A regional approach to determine economic, environmental and social impacts of different sugarcane production systems in Brazil

Biomass and Bioenergy 120 (2019) 9–20

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Research paper

A regional approach to determine economic, environmental and social impacts of different sugarcane production systems in Brazil

T

T.F. Cardosoa,∗, M.D.B. Watanabea, A. Souzaa, M.F. Chagasa,b, O. Cavaletta, E.R. Moraisa, L.A.H. Nogueirac, M.R.L.V. Leala, O.A. Braunbeckd, L.A.B. Cortezd, A. Bonomia,b a

Laboratório Nacional de Ciência e Tecnologia do Bioetanol (CTBE), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), CEP 13083-970, Campinas, São Paulo, Brazil b Faculdade de Engenharia Química (FEQ), Universidade Estadual de Campinas (Unicamp), Av. Albert Einstein, 500, CEP 13083-852, Campinas, SP, Brazil c Instituto de Recursos Naturais (IRN), Universidade Federal de Itajubá (UNIFEI), Campus Universitário Pinheirinho, Itajubá, Minas Gerais, CEP 37500-050, Brazil d Faculdade de Engenharia Agrícola (FEAGRI), Universidade Estadual de Campinas (Unicamp), Av. Cândido Rondon, 501, CEP 13083-875, Campinas, SP, Brazil

ARTICLE INFO

ABSTRACT

Keywords: Sugarcane harvesting technologies Social assessment Techno-economic analysis Environmental impacts Sugarcane straw recovery Biorefineries

Brazilian sugarcane sector plays a very important role in the economy of the country, considering sugar, ethanol and bioelectricity production. Over the last decade, a variety of economic, social and environmental elements have pushed the sugarcane sector to increasing adoption of mechanically-based agricultural operations, especially in Center-South region of Brazil. Manual and mechanized sugarcane harvesting technologies were evaluated (with and without burning), as well as straw recovery, in three representative sugarcane production regions in Brazil: São Paulo state, the largest national producer, Northeast, a traditional region of sugarcane production, and Center-West region, sugarcane expansion area. Sugarcane production systems were compared using metrics from Engineering Economics, Life Cycle Assessment, and Social Life Cycle Assessment. The mechanized harvesting presented lower production costs in the São Paulo and Center-West regions, whereas manual harvesting had lowest cost in the Northeast region. When considering the verticalized production system (agricultural and industrial phases), mechanized with straw recovery - operating during both season and offseason periods - presented the best techno-economic performances when compared to the other scenarios in all the regions. Manual harvesting presented higher job creation while mechanized sugarcane systems show better working conditions and workers with higher average income, especially in the agricultural phase. Considering environmental impacts, scenarios with mechanized harvesting without burning and straw recovery presented the best comparative balance of environmental impacts.

1. Introduction Brazilian sugarcane sector plays a very important role in the national economy, considering both sugar and energy (ethanol and electricity) production. The sector has experienced significant changes over the years, especially at the sugarcane productions systems. Historically, the technology involving sugarcane production in Brazil has been based on manpower associated with the pre-harvesting burning of straw due to its low investment, reduced production cost, improvement of field conditions for workers by previous clearing off the sugarcane straw [1]. However, working conditions in the manual cutting of sugarcane, is recognized as exhaustive, associated with excessive physical stress due to the repetition of the movements in long working days and high exposure to sun as well as heavy loads of harvested feedstock. Mechanized

∗

harvesting presents better working conditions, however, it requires skilled labor and higher investment in agricultural machinery [2,3]. For the 2017/2018 harvesting season, approximately 90% of sugarcane is expected to be mechanically harvested in Brazil [4]. The increase in such participation occurs mainly due to the scarcity of available workforce, combined with the need for meeting harvesting schedules, increased environmental pressures and cost reduction targets [1]. The objective of this work is to assess the economic, environmental, and social impacts of sugarcane harvesting technologies and the potential contribution of straw recovery considering the key characteristics of the three-main representative sugarcane production regions in Brazil. In order to perform such assessment, this study was divided in two parts. Firstly, a comparison of technological scenarios with either

Corresponding author. E-mail address: [email protected] (T.F. Cardoso).

https://doi.org/10.1016/j.biombioe.2018.10.018 Received 12 April 2018; Received in revised form 16 October 2018; Accepted 29 October 2018 0961-9534/ © 2018 Published by Elsevier Ltd.

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production [5,6]. Considering the TRS parameter, CW region is more associated with autonomous rather than annexed plants. 2. Material and methods The Brazilian Bioethanol Science and Technology Laboratory (CTBE/CNPEM), has developed the Virtual Sugarcane Biorefinery (VSB), an integrated computer simulation platform that evaluates technologies in use or under development, including the entire sugarcane production chain (agricultural and industrial phases) considering the three pillars of sustainability: economic, environmental and social impacts [7]. In this study, the VSB (Fig. 2) was used to perform simulations which give support to the technological and sustainability assessments.

Fig. 1. Sugarcane producing regions – Brazil 2017/2018 harvesting season [6].

100% manual harvesting (Man scenario) or 100% mechanized harvesting (Mech and Bales scenarios). Then, in the second part, an assessment considering the average technological characteristics of each region was carried out. This study emphasizes three representative sugarcane producing areas in Brazil, as presented in Fig. 1:

2.1. Definition of scenarios The description of the scenarios considered in this study are listed in Table 2. They represent different conditions for SP. NE and CW regions. An assumption for all of them is that the agricultural systems are vertically integrated to the industrial phase. Therefore, agricultural and industrial scenarios affect each other (for instance, industrial residues affect the agricultural model by replacing part of the fertilizer used in sugarcane growth). For the economic analysis a vertically integrated system means that the biomass production costs – calculated with CanaSoft, the agricultural model of VSB – will be transferred as operating costs to the industrial stage.

- São Paulo state (SP), the largest national producer, with approximately 52% of the sugarcane area in Brazil and 55% of the national production; - Northeast region (NE), which is a traditional region of sugarcane production in Brazil, with approximately 10% of the total sugarcane area and 7% of sugarcane production; - Center-West region (CW), where occurred the recent sugarcane expansion in Brazil, related to 20% of the sugarcane area and 21% of the national sugarcane production.

2.1.1. Agricultural phase In the analysis of sugarcane production systems, agricultural phase, is performed using the CanaSoft model, a computational tool for modelling and simulation of agricultural phase in VSB platform. CanaSoft model is based on interconnected spreadsheets integrating diverse calculation modules and a database that includes all important operations involved in the sugarcane production systems in Brazil. These operations include machinery, workforce, inputs and other factors required to calculate sugarcane production costs and to generate complete sets of inventories for environmental and social assessments [8]. In the agricultural phase, “Man” refers to manual harvesting with pre-harvesting burning of straw; “Mech” refers to mechanized sugarcane harvesting without pre-harvesting burning of straw (green cane) scenarios, without straw recovery and “Bales” refer to the scenarios of mechanized sugarcane harvesting of green cane with straw recovery. The summary of the details and basic assumptions for the agricultural phase of the scenarios are shown in Table 3. When sugarcane is manually harvested (whole stalk), the sugarcane stalks entangle and do not allow the sugarcane transshipment equipment to be used. In this way, trucks entering the plantation must be lighter to avoid damage on both sugarcane ratoons and soil structure. Therefore, sugarcane stalks are assumed to be transported in trucks of 60 m3 volumetric loading capacity for manual harvesting, while 184 m³ capacity is assumed in the case of mechanized harvesting with transshipment equipment [9]. The straw in the soil after the harvesting without burning may, depending on the quantity, reduce ratoon sprouting, make it difficult to control pests and the mechanized cultivation, requiring an additional operation to remove the straw from the sugarcane ratoon. However, the incorporation of straw into the soil improves soil physical conditions and could increase the productivity of sugarcane [10]. For scenarios with straw recovery, baling system is considered. In such system, straw is windrowed around 10–15 days after harvesting when moisture drops below 15%. Then, straw is collected and compacted in bales by a specific implement. These are then loaded and transported separately from the stalks to the plant [11]. In this study, 50% of the total straw available at the field (140 kg of straw, dry mass,

These selected regions account together for more than 80% of the national sugarcane industry [5]. In the 2017/2018 sugarcane harvesting season, SP showed 96% participation of mechanized harvesting technology. The NE region, on the other hand, presented roughly only 17% of mechanized harvest participation in the total harvested area. Such low values in the NE area can be mostly explained by the more difficult areas with steeper slope and relative higher availability of unskilled workforce labor in the region. In CW region, the current sugarcane expansion area, the mechanized harvesting participation reached about 96.5% of the total area [4]. The industrial configuration also has particular characteristics in the different regions. The sugarcane plants can be divided in three main configurations: sugar plant (producing both sugar and electricity), autonomous plants (whose main outputs are ethanol and electricity) and annexed plants (that produce ethanol, sugar and electricity). Table 1 shows the profile of production units in the different regions. The NE is a traditional sugar producer region where 10% of the total plants are sugar plants with a region average of 55% TRS (Total Recoverable Sugar) destination to the sugar production. On the other hand, in SP region most of the plants are annexed ones (87%) and, in average, approximately 50% of TRS is diverted to sugar production. In CW region, 41% of industrial plants are autonomous ones and 59% are annexed plants. Although there is a relative large number of annexed plants in this region, less than 30% of the TRS is diverted to sugar Table 1 Profile of production units (based on 2014/2015 season – in number of industrial plants) [6]. Plant type

Annexed plants

Autonomous plants

Sugar plants

São Paulo State Northeast region Center-West region Brazil

87% 63% 59% 72%

12% 27% 41% 25%

1% 10% 0% 3%

10

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Fig. 2. Simplified scheme of the VSB. Source: based on [7]. Table 2 Description of sugarcane processing scenarios. Scenarios

SP Man SP Mech SP Bales NE Man NE Mech NE Bales CW Man CW Mech CW Bales a b c

Agricultural

Industrial

Harvesting

Straw recovery

Processing capacity (MM TC/season)a

Type of plant

Manual Mechanized Mechanized Manual Mechanized Mechanized Manual Mechanized Mechanized

no no 50%c no no 50%c no no 50%c

3 3 3 2 2 2 4 4 4

Annexed (50:50)b Annexed (50:50) Annexed (50:50) Annexed (50:50) Annexed (50:50) Annexed (50:50) Autonomous Autonomous Autonomous

MM TC: million metric ton of stalks. 50% of sugarcane juice is used for sugar production and the other 50% is used for ethanol production. 50% of the total available straw at the harvesting operation (bales + vegetal impurities in stalks).

Table 3 Summary of the agricultural operations in evaluated scenarios. Man Planting Semi-mechanized Seedlings (TC/ha)a 12 Pre-harvesting Burned Harvesting Manual Straw recovery no Transport - loading capacity (m³) Sugarcane (stalks) 60 Straw (bales) no

Mech

Table 4 Main regional agricultural parameters in assessed regions. Bales

Mechanized 16 Green cane Mechanized no

Mechanized 16 Green cane Mechanized 50%b

184 no

184 184

a

Productivity (TC/ha) Transport distance (km) Harvesting efficiency (TC/day) Manual operation (per worker)b Mechanized operation (per harvester)c Cost of land (US$/ha.year)d a b

a

TC: metric ton of stalks. 50% of the total available straw in harvesting operation (bales + vegetal impurities in stalks).

c

b

d

SP

NE

CW

78.0 32.0

51.4 27.0

74.6 35.4

8 500 303.40

7 430 81.9

8 580 97.1

TC: metric ton of stalks. Source [4]: Source [13]: Source [14]:

The opportunity cost of land was considered in this analysis since it is an important component of sugarcane production cost. The cost of land for the regions NE and CW represent, respectively, around 32% and 27% of land cost assumed for sugarcane production in SP, according to a previous study about sugarcane land cost in Brazil [14]. Regarding the labor cost, it was also considered a difference for the NE lower (around 20%) than for the other regions [15].

per metric ton of sugarcane stalks) [12] was assumed to be recovered, including the vegetal impurities in the sugarcane stalks. The bales are then transported to the sugarcane plant, also in trucks with 184 m³ of loading capacity. In addition to the technologies defining the scenarios, regional agricultural characteristics were also considered (Table 4). In the NE region, lower productivity is observed and the majority of areas has steeper slopes, then resulting in lower harvesting yields (both manual and mechanized). In the CW region, mechanized harvesting presents higher yields due to improved administration of the areas. In the SP region, both higher productivity and higher cost of land are observed [13].

2.1.2. Industrial phase To assess impacts of different harvesting technologies in the biorefinery systems, some sugarcane production scenarios are proposed to be vertically integrated to the industrial processing facilities. The choice of sugarcane production scenarios affects the investment 11

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Fig. 3. Main industrial processing steps for the considered annexed (a) and autonomous (b) plants. Source: based on [16].

decisions and the fixed capital related to industrial equipment acquisitions. For instance, straw recovered using baling system must be conditioned before being fed into the cogeneration system at the industrial facility. This conditioning involves the use of unbaling system and chopper. Moreover, sugar, ethanol and electricity yields may vary significantly according to the biomass inputs which are directly associated with the chosen agricultural production scenarios. In the VSB framework, the industrial conversion scenarios are simulated using Aspen Plus® process simulator to establish complete mass and energy balances of sugarcane processing operations. Despite variations in industrial equipment and adjustments related to different ethanol and electricity yields, all assessed industrial scenarios are represented by annexed and autonomous plants as described in scenarios definition (Table 2). The simulated processes are illustrated in Fig. 3 for both type of industrial plants. The main products are sugar, anhydrous ethanol and surplus electricity – whose yearly production will vary depending on the scenario. The processing capacities and industrial facility configurations are based on regional averages and might vary according to the considered region; e.g. the facilities located in SP region process 3 million metric ton of sugarcane stalks during 190 days of operation per season in annexed plants whereas in the CW region 4 million metric ton are processed in autonomous plants during 170 days. In the NE region, the harvesting season lasts 135 days with an average processing capacity of 2 million metric ton. The plant configuration might also vary according to the use of straw. When mechanized harvesting and straw recovery are considered, all available biomass (bagasse and straw) is burned in 65 bar pressure boilers. These scenarios also consider the use of condensing-extraction turbines which significantly increase the electricity generation in the combined heat and power (CHP) system. In all the proposed scenarios, the use of azeotropic distillation as a dehydration process was considered. A reduction of 20% in process steam consumption (2.5 bar) was assumed for autonomous plants, which would be obtained with thermal integration [16]. Table 5 shows the main assumptions considered for the industrial sugarcane processing of the selected harvesting scenarios.

performed for each region. The evaluation reported in Table 6 was based on the proportion of harvesting technologies [4] and considered industrial configurations [6]. 2.3. Economic analysis The internal rate of return and net present value are calculated according to the discounted cash flow analysis. Also, the minimum selling prices (MSP) of each output will correspond to the values by which the cash flow's net present value equals zero (i.e., when the internal rate of return equals the minimum acceptable rate of return of 12% per year, which is typical for Brazilian sugarcane industry). This study assumes that agricultural and industrial scenarios are vertically integrated, i.e., biomass production costs calculated with CanaSoft model are computed as biomass operating costs in the industrial cash flows. Moreover, industrial residues from industrial scenarios (such as ashes and vinasse, for instance), which partially return to the sugarcane plantation, affect the biomass production costs. The reference date of this study is December 2017, when the exchange rate was R$ 3.29 per U.S. dollar. For all greenfield projects, a 25-year lifetime span was assumed. The depreciation rate was assumed to be linear and equal to 10% per year. The total capital expenditures (CAPEX) and operating costs (OPEX) were estimated according to the Virtual Sugarcane Biorefinery database [7]. The industrial process simulations and outputs (anhydrous ethanol, sugar and electricity) were obtained using Aspen Plus® process simulator. To estimate the revenues related to the different scenarios, the main regional differences were related to the internal markets for ethanol and sugar for each region while electricity is sold according to the average price in national auctions. Table 7 summarizes historical prices in Brazil. For example, in NE region, prices were historically higher for both ethanol and sugar, when compared with the other two regions. NE region prices represent the weighted average of Alagoas, Pernambuco and Paraíba states. For CW region, data from Goiás and Mato Grosso states were accounted, based on data availability and on the weight of each state in the overall's region production. For all selected states, the average price represents the 6-year moving average of monthly prices for a decade obtained from CEPEA (2018) database [19]. For electricity, although there are specific prices for different regions for the free market (spot prices), such option still represents more risk to long-term greenfield projects. For this reason, surplus electricity was assumed to be traded in long-

2.2. Current average condition Likewise, an assessment of the current average condition considering both the actual mix of harvesting technologies and the main destination of the Total Recoverable Sugar in the industrial phase was 12

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Table 5 Main industrial parameters in assessed regions. Industrial configuration

units

Without straw recovery SP

Type of distillery Crushing capacity Total Recoverable Sugarb Use of straw Extraction Drivers Overall extraction lossesc Cogeneration Boilers pressure Boilers efficiencyc Use of condensing turbine Ethanol producion Fermentation yieldc Sugar Production Sugar purity a b c

With straw recovery

NE

CW

SP

NE

CW

– TC/seasona kg/TC –

Annexed (50:50) Annexed (50:50) 3 2 134.52 128.05 only vegetal impurities

Autonomous 4 137.59

Annexed (50:50) 3 134.52 50% of total available

Annexed (50:50) 2 128.05 straw

Autonomous 4 137.59

– %

Electric 8.75

Mechanical 9.15

Electric 9.55

Electric 8.75

Electric 9.15

Electric 9.55

bar % –

45 81.6 no

22 76.3 no

45 81.6 no

65 87.8 yes

65 87.8 yes

65 87.8 yes

%

89.0

88.6

88.7

89.0

88.6

88.7

wt.%

99.9

99.9

–

99.9

99.9

–

TC: metric ton of stalks. Source [6]: Source [17,18]:

Table 6 Current sugarcane production and industrial mode representing average characteristics for each region. Agricultural

Industrial

a

Harvesting

SP Average SP Average Bales NE Average CW Average CW Average Bales a b c

Manual (%)

Mechanized (%)

4.1 4.1 83.3 3.5 3.5

95.9 95.9 16.7 96.5 96.5

Straw recovery

Crushing capacity (MM TC season)b

Type of plant

no 50%c No No 50%c

3 3 2 4 4

Annexed (50:50) Annexed (50:50) Annexed (50:50) Autonomous Autonomous

Source [4]: MM TC: million metric ton of stalks. 50% of the produced straw (bales + vegetal impurities in stalks).

The present study considered workers' fair salary, health and safety, and local employment in local community's impact subcategories. Accordingly, the following social metrics were addressed: level of jobs created (local employment subcategory), level of occupational accidents (health and safety subcategory) and average wage of workers (fair salary subcategory). The social assessment used the outputs of VSB simulations and official data from Brazilian public agencies and companies. The working hours and working hour costs of all agricultural activities required in each scenario were obtained through the VSB agricultural model (CanaSoft). The number of occupational accidents in the agricultural phase were estimated in a two-steps procedure: first, the occupational accidents rate (accidents per worker) was calculated using the level of mechanization of each scenario using a linear correlation model; second, the accident rate was multiplied by the number of jobs, resulting the total number of accidents. The linear correlation model between the incidence of accidents (number of accidents per worker) in the sugarcane production and the level of mechanization (%) was established by Souza et al. (2018) [3] for the years 2009–2015 using occupational accidents data from the Secretary of Social Security – INSS (Brazilian Ministry of Finance) and level of harvesting mechanization from the national supply company – CONAB (Brazilian Ministry of Agriculture). The correlation from Ref. [3] shows that the higher the mechanization level, the lower the probability of occupational accidents. In the industrial phase, the number of jobs were estimated considering data from national reports [23]. The rate of occupational

Table 7 Historical long-term prices in different regions in Brazil (domestic market). Products

SP

NE

CW

Anhydrous ethanol price (US$/liter)a Sugar price (US$/kg)a Electricity price (US$/MWh)b

0.540 0.396 60.54

0.625 0.520 60.54

0.559 – 60.54

a b

Source [19]: Sources [20,21]:

term contracts in the regulated electricity market. Such average price for a decade in Brazil were obtained in both MME (2014) [20] and CCEE (2018) databases [21]. 2.4. Social assessment The social assessment framework was performed based on the Social Life Cycle Assessment (SLCA) methodology. SLCA is a product-oriented method that aims at assessing social and socioeconomic aspects of products by using the best available methodology to collect consolidated data and reports about positive and negative social impacts in product life cycles [22]. One of the uses of the SLCA is to forecast social issues considering future scenarios. This approach allows the estimation of potential social effects of the adoption of a new technology. The Guidelines for Social Life Cycle Assessment [22] sets 32 impacts subcategories, which were classified in five stakeholder categories (workers, local community, society, consumers and value chain actors). 13

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accidents was considered according the selected regions [24]. The industrial average wage estimated considering information obtained from the Ministry of Labor's database [15], was also considered the same for all scenarios.

to the calculations using CanaSoft model (US$ 22,84 per metric ton – see Table 8). The reason to use CanaSoft model is to have the possibility of varying details of sugarcane production technologies when comparing a group of different agricultural scenarios. The mechanized harvesting scenarios present lower costs in SP and CW. Although average productivity in SP is slightly higher than in other regions, production costs in CW are lower, mostly due to higher efficiency in mechanized harvesting operation and lower cost of land. In the NE region, the manual harvesting has a relative lower cost per hectare, when compared to the other two scenarios in this region, mainly due to the low efficiency of the mechanized harvesting system when operating in areas with steeper slopes and lower sugarcane productivity. However, when compared to the other two regions, the NE presents higher production costs per metric ton of sugarcane stalks, mostly due to the low productivity of sugarcane and the low efficiency of the sugarcane harvesting operation (both manual and mechanized). In the economic analysis, the costs per metric ton of stalks and straw are used to account for the biomass cost in the sugarcane industry, instead of the cost per hectare. The manual and mechanized harvesting operations play an important role in sugarcane production cost, representing around 35% of the total sugarcane costs [11]. Mechanized harvesting scenario (Mech) presents lower costs per metric ton of stalk in SP and CW, whereas manual harvesting scenario (Man) shows lower production per metric ton of stalk costs in NE. The sensitivity analysis varying the harvesting efficiency highlights the importance of this operation in the production costs, eventually changing the conclusions for the regions, depending on the observed efficiency, as shown in Fig. 4. In this sensitivity analysis, a ± 10% and ± 20% variation in the harvesting efficiency for both manual and mechanized harvesting systems, was considered. For example, a reduction of 10% in both manual and mechanized harvesting efficiencies will make manual cheaper than mechanized harvesting in SP state. The sugarcane production cost breakdown for the scenarios representing average characteristics of each region is presented in Table 9. SP and CW regions have a behavior which is similar to the mechanized harvesting scenario, mostly because the level of mechanization in these regions is relatively high in both cases (around 95%). On the other hand, sugarcane production cost of NE is closer to manual harvesting scenario, because the average level of mechanization in this region is low (around 17%). Table 10 shows the ethanol, sugar and surplus electricity production and the economic results of each scenario. The amount of total sugar, ethanol and electricity production depends on sugarcane composition, configuration of the industrial plant and global industrial losses which can vary according to the region (as described in Table 5). For ethanol production yield, CW presents the highest values because the scenarios represent autonomous plants where all available sugars are diverted to ethanol production. When comparing SP and NE ethanol yields (both

2.5. Environmental assessment The environmental impacts were evaluated through the Life Cycle Assessment (LCA) methodology, described in ISO 14040 and 14044 series of standards [25,26], and performed considering 1 kg of anhydrous ethanol at factory gate as the functional unit to compare different scenarios. The SimaPro® software was used for this environmental assessment. The ecoinvent database v2.2 [27], adapted to Brazilian conditions according to Chagas et al. (2013) [28], was used to obtain the environmental profile of the main inputs of the assessed scenarios (e.g. diesel, fertilizers, pesticides and other chemicals used as input in the processes). The life cycle inventories for the sugarcane production systems were generated using the CanaSoft model [7]. For the industrial phase, mass and energy balances from Aspen Plus® simulations were used to generate the life cycle inventories. The compilation of emission factors used to generate the agricultural and industrial inventories can be found in Chagas et al. (2016) [29]. Since multiple products are obtained (ethanol, sugar and electricity) in all the evaluated production systems, it is necessary to split part of the environmental impacts to each one of the products. In this study, the allocation procedure based on economic relationships between ethanol and the co-products was employed, considering that economic value created is the driver of that process [30]. Revenues were used to establish the allocation factors, based on the products’ selling prices presented in Table 7. The selected environmental impact categories from the ReCiPe Midpoint (H) V1.05 method [31] were: climate change (kg CO2 eq.), terrestrial acidification (kg SO2 eq.), human toxicity (kg 1,4-DB eq.), particulate matter formation (kg PM10 eq.), and fossil depletion (kg oil eq.). The choice of the impact categories was based on their relevance for biofuel production and the consequences of changing the mechanization level [30]. 3. Results and discussion 3.1. Techno-economic assessment Sugarcane biomass (stalks and straw) production costs are summarized in Table 8, highlighting the main costs components – such as mechanized operations, manual operations, inputs and transport for the assessed scenarios. The assumption of vertically integrated system, which assumes that the total sugarcane stalks production costs are included in the industrial operating costs, does not affect the analysis in this study. For example, historical sugarcane price in SP state (US$ 22,66 per metric ton, according to the sugarcane market) is very close Table 8 Main components of agricultural production costs. Production costs

(US$/ha)

Mechanized operationsa Manual operationsa Inputs Transportb Total

Stalks (US$/TC)c Straw (US$/tondb)d a b c d

SP

NE

CW

Man

Mech

Bales

Man

Mech

Bales

Man

Mech

Bales

196.71 446.53 372.70 356.23 1747.81 23.11 –

655.11 20.09 431.53 227.93 1709.63 22.84 –

727.22 20.09 440.31 242.61 1805.21 22.84 32.39

186.19 283.11 382.73 216.39 1193.84 24.35 –

503.74 16.05 443.04 144.84 1232.42 25.55 –

559.57 16.05 448.57 153.51 1302.45 25.55 36.83

201.72 430.24 364.10 405.60 1575.06 21.82 –

585.67 20.09 422.21 265.69 1466.26 20.54 –

653.82 20.09 430.56 282.36 1559.42 20.54 33.10

Sugarcane stalks and straw. Sugarcane stalks, straw and inputs. TC: metric ton of stalks. tondb: metric ton in dry basis. 14

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consider annexed plants), a slightly higher value is observed in SP whose total sugar losses in the plant are lower. For sugar, a similar behavior is observed. For electricity, different configurations are considered for the cogeneration systems. For scenarios with straw recovery (Bales), scenarios in SP and CW present lower surplus electricity per metric ton of stalk when compared to NE whose biomass has a higher content of fibers (15%). In the scenarios without straw recovery (Man and Mech), a higher surplus electricity is observed in CW, due to the higher steam consumption of these autonomous plants which, in turn, increase the electricity generation, since these plants do not have condensing turbines in their CHP system. As the simulation highlights, the best technology will depend on the region and on the configuration of the plant (either autonomous or annexed plants). It is possible to observe that in Sao Paulo state, the best alternative is related to the Bales scenario, whose plant produces ethanol and sugar during the season and surplus electricity during both the season and off-season periods – highest IRR and lower MSP of products. Considering that in Sao Paulo all scenarios are annexed plants, the main difference among them can be attributed to the higher surplus electricity (in Bales scenario), which allows for enhancing its profitability. The better performance depends firstly on the additional revenues from higher straw recovery and, secondly, on the operation of the cogeneration area during both the season and off-season periods that reduces capital expenditures associated with boilers and turbines due to the additional days of operation. A similar effect is observed in the CW region whose best alternative is associated with Autonomous/ Bales scenarios and the worst alternative is related to the manual harvest (i.e. lower IRR and highest MSP of products). Again, when compared to the other autonomous scenarios (either Man or Mech), the straw recovery using bales technology increases the internal rate of return and the NPV/Investment. For NE region, the best scenario is related to an Annexed plant with Bales technology, the second-best scenario is Man and the worst scenario is Mech without straw recovery, which is economically not attractive considering a minimum acceptable rate of return of 12% per year. First, this occurs because both Man and Mech scenarios do not have additional revenues from electricity which plays an important role on increasing IRR (of Bales scenario). Second, when comparing Man and Mech, the main difference is that Man scenario relies on relatively lower labor cost, which decreases sugarcane production cost. Moreover, scenarios involving mechanized harvesting operations are still related to low harvesting efficiency in NE region which, in turn, contributes to increase sugarcane production costs. As the results related to Bales scenario show, the additional electricity output produced from straw burning is also advantageous for NE region, since it operates during both season and off-season periods. Also, it contributes to decrease the minimum product selling prices of both ethanol and electricity of NE Bales scenario. Considering the influence of biomass on the overall results of vertically integrated scenarios, Fig. 5 shows a sensitivity analysis of sugarcane production costs on the internal rates of return for the scenarios in different Brazilian regions. As Fig. 5 highlights, a 10% variation on the biomass cost was performed in the scenarios for the different regions. The results show that in SP, NE and CW regions, Bales scenarios are always associated with the higher IRRs due to the additional revenues with electricity. In SP, it is possible to infer that a 10% increase in biomass cost could decrease the IRR to levels below 12%, thus generating a negative net present value; in other words, Man and Mech configurations would not be economically viable in such extreme scenario. Bales scenario, on the other hand, would remain economically feasible even with a 10% increase in sugarcane cost.

Fig. 4. Sensitivity analysis of harvesting efficiency for both manual and mechanized operations in assessed scenarios. a metric ton of stalks per day per worker (Man); b metric ton of stalks per day per harvester (Mech). Table 9 Breakdown of sugarcane production systems representing average characteristics of each region. Production costs (US$/ha)

SP Average

Mechanized operations Manual operations Inputs Transporta Total Stalks (US$/TC)b Straw (US$/tondb)c a b c

NE

CW

Average Bales

Average

Average

Average Bales

636.78

706.50

233.32

572.58

642.44

34.78 430.23 233.17 1709.93

34.78 439.00 248.00 1803.24

247.10 388.24 204.38 1198.47

32.06 420.76 270.55 1468.54

32.06 429.10 287.23 1563.42

22.85 0.00

22.85 30.33

24.44 0.00

20.57 0.00

20.57 32.53

Sugarcane stalks, straw and inputs. TC: metric ton of stalks. tondb: metric ton in dry basis.

15

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Table 10 Main results from techno-economic analysis for the different assessed scenarios. Results

Industrial outputs Anhydrous ethanol (L/TC)a Sugar (kg/TC)a Surplus electricity (kWh/TC)a Industrial investment Total Capex (US$ MM) Cash flow analysis IRR (% per year) NPV (US$ MM) NPV/Investment Minimum selling price Anhydrous ethanol (US$/L) Sugar cost (US$/kg) Electricity (US$/MWh) a

SP

NE

CW

Man

Mech

Bales

Man

Mech

Bales

Man

Mech

Bales

44.6 43.8 34.9

44.6 43.8 34.9

44.6 43.8 169.5

42.2 41.6 –

42.2 41.6 –

42.2 41.6 187.5

72.3 – 38.8

72.3 – 38.8

72.3 – 164.1

213

213

251

174

174

228

291

291

322

13.4% 22 0.10

13.6% 25 0.12

15.7% 73 0.29

12.3% 4 0.02

11.5% −6 −0.04

13.4% 24 0.10

13.7% 39 0.13

14.7% 60 0.21

17.2% 134 0.42

0.52 0.38 58.09

0.52 0.38 57.72

0.48 0.35 53.63

0.62 0.52 –

0.64 0.53 –

0.59 0.50 57.60

0.53 – 57.21

0.51 – 55.40

0.47 – 50.78

TC: metric ton of stalks. Table 11 Main results from techno-economic analysis for the average scenarios. Results

Industrial outputs Anhydrous ethanol (L/TC)b Sugar (kg/TC)b Surplus electricity (kWh/TC)b Total investment CAPEX (US$ MM) Cash flow analysis IRR (% per year) NPV (US$ MM) Profitability index (NPV/Investment) Minimum selling price Anhydrous ethanol (US$/L) Sugar cost (US$/kg) Electricity (US $/MWh)

Fig. 5. Sensitivity analysis of sugarcane costs on IRR in assessed scenarios.

In the CW region, a similar trend is observed. On the other hand, the difference between mechanized and manual harvesting systems (i.e., the distance between Mech and Man curves in Fig. 5) is larger than that observed in SP scenarios. This occurs because the average harvester efficiency in CW region is higher than the registered in SP, whereas the efficiency of manual operations in both regions are the same. In general, all scenarios in CW region were associated to IRRs above 12% per year, for an increase of 10% in sugarcane stalk cost. In the NE region, there is a smaller difference among IRRs of scenarios, whose values are all very close to the 12% minimum acceptable rate of return. In general terms, Man and Mech scenarios were also more sensitive to variations in sugarcane stalk cost. When the sugarcane stalk cost reaches +10% variation, Bales is the only scenario which remains economically feasible. Considering that the average technology observed in the different regions of Brazil is a composition of Mech, Man and Bales scenarios, it is possible to highlight the differences among regions when considering the actual agricultural mix of technologies in SP, NE and CW (see Table 11). Moreover, some extreme scenarios such as 100% mechanization in regions like NE is unlikely to be feasible due to soil characteristics of the region. As Table 11 shows, all greenfield scenarios are economically feasible for all regions when considering the current average characteristics of each region. The best results are related to the CW region which is associated to the lowest MSPs for ethanol and electricity and highest IRRs. This occurs mainly because CW scenarios are related to the lowest biomass costs among all scenarios and higher plant capacities when compared to SP and NE regions; also, CW region presents a slightly higher anhydrous ethanol selling price when compared to SP. When observing the MSPs, the highest values are related to the NE region, mainly because of higher sugarcane stalks and straw costs when compared to SP and CW regions. Also, the lower plant's crushing

SP

NEa

CW

Average

Average Bales

Average

Average

Average Bales

44.6

44.6

42.2

72.3

72.3

43.8 34.9

43.8 167.0

41.6 –

– 38.8

– 162.2

213

250

174

291

320

13.5% 25 0.12

15.7% 73 0.29

12.2% 3 0.02

14.6% 59 0.20

17.2% 134 0.42

(MSP) 0.52

0.48

0.62

0.51

0.47

0.38 57.73

0.35 53.59

0.52 –

– 55.44

– 50.79

a In the NE region, there is no Average Bales scenario due to low mechanization (16.7% - see Table 6). b TC: metric ton of stalks.

capacity (2 million metric ton of sugarcane stalks per year) contributes to increase the operating costs. Among all scenarios, CW “Average Bales” is related to the lowest anhydrous ethanol MSP (US$ 0.47 per liter) and the lowest sugar MSP occurs in SP “Average Bales” scenario (US$ 0.35/kg). In the NE region, the internal rate of return is very close to the minimum acceptable rate of return (12% per year), even though the historical prices for sugar and ethanol are significantly higher in such region. 3.2. Social assessment Fig. 6a shows the number of jobs per million metric ton of processed stalks. The scenarios with manual sugarcane harvesting presented the highest job creation level. The NE region presented the largest gap between manual and mechanized scenarios. In comparison with the other manual scenarios, a lower sugarcane productivity leads to a higher cultivation area and, consequently, more workers (jobs) are required. In addition, the lower manual harvesting efficiency in NE region also leads to more job creation. The difference between the mechanized 16

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Fig. 6. Job creation (a) and accident (b) levels in assessed scenarios.

scenarios were small; the scenario with straw recovery shows slightly higher job creation level. Within the same region, whereas the agricultural phase changed from one scenario to another, there were no major differences in job creation level in the industrial phase. On the other hand, the difference observed among the regions for the same harvesting category (Man, Mech or Bales) can be explained by the differences in some industrial technical characteristics such as size (scale), plant configuration (annexed or autonomous) and industrial yields and agricultural characteristics, for instance, sugarcane productivity and manual harvesting efficiency. The occupational accidents are presented in Fig. 6b. Similarly to the results of job creation, the manual harvesting scenarios presented the highest job creation level and the NE region the largest gap between manual and mechanized scenarios, although the gap in occupational accidents is larger than in the job creation. Occupational accidents are correlated to both the number of workers and the accidents rate. In other words, the lower the level of mechanization – which is the case in manual harvesting scenarios – the higher is the number of jobs created (workers involved) and the higher the probability of occupational accidents for a given number of workers. Fig. 7 shows the average wages considering a vertical model for all assessed scenarios. The mechanized harvesting scenarios (Mech and Bales) presented slightly higher average wages when compared to those related to manual harvesting, considering all evaluated regions. Manual operations are generally associated with low-qualified jobs which, consequently, means lower wages. In this way, the higher the occurrence of manual operations the lower the average wage. Once again, the average wages in the industrial phase are exactly the same due to the similar industrial configuration in all scenarios. Comparing the evaluated regions, the NE presents the lower average wage. Due to

socioeconomic characteristics in NE, for instance cost of living, wages, in average, are lower when compared to CW and SP regions. Fig. 8a shows the results for job creation level of “Average” and “Average Bales” scenarios in SP, NE and CW. NE region presented the highest job creation level, which can be explained mostly by the low incidence of mechanized harvesting (16.7%). Beyond that, as mentioned in the previous comparison between manual and mechanized harvesting, the lower sugarcane growing productivity and manual harvesting efficiency also contribute for the high job creation level. Regarding technological configuration, “Average Bales” scenarios presented a slightly higher job creation level in all the regions. On the other hand, it is difficult to perceive the difference between CW and SP for the same configuration (“Average” or “Average Bales”) since both scenarios have very similar harvesting technologies. Fig. 8b shows the occupational accidents level of “Average” and “Average Bales” scenarios in SP, NE and CW. Similar to job creation, the occupational accidents were much larger in NE than in SP and CW. The low level of mechanized harvesting is associated to a higher rate of occupational accidents (accidents per thousand workers) in comparison to SP and CW. In addition, the number of workers (jobs) is larger in NE than in SP and CW regions. The combination of these two factors explain the huge gap between NE and the other regions. The comparison of the average wage of “Average” and “Average Bales” scenarios is shown in Fig. 9. The results for SP and CW “Average” scenarios, basically, are the same. A similar situation is observed for “Average Bales” scenarios. On the other hand, NE presented the lowest average wage, which can be explained by the low level of mechanization in the harvesting operation. As mentioned before, manual operations are associated with low-qualified jobs, which can be translated in lower wages. 3.3. Environmental assessment The results of the environmental impact for different scenarios evaluated in this study are presented in Fig. 10. In general, due to higher fertilizer and diesel consumption in mechanized agricultural operations, sugarcane production systems with mechanized operations present slightly higher environmental impacts than manual sugarcane harvesting system. Only for particulate matter formation impact category, manual harvesting presented worse results; this is mostly due to emissions from sugarcane straw burning previous to harvesting and higher diesel consumption in the harvesting and transport operations. For most selected impact categories, results indicate that scenarios with straw recovery present lower environmental impacts in all the regions, because of higher electricity yields. Again, the exception is for particulate matter formation, because higher boiler emissions are associated with straw used in the industry.

Fig. 7. Monthly average wage in assessed scenarios. 17

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Fig. 8. Job creation (a) and accident (b) levels of current average condition in assessed scenarios.

since the manual harvesting system is still predominant in this region. CW region has higher impacts on fossil depletion category, strongly dependent on diesel consumption since higher crushing capacity implies on longer transport distances. 3.4. General discussion The results in this paper are in accordance to the actual technologies employed in each region. For instance, in SP state, mechanized harvesting scenario presented the best results. Indeed, this technology is currently predominant in SP, mainly due to lack of labor and environmental law enforcement. Moreover, sugarcane without burning would increase the sugarcane stalks production cost in manual harvesting. However, for the NE region, where mechanized harvesting efficiency is low and labor costs are lower, scenarios with manual harvesting have economic advantages. Mechanization will become the major solution for the Brazilian sugarcane regions due to environmental pressure to harvest sugarcane without burning and to the fact that manual harvesting of green cane is economically unfeasible [11]. Therefore, the sugarcane production sector will encourage the development of technological solutions for mechanized harvesting of green cane in the steeper slope areas, considering economic, environmental and social impacts.

Fig. 9. Monthly average wage for current average conditions in assessed scenarios.

When the same technology is compared across the regions, SP presents the lowest impacts, mainly due to higher sugarcane productivity and harvesting efficiency. NE presents always the higher impacts, due to lower agricultural yields. In this region, mechanized harvesting presented higher impacts when compared to manual harvesting due to the low efficiency of harvester, increasing diesel consumption. Fig. 11 shows the environmental impacts for current average condition, with comparable results to those observed in the technological scenarios comparison (Fig. 10). NE region presents the worse results,

4. Conclusions This study compares different technologies in the main sugarcane producing regions in Brazil and shows the importance of integrated analysis (agriculture and industry). The analysis of the scenarios (Man,

Fig. 10. Relative environmental impacts for ethanol production in assessed scenarios. 18

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Fig. 11. Relative environmental impacts of ethanol production for current average conditions in assessed scenarios.

Mech and Bales) allowed to compare the impacts related to the change of harvesting technology. Considering current average of the technologies observed in the different regions with their characteristics (productivity, harvesting efficiency, land and labor costs, raw material quality, industrial yields and product prices), generated a regional characterization of Brazilian sugarcane production. The results obtained in this study show that the mechanized sugarcane harvesting system presents lower production costs per metric ton of stalk in the region of SP and CW, whereas in the NE region lower costs are associated with the manual harvesting operations. These results are strongly related to harvesting efficiency, land and labor costs. Moreover, the difference of sugarcane stalks production costs among the regions is mainly due to sugarcane productivities. When considering broader effects of verticalized production systems, mechanization with straw recovery presented the best technoeconomic performances when compared to the non-recovery scenarios in all the three regions. The results for Bales, however, are very dependent on the cogeneration operation in the plant which was assumed to operate during both sugarcane season and off-season periods. When generation of surplus electricity just occurs during the season, the idle capacity of boilers and turbines during the off-season period would worsen the economic performance. Considering social effects, the manual harvesting scenarios presented the best level of job creation. However, these jobs are associated with higher rates of occupational accidents and lower wages. NE presented the highest level of job creation and occupational accidents influenced mainly due to the lower participation of mechanized harvesting and lower sugarcane productivity. Moreover, NE presented the lowest average wage due to the high incidence of manual (unskilled) jobs when compared to the other regions. SP and CW presented very close results. Environmental assessment showed that the scenarios with mechanized harvesting with straw recovery presented the best performance for all regions. NE region presented the worst results, caused mainly by lower agricultural productivity.

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Acknowledgments The authors would like to thank FAPESP (2012/15359-1 - Economic input-output Life Cycle Assessment as a tool for Virtual Sugarcane Biorefinery), CAPES/CNPEM (Process 23038.006737/2012-56 – Applying of the Social Life Cycle Assessment in the sugarcane Chain in Brazil), FAPESP/BIOEN (2012/00282-3 – Bioenergy contribution of Latin America, Caribbean and Africa to the GSB project – LACAf - Cane I), and Global Environment Facility – GEF (BRA/10/G31 - Sugarcane Renewable Electricity - SUCRE), for the financial support. 19

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