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Oil  Palm  for  Biodiesel  in  Brazil  –  Risks  and  Opportunities  

Oskar  Englund,  Göran  Berndes,  Martin  U.  Persson  and  Gerd  Sparovek  Supplementary  Information  

Main  uncertainties  The discount rate represents the opportunity cost of capital. It should therefore reflect the (risk-free) expected returns from investments, i.e., market interest rates. The benchmark interest rate in Brazil has averaged almost 16% from 1999 until 2014, but in the latest decade it has averaged around 10% per year (Segura-Ubiergo 2012). We therefore used a 10% discount rate as a baseline assumption but stress that results would change significantly with another discount rate: for example, without a LUC carbon-pricing scheme, using a discount rate of 5% increases the total area with positive NPV with an average 16% for establishment year 2013, and 12 % for establishment year 2025. Using a discount rate of 15%, the profitable area instead decreases with 29% and 22%, respectively. Since this study considers a potential carbon cost from LUC to be a start-up cost, the impact of a LUC carbon price decreases using a lower discount rate, and increases using a higher discount rate (Supplementary figure 1). Similar observations have been made by Persson and Azar (U. M. Persson & Azar 2010).

Supplementary Figure 1: Sensitivity analysis using different discount rates. Green columns (above the x-axis) show the total area where NPV is positive, and orange columns (below the x-axis) show the area where NPV is negative (cf. figure 1 in main paper), in each of the main 18 scenarios. Error bars show results using a discount rate of 5% (upper end) and 15% (lower end).

As seen in figure 2-3 (main paper), the LUC carbon price level is the most important factor behind the variations in NPV. The small differences between WEO scenarios, at a given LUC carbon price level, can be explained partly by relatively small differences in oil price projections, and partly by assumptions made in the WEO model that generates the interconnected projections of oil, carbon, and coal. By 2035, the range of oil price projections in the WEO scenarios that were used in this study is $97-140, while in the EIA Annual Energy Outlook (EIA 2014) the range is $73-188. Unless biodiesel demand is determined by policies including

-600

-400

-200

0

200

400

600

CP NP 450 CP NP 450 CP NP 450 CP NP 450 CP NP 450 CP NP 450

2013 2025 2013 2025 2013 2025

No LUC C price Mid LUC C price High LUC C price

Mha

Positive NPV Negative NPV

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quantitative biodiesel targets, the oil and carbon prices determine the willingness to pay for biodiesel. Thus, the NPV of establishing palm oil plantations is higher in a scenario with higher oil prices, unless the higher carbon prices that would typically occur in a scenario with lower oil prices outweigh the effect of the oil price difference by increasing the price for petrodiesel. This is the case in the WEO scenarios, where the willingness to pay for biodiesel varies little compared with the oil price projections (by 2035: $832-941). By 2035, the biodiesel price is actually higher in the 450 ppm scenario than in the New policies scenario, even though the oil price is higher in the latter. The seemingly small effect of oil price variations on NPV of establishing oil palm plantations is however an effect of assumptions in the model that generate interconnected prices of oil, carbon and coal. Other assumptions may result in larger variations in the resulting willingness to pay for biodiesel, and thus also in NPV of establishing oil palm plantations.

Methodology  (S1) was used to spatially determine the NPV of establishing oil palm plantations for biodiesel production in Brazil. 𝑁𝑃𝑉!"#$%#& 𝑡 =  −𝐿𝑎𝑛𝑑  𝑝𝑟𝑖𝑐𝑒 + 𝑅𝑒𝑣𝑒𝑛𝑢𝑒  𝑓𝑟𝑜𝑚  𝑡𝑖𝑚𝑏𝑒𝑟 − 𝐶𝑜𝑠𝑡  𝑜𝑓  𝑒𝑠𝑡𝑎𝑏𝑙𝑖𝑠ℎ𝑖𝑛𝑔  𝑝𝑙𝑎𝑛𝑡𝑎𝑡𝑖𝑜𝑛𝑠 −𝐶𝑜𝑠𝑡  𝑜𝑓  𝑒𝑠𝑡𝑎𝑏𝑙𝑖𝑠ℎ𝑖𝑛𝑔  𝑚𝑖𝑙𝑙 − 𝐶𝑜𝑠𝑡  𝑜𝑓  𝐿𝑈𝐶  𝑐𝑎𝑟𝑏𝑜𝑛  𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠   +

!"#"$%"  !!"#$%&'$%()  !"#$!!"##"$%  !"#$!!"#$%&'"(  !"#$!!"#$  !"  !!!  !"#$$#%&$!!! !

!"!!! (S1)

(S1) was then reformulated to (S2) to facilitate easier implementation in the GIS software and to reduce computation needs. 𝑁𝑃𝑉!"#$%#& 𝑡 = 𝑎 − 𝑏 − ℎ − 𝑖 − 𝑐 − 𝑒 + 𝑔 ∗ 𝑌!"# − 𝑑 ∗ 𝐸!"# − 𝑓 ∗ 𝑌!"# ∗ 𝐶!"#$%&'"! − 𝐶!"#$ (S2) Where: 𝑎 = 𝑅!"#$%& (only in cells where LULC = forest) 𝑏 = 𝐶!"!!"#$%#%&'$( 𝑐 = 𝐶!"!!"## 𝑑 = −𝑃! 0 (negative since emission values are negative for a decrease in carbon stock)

𝑒 =𝑌% 𝑛 ∗ 𝑃 !"#$%&# 𝑛 + 𝐾!"!!"# ∗ 𝑃!"#$%$&'( 𝑛

1 + 𝑟 !

!"

!!!

𝑓 =𝑌%(𝑛)1 + 𝑟 !

!"

!!!

𝑔 =𝑌% 𝑛 ∗ 𝐶!"##

1 + 𝑟 !

!"

!!!

ℎ =𝐶!"#$%&'$%()(𝑛)

1 + 𝑟 !

!"

!!!

𝑖 =𝐸!!! ∗ 𝑃!(𝑛)

1 + 𝑟 !

!"

!!!

(S2) was calculated for each hectare in Brazil for a total of 27 scenarios, using ArcGIS map algebra. See main paper for information about the scenarios. All prices are expressed in constant (inflation adjusted) USD for the year 2010. See Supplementary table 1 for description of parameters.

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Supplementary table 1: Description of parameters in the NPV formula

Parameter Description Further information

Ypot Raster dataset. Maximum potential production capacity of palm oil and palm kernel oil, based on GAEZ 3.0.

Section Potential Yield under Spatial Data

ELUC Raster dataset. Change in carbon stock from establishing oil palm plantations Section Effects on Carbon Stock from Oil Palm Establishment under Spatial Data

Ctransport Raster dataset. Costs for transporting palm oil to an export port Transportation Cost under Spatial Data

Cland Raster dataset. Cost of purchasing land in each cell, or the opportunity cost of not choosing an alternative land use for a land owner. The land prices for natural vegetation and agricultural land in each micro region, as reported by FNP (2012), was assigned to individual cells in that micro region based on their LULC class.

Rtimber In cells classified as forest, a revenue of $339 from selling the timber is assumed (Busch et al. 2009). Cest_plantations Cost of establishing plantations. In order to include only costs independent of policy, taxes and fixed

charges ($28.3 $/ha), and costs of upholding a conservation area required by policy (98.2 $/ha), were excluded.

Supplementary table 2

Cest_mill Cost of establishing a palm oil mill refer to construction of a mill with an annual capacity of 5256.67 t PO+PKO. Each grid cell (ha) need to cover a certain part of these costs, based on the average yield in the cell. Share of establishment cost ($/ha): Total establishment cost * average annual yield per ha (0.77*Ypot) / annual processing capacity of mill -> Share of establishment cost ≈ 601 $/t PO+PKOmax

Supplementary table 3

PC (n) Carbon price projections are taken from the three scenarios Current Policies (CP), New Policies (NP), and 450 ppm, in World Energy Outlook (WEO) (IEA 2012), and applied in the model using the following assumptions: (i) Since EU currently has the most ambitious carbon pricing scheme and the EU biofuel market is sufficiently important to affect global market dynamics, the global biodiesel price is assumed to depend on the global crude oil price and the carbon price in the EU; (ii) Since only a small fraction of coal consumption is traded internationally (IEA 2012) coal prices are assumed to depend only on carbon pricing schemes in the region where the coal is produced/used. The same is assumed for bioenergy from residues, since it competes primarily with coal. The price for bioenergy from residues therefore depends on the coal price and the national or regional carbon pricing policy. In this case, the projected average OECD steam coal price and the national carbon pricing policy for Brazil, which exists is one of the WEO scenarios (450 ppm); (iii) The price of carbon emissions due to land use change depends on whether a global or national LUC C pricing scheme exists. In this study, we use three LUC C price scenarios, No, Mid and High. The Mid price scenario starts at 6 $/t CO2e, i.e., approximately the current average price on voluntary carbon markets (Peters-Stanley et al. 2013), and the High price scenario starts at 18 $/t CO2e, i.e., approximately the current EU ETS price. The High scenario then follows the EU carbon price development in the WEO 450 ppm scenario, while the Mid scenario increases each year with the same relative change.

Supplementary figure 2

Ppalmoil (n)

The biodiesel price is assumed to be equivalent with the diesel price, which is calculated using: Pbiodiesel = Pdiesel = Poil + CrefiningDIESEL + Ccarbon Where Poil is the oil price projections in the three policy scenarios in IEA world energy outlook 2012. CprocessingDIESEL is the cost of refining crude oil into diesel fuel, estimated at 162.5 $/t (Li et al. 2012) ≈ 3.77 $/GJ. Ccarbon is the cost for carbon emissions calculated per energy unit using the carbon price in the EU for each year in the IEA scenarios multiplied with the CO2 emission factor for diesel. Calorific values and conversion factors were obtained from Edwards et al. (2013). The price of palm oil for biodiesel is calculated using: Ppalmoil = Pbiodiesel – CrefiningPO

Where CrefiningPO is estimated at 73.9 $/t (SUFRAMA 2003).

Supplementary figure 3

Y% (n) Percent of the maximum yield achieved each plantation year. Supplementary table 4

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KPO-res

Relationship between Ypot (potential yield of PO+PKO) and the amount of residues that can be used for bioenergy. Mainly fiber, palm kernel shell, and in some cases empty fruit bunches (EFB) are utilized for energy purposes. As shown by Menon et al. (2003), this has economic advantages over mulching, which is the regular practice, while fronds are used for mulching and the kernel cake is sold as animal feed. Although there is limited experience with removing larger shares of biomass, Corley & Tinker(2003) finds it unlikely that consequences for soil properties will be negative, as long as the loss in nutrients are replaced in the form of fertilizers. Here, it is assumed that, in addition to fiber and shell, EFB, kernel cake, and biogas from palm oil mill effluent (POME) are utilized for bioenergy (i.e., all residues but the fronds). Trunks are not included as a source for bioenergy since replanting is considered to be part of establishing the next plantation cycle (i.e. year 26). If 100 % of fiber, kernel shell, EFB, kernel cake, and biogas from palm oil mill effluent (POME) (i.e., all residues but the fronds) are utilized for bioenergy, it would correspond to ~22.1 GJ /t PO+PKO (Persson 2012).

Pbioenergy (n)

The price for residues used for bioenergy year n is assumed to be equivalent with the price for steam coal: Pbioenergy = Pcoal + Ccarbon Where Pcoal is taken from the coal price development in the WEO scenarios and Ccarbon is the cost for carbon emissions calculated per energy unit using the carbon price in Brazil for year n in the IEA scenarios (the 450 ppm scenario is the only scenario where Brazil implements a carbon pricing scheme), multiplied with the CO2 emission factor for hard coal. Calorific values and conversion factors are from Edwards et al. (2013).

Supplementary figure 4

r

Discount rate, set at 10 % for the main analysis. Sensitivity analysis performed using discount rates of 5% and 15%.

Main paper, Supplementary figure 1

Cmill Cost of producing palm oil for an oil palm mill. Only costs independent of policy are included (i.e. taxes and fixed charges are excluded). For comparison, Persson (2012) reports a high estimate of milling costs at 15 $/t FFB, which corresponds to 76.5 $/t PO+PKO, with a PO extraction rate of 22.5 % (Fischer et al. 2012) and a PKO content of 2.4 % (Persson 2012).

Supplementary table 5

Ccultivation (n)

Annual costs of cultivating oil palm. In order to include only costs independent of policy, taxes and fixed charges, and costs of upholding a conservation area required by policy, are excluded.

Supplementary table 6

EN2O

N2O emissions of 5.2 kg N2O/ha/a were assumed (Persson 2012). Using a Global Warming Potential of 298 for N20 (Forster et al. 2007), this corresponds to 1.55 t CO2-eq = 0.422 t C/ha/a.

Supplementary table 2: Costs of establishing plantations (SUFRAMA 2003)

Specification Cost ($/ha) Buildings & facilities 4.5 Machines and equipment 1.9 Tools and accessories 4.6 Furniture and utensils 0.9 Computers 2.5 Vehicles 254 Manual land preparation 20.6 Mechanized land preparation 336.3 Plantation material 1205.9 Labour 21.8 Insurance 18.4 Total plantation establishment cost 1871.4

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Supplementary table 3: Cost of establishing a palm oil mill with an annual capacity of 5256.67 t PO+PKO (SUFRAMA 2003)

Specification Cost ($) Facilities 125,664 Installations 308,240 Machines and equipment 3,082,400 Accessories and utensils 2,622 Computers 2,042 Vehicles 583,531 Total mill establishment cost 4,104,500

Supplementary Figure 2: Developments of the LUC carbon price (orange and green lines) and the Brazilian carbon tax (black line) in the WEO scenarios. CP/NP = Current-/New Policies; 450 = 450 ppm

Supplementary Figure 3: Development of the price of palm oil for biodiesel

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450"ppm"0"high" 450"0"Carbon"tax"in"Brazil"

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Supplementary table 4: Yield profile over a plantation lifetime of 25 years (Persson 2012)

Plantation year Share of max potential yield, Ypot 1 0,00 2 0,00 3 0,00 4 0,36 5 0,61 6 0,81 7 0,93 8 0,98 9 1,00 10 1,00 11 0,99 12 0,98 13 0,97 14 0,96 15 0,95 16 0,94 17 0,93 18 0,91 19 0,90 20 0,88 21 0,86 22 0,84 23 0,82 24 0,80 25 0,78 Average 0,77

Supplementary Figure 4: Development of the price of bioenergy from residues in Brazil

Supplementary table 5: Costs of producing palm oil for an oil palm mill (SUFRAMA 2003)

Average production costs ($/ t PO+PKO / y) Fixed costs 44.9 Variable costs 29.0 Total annual production costs 73.9

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Supplementary table 6: Cost of oil palm cultivation (SUFRAMA 2003) ($/t/y)

Plantation year 1 2 3 4 5 6-13 14-25 Fixed costs 29.7 29.7 29.7 29.7 29.7 29.7 29.7 Variable costs 494.2 460.9 543.6 569.6 569.6 570.3 358.1 Total production costs 523.9 490.6 573.3 599.3 599.3 600.0 387.8

Spatial  data  This section describes the spatial data used as input for the NPV model.

Potential  Yield  

Spatial data on potential yield for palm oil was taken from the GAEZ 3.0 model (IIASA & FAO 2012).

GAEZ  input  parameters    

Model Total production capacity (t/ha) Crop Oilpalm Water supply Rain-fed Input level High Time period Baseline (1960-1990) Climate model none CO2 fertilization No Geographic area Brazil

• Input level was set to high since it was the option most similar to the current situation for oil palm

cultivation in Brazil.

• The output data was resampled to 100 m using bilinear convolution, to match the LULC map.

• Negative values and cells classified as water according to the LULC map were set to zero.

• The output of the model is t palm oil / ha. However, palm kernel oil (PKO) may also be used for biodiesel production so the values were changed to include PO + PKO as follows:

Extractable PO constitute 22.5 % of FFB in GAEZ (Fischer et al. 2012) and PKO constitute 2.4 % of FFB (Persson 2012).

=> PO + PKO = 1.107 * PO

Residues  

Revenues from selling residues for bioenergy are included in the NPV formula. The potential of using residues for energy purposes was spatially estimated based on the potential yield data. Mainly fiber, palm kernel shell, and in some cases empty fruit bunches (EFB) are utilized for energy purposes. As argued by Menon et al. (2003), this has economic advantages over mulching, which is the regular practice, while fronds are used for mulching and the kernel cake is sold as animal feed. Although there is limited experience with removing larger shares of biomass, Corley & Tinker (2003) finds it unlikely that consequences for soil properties will be negative, as long as the loss in nutrients are replaced in the form of fertilizers. Here, it is assumed that, in addition to fiber and shell, EFB, kernel cake, and biogas from palm oil mill effluent (POME) are utilized for bioenergy (i.e., all residues but the fronds). Trunks are not included as a source for bioenergy since replanting is considered to be part of establishing the next plantation cycle (i.e. year 26).

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If 100 % of fiber, kernel shell, EFB, kernel cake, and biogas from palm oil mill effluent (POME) (i.e., all residues but the fronds) are utilized for bioenergy, it would correspond to ~22.1 GJ /t PO+PKO, as specified in Supplementary table 7.

Supplementary table 7: Energy recoverable from residues per ton of PO+PKO produced (recalculated from Persson (2012))

FFB constitutes: wt/ t FFB PO 22,5%

PKO 2,4% Kernel cake 2,8%

Fibre 13,8% Shell 6,2% EFB 22,9%

Moisture content: Kernel cake 36,9%

Fibre 36,9% Shell 9,3% EFB 49,7% Trunks & fronds

Fronds 11 (tDM/ha/yr) Share of fronds used for mulching 100%

Trunks at replanting 88 (tDM/ha) Energy params:

CPO 39,6 (GJ/t) PKO 38,0 (GJ/t) Fibre 18,5 (GJ/tDM) Shell 20,3 (GJ/tDM)

Kernel cake 18,9 (GJ/tDM) EFB 17,5 (GJ/tDM)

Trunks & fronds 14,9 (GJ/tDM) Biogas from POME 0,4 (GJ/tFFB)

Electricity demand milling 40,0 (kWh/tFFB) Steam demand milling 1,5 (GJ/tFFB)

Efficiency co-generation 85% Palm oil biodiesel energy content 40,0 (GJ/t)

Mass yield biodiesel production 96% Natural gas demand for biodiesel prod. 2,9 (GJ/t biodiesel)

Energy from residues / t FFB 5,49 GJ/t FFB Energy from residues / t PO 22,1 GJ/t PO+PKO

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Effects  on  Carbon  Stock  from  Oil  Palm  Establishment  This section explains the methodology used to spatially estimate how the establishment of oil palm plantations affect the carbon stock. Three C pools were considered: aboveground C (AGC), belowground C (BGC), and litter C (LC).

Current  C  stocks  

Aboveground  C  

Recent carbon maps from Baccini et al. (2012) and Saatchi et al. (2011) were evaluated. The latter is of coarser resolution and therefore does not capture deforested areas near roads as well as the former. In addition, it does not match waterways in the LULC map as well. Therefore, the dataset by Baccini et al. (2012) was used for this study. The dataset was resampled to 100 m to match the LULC map. This was done using cubic convolution in order to reproduce the statistical and spectral properties of the original grid as good as possible. Since the original dataset was expressed in tons of biomass, it was converted to tons of carbon assuming a default carbon fraction of biomass of 0.5 (IPCC 2006a).

Data  manipulation  

Unvegetated areas (ID 6 in simple LULC map) were set to zero AGC, as well as negative values that occurred in the resampling process. The AGC values for pastures, croplands and grasslands were evaluated and they proved much higher than expected; 68.7, 65, and 101.2 t C/ha for pastures, croplands, and grasslands, respectively. The original dataset therefore seem to overestimate AGC values in cells classified as pastures, cropland and grassland by an order of magnitude. The lead author confirmed in personal communication that the model overestimates carbon values in the lower end and that most of the calibration data for the model were located in tropical forests, making the model less suitable for estimating AGC values on non-forested land. To correct for this, AGC values in non-forest cells were downscaled, so that the mean AGC value corresponds to the IPCC mean default AGC values for pastures, cropland and tropical grasslands (IPCC 2006b): 3.1, 4.7, and 3.1 t C/ha, respectively. This approach facilitated that the relative differences in AGC between all individual cells within the same LULC class were preserved, but their values were taken down to a realistic level. The AGC data on pastures, croplands and grasslands was thus modified as follows: 0.045 * original AGC values in pasture cells (3.1 / 68.7 = 0.045) 0.072 * original AGC values in cropland cells (4.7 / 65 = 0.072) 0.031 * original AGC values in grassland cells (3.1 / 101.2 = 0.031) The modified carbon rasters for pasture, cropland and grasslands replaced the corresponding grid cells in the original carbon raster, using raster calculator:

Belowground  C  

Belowground C was estimated using IPCC root-to-shoot ratios (IPCC 2006a). Depending on the LULC code and the AGC value, each cell was assigned a BGC value (Supplementary table 8)). Pastures were assumed to have the same AGC:BGC ratio as natural grasslands.

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Supplementary table 8: AGC:BGC ratios used to spatially estimate BGC values

ID Name AGC:BGC ratio

1 Cropland 0.50 2 Cropland/vegetation 0.46 3 Vegetation/cropland 0.41 4 Pasture 1.60 10 Closed to open evergreen forest 0.37 11 Closed deciduous forest 0.24 12 Open deciduous forest 0.20 13 Closed to open broadleaved forest, regularly flooded 0.37 14 Closed broadleaved forest, permanently flooded 0.37 20 Mosaic Forest-Shrubland-Grassland 0.74 21 Shrubland 0.40 22 Grassland 1.60 23 Closed to open vegetation, regularly flooded 0.40 30 Sparse vegetation 0.40 31 Artificial areas 0.00 32 Bare areas 0.00 40 Water 0.00 After reviewing the results, a few grid cells show BGC values higher than 84, which is assumed to be the theoretical maximum (i.e. the maximum AGC value times the maximum root-to-shoot ratio for forests). These grid cells are likely forests that are misclassified as non-forest LULC types, or correctly classified but assigned an overestimated AGC value. To correct for this, all BGC values above 84 are recalculated using a root-to-shoot ratio of 0.37 (closed to open evergreen forest). Maia et al. (2010) report that perennial cropping has a minimal impact on soil organic carbon (SOC) stocks after 20 years, estimated at a factor value of 0.98±0.14, suggesting these systems maintain about 98% of the SOC stock found under native vegetation. Therefore, soil C was omitted when estimating changes in BGC. It should be noted that productive pastures can be high in soil C (Cederberg et al. 2011), so it is possible that the soil C pool would decrease by converting productive pastures to oil palm. It is however not considered in this study due to the ambiguous literature.

Litter  C  

The UNFCCC (2012) provides a “conservative default factor expressing carbon stock in litter as a percentage of carbon stock in tree biomass” (Supplementary table 9): Supplementary table 9: UNFCCC conservative default factor expressing carbon stock in litter as a percentage of carbon stock in tree biomass, depending on elevation and annual precipitation

Biome Elevation Annual Precipitation DFDW Tropical <2000 <1000 mm 4 % Tropical <2000 1000-1600 mm 1 % Tropical <2000 >1600 1 % Tropical >2000 All 1 % Temperate/Boreal All All 4 %

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Elevation was considered to be lower than 2000 m for all of Brazil. Precipitation data (Hijmans et al. 2005) was resampled and snapped to match the AGC dataset. Map Algebra was used to combine the data and produce a dataset of litter C.

Carbon  stocks  in  oil  palm  plantations  

Carbon stocks in oil palm plantations varies between sites. It is assumed that yields (Y) and carbon stocks (C) are positively correlated. A linear relationship was assumed. In order to estimate the carbon stocks in presumed oil palm plantations, it was assumed that there is a linear relationship also between potential yield (potY) and potential carbon stocks (potC): k = C/Y = potC/potY In order to determine k, the maximum C content in aboveground biomass was divided by the maximum potential yield in the GAEZ 3.0 potential production capacity dataset (IIASA & FAO 2012). IPCC (2006b) reports that the aboveground biomass in oil palm plantation ranges between 62-202 t/ha. 202 t/ha biomass corresponds to 84.23 tC/ha using a conversion factor of 41.7 (Syahrinudin 2005). The global maximum potential yield is 7.414: k = Cmax/potYmax = 84.23 / 7.414 = 11.4 for aboveground biomass. Following the relationship between different carbon stocks in oil palm plantations as reported by Syahrinudin (2005), k was determined also for the other carbon pools: 4.5 for BGC and 1.4 for LC. The average C content over a plantation lifetime of 25 years, assuming a linear increase in biomass, in each of the three C stocks was then estimated for each grid cell using: AGC: Potential yield * 11.4 / 2 BGC: Potential yield * 4.5 * / 2 LC: Potential yield * 1.4 / 2

Creating  final  C  stock  change  dataset  

The net C stock change in case of oil palm establishment was then calculated for each grid cell by subtracting the current total C stocks from the total estimated oil palm C stocks, using raster calculator. Values are expressed in t C for each cell (= t C/ha). Negative values indicate a loss in C stocks if oil palm plantations are established (i.e. net LUC C emissions) and positive values indicate a gain in C stocks (i.e. net C sequestration).

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Transportation  Cost  In this study, transportation costs include the costs of transporting palm oil to an export port. Since FFB need to be processed into palm oil within ca. 48 hours, the processing must take place near the plantations. For simplicity, it is assumed to happen within each grid cell. The cost of transporting FFB to a palm oil mill is not included. Plantations are unlikely to be established far from existing access roads. Therefore, all grid cells are assumed to be connected by an access road to the main infrastructure network, since that is likely to be the case at the time when plantations are established. The result for individual cells is thus, in most cases, not reflecting the current conditions, but what the transportation cost would be like at the time when plantations are established there. The cost distance tool in ArcGIS was used to estimate transportation costs for each cell. Required inputs include (1) a cost raster, i.e., a raster where the value for each cell corresponds to the cost per meter to traverse it; and (2) a source dataset, i.e. a shapefile or raster where the destinations are included (in this case the export ports). The tool then calculates, for each cell, the lowest possible cost to get to a destination. Roads in poor condition result in higher variable costs of operation because they (i) reduce fuel efficiency; (ii) damage the vehicles, leading to higher maintenance and higher operation costs; (iii) reduce the life of tires; (iv) reduce vehicle utilization because of lower speeds; and (v) reduce the life of the truck (Teravaninthorn & Raballand 2009). Therefore, a literature review was made to estimate the cost of traversing different surfaces (Supplementary table 10). The reviewed literature showed differences both in methodologies and in targeted commodity. Values for this study were therefore chosen by qualitatively comparing the reviewed studies, instead of by calculating mean values. No study reported different costs for different types of cleared vegetation. Therefore, the cost of transporting goods on an established access road is assumed not to depend on the vegetation type that was cleared in order to establish the road. Therefore, five transport surfaces were considered when estimating the cost of transporting palm oil to export ports, as presented in Supplementary table 11.

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Supplementary table 10: Cost of traversing different surfaces, expressed in USD2010

LULC type [a] [b]

[c]

[d] [e]

[f]

[g]

[h]

[i]

Used ($/t/km)

Paved road 0.05 0.0681 0.1542

0.08-0.16 0.06-0.17 0.08

Unpaved road 0.15 0.1361- 0.2312 0.2642

0.4663 0.17-0.215

0.24-0.286

0.16

Agriculture 0.2 0.272 0.24 0.7624 0.32 Cleared grassland and Savanna

0.3 0.272 0.24 0.7624 0.32

Cleared forest

0.272 0.24 0.7624 0.32

Water Bodies

0.272 0.32

Navigable rivers 0.095 0.115 0.058 0.02-0.068 0.06

[a] (Vera-Diaz et al. 2009). Expressed in [$/t/m] [b] (Stone 1998). Expressed in [$/m3/m] [c] (Veríssimo et al. 1992). Expressed in [$/m3/m] [d] (Veríssimo et al. 1995). Expressed in [$/m3/m] [e] (Barros & Uhl 1995). Expressed in [$/m3/m] [f] (Merry et al. 2009). Expressed in [$/m3/m] [g] (Verissimo et al. 2002). Expressed in [$/m3/m] [h] (Lentini et al. 2005). Expressed in [$/m3/m] [i] (Salin 2011). Expressed in [$/t/m]

1) Large truck (30 m3) 2) Medium truck (13 m3) 3) Small truck (5 m3) 4) Unpaved road (poor condition or maintenance) 5) Regular quality 6) Poor quality 7) Road type not specified. Cost depend on distance, the longer the less expensive per unit of distance 8) Transport mode (raft/barge/ferry) not specified. Cost depend on distance, the longer the less expensive per unit of distance

Supplementary table 11: Transport surfaces considered when estimating the cost of transporting palm oil to export ports

Transport surface (code) Transport surface (name) Cost ($ 2010 /t/km)

1 Paved roads 0.08 2 Unpaved roads 0.16 3 Access roads (all grid cells not classified as any transport mode) 0.32 4 Navigable rivers 0.06 5 Planned roads 0.081

1) It is assumed that all new roads will be paved

Existing  and  future  road  network  A road dataset from The Ministry of Transport (Ministério dos Transportes 2009) was reclassified using general rules, as described below, to obtain a dataset with the desired transportation surfaces (Supplementary table 11). In some cases where roads were obviously misclassified following a visual review of the data, roads were reclassified manually. The status of unclassified roads (‘’) was investigated by comparison with another road dataset from The Ministry of Transport (Ministério dos Transportes 2008), various satellite images, and above ground carbon data (Baccini et al. 2012). It seems like unclassified roads are in most cases not constructed (i.e. planned) and were therefore classified as planned roads along with roads classified as planned (“Planejada”). To cross a river on a ferry (“Travessia”) is likely more costly than to drive on the connecting roads (i.e. there is likely a fee involved). This cost may however vary. Due to this, roads classified as ferry crossings (“Travessia”) were assumed to be equally costly as access roads to traverse, and thus classified accordingly. Roads classified as motorways (“Duplicada”), under motorway construction (“Em obras de duplicação”), and paved (“Pavimentada”) were classified as paved roads.

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Roads under construction (“Em obras de implantação”), being paved (“Em obras de pavimentação”), with natural road bed (“Leito natural”), and already established roads (“Implantada”) were classified as unpaved roads. New shapefiles were created based on the above rules (and exceptions), rasterized and reclassified according to Supplementary table 11, with a resolution of 100 m.

Navigable  waterways  

Several datasets on waterways were assessed and the National Agency for Waterway Transportation(Antaq 2013) dataset was found to be the most spatially correct one, when comparing with waterways in the LULC map. Rivers specified as “Não navegável” (not navigable), “Navegação inexpressive” (meaningless to traffic) and ““ (unspecified – mainly ocean routes), were considered as not navigable. The navigability of all remaining rivers was evaluated through a literature review. The features were then clipped using a polygon of Brazil. In order to avoid rivers on the border to other countries to be fragmented in the process, the polygon was slightly expanded where needed. The shapefile was then rasterized and reclassified according to Supplementary table 11.

Export  points  in  Brazil  

Export ports in Brazil include ports classified as “Ports with Container Liner Service” according to the world port source (http://www.worldportsource.com/shipping/country/ports/BRA.php) Supplementary table 12: Ports with Container Liner Service in Brazil (ref). Ports in bold were included as possible export points for estimating transportation costs

Port Full name Comments Aratu Port of Aratu Belem Port of Belem Fortaleza Port of Fortaleza Imbituba Port of Imbituba Excluded: nearby Santos Ipojuca Suape Marine Terminal Itaguai Port of Itaguai Excluded: nearby Rio de

Janeiro Itajai Port of Itajai Excluded: nearby Santos Manaus Port of Manaus Navegantes Port of Navegantes Excluded: nearby Itajai Paranagua Port of Paranagua Pecem Port of Pecem Excluded: nearby Fortaleza Rio de Janeiro Port of Rio de Janeiro Rio Grande Port of Rio Grande Salvador Port of Salvador Excluded: nearby Aratu Santos Port of Santos Sao Francisco do Sul Port of Sao Francisco do

Sul Excluded: nearby Santos

Vila do Conde Port of Vila do Conde Excluded: nearby Belém Vitoria Port of Vitoria In addition to the ports in Supplementary table 12, Santarem and Itaquí were included, since they are important soy export ports(Vera-Diaz et al. 2009). A shapefile was created with the location of the selected ports.

Export  through  Peru  

In addition to using ports in Brazil, the possibility to export through Peru was included since it might be cheaper for producers in the north-western parts of Brazil.

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BR-­‐364  to  Lima  

An export point was added at the Peruvian border, approximately where the planned BR-364 would cross the border if completed. The grid cells surrounding this export point were given a transport cost equal to the approximate cost of transportation from the Peruvian border to Lima, using the Pucallpa-Lima highway: There is approximately 100 km without existing roads between the border and Pucallpa. From Pucallpa to Lima there are 854 km on the Amazon Central highway. This highway is mostly paved but in some areas the road conditions can be very poor during the rain season. In addition, it traverses the Andes, which reduces fuel efficiency significantly. Therefore, the transport cost (USD/t/km) on this road is assumed to be equal to an unpaved highway in Brazil. Total cost from the export point at the border to Lima: 100 km * 0,27 (cost on access road) + 854 km * 0,17 (cost on unpaved road) = 172 USD/t. Since the cost for traversing each cell needs to be expressed in cost per meter when using the cost distance tool in ArcGIS (as applied in later steps), and the resolution is 100 m, the cells surrounding the export point were given the value 1.72

BR-­‐317  to  San  Juan  de  Marcona  

Another access point was added where the BR-317 crosses the border to Peru, to allow for export through the San Juan de Marcona port via the interoceanic highway. Most of the road is paved but it traverses the Andes, which reduces fuel efficiency significantly. Therefore, the average transport cost (USD/t/km) on this road is likely higher than on a paved road in Brazil. Since no estimations on transportation costs on this road was found in literature, 0,1 $/t/km was assumed, more costly than a paved road but less so than unpaved highways. The total cost from the export point at the border to San Juan de Marcona: 1345 km * 0,1 = 135 USD/t The cells surrounding the export point were thus given the value 1.35

Creating  the  cost  raster  The cost raster was created by combining the data on roads, navigable waterways, and export costs (through Peru). All other grid cells were classified as access roads, as explained earlier. After setting all “NoData” values to zero and combining the three datasets, the resulting dataset was clipped using a polygon of Brazil, slightly modified to make sure that the export cost cells for export through Peru could not be bypassed. By assigning corresponding costs from Supplementary table 11 in the attribute table, a cost raster was produced for both current (Supplementary figure 5) and future infrastructure using Raster calculator and Lookup.

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Supplementary Figure 5: Cost distance raster used as input to the NPV model. Values range between 0-224 USD/t. Green indicates low values and red indicates high.

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Land  Use  /  Land  Cover  The LULC map used in this study is based on the “Harmonized Land Use Map of Brazil”, compiled by Gerd Sparovek, Alberto Barretto and Marcelo Matsumoto, at the University of Sao Paolo and the Nature Conservancy in Brazil. It has a resolution of 100 m and was developed by compiling several regional land cover studies, which were based on observations. It was considered the best alternative for the purpose of this study given its high resolution and accuracy. The dataset differentiates between pasture and cropland, but does not specify vegetation types. It also has some data gaps. Therefore, Globcover (Bontemps et al. 2009) was used for specifying vegetation types and filling data gaps. Cells specified as “natural vegetation”, “unclassified” and “other” in the LULC map were reclassified according to Globcover (Supplementary table 13): The raster was then modified to include water instead of “NoData” in north-eastern Brazil, around Belem, so that water transportation to the port of Belém would be possible when estimating transportation costs. For analyzing the results, a simplified LULC map was needed. Therefore the LULC classes were reduced as described in Supplementary table 14:

Supplementary table 13: Reclassification of cells classified as “natural vegetation”, “unclassified” and “other” in the LULC map

Globcover name Globcover value

LULC value LULC name

Rainfed croplands 14 1 Cropland Mosaic croplands/vegetation 20 2 Cropland/vegetation Mosaic vegetation/ croplands 30 3 Vetegation/cropland 15 4 Pastures Closed to open broadleaved evergreen or semi-deciduous forest 40 10 Closed to open evergreen forest

Closed to open mixed broadleaved and needleleaved forest 100 10 Closed to open evergreen forest Closed broadleaved deciduous forest 50 11 Closed deciduous forest Open broadleaved deciduous forest 60 12 Open deciduous forest Closed to open broadleaved forest regularly flooded (fresh-brackish water) 160 13 Closed to open broadleaved forest,

regularly flooded Closed broadleaved forest permanently flooded (saline-brackish water) 170 14 Closed broadleaved forest, permanently

flooded Mosaic Forest-Shrubland/Grassland 110 20 Mosaic Forest-Shrubland-Grassland Mosaic Grassland/Forest-Shrubland 120 20 Mosaic Forest-Shrubland-Grassland Closed to open shrubland 130 21 Shrubland Closed to open grassland 140 22 Grassland

Closed to open vegetation regularly flooded 180 23 Closed to open vegetation, regularly flooded

Sparse vegetation 150 30 Sparse vegetation Artificial areas 190 31 Artificial areas Bare areas 200 32 Bare areas Permanent snow and ice 220 32 Bare areas Water bodies 210 40 Water

Supplementary table 14: Reclassification of the LULC map into fewer LULC classes

Value LULC Old value 1 Cropland 1 2 Pasture 4 3 Mosaic cropland 2,3 4 Forest 10,11,12,13,14 5 Other natural vegetation 20,21,22,23 6 Unvegetated areas 30,31,32,40

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