Appendices

ContentsA. The global LUC emission factor (LUCglobal).................................................................................................1

B. The discounted Global Warming Potential factor for land-use change emissions with dynamic land-use baseline (GWPLUC)............................................................................................................................................3

B.1 The LUCglobal factor with a dynamic baseline method............................................................................5

C. The GHG protocol method for GHG emission accounting of unknown LUC.............................................5

D. LUC emissions with the GLOBIOM model (Valin et al. 2015)...................................................................7

D.1 Sugarcane ethanol..................................................................................................................................7

D.2 Palm-oil biodiesel..................................................................................................................................7

D.3 Corn ethanol..........................................................................................................................................7

E. Life cycle GHG emissions of the four biofuel study cases...........................................................................8

E.1 Danish willow for cogeneration of heat and power (CHP).....................................................................8

E.2 Brazilian sugarcane ethanol..................................................................................................................10

E.3 Malaysian palm-oil biodiesel...............................................................................................................11

E.4 US corn ethanol....................................................................................................................................12

F. Dynamic baseline methods for GHG emission accounting from LUC.......................................................12

F.1 ILUC causality and system boundaries of biofuel studies with dynamic baseline methods..................12

F.2 Mass conservation in dynamic baseline methods.................................................................................13

E2.1 Mass conservation of GHG emissions in global biofuel production...............................................14

F.2.2 Mass conservation of food production in dynamic baseline methods............................................18

F.3 A last consistency check: current atmospheric CO2 trend as dynamic atmospheric baseline?..............19

References......................................................................................................................................................20

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A. The global LUC emission factor (LUCglobal) A global generic land-use change (LUC) emission factor (LUCglobal) is presented here, which is applied in the second dynamic baseline method (DBM2) and as a LUC accounting method. Greenhouse gas (GHG) emissions related to deforestation in the LUCglobal factor are reported separately to facilitate the application of different global warming potential (GWP) factors (Table A1). Indirect emissions from intensification are also reported separately as annual additional fertilizer use (here restricted to N synthetic fertilizer), in line with previous publications (Saez de Bikuña et al., 2016; Tonini et al., 2016). These can be excluded if fertilizer use is known. Intensification effects have been nonetheless considered to calculate the global land area-equivalent demand. The key difference with previous annual iLUC factors is that GHG emissions in LUCglobal are not discounted (Kløverpris and Mueller, 2012; Schmidt et al., 2015) nor previously amortized (over 100 years, like in Tonini et al. 2015).

FAO deforestation statistics (FAO, 2011) were combined with data of affected biomes by agricultural land expansion (Tonini et al., 2016) and IPCC data for above-ground biomass in world forests (IPCC, 2006a). The global (additional) demand for productive land was calculated considering that the gross deforestation (10.25 Mhaexp year-1, average of 2000-2010 (FAO, 2011)) corresponds to the 37% share of the yearly demand for new land (0.37 haexp ha-1

dem) (Schmidt et al., 2015; Tonini et al., 2016). Calculated this way, the global demanded equivalent area results in 27.7 Mhadem year-1. Identical expansion-intensification shares for global crop production (37% and 63%, respectively) were derived from FAO statistics by two independent studies (Schmidt et al., 2015; Tonini et al., 2016).

We take the estimate of global C stock changes and N2O and CH4 emissions from agricultural expansion from Table S6 in Appendix S4 (Saez de Bikuña et al., 2016). The related emissions represent an average C stock loss from deforestation (mean global figure between 2000 and 2010 (FAO, 2011)) and the respective GHG emissions (from IPCC emission factors, Table 2.5 (IPCC, 2006b)) considering the biomes affected. The global annual increase of synthetic N-fertilizer use is estimated to be 166 kg N per new hectare intensified (Tonini et al., 2016), which corresponds to 105 kg N per additional hectare demanded. Taking an emission factor for urea of 2.97 kg CO2 kg-1 and a N content of 26% per kilo urea (Ecoinvent, 2014), we got the average annual emissions from N-fertilizer production. To derive the resulting field emissions, the N2O emission factors (direct and indirect) from IPCC were finally applied to the additional N fertilization (De Klein et al., 2006; Smith et al., 2002).

Table A1. GHG emission factors and the total LUCglobal emission factor (calculated with GWP100) from agricultural land expansion per additional hectare demanded. Annual GHG emissions from intensification calculated as additional N-fertilizer (production and use), corresponds to a 63% share of the additional global food production.

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We present the LUCglobal as aggregated GHG emissions per additional hectare demanded that LCA practitioners need to amortize over the lifetime of the occupation period (Table A1). On the contrary, intensification emissions are calculated and reported in an annual basis, which LCA practitioners need to aggregate over the relevant occupation period. The presented LUCglobal emission factor represents thus the potential global warming (GW) effect (i.e. its intrinsic GW capability, not to be confused with the GWP factor) of the GHG emissions from deforestation and intensification that a land-demanding biofuel system creates on average when demanding an additional productive hectare. For the compilation of the respective GHG emissions the IPCC’s GWP factors for a 100 year time horizon (GWP100) have been applied to convert them to CO2-equivalents (Myhre et al., 2013), but other time horizons (with other GWP factors) can still be used.

As a validation of our LUCglobal factor proposal, we estimate the equivalent figure from latest IPCC data for annual LUC emissions (4.3 to 5.5 Pg CO2 yr-1) (Smith et al., 2014). Taking IPCC’s low estimate for global LUC emissions and the calculated 27.7 Mhadem yr-1, it yields a LUCglobal of 155.2 Mg CO2 hadem

-1 yr-1, which is in line with our LUCglobal factor. Going the other way around, the estimated total (global) LUC emissions with our approach are 4.6 Pg CO2e yr-1 or 1.1 Pg C yr-1 (excluding other non-CO2 emissions for the latter), which is in agreement with latest global C budget and CO2 flux estimates from LUC (Li et al., 2016).

B. The discounted Global Warming Potential factor for land-use change emissions with dynamic land-use baseline (GWPLUC) The dynamic baseline concept resembles the land-use trend factor referred to in the GHG protocol for project accounting (The Greenhouse Gas Protocol, 2005), where it is to be applied in the candidate baselines “to ensure that the baseline GHG emissions and removals more closely reflect an area’s changing conditions” (Chapter 8, page 40). There are also other approaches that consider the timing of carbon emissions from land-use and LUC (Fearnside et al., 2000; O’Hare et al., 2009). Notwithstanding, we followed here a dynamic baseline approach for LUC accounting as described in Kløverpris and Mueller 2012 and Schmidt et al. 2015. The mathematical formulae developed here are an application of the methods described there, with the last updated coefficients from IPCC and the terminology used in their last AR5 report (Myhre et al., 2013). We denote the resulting GWP factor as GWPLUC. For simplicity, and as a first proxy, only CO2 emissions from LUC are considered.

Taking a dynamic land-use baseline, the total global warming effect of a generic LUC emission (GWLUC) is the difference between the cumulated radiative forcing (RF) of the assessed LUC emissions respect to the baseline:

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GHG emission factors Value UnitsCO2EFexp, total 141.6 Mg CO2 ha-1

dem

N2OEFexp, total 16.3 kg N2O ha-1dem

CH4EFexp, total 555.0 kg CH4 ha-1dem

LUCglobal 165.5 Mg CO2e ha-1dem

N-fertilizer production 1195 kg CO2e ha-1dem yr-1

Field emissions (direct + indirect) 2.9 kg N2O ha-1dem yr-1

Intensification 2.1 Mg CO2e ha-1dem

GW LUC=∫0

HRF LUC( t )dt−∫0

HRFBaseline( t )dt

The cumulated RF of a GHG (the total GW) has been defined by the IPCC as the absolute global warming potential (AGWP) of substance i (Myhre et al., 2013). For a CO2 pulse emission over the chosen time horizon H we have then:

AGWPCO 2=∫0

HRFCO2

( t )dt

We define the GWPLUC factor in similar terms as the GWP of the IPCC, and after (Kløverpris and Mueller, 2012; Schmidt et al., 2015):

GWP LUC=AGWPCO 2 , LUC−AGWPBaseline

AGWPCO2

Where AGWPCO2,LUC is the cumulated RF of a one mass unit CO2 pulse emission (from LUC origin), AGWPBaseline is the cumulated RF of the same CO2 pulse emission from the dynamic baseline (one year later) and AGWPCO2 is the cumulated RF of a reference pulse emission of one mass unit CO2. To account for the effect of anticipating one year the LUC emissions, the resulting GWPLUC factor can be thus multiplied to a generic LUC emission.

The RF is given by the multiplication of the radiative efficiency (RE) of CO2 and its time dependent atmospheric abundance (RCO2). RCO2 is thus the fraction of CO2 remaining in the atmosphere after a pulse emission (Myhre et al., 2013):

RFCO 2=RECO2

¿ RCO2

Since we focus in carbon emissions, the RE of CO2 can be neglected from the calculation of the GWPLUC factor. The reason is that the RE is a constant of GHG, and it can be then extracted from the integrals of the AGWP. When this is done, the RECO2 in the nominator is cancelled out with the RECO2 in the denominator. We then have that, when considering only CO2 emissions, the GWPLUC factor is only dependent on the CO2 fractions in the atmosphere from the assessed LUC pulse emission and the dynamic baseline. That is:

GWPLUC=∫0

HRCO 2

( t )dt−∫0

H−1RCO2

( t )dt

∫0

HRCO2

( t )dt

Taking a time horizon of 100 years, we then have that the GWPLUC factor is:

GWPLUC=∫0

100RCO2

( t )dt−∫0

99RCO2

( t )dt

∫0

100RCO2

( t )dt

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The decay of a CO2 fraction in the atmosphere is given by the Bern model, as a sum of exponentials representing the different carbon sinks of the Earth ecosystems and their dynamics (Myhre et al., 2013). If we take the updated coefficients from IPCC (Myhre et al., 2013) (Table 8.SM.10) and we integrate the different CO2 fractions over the selected horizon, we get that the discounted GWPLUC factor is 0.0077 or 0.77%.

Dynamic baseline methods (DBM) anticipate LUC emissions from biofuels one year respect to the dynamic baseline, but the methods are not dynamic themselves. The derived factor can be applied to any LUC, be it direct (dLUC) or indirect (iLUC), provided that it occurs in an area of a (developing) country where agricultural expansion is ongoing and under the condition that the assessed LUC (i.e. the induced agricultural expansion) area is not greater than the estimated expansion area in the baseline (Kløverpris and Mueller, 2012). We consider that this is the case for the iLUC and dLUC emissions of the four biofuel examples presented, which was also estimated in (Kløverpris and Mueller, 2012).

For the first dynamic baseline method (DBM1), the GWPLUC factor is applied to a total emission factor (e.g. in Mg CO2 hadem

-1). The resulting time-discounted LUC emissions are in the same physical units as the original LUC emissions (i.e. in Mg CO2 hadem

-1) and are hence added once to the life-cycle of the related biofuel production (Kløverpris and Mueller, 2012) (see Table 1).

For the second dynamic baseline method (DBM2), the GWPLUC factor is applied to an annual emission factor (in Mg CO2 hadem

-1 yr-1), since the deforestation baseline is considered to be a trend that occurs every year. The resulting time-discounted LUC emission factor is in the same physical units as the intensification emission factor (i.e. in Mg CO2 hadem

-1 yr-1) and it is hence added as many times as years long is the expected occupation for biofuel production (Schmidt et al., 2015).

B.1 The LUCglobal factor with a dynamic baseline method. For the DBM2 (Schmidt et al., 2015), we take the respective CO2 emissions from the LUCglobal factor presented in Table A1 and multiply them with the GWPLUC factor. The second dynamic baseline method considers the respective LUC from deforestation as annual CO2 emissions, and therefore the the global LUC factor results in an annual, time-discounted version of the LUCglobal factor:

LUCglobal_CO2, DBM 2 = LUCglobal_CO2·GWPLUC = 141.6*0.008 = 1.1 Mg CO2 hadem-1 yr-1

To this annual CO2 emissions from ‘anticipated deforestation’, the related indirect GHG emissions from intensification are added (Table A1). Taking a GWP100 factor for the N2O emissions, the time-discounted LUCglobal annual factor used for in the DBM2 yields a total of 3.2 Mg CO2eq hadem

-1 yr-1.

C. The GHG protocol method for GHG emission accounting of unknown LUC According to this method, land-use area cover time series is collected for the countries where the product or the feedstock originates from (from FAO stat database), in order to calculate the cropland area changes for the main crops of those countries. This way, the net area expansion of sugarcane in Brazil and oil-palm in Malaysia on former arable land and native forest were estimated. Since a consequential approach was taken to calculate the LUC emissions (of additional feedstock production), only the land from the additional expansion was considered in the land-use

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mix (i.e. the additional crop production originates from the converted, new land). Here an attributional LUC result is presented for illustration, even though they are not directly comparable. To calculate this, an average land-use mix for sugarcane and palm-oil could be considered (i.e. the crop production is taken as an average of the whole plantation area, the new and former plantations).

Table C1. Historical land-use area cover (ha) for Malaysia and land-use mix shares used for attributional (ALCA) and consequential (CLCA) calculations of the method.

Land-use area cover (Malaysia) 2003 2014 Difference

Cereals (total) 704,700 681,399 -23,301

Coarse Grain, Total 24,000 9,720 -14,280

Coconuts 178,000 87,974 -90,026

Fruit excl Melons,Total 98,913 91,523 -7,390

Oilcrops Primary 3,682,063 4,762,900 1,080,837

Treenuts, total 13,152 7,451 -5,701

Vegetables&Melons, total 39,387 62,736 23,349

Rubber, natural 1,275,000 1,057,271 -217,729

Total 6,015,215 6,760,974 745,759

Land-use area, oil-palm Area ALCA CLCAFormer oil palm 3,682,063 54% 0%New oil palm, former arable 350,639 5% 32%New oil palm, former forest 746,415 11% 68%

For the Malaysian case, oil-palm plantations take all the share on the gross expansion on forest and former arable lands (see Table C1). The LUC emissions are taken from the aboveground estimates given in Wicke et al. 2008 (corresponding to 629 Mg CO2-eq ha-1 for natural rainforest), while considering zero emissions for cultivation on former arable land. These are then combined with the corresponding land shares for ALCA and CLCA approaches. The figure presented in Table 1 in the main text (LUCGHGP) is thus calculated by multiplying 629 Mg CO2-eq ha-1 with 0.68 (zero GHG emissions from the remaining 32% expansion on former arable land).

Table C2. Historical land-use area cover (ha) for Brazil and land-use mix shares used for attributional (ALCA) and consequential (CLCA) calculations of the method.

Land-use area cover (Brazil) 2004 2013 Difference

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Cereals,Total20,426,199 21,850,734 1,424,535

Citrus Fruit,Total938,676 802,862 -135,814

Fibre Crops Primary1,392,410 1,127,639 -264,771

Fruit excl Melons,Total2,383,545 2,294,851 -88,694

Oilcrops Primary23,417,829 32,188,480 8,770,651

Pulses,Total4,018,796 3,209,184 -809,612

Roots and Tubers,Total1,970,276 1,765,146 -205,130

Treenuts,Total696,003 701,718 5,715

Vegetables and Melons, Total423,418 468,698 45,280

Sugarcane5,631,741 10,437,567 4,805,826

Total61,298,893 74,846,879 13,547,986

Arable land reduction-1,504,021

Net expansion13,547,986

Area (ha) ALCA CLCA

Former sugarcane 5,631,741 54%

New sugarcane (former arable) 533,516 5% 11%

New sugarcane (former forest) 4,272,310 41% 89%

For Brazil, the expansion of sugarcane plantations (32% of the gross expansion) it is shared with oilcrops (mainly soybean, 58%) and cereals (9%). Hence, the sugarcane share of LUC on former arable and forest land result in 11% and 89%, respectively. To calculate the LUC applied with this method, 490 Mg CO2-eq ha-1 of emissions are taken for former forest land (forest to rangeland, Table 2A, Lapola et al. 2011) and -74 Mg CO2-eq ha-1 for the expansion onto former arable land (Valin et al., 2015). The negative LUC represents the C sequestration of sugarcane plantations established on former land, that was used for pulses and annual crops (see Table C2). Multiplying these emission factors with the corresponding land shares of the CLCA approach, the average LUCGHGP factor for Brazilian sugarcane is obtained.

D. LUC emissions with the GLOBIOM model (Valin et al. 2015)

D.1 Sugarcane ethanolThe modelled sugarcane ethanol production would be imported to the EU and this extra production would take place mainly in South America through expansion (page 58). Therefore it is considered

S7

to be representative for the Brazilian sugarcane ethanol study case. The total LUC emissions given by the model were 43 MtCO2eq which are distributed among the total area acreage (1 Mha), to provide results per hectare demanded (43 Mg CO2eq hadem

-1).

D.2 Palm-oil biodieselThe modelled palm-oil biodiesel that would be imported to EU would come from South East Asia, which we consider again representative for the Malaysian study case (page 63). Only 44% of the demand would be covered by additional production, which would require an acreage of 1.2 Mha. The related expansion partly takes place in forested land and peatland with estimated total (net) LUC emissions of 569 MtCO2eq. Distributing these emissions among the demanded area (2.7 Mha), it results in 211 Mg CO2eq hadem

-1.

D.3 Corn ethanolThe modelled corn ethanol would be mainly produced within the EU, requiring an acreage of 1.3 Mha (page 51). Around 45% of the additional feedstock production is achieved with expansion, 11% through intensification (yield improvement) and the rest is met with reduced feed demand and substitution effects. The total LUC emissions are 35 MtCO2eq which are then distributed over the total area demanded (2.9 Mha), resulting in 12 Mg CO2eq hadem

-1.

E. Dynamic baseline methods for GHG emission accounting from LUC

E.1 ILUC causality and system boundaries of biofuel studies with dynamic baseline methodsUnder the basic assumption of an increasing global food demand, and given the globalization of food markets, occupying arable land to establish a bioenergy system displaces the food crop production elsewhere (Fritsche et al., 2010; Hertel et al., 2010; Hiederer et al., 2010; Kløverpris, 2008; Lapola et al., 2010; Searchinger et al., 2008). In other words, it is the occupation of arable land for energy purposes which displaces food production and makes iLUC exist. Therefore, the fundamental causality of iLUC implies that the iLUC effect continues as long as the occupation of arable land (i.e. the primary cause) and the initial food demand continue. Dynamic baseline methods (Kløverpris and Mueller, 2012; Schmidt et al., 2015) (DBM) implicitly break this funamental causality by assuming that the iLUC affected land is deforested after one year. This assumption implies that after one year the new arable land is part of the regional or global LUC trend. But the assumed LUC trend, which is taken as dynamic baseline during the existence of the bioenergy system, is caused by the demand of other product systems.

The dynamic baseline cannot be considered either a system expansion that credits ‘avoided deforestation’ emissions (ISO, 2006a, 2006b), because the products creating the deforestation are not being substituted or replaced (they are unknown). Once the specific land-demanding biofuel (the cause) is assigned with a specific LUC emission (the effect), there is no further cause-effect between the ongoing global or regional LUC trend and the assessed biofuel during its life-cycle. On the contrary, once the dynamic baseline overlaps with the iLUC affected land, it becomes part of the

S8

LUC trend itself. But DBM assign no responsibility to the LUC trend itself, as if it was a natural phenomenon and not a human-induced event. On this basis, we believe that the LUC trend cannot be taken as a baseline as long as the energy crop occupies the land. That is, if all products creating LUC would fully account for the LUC they generate, there would not be any LUC trend (and neither a dynamic baseline) but a sum of LUC generated by multiple products. In summary, the ongoing deforestation is necessarily outside the system boundaries of any bioenergy system that occupies land, because the multiple factors that cause it are external to it. Therefore, global or regional LUC trends should not be considered within the temporal scope boundary of LCAs of any land-demanding bioenergy system.

Notwithstanding, DBM can be valid and useful for assessments of products whose long-term land occupation is not determined by a considerable infrastructure which is part of the product system (like a biorefinery or power-plant) and could help estimate more accurately the total long-term emissions and GW effects attributable to the post-occupation or the land release of such land-based products.

E.2 Mass conservation in dynamic baseline methodsThe main and most concerning mass balance inconsistency regards the discounted GHG emissions, which will remain unaccounted for (see Table E1). Not surprisingly, DBM “show significantly reduced iLUC impacts when future land use trends are included” (Chapter 11, page 880) (Smith et al., 2014). But these reduced impacts are basically unaccounted LUC emissions, because these are considered that will happen anyway and no other product will be hold responsible for the remaining unaccounted emissions.

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Table E1. GHG emissions from LUC for different biofuels and their relative accounting error (ε) with different methods. DK for Denmark, BR for Brazil and MY for Malaysia.

Occupation (years)

Expansion LUC (Mg CO2eq hadem

-1)

Accounted LUC(Mg CO2eq hadem

-1) Unaccounted LUC (%)

DBM 1 DBM 2 DBM 1 DMB 2

Willow (DK cropland) 20 283 2 42 99 85

Sugarcane (BR cropland ) 30 311 4 63 99 89

Oil-palm (MY forest) 25 388 6 52 99 93

Corn (US cropland) 30 169 1 63 99 63

E2.1 Mass conservation of GHG emissions in global biofuel productionTo make clearer the underestimation bias of DBM, their application is extrapolated to the global biofuel production. We define the relative GHG accounting error ε of LUC emissions as the difference between the actual LUC emissions (i.e. the upfront GHG emissions from LUC that are “seen” by the Earth ecosystem) and the LUC emissions that are accounted with the DBM, divided by the actual LUC emissions. In order to analyze the overall magnitude of the GHG accounting error ε, we take statistics on global bioethanol and biodiesel production to extrapolate the case-specific error to the whole world. Current bioenergy production from short-rotation perennial crops like willow was estimated to be negligible, and was disregarded for data consistency1.

For the first dynamic baseline method (LUCDBM1), the GWPLUC factor is multiplied with the actual LUC emissions to get the time-discounted LUC emission factor. The relative error is simply the applied constant discounting, which is independent of the energy crops’ life-cycle or any other definition of the LCA’s time scope:

ε DBM 1=(LUC−LUC⋅GWPLUC )⋅ 1LUC

=1−GWPLUC=99 .2%

For the second dynamic baseline method (LUCDBM2), the LUC emissions correspond to the LUCglobal

emissions (Table A1), which is then time-discounted (see B.1) and aggregated over the technological time scope of the study to calculate the accounted LUC emissions. We have then that the relative accounting error for the LUCDBM2 is:

1 The whole EU-28 had 37.6 kha of dedicated energy crops (retrieved from Eurostat’s data explorer: crop statistics by area, energy crops, 2016: http://ec.europa.eu/eurostat/data/database) while no statistics could be found in US government official sites. No further information could be found regarding the specific plant types to deduce their lifetimes. This area would anyway yield about 6 PJ of bioenergy, assuming it was planted with willow (same yields as in (Saez de Bikuña et al., 2016)), a negligible amount when compared to the bioethanol or biodiesel production in the same region.

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ε DBM 2=1−GWPLUC⋅Toccup

For LUCDBM2, the error is dependent on the identified occupation time horizon used in the LCA, while LUCDBM1 is independent from it. To the date, different LCA studies take different time horizons for their assessments, so the global average error (E) of LUC emission accounting of biofuels will be proportional to the defined amortization and occupation time horizons. Since DBM are proposed to avoid the amortization period choice, we assume that the inventory modelling period will be based on the energy crop’s plantation lifetime. The error E is then calculated by weighting the accounted LUC of the specific energy crop with a lifetime i, taken as (1 – εi), with the global production fraction of all plantations with lifetime i (pi). The globally accounted LUC emissions (1 – E) are then mathematically described by:

1−Ε= ∑i=1,6 ,25

[(1−εi )⋅pi ]

Where pi represents the global production fraction (on an energy content basis) of a certain biofuel (either bioethanol or biodiesel) with a plantation lifetime Toccup,i. Where the plantations’ lifetime (Toccup,i) is equal to 1 (for annual crops), 6 (for sugarcane plantations) and 25 (for oil-palm plantations):

ΕDBM 1=99 .2 %

ΕDBM 2=1−GWP LUC⋅ ∑i=1,6 , 25

[T scope , i⋅pi ]

The different global biofuel production fractions (pi) are calculated with an estimate of the total production quantities of the annual, 6-annual and 25-annual energy crops (Pi) and the world’s total biofuel energy production in 2015 (bioethanol and biodiesel) (∑Pi). That is:

pi=P i

∑i=1,6 ,25

Pi

In Figure E1 we can see the global biofuel production fractions (right axis) and the relative accounting error (left axis) with the time scope. As it is shown in Figure E1 and Table E1, the LUCDBM1 would leave 99% of LUC emissions unaccounted, while an LCA applying the LUCDBM2 would need to define a very long time scope (>> 30 years) to fully account for the induced agricultural expansion emissions. Taking the global biofuel production of 2015, we estimated that the LUCDBM2 would omit on average 97% of the deforestation emissions, since most of the global biofuel production comes from annual crops.

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Fig. E1 The share of unaccounted LUC for amortization-free dynamic baseline methods (left) and the fraction of global liquid biofuel production for year 2015 (right; bars) as a function of the plantation lifetime of bioenergy crops (depicted as the occupation time of the LCA).

The calculated pi fractions of biodiesel and bioethanol production are presented in Figure E1 (right axis) and the values are gathered in Table E1 and Table E2 below. The world biodiesel production in 2015 was 29.8 billion liters, which approximates to 964.4 PJ using a calorific value of 32.384 MJ/liter (2). The world bioethanol production in 2015 was 25.6 billion gallons, which approximates to 2208 PJ using a calorific value of 22.8 MJ/liter (Seabra et al., 2011) (and a conversion factor of 0.264 gallons per liter). The estimated global biofuel production is thus 3173 PJ. The main global biofuel production comes from annual crops (mainly soybean, oil crops and cereal crops like wheat and corn), which yielded a fraction p1 of 72%. The global biodiesel production from palm-oil (P25) was approximated by the palm-oil production from Indonesia, Malaysia, Colombia, Thailand, Nigeria and India. The resulting fraction p25 was 5%. The global bioethanol production from sugarcane (P6) was approximated by the ethanol production from Brazil, Thailand (70% of its ethanol production) and India (50% of its ethanol production assumed). This yielded a fraction p6 of 23%.

Table E2. Global biodiesel production per country or region in 2015.

2 Retrieved from: http://ec.europa.eu/eurostat/documents/3217494/7571929/KS-EN-16-001-EN-N.pdf/28165740-1051-49ea-83a3-a2a51c7ad304

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BIODIESEL

Country/Region Quantity Unit Comment/Source

US 1,268 million gallons (3) and (4) annual oil-crops (mainly soybean)

EU-28 11,248.90 Gg oil eq. Figure from 2014 (5); annual oil-crops

Brazil 4.1 billion liters (8); 81% from soybean (6)

Argentina 1.15 billion liters (8); mainly soybean (7)

Indonesia 2.1 billion liters (8); mainly palm-oil (11)

Malaysia 0.53 billion liters (8); mainly palm-oil (11)

Thailand 1.2 billion liters (8); mainly palm-oil (11)

Colombia 0.6 billion liters (8); mainly palm-oil (11)

China 0.36 billion liters (8); mainly soybean (11)

Canada 0.3 billion liters (8); mainly soybean (11)

India 0.1 billion liters (8); jatropha-oil assumed

Nigeria 0.1 billion liters (8); palm-oil assumed

World 29.8 billion liters

3 Retrieved from (July 2015): http://www.eia.gov/biofuels/biodiesel/production/ 4 Retrieved from (July 2015): http://www.statista.com/statistics/271472/biodiesel-production-in-selected-countries/ 5 Retrieved from (July 2015): http://ec.europa.eu/eurostat/tgm/refreshTableAction.do?tab=table&plugin=1&pcode=ten00081&language=en 6 Retrieved from (July 2015): http://aprobio.com.br/wp-content/uploads/2016/06/Boletim-ANP-maio-de-2016.pdf 7 Retrieved from (July 2015): http://gain.fas.usda.gov/Lists/Advanced%20Search/AllItems.aspx

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Table E3. Global bioethanol production per country or region in 2015.

BIOETHANOL

Country/Region Quantity Unit Comment/Source

US 14.8 billion gallons (8) and (9); mainly corn.

EU-28 2640 Kt oeFigure from 2014; it includes “biogasolines” and

“other liquid biofuels” (10); cereal origin.

Brazil 7 billion gallons (12); mainly sugarcane (15)

Thailand 334 million gallons (12); 70% sugarcane, 30% cassava (11)

China 813 million gallons (12); mostly cassava, corn and wheat (15)

Canada 436 million gallons (12); mostly cereal origin (15)

Argentina 211 million gallons (12); mostly cereal origin (15)

India 211 million gallons (12); 50% annual, 50% sugarcane assumed

Rest of the world 391 million gallons (12); annual-crop origin assumed

World 25.6 billion gallons

E.2.2 Mass conservation of food production in dynamic baseline methodsBesides omitting part of the LUC emissions, DBM bring about other mass balance inconsistencies. Let us imagine the iLUC as a “virtual food production contract” with distant farmers that will produce the displaced food crops during the whole land occupation period (the time horizon that would appear in this contract). We can illustrate this with a Danish farmer who establishes a bioenergy crop on a Danish arable field. Let us assume that, ignoring substitution and price elasticity effects for sake of simplicity, this energy cropping will displace the production of an average wheat yield in Denmark to different distant farmers (see Figure E2). The “virtual food production contract” between the Danish farmer and these distant farmers will hence state that 7.25 Mg ha-1 yr-1 of wheat must be produced as long as the Danish farmer does not supply this wheat (i.e. the occupation period for energy cropping) (see Figure E1). Let us further assume now that this Danish farmer will occupy the 1 ha field for energy cropping for 10 years. The “virtual food production contract” will thus require from the distant farmers a total production of 72.5 Mg of wheat spread in 10 years (see Figure E2). Applying DBM will break this “virtual food production contract”, freeing the distant farmers from the remaining wheat production (after the first year) 8 Retrieved from (July 2015): http://ethanolrfa.org/resources/industry/statistics/#1454098996479-8715d404-e546 9 Retrieved from (July 2015): http://www.eia.gov/todayinenergy/detail.cfm?id=27452 10 Retrieved from (July 2015): http://ec.europa.eu/eurostat/tgm/refreshTableAction.do?tab=table&plugin=1&pcode=ten00081&language=en 11 Retrieved from (July 2015): http://gain.fas.usda.gov/Lists/Advanced%20Search/AllItems.aspx

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without considering any further leakage or downstream effects. Since the displaced Danish wheat production is not (directly) compensated for after the first year, there would be 65.25 Mg of wheat missing from the assessed product system when a DBM is used. If the iLUC effects are not taken into account after the first year, “then it is implicitly assumed that the displaced crop is compensated without causing any effects (which is clearly not possible)” (Schmidt et al., 2015). LUC emissions after the occupation Toccup (post-occupation effects) will depend on the land uses on the field used for bioenergy production (A1) and on the land uses on the iLUC field (A2).

Figure E2. Illustration of the iLUC causality through the “virtual food production contract”. Before the energy crop establishment, A1 produces a certain amount of wheat in Denmark (DK), W. During the occupation period, A1 produces a certain amount of biomass per year (Y) and W production is displaced to the rest of the world (ROW), converting and occupying A2. ILUC emissions occur at the beginning of the bioenergy project from the conversion of A2.

E.3 A last consistency check: current atmospheric CO2 trend as dynamic atmospheric baseline?As a final illustration, let us take a straightforward analogy: the current trend of global GHG emissions and the fossil fuel consumption trend. Taking a similar dynamic baseline approach, and to be consistent with it, we consider now the current atmospheric CO2 concentration trend (Meinshausen et al., 2011) as the dynamic baseline to account for the GHG emissions of the additional fossil oil combustion that a prospective energy system would induce. With the very same logic of the DBM (which give for granted that forests will be all cleared at some point), it could be similarly assumed that fossil oil will be all depleted at some point (Greene et al., 2006; Lloyd, 2007). Therefore, the GW effect from additional fossil oil combustion should be considered as

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“anticipated fossil fuel combustion”. We could then apply a similar discounted GWP factor to these fossil emissions. If we assume that the induced fossil fuel demand is smaller than the baseline demand, we will get a discounted GWP factor of 0.8% (see Figure 1 and GWPLUC calculations) which would imply that the net effect of these fossil emissions is their anticipation by one year. That is, for every additional tonne of fossil oil that is burnt we would account for a total GHG emission release of 269 kg CO2eq instead of 34,924 kg CO2eq12. This way we would allow ourselves to discount our own fossil emissions by 99.2% on the grounds of the existing global increasing trend of (anthropogenic) CO2 emissions.

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