Economic-energy-environment analysis of prospective sugarcane bioethanol production in Brazil

Applied Energy 181 (2016) 514–526

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Economic-energy-environment analysis of prospective sugarcane bioethanol production in Brazil Ariovaldo Lopes de Carvalho a,d,⇑, Carlos Henggeler Antunes a,b, Fausto Freire a,c a

Energy for Sustainability Initiative, Faculty of Sciences and Technology – University of Coimbra, Rua Luís Reis dos Santos, 3030-788 Coimbra, Portugal1 INESC Coimbra, Dept. of Electrical and Computer Engineering and INESC Coimbra, Polo 2, University of Coimbra, 3030-290 Coimbra, Portugal2 c ADAI-LAETA Department of Mechanical Engineering, Faculty of Sciences and Technology, University of Coimbra, Rua Luís Reis Santos, 3030-788 Coimbra, Portugal d Federal Institute of Mato Grosso, Campus Diamantino, Rodovia Roberto Campos, 78400-970 Diamantino, Brazil3

b

h i g h l i g h t s
A Hybrid IO-MOLP model is formulated for energy-economic-environmental analysis.
Scenarios for sugarcane cultivation and 1st- and 2nd-generation bioethanol production.
Higher energy use and GHG emissions due to chemicals in 2G processes.
Lower overall employment level in the 1G + 2G scenarios compared to the 1G scenario.
Policies and technological choices should consider direct and indirect effects of 2G.

a r t i c l e

i n f o

Article history: Received 10 February 2016 Received in revised form 24 July 2016 Accepted 28 July 2016

Keywords: Input-output analysis Hybrid modeling Multi-objective linear programming Multi-sectoral economy-energyenvironment models Lignocellulosic bioethanol

a b s t r a c t Bioethanol from sugarcane can be produced using first-generation (1G) or second-generation (2G) technologies. 2G technologies can increase the capacity of production per sugarcane mass input and are expected to have a key role in future reductions of environmental impacts of sugarcane bioethanol. A hybrid Input-Output (IO) framework is developed for Brazil coupling the System of National Accounts and the National Energy Balance, which is extended to assess Greenhouse Gas (GHG) emissions. Lifecycle based estimates for two sugarcane cultivation systems, two 1G and eight 2G bioethanol production scenarios, are coupled in the IO framework. A multi-objective linear programming (MOLP) model is formulated based on this framework for energy-economic-environmental analysis of the Brazilian economic system and domestic bioethanol supply in prospective scenarios. Twenty-four solutions are computed: four ‘‘extreme” solutions resulting from the individual optimization of each objective function (GDP, employment level, total energy consumption and total GHG emissions - 1G scenario), ten compromise solutions minimizing the distance of the feasible region to the ideal solution (1G, 1G-optimized and prospective 1G + 2G scenarios), and ten solutions maximizing the total bioethanol production (1G, 1G-optimized and prospective 1G + 2G scenarios). Higher diesel oil and lubricants consumption in the mechanical harvesting process has counterbalanced the positive effects of more efficient trucks leading to higher energy consumption and GHG emissions. Lower overall employment level in the 1G + 2G scenarios is achieved such that policies linked to reabsorption of sugarcane cutters in alternative activities are positive. Indirect effects from maximizing the bioethanol production increase the total energy consumption and the GHG emissions thus requiring efficiency measures and fossil energy substitution by cleaner sources. The integrated- or country-based analysis of the whole economic system has complemented the process design and process-based analysis, contributing to identify direct and indirect effects that can offset the benefits. Direct and indirect effects on the whole economic system have to be considered in policies and technological choices for prospective bioethanol production, since positive direct effects of 1G + 2G plants can be counterbalanced by indirect impacts on other sectors, mainly from chemicals in the process. Ó 2016 Elsevier Ltd. All rights reserved.

⇑ Corresponding author at: Federal Institute of Mato Grosso, Campus Diamantino, Rodovia Roberto Campos, 78400-970 Diamantino, Brazil. 1 2 3

E-mail address: [email protected] (A.L. de Carvalho). http://www.uc.pt/efs. http://www.uc.pt/en/org/inescc. http://www.ifmt.edu.br.

http://dx.doi.org/10.1016/j.apenergy.2016.07.122 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

Acronyms 1G first generation bioethanol 1G + 2G combined and integrated 1G and 2G technologies 2G second generation bioethanol CH4 methane carbon dioxide CO2 EH enzymatic hidrolysis EIO-LCA economic input-output life cycle analysis GDP Gross Domestic Product GHG Greenhouse Gases IO Input-Output IOA Input Output Analysis IO-MOLP input-output multi-objective linear programming ISO International Organization for Standardization LCA Life Cycle Assessment LP Linear Programming MOLP Multi-objective Linear Programming NO2 nitrous oxide NPISH non-profit institution serving households PROALCOOL Brazilian Alcohol Program R$ Brazilian Real WIS water insoluble solids

1. Introduction The 1970s worldwide oil crisis impelled Brazil to increase the production of the first-generation (1G) bioethanol based entirely on the fermentation of sugar juice from sugarcane and/or molasses as an alternative fuel in the transportation sector. After some decades, policies in the scope of the Brazilian Alcohol Program (PROALCOOL) have been responsible to consolidate the agriculture and industrial supply by increasing investments on sugarcane cultivation and construction of new bioethanol plants in Brazil. In addition, the PROALCOOL has been responsible for the creation of an important domestic market for this fuel by incentivizing the substitution of petrol with bioethanol as much as the bioethanol price was made competitive due to taxes on petrol and subsides on bioethanol production. Nowadays, Brazil is the second major bioethanol producer with 28% of the total worldwide, while the United States, which is the major producer, is responsible for 58.5% of the total [1]. In the 2014/2015 harvest, 634.8 Mt of sugarcane have been produced in Brazil, resulting in 11.7 GL of anhydrous and 16.9 GL of hydrous bioethanol (a total of 28.7 GL) [2]. In 2014 bioethanol was responsible for 32.3% of the total energy consumed in light vehicles in Brazil [3]. In Brazil, bioethanol is produced in mixed sugar-bioethanol plants (the most common type of bioethanol plants, producing both bioethanol and crystallized sugar) and in autonomous distilleries (producing only bioethanol). A lignocellulosic residue (called bagasse) is also produced in the sugarcane processing. The bagasse is burnt in boilers to generate heat and electricity that are used in the bioethanol plant. Electricity surplus can be exported to the national electricity system. More efficient and expensive boilers for the combustion of bagasse have improved the capacity of the plants in generating electricity surpluses, therefore allowing to increase the return from each plant [4,5]. Prospective technologies for lignocellulosic bioethanol production, also referred to as the second-generation (2G) bioethanol, increase the role of bagasse in the process [6,7]. The use of bagasse in 2G technologies as a raw material for bioethanol production can increase the total capacity of production per unit of sugarcane. The 2G technologies are expected to have a key role in future reductions of environmental impacts of sugarcane bioethanol by using

Notation ba bh ci debt ec emp exp gdp gdpcurr gfcf ggb ghg gva imp pc rc sc ts ydcurr

515

anhydrous bioethanol hydrous bioethanol change in inventories public debt total energy consumption employment level exports gross domestic product gross domestic product at current prices gross fixed capital formation public administration global balance total greenhouse gas emissions gross value added imports public consumption resident consumption sugarcane taxes less subsides on products resident’s disposable income at current prices

sugarcane leaves and tops in bioethanol production. Instead of being burnt in the field as in the near past (nowadays most of the sugarcane is mechanically harvested in Brazil) or discarded as residues of the mechanical harvesting (being waived or burnt), leaves and tops can be used as energy sources to replace bagasse burnt in the boilers or even used to produce 2G bioethanol. However, the 2G technologies have not been commercially competitive in Brazil due to high production costs (compared to the 1G technology) and some bottlenecks regarding the conversion of lignocellulose into fermentable sugars and the downstream processing pose a challenge for this option in the near future [8–11]. Combined and integrated 1G and 2G technologies (1G + 2G) for bioethanol production can also be implemented. Macrelli et al. [12] performed a techno-economic evaluation of the integration of 1G + 2G bioethanol production from sugarcane for fourteen scenarios, considering several operating conditions and process layouts. According to the simulations, the production of 2G bioethanol from sugarcane bagasse and leaves in Brazil is already competitive (without subsidies) with 1G starch-based bioethanol production in Europe. Moreover 2G bioethanol could be produced at a lower cost if subsidies were used to compensate for the opportunity cost from the sale of excess electricity and the cost of enzymes continues to fall. In addition, other factors as energy prices, plant efficiency and costs, type of electricity substituted, and policy instruments can influence the sugarcane biomass use for 2G or electricity production [13]. The International Organization for Standardization (ISO) 14040 standard defines Life Cycle Assessment (LCA) as ‘‘a compilation and evaluation of the inputs, outputs and potential environmental impacts from a productive system throughout its life cycle” [14]. The LCA methodology assesses the environmental impacts associated with the life cycle of the product under study [15,16]. LCA has an important role on public and private environmental management, comparing alternative products or helping in the development of new products with lower environmental impacts [17]. Some studies [18–21] have used LCA to investigate the energy and Greenhouse Gases (GHG) balances of sugarcane-based bioethanol and, in a smaller number of cases, a wide-range of impacts [22]. However, setting tight boundaries in the supply chain of the analyzed system as required by the LCA approach can

516

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

restrict the analysis, since upstream and downstream impacts that can propagate through the economy are neglected. The cost and time required for (full and exhaustive) LCA studies are a shortcoming of this methodology, which has also motivated the research of methods that simplify the analysis while still providing consistent information to satisfy the overall assessment goals [23]. In this sense, the environmental extended input-output models can be a useful approach to overcome some weaknesses of LCA studies, since the overall environmental impact of an entire sector of the economy can be accounted for. Input-Output Analysis (IOA) has been traditionally used to study the inter/intra-relationships among different sectors in the economic system, describing the relationship between the inputs used and the outputs produced. Some authors have proposed different approaches to modify the Input-Output (IO) models to account for environmental impacts using generalized IO models [24], economic ecological-models [25] and commodity by industry models [26]. An external expansion of the IO framework can also be made to incorporate the environmental impacts, assuming a proportional relation between the output of the sectors and the corresponding impact levels [27]. IO models have also been adapted for the analysis of the energy sector (e.g., [28–31]) and energy and environmental interactions (e.g., [32,33]). Some studies have developed basic and extended IO models to assess the interactions of the Brazilian bioethanol sector with the overall Brazilian economic system for different purposes: to assess the impacts from sugar and bioethanol exports [34,35]; to estimate the impacts from new bioethanol factories [36]; to prospective studies of demand increase [37]; to assess the impacts on socioeconomic indicators of different prospective technological mixes [38–40]. However, most of these studies have just performed an economic analysis and, in a small number of cases, an analysis of the employment level. Unfortunately, these studies have not deeply assessed the energy and GHG aspects related to the bioethanol sector and the whole economic system. Other studies using the IO framework have also addressed environmental and energy issues. Compeán and Polenske [41] developed an environmentally extended interregional input-output model using hybrid units accounting for energy- and carbon-intensities to analyze the direct and indirect impacts of different bioethanol production technologies. However, this study has a limited number of sectors and fuels, the model does not include the employment analysis and the impacts from 2G bioethanol are not assessed. Watanabe et al. [42] applied the so-called Economic Input-Output Life-Cycle Analysis (EIO-LCA) to the assessment of economic and GHG emissions of different 1G and 2G sugarcane based bioethanol production technologies in Brazil. However, this study did not address the effects on the employment level, as well as the impacts on the energy intensities and on the composition of the national energy mix. The EIO-LCA is one of the methods utilized to integrate the IOA with the LCA of specific sectors, such as hybrid approaches [27,43]. Alternatively, there are some studies, although not linked with the bioethanol system, which have used IO hybrid models to assess the energy sector (see e.g., [44,45]) and to assess the Brazilian economic system (see e.g., [46–48]). It is important to refer that ‘‘hybrid” does not refer here to the linkage between IOA and LCA methodologies, but to the combination of physical and monetary units into the transaction and technical coefficients matrices of the IOA. In this approach the basic IO model is adjusted and new rows and columns are included in the transaction matrix to allocate the energy sectors (or commodities), substituting flows in monetary units by flows in physical units for the energy sectors (or commodities) into the inter-sector transaction and final demand matrices. These new matrices are then utilized to generate new technical coefficient matrices, which are then applied to the IO model (see [46,49]).

Some studies have developed linear programming (LP) models coupled with the IO framework for different purposes [50–52]. However, Multi-objective Linear Programming (MOLP) models coupled with IO framework can provide a more thorough assessment of different axes of evaluation of potential policies, enabling to explore the trade-offs between competing objectives through the computation of non-dominated solutions, i.e. solutions for which no other feasible solution exists improving the value of a given objective function without worsening the value of, at least, other objective function. IO-MOLP models have been applied to study the impacts of national and regional policies on the employment, water pollution, energy requirements, carbon dioxide (CO2) emissions, foreign trade balance, etc. [53–56]. There are also IO-MOLP models using hybrid frameworks and external expansions of the IO model to assess en ergy-environment-economic-social objectives [57–60]. These models are used as basis for the extended hybrid IO-MOLP model herein presented and applied to the Brazilian economic system, which will be briefly outlined in the next section. The literature review revealed a lack of studies using the IOMOLP framework encompassing economic, energy, environmental (especially GHG emissions) and social (especially employment levels) spheres coupled with LCA estimates for energy commodities analysis, as well as for the assessment of the 2G bioethanol sector specifically devoted to the Brazilian economic system. Therefore, a comprehensive approach is proposed for prospective analysis of the economic system and specific energy commodities, which applies extended and hybrid IO frameworks coupled with multi-objective modeling and LCA estimates. The aim is to assess the impacts on the total energy consumption, GHG emissions, several economic variables and employment level associated with the bioethanol production according to the current and prospective technologies (1G and 2G) in the horizon of 2018. The integration of LCA and hybrid IO-MOLP models herein proposed improves the scope of analysis of energy products, especially 2G bioethanol. This methodological framework allows incorporating different processes into the model and expanding the boundaries of the analysis to the entire economic system such that direct and indirect effects in an integrated- or country-basis analysis are accounted for. Additionally, the model overcomes the lack of studies integrating IOMOLP models devoted to energy products, particularly for the analysis of 2G bioethanol systems, as well as for the integrated assessment of energy, environmental, economic and social spheres. Such comprehensive modeling and analysis has not been developed previously, especially applied to the Brazilian bioethanol system. This methodological framework can be adapted to other economic systems, countries and target sectors to assess the trade-offs between those axes of evaluation and support the design of appropriate policies. The methods used to estimate the sugarcane and bioethanol scenarios as well as the hybrid IO model formulated in this study are presented in Section 2. Some illustrative results are presented in Section 3. Conclusions and future developments are drawn in Section 4.

2. Materials and methods 2.1. Sugarcane scenarios The first step to build up the Extended Hybrid IO-MOLP model is to construct the sugarcane sector (or disaggregate it from the sector in which it appears), which is not explicitly available in the Brazilian IO tables. For this purpose, the methodology proposed in [40] to estimate the average utilization and costs of the main inputs (such as diesel oil, lubricants, agriculture inputs, salary, etc.) in the life cycle of bioethanol was jointly used with the total

517

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

output of the sugarcane sector. Two different sugarcane production scenarios are considered: a current sugarcane scenario considering the sugarcane cultivated using average agricultural technology of Brazil in 2009, and a future sugarcane scenario considering optimized conditions of cultivation, mechanical harvesting (with straw removal and delivery to the bio-refinery) and transportation.

Table 2 Production cost for combined 1G + 2G plants (R$/R$ of Bioethanol) (based on [12])a.

2.2. Bioethanol scenarios

Inputs

2G-A

2G-B

2G-C

2G-D

2G-E

2G-F

2G-G

2G-H

Sugarcane + leaves Enzymes Acid Base

0.45

0.43

0.43

0.39

0.38

0.33

0.35

0.33

0.11 0.02 0.01

0.12 0.03 0.01

0.13 0.03 0.01

0.19 0.02 0.01

0.21 0.03 0.01

0.26 0.03 0.01

0.21 0.03 0.01

0.26 0.03 0.02

a The scenarios 2G-A to 2G-H represent the values for combined 1G + 2G plants without mixing the material streams. The 1G process (autonomous distillery) comprises the traditional Melle-Boinot steps, while the 2G bioethanol production from sugarcane bagasse and leaves using separate hydrolysis and fermentation [12].

Results from [12] have been used in our study as input data to eight scenarios regarding current and prospective 1G and 2G technologies. The simulations of [12] have considered that 1G plants utilize sugarcane as raw material (165 dry t/h), fresh water (310 m3/h) and Bagasse to boiler (74 dry t/h), producing 46 m3/h of bioethanol, 370 m3/h of Vinasse and 77 MWel of electricity. The integration of 1G and 2G bioethanol production was carried out sequentially investigating eight scenarios (2G-A to 2G-H) for process variables found to have significant impact on the plant design and capital cost. Firstly, a base case (Scenario 2G-A, representing the combined 1G + 2G plant from [12]) is considered. Four combinations of enzyme dosage and enzymatic hydrolysis (EH) residence time were investigated in scenarios 2G-B, 2G-C, 2G-D and 2G-E to identify the scenario with lowest bioethanol selling price. The resulting scenario 2G-E was then used as the starting point for the evaluation of leaves addition (Scenario 2G-F), heat integration (Scenario 2G-G) and leaves addition together with heat integration (Scenario 2G-H) (see Table 1). The estimates of [12] for eight different configurations of combined 1G + 2G bioethanol production plants (2G-A to 2G-H) were used to calculate the cost of the main inputs utilized in each combined 1G + 2G process to produce a monetary unit of bioethanol (presented in Table 2). Those estimates were used to compute eight different sets of technical coefficients for the Brazilian bioethanol sector weighted by the total bioethanol produced in the 1G + 2G plants for each scenario (see Appendix A). These sets of technological coefficients of bioethanol production were used to substitute the technical coefficient of the bioethanol sector into the 2009 Brazilian technical coefficient matrix in each optimization. Moreover, as referred to previously, two sugarcane production scenarios will be considered and also inserted into the technical coefficient matrix according to the scenarios to be optimized. Therefore each bioethanol plant uses different sugarcane types as input: for the current scenario (1G) the current sugarcane scenario is considered, whereas for 1G-optimized and 1G + 2G (2GA to 2G-H) scenarios the future sugarcane scenario is considered.

the outputs of other sectors in fixed proportions in order to produce its own single output and the inputs into a sector are proportional to the total output of that sector [61,49]. The basic IO relationship is:

x¼A xþy

ð1Þ

where A is a matrix of inter-sectoral direct requirements (or technical coefficients matrix) in which each element aij represents the value of input required from sector i to produce one monetary unit worth output of sector j (i = 1, . . . , n; and j = 1, . . . , n), x is the vector of total outputs and y is the vector of the final demand. In this step, the energy flows in the Brazilian National Energy Balance [62] are incorporated into the 2009 Brazilian IO table [63] by considering artificial sectors (see also [57,58]). For this purpose some adjustments and inclusion of new rows and columns in the IO table are necessary in order to incorporate the different energy sectors (or commodities). Thus a new inter-sectoral (or transaction) matrix and vectors for final demand and total output are obtained, in which energy flows are considered in physical quantities of energy (tons of oil equivalent, toe) and all nonenergy sector flows are measured in monetary units. The technical coefficients for the inter-sectoral matrix and final demand vectors with hybrid units (monetary/physical, monetary/monetary, physical/physical or physical/monetary) are then obtained from this new IO framework. The adjustments performed in the IO framework provide: a square matrix with 112 activity sectors split into 53 economic sectors, 5 energy producing sectors, 5 artificial sectors used for distributing the energy consumed by each means of transportation and 49 artificial sectors for energy commodities; 6 column vectors with the components of final demand (exports – exp; public consumption – pc; resident consumption – rc; gross fixed capital formation – gfcf; and changes in inventories – ci); 1 column vector for competitive imports (considered for energy commodities only); and 6 row vectors for the primary inputs (wages, gross mixed income, gross operating surplus, other production taxes and other production subsides).

2.3. Hybrid IO-MOLP model The third step to build the Extended Hybrid IO-MOLP model consists in rearranging the IO tables to obtain technical coefficients in hybrid units. The IO model assumes that each sector consumes Table 1 Summary of the conditions for the 1G + 2G scenarios investigated (based on [12]). Parameters

1G + 2G scenarios 2G-A

1G heat Integration Leaves WIS (%)a Enzyme dosage Hydrolysis time (h) Glucose yield from EH, (%)a Steam Dryer 1G + 2G bioethanol, L/dry t of sugarcane a b

7 Low 72 47 No 329

2G-B

7 Low 72 47 Yes 341

When two values are given the first is for bagasse and the second for leaves. The high enzyme loading is twice the low enzyme loading.

2G-C

7 Low 48 42 Yes 337

2G-D

7 Highb 72 73 Yes 360

2G-E

7 Highb 48 66 Yes 360

2G-F Yes 7/7 Highb 72 73/96 Yes 435

2G-G

2G-H

Yes

Yes Yes 7/14 Highb 72 73/76 Yes 420

7 Highb 72 73 Yes 379

518

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

An external expansion of the IO model is made to estimate GHG emissions from energy combustion, industrial processes, agriculture activities, waste management, wastewater treatment and discharge, and fugitive emissions. In this step, which is based on the IPCC [64] methodology, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emission factors are used in combination with the activity level of specific sectors and final demand components. These estimates give a vector with the environmental impacts per unit of output of the sectors and the final demand, considering the corresponding Global Warming Potential (100-year horizon: 25 for CH4 and 298 for NO2) relative to CO2 [65]. Finally, the MOLP model based on IOA proposed by [57] is used as basis and adapted to the Brazilian reality. In this model a set of 443 variables is considered including: the total output and exports (at basic and consumer prices) of each sector (a total of 336 variables); competitive imports of energy commodities (49 variables); several economic variables (58 variables). The model includes (internal) coherence constraints derived from the IOA and other sets of constraints associated with the structure of the economic system, employment and energy consumption, which are briefly described below. Further details about the MOLP model can be found in [59].

2.4. Model constraints Coherence constraints are used to determine that the intermediate consumption and final demand of each activity sector shall not exceed the corresponding total amount available from national production and competitive imports. The Gross Domestic Product (GDP, expenditure approach) is computed considering the final demand minus imports at FOB (free on board) prices (including tourism). The GDP (production approach) is computed by the sum of gross value added (gva) and the total of taxes less subsides on products (ts) that are not included in the production. The gross value added is given by the sum of wages, gross mixed income, gross operating surplus, other production taxes minus other production subsides. Taxes less subsidies on goods or services are calculated for the intermediate consumption and final demand items. The model also establishes some assumptions for several consumption relations: the households’ consumption on the territory includes the consumption on the territory by resident and nonresident households; the residents’ consumption includes the consumption of households and Non-profit Institutions Serving Households (NPISH); the resident households’ consumption on the territory is linearly dependent on the available income; and the tourism imports is given as a proportion of the residents’ household consumption. The GDP at current prices (gdpcurr) is estimated considering the components of GDP (expense approach) at constant prices and the corresponding deflators. Additionally, the consumption of goods and services by the public administration at current prices and the gross fixed capital formation (gfcf) at current prices are exogenously defined. The residents’ disposable income at current prices (ydcurr) is computed by subtracting the public administration and (nonfinancial and financial) corporations’ disposable incomes from the National Disposable Income. Public debt (debt) is given by the summation of the previous period debt with the symmetrical value of the public administration’s global balance, plus an adjustment variable. Public administration’s global balance (ggb) is computed by subtracting the public administration’s expenditures from the public administration’s revenues.

The employment level (emp) is obtained by using labor gross productivity coefficients for each sector. The total energy consumption for energy purposes (ec) is obtained from the sum of national and imported energy excluding the energy consumed for non-energy purposes, i.e. the energy commodities that are used as raw materials (e.g., primary fuels used to produce secondary fuels, products for the chemical industry, etc.). Specific technical coefficients are applied to the intermediary consumption and final demand. 2.5. Objective functions The objective functions considered in the model are: maximization of GDP (at 2009 basic prices – due to the most recent data available) (gdp), maximization of the employment level (emp), minimization of the total energy consumption for energy purposes (ec) and minimization of the total GHG emissions (ghg). Firstly, each objective function was optimized individually for the 1G scenario to obtain four distinct non-dominated solutions (solutions 1–4). These solutions provide an overview of the range of variation of the objective values within the non-dominated region. Then, for the 1G scenario, the solution that minimizes the Tchebyscheff distance to the ideal solution is computed (solution 5), which allows obtaining a ‘‘balanced” compromise nondominated solution to the MOLP model since this surrogate scalar function enables to obtain the non-dominated solution that minimizes the maximum deviation to the best value reachable in the feasible region for each objective function (which cannot be obtained simultaneously since the objective functions are conflicting). The same procedure is then applied for the 1G-optimized (substituting the 2009-average coefficient vector of the sugarcane sector by the optimized one, solution 6) and prospective technologies (1G + 2G) scenarios (substituting the 2009-average coefficient vector of the sugarcane sector by the optimized one – in all scenarios – and the coefficient vector of the bioethanol sector by the corresponding 1G + 2G vector – in each scenario, solutions 7–14). In this step, two additional constraints are included in the model in order to establish the values of GDP (at 2009 basic prices) and the total (anhydrous + hydrous) bioethanol production at the same levels obtained in the 1G scenario. This procedure enables to compare the impacts due to the specific technologies on the other objectives (and selected decision variables) assuring the same economic performance and bioethanol production level. In order to compare the potential of these new technologies in increasing the total bioethanol production, as well as the impacts on the economic, environmental, energy and employment levels, the maximization of the total (anhydrous + hydrous) bioethanol production in each of the 10 different scenarios (1G, 1G-optimized, 2G-A, 2G-B, 2G-C, 2G-D, 2G-E, 2G-F, 2G-G and 2G-H) are considered (solutions 15–24). Table 3 summarizes the objective functions and scenarios considered in the 24 non-dominated solutions obtained. The MOLP model has been supplied with realistic data gathered from several sources [12,62–64,66–68]. The IO framework has been compiled using a workbook structure of multiple linked spreadsheets (using Microsoft Excel), while the optimizations was performed using Opensolver (OS; http://opensolver.org). The results of all optimizations and the discussion will be presented in the next section. 3. Results and discussion 3.1. Individual optimization of each objective function Tables 4 and 5 outline the results for the objective functions and main variables of 4 non-dominated solutions corresponding to the individual optimization of each objective function (GDP, employ-

x x

x x

x x

x

x

x x

x x x

x x

x

x

x

x x x x x

x

x

x

x

x


a reduction between 1.17 and 1.32 million of employees compared to the solutions that individually optimize GDP and employment objectives;
an increase of approximately 1.11–1.26 million of employees compared to the solutions that individually optimize energy consumption and GHG emissions;
energy consumption and GHG emissions in solutions pertaining to the bioethanol scenarios present a reduction between 8.94– 9.36 Mtoe and 41,846–57,450 Gg CO2eq, respectively, compared to the solutions that individually optimize GDP and employment objectives;
the solutions pertaining to the bioethanol scenarios present an increase of 7.13–7.79 Mtoe and 34,828–36,854 Gg CO2eq, respectively, compared to the solutions that individually optimize the energy consumption and GHG emissions.

x

x

x

x

x x x x

x

x x

x

x

x

x x x x x

x Bioethanol Bioethanol Bioethanol Bioethanol Bioethanol Bioethanol Bioethanol Bioethanol Bioethanol Bioethanol Scenarios

1G 2G-A 2G-B 2G-C 2G-D 2G-E 2G-F 2G-G 2G-H

x Sugarcane Current Sugarcane Optimized Sugarcane Scenarios

x

x Max gdp Max emp Min ghg Min ec Min Tchebyscheff Distance Max Total Bioethanol Objective functions

x

x x x x x x x x x x x x x

ment level, total energy consumption and total GHG emissions) for the 1G scenario. The 2009 values of each objective function and selected variables are included in Tables 4 and 5 for comparison purposes.

Tables 6 and 7 outline the results of the objective functions and main variables of 10 non-dominated compromise solutions obtained by minimizing the distance of the feasible region (using a weighted Tchebyscheff metric, see e.g., [69]) to the ideal solution for the 1G, 1G-optimized and prospective 1G + 2G scenarios (solutions 5–14 in Tables 6 and 7). Solutions that minimize the distance of the feasible region to the ideal solution present objective function values that are well balanced in comparison to the solutions individually optimizing each objective (which are more ‘‘extreme”). In a preliminary overall analysis, solutions considering different bioethanol scenarios (1G, 1G-optimized and 1G + 2G) display:

Sol. 1

x

x

x x x x

Sol. 21 Sol. 20 Sol. 19 Sol. 18 Sol. 17 Sol. 16 Sol. 15 Sol. 14 Sol. 13 Sol. 12 Sol. 11 Sol. 10 Sol. 9 Sol. 8 Sol. 7 Sol. 6 Sol. 5 Sol. 4 Sol. 3 Sol. 2

519

3.2. Minimization of the distance of the feasible region to the ideal solution

Solutions?

Table 3 Summary of the objective functions and scenarios considered in each non-dominated solution analyzed.

x

Sol. 24 Sol. 22

Sol. 23

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

Solution 6 (which minimizes the Tchebyscheff distance to the ideal solution for the 1G-optimized scenario) presents marginal impacts in terms of energy consumption and GHG emissions, as well as unexpressive variations in the employment level in relation to solution 5 (which minimizes the Tchebyscheff distance to the ideal solution for the 1G – 2009 average – scenario). In solution 6 the total energy consumption and GHG emissions are raised by 0.57 ktoe and 2.75 Gg CO2eq, respectively, compared to the 1G scenario. Although the more efficient and higher load capacity trucks for sugarcane transportation demand relatively less diesel oil and lubricants, the requirements of these products are higher as much as the mechanical harvesting process are applied, therefore counterbalancing the impacts. It is noteworthy that due to a lack of a reliable methodology and data shortage, the model has not considered the substitution effects on the direct employment requirements from the harvesting and transportation activities in the 1G-optimized scenario in comparison to the 1G scenario. Thus, the marginal variation observed in the total number of employees is mainly due to indirect effects on the total output of other sectors. The total GHG emissions and energy consumption are dissimilar between the 1G + 2G scenarios:
Solutions 7–9 display lower or similar values compared to the 1G and 1G-optimized scenarios.
Solutions 10–14 present relatively higher values of total GHG emissions and energy consumption than solutions 7–9. In addition, the same differences are observed in the general employment level:

520

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

Table 4 Values of the objective functions in the non-dominated solutions computed in the individual optimization of each objective function. Notation

Description

gdp emp ec ghg

Units

Gross Domestic Product Employees Energy consumption – total GHG emissions – total

a

MR$ Emp.  103 ktoe Gg CO2eq

2009

Max gdp

Max emp

Min ec

Min ghg

3,239,404 41,208 205,679 2,406,175

4,351,337 55,200 273,740 2,738,023

4,351,337 55,319 273,526 2,752,640

4,133,118 52,883 256,800 2,660,362

4,126,511 52,766 257,254 2,659,322

The values in bold represent the ideal solution of each objective function. a Constant 2009 prices.

Table 5 Values of the main variables in the non-dominated solutions computed in the individual optimization of each objective function. Notation

Description

sc ba bh rc pc ci gfcf imp exp ts gva gdpcurr ydcurr ggb debt a b c

Units

Sugarcane Bioethanol Anhydrous Bioethanol Hydrous Resident´s consumption Public consumption Changes in inventories Gross fixed capital formation Imports Exports Taxes Gross value added GDP current Residents’ disposable income Public adm. global balance Public debt

a

MR$ GL GL MR$a MR$a MR$a MR$a MR$a MR$a MR$a MR$a MR$b MR$b MR$b MR$b

2009c

Max gdp

Max emp

Min ec

Min ghg

21,098 7014 19,089 1,979,751 687,001 7471 585,317 360,847 355,653 445,025 2,794,379 3,239,404 2,091,051 149,309 2,167,856

26,612 8836 24,929 2,668,603 922,816 10,082 861,608 436,793 345,186 593,996 3,757,340 6,735,909 4,726,263 130,120 4,558,698

26,703 8922 24,633 2,574,038 922,816 10,221 861,608 436,188 439,284 592,700 3,758,636 6,776,766 4,558,783 139,629 4,568,207

24,569 7014 22,521 2,475,401 922,816 9527 816,599 411,438 339,267 558,856 3,574,262 6,447,358 4,384,091 245,756 4,674,334

25,050 8325 23,357 2,494,412 922,816 9405 786,297 408,272 340,663 557,783 3,568,728 6,486,833 4,417,761 245,774 4,702,954

Constant 2009 prices. Current 2018 prices. The values for gdpcurr, ydcurr, ggb and debt in the column 2009 are in current 2009 prices.

Table 6 Values of the objective functions in the non-dominated solutions computed by minimizing the distance of the feasible region to the ideal solution.

a

Notation

Units

Sol. 5 1G

Sol. 6 1G Opt.

Sol. 7 2G - A

Sol. 8 2G - B

Sol. 9 2G - C

Sol. 10 2G - D

Sol. 11 2G - E

Sol. 12 2G - F

Sol. 13 2G - G

Sol. 14 2G - H

gdp emp ec ghg

MR$a Emp.  103 ktoe Gg CO2eq

4,236,658 54,024 264,410 2,695,334

4,236,658 54,024 264,410 2,695,337

4,236,658 54,029 264,379 2,695,190

4,236,658 54,024 264,409 2,695,333

4,236,658 54,024 264,405 2,695,314

4,236,658 54,016 264,453 2,695,541

4,236,658 54,013 264,474 2,695,641

4,236,658 53,998 264,588 2,696,177

4,236,658 54,005 264,519 2,695,853

4,236,658 54,000 264,581 2,696,145

Constant 2009 prices.

Table 7 Values of the main variables in the non-dominated solutions computed by minimizing the distance of the feasible region to the ideal solution.

a b

Notation

Units

Sol. 5 1G

Sol. 6 1G Opt.

Sol. 7 2G - A

Sol. 8 2G - B

Sol. 9 2G - C

Sol. 10 2G - D

Sol. 11 2G - E

Sol. 12 2G - F

Sol. 13 2G - G

Sol. 14 2G - H

sc ba bh rc pc ci gfcf imp exp ts gva gdpcurr ydcurr ggb debt

MR$a GL GL MR$a MR$a MR$a MR$a MR$a MR$a MR$a MR$a MR$b MR$b MR$b MR$b

25,536 8439 23,867 2,551,100 922,816 9870 861,608 425,656 336,660 575,572 3,661,086 6,544,865 4,518,159 193,215 4,621,794

25,536 8439 23,867 2,551,101 922,816 9870 861,608 425,656 336,660 575,574 3,661,083 6,544,865 4,518,160 193,239 4,621,818

25,021 8445 23,862 2,549,135 922,816 9875 861,608 426,333 339,306 575,636 3,661,022 6,544,865 4,514,679 193,232 4,621,811

24,423 8444 23,862 2,549,557 922,816 9874 861,608 426,260 338,811 575,661 3,660,997 6,544,865 4,515,426 193,190 4,621,769

24,473 8444 23,862 2,549,827 922,816 9874 861,608 426,309 338,589 575,664 3,660,994 6,544,865 4,515,904 193,152 4,621,731

23,455 8443 23,863 2,551,814 922,816 9869 861,608 426,363 336,653 575,732 3,660,926 6,544,865 4,519,422 192,900 4,621,478

23,152 8485 23,822 2,552,048 922,816 9479 861,608 426,093 335,758 575,729 3,660,929 6,544,865 4,519,838 192,912 4,621,490

21,565 8502 23,805 2,551,951 922,816 9238 861,608 425,689 335,209 575,776 3,660,882 6,544,865 4,519,666 192,962 4,621,540

22,238 8461 23,846 2,551,637 922,816 9700 861,608 425,965 336,262 575,754 3,660,904 6,544,865 4,519,110 192,980 4,621,559

21,739 8516 23,791 2,552,113 922,816 9108 861,608 425,681 334,911 575,768 3,660,890 6,544,865 4,519,952 192,946 4,621,525

Constant 2009 prices. Current 2018 prices.

521

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526


the employment level increases or has similar values in solutions 7–9 (achieving the highest values in scenario 2G-A, representing an increase of approximately 5.19 thousand employees compared to the 1G scenario);
the employment level decreases in solutions 10–14 (achieving the lowest values in scenario 2G-F, representing a reduction of almost 25.80 thousand employees compared to the 1G scenario). These differences regarding the energy consumption, GHG emissions and employment level can be explained by the higher use of chemicals, especially enzymes (see Table 1), in the scenarios 10–14 in relation to scenarios 7–9. This increases the output of the chemical sector leading to direct and indirect effects on the energy consumption (and consequently on the GHG emissions), as well as on the overall employment level. Another important characteristic is that all 1G + 2G scenarios display a reduction in the total sugarcane outputs, achieving the lowest value in the scenario 2G-F (MR$ 21,565), validating the assumption that less sugarcane is demanded in combined 1G + 2G plants in relation to 1G plants to produce the same bioethanol amount. It is possible to verify in Tables 6 and 7 that even fixing the GDP and the total bioethanol values in those optimizations (solutions 5– 10), the economic structure is remarkably dissimilar in each solution (the same occurs for the amount of hydrous and anhydrous bioethanol produced). The main characteristics of those solutions include: an overall increase of exports, imports, taxes and gross value added in the 1G + 2G scenarios compared to the 1G scenario, while the values of public administration’s global balance (in absolute terms) and public debt have a marginal overall decrease.

3.3. Maximization of the total bioethanol production The results regarding the maximization of the total (anhydrous + hydrous) bioethanol production in each scenario (solutions 15– 24) are presented in Tables 8 and 9. It is possible to verify that:


All solutions achieved the upper limit established for the resident’s consumption (2.75% higher than the values obtained in the optimization of GDP), total exports and GFCF.
All bioethanol scenarios have negative impacts in terms of employment level. The minimum value is obtained in solution 22 that is 5.58% lower than in the solution optimizing the employment level. This fact can be explained by the negative influence on the outputs of the public services (the sectors presenting the major output decreases) due to the lowest level of public consumption in those solutions.
All 1G + 2G scenarios provided improvements in terms of total bioethanol production, achieving the highest level in solution 24 with a surplus of 15.47 ML of bioethanol compared to the 1G scenario (solution 15).
Even with a higher bioethanol production the total sugarcane requirements are reduced in all 1G + 2G scenarios, achieving the lowest value in solution 22 (MR$ 23,790).
The values of imports and taxes in all scenarios increase between 1.78–1.84% and 2.34–2.40%, respectively, compared to the solution optimizing the GDP. An important characteristic of these solutions is that even obtaining highest values (in absolute terms) of public debt, the ratio between the public debt and GDP are lower than in the solutions optimizing the energy consumption and GHG emissions. The average values in solutions 15–24 represent 67.7% of the GDP, while in the solutions optimizing the energy consumption and GHG emissions this ratio achieves 72.5%. The major drawbacks of solutions 15–24 are the negative impacts on the total energy consumption and GHG emissions, which achieved in the best case (solution 17) 284,500 ktoe and 2,803,701 Gg of CO2eq, respectively. This can be explained by the influence of higher production of bioethanol on the overall production of all sectors (demonstrated in higher levels of gross value added) and exports, as well as direct and indirect effects from higher consumption of chemical products in the 1G + 2G scenarios.

Table 8 Values of the objective function in the non-dominated solutions considering the maximization of the bioethanol production in each scenario. Notation

Units

Sol. 15 1G

Sol. 16 1G Opt.

Sol. 17 2G - A

Sol. 18 2G - B

Sol. 19 2G - C

Sol. 20 2G - D

Sol. 21 2G - E

Sol. 22 2G - F

Sol. 23 2G - G

Sol. 24 2G - H

ba + bh

GL

35,885

35,885

35,894

35,895

35,895

35,898

35,899

35,900

35,897

35,901

Table 9 Values of selected variables in the non-dominated solutions considering the maximization of the bioethanol production in each scenario.

a b

Notation

Units

Sol. 15 1G

Sol. 16 1G Opt.

Sol. 17 2G - A

Sol. 18 2G - B

Sol. 19 2G - C

Sol. 20 2G - D

Sol. 21 2G - E

Sol. 22 2G - F

Sol. 23 2G - G

Sol. 24 2G - H

gdp emp ec ghg sc ba bh rc pc ci gfcf imp exp ts gva gdpcurr ydcurr ggb debt

MR$a Emp.  103 ktoe Gg CO2eq MR$a GL GL MR$a MR$a MR$a MR$a MR$a MR$a MR$a MR$a MR$b MR$b MR$b MR$b

4,276,212 52,252 284,614 2,803,884 28,143 9703 26,182 2,741,955 687,001 9031 861,608 444,605 439,284 607,924 3,668,287 7,145,234 4,856,175 264,535 4,834,887

4,276,211 52,252 284,614 2,803,884 28,143 9703 26,182 2,741,955 687,001 9032 861,608 444,606 439,284 607,925 3,668,286 7,145,224 4,856,175 264,535 4,834,887

4,276,609 52,256 284,500 2,803,701 27,554 9777 26,117 2,741,955 687,001 8392 861,608 444,848 439,284 608,061 3,668,548 7,145,641 4,856,175 263,934 4,834,887

4,276,671 52,251 284,572 2,803,946 26,902 9777 26,118 2,741,955 687,001 8397 861,608 444,781 439,284 608,089 3,668,582 7,145,657 4,856,175 263,833 4,834,887

4,276,693 52,253 284,550 2,803,985 26,955 9781 26,114 2,741,955 687,001 8356 861,608 444,800 439,284 608,091 3,668,602 7,145,676 4,856,175 263,799 4,834,887

4,276,899 52,248 284,617 2,804,657 25,840 9799 26,098 2,741,955 687,001 8202 861,608 444,746 439,284 608,160 3,668,740 7,145,793 4,856,175 263,472 4,834,887

4,276,986 52,247 284,620 2,804,900 25,519 9809 26,090 2,741,955 687,001 8121 861,608 444,741 439,284 608,182 3,668,804 7,145,844 4,856,175 263,336 4,834,887

4,277,124 52,234 284,833 2,805,540 23,790 9805 26,095 2,741,955 687,001 8157 861,608 444,568 439,284 608,264 3,668,860 7,145,885 4,856,175 263,112 4,834,887

4,276,972 52,238 284,793 2,804,993 24,518 9787 26,110 2,741,955 687,001 8309 861,608 444,568 439,284 608,203 3,668,769 7,145,768 4,856,175 263,348 4,834,887

4,277,155 52,237 284,782 2,805,578 23,983 9814 26,087 2,741,955 687,001 8081 861,608 444,612 439,284 608,262 3,668,893 7,145,918 4,856,175 263,064 4,834,887

Constant 2009 prices. Current 2018 prices.

522

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

3.4. Policy implications Illustrative results have provided valuable insights about the trade-offs involved and allowed identifying the performance and trends of the main variables. The results can be useful for future policies and technological choices related to the bioethanol sector, such as:
Higher diesel oil and lubricants consumption in the mechanical harvesting process in 1G-optimized scenario (solution 6) has counterbalanced the positive effects of more efficient and higher load capacity trucks for sugarcane transportation. This fact lead to negative marginal impacts in terms of total energy consumption and GHG emissions compared to the 1G scenario (solution 5). Thus, more efficient harvesters or engine fueled with other cleaner energy source (e.g., biodiesel) would be useful in reducing the fossil fuel consumption and the GHG emissions associated with the mechanical harvesting.
A reduction in the total sugarcane outputs is obtained in all 1G + 2G scenarios (solutions 7–14). Therefore, the assumption that less sugarcane is required in 1G + 2G plants than the 1G plant to produce the same bioethanol amount is validated.
All solutions maximizing the total (anhydrous + hydrous) bioethanol production (solutions 15–24) achieved the upper bound defined for resident’s consumption, total exports and GFCF. Hence, internal and external expansions of the bioethanol market as well as new investments are necessary to guarantee major production levels.
A lower overall employment level is obtained in the 1G + 2G scenarios (solutions 17–24) compared to the 1G and 1Goptimized scenarios (solutions 15 and 16). This fact is due to relatively less agricultural and industrial employment requirements in the 1G + 2G scenarios. Thus, improving policies to promote training and reabsorption of sugarcane cutters as machine operators or in support and maintenance activities will be positive to reduce the impacts on the sugarcane cultivation process. In addition, the lesser industrial employment requirements by unit of bioethanol produced in the 2G process would be counterbalanced if the productivity of the 2G technology also provides cost reduction in the process, increasing the competitiveness of the product and improving the bioethanol market. It is also important to refer that the 2G technology would be useful in reducing the employment seasonality in the sector, given that the raw material to produce 2G bioethanol can be stocked and used in the sugarcane off season.
An important negative effect in all scenarios maximizing the total bioethanol production (solutions 15–24) is the increase of the total energy consumption and the GHG emissions caused by the higher production of bioethanol on the overall production of all sectors and exports. Possibly this fact is related to the fossil fuel intensity of the Brazilian economy, such that indirect effects from higher bioethanol production are propagated through the whole economy. Policies envisaging the reduction of the coefficients of energy consumption, and consequently the associated GHG emissions, will have a remarkable positive impact. Possible options include, for example, efficiency measures and the substitution of fossil energy by other cleaner sources, such as: increasing the wind and solar energy production, reducing the thermal power generation, increasing the share of biodiesel, bioethanol or even electricity in the transportation sector.
Direct and indirect effects from different patterns of chemical utilization (especially enzymes) in the 2G processes have influenced the total GHG emissions, energy consumption and

employment level. Nevertheless, the cost of chemicals is expected to decrease in the medium to long term as far as new technological improvements and economies of scale are attained. Hence, the negative influence of chemicals on the overall cost and consumption pattern of the 2G processes can be reduced, as well as the corresponding direct and indirect effects. 4. Conclusions An extended hybrid IO-MOLP model to assess the impacts of different 1G and 1G + 2G bioethanol production processes on the Brazilian economic system and the bioethanol supply in prospective scenarios was presented. Two sugarcane cultivation, two 1G and eight 2G bioethanol production processes scenarios have been set. Four objective functions and several defining, environmental and economic constraints have been considered in the MOLP model developed in this study. Relevant indicators involving economic, social, environment and energy concerns have been evaluated, considering several activity sectors and a scenario for 2018. Illustrative results are presented and the main characteristics and trade-offs are identified. The integration of IO hybrid formulation and LCA approaches has allowed incorporating different processes into the model and expand the boundaries of analysis. The integrated- or countrybasis analysis, as presented in this study can complement the plant- or process-basis analysis of the energy commodities. The integrated- or country-basis analysis would be useful in developing the product, specially the 2G bioethanol, improving the (renewable) energy supply, reducing the overall GHG emissions and affording the employment level sustainability. It is worthwhile noting that policies and technological choices for prospective bioethanol production will have to take into account the direct and indirect effects on the whole economic system, since positive direct effects of 1G + 2G plants can be counterbalanced by indirect impacts on the output of other sectors, mainly from the utilization of chemicals in the process. The heterogeneity of sectors and the uncertainty associated with prospective scenarios of the Brazilian economic system and the inter-relationships between sectors are limitations inherent to this type of model and can lead to a bias in the estimates. The assumptions and simplifications of IO models, such as fixity of coefficients, constant returns to scale, and homogeneity of production, may lead to criticisms about the exclusion of joint production, the uniqueness of technological process to each sector and the capacity of economic agents in responding to exogenous shocks. However, these assumptions only become limitations when they compromise the integrity of the conclusions that are being drawn from the research, by which some adjustments can be made [70,71]. Nevertheless, the IO framework coupled with the MOLP model has provided a useful tool to assess the impacts from different processes as a result of changes in the output of economic sectors in prospective scenarios. This methodological framework can be extended to other countries, target sectors or energy products to shape appropriate policies. Since only the main inputs in the sugarcane and 2G bioethanol life cycle are considered, further improvements can include other less expressive inputs and outputs of those processes within a more detailed framework. In addition, different electricity cogeneration options can be investigated. The deterministic characteristics of the coefficients and variables, as well as the uncertainty inherent to the calculation of environmental impacts and economic projections are issues that can be avoided by including the treatment of uncertainty in future extensions of this model using, for example,

523

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

and PTDC/SEN-TRA/117251/2010 and doctoral grant SFRH/ BD/42960/2008. This work has been developed under the Energy for Sustainability Initiative of the University of Coimbra.

interval programming techniques to reach robust recommendations compatible with a range of input parameters. Acknowledgements

Appendix A This work has been supported by the Portuguese Foundation for Science and Technology under projects UID/MULTI/00308/2013

See Table A1.

Table A1 Technical coefficients for the 1G and 2G scenarios investigated (R$/toe or toe/toe). Sectors/fuels

1G

2G-A

2G-B

2G-C

2G-D

2G-E

2G-F

2G-G

2G-H

Agriculture and forestry Production of sugarcane Production of wood and charcoal Wood Charcoal Livestock and fishing Petroleum and natural gas Crude oil Natural gas wet Natural gas dry Iron ore Other extractive industry Coal production Steam coal 3100 Steam coal 3300 Steam coal 3700 Steam coal 4200 Steam coal 4500 Steam coal 4700 Steam coal 5200 Steam coal 5900 Steam coal 6000 Steam coal without specification Metallurgical coal Food and beverage Tobacco products Textiles Clothing and accessories Leather goods and footwear Wood products - except furniture Pulp and paper products Newspapers, magazines, CDs and other products recorded Petroleum refining and coke Automotive gasoline Fuel oil Diesel oil Aviation gasoline Liquefied petroleum gas Naphtha Kerosene Illuminated Jet Kerosene Gas coke Coke coal Refinery gas Petroleum coke Other energy petroleum products Tar Asphalt Lubricants Solvents Other non-energy petroleum products Alcohol Anhydrous bioethanol Hydrous bioethanol Sugarcane Juice Molasses Sugarcane Bagasse Chemicals Manufacture of resins and elastomers Pharmaceutical products Agrochemicals

0.00012531 0.17934350 0.00000000 0.00000000 0.00000000 0.00366046 0.00000000 0.00000000 0.00000000 0.00000000 0.00000083 0.00000151 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.01037657 0.00000002 0.00060517 0.00000244 0.00000098 0.00000258 0.00068549 0.00000197

0.00010817 0.17229684 0.00000000 0.00000000 0.00000000 0.00000003 0.00000000 0.00000000 0.00000000 0.00000000 0.00000072 0.00000134 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00895729 0.00000002 0.00052240 0.00000211 0.00000084 0.00000223 0.00059173 0.00000146

0.00010436 0.16409073 0.00000000 0.00000000 0.00000000 0.00000003 0.00000000 0.00000000 0.00000000 0.00000000 0.00000069 0.00000130 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00864207 0.00000002 0.00050402 0.00000203 0.00000081 0.00000215 0.00057090 0.00000140

0.00010560 0.16476892 0.00000000 0.00000000 0.00000000 0.00000003 0.00000000 0.00000000 0.00000000 0.00000000 0.00000070 0.00000131 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00874465 0.00000002 0.00051000 0.00000206 0.00000082 0.00000217 0.00057768 0.00000142

0.00009885 0.15080008 0.00000000 0.00000000 0.00000000 0.00000003 0.00000000 0.00000000 0.00000000 0.00000000 0.00000066 0.00000123 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00818596 0.00000002 0.00047741 0.00000193 0.00000077 0.00000203 0.00054077 0.00000133

0.00009885 0.14678138 0.00000000 0.00000000 0.00000000 0.00000003 0.00000000 0.00000000 0.00000000 0.00000000 0.00000066 0.00000123 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00818596 0.00000002 0.00047741 0.00000193 0.00000077 0.00000203 0.00054077 0.00000133

0.00008181 0.12503387 0.00000000 0.00000000 0.00000000 0.00000002 0.00000000 0.00000000 0.00000000 0.00000000 0.00000054 0.00000102 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00677459 0.00000002 0.00039510 0.00000159 0.00000064 0.00000168 0.00044754 0.00000110

0.00009390 0.13412254 0.00000000 0.00000000 0.00000000 0.00000002 0.00000000 0.00000000 0.00000000 0.00000000 0.00000062 0.00000117 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00777559 0.00000002 0.00045348 0.00000183 0.00000073 0.00000193 0.00051366 0.00000126

0.00008473 0.12746704 0.00000000 0.00000000 0.00000000 0.00000002 0.00000000 0.00000000 0.00000000 0.00000000 0.00000056 0.00000105 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00701654 0.00000002 0.00040921 0.00000165 0.00000066 0.00000174 0.00046352 0.00000114

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.00042538 0.00000052

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.05274093 0.00000045

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.06053489 0.00000043

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.06189334 0.00000044

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.08461411 0.00000041

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.09261657 0.00000041

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.11439605 0.00000034

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.09402625 0.00000039

0.00000000 0.00000000 0.00000000 0.18429477 0.05141612 0.21000209 0.11577290 0.00000035

0.00000259 0.00002671

0.00000223 0.00002306

0.00000216 0.00002225

0.00000218 0.00002251

0.00000204 0.00002107

0.00000204 0.00002107

0.00000169 0.00001744

0.00000194 0.00002002

0.00000175 0.00001806

(continued on next page)

524

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

Table A1 (continued) Sectors/fuels

1G

2G-A

2G-B

2G-C

2G-D

2G-E

2G-F

2G-G

2G-H

Perfumes, soaps and cleaning supplies Paints, varnishes, enamels and lacquers Chemical products and preparations Rubber and plastic Cement Other products of nonmetallic minerals Manufacture of steel and steel products Metallurgy of nonferrous metals Metal products - except machinery and equipment Machinery and equipment, including maintenance and repairs Appliances Office machines and computer equipment Machinery, equipment and material Electronic and communication equipment Apparatus/instruments healthcare, optical and measurement Cars, vans and utilities Trucks and buses Parts and accessories for motor vehicles Other transportation equipment Furniture and products from other industries Gas, water, sewer and street cleaning Electricity Distribution Uranium (U308) Hydro Bleach Other Renewable Nuclear power plants (Uranium C, UO2) Public service power plants Cogeneration Construction Trade Transport, storage and mail Road transport Pipelines Railway transport Water-borne transport Aviation transport Information services Financial intermediation and insurance Real estate and rents Maintenance and repair Accommodation services and meals Business services Private education Private health Services for families Public education Public health Public service and social security

0.00000233

0.00000201

0.00000194

0.00000197

0.00000184

0.00000184

0.00000152

0.00000175

0.00000158

0.00000212

0.00000183

0.00000177

0.00000179

0.00000167

0.00000167

0.00000138

0.00000159

0.00000143

0.00022850

0.00019725

0.00019031

0.00019256

0.00018026

0.00018026

0.00014918

0.00017122

0.00015451

0.00341800 0.00000755 0.00082011

0.00295050 0.00000652 0.00070794

0.00284667 0.00000629 0.00068303

0.00288045 0.00000636 0.00069113

0.00269643 0.00000595 0.00064698

0.00269643 0.00000595 0.00064698

0.00223152 0.00000493 0.00053543

0.00256125 0.00000566 0.00061454

0.00231122 0.00000510 0.00055455

0.00004896

0.00004227

0.00004078

0.00004126

0.00003863

0.00003863

0.00003197

0.00003669

0.00003311

0.00006440 0.00425346

0.00005559 0.00367168

0.00005363 0.00354247

0.00005427 0.00358452

0.00005080 0.00335551

0.00005080 0.00335551

0.00004204 0.00277697

0.00004825 0.00318729

0.00004354 0.00287615

0.00394162

0.00340249

0.00328276

0.00332172

0.00310950

0.00310950

0.00257338

0.00295361

0.00266529

0.00000794 0.00000985

0.00000685 0.00000850

0.00000661 0.00000820

0.00000669 0.00000830

0.00000626 0.00000777

0.00000626 0.00000777

0.00000518 0.00000643

0.00000595 0.00000738

0.00000537 0.00000666

0.00019839

0.00017125

0.00016523

0.00016719

0.00015651

0.00015651

0.00012952

0.00014866

0.00013415

0.00001908

0.00001647

0.00001589

0.00001608

0.00001505

0.00001505

0.00001246

0.00001430

0.00001290

0.00000550

0.00000474

0.00000458

0.00000463

0.00000434

0.00000434

0.00000359

0.00000412

0.00000372

0.00000047 0.00000927 0.00018460

0.00000041 0.00000800 0.00015935

0.00000039 0.00000772 0.00015375

0.00000040 0.00000781 0.00015557

0.00000037 0.00000731 0.00014563

0.00000037 0.00000731 0.00014563

0.00000031 0.00000605 0.00012052

0.00000035 0.00000695 0.00013833

0.00000032 0.00000627 0.00012483

0.00000275 0.00000777

0.00000238 0.00000670

0.00000229 0.00000646

0.00000232 0.00000654

0.00000217 0.00000612

0.00000217 0.00000612

0.00000180 0.00000506

0.00000206 0.00000581

0.00000186 0.00000525

0.00098510

0.00036686

0.00035395

0.00035815

0.00033527

0.00033527

0.00027747

0.00031846

0.00028738

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00641254 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

0.00000000 0.00000000 0.00000000 0.00000000 0.00005561 0.00470288 0.00760046 0.00000000 0.00000000 0.00000000 0.00068096 0.00719549

0.00000000 0.00000000 0.00000000 0.00000000 0.00004801 0.00406650 0.00656088 0.00000000 0.00000000 0.00000000 0.00058121 0.00621130

0.00000000 0.00000000 0.00000000 0.00000000 0.00004632 0.00392340 0.00633000 0.00000000 0.00000000 0.00000000 0.00056076 0.00599272

0.00000000 0.00000000 0.00000000 0.00000000 0.00004687 0.00396997 0.00640513 0.00000000 0.00000000 0.00000000 0.00056741 0.00606385

0.00000000 0.00000000 0.00000000 0.00000000 0.00004387 0.00371633 0.00599592 0.00000000 0.00000000 0.00000000 0.00053116 0.00567644

0.00000000 0.00000000 0.00000000 0.00000000 0.00004387 0.00371633 0.00599592 0.00000000 0.00000000 0.00000000 0.00053116 0.00567644

0.00000000 0.00000000 0.00000000 0.00000000 0.00003631 0.00307558 0.00496214 0.00000000 0.00000000 0.00000000 0.00043958 0.00469774

0.00000000 0.00000000 0.00000000 0.00000000 0.00004167 0.00353002 0.00569533 0.00000000 0.00000000 0.00000000 0.00050453 0.00539187

0.00000000 0.00000000 0.00000000 0.00000000 0.00003761 0.00318542 0.00513936 0.00000000 0.00000000 0.00000000 0.00045528 0.00486552

0.00096477 0.00015882 0.00047220

0.00083281 0.00013710 0.00040761

0.00080351 0.00013227 0.00039327

0.00081304 0.00013384 0.00039794

0.00076110 0.00012529 0.00037251

0.00076110 0.00012529 0.00037251

0.00062988 0.00010369 0.00030829

0.00072294 0.00011901 0.00035384

0.00065237 0.00010739 0.00031930

0.00387139 0.00003501 0.00007377 0.00059180 0.00000759 0.00000008 0.00031084

0.00334187 0.00003022 0.00006368 0.00051086 0.00000655 0.00000007 0.00026833

0.00322427 0.00002916 0.00006144 0.00049288 0.00000632 0.00000007 0.00025888

0.00326254 0.00002950 0.00006217 0.00049873 0.00000640 0.00000007 0.00026196

0.00305410 0.00002762 0.00005819 0.00046687 0.00000599 0.00000007 0.00024522

0.00305410 0.00002762 0.00005819 0.00046687 0.00000599 0.00000007 0.00024522

0.00252753 0.00002286 0.00004816 0.00038637 0.00000496 0.00000005 0.00020294

0.00290099 0.00002623 0.00005528 0.00044346 0.00000569 0.00000006 0.00023293

0.00261780 0.00002367 0.00004988 0.00040017 0.00000513 0.00000006 0.00021019

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526

References [1] Renewable Fuel Association. World fuel ethanol production – 2015 Available atAvailable from: 2016 [accessed 08 Jun 2016]. [2] Companhia Nacional de Abastecimento (CONAB). CONAB database Available atAvailable from: 2015 [accessed 05 May 2015]. [3] Empresa de Pesquisa Energética (EPE). Brazilian energy balance – year 2014. Empresa de Pesquisa Energética. Brazil: Empresa de Pesquisa Energética; 2015. [4] Macedo I, Leal M, Hassuani S. Sugar cane residues for power generation in the sugar/ethanol mills in Brazil. Energy Sust Dev 2001;5(1):77–82. http://dx.doi. org/10.1016/S0973-0826(09)60022-3. [5] Ensinas A, Arnao J, Nebra S. Increasing energetic efficiency in sugar, ethanol, and electricity producing plants. In: Cortez L, editor. Sugarcane bioethanol: R&D for productivity and sustainability. São Paulo (Brazil): Edgard Blucher Ltda; 2010. p. 583–600. [6] Dwivedi P, Alavalapati J, Lal P. Cellulosic ethanol production in the United States: Conversion technologies, current production status, economics, and emerging developments. Energy Sust Dev 2009;13:174–82. http://dx.doi.org/ 10.1016/j.esd.2009.06.003. [7] Mosqueira-Salazar K, Palacios-Bereche R, Chávez-Rodríguez M, Seabra J, Nebra S. Reduction of water consumption in an integrated first- and secondgeneration ethanol plant. Energy Sust Dev 2013;17:531–5. http://dx.doi.org/ 10.1016/j.esd.2013.08.001. [8] Rosillo-Calle F, Walter A. Global market for bioethanol: historical trends and future prospects. Energy Sust Dev 2006;10(1):20–32. http://dx.doi.org/ 10.1016/S0973-0826(08)60504-9. [9] Hahn-Hägerdal B, Galbe M, Gorwa-Grauslund M, Lidén G, Zacchi G. Bioethanol - the fuel of tomorrow from the residues of today. Trends Biotechnol 2006;24 (12):549–56. http://dx.doi.org/10.1016/j.tibtech.2006.10.004. [10] Galbe M, Sassner P, Wingren A, Zacchi G. Process engineering economics of bioethanol production. Adv Biochem Eng Biotechnol 2007;108:303–27. http:// dx.doi.org/10.1007/10_2007_063. [11] Cardona C, Quintero J, Paz I. Production of bioethanol from sugarcane bagasse: status and perspectives. Bioresour Technol 2010;101(13):4754–66. http://dx. doi.org/10.1016/j.biortech.2009.10.097. [12] Macrelli S, Mogensen J, Zacc G. Techno-economic evaluation of 2nd generation bioethanol production from sugar cane bagasse and leaves integrated with the sugar-based ethanol process. Biotechnol Biofuels 2012;5(22):1–18. http://dx. doi.org/10.1186/1754-6834-5-22. [13] Khatiwada D, Leduc S, McCallum I. Optimizing ethanol and bioelectricity production in sugarcane biorefineries in Brazil. Renew Energy 2016;85:371–86. http://dx.doi.org/10.1016/j.renene.2015.06.009. [14] International Standard Organization (ISO). Environmental management – life cycle assessment – principles and framework – TC 207/SC 5 ISO 14040. Geneva: International Standard Organization; 1997. [15] Rebitzer G, Ekvall T, Frischknecht R, Hunkeler D, Norris G, Rydberg T, et al. Life cycle assessment. Part 1: Framework, goal and scope definition, inventory analysis, and applications. Environ Int 2004;30(5):701–20. http://dx.doi.org/ 10.1016/j.envint.2003.11.005. [16] Narayanaswamy V, Althman W, Berkel R, McGregor M. A primer on environmental Life Cycle Assessment (LCA) for Australian grains. Australia: Curtin University of Technology, Centre of Excellence in Cleaner Production; 2002. [17] Guinée J, Gorrée M, Huppes R, Kleijn R, Koning A, Oers L, et al. Life cycle assessment - an operational guide to the ISO standards. Netherlands: Leiden University, Centre of Environmental Science; 2001. [18] Ometto A. Avaliação do ciclo de vida do álcool etílico hidratado combustível pelos métodos EDIP. Brasil: Tese de doutoramento em Engenharia, Universidade de São Paulo; 2005. [19] Oliveira M, Vaughan B, Rykiel Jr E. Ethanol as fuel: energy, carbon dioxide balances, and ecological footprint. Bioscience 2005;55(7):593–602. [20] Macedo I. Greenhouse gas emissions and energy balances in bio-ethanol production and utilization in Brazil (1996). Biomass Bioenergy 1998;14– 1:77–81. [21] Macedo I, Seabra J, Silva J. Green house gases emissions in the production and use of ethanol from sugarcane in Brazil: the 2005/2006 averages and a prediction for 2020. Biomass Bioenergy 2008;32:582–95. [22] Luo L, Voet E, Huppes G. Life cycle assessment and life cycle costing of bioethanol from sugar cane in Brazil. Renew Sust Energy Rev 2008;615:1–7. [23] Matthews H, Small M. Extending the boundaries of life-cycle assessment through environmental economic input-output models. J Ind Ecol 2000;4 (3):7–10. http://dx.doi.org/10.1162/108819800300106357. [24] Leontief W. Environmental repercussions and the economic structure: an input-output approach. In: Leontief W, editor. Input-output economics. New York (USA): Oxford University Press; 1970. p. 241–60. 1986. [25] Daly H. On economics as a life science. J Polit Econ 1968;76(3):392–406. http://dx.doi.org/10.1086/259412. [26] Victor P. Pollution: economics and environment. London (UK): George Allen & Unwin; 1972. [27] Suh S, Huppes G. Methods for life cycle inventory of a product. J Clean Prod 2005;13(7):687–97. http://dx.doi.org/10.1016/j.jclepro.2003.04.001.

525

[28] Gay P, Proops J. Carbon-dioxide production by th UK economy: an input output assessment. Appl Energy 1993;44:113–30. http://dx.doi.org/10.1016/03062619(93)90057-V. [29] Cruz Jr J, Tan R, Culaba A, Ballacillo J. A dynamic input–output model for nascent bioenergy supply chains. Appl Energy 2009;86:S86–94. http://dx.doi. org/10.1016/j.apenergy.2009.04.007. [30] Chen S, Chen B. Urban energy consumption: different insights from energy flow analysis, input–output analysis and ecological network analysis. Appl Energy 2015;138:99–107. http://dx.doi.org/10.1016/j.apenergy.2014.10.055. [31] Zhang B, Qiao H, Chen Z, Chen B. Growth in embodied energy transfers via China’s domestic trade: evidence from multi-regional input–output analysis. Appl Energy; 2015 [in press]. http://dx.doi.org/10.1016/j.apenergy.2015.09. 076. [32] Chung W, Tohno S, Shim S. An estimation of energy and GHG emission intensity caused by energy consumption in Korea: an energy IO approach. Appl Energy 2009;86:1902–14. http://dx.doi.org/10.1016/j. apenergy.2009.02.001. [33] Chung W, Tohno S, Choi K. Socio-technological impact analysis using an energy IO approach to GHG emissions issues in South Korea. Appl Energy 2011;88:3747–58. http://dx.doi.org/10.1016/j.apenergy.2011.03.033. [34] Burnquist H, Costa C, Guilhoto J. Impacts of changes in regional sugar and ethanol exports upon Brazilian overall economy. In: Input-output and general equilibrium: data, modeling and policy analysis conference, 2 – 4 September, Free University of Brussels, Brussels, Belgium. Available at:Available from: [accessed 25 February 2014]. [35] Costa C, Burnquist H, Guilhoto J. Impacto de alterações nas exportações de açúcar e álcool nas regiões Centro-Sul e Norte-Nordeste sobre a economia do Brasil. Revis Econ Sociol Rural 2006;44(4):609–27. http://dx.doi.org/10.1590/ S0103-20032006000400001. [36] Terciote R. Impactos econômicos da implementação das novas usinas de canade-açúcar. In: Proceedings of the 6th Encontro de Energia no Meio Rural, Campinas, Brazil. Available at:Available from: [accessed 15 February 2014]. [37] Filho J, Filho J. Os impactos econômicos da expansão da produção de etanol sob a ótica da matriz de contabilidade social brasileira. In: Proceedings of the XLVII Congresso da Sociedade Brasileira de Economia, Administração e Sociologia Rural, 26 – 30 July, Porto Alegre, Brazil. Available at:Available from: [accessed 20 February 2014]. [38] Scaramucci J, Cunha M. Bioethanol as basis for regional development in Brazil: an input-output model with mixed technologies. In: Intermediate international input-output meeting, 26 – 28 July, Sendai, Japan. Available at: Available from: [accessed 10 January 2014]. [39] Scaramucci J, Cunha M. Aspectos socioeconômicos do uso energético da biomassa de cana-de-açúcar. In: Cortez L, Lora E, Gómez E, editors. Biomassa para Energia, Editora Unicamp, Brazil; 2008. p. 699–730. [40] Cunha M, Scaramucci J. The construction of an updated economic database for energy studies: an application to the Brazilian sugarcane agroindustry. In: International conference on regional and urban modeling, 1 – 3 June, Brussels, Belgium. Available at:Available from: [accessed 10 February 2014]. [41] Compeán R, Polenske K. Antagonistic bioenergies: technological divergence of the ethanol industry in Brazil. Energy Policy 2011;39:6951–61. http://dx.doi. org/10.1016/j.enpol.2010.11.017. [42] Watanabe M, Chagas M, Cavalett O, Cunha M, Bonomi A. Integrating Life Cycle Assessment and Input-Output Analysis for the assessment of ethanol greenhouse gases emission in Brazil. In: 4th international workshop: advances in cleaner production - integrating cleaner production into sustainability strategies, 22 – 24 May, São Paulo, Brazil. Available at: Available from: [accessed 15 January 2013]. [43] Rojas S. Integração do enfoque Input-Output na Avaliação de Ciclo de Vida. Instituto Brasileiro de Informação em Ciência e Tecnologia, Brazil; 2009. Available at: [accessed 10 January 2014]. [44] Bush R, Jacques D, Scott K, Barrett J. The carbon payback of micro-generation: an integrated hybrid input–output approach. Appl Energy 2014;119:85–98. http://dx.doi.org/10.1016/j.apenergy.2013.12.063. [45] Igos E, Rugani B, Rege S, Benetto E, Drouet L, Zachary D. Combination of equilibrium models and hybrid life cycle-input–output analysis to predict the environmental impacts of energy policy scenarios. Appl Energy 2015;145:234–45. http://dx.doi.org/10.1016/j.apenergy.2015.02.007. [46] Hilgemberg E. Quantificação e Efeitos Econômicos do Controle de Emissões de CO2 Decorrentes do Uso de Gás Natural, Álcool e Derivados de Petróleo no Brasil: Um modelo Inter-regional de Insumo-Produto PhD Thesis. Piracicaba (Brazil): University of São Paulo; 2004. Available at:Available from: [accessed 20 February 2014]. [47] Figueiredo N, Júnior I, Perobelli F. Construção da matriz de insumo-produto híbrida para o estado de Pernambuco e avaliação da intensidade energética e de emissões de CO2 setorial. In: Proceedings of the Fórum Banco do Nordeste

526

[48]

[49] [50]

[51]

[52]

[53]

[54]

[55] [56]

[57]

[58]

[59]

A.L. de Carvalho et al. / Applied Energy 181 (2016) 514–526 do Brasil de desenvolvimento – XIV Encontro regional de economia, 16 – 17 July, Banco do Nordeste do Brasil, Fortaleza, Brazil. Available at:Available from: [accessed 1 March 2014]. Santiago F. Um modelo econométrico + insumo-produto para a previsão de longo prazo da demanda de combustíveis no Brasil MSc Thesis. Brazil: Universidade Federal de Juiz de Fora; 2009. Miller R, Blair P. Input-output analysis: foundations and extensions. 2nd ed. New York (USA): Cambridge University Press; 2009. Moulik T, Dholakia B, Dholakia R, Ramani K. Energy planning in India: the relevance of regional planning for natural policy. Energy Policy 1992;20 (9):836–46. http://dx.doi.org/10.1016/0301-4215(92)90120-Q. Hristu-Varsakelis D, Karagianni S, Pempetzoglou M, Sfetsos A. Optimizing production with energy and GHG emission constraints in Greece: an input– output analysis. Energy Policy 2010;38(3):1566–77. http://dx.doi.org/ 10.1016/j.enpol.2009.11.040. Tan R, Aviso K, Barilea I, Culaba A, Cruz Jr J. A fuzzy multi-regional input– output optimization model for biomass production and trade under resource and footprint constraints. Appl Energy 2012;90:154–60. http://dx.doi.org/ 10.1016/j.apenergy.2011.01.032. Cho C. The economic-energy-environmental policy problem: an application of the interactive multiobjective decision method for Chungbuk Province. J Environ Manage 1999;56(2):119–31. http://dx.doi.org/ 10.1006/jema.1999.0264. Hsu G, Chou F. Integrated planning for mitigating CO2 emissions in Taiwan: a multi-objective programming approach. Energy Policy 2000;28(8):519–23. http://dx.doi.org/10.1016/S0301-4215(00)00006-9. Chen T. The impact of mitigating CO emissions on Taiwan’s economy. Energy Econ 2001;23:141–51. http://dx.doi.org/10.1016/S0140-9883(00)00060-8. Kravtsov M, Pashkevich A. A multicriteria approach to optimization of the gross domestic product. Autom Remote Control 2004;65(2):337–45. http://dx. doi.org/10.1023/B:AURC.0000014730.28129.e5. Oliveira C, Antunes C. A multiple objective model to deal with economyenergy-environment interactions. Eur J Oper Res 2004;153:370–85. http://dx. doi.org/10.1016/S0377-2217(03)00159-0. Oliveira C, Antunes C. A multi-objective multi-sectoral economy-energyenvironment model: application to Portugal. Energy 2011;36(5):2856–66. http://dx.doi.org/10.1016/j.energy.2011.02.028. Carvalho A, Antunes C, Freire F, Henriques C. A hybrid input-output multiobjective model to assess economic-energy-environment trade-offs in Brazil. Energy 2015;82:769–85. http://dx.doi.org/10.1016/j.energy.2015.01.089.

[60] Carvalho A, Antunes C, Freire F, Henriques C. A multi-objective interactive approach to assess economic-energy-environment trade-offs in Brazil. Renew Sust Energy Rev 2016;54:1429–42. http://dx.doi.org/10.1016/j. rser.2015.10.064. [61] Leontief W. Input-output analysis. In: Leontief W, editor. Input-output economics. New York (USA): Oxford University Press; 1985. p. 19–40. 1986. [62] Ministério das Minas e Energia (MME). Balanço Energético Nacional, Ministério das Minas e Energia, Brazil Available at:Available from: 2010 [accessed 10 June 2013]. [63] Guilhoto J, Sesso Filho U. Estimação da Matriz Insumo-Produto Utilizando Dados Preliminares das Contas Nacionais: Aplicação e Análise de Indicadores Econômicos para o Brasil em 2005. Econ Technol 2010;23:53–62. http://dx. doi.org/10.2139/ssrn.1836495. [64] Intergovernmental Panel on Climate Change (IPCC). IPCC guidelines for national greenhouse gas inventories, intergovernmental panel on climate change/institute for global environmental strategies (IGES). Japan: Hayama; 2006. [65] Intergovernmental Panel on Climate Change (IPCC). Climate change 2007 – the physical science basis. New York (USA): Intergovernmental Panel on Climate Change, Cambridge University Press; 2007. [66] Instituto Brasileiro de Geografia e Estatística (IBGE). Matriz Insumo-Produto Brasil 2000/2005, Instituto Brasileiro de Geografia e Estatística, Brazil; 2008. [67] Ministério da Ciência e Tecnologia (MCT). Segunda Comunicação Nacional do Brasil à Convenção-Quadro das Nações Unidas sobre Mudança do Clima, Ministério da Ciência e Tecnologia, Brazil Available at:Available from: 2010 [accessed 15 October 2013]. [68] International Monetary Fund (IMF). World economic outlook database, international monetary fund Available at:Available from: 2013 [accessed 10 January 2014]. [69] Steuer R, Choo E. An interactive weighted Tchebycheff procedure for multiple objective programming. Math Program 26, 326–344. http://dx.doi.org/10. 1007/BF02591870. [70] Bickneel K, Ball R, Cullen R, Bigsby H. New methodology for the ecological footprint with an application to the New Zealand economy. Ecol Econ 1998;27 (2):149–60. [71] Cruz L. A Portuguese energy-economy-environment input-output model: policy applications Ph.D. Thesis. Keele (UK): Keele University, School of Politics International Relations and the Environment; 2002.