the iron and steel industry’scarbon-intensity is comparatively low. This is mainly caused by theabsence of coal, a major energy fuel in the iron and steel makingprocesses, as an energy source in our analysis.

It is important to emphasize that ethanol explains less than 1%of land-transport’s carbon-intensity. This finding is extremelyrelevant in terms of environmental policy. Ethanol may helpdecrease the transport sector’s carbon-intensity if its use werefurther diffused as a fuel substitute for gasoline (Teixeira Coelhoand Goldemberg, 2004).

5. Conclusion

In this paper, we analyzed both the causes and effects of thistechnology gap. We concluded that the environmental efficiency(output per unit of greenhouse gases) and productivity differencesbetween the labor-intensive and capital-intensive ethanol produc-tion processes are explained by economic, political, and spatialfactors, particularly the economies of scale and mechanization ofsugarcane harvesting, access to financial resources, the policiesderived from the National Ethanol Program as well as currentenvironmental legislation, and geographical advantages for theSouth region in terms of climate, soil, and topography.

In addition, we used an input–output model in hybrid units inorder to obtain direct and indirect carbon-intensities, for four typesof energy, namely natural gas, ethanol, oil by-products, andelectricity. We showed that ethanol has the lowest CO2 emissionsamong the energy sources examined. We also evidenced that eventhough ethanol technologies are, in fact, more carbon-efficient thangasoline and other oil by-products, a labor-intensive ethanolproduction process is likely to produce more pollution than acapital-intensive one. This is an important consideration: depend-ing on their production technology, biofuels might offer viablealternatives to alleviating the current ecological crisis and facingthe challenge of climate change. The labor-intensive alternative,

Table 6Regional total carbon-intensity (gCO2/R$) by industry and energy source, Brazil, 2002.

Source: The authors, based on data provided by Centro de Estudos Avanc-ados em Economia Aplicada and Ministerio de Minas e Energia.

Region and sector Natural gas Ethanol Oil by-products Electricity Total carbon-intensitya

North

1. Agriculture 18.669 1.628 132.585 5.605 158.487

2. Mineral mining 40.113 3.473 285.614 28.904 358.103

3. Petroleum and gas 63.575 0.109 4.000 0.801 68.484

4. Non-metallic minerals 29.454 2.008 214.136 22.240 267.838

5. Iron and steel 16.209 2.496 116.290 35.852 170.847

6. Pulp and paper 16.903 2.931 124.289 19.941 164.064

7. Chemicals 37.274 4.233 274.133 9.738 325.378

8. Ethanol 0.883 54.541 6.296 1.007 62.728

9. Oil refinery 9.082 0.109 80.514 0.310 90.015

10. Textiles 21.381 3.365 156.140 21.723 202.608

11. Food and beverages 21.948 3.853 156.806 13.018 195.626

12. Machineries and others 12.740 2.008 91.476 9.454 115.678

13. Electricity and public utilities 0.568 0.163 4.370 39.339 44.440

14. Non-hydro energy 54.997 2.659 394.719 13.251 465.626

15. Commerce and services 38.536 17.041 276.651 9.247 341.475

16. Land transport 96.056 5.156 698.258 8.214 807.683

17. Other transport 69.755 3.039 500.417 7.207 580.418

18. Public administration 8.767 3.093 62.811 10.358 85.029

Northeast

1. Agriculture 23.147 2.008 161.769 6.303 193.226

2. Mineral mining 36.770 3.148 256.727 25.288 321.932



5. Iron and steel 19.615 2.822 136.955 37.815 197.207

6. Pulp and paper 18.543 3.419 129.697 22.756 174.414

7. Chemicals 81.550 5.264 569.524 13.819 670.157

8. Ethanol 1.072 54.541 7.555 1.162 64.331

9. Oil refinery 11.857 0.109 82.588 0.387 94.941

10. Textiles 25.859 3.907 180.953 24.513 235.232




14. Non-hydro energy 32.796 2.388 229.321 12.321 276.826


16. Land transport 111.066 5.807 776.402 9.144 902.419

17. Other transport 82.432 3.093 574.413 7.387 667.326


Center-West

1. Agriculture 33.175 4.070 232.580 9.428 279.253

2. Mineral mining 44.969 5.861 314.131 33.992 398.953



5. Iron and steel 22.957 4.559 159.473 42.723 229.712

6. Pulp and paper 26.868 6.078 187.842 29.265 250.053

7. Chemicals 39.608 10.040 277.022 18.029 344.699

8. Ethanol 0.883 54.541 6.222 0.904 62.550

9. Oil refinery 14.632 0.163 84.292 0.491 99.577

10. Textiles 29.454 6.024 206.137 29.550 271.164




14. Non-hydro energy 28.318 4.939 197.174 21.646 252.077


16. Land transport 121.157 8.249 849.731 11.004 990.141

17. Other transport 93.722 5.644 652.853 9.764 761.983


South

1. Agriculture 23.210 2.334 163.472 6.018 195.034

2. Mineral mining 40.302 4.450 286.947 27.406 359.105



5. Iron and steel 18.290 3.093 128.586 26.579 176.548

6. Pulp and paper 20.624 4.450 145.473 23.609 194.156

7. Chemicals 59.917 5.644 426.199 16.040 507.800

8. Ethanol 0.820 54.487 5.926 0.904 62.137

9. Oil refinery 11.416 0.109 81.921 0.336 93.781

10. Textiles 24.471 4.450 172.213 22.989 224.123




14. Non-hydro energy 13.055 2.605 92.217 14.826 122.704


Table 6 (continued )

Region and sector Natural gas Ethanol Oil by-products Electricity Total carbon-intensitya


16. Land transport 110.499 6.621 783.142 8.989 909.251

17. Other transport 63.259 4.179 450.346 7.258 525.042


Southeast

1. Agriculture 23.084 2.171 160.880 6.716 192.850

2. Mineral mining 31.346 2.768 218.655 23.118 275.886



5. Iron and steel 18.416 2.496 128.215 31.823 180.951

6. Pulp and paper 20.624 3.690 144.437 25.107 193.858

7. Chemicals 49.195 6.024 347.536 13.612 416.367

8. Ethanol 0.694 54.487 5.111 0.852 61.144

9. Oil refinery 11.857 0.109 82.144 0.362 94.471

10. Textiles 23.777 3.853 166.509 24.642 218.782




14. Non-hydro energy 13.875 2.714 96.513 22.265 135.368


16. Land transport 105.075 5.373 729.293 8.782 848.523

17. Other transport 64.647 2.931 448.716 6.819 523.113


aCarbon-intensity is defined as CO2 emissions from the consumption of energy in grams per real.Total carbon-intensity represents direct plus indirect carbon-intensity.


however, might be a more strenuous path than the capital-intensive technological choice to achieve these goals. In the caseof sugarcane ethanol in Brazil, the technological change thattransformed the labor-intensive ethanol making process into abiotechnologically advanced, mechanized industry improved thesustainability of the fuel and made it less carbon-intensive.

The biotechnologically enhanced industries of the South andSoutheast regions proved to be the most carbon-efficient ethanolsectors. In general, we found that the ethanol sectors are con-sidered to be clean industries and that, in contrast, the land-transport sectors of the Northeast, Southeast and South regionswere classified as major CO2 emitters. This finding is extremelyrelevant in terms of energy and environmental policy design, asethyl alcohol may help decrease the transport sector’s energy andcarbon-intensities if its use were further diffused as a fuelsubstitute for gasoline (Teixeira Coelho and Goldemberg, 2004).This could be the first step to construct a computable generalequilibrium model capable of representing the economic andenvironmental repercussions of Brazil’s dual technological pro-cesses and simulating regionally the responses to various kinds ofeconomic policy shocks.

In terms of policy, this implies that a hypothetical technologychange towards the implementation of more modern technologiesin the labor-intensive ethanol regions is likely to generate envir-onmental gains and energy savings. Therefore, policy measures toencourage the upgrading of traditional technologies need to beconsidered seriously. However, as discussed before, state regula-tions that require mechanized harvesting are still rare in theNortheastern states. Likewise, having the government or the Northand Northeast poorer ethanol producers finance and implementcostly technical innovations is an unlikely policy objective. Thus, astrong case exists for the continuation of traditional technologiesalong with the biotechnologically enhanced production techni-ques. For the time being, and given that significant technologychanges are out-of-sight for the traditional ethanol sectors, onemeans to improve productivity would be to improve education andhuman capital endowments in general, which are significantlylower in the Northeastern regions (Guerrero Compean, 2008).Switching cultures, or the production of a different kind ofsugarcane for specific markets, like organic sugar, are alternative

possibilities. On the energy front, government policies in support ofthe alternative fuel should be continued. The development ofethanol ‘‘made in Brazil’’ as a substitute for gasoline has inherentimplications on Brazil’s energy-security policy, because as moreethanol is demanded for motor vehicles given its currently lowercost of production, less oil will be required. The ethanol produced,together with the oil Brazil pumps, may sustain the country’senergy independence. Besides, in the process of securing energyindependence, reduced petroleum imports will continue toimprove Brazil’s balance of payments, avoid foreign debt, andinsulate Brazil from disruptions in fossil energy supply or oil priceshocks. In addition, some analysts have demonstrated that percapita investment costs in the ethanol sector may be up to 94%lower than those of the petrochemical industry (Geller, 1985) and,similarly, Goldemberg et al. (2004) and Guerrero Compean (2008)have shown that ethanol production costs have been lower thangasoline’s since 2004.

Analysts could consider certain possible refinements and keydevelopments to improve this study and stimulate research onrelatively unexplored areas of technology choice studies in thedeveloping world, and particularly in Brazil. They could breakdown the labor-intensive and capital-intensive ethanol industriesintrarregionally, in order to study the economic effects of the labor-intensive ethanol distilleries still operating in the modern South-east region or, conversely, the environmental impacts of the fewcapital-intensive ethanol plants in the impoverished Northeast.This would enrich the technology-choice analysis by eliminatingthe artificial simplifying assumption of technological dualismconsidered here. Likewise, we can improve the energy analysiswhen more sectorally disaggregated data become available. Lack ofstatistical information at the regional level was the major cause forthe omission of coke as an energy input, which was reflected in theseemingly low carbon-intensities of the iron and steel sector. Aspointed out by Khan and Thorbecke (1988), the inadequacy of datafor multisectoral planning models is as big a handicap as inade-quate conceptualization. Similarly, future research could involveother techniques of modeling and analysis, in particular geographicinformation systems and computable general equilibrium eco-nomic simulations, to draw additional empirical conclusions help-ful for policy and planning processes design.


Acknowledgments

We would like to acknowledge Joaquim Guilhoto and JoseGoldemberg for their general support of the line of work reportedin this paper. Particular contributions due to them are mentioned inthe text. We wish to thank two anonymous referees for valuablecomments and suggestions. We are particularly grateful to JoAnnCarmin for reviewing an early draft of this paper. This work wassupported by the Multiregional Planning Research Team at theMassachusetts Institute of Technology.

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antagonistic bioenergies: technological divergence of the ethanol industry in brazil

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