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ELECTRONIC SUPPLEMENTARY MATERIAL

1 Generalized input-output approach

Miller and Blair (2009) mention that one essential set of data for an input-output (IO) model is monetary values of the transactions between pairs of sectors. Thus, an IO model consistent of a system of linear equation, each one describes the distribution of sector’s output throughout the economy. Originally, the Brazilian IO system is structured on 55 sectors and 110 products. The sectoral classification of the Brazilian IO system used in this work is presented in table A. Table A Classification Code for Brazilian Sectors Symbo Aggregated sectors IO table classification 2005 l Alf Agriculture-Livestock-Fishing Agriculture; Livestock; Fishing Sc Sugarcane Sugarcane Min Mining Mining; other extractive industries F&B Food-Beverage Food and Beverage Tx Textiles Textiles Pp Paper-Pulp Pulp and paper products; magazines, newspapers En Energy Extraction oil crude and natural gas; refining process FGe Sugarcane-derived ethanol Sugarcane-derived ethanol (industrial stage) Che Chemicals Chemicals; Resins and elastomers; pesticides, fertilizers; paints, varnish, lacquer. Oche Other chemical Enzyme and diverse chemical preparations Cem Cement Cement Cer Ceramics Ceramic and others Met Metallurgy Steel and steel derivations, non-ferrous metals, metal products, excluding machinery and equipments Tra Transport All types of transports Os Other industrial sectors 18 industrial sectors such as cloths, pharmaceutical products, plastic products, auto parts, transport equipments, electronic machinery and equipments SIUP Industrial Services for Public Utility Distribution of electricity and gas, sanitation system Co Commerce-and-services 10 diverse service sectors such as private health Pu Public Public education, health and administration

In many cases, according to Miller and Blair (2009), the sector’s output is not the most important measure of the economic impact following a change is the final demand (household consumption, government expenditures, investments, exports). The sector’s output can be translated to employment effects, energy consumption, and pollution emissions. To identify those impacts is necessary to incorporate the respective coefficients into the intervention matrix with environmental and economic coefficients in eq. 1. (1)

Where is the vector with the total employment effects, energy consumption and pollution emissions, is the intervention matrix with environmental and economic coefficients, -1 (I-A) (18,18) is Leontief matrix, and Y(18,1) is the final demand. Table B shows the respective coefficients for the 18 sectors of the IO system.

Table B Employment, CO2e emissions and energy consumption coefficients Sectors Employment Energy Consumption (jobs) Kg CO2e emissions (MJ) Alf 0.000109 0.09 0.80 Sc 0.000061 0.65 6.81 Min 0.000006 0.21 0.97 F&B 0.000009 0.20 0.11 Tx 0.000029 0.05 0.15 Pp 0.000009 0.32 0.47 En 0.000001 0.23 0.23 FGe 0.000006 0.17 0.17 Che 0.000002 0.15 0.31 Oche 0.000007 0.01 0.02 Cem 0.000002 1.55 1.97 Cer 0.000021 0.41 0.77 Met 0.000006 0.52 1.93 Tra 0.000021 0.77 13.56 Os 0.000017 0.01 0.04 SIUP 0.000002 0.01 0.02 Co 0.000032 0.00 0.02 Pu 0.000021 0.01 0.07 Sources: Brazilian Institute of Geography and Statistics (IBGE), National Energy Balance

The Brazilian technical coefficient matrix (Ä) is obtained from the Make and Use matrix context within the industry technology approach, as depicted in eq. 2. (2)

Where D is the share market matrix and is the parallel to the ordinary technical coefficients matrix Ä in the context of the Use matrix.

Process-based Approach Before constructing the process-based matrix, the processes were modeled in the GaBi software with GaBi database and secondary sources. The process-based matrix was structured, following the approach described by Suh (2005) and Heijungs and Suh (2002) and depicted in eq. 3. (3)

Where is the vector with the total employment effects, energy consumption and pollution emissions, is the intervention matrix with environmental and economic coefficients for all -1 processes within the system boundary (s), Ã (s,s) denotes the inverse matrix of the technological matrix Ã, and f(s,1) is the vector that shows the functional unit of the system. Modeling the technological matrix, negative values denotes inputs and positive values are outputs. Since in this work was carried out a Structural Path Analysis (SPA) on the Hybrid LCA matrix, the process-based matrix needs to fulfill some mathematical conditions for the process-based LCA matrix can be expressed as a power series expansion. According to Waugh (1950) and Suh and Heijungs (2007), the IO matrix always has a Leontief matrix that can be expressed as a power series. According to Suh and Heijungs (2007), the necessary and sufficient condition for a real, invertible Ã to be used for SPA using its power series form, Ã-1=I+(I- Ã)+(I- Ã)2+(I-Ã)3+…, is to satisfy all of the following three conditions:

1. ãss=1,

2. ãsk≤ 0 for all s,k (s≠k), and

3. all eigenvalues are within the unity in modulus for (I- Ã).

To satisfy these conditions, it was structured two technological matrices. The first one models, in detailed way, the bagasse-derived ethanol (SGe) production. The second one is the main matrix for this work, and it models the use phase matrix. Figure A depicts the technological matrix to assess the environmental and economic impacts to travel a distance of 500 km with hydrated SGe with a Brazilian average Flex Fuel Vehicle 1.0 (FFV).

cradle-to-gate cradle-to-gate gate-to-gate ethanol DistributionTransport Use FFV Ethanol (lts) 1.000 0.000 -0.101 Transport (cargo) 0.000 1.000 -0.101 USe FFV (Km) 0.000 0.000 1.000 Figure A. LCA Technological Matrix

The employment, CO2e emissions and energy consumption coefficients of each cradle-to- gate process and gate-to-gate process is presented in table C. The environmental coefficients are resulted from the process-based modeling in GaBi software. The employment coefficients of each cradle-to-gate and gate-to-gate process are calculated by summing all flows according to the functional unit of each process.

Table C Employment, CO2e emissions and energy consumption coefficients Sectors Employment Energy Consumption (jobs) Kg CO2e emissions (MJ) SGe 0.0000107 0.52 4.70 Transport 0.0000001 0.03 0.48 Use FFV 0.0000001 0.00 0.00 Bagasse 0.000605673763 17.64 223.00 Enzyme 0.000004353729 2.46 1.13 Sulfuric Acid 0.000004473635 0.31 11.80 Lime 0.000001432104 0.97 4.70 DAP 0.000004376291 0.82 11.41 Bioelectricit y 0.000000136640 0.00 0.05 Steam 0.000000000010 0.00 0.00 Sources: GaBi database; company reports and sectoral associations

2 Construction of the upstream cutoff matrix Cu

The construction of the upstream cutoff matrix from the hybrid LCA matrix was structured, following the approach described by Wiedmann et al (2011), Suh and Huppes (2005) and Suh (2004). The construction is described here in the following 5 steps. Step 1: Create concordance between processes and IO system sectors. Step 2: Create technical coefficient matrix matching process matrix, referred as C. The columns of the upstream non-weighted cutoff matrix C are occupied with technical coefficients of Ä. Technical coefficients äij placed into cells cik of the matrix where i is a row of sectors and j the sector matching process k. For example, sector “Chemical” matches “Lime” process. Step 3: calculate multiplier p. is the value of process s, in basic monetary units, divided by the volume of s. p is in monetary units (R$) per respective functional unit (example kg). Step 4: Columns of the matrix C from Step 2 are multiplied with the multiplier p of the u corresponding based-process products to yield price-weighted coefficients c ik, as shown in eq. 4. (4)

u Step 5: Correcting double accounting. Those C sk that are already represented in the u process-based matrix have to be deleted. Thus, c ik=0 ↔ ãsk≠0. 3 Sensitivity analysis

The sectoral aggregation of I-O system and the weighting of Cu elements can be the main sources of uncertainties. Since the elements depend on the values of process k, in basic monetary units, this sensitivity analysis identifies the effects of changes of the values of process s, in basic monetary units, in ±20 % for the final results of employment generation, CO2 emissions and energy consumption. The results depict that changes in the weighting multiplier of the Cu elements are more sensitive for the employment generation category. Table D illustrates the sensitive of the impact categories due to changes in Cu elements. Table D Effect due to the change in the multiplier p of process k

kg CO2e emissions Energy Consumption Employment generation MJ (jobs) Hybrid LCA (500 km) 55.4 401.4 0.0037 Multiplier p +20 % 57.7 429.4 0.00427 Multiplier p -20 % 53.1 373.4 0.00305 % change ±4.18 ±6.97 ±16.73 Hybrid LCA (50 liters) 28.8 251.9 0.00094 Multiplier p +20 % 29.5 254.5 0.00102 Multiplier p -20 % 28.5 249.3 0.00086 % change ±1.74 % ±1.05 % ±8.42 %

The uncertainties about the representativeness of upstream inputs used in employment u generation are also assessed here. In other words, we added more C ik elements, since many of those elements were eliminated in step 5 to correct the double accounting, according to the environmental modeling. To avoid double accounting, the new upstream inputs depend on the inputs considered in the original employment life cycle inventory. The added inputs are: 1. Bagasse: added inputs from mining and chemical sectors; 2. Enzymes: added inputs from mining, cement and chemical sectors; 3. Sulfuric Acid: added inputs from agriculture, sugarcane, mining, cement and chemical sectors; 4. Lime: added inputs from agricultural, sugarcane, mining, cement and chemical sectors; 5. DAP: added inputs from agricultural, sugarcane, mining, cement and chemical sectors; 6. Bioelectricity: added inputs from mining, cement, chemical and other chemical sectors; 7. Steam: added inputs from mining, cement, chemical and other chemical sectors; 8. SGe: no added inputs; 9. Transport (cargo): added inputs from agriculture, mining, cement, chemical and other chemical sectors; 10. Use phase: added inputs from agricultural, sugarcane, mining, cement and chemical sectors; This analysis identified that the inclusion of those new upstream inputs resulted in a change of only 5 % of the total employment generation. Table E shows the results of the sensitivity analysis.

Table E Change in the technological structure of the inputs to consolidate the employment life cycle Description Employment Generation Hybrid LCA (500 km) 0,0037 Maximum of inputs 0,0038 % Change +4,94 Hybrid LCA (50 liters) 0,00094 Maximum of inputs 0,00100 % Change +6,73 References

Miller R, Blair P (2009) Input-Output Analysis: Foundations and Extensions. Cambridge University Press. ISBN 978-0-521-51713-3 Heijungs R, Suh S (2002) The Computational Structure of Life Cycle Assessment. Kluwer Academic Publishers. ISBN 1-4020-0672-1 Suh S (2004) Functions, commodities and environmental impacts in an ecological– economic model. Ecol Econ 48(4):451-467 Suh S, Heijungs R (2007) Power Series Expansion and Structural Analysis for Life Cycle Assessment. Int J Life Cycle Assess 12(6):381-390 Suh S, Huppes G (2005) Methods in the Life Cycle Inventory of a Product. J Clean Prod 13(7):687-697 Wiedmann T, Suh S, Feng S, Kuishuang F, Lenzen M, Acquaye A, Scott K, Barrett J (2011) Application of hybrid cycle life approaches to emerging energy technologies: the case of wind power in the UK. Environ Sci Technol 45:5900-5907