A hybrid input–output multi-objective model to assess economic–energy–environment trade-offs in Brazil

Energy 82 (2015) 769e785

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A hybrid inputeoutput multi-objective model to assess economiceenergyeenvironment trade-offs in Brazil Ariovaldo Lopes de Carvalho a, *, Carlos Henggeler Antunes a, c, Fausto Freire b, Carla Oliveira Henriques a, d a

R&D Institute INESC Coimbra, R. Antero Quental 199, 3000-033 Coimbra, Portugal ADAI-LAETA, Department of Mechanical Engineering, University of Coimbra, Polo II Campus, Rua Luis Reis Santos, 3030-788 Coimbra, Portugal Department of Electrical and Computer Engineering, Polo II Campus, University of Coimbra, 3030-290 Coimbra, Portugal d Polytechnic Institute of Coimbra, Quinta Agrícola, Bencanta, 3040-316 Coimbra, Portugal b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 April 2014 Received in revised form 21 December 2014 Accepted 8 January 2015 Available online 5 March 2015

A multi-objective linear programming (MOLP) model based on a hybrid InputeOutput (IO) framework is presented. This model aims at assessing the trade-offs between economic, energy, environmental (E3) and social objectives in the Brazilian economic system. This combination of multi-objective models with InputeOutput Analysis (IOA) plays a supplementary role in understanding the interactions between the economic and energy systems, and the corresponding impacts on the environment, offering a consistent framework for assessing the effects of distinct policies on these systems. Firstly, the System of National Accounts (SNA) is reorganized to include the National Energy Balance, creating a hybrid IO framework that is extended to assess Greenhouse Gas (GHG) emissions and the employment level. The objective functions considered are the maximization of GDP (gross domestic product) and employment levels, as well as the minimization of energy consumption and GHG emissions. An interactive method enabling a progressive and selective search of non-dominated solutions with distinct characteristics and underlying trade-offs is utilized. Illustrative results indicate that the maximization of GDP and the employment levels lead to an increase of both energy consumption and GHG emissions, while the minimization of either GHG emissions or energy consumption cause negative impacts on GDP and employment. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Greenhouse gas (GHG) Inputeoutput analysis (IOA) Multi-objective linear programming Multi-sectoral economyeenergy eenvironment models

1. Introduction The interactions between energy consumption and economic growth, as well as environmental impacts, have been widely studied in energy economics literature by different methods (e.g. Refs. [1e3]), including a co-integration test to investigate the causal relationship between renewable and non-renewable energy consumption and economic growth in Brazil over the period 1980e2009 [4]. Even though this causal relationship is not consensual (e.g. Refs. [5e7]) the correlation between these indicators is determinant for the energy and economic policy making process. On one hand, economic growth usually leads to an

* Corresponding author. Tel.: þ351 239 851040/9; fax: þ351 239 824692. E-mail addresses: [email protected] (A.L. Carvalho), ch@ deec.uc.pt (C.H. Antunes), [email protected] (F. Freire), [email protected] (C.O. Henriques). http://dx.doi.org/10.1016/j.energy.2015.01.089 0360-5442/© 2015 Elsevier Ltd. All rights reserved.

increase of energy consumption and consequently environmental impacts, since the current patterns of energy consumption heavily rely on fossil fuels, which are finite resources and important sources of Greenhouse Gas (GHG) emissions. On the other hand, energy and environmental policies may have a negative (constraining) impact on economic growth and social welfare. Therefore, it is important to assess the trade-offs between economic growth, energy demand/supply, as well as environmental and social effects, in order to provide reliable tools for planners and decision makers [8]. Social welfare and infrastructure levels have been recording a steady increase in Brazil mainly due to the recent period of economic growth, which has also influenced energy consumption. Between 2000 and 2012, Brazil's economic growth exceeded the population growth. While the population increased at an annual rate of 1.06%, GDP (gross domestic product) has presented an annual growth rate of 3.16% during the same period [9]. In 2013 Brazil's GDP achieved US$ 2.242 trillion (R$ 4.838 trillion) and a value per capita of US$ 11,153.68 (R$ 24,065.49) [10]. Brazil has also

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been improving the energy supply and the contribution of renewable sources in the energy mix, which represented 42.4% of the domestic energy supply in the country in 2012 [11]. Almost 85% of the Brazilian electricity derived from renewable sources in 2012, in which the domestic hydro generation accounted for 70% of the supply whereas the production of electricity from wind power increased 87% between 2011 and 2012. Moreover, 23,476,667 m3 of bioethanol and 2,717,483 m3 of biodiesel were consumed in the country in 2012, representing approximately 27% and 5% of the total energy consumed in light and heavy vehicles, respectively [11]. However, the fossil fuel production has also risen due to the exploitation of new oil extraction areas in order to satisfy this economic growth, achieving a record production of 2.23 million barrels per day in January 2012 [12]. The average daily production of natural gas achieved 70.6 million m3 per day in 2012 (which represents 11.5% of the energy produced in the country). Although the largest share of CO2 emissions in Brazil results from land use change (LUC) (representing 79% of the total CO2 emissions in 2005), the energy sector was the second major emission source in 2005 (20% of the total) mainly due to road transportation and industry (responsible for 39% and 27% of the emissions of this sector, respectively). Since fossil fuels represent a significant part (almost 57% in year 2012) of the final energy consumption in the country, the impacts of GHG emissions are a drawback for the current and prospective economic growth [11,13]. InputeOutput 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 [14,15]. IO models have then been modified to account for environmental impacts [16e18] using different modeling approaches: generalized IO models [19,20]; economic-ecological models [21,22]; commodity by industry models [23]; and externally expanded IO models [24,25]. IO models have also been adapted for the explicit analysis of the energy sector (e.g. Refs. [26e29]) and to analyze E3 interactions [30e33]. Some studies have applied IO models using hybrid frameworks considering the monetary values regarding the energy sectors in equivalent physical units [15,34]. IO models with hybrid units have been developed to assess the Brazilian economic system: Hilgemberg and Guilhoto [35] applied an IO hybrid model to assess the interactions between carbon and energy intensity, as well as sector output and employment levels for 18 main sectors in the Brazilian economic system; Figueiredo et al. [36] applied an IO hybrid model to assess the energy intensity and CO2 emissions for a specific region in Brazil. Since the seminal work by Dorfman et al. [37], several studies have used linear programming (LP) models coupled with the IO framework for different purposes (see e.g. Refs. [38e40]). The generalization of the IO model using LP enables investigating efficient combinations of inputs and outputs on the boundary of the production possibility frontier [41]. Some studies have introduced environmental and energy (and combinations of both) objectives into IO-LP models, either as the only objective function or combined with economic objectives [42e44]. Nevertheless, models become more able to capture the complexity of real world problems if multiple, conflicting and incommensurable axes of evaluation of distinct potential policies are explicitly considered, which enables to exploit the trade-offs between those competing objectives and analyze a larger set of diversified 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; in general, the concept of efficiency refers to the decision variable space and the concept of non-dominance refers to the objective function space). In this context, multi-objective LP

(MOLP) models coupled with the IO framework have also been developed to assess energy, environmental and economic objectives and their trade-offs. Ni et al. [45] applied an economicenvironmental model to assess the dynamic relationships between economic development, water pollution and the subsequent waste-load allocation in different economic sectors of the Shenzhen Province in China, considering the maximization of GDP and the minimization of the total chemical oxygen demand (COD). Kravtsov and Pashkevich [46] developed an economic-energy model to analyze alternative development options for the national economy of Belarus, considering the maximization of GDP and foreign trade balance and the minimization of the energy requirements. A set of models to assess E3 interactions can also be identified: Cho [47] developed an interactive model to study a regional policy problem in the Chungbuk Province in South Korea, considering an economy with 12 sectors and the optimization of employment, water pollution and energy consumption; Chen [48] developed an MOLP model to investigate the impact of mitigating CO2 emissions on Taiwan's economy, considering the maximization of the GDP and the minimization of CO2 emissions associated with energy consumption for 33 sectors; Antunes et al. [49] developed an IO MOLP model using the TRIMAP interactive environment to analyze the interactions of the energy system with the economy for
bal [50] analyzed how targets for the emissions Portugal; San Cristo of GHGs may be reached and can affect the composition of production activity in Spain considering a goal programming model with 91 sectors, minimizing GHG emissions, waste emissions, and energy requirements, and maximizing employment and output levels. Oliveira and Antunes [51] and Henriques and Antunes [52] constructed IO MOLP models for the Portuguese economy to assess the trade-offs between the maximization of GDP and employment level, and the minimization of energy imports and environmental impacts, tackling the uncertainty of model coefficients using interval programming. Since, in general, energy, economic, environmental and social concerns have conflicting interactions, a broad scrutiny of these evaluation aspects and a thorough appraisal of the trade-offs at stake are required to assess the merits of adopting distinct policies associated with different non-dominated solutions to the MOLP model. Hence, a hybrid IO MOLP model is herein presented and applied to the Brazilian economic system with the aim of assessing the trade-offs associated with the maximization of GDP and employment levels and the minimization of the total energy consumption and GHG emissions, considering 2018 as the planning horizon. The methodology followed in this study and the model formulation are presented in Section 2. The search of nondominated solutions as well as some illustrative results are presented in Section 3. Finally, some conclusions and future developments are drawn in Section 4.

2. Extended hybrid inputeoutput model The IO model assumes that each sector consumes 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 [14,15]. The basic IO relationship is:

x ¼ Ax þ y

(1)

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 ¼ 1, …, n; and j ¼ 1, …, n), x is the vector of total outputs and y is the vector of the final demand.

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In order to assess energy consumption, the 2009 Brazilian IO table [53] is rearranged to include the energy flows in physical units. For this purpose, the data of production and consumption of the different energy commodities available in the Brazilian National Energy Balance [54] are incorporated into the IO table through the consideration of ‘artificial sectors’ (identified with a letter A in the first column of the Table A.3 in Appendix A). The rows and columns associated with the ‘artificial sectors’ for representing the different energy commodities are included in the transactions matrix to allocate the energy flows (in physical units) between energy and non-energy sectors (identified with the letter E and N in the first column of the Table A.3 in Appendix A, respectively). Furthermore, new rows associated with those ‘artificial sectors’ representing the different energy commodities are also included in the final demand and total output vectors. The rows in the transactions table, the final demand and the total output vectors representing the energy sectors, originally computed in monetary units, are substituted by flows in physical units to allocate the energy produced in the corresponding sector (see also Refs. [8,51,52]). I.e., the cells representing the commodities produced in that sector (e.g. anhydrous bioethanol produced in the bioethanol sector) display the values of production of that commodity in the respective sector, while the other cells have null values. This procedure generates a new transactions matrix and new vectors y* and x* with hybrid units, where energy flows are considered in physical quantities of energy (tons of oil equivalent e toe) and all non-energy sector flows are measured in monetary units (Brazilian Real e R$). These new matrix and vectors are used to generate a new hybrid technical coefficients matrix A*, in which the entries are measured in different units (monetary/physical, monetary/monetary, physical/physical or physical/monetary) according to the corresponding activity sectors (and commodities). Thus, from Eq. (1) it is possible to get:

x* ¼ A* x* þ y*

(2)

The adjustments performed in the IO framework provide: a square matrix with 109 activity sectors split into 52 economic sectors, 6 energy producing sectors, 5 artificial sectors used for distributing the energy consumed by each means of transportation and 46 artificial energy-product sectors (see Table A.3 in Appendix A); 6 column vectors with the components of final demand (exports, public consumption, private consumption, gross fixed capital formation e GFCF, and changes in inventories); 1 column vector for competitive imports (considered for energy products only); and 6 row vectors for the primary inputs (wages, gross mixed income, gross operating surplus, other production taxes and other production subsidies). The IO model is expanded in this study in order to estimate GHG emissions from industrial processes, agriculture activities, waste management, wastewater treatment and discharge, and fugitive emissions. The extension of IOA is made by assuming that the amount of GHG generated by a sector is proportional to the amount of output of the sector [25]. The IO system is then extended to assess GHG emissions from energy combustion. The level of activity in each sector is associated with its energy demand (by fuel source). Thus, specific GHG emission factors per unit of each fuel consumed are applied to the total energy consumed in each sector, in order to obtain the total emissions of each activity sector and the whole economy [8]. Data vectors indicating environmental impacts per unit of output are used in the following system of equations [55]:

e ¼ Rx

(3)

where e is a vector with the total direct and indirect GHG emissions and R is an emission coefficient matrix which shows the amount of GHG emitted per output unit of each sector.

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In this study, the MOLP model based on IOA proposed in Refs. [8,51,52] for Portugal is adapted for the Brazilian economic system, which has a very different structure leading to important changes in the mathematical model. The model includes (internal) coherence constraints derived from the IOA, several economic and environmental constraints, as well as constraints for the employment level and energy consumption, which are described in the next sections. The decision variables, the parameters of the model, the sectors considered and the indexes used in the model are presented in the Tables A.1eA.4 in Appendix A. The notation used for both variables and model parameters considers bold letters for vectors, small letters for scalars, capital letters for matrices and the upper “T” designates the transpose of a matrix or a vector.

2.1. Model constraints Coherence constraints establish 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:

Ax þ FDy  x þ impc

(4)

The computation of GDP (at constant prices) according to the expenditure approach is estimated considering the final demand less imports at FOB (free on board) prices (including tourism):

gdpexp ¼ expcpt þ g þ cr þ gfcf±ci  impfob

(5)

The computation of GDP (at constant prices) according to the production approach is the sum of gross value added and the total of taxes less subsidies on products that are not included in production:

gdppro ¼ gva þ ts

(6)

The gross value added is given by the sum of wages, gross mixed income, gross operating surplus, other production taxes and other production subsidies. Specific technical coefficients are used for each item:

gva ¼ awag x þ agmi x þ agos x þ aopt x  aops x

(7)

Taxes less subsidies on goods or services are calculated for the intermediate consumption and final demand items:

ts ¼ Ats x þ FDts y

(8)

The model also establishes some assumptions for several consumption relations: the households' consumption in the territory includes the consumption in the territory by resident and nonresident households; the consumption of residents includes the consumption of households and Non-profit Institutions Serving Households (NPISH); the resident households' consumption in the territory is linearly dependent on the available income; and the tourism imports is given as a proportion of the residents' household consumption. GDP at current prices is estimated considering the components of GDP (at constant prices) according to the expenditure approach and the corresponding deflators. Additionally, the consumption of goods and services by the public administration at current prices and the GFCF at current prices are exogenously defined:

gdpcurr ¼ ðcrÞðpcr Þ þ gcurr þ gfcf curr ±ðciÞðpci Þ 



þ expcpt pexpcpt  impfob pimpfob

(9)

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The residents' disposable income at current prices is estimated by subtracting the public administration and (non-financial and financial) corporations' disposable income from the National Disposable Income:



 ydcurr ¼ gdpcurr 1  psgdpcurr þ piþ  pi þ itsub  itsubg 
 þ ctr  dt  dtc  ssc  itsub  piþ g  pig þ trgh

(10) Public debt is given by the summation of the previous period debt with the symmetrical value of the public administration global balance, plus an adjustment variable:






 debt ¼ debt1  gbþ g  gbg þ dat

(11)

Public administration's global balance is computed by subtracting the public administration's expenditures from the public administration's revenues:




 
  gbþ ¼ dt þ dtc þ ssc þ itsubg þ piþ g  pig  gcurr g  gbg

eiswincinw and ecwincinw represent the corresponding CH4 and N2O values; and elucw is a fixed exogenous variable with the amount of the corresponding CO2, CH4 and N2O emissions from LUC in 2000 provided in the Brazilian National GHG Inventory [13]. Thus the total emissions of CO2 (teco), CH4 (tech) and NO2 (teno) are multiplied by the corresponding Global Warming Potential (100-year horizon: 25 for CH4, 298 for N2O) relative to CO2 [57] to obtain the total GHG emissions (in Gg of CO2 equivalent):

ghg ¼ teco þ 25ðtech Þ þ 298ðteno Þ

(18)

2.2. Objective functions According to the aims of this study, which is assessing the interactions between the economic growth, energy use, GHG emissions and employment level in Brazil for 2018, the model herein proposed explicitly considers four competing objective functions as follows.

The employment level is obtained by using labor gross productivity coefficients for each sector:

2.2.1. Maximization of GDP The GDP is an indicator of global economic performance, which is able to capture variations in production, income and consumption levels in a country. In this sense, the maximization of GDP (at constant prices) is considered:

emp ¼ lp x

Max z1 ¼ gdpexp

 trgh þ ctrg  jurg þ tk þ trkg  gfcf g (12)

(13)

The total energy consumption per sector is obtained from the sum of national and imported energy excluding the energy consumed for non-energy purpose (i.e. energy used as raw materials). Specific technical coefficients are applied to the intermediary consumption and final demand:

cfe ¼ ðAE x  ANE xÞ þ ðFDE y  FDNE yÞ

(14)

The model considers environmental constraints for GHG emissions from carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emission sources. In this step, based on the IPCC methodology [56], emission factors are used in combination with the level of activity of specific sectors and final demand components. A detailed description of those constraints can be consulted in Appendix B. The total emissions of CO2 (teco), CH4 (tech) and N2O (teno) are calculated by adding the corresponding CO2, CH4, and N2O emissions from energy consumption, fugitive emissions, agriculture emissions, solid waste disposal and wastewater and sludge:

teco ¼ ecw þ tfew þ eipw þ elucw þ eswincinw X X emmef rw þ ecrbQw tech ¼ ecw þ tfew þ eipw þ r

(15)

Q

X X þ ericeHw þ elucw þ emswswduw þ emswbtw

2.2.2. Minimization of total energy consumption Although energy consumed in Brazil is mostly produced within the country and new oil reserves have been discovered while the production of renewable fuels have increased, the energy supply/ demand relation can limit the economic growth. Moreover, the oil scarcity highlights the need to improve its efficient utilization. In this sense, the minimization of the energy consumption is considered, in order to assess its impacts on economic growth and GHG emissions as well as on the energy mix. cfe is the sum of all elements of cfe, i.e. for all sectors:

Min z2 ¼ cfe

(20)

2.2.3. Minimization of GHG emissions The correlation between the economic activity (and energy use) and GHG emissions as well as the international agreement on the need to reduce GHG emissions motivate the assessment of the trade-offs associated with GHG emissions, economic growth and energy consumption:

Min z3 ¼ ghg

(21)

u

H

þ eisworgswdw þ eiswincinw þ ecwincinw þ ecdwww X þ echiwwyw y

(16) X X X teno ¼ ecw þ tfew þ eipw þ emmrw þ ecrQw þ esf dw r

þ

(19)

Q

d

X ecrbQw þ elucw þ emswbtw þ eiswincinw Q

þ ecwincinw þ en2owww (17) in which ecw, tfew and eipw are the corresponding total of CO2, CH4 and N2O from the vectors ecw, tfew and eipw; ecrbQW, emswbtw,

2.2.4. Maximization of employment level In order to include the social dimension in this analysis, the employment level, which can be considered as a social welfare indicator, is maximized:

Max z4 ¼ emp

(22)

The application of this methodological approach to a country with the characteristics of Brazil has led to significant modifications in the mathematical model compared to the previous studies applied to Portugal (see Refs. [8,51,52]). The number of sectors and fuels assessed is higher than in previous studies. Apart the GHG emissions from energy combustion, industrial processes, agricultural activities, wastewater, waste treatment and fugitive emissions computed in previous versions of the model, the GHG emissions

A.L. Carvalho et al. / Energy 82 (2015) 769e785

from rice cultivation, fugitive emissions from coal and natural gas production and LUC and forestry emissions are also considered in this model. The model also contains other important changes compared to previous versions, which are not exclusively related to the necessary adaptations due to its application to a different country. The GHG emissions from energy combustion have been estimated by using a bottom-up approach for all GHG (CO2, CH4 and N2O), whereas in the previous studies a top-down approach for CO2 and a bottom-up approach for CH4 and N2O were considered. Furthermore, the computation of CH4 emissions from industrial wastewater is based on the methodology used in the II Brazilian Inventory of GHG emissions, in which the biochemical oxygen demand (BOD) was applied instead of the COD as suggested by the IPCC methodology [56]. This methodology is also different from the one used in the previous studies in which emission factors provided by the Portuguese Inventory of GHG emissions were considered. Finally, the procedure (which is based on the STEM (Step method) interactive method) used to compute the non-dominated solutions is also different from previous studies. 3. Results and discussion The MOLP model has been supplied with real-world data gathered from several sources [9,13,53,54]. The IO framework has been compiled using a workbook structure of multiple linked spreadsheets (using Microsoft Excel), while the multi-objective optimization was performed using Open Solver (OS; http:// opensolver.org). OS is fully adapted to run within Microsoft Excel framework, which avoids a data exporting process to other optimization solvers. Firstly, each objective function was optimized individually, resulting in 4 distinct non-dominated solutions (see Table 1). These solutions provide an overview of the range of variation of the objective values within the non-dominated region. Table 2 displays the values of some of the main economic variables for these non-dominated/efficient solutions, which individually optimize each objective function (Table 1). Solution 1: a) The optimal level for GDP reaches the upper limit established in our projections; b) Positive effects on national production are confirmed by higher values for the gross value added (similar to solution 4); c) The highest direct and indirect tax level is obtained in this solution; d) Lowest values (in absolute terms) of the public administration global balance and public debt (corresponding to 1.93% and 67.68% of current GDP, respectively) e) Higher values for energy consumption e 6.42% higher than the optimal value in solution 2; f) Higher values of GHG emissions e 2.98% higher than the optimal value in solution 3, but with slightly lower values than in solution 4; g) Sectors with the highest variation rate of the output level in comparison to its corresponding output level in the base year are linked to oil and natural gas extraction, oil refining and coke, electricity generation, construction and services for families.

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Solution 2: a) Overall contraction of the economic activity due to lower levels for GDP and gross value added; b) Lower values of indirect taxes lead to negative pressures on the public debt (72.5% of current GDP); c) The most negative value of public administration global balance (3.81% of current GDP); d) Lowest level for the residents' disposable income leads to the lowest private consumption (the main item contributing to the GDP in the expenditure approach); e) The employment level decreases 4.41% in comparison to the optimal solution; f) The optimal energy consumption does not lead to the lowest fossil energy consumption (see Table 6) (slightly higher than in solution 3); g) The energy producing sectors and the energy intensive sectors (chemicals, pulp and paper products, manufacture of steel and steel products, metallurgy of nonferrous metals, iron ore, and other extractive industries) display the lowest variation rate of the output level in comparison to the corresponding output level in the base year. Solution 3: a) The results in solution 3 are quite correlated with the results in solution 2 (since the fossil fuel combustion is one of the main GHG emission sources; see Table 7); b) The overall contraction of the economic activity is also confirmed by the lowest levels for GDP, gross value added and GFCF achieved in this solution; c) The employment level achieves the lowest values between all solutions (4.7% lower than its optimal value in solution 4); d) Lowest level of direct and indirect taxes contributing for negative impacts on the public administration global balance (3.78% of current GDP) and the highest level of public debt in absolute terms (corresponding to 72.5% of current GDP); e) The energy producing sectors and the energy intensive sectors (as in solution 2) display the lowest variation rate of the output level in comparison to the corresponding output level in the base year. Solution 4: a) The results are similar to the ones obtained optimizing GDP (solution 1); b) The upper limit estimated for GDP is also reached, characterizing an alternative optimum to GDP; c) The highest level of gross value added, exports and GFCF (similar to the values in the solution optimizing GDP); d) Lower values of public debt in relation to the GDP (67.40% of current GDP) of these four solutions (slightly higher than solution 1 in absolute terms); e) Lower values of public administration global balance levels (2.06% of current GDP);

Table 1 Optimal solutions to each objective function. Notation

Objective functions

Units

Sol. 1 Max gdp

Sol. 2 Min cfe

Sol. 3 Min ghg

Sol. 4 Max emp

gdpexp cfe ghg emp

Gross domestic product Total energy consumption GHG emissions Employment

R$  106 (2009 constant prices) toe  103 Gg CO2eq Employees  103

4,351,337 273,748 2,759,553 55,199

4,132,918 257,244 2,681,762 52,882

4,124,096 257,505 2,679,694 52,722

4,351,337 274,865 2,777,564 55,324

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Table 2 Values of some economic variables for the individual optima to each function. Notation

Main indicators

Units

cr g ci gfcf impfob exp gva ts gdpcurr ydcurr  gbþ g  gbg debt dt þ dtc

Consumption of residents Public administration consumption Changes in inventories Gross fixed capital formation Imports including tourism Exports including the tourism Gross value added Total indirect taxes (less subsidies) GDP current Households disposable income Public administration global balance Public debt Total direct taxes

R$ R$ R$ R$ R$ R$ R$ R$ R$ R$ R$ R$ R$

            

106 106 106 106 106 106 106 106 106 106 106 106 106

(2009 (2009 (2009 (2009 (2009 (2009 (2009 (2009 (2018 (2018 (2018 (2018 (2018

constant prices) constant prices) constant prices) constant prices) constant prices) constant prices) constant prices) constant prices) current prices) current prices) current prices) current prices) current prices)

f) Higher levels of indirect taxes (following the results in solution 1); g) Higher values for imports (slightly lower than solution 1); h) The energy consumption achieved the highest levels (6.85% higher than the optimal value in solution 2); i) GHG emissions achieved the highest levels (3.65% higher than the optimal value in solution 3); j) Sectors with the highest variation rate of the output level in comparison to the corresponding output level in the base year are other products of nonmetallic minerals, metal products e except machinery and equipment, machinery and equipment, including maintenance and repairs, office machines and computer equipment, trucks and buses, and cement. Some general remarks can be made based on these four solutions: a) It is possible to recognize a strong correlation between economic growth and social welfare (measured through GDP and employment levels) for the Brazilian economic system. In fact, the recent Brazilian GDP growth, which led to an almost full employment condition in the country (the unemployment rate has reached almost 4.8% in January 2014 [58]), validates this statement; b) A correlation between energy consumption and GHG emissions can also be stated, since energy combustion has been one of the main GHG emission sources in this country (see Ref. [13]). Although the upward demand for energy has been mainly supplied by new oil and hydropower facilities, the renewable energy mix is expected to be enlarged by biomass, wind and solar power, which will be a key policy in reducing GHG emissions; c) An antagonism between solutions 1 and 4 and solutions 2 and 3 can also be identified, since the optimal values in solutions 1 and 4 result in higher values of energy consumption and GHG emissions; also the optimal values in solutions 2 and 3 result in lower GDP and employment levels; d) The highest level of gross value added, exports and GFCF (similar to the values in the solution optimizing GDP) obtained in solution 4 indicates that investments have an important role in the job creation process, coupled with the exports increase. Hence, institutional and regulatory reforms that increase the attractiveness of investments will have positive effects on the Brazilian economy and employment level. The reformulation of the taxpaying system by simplifying and reducing the tax burden (that represented 35.85% of GDP in 2013 [59]) for the productive sector is also a point to be considered. These measures will be a key issue in

Sol. 1 Max gdp

Sol. 2 Min cfe

Sol. 3 Min ghg

Sol. 4 Max emp

2,669,420 922,816 10,081 861,608 436,796 344,369 3,757,331 594,005 6,735,909 4,727,712 130,037 4,558,615 637,440

2,475,466 922,816 9,526 816,356 411,469 339,276 3,574,092 558,827 6,447,431 4,384,205 245,809 4,674,387 601,944

2,501,523 922,816 9,359 775,011 407,078 341,183 3,566,702 557,394 6,501,610 4,430,355 245,771 4,713,667 607,543

2,574,224 922,816 10,221 861,608 436,375 439,284 3,758,626 592,710 6,777,279 4,559,113 139,522 4,568,101 629,869

mitigating the risks of investors and create a better environment for investments; e) The optimal energy consumption in solution 2 does not lead to the lowest fossil energy consumption. This fact sheds light on the fossil fuel intensiveness of the Brazilian economy. Although the use of renewable energy has been improved, the Brazilian economy still is very dependent of nonrenewable sources. Therefore, policies seeking an overall reduction in the total energy consumed in the country will affect both renewable and non-renewable energy due to direct and indirect effects, unless the process of substitution of non-renewable by renewable energy sources would be intensified in the future. In this sense, policies should look for harnessing the renewable energy production potential and continuing gradually improving the installed base of hydro, wind and solar power plants, expanding the biomass cogeneration, progressing the lignocellulosic bioethanol technology, increasing the content of bioethanol in the total gasoline and the content of biodiesel in the total diesel oil, whereas developing energy efficiency measures to reduce the fossil fuel intensiveness of the economy; f) It is noteworthy that the deterministic nature of the technical coefficients e which are constant and reflect the economic structure in the base year, in which more fossil fuels are consumed e does not allow capturing internal variations in the pattern of utilization of energy in each sector (e.g. substitution of non-renewable by renewable energy in the transportation sector, etc.) that can occur in the prospective scenario. For this purpose research is currently underway to deal with uncertainty thus enabling a thorough analysis of the changes in the model coefficients to take distinct scenarios into account. In order to complement the analysis of trade-offs, each objective function has been individually optimized considering the optimal values of each objective as constraints in each run. For example, GDP was maximized including, one at a time, bounding constraints for: GHG emissions (i.e., total GHG emissions lower than or equal to its optimal value: ghg  2,679,694 Gg CO2eq), energy consumption (i.e., total energy consumption lower than or equal to its optimal value: cfe  257,244 toe  103), employment (i.e., total employment higher than or equal to its optimal value: emp  55,323,521 employees), and the bounding constraint gdp  4,351,337 R$  106 is applied for the other objective functions. The same procedure has been applied for each objective function. This allowed obtaining the marginal rate of change associated with the bounding constraints of all objectives (regarding their optimal values) for all objective functions presented in Table 3.

A.L. Carvalho et al. / Energy 82 (2015) 769e785 Table 3 Shadow prices for each objective function considering the optimal values of each objective as individual constraints in each optimization. Constraints

Objective functions Max gdp Max emp Min ghg Min cfe 6

gdpexp  4,351,337 (R$  10  2009 1.00 constant prices) emp  55,323,521 (Employees) e (8.78) ghg  2,679,694 (Gg CO2eq) cfe  257,244 (toe  103) (30.88)

e

0.39

0.08

1.00 (166.23) (416.92)

1.37 1.00 (2.87)

0.33 (0.45) 1.00

Hence, the interpretation of the shadow prices obtained is that a marginal increase: - of GHG emissions results in an increase of 8.78 R$  106 of GDP; - of energy consumption leads to an increase of 30.88 R$  106 of GDP; - of GHG emissions results in an increase of 166.23 employees; - of energy consumption leads to an increase of 416.92 employees; and a marginal reduction: - of GDP (R$  106) results in a reduction of 0.39 Gg CO2eq; - of employment leads to a reduction of 1.37 Gg CO2eq; - in the total energy consumption (toe  103) results in an increase of 2.87 Gg CO2eq; - of GDP (R$  106) results in a reduction of 0.08 toe  103; - of employment leads to a reduction of 0.33 toe  103; - in the total GHG emissions (toe  103) results in an increase of 0.45 toe  103. Other more balanced solutions have also been computed using the Step method (STEM) [60]. This method computes the solution that minimizes the worst deviation of all objectives with respect to the “ideal” solution (the optimal values to each objective function, which in general cannot be obtained simultaneously since the objective functions are in conflict) by progressively reducing the feasible region according to the preferences of the decision maker (DM). Non-dominated solutions are computed by using a minemax (Tchebycheff) surrogate scalar function. The solution is presented to the DM, who should specify whether he/she considers this solution as a satisfactory balance between the competing objectives. If any of the objective functions does not yet attain a satisfactory value,

775

the DM should specify the objectives he/she is willing to accept degrading (relaxing) in order to improve the not yet satisfactory objectives. This leads to imposing further constraints on the objective function values and a corresponding reduction of the feasible region. Therefore, the solution search process is guided according to the DM's preferences to the regions where compromise solutions that more closely correspond to preference information are located, which also decreases the computational effort by narrowing down the scope of the search. The process continues until the DM identifies a good compromise solution. The STEM interactive method offers a user-friendly interaction with a DM since all the “dialogue” is made in terms of objective functions values, which is the space more familiar to the DM. This interactive solution search process provides comprehensive information about the solution structure and trade-offs that are at stake between the competing objectives in different regions of the search space, which is progressively reduced. The non-dominated solutions computed using the interactive STEM method are displayed in Table 4. In this first application of the STEM method (1st round) we have considered only two objective functions (maximization of GDP and minimization of GHG emissions; see the corresponding individual optimal solutions in Table 1). Solution 5 (the initial solution computed by STEM) minimizes a weighted Tchebycheff (minemax) distance to the ideal solution (the values in bold in Table 1). For the second STEM iteration a relaxation of 0.5% of the GDP value obtained in solution 5 was specified. This information involves reducing the feasible region in such a way that a new solution (6) is computed trying to improve the total GHG emissions at the expense of letting GDP worsening at most 0.5% with respect to solution 5. Let us suppose that after analyzing solution 6, the DM decides to relax the value of GHG emissions. For this purpose a constraint allowing an increase of 1% in the GHG emissions obtained in solution 6 is included in the model in order to improve the GDP values, leading to solution 7. Finally, the DM decides to assess the effects of a further reduction of 1.5% in the GDP values obtained in solution 7 in order to improve the GHG emission level, thus obtaining solution 8. The main characteristics of these solutions are described in Table 4. Since the main feature of multi-objective models is allowing the explicit consideration of the various axes of evaluation to enable the exploration of trade-offs, we have complemented the analysis by computing non-dominated solutions using the interactive STEM method applied to the four objectives previously specified (2nd round), which are displayed in Table 5. Solution 9 (the initial

Table 4 Results of STEM e 1st round. Objective functions

Units

Sol. 5

Sol. 6

Sol. 7

Sol. 8

GDP GHG

R$  106 (2009 constant prices) Gg CO2eq

4,293,692 2,737,339

4,272,223 2,729,515

4,344,362 2,756,810

4,279,197 2,732,056

Main indicators Total energy consumption Employment Consumption of residents Public administration consumption Changes in inventories Gross fixed capital formation Imports including tourism Exports including the tourism Gross value added Total indirect taxes (less subsidies) GDP current Households disposable income Public administration global balance Public debt Total direct taxes

toe  103 Employees  103 R$  106 (2009 constant prices) R$  106 (2009 constant prices) R$  106 (2009 constant prices) R$  106 (2009 constant prices) R$  106 (2009 constant prices) R$  106 (2009 constant prices) R$  106 (2009 constant prices) R$  106 (2009 constant prices) R$  106 (2018 current prices) R$  106 (2018 current prices) R$  106 (2018 current prices) R$  106 (2018 current prices) R$  106 (2018 current prices)

269,206 54,603 2,614,228 922,816 9,969 861,608 431,196 336,204 3,708,883 584,809 6,639,878 4,629,963 187,569 4,616,148 626,589

267,625 54,384 2,590,433 922,816 9,932 861,608 429,082 336,380 3,690,876 581,347 6,604,114 4,587,819 175,914 4,604,493 622,214

273,185 55,126 2,663,140 922,816 10,067 861,608 436,122 342,987 3,751,465 592,897 6,724,290 4,716,589 133,775 4,562,354 636,168

268,138 54,455 2,598,162 922,816 9,944 861,608 429,768 336,323 3,696,725 582,472 6,615,731 4,601,508 179,700 4,608,279 623,635

776

A.L. Carvalho et al. / Energy 82 (2015) 769e785

Table 5 Results of STEM e 2nd round. Objective functions

Units

Optimal values

Sol. 9

Sol. 10

Sol. 11

GDP Energy consumption GHG Employment

R$  106 (2009 constant prices) toe  103 Gg CO2eq Employees  103

4,351,337 257,244 2,679,694 55,324

4,238,428 265,136 2,717,199 54,040

4,242,222 264,925 2,719,296 54,074

4,244,182 265,560 2,719,296 54,098

Main indicators Consumption of residents Public administration consumption Changes in inventories Gross fixed capital formation Imports including tourism Exports including the tourism Gross value added Total indirect taxes (less subsidies) GDP current Households disposable income Public administration global balance Public debt Total direct taxes

R$ R$ R$ R$ R$ R$ R$ R$ R$ R$ R$ R$ R$

e e e e e e e e e e e e e

2,552,974 922,816 9,873 861,608 425,754 336,657 3,662,530 575,898 6,547,814 4,521,477 192,203 4,620,782 615,327

2,557,139 922,816 9,880 861,608 426,073 336,611 3,665,729 576,493 6,554,134 4,528,855 190,127 4,618,706 616,096

2,559,351 922,816 9,883 861,608 426,320 336,610 3,667,356 576,826 6,557,399 4,532,772 188,986 4,617,564 616,499

            

106 106 106 106 106 106 106 106 106 106 106 106 106

(2009 (2009 (2009 (2009 (2009 (2009 (2009 (2009 (2018 (2018 (2018 (2018 (2018

constant prices) constant prices) constant prices) constant prices) constant prices) constant prices) constant prices) constant prices) current prices) current prices) current prices) current prices) current prices)

solution computed by STEM) minimizes a weighted Tchebycheff distance to the ideal solution (“optimal values” column in Table 5). For the second STEM iteration a relaxation of 1.5% of the GHG emissions value obtained in solution 9 was specified. This information involves reducing the feasible region in such a way that a new solution (10) is computed trying to improve the objective functions that are not yet considered as having a satisfactory value, at the expense of letting the GHG emissions worsen at most 1.5% with respect to solution 9. Since the values for solution 10 were not yet satisfactory a relaxation value of 1.5% of increase in the energy consumption was specified, leading to solution 11. The main characteristics of these solutions are: Solution 9: a) All objective functions achieved intermediate values in the range of variation between the best and worst values in Table 1; b) GDP decreased about 2.59% and the employment level 2.32% compared to their optimal values; c) Energy consumption and the GHG emissions increased 3.07% and 1.40% compared to their optimal values, respectively. Solution 10: a) The relaxation of GHG emissions by 1.5% gives room for slight improvements in the other objective functions; b) The GDP and the employment level increased about 0.09% and 0.064% in relation to their values in solution 9; c) The energy consumption decreased 0.08% in relation to solution 9; d) Even allowing to increase the value for GHG emissions in 1.5% with respect to solution 9, the GHG emissions do not reach the maximum acceptable value defined for the relaxation (approximately 2,758 Tg CO2eq) increasing only 0.08%. Solution 11: a) The relaxation of the energy consumption level by 1.5% is also not fully used - this objective only increased 0.24%; b) Slight improvements in GDP (increased 0.046% with respect to solution 10); c) Slight improvements in the employment level (increased 0.044% with respect to solution 10); d) The GHG emissions values did not change.

Further relaxations, for instance, in the GDP, would not result in the variation of any of the objective function values. The reduced feasible region after the previous STEM iterations becomes too rigid, in such a way that a relaxation of GDP is not able to improve the values in any of the other objectives. Thus, solution 11 can be considered a well-balanced solution according to the preferences revealed by the DM, or the interactive procedure may be restarted in order to assess the trade-offs unveiled by different relaxations. The values of the objective functions in all solutions are displayed in Fig. 1. In this figure each quadrant represents a graph with the values of the objective function (in columns) for each nondominated solution obtained in the individual optimization of each objective and using the STEM method (in rows). It is possible to recognize the trade-offs between the competing objectives as relaxation values are specified with the aim to improve the objectives whose values are not yet considered satisfactory. The employment level displays a higher sensitivity to the preference information supplied by the DM (in the form of relaxations of some objective functions), which provides hints on how the trade-offs between the objective functions may be exploited in policy design. The energy consumption by energy source obtained in the solutions which individually optimize each objective (solutions 1e4) and in the 2nd round of STEM (solutions 9e11) is displayed in Table 6. The consolidated values calculated by the Brazilian Energy Research Center (Empresa de Pesquisa Energ
etica e EPE) for 2012 (EPE e 2012) and the prospective values for 2018 (EPE e 2018) [11,61] are displayed in the first and second columns as references. The values attained in almost all energy sources are higher than the values obtained in 2012 (confirming the assumption that energy consumption follows the economic growth trend) and lower than the values estimated by Ref. [61], although some variations between all solutions are verified. The highest deviation occurs in the gasoline and bioethanol consumption. This fact is explained because [61] considered that a substitution between the consumption of both may occur in the next years in the transportation sector. This suggests a weakness of this kind of modeling formulation, which is unable to capture these effects. However, the values obtained in all simulations are closer to the consolidated values in 2012. This fact supports that fuel consumption in the transportation sector would not attain the substitution effect from gasoline to bioethanol estimated in Ref. [61] unless an abrupt structural change occurs in the next years. This change could be in the supply side of the fuel market through the progress of the lignocellulosic-based

A.L. Carvalho et al. / Energy 82 (2015) 769e785

777

Fig. 1. Objective function values for the individual optimal solutions and the solutions computed using STEM (2nd round).

bioethanol production, which can expand the supply and lower the price of this fuel in the market. Policies providing comparative advantages for the consumption of bioethanol in relation to the consumption of gasoline or liquefied petroleum gas, such as a reduction in the subsidies of gasoline production, overtaxing the gasoline or subsidizing the bioethanol production, can also influence the demand side to push up the bioethanol consumption. In Table 7 the GHG emissions of different emission sources obtained in each solution (1e4 and 9e11) are displayed. The GHG emissions for 2005 estimated in the Brazilian National GHG Inventory [13] and the prospective values for 2020 from Ref. [62] are displayed in the first and second columns as references. It is possible to verify that the emissions from energy combustion in other industries, fugitive emissions and solid waste and wastewater are the items presenting highest increases (at least 60%, 85% and 142%, respectively) compared to the official results in the Brazilian National GHG Inventory [13], whereas the other items present

lower improvements varying between 22.95% (for industrial processes) and 41.6% (for agricultural processes). It is noteworthy that, in addition to the energy combustion, the major source of reduction of GHG emissions in solutions 2 and 3 is derived from agriculture activities. Thus, it is important to Brazil continuing promoting the substitution of fossil for renewable energy enlarging the hydro, wind and solar power framework, as well as investing in energy efficiency measures. Moreover, a reduction of the energy intensiveness in the agriculture sector and adoption of procedures to decrease the emissions specially from enteric fermentation will provide remarkable benefits in reducing the total GHG emissions in Brazil, since the country is one of the world's largest agricultural and livestock producers. From the results herein presented it might be concluded that it will be a difficult challenge for Brazil (according to 2009 technology coefficients) achieving the prospective economic growth (about 2.5e3.5% [9]), guaranteeing improvements in social welfare, while

Table 6 Estimated Brazilian energy mix in 2018 (toe  103). EPE-2012

EPE-2018

Sol. 1 Max gdp

Sol. 2 Min cfe

Sol. 3 Min ghg

Sol. 4 Max emp

Sol. 9

Sol. 10

Sol. 11

Total fossil fuels Diesel Fuel oil Gasoline GLP Kerosene Natural gas Coal and coke Other fossil fuels

128,500 46,280 4,170 24,512 8,023 3,769 17,349 11,588 12,809

161,794 57,919 5,655 28,066 9,360 4,981 25,512 15,543 14,759

137,848 48,761 7,862 19,600 9,986 3,780 20,516 10,527 16,817

129,957 45,823 7,445 18,407 9,307 3,550 19,426 10,039 15,960

129,494 45,864 7,402 18,416 9,370 3,552 19,303 9,859 15,729

140,378 49,132 8,158 19,657 9,711 3,791 21,243 11,229 17,457

133,673 47,117 7,648 18,941 9,593 3,653 19,920 10,339 16,462

133,879 47,186 7,655 18,969 9,607 3,658 19,985 10,345 16,474

133,877 47,200 7,658 18,974 9,614 3,659 19,949 10,346 16,477

Total renewable Wood Charcoal Sugarcane bagasse Electricity Bioethanol Other renewables

108,243 16,428 4,646 28,391 42,862 9,916 6,000

152,737 16,619 6,974 38,814 56,296 20,599 13,436

135,900 21,987 5,106 36,581 50,033 15,701 6,491

127,287 20,556 4,845 33,906 47,081 14,746 6,154

128,012 20,585 4,779 34,554 47,172 14,753 6,169

134,487 21,800 5,350 36,910 47,913 15,747 6,767

131,463 21,204 4,993 35,186 48,619 15,173 6,288

131,046 21,230 4,997 34,797 48,532 15,196 6,294

131,683 21,245 4,998 35,255 48,688 15,200 6,297

Total

236,744

314,531

273,748

257,244

257,505

274,865

265,136

264,925

265,560

778

A.L. Carvalho et al. / Energy 82 (2015) 769e785

Table 7 Total GHG emissions by emission source in each solution (Gg CO2eq). 2005 (MCT, 2010)

2020 (Brasil, 2010)

18,679 312,092 52,726 78,910 134,575 45,881

a þ b ¼ 868,000

c-Industrial processes d-Solid waste and wastewater e-Agriculture f-Forestry and LUC (fixed values)

72,553 47,857 461,048 1,340,979

c þ d ¼ 234,031

Total

2,253,208

a-Fugitive emissions b-Energy combustion Energy industry Other industries Transportation Others sources

Sol. 1 Max gdp

Sol. 2 Min cfe

Sol. 3 Min ghg

Sol. 4 Max emp

Sol. 9

Sol.10

Sol.11

36,557 474,074 76,003 135,078 209,623 53,370

34,706 446,959 71,748 128,436 196,874 49,901

34,558 445,306 71,752 126,308 196,965 50,281

38,473 481,122 76,368 141,533 210,242 52,979

34,814 460,016 73,687 132,465 202,576 51,289

35,592 460,543 73,762 132,544 202,880 51,356

34,916 460,699 73,799 132,569 202,935 51,397

729,752 1,403,521

95,829 116,559 696,340 1,340,193

91,160 115,911 652,833 1,340,193

89,205 115,982 654,450 1,340,193

100,010 116,657 701,108 1,340,193

94,274 116,164 671,737 1,340,193

94,326 116,176 672,465 1,340,193

94,334 116,184 672,969 1,340,193

3,235,305

2,759,553

2,681,762

2,679,694

2,777,564

2,717,199

2,719,296

2,719,296

e e e e

decreasing the energy consumption and accomplishing GHG emission reduction targets. Brazil has been a leading country in voluntarily assuming an ambitious reduction target e decrease between 36% and 39% the prospective GHG emissions in 2020 (from approximately 3,236 Tg CO2eq to a total ranging from 1,977 to 2,068 Tg CO2eq; see Table 7) [62] e compared to USA, which is willing to cut between 26% and 28% of GHG emissions by 2025, Europe (a reduction of 40% until 2030) and China (stabilize the emissions in 2030). As the owner of the major rainforest worldwide, the Amazon forest, Brazil has mainly focused on the impacts from LUC and deforestation to lower its emissions. Although deforestation has been marginally reduced in recent years, it is a complex socio-economic phenomenon with its own inertia and difficult to control. The development of infrastructure in more remote areas has created pressures on the forest that make it difficult controlling deforestation. As a leading agriculture producer country, Brazil must pay special attention to the agricultural and livestock sector. This is also essential to reduce the pressure on deforestation and LUC due to the expansion of the agriculture and livestock frontier and consequently the overall reduction of GHG emissions. The results herein obtained are quite far from these targets even in a best case scenario. However, it should be mentioned that the LUC and forestry emissions are considered in this study as a constant value of year 2000 according to data obtained from the Brazilian National GHG Inventory [13]. Since LUC and forestry emissions are responsible for almost 50% of the total GHG emissions in the country, the results obtained may be somewhat biased. Therefore, if better results of this item in future updates of National GHG Inventories are considered in the model, it will also be possible a downward variation of the total GHG emissions estimates presented in this study. Endogenizing the estimates of the emissions from these sources in this type of analysis in future upgrades of this model can also contribute to diminish this bias problem. 4. Conclusions This paper presents a hybrid IO framework, which is used to develop an MOLP model applied to the Brazilian economic system. In this model the Brazilian 2009 IO table is rearranged to allocate endogenously the Brazilian energy balance, and externally extended to assess GHG emissions and employment levels. The MOLP model considers four objective functions and several sets of constraints. Relevant indicators involving economic, social, environment and energy concerns are evaluated, considering several activity sectors and a scenario for 2018. The illustrative results obtained with this model provide valuable insights about the tradeoffs involved and allow identifying the performance and trends of the main variables.

The results suggest positive correlations between the GDP growth and the employment level, as well as for energy consumption and GHG emissions. Conflicting interactions between objective functions are also identified, namely the optimal solutions for GDP and employment levels lead to more energy consumption and GHG emissions, while the minimization of GHG emissions and energy consumption result in negative impacts on GDP and the employment level. In this sense, the policies to undertake should take these conflicting interactions into account, for instance engaging in strong energy efficiency programs to cope with potential increases in energy consumption and GHG emissions if a steady growth of GDP is envisaged. Policies aiming a significant reduction in LUC and forestry emissions (such as improving the control over deforestation, mainly from the expansion of the agriculture frontier) can allow the country obtaining considerable progress toward the reduction targets. Despite the high rate of renewable energy deployment, the share of fossil fuels in the total energy consumption is high in all solutions. Therefore, policies envisaging the reduction of the coefficients of energy consumption, and consequently the GHG emissions associated, will have a remarkable positive impact on achieving better performance in these objectives. Possible options include, for example, the substitution of fossil energy by other cleaner sources (e.g. increasing the national production of wind and solar energy, substituting the thermal power generation, increasing the share of biodiesel, bioethanol or even electricity in the transportation sector), encouraging energy efficiency and conservation actions (e.g. improving the public transport system to reduce the use of individual transportation and increase the rate of people transported per unit of energy, raising the use of pipelines, railway and waterborne transport systems to increase the weight transported per unit of energy consumed). The commitment to reduce GHG emissions from energy sources is mainly focused on the improvement of hydro and alternative sources as well as expansion of biofuels, namely the bioethanol from sugarcane and future lignocellulosic production. As referred to previously, these policies to reduce these total amounts are quite different from other nations due to particular characteristics of the country. However, there are some limitations inherent to this type of models. Since IO models comprise sectors rather than simple processes and sectors may be too heterogenous, the results may not correctly reflect the real conditions in each sector. In addition, due to the lack of recent official statistical information, the IO 2009 system was utilized. Since the economic system in Brazil will be possibly different in 2018, the inter-relationships among sectors can also be different leading to a bias in the estimates. The IO framework coupled with the MOLP model provided an important tool to assess the interactions and trade-offs along four

A.L. Carvalho et al. / Energy 82 (2015) 769e785

distinct axes of evaluation of policies resulting from changes in the output of economic sectors in Brazil for prospective scenarios. The incorporation of uncertainty treatment in future developments of this model will be useful to provide robust conclusions, i.e. unveiling those policies which reveal more “immune” to uncertainty sources such as, for instance, emission estimates, electricity generation mix and economic projections. Also, as LUC and other emission sources are considered constant and having the same values of 2000, solutions should take it into account. Since LUC is the main GHG emission source in Brazil, it is planned to be object of further improvements of this model.

Table A.1 (continued ) Notation

Description

Unit

ydcurr

Household and NPISH disposable income at current prices. Proportion of corporation savings on the GDP at current prices. Balance of primary incomes (exogenous variable) (where piþ is the income received from abroad by resident primary factors and pi the income paid to non-resident primary factors). Total indirect taxes less subsidies (exogenous variable). Indirect taxes less subsidies to public administration (exogenous variable). Current transfers (exogenous variable). Direct tax on households and NPISH (exogenous variable). Direct tax on corporation's income (exogenous variable). Social contributions (exogenous variable). Balance of property incomes (exogenous variable). Transferences from public administration to households (exogenous variable). Public debt. Public debt in the previous period (exogenous variable). Public administration global balance. Public debt adjustment variable (exogenous variable). Public administration current transferences (exogenous variable). Public debt interest. Capital tax (exogenous variable). Public administration capital transferences (exogenous variable). Public administration gross fixed capital formation (exogenous variable). Employment level. Total energy consumption. Total GHG emissions from fuel combustion. Total fugitive emissions from coal production. Total fugitive emissions from gas and oil production activities. Total fugitive emissions. Emissions from industrial processes. Direct N2O emissions from manure management. CH4 emissions from manure management and enteric fermentation Amount of N2O in crop residues (above and below ground), including N-fixing and from forage/pasture renewal, returned to soils annually. N2O emissions from synthetic fertilizers.

Million R$ (current prices)

psgdpcurr

(piþ  pi)

Acknowledgments itsub

This work has been supported by the Portuguese Foundation for Science and Technology under projects MIT/SET/0014/2009, PEstOE/EEI/UI0308/2014 and PTDC/SEN-TRA/117251/2010 and doctoral grant SFRH/BD/42960/2008. This work has been framed under the Energy for Sustainability Initiative of the University of Coimbra and supported by the R&D Project EMSURE (CENTRO-070224-FEDER-002004).

ctr

Appendix A. Model components

ssc

itsubg

dt dtc

(piþ g Table A.1 Model variables. Description

Unit

x

Vector of total national production (output).

Million R$ (constant basic prices) or toe according to non-energy or energy sector Million R$ (constant consumer prices) or toe according to non-energy or energy sector. toe Million R$ (constant prices)

y

Vector of the final demand components.

impc gdpexp

Vector of competitive imports. GDP in the expenditure approach. Total exports (including the tourism) at consumer prices. Total public administration consumption. Consumption of residents.

expcpt g cr gfcf ci impfob gdppro gva ts gdpcurr gcurr

gfcfcurr

pcr pci pexpcpt pimpfob



pi g)

trgh

Notation

Total gross fixed capital formation. Total changes in inventories. Total imports including tourism. GDP in the production approach. Gross value added. Taxes less subsidies on goods or services. GDP at current prices. Total public administration consumption at current prices (exogenous variable). Gross fixed capital formation at current prices (exogenous variable). Private consumption deflator (exogenous variable). Changes in inventories deflator (exogenous variable). Exports deflator (exogenous variable). Imports deflator (exogenous variable).

Million R$ (constant consumer prices) Million R$ (constant consumer prices) Million R$ (constant consumer prices) Million R$ (constant consumer prices) Million R$ (constant consumer prices) Million R$ (constant FOB prices) Million R$ (constant prices) Million R$ (constant prices) Million R$ (constant prices) Million R$ (current prices) Million R$ (current prices)

debt debt-1  gbþ g  gbg

dat ctrg

jurg tk trkg

gfcfg

emp cfe ecw fecpw fegopw

tfew eipw emmrw

Million R$ (current prices)

No dimension (to transform constant to current prices) No dimension (to transform constant to current prices) No dimension (to transform constant to current prices) No dimension (to transform constant to current prices)

779

emmfrw

ecrQw

esfdw

%

Million R$ (current prices)

Million R$ (current prices) Million R$ (current prices)

Million R$ (current prices) Million R$ (current prices) Million R$ (current prices) Million R$ (current prices) Million R$ (current prices) Million R$ (current prices)

Million R$ (current prices) Million R$ (current prices) Million R$ (current prices) Million R$ (current prices) Million R$ (current prices)

Million R$ (current prices) Million R$ (current prices) Million R$ (current prices)

Million R$ (current prices)

Number of employees toe Gg Gg Gg

Gg Gg Gg Gg

Gg

Gg (continued on next page)

780

A.L. Carvalho et al. / Energy 82 (2015) 769e785

Table A.1 (continued )

Table A.2 (continued)

Notation

Description

Unit

Notation

Description

Unit

ecrbQw

CH4 and N2O emissions from crop residues burnt. CH4 emissions from rice cultivation. Emissions from land use change (exogenous variable). CH4 emissions from Solid Waste Disposal Sites (SWDS). Emissions from biological treatment. Emissions from organic Industrial Solid Waste (ISW) in SWDS. Emissions of ISW incinerated. Emissions from Clinical Waste (CW) incineration. CO2 emissions from solid waste incineration. CH4 emissions from domestic wastewater. Human population in the country (exogenous variable). CH4 emissions from on-site industrial wastewater treatment. N2O emissions from wastewater effluent. Total emissions of CO2. Total emissions of CH4. Total emissions of NO2. Total greenhouse gas emissions.

Gg

Ats

Matrix with the coefficients of net taxes on goods and services on the total output of each sector. Diagonal matrix whose main diagonal is the vector with the coefficients of net taxes on goods and services on the total values of final demand items. Labor productivity (ratio of employees by total production) in each sector. Sub-matrix of matrix A with energy consumption coefficients. Sub matrix of matrix FD with energy consumption coefficients. Technical coefficient matrix of intermediary energy consumption for non-energy purpose. Technical coefficient matrix of final demand energy consumption for non-energy purpose. Matrix with the emission factors to the pollutants w (CO2, CH4 and N2O) for energy combustion by fuel type and sector. Diagonal matrix of transformation factor from toe to Terajoules by fuel. Emission factor for coal (underground or surface) mining. Diagonal matrix with transformation factors from toe to cubic meters by fuel. Ratio of underground or surface mining in the total coal production. Emission factors for gas and oil production activities. Emission factors for industrial processes. Coefficient of manure production by each livestock species/category. Coefficient of number of heads of livestock species/category by million of output of the livestock and fishing sector. Matrix with the fraction of total annual nitrogen excretion for each livestock species/category r that is managed in manure management system s in the country. The emission factors for direct N2O emissions from manure management systems. Emission factors for CH4 emissions from manure management and enteric fermentation Coefficient of productivity of each crop. Ratio of dry matter (d.m.) produced by each crop output. Ratio of above ground residues (d.m.) in the total dry matter produced by each crop.

Million R$ (constant prices)/Million R$ (constant basic prices)

ericeHw elucw emswswduw emswbtw eisworgswdw

eiswincinw ecwincinw eswincinw echdwww p echiwwyw

en2owww teco tech teno ghg

Gg Gg

FDts

Gg Gg lp Gg AE Gg Gg FDE Gg Gg

ANE

Habitants Gg

FDNE

Gg EFw Gg Gg Gg Gg of CO2 equivalent FCTJ

Table A.2 Coefficients and parameters.

efcpw

Notation

Description

Unit

A

Technical coefficient matrix. This matrix includes nonenergy sectors, energy sectors and energy products (fuels). Each element aij represents the quantity of good or service i used to produce one unity of good or service j.

Million R$ (constant basic prices)/Million R$ (constant basic prices), for flows between non-energy sectors; Million R$ (constant basic prices)/toe, for flows between nonenergy and energy sectors; toe/Million R$ (constant basic prices), for flows between energy and nonenergy sectors; toe/toe, for flows between energy sectors. Million R$ (constant basic prices)/Million R$ (constant basic prices), for flows of non-energy sectors; toe/ Million R$ (constant basic prices), for flows of energy sectors. Million R$ (constant prices)/Million R$ (constant basic prices) Million R$ (constant prices)/Million R$ (constant basic prices) Million R$ (constant prices)/Million R$ (constant basic prices) Million R$ (constant prices)/Million R$ (constant basic prices) Million R$ (constant prices)/Million R$ (constant basic prices)

FD

awag

agmi

agos

aopt

aops

Diagonal matrix whose main diagonal corresponds to the coefficients of consumption of energy and non-energy commodities by the final demand items. Vector with the weights of wages on the production of each sector. Vector with the weights of gross mixed income on the production of each sector. Vector with the weights of gross operating surplus on the production of each sector. Vector with the weights of other production taxes on the production of each sector. Vector with the weights of other production subsidies on the production of each sector.

FCtm3

msr

efgopw efipw ampr

aepr

Amars

Efn2osw

Efchrw

aprodQ admqpQ admagQ

Million R$ (constant prices)/Million R$ (constant consumer prices)

Employees/Million R$ (constant basic prices) toe/Million R$ (constant basic prices) toe/Million R$ (constant consumer prices) toe/Million R$ (constant basic prices)

toe/Million R$ (constant consumer prices)

g/TJ

TJ/toe

Gg/m3

m3/toe

%

Gg/m3 Gg/Million R$ (constant basic prices) ton/thousand of heads

Thousand of heads/Million R$ (constant basic prices)

%

ton/ton

ton/thousand of heads

kg/ha kg (d.m.)/kg kg (d.m.)/kg (d.m.)

A.L. Carvalho et al. / Energy 82 (2015) 769e785 Table A.2 (continued)

781

Table A.2 (continued)

Notation

Description

Unit

Notation

Description

Unit

aprodutQ

Coefficient of area cultivated by unitary economic production of each crop Q. Vector with the weights of the output of each crop in the total output of the agriculture sector. Ratio of the burnt area in the total area (considered only for sugarcane). N content of above-ground residues for crop Q. Ratio of below ground residues (d.m.) in the total dry matter produced by each crop. N content of below-ground residues for crop Q. Emission factor for N additions from crop residues as a result of loss of soil carbon. Coefficient of fertilizers utilization in the agricultural sector. Fraction of synthetic fertilizer N that volatilizes as NH3 and NOx. Fraction of all N added to/ mineralized in managed soils in regions where leaching/runoff occur that is lost through leaching and runoff. Emission factor for N2O emissions from N volatilization. Ratio of residues (d.m.) burnt in the total d.m. produced by each crop. Emission factor for dry matter burnt for each GHG. Annual emission factor for H and j conditions. Coefficient of annual harvested area of rice by H and j conditions. Sub-matrix of matrix A with the coefficients for MSW production from private consumption from service and trade sectors. Sub matrix of matrix FD with the coefficients of MSW production by specific final demand components. Fraction of MSW disposed to SWDS. Coefficient of MSW by residue type. CH4 correction factor for aerobic decomposition in the year of deposition. Fraction of degradable organic carbon (DOC) in the year of deposition. Fraction of DOC that can be decomposed. Fraction of CH4 in generated landfill gas. Fraction of MSW carried to composting and anaerobic digestion processes on total MSW. Emission factor for biological treatment of MSW. Recovery factor of CH4. Coefficient of ISW production by output monetary unity.

ha/Million R$ (constant basic prices)

fnhwisw

Coefficient of non-hazardous waste in the industrial waste. Fraction of industrial solid waste to solid waste disposal. Fraction of organic waste in the industrial waste. Emission factor for organic ISW disposed. Fraction of incinerated ISW without economic value in the total ISW. Emission factor for ISW incineration. Coefficient of incinerated clinical waste per output unit of the private public and health sectors. Emission factor for clinical waste incinerated. Fraction of MSW incinerated. Fraction of ISW incinerated. Carbon content in MSW and ISW. Fraction of fossil carbon in MSW and ISW. Carbon content in CW. Fraction of fossil carbon in CW. Combustion efficiency of incinerators. Maximum CH4 producing potential for domestic wastewater. Methane correction factor for the wastewater treatment and discharge system. Degree of utilization of treatment/discharge pathway or system. Biochemical Oxygen Demand (BOD) per person. Coefficient of CH4 recovered by CH4 produced in the inventory year. Coefficient of sector production per unit of output.

%

apatsQ

aburnQ

nagQ admbgQ

nbgQ efn2onw

afertd

afnvd afnld

efnvldw admbdmQ

efburnQw EFriceHbw AriceHb

Amsw

FDmsw

fmswswd amswu mcfmswu

docmswu

docfmswu fch4glg fmswbt

efmswbtw rfemmsww Aisw

fiswswd Million R$ (constant basic prices)/Million R$ (constant basic prices) %

fisworgswd efiworgswdw fiswincwev

kg N/kg (d.m.) kg (d.m.)/kg (d.m.)

efiswincinw Acw

kg N/kg (d.m.) kg N2O/kg N efcwincinw kg N/Million R$ (constant basic prices) kg N volatilized/kg of N applied kg N/kg of N additions

fmswincin fiswincin ccmswisw ffcmswisw cccw ffccw ceinc

kg N2O/kg N

bow

kg (d.m.)/kg (d.m.) mcfj kg/kg (d.m.) adwwtj kg CH4/ha ha/Million R$ (constant basic prices)

bod adwwrec

Gg/Million R$ (constant basic prices) aprody

Gg/Million R$ (constant consumer prices)

aprosecyp

%

bodp

% efiwwpw % achrecp Gg C/Gg waste

pcprot fnprot

Fraction of each selected product in the total output of the respective sector. Biochemical oxygen demand in the wastewater from each product. Emission factor of CH4 by BOD produced for each product. CH4 recovery coefficient from wastewater. Annual per capita protein consumption. Fraction of nitrogen in protein.

% fncprot % %

findcom

asludge ton/Gg efn2owww % Gg/Million R$ (constant basic prices)

Factor for non-consumed protein added to the wastewater. Factor for industrial and commercial co-discharged protein into the sewer system. Coefficient for nitrogen removed with sludge. Emission factor for N2O emissions from wastewater.

% % Gg/Gg %

kg/Gg Gg/Million R$ (constant basic prices)

kg/Gg % % % % % % % kg CH4/kg BOD

%

%

kg BOD/habitants/year %

ton/Million R$ (constant basic prices) or (m3/Million R$ (constant basic prices) for beer production %

kg BOD/ton of product or kg BOd/m3 for beer production kg CH4/kg BOD % kg/person/year kg N/kg protein (default ¼ 0.16) %

%

kg N/kg N/year (default ¼ 0) kg N2O/kg N

782

A.L. Carvalho et al. / Energy 82 (2015) 769e785

Table A.3 Sectors and fuels included in the model. N01 E01

Table A.3 (continued )

A39 N13

Sugarcane bagasse Chemicals

A01

Agriculture and forestry Production of wood and charcoal Wood

N14

A02

Charcoal

N15

N02 E02

N16 N17

A03

Livestock and fishing Petroleum and natural gas Crude oil

N18

A04

Natural gas wet

N19

A05 N03 N04

N20 N21 N22

E03

Natural gas dry Iron ore Other extractive industry Coal

A06

Steam coal 3100

N24

A07

Steam coal 3300

N25

Manufacture of resins and elastomers Pharmaceutical products Agrochemicals 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 e 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

A08

Steam coal 3700

N23

N26

A09 A10

Steam coal 4200 Steam coal 4500

N27 N28

A11

Steam coal 4700

N29

A12

Steam coal 5200

N30

A13

Steam coal 5900

N31

A14 A15

N32 N33

A16

Steam coal 6000 Steam coal without specification Metallurgical coal

N05

Food and beverage

N35

N06

Tobacco products

N36

N07

Textiles

N37

N08

Clothing and accessories Leather goods and footwear Wood products e except furniture Pulp and paper products Newspapers, magazines, CDs and other products recorded Petroleum refining and coke Automotive gasoline

E06

Parts and accessories for motor vehicles Other transportation equipment Furniture and products from other industries Gas, water, sewer and street cleaning Electricity distribution

A40

Uranium (U308)

A41

Hydro

A42

Bleach

A43

Other renewable

A44

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

N09 N10 N11 N12

E04 A17 A18 A19 A20 A21 A22 A23 A24 A25

Fuel oil Diesel oil Aviation gasoline Liquefied petroleum gas Naphtha Kerosene illuminated Jet kerosene Gas coke

N34

A45 A46 N38 N39 N40 A47 A48 A49 A50

A26 A27 A28

Coke coal Refinery gas Petroleum coke

A51 N41 N42

A29

Other energy petroleum products Tar Asphalt

N43

A30 A31 A32 A33 A34 E05 A35 A36 A37

Lubricants Solvents Other non-energy petroleum products Alcohol Anhydrous bioethanol Hydrated bioethanol Sugarcane juice

A38

Molasses

N44 N45 N46 N47 N48 N49 N50 N51 N52

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

Table A.4 Index in the model. Notation Description cp gop w r lf s Q af d H b u

k msw isw cw j

y p

Coal production sector. Petroleum and natural gas sector. GHG gases: CO2, CH4 and N2O. Animal types: cattle, buffaloes, pigs, chickens, turkeys, ducks, lamb, sheep, horses, mules and asses. Livestock and fishing sector. Pasture, anaerobic lagoon, solid storage and anaerobic digester. Rice, sugarcane, maize, soybean, bean and cassava (the main cultures in the Brazilian economic system). Agriculture and forestry sector. Direct deposition, volatilization and leaching. Different ecosystems: irrigated, rainfed and deep water rice production. Water regimes: continuously and intermittently flooded; regular rainfed; and deep water. Food waste; garden (yard) and park waste; paper and cardboard; wood; textiles; nappies (disposable diapers); rubber and leather; plastics; metal; glass (and pottery and china); other (e.g., ash, dirt, dust, soil, electronic waste). The reaction constant (k ¼ ln(2)/t1/2) and n represents the period considered (1998e2018). MSW produced by residents, trade and service sectors and public treatment. ISW generated by industrial sectors. CW generated in the private and public health sectors. Represents each treatment/discharge pathway or system: septic system; anaerobic lagoon; sea, river and lake discharge; anaerobic reactor; stagnant sewer; flowing sewer (open or closed); centralized, aerobic treatment plant; and anaerobic digester for sludge. Sectors: bioethanol; food and beverages; agriculture and forestry; pulp and cellulosic; and livestock and fishing. Products: bioethanol, sugar, beer, milk, cotton, paper, swine, cattle and poultry.

Appendix B. Description of the environmental constraints of the model Energy combustion: emissions from fossil energy combustion include emissions from the intermediary sectors (energy industries; trade and services; agriculture, forestry, fishing and livestock; manufacturing industries; transports and construction) and final demand (excluding exports and GFCF). It is also assumed that non-energy oil products have null emission factors; only the energetic oil for the refinery own use is taken into account; the fuel use with non-energetic purposes is excluded from combustion.

A.L. Carvalho et al. / Energy 82 (2015) 769e785

 
 ecw ¼ ðEFw ÞT FCTJ ½ðAE x  ANE xÞ þ ðFDE y  FDNE yÞ 109 (A.1) Fugitive emissions: this category comprises emissions from the extraction, processing and delivery of fossil fuels to the point of final use. This group includes: coal production, gas production, gas transmission, gas storage, gas distribution, liquefied petroleum gas (LPG) transport, oil production and oil transport. In this step, the IPCC (2006) methodology for developing countries is considered. Coal production e includes emissions from mining and postmining operations from underground and surface mining systems:


   fecpw ¼ ðefcpw ÞT FCtm3 AE xcp þ FDE y ðmsrÞ 109

(A.2)

Gas and oil production and distribution e comprises emissions from gas production and distribution activities (venting, flaring, transmission, storage, distribution and other fugitive sources) and oil production and transport (venting, flaring, transport and other fugitive releases):


9    10 fegopw ¼ ðefgopw ÞT FCtm3 AE xgop þ FDE y

(A.3)

Thus, the total fugitive emissions can be computed from Eqs. (A.2) and (A.3) by the following equation:

tfew ¼ fecpw þ fegopw

(A.4)

Emissions from industrial processes: the emissions from industrial processes are based on the output of the activity sectors.

eipw ¼ ðefipw ÞT ðxÞ

(A.5)

Emissions from agriculture, forestry, livestock and land use change (AFOLU): this category includes emissions from: manure management, enteric fermentation, managed soils, fire on managed land and rice cultivation. Other emission sources linked to the AFOLU sector, such as land use change, are considered as having the same values estimated for 2000 in the Brazilian GHG Emissions Inventory Report (MCT, 2010). N2O emissions from manure management: in this category, the IPCC (2006) tier 2 methodology is applied to estimate direct emissions. For this purpose livestock population data by animal species/category, climate region, manure management practices and a combination of IPCC default emission factors and specific Brazilian's emission factors are considered:

783

pasture renewal, returned to soils annually is estimated in (A.8) using as basis the Tier 1b methodology:

    
 ecrQw ¼ aprodQ admqpQ admagQ aprodutQ xaf      ðapatsQ 1  aburnQ nagQ þ admbgQ 
 ðnbgQ ðefn2onw Þ 106 (A.8) The meaning of index Q and af is clarified in Table A.4 in Appendix A. Synthetic fertilizer emissions: the N2O emissions from synthetic fertilizers are calculated according to the area cultivated by each fertilization application:



esf dw ¼ ðafertd Þ xaf ðafnvd Þðafnld Þðefnvldw Þ 106

(A.9)

The meaning of index d is clarified in Table A.4 in Appendix A. Emissions from burning of agriculture residues: the emissions of this category in Brazil are exclusively related to the sugarcane burnt, whilst the mechanized harvesting has been mitigating this procedure. The amount of dry matter burnt and oxidation and emission factors are considered for this calculation in (A.10). In this estimates, only CH4 and N2O emissions are computed since biogenic CO2 emissions are considered as null:

    
 ecrbQw ¼ aprodQ admqpQ admbdmQ aprodutQ xaf    
ðapatsQ aburnQ efburnQw 106 (A.10) CH4 emissions from rice cultivation: for this estimate, the rice harvested area is disaggregated into sub-units, in order to take into to account the variability between different ecosystems and water regimes. The total annual emissions are equal to the sum of emissions from each sub-unit of harvested area:

ericeHw ¼

Xh


 i
6  10 ðEFriceHbw ÞðAriceHb Þ xaf apatsQ

b

(A.11)

A.

The meaning of index H and b is clarified in Table A.4 in Appendix A. Municipal Solid Waste (MSW): This category includes emissions from solid waste disposal sites (SWDS) and biological treatment from private consumption, trade and service sectors and public treatment. CH4 emissions of MSW in SWDS: these emissions are estimated by the First Order Decay (FOD) methodology (IPCC, 2006). The MSW which is carried to SWDS is allocated by category or type/ material and estimated from specific technical coefficients:

CH4 Emissions from manure management and enteric fermentation: the IPCC (2006) Tier 1 methodology is used to estimate the emissions from both sources:

emswswduw ¼

(A.6) The meaning of index r and lf is clarified in Table A.4 in Appendix

emmef rw

 
¼ aepr xlf ðEFchrw Þ 103


(A.7)

N2O emissions from managed soils: this category includes direct N2O emissions from crop residues and synthetic fertilizers, as well as indirect N2O emissions from volatilization and leaching and runoff of Nitrogen (N) from land. Crop residues emissions: the amount of N2O in crop residues (above and below ground), including N-fixing and from forage/

X


 1ek ðAmsw xmsw þFDmsw ymsw Þðf mswswd Þ

n

ðamswu Þðmcfmswu Þðdocmswu Þðdocfmswu Þ
  
 f ch4glg 16 12 1e­ku ð2018nÞ =




emmrw ¼ ðampr Þ ðaepr Þ xlf ½ðAmars ÞðEFn2osw Þ 103

(A.12) where k is the reaction constant and n represents the period considered (1998e2018). The meaning of index u is clarified in Table A.4 in Appendix A.

784

A.L. Carvalho et al. / Energy 82 (2015) 769e785

N2O and CH4 emissions from biological treatment of MSW: biological treatment of MSW includes composting and anaerobic digestion processes:

emswbtw ¼ðAmsw xmsw þ FDmsw ymsw Þðf mswbt Þðefmswbtw Þ 
ð1  rfemmsww Þ 103 (A.13) Industrial Solid Waste (ISW): this category includes solid waste production and treatment from industrial sectors, including refinery activities. CH4 emissions from organic ISW in SWDS: these emissions are calculated by the fraction of industrial solid waste carried to solid waste disposal, the fraction of organic waste in the industrial waste, a coefficient of non-hazardous waste in the industrial waste and a default emission factor for organic ISW disposed:

iT 
Xnh
o ðpÞðbodÞ ðbow Þ mcf j ecdwww ¼ adwwtj j


 ð1  adwwrecÞ 106 (A.18) The meaning of index j (treatment/discharge pathway or system) is clarified in Table A.4 in Appendix A. CH4 emissions from Industrial wastewater: the 10 major industrial producing sectors (and respective products) of wastewater with high CH4 gas production potential are considered in this study, such as recommended by the II Brazilian Inventory of GHG emissions methodology (MCT, 2010):

echiwwyw ¼

p





eisworgswdw ¼ðAisw xisw Þðf nhwisw Þðf iswswd Þ f isworgswd

efiwwpw



1eachrecp



106 (A.19)

ðefiworgswdw Þ (A.14) Emissions from ISW without energy value incinerated (excluding CO2): the emissions of ISW incinerated are calculated by multiplying the total ISW without energy value by a default emission factor for ISW incineration. Only the ISW without energy value is considered, since other ISW with energy value are considered in the energy combustion calculation:

The meaning of index y (sectors) and p (products) is clarified in Table A.4 in Appendix A. N2O emissions from wastewater: the N2O emissions from wastewater effluent are calculated in (A.20) based on the tier 1 IPCC (2006) methodology:



en2owww ¼ðpÞðpcprotÞ f nprot f ncprot ðf indcom Þð1  asludgeÞ 
ðefn2owww Þ 106


eiswincinw ¼ ðAisw xisw Þðf iswincwev Þðefiswincinw Þ 106

(A.20) (A.15)

Clinical Waste (CW): it is considered that all the CW produced by the public and private health sectors are carried to incineration. The emissions from CW incineration (excluding CO2) are estimated by multiplying the amount of CW incinerated by a default emission factor for clinical waste incinerated:


ecwincinw ¼ ðAcw Þðxcw Þðefcwincinw Þ 106

T


 X
xy aprosecyp bodp aprody

(A.16)

CO2 emissions from solid waste incineration: since the methodology to estimate CO2 emissions from solid waste incineration is different from other GHG emissions calculation, a specific formula is applied to the fraction of MSW, ISW and CW incinerated:

n eswincinw ¼ ½ðAmsw xmsw þ FDmsw ymsw Þðf mswincin Þ 
þ ðAisw xisw Þðf iswincin Þðccmswisw Þ f fcmswisw

o
44 12 ðce Þ þ ðAcw xcw Þðcccw Þ f fccw inc =

(A.17) Wastewater and sludge emissions: due to different sources (domestic, commercial and industrial) and treatment methods (on site, sewered to a centralized plant or disposed untreated nearby or via an outfall) it is necessary to consider different sections to estimate the emissions from wastewater and sludge as follows. Additionally, since CO2 emissions from wastewater are considered as biogenic, they shall not be included (IPCC, 2006). CH4 emissions from domestic wastewater: domestic wastewater is basically produced by household water use.

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