GHG PERFORMANCE JATROPHA BIODIESEL - … · GHG-PERFORMANCE JATROPHA BIODIESEL 1 1 Introduction 1.1...
Transcript of GHG PERFORMANCE JATROPHA BIODIESEL - … · GHG-PERFORMANCE JATROPHA BIODIESEL 1 1 Introduction 1.1...
Ecofys bv
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Bart Dehue
Willem Hettinga
2 June 2008
Copyright Ecofys 2008
Ecofys reference: PBIONL073010
Commissioned by: D1 Oils
GHG PERFORMANCE JATROPHA BIODIESEL
GHG-PERFORMANCE JATROPHA BIODIESEL II
Acknowledgements
The authors are thankful to all the Indian team members for demonstrating to us the real
life plantations, providing the required data, their hospitality and openness during the field
trip. Special thanks go to Suresh and Sreenivas for their professional input and pleasant
company. In addition we would like to thank Neil Judd and Kathleen Bottriell from
Proforest for their open cooperation during the project.
GHG-PERFORMANCE JATROPHA BIODIESEL III
Content
1 Introduct ion 1
1.1 Why GHG-performance is important 1
1.2 RTFO default for Jatropha 2
1.3 Overview Jatropha biodiesel supply chain 2
1.4 Structure of the report 3
2 GHG-performance: Base Case 6
2.1 Base Case: Jatropha biodiesel reduces GHG-emissions by 66% to 68% 6
2.2 Base case description and assumptions 8
3 Comparison of Jatropha to other crops 14
3.1 Jatropha biodiesel compared to RTFO defaults 14
3.2 Jatropha biodiesel compared to EC defaults 15
3.3 Understanding the differences 16
4 Impacts of Land Use Change 18
4.1 Using RTFO or EC default values for Land Use Change 18
4.2 IPCC Tier 1 approach for LUC: the effect of climate zones 20
4.3 Using site specific data for Land Use Change 21
5 Opportunit ies to improve GHG-performance 22
5.1 Changes to the production system 22
5.2 Sensitivity analysis 27
5.3 Worst and best case scenario 30
6 Conclusions and discussion 32
6.1 GHG-performance Jatropha versus other crops 32
6.2 Performance under RTFO versus EC methodology 32
6.3 Areas for improvement 33
6.4 Land Use Change 33
GHG-PERFORMANCE JATROPHA BIODIESEL IV
References 35
Annex A Key dif ferences between the RTFO and
EC proposal 36
Annex B Jatropha yield 39
Annex C IPCC defini t ions 40
Annex D LUC calculat ions IPCC Tier 1 43
GHG-PERFORMANCE JATROPHA BIODIESEL 1
1 Introduction
1.1 Why GHG-per formance i s important
Sustainability in production is an important issue for D1 and D1 aims to maximize the
greenhouse gas emission savings of its Jatropha oil/biodiesel. Besides the internal drive to
maximize the greenhouse gas GHG-performance, the following policy and market
developments make GHG-performance an important and valuable parameter in the
biofuel market.
• RTFO. As of April 2008, parties wishing to earn Renewable Transport Fuel
Certificates under the Renewable Transport Fuel Obligation (RTFO), need to report
on the carbon intensity and sustainability of their biofuels. The RTFO starts with a
reporting obligation in which obligated companies such as Shell and BP will be
required to report on the carbon intensity and sustainability of their biofuels. This
information will be made publicly available and it is expected that this will create a
strong moral pressure on these companies to source sustainably produced biofuels.
Recently the Department for Transport even announced that the UK Government:
“Aims to reward biofuels under the RTFO in accordance with the carbon savings that
they offer from April 2010.” This will create a higher value for biofuels with higher
GHG-performance as these biofuels enable fuel suppliers to meet their obligation with
fewer litres of biofuel.
• EU Renewable Energy Directive. The proposal for a Renewable Energy Directive
(RED) from the EC contains a 10% biofuel target with a proposed minimum GHG-
emission saving requirement of 35%. Proposed amendments to this directive show
this minimum GHG-emission saving level may rise in the future.
• EU Fuel Quality Directive. The proposal for a Fuel Quality Directive (FQD)
contains a 10% GHG-emission reduction target for the total transport fuel pool. A
significant amount of these savings will need to come from biofuels. Obligated
companies will need less biofuels to meet this target if they source biofuels with a
higher GHG-performance. Again, this creates a monetary value for biofuels with a
higher GHG-performance.
In summary, with current policy proposals a higher GHG-performance will directly
translate into a higher biofuel value. This makes both the absolute GHG-performance of
Jatropha biodiesel and its relative performance compared to other feedstocks important
parameters for the market value of Jatropha oil/biodiesel.
GHG-PERFORMANCE JATROPHA BIODIESEL 2
1.2 RTFO defaul t for Jatropha
Under the RTFO and EC-proposal, obligated parties can use conservative default values
as well as real values for the GHG-performance. Defaults are set conservative to stimulate
parties to report actual data on their GHG-performance. Defaults exist for a limited
number of fuel chains and for Jatropha biodiesel no such default currently exists under the
RTFO or EC-proposal. It is expected that in the next year a default value for Jatropha will
be developed for the RTFO.
The findings of this project can serve as input for the development of a default value for
Jatropha. In addition, this project gives D1 an insight into what the important parameters
are for the GHG-performance of Jatropha biodiesel. Even if a default value is defined for
Jatropha it will be set conservatively and this project enables (buyers of) D1’s Jatropha
oil/biodiesel to report the better performance calculated in this project based on actual
values.
1.3 Overview Jatropha b iodiese l supply chain
Figure 1 on the next page shows the Jatropha biodiesel supply chain as it was defined for
this project. This represents production chains based on D1’s Jatropha plantations in
North and North-east India. The results of this project can not be taken to be valid for
other Jatropha chains although the lessons learned in this project provide valuable insight
for other Jatropha chains as well.
GHG-PERFORMANCE JATROPHA BIODIESEL 3
1. Cultivation
2. Drying and storage(in the field)
Fruit
Hull
Seed4.5 t/ha
Shell23%
4. Oil extraction
(expeller in India)
3. Seed transport(field� expeller)
5. Oil transport(expeller� harbour)
(harbour� UK)
6. Transesterification(in UK)
7. Biodiesel transport(outside scope)
Kernel
75%
Seedcake
67%
Oil33%
Oil
Oil
-Glycerine &
-K2SO4 9%
Biodiesel91%
Biodiesel1.0 t/ha
ElectricityShells for steam generationSolvent
Transportation modesFuel efficiencyDistance
Transportation modusDistance Fuel efficiency
Natural gasElectricity
MethanolPotassium hydroxide
Cultivation inputs
Seed
Seed
Shell + dust
23% + 2%
8. Land use change (LUC) Previous land use, climate region, crop type, …
Figure 1 . Jatropha bi odiesel suppl y chain with the yie lds per phase. Words
in i ta l i c represent parameters that have been var ied in
scenarios/sens i t iv i ty analysi s. Inputs are shown on the r ight .
1.4 Structure of the repor t
The remainder of this report is structures as follows:
• Chapter 2 establishes the GHG-performance of the Base Case Jatropha Biodiesel
chain. It does this for both the current RTFO methodology and the RTFO
methodology adapted to fit the methodology from the draft RED from the EC.
• Chapter 3 compares the GHG-performance of the Base Case Jatropha chain with that
of other biodiesel chains with which it will compete in the market.
• The impact of Land Use Change on GHG-performance is discussed in Chapter 4.
• Chapter 5 analyses the possibilities for further improvement of the Base Case as well
as potential risks. This is done by analysing several what-if scenarios as well as
performing a sensitivity analysis.
• Conclusions and recommendations are given in Chapter 6.
• A separate analysis on the value of Renewable Transport Fuel Certificates is included
in Chapter 7.
GHG-PERFORMANCE JATROPHA BIODIESEL 4
Figure 2 shows the structure of our analysis as a guidance throughout the report.
Figure 2 . Overview of the st ructure of the GHG-analysi s.
A more technical discussion on the differences in the GHG methodology between the
RTFO and the EC is provided in Annex A.
Terminology
A list of terms used in this report is included below. Figure 3 on the next page clarifies the
names of the main Jatropha products.
EC European Commission
FQD Fuel Quality Directive
GHG Greenhouse Gas
Hull Product from the fruit that encapsulates the seeds
IPCC Intergovernmental Panel on Climate Change
Kernel Product from the seed
LUC Land Use Change
Jatropha Oil Product from the kernel
RED Proposal for a directive of the Parliament and the Council on the
promotion of the use of energy from renewable sources. Version 15.4,
23 January 2008.
RTFO Renewable Transport Fuels Obligation
Seed Jatropha seed (product from the fruit)
Seedcake Product from the kernel
Shell Product from the seed that encapsulates the kernel
S
y
s
t
e
m
EC
RTFO
Without LUC
Without LUC
Yields
Inputs
Transportation
Oil expeller
Base cases Chapter 2
Scenarios and comparison
Chapter 3, 4 & 5
Methodology Throughout report
(details in Annex A)
Best case
Optimal
Chapter 5 & 6
Comparison other crops
Recommendations
LUC
With LUC (IPCC Tier 1)
GHG-PERFORMANCE JATROPHA BIODIESEL 5
Fruit
Hull (pericarp)
Seed
Shell
Kernel
Seedcake
Oil
Figure 3 . Overview of the Jatropha product ion cha in with the terminology
that is used throughout the report .
GHG-PERFORMANCE JATROPHA BIODIESEL 6
2 GHG-performance: Base Case
Jatropha biodiesel saves 66% of the GHG-emissions compared to fossil diesel, even
when Land Use Change from grassland to Jatropha plantation is taken into account.
GHG-performance of the Base Case Jatropha biodiesel supply chain is nearly similar
under the RTFO-methodology as under the proposed RED-methodology, respectively
1093 kgCO2e and 1040 kgCO2e per tonne of biodiesel. Two Base Cases have been
assessed: one in which no Land Use Change (LUC) has been included and one in
which the GHG-effects of a LUC from grassland to Jatropha has been included. In
subsequent chapters, the Base Case including LUC is used to 1) compare the GHG-
performance of Jatropha biodiesel with other crops and fuels, 2) show the effect of
different types of LUC and 3) analyse how further improvements van be made and what
the main risks are.
S
y
s
t
e
m
EC
RTFO
Without
LUC
Without
LUC
Inputs
Transportatio
n modus
Transportation distance
Oil expeller
Base cases Chapter 2
Scenarios and comparison Chapter 3 & 4
Methodology Throughout report
Best case
Optimal
Chapter 4 & 5
Comparison
other crops
Recommendations
Yields
With LUC (IPCC Tier 1)
2.1 Base Case: Jatropha b iodiese l reduces GHG-
emiss ions by 66% to 68%
Figure 4 shows the GHG-performance of the Base Case with and without LUC by using
both the RTFO and EC methodology. Without LUC, the Base Case for Jatropha biodiesel
under the RTFO scores slightly better than under the EC methodology. If we include LUC
in the Base Case the overall GHG-emission savings are reduced by 4.8%-pt and 2.8%-pt
dependent on the methodology. Including LUC, Jatropha biodiesel performs slightly
better under the EC-methodology than under the current RTFO methodology.
A more detailed discussion on the inputs used for the Base Case is given in section 2.2
below.
GHG-PERFORMANCE JATROPHA BIODIESEL 7
71% 70%
68%
66%
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
without LUC with LUC (IPCC Grassland) without LUC with LUC (IPCC Grassland)
RTFO EC
GH
G p
erf
orm
an
ce [
kg
CO
2e
/t b
iod
iese
l]
63%
66%
69%
72%
75%
78%
81%
84%
88%
91%
94%
97%
100%
GH
G s
avin
g [
%]
8. Land Use Change
7. Biodiesel transport
6. Transesterification
5. Oil transport
4. Oil extraction
3. Seed transport
2. Drying and storage
1. Jatropha cultivation
Figure 4 . GHG-per formance o f the Base Case Jatropha biodiesel chain with
and without LUC by us ing both the RTFO and EC methodologies.
The r ight axis shows the net GHG-emission saving compared wi th
foss i l diesel . The Base Cases with LUC from grass land to
Jatropha (as calcu lated by IPCC) is used for fur ther analysi s.
2.1.1 RTFO
The Base Case Jatropha biodiesel without LUC has a GHG-performance of 934 kgCO2e/t
biodiesel under the RTFO. This increases to 1093 kgCO2e/t biodiesel if Land Use Change
from grassland to perennial cropland is taken into account by using IPCC Tier 1
calculations. In Chapter 4 it is shown that the impact of LUC becomes larger if plantations
are located in other climate zones and becomes extremely large if default values from the
RTFO or EC are used. Including LUC, Jatropha biodiesel reduces GHG-emissions by
66% compared to fossil diesel.
The largest GHG-emission contributor for the Base Case Jatropha chain is the
transesterification process at 43%. Oil transport (by truck and ship) is the second largest
contributor at 34%. Land Use Change causes 15% of the total emissions. GHG-emissions
from cultivation are zero, as no inputs are being used. Furthermore seed transport, drying
and storage and oil extraction all have very little GHG-emissions.
2.1.2 EC
Under the methodology of the EC proposal, the Base Case Jatropha biodiesel has a GHG-
performance of 945 kgCO2e/t biodiesel. This increases to 1040 kgCO2e/t biodiesel if Land
Use Change from grassland to perennial cropland is taken into account. Biodiesel saves
68% of the GHG-emissions compared to fossil diesel under the EC directive.
GHG-PERFORMANCE JATROPHA BIODIESEL 8
Also in the methodology from the EC the transesterification step is most GHG intensive,
overall responsible 48% of the GHG-emissions, followed by oil transport with 38%. Land
Use Change causes 9% of the emissions. Again, the other steps only contribute marginally
to the total GHG-emissions.
2.1.3 Differences between RTFO and EC
The difference in the GHG-performance of the Base Case Jatropha biodiesel chain using
the RTFO methodology and EC methodology is small. The differences that exist are
caused by the fact that co-products are dealt with in a different way in the EC and RTFO
methodologies. This especially influences GHG-emissions from LUC. This is discussed in
more detail in section 2.2.
2.2 Base case descr ipt ion and assumpt ions
The GHG-performance is the result of a fairly limited number of contributors in the
Jatropha biodiesel production chain. Table 1 summarises the key inputs and assumptions
for the Base Case. Where no specific information is given we have used default RTFO
values (e.g. the efficiencies of different transport modes.)
Table 1. Inputs and assumpt ions in the Base Case Jatropha biodiesel chain.
Phase Parameter / assumption
Assumption Remark
1. Cultivation -Seed yield: -Zero inputs (a -Manual harvesting
4.5 t/ha Harvested yield (b
2. Drying and storage -Dried by the sun
3. Seed transport -By truck: 150 km
4. Oil extraction -Mechanical expeller: -Electricity use:
25% oil recovery 6 kWh/t oil
5. Oil transport -By truck: -By ship:
750 km 14,500km
6. Transesterification -Biodiesel yield:
-Natural gas inputs: -Electricity inputs:
-Methanol addition: -KOH addition:
91%
1,690 MJ/t biodiesel 335 MJ / t biodiesel
113 kg / t biodiesel 26 kg / t biodiesel
-Takes place in UK
-Defaults from RTFO
7. Biodiesel transport Outside system boundaries
For Base Case with LUC
Land Use Change Grassland to perennial cropland conversion (c
-climate zone: tropical dry - 0.211 t CO2/ha/yr
IPCC Tier 1 calculation (d
a) In fact, organic manure is applied once. But resulting GHG-emissions are not accounted for in the methodologies, it has therefore been excluded from the analysis.
b) From the seed yield figures provided by D1 Oils, we have calculated an average harvested yield over 20 year plantation lifetime, see Annex B.
c) For LUC, the default has been set at a Land Use Change from grassland to cropland, assuming that D1’s plantations will be established on former grassland and not on forests.
d) We analyse the impact of LUC in the Base Case using an IPCC Tier 1 methodology. Both the RTFO and EC methodology allow producers to do so. More info on this methodology as well as the effects of using the default LUC figures given in the EC and RTFO methodology is discussed in Chapter 4.
GHG-PERFORMANCE JATROPHA BIODIESEL 9
2.2.1 Allocation factors
The difference in the outcomes between the RTFO and EC methodology is the result of
difference in the way they deal with co-products.
RTFO
The RTFO methodology uses a substitution approach if possible. If insufficient data is
available for a substitution approach, economic allocation is used. Both are briefly
explained below.
A substitution approach works on the principle that the co-product of the biodiesel chain
replaces another product. This saves the emissions that would have been emitted by
producing the replaced product. For example, rapeseed meal is assumed to replace
soybean meal. The emissions that would have been caused by this soybean meal
production are now avoided and this forms an GHG-credit for the rapeseed biodiesel
chain. The practical difficulty with this approach is that one needs to determine what
product is replaced by the biofuel co-product, in what quantities and how much emissions
are avoided by this. When this information is not available, economic allocation is used in
the RTFO.
Allocation works on the logic that each product is partially responsible for the
environmental impacts which have occurred up to this point in the supply chain and
should be allocated a portion of these impacts. In economic allocation the total emissions
caused to produce two products (e.g. rapeseed oil and rapeseed meal) are allocated to the
two products based on their economic value. The rationale for this is that the product with
the highest value should also carry the highest GHG burden. The practical difficulty with
this approach is that market prices fluctuate which changes the allocation factor over time.
This is why an average market value over a certain period is typically used. Another
challenge is formed where no mature markets exist such as for Jatropha seed cake – in
these cases it is more difficult to determine the ‘market value’.
EC
The EC also uses an allocation approach but allocates the burden to the various products
based on the energy content of these products. This has the advantage of being constant
over time but in general allocates a relatively large part of the GHG burden to residual
products with a low market value.
Allocation factors for the Jatropha chain
The Jatropha biodiesel production chain has three main co-products: seedcake (co-product
from the seed expeller) and glycerine and potassium hydroxide (both co-products from the
transesterification process). All upstream emissions have to be allocated accordingly.
Tables 2 and 3 below summarise the key allocation factors for both methodologies. These
have been used to calculate the results discussed in section 2.1.
GHG-PERFORMANCE JATROPHA BIODIESEL 10
Note that agricultural residues such as the hull do not exit the system and therefore no
emissions are allocated to these residues.
Table 2. Al locat ion factors for seedcake in the RTFO (al locat ion by market
value) and EC proposal (al locat ion by energy content) .
RTFO Market value [GBP/tproduct]
Yield [tseedcake/ toil]
Total value [GBP/toil]
Allocation factor [%]
Seedcake 36 2.03 72 14.4% Oil 427 1 427 85.6%
Total 499
EC Energy content [GJ/ tproduct]
Yield [tseedcake/ toil]
Total energy [GJ/toil]
Allocation factor [%]
Seedcake 20 2.03 40 51.4% Oil 37.8 1 37.8 48.6% Total 77.8
It can be seen from the table above that allocation by energy content (EC methodology) is
more beneficial for the seedcake allocation factor – compared to economic allocation
(RTFO) more emissions are allocated to the seedcake and therefore fewer emissions are
allocated to the oil. This is caused by the fact that Jatropha seedcake has a relative low
price compared to its energy content. In the sensitivity analysis (Section 5.2) prices have
been varied to determine the effect on GHG-performance.
Table 3. A l locat ion factors for glycer ine and potass ium sulphate in the RTFO
(al locat ion by market value) and EC proposal (a l locat ion by
energy content) .
RTFO Market value [GBP/tproduct]
Yield [tseedcake/ tbiodiesel]
Total value [GBP/tbiodiesel]
Allocation factor [%]
Glycerine 345 0.1 34.5 9% Potassium sulph. 75 0.04 3.0 1% Biodiesel 340 1 340 90%
Total 377.5
EC Energy content [GJ/ tproduct]
Yield [tseedcake/ tbiodiesel]
Total energy [GJ/tbiodiesel]
Allocation factor [%]
Glycerine 19 0.1 1.9 5% Potassium sulph. Not applicable (a 0.04 0 0% Biodiesel 1 37.2 95%
Total 39.1
a) According to the EC methodology: “Co-products that have a negative energy content
shall be considered to have an energy content of zero for the purpose of the calculation”
For the transesterification co-products, allocation by market value (RTFO) turns out
slightly more beneficial than by energy content (EC). This has two reasons. Firstly,
potassium sulphate has a negative energy content and therefore no emissions are allocated
in the energetic allocation method (EC). Secondly, the RTFO assumes relatively high
glycerine prices compared to the biodiesel price, which leads to a lower allocation of
GHG-emissions to the biodiesel. In the sensitivity analysis (Section 5.2) prices have been
varied to demonstrate the effect on GHG-performance.
GHG-PERFORMANCE JATROPHA BIODIESEL 11
Overall the Base Case without LUC scores better with economic allocation (RTFO), the
Base Case with LUC scores better by using energetic allocation (EC). This difference is
caused by the amount of GHG-emissions from LUC. Without LUC, only few emissions
take place that can be attributed to the seedcake, which makes the allocation of glycerine
more important and that is more beneficial under the RTFO. However, if we take LUC
into account this is responsible for a large part of the emissions and suddenly the seedcake
allocation becomes more important, which is more beneficial under the EC.
In other words, if the RTFO has to change its co-product methodology to allocation by
energy content to be consistent with the EC proposal, the GHG-performance of Jatropha
biodiesel including LUC improves slightly.
2.2.2 Land Use Change
Land use change can make or brake GHG-performance
LUC can have a significant impact on the GHG-performance of biofuels and can even
cancel out all GHG-emission savings of a biofuel.
Direct and indirect LUC
Currently both the RTFO and the EC only include emissions caused by direct LUC. In the
RTFO, emissions from LUC that occurred after November 2005 must be included. In the
EC-proposal the reference date is January 2008. This means that existing plantations do
not have to include emissions from LUC. This will normally be a disadvantage for
Jatropha as most Jatropha plantations are established after the reference year while most
competing crops have ample plantations with establishment dates before the reference
year.
Indirect LUC, in which biofuel feedstock production displace other land functions to other
areas where they may cause LUC emissions, are not included in the present RTFO and EC
proposal. However, both the recent review of the 10% target commissioned by the UK
government and the rapport of EP rapporteur Wijkman acknowledge these indirect
impacts. If emissions from indirect LUC are somehow included this could be a relative
benefit for Jatropha as any emissions for indirect LUC would only apply to existing
agricultural land and not to newly established cropland. The remainder of this report will
focus on emissions from direct LUC.
Emissions from LUC in RTFO and EC
The RTFO and EC both present default GHG-emissions from LUC that have to be added
to the GHG-performance of the biofuel, if more detailed information is not available.
Especially the use of default values has very large and negative impacts – see Chapter 4.
However, both methodologies allow producers to provide more detailed data based on
IPCC methodologies. In the Base Case calculations we have included the effects of LUC
using an IPCC Tier 1 methodology because the default numbers from the RTFO and EC
lead to a negative GHG performance.
GHG-PERFORMANCE JATROPHA BIODIESEL 12
IPCC Tier 1 approach for the Base Case
The impact of LUC on GHG-emissions in an IPCC Tier 1 approach is dependent on
various variables and assumptions, e.g. region, climate zone, original land use and the
crop replacement type. Relevant assumptions for D1 are:
• Region: We used IPCC default numbers for Continental Asia in our calculations.
• Climate zone: Rajasthan is located in a tropical dry climate whereas the north east
regions have a tropical moist climate. We have used IPCC data for a tropical dry
climate in our Base Case calculations.
• Previous land use: Either forest or grasslands can be chosen as a previous land use
(cropland to cropland in principle does not form a LUC). Grassland has been used in
the Base Case calculations.
• Replacement crop-type: Jatropha is a perennial crop, in contrast to annual crops.
The IPCC Tier 1 separates GHG-emissions associated with LUC from three different
carbon stocks:
• (Above ground) Biomass:
� Below ground biomass is assumed to remain unchanged in the Tier 1 level
methodology.
� Differences in above ground biomass before and after LUC are included in
the calculations. For the above ground biomass of the land use before
conversion we used IPCC defaults for tropical dry grassland, in line with
the above assumptions. Above ground biomass immediately after
conversion is assumed to be zero in a Tier 1 approach.
� For perennial crops, the IPCC Tier 1 approach provides default values for
the amount of annual carbon build up in above ground biomass. However,
in our opinion these numbers are not representative for Jatropha plantations
as they are focussed on (short) rotation forest crops in which the total tree is
harvested periodically. To avoid inappropriate use of IPCC default values
we have taken a conservative approach here and assumed zero carbon build
up in Jatropha in above ground biomass in the Base Case. In order to claim
the GHG-benefits of carbon build up in above ground biomass in Jatropha
plantations we recommend a Tier two or three approach which will provide
more representative results. Applying such an approach can deliver
significant GHG-benefits and is analysed in Chapter 4.1
• Dead Organic Matter (DOM) and litter:
� For grasslands carbon stocks in DOM are assumed to be zero before LUC
(in line with IPCC Tier 1). For the carbon stocks in DOM in forests we
used the IPCC Tier 1 default values for broadleaf deciduous tropical forest.
� All DOM is removed after LUC to cropland, leading to zero carbon stored
in DOM after conversion.
1 The IPCC Tier 1 approach for annual crops assumes annual growth in biomass carbon stock
equals annual losses in carbon stock (harvesting) - the resulting net build up therefore amounts
to zero.
GHG-PERFORMANCE JATROPHA BIODIESEL 13
• Soil carbon:
� For perennial crops the Tier 1 approach applies a soil carbon stock change
factor of 1. This implies that zero changes in soil carbon take place in
conversion from grassland or forest to Jatropha.
In Figure 5 the IPCC Tier 1 calculation is displayed for LUC from grassland to Jatropha
plantations in a tropical dry climate.
Total
- 1.15 t C
=
+ 0.211tCO2/ha/y
Above ground biomass- 1.15 t C
Below ground biomass+ 0 t C
Dead Organic Matter (DOM)+ 0 t C
Soil carbon+ 0 t C
Annual C built up (growth) 0 t CAssume
Annual C losses 0 t C
C before LUC 1.15 t CIPCC
C after LUC 0 t CTie 1 definition
DOM / litter stock under old LU+ 0 t CGrassland
DOM / litter stock under new LU:+ 0 t CAssume
Change in carbon stock in mineral soils+ 0 t C
No change (stock change factors for perennials ≠Tier 1)
N2O emissions from mineralised N
(result of carbon stock change, so effect under Tier 1 is zero)
Delta built up+ 0 t C
Delta biomass- 1.15 t C
Not included in Tier 1 approach
(In-)Direct N2O emission factor
0.01 + 0.00225
Amount of N
Dependent on change in management
Reference carbon stock +34.5 t C
IPCC
Stock change factors
No change after LUC All: 1
From Grassland to perennial Jatropha plantations (tropical dry climate)
Figure 5 . Breakdown of the IPCC Tier 1 calcu lat ion in four categories o f
carbon stock and the under ly ing input data for a LUC from
grassland to perennial c ropland in a t ropical dry cl imate.
The above analysis shows how LUC has been included in the Base Case. Chapter 4
includes a more detailed analysis of LUC. It discusses the effects of using the default
LUC numbers from the RTFO and EC, the effects of applying an IPCC Tier 1 approach
with different assumptions (e.g. different climate zone and vegetation type), and the
potential GHG-benefits of using field survey data to claim the carbon build up in Jatropha
plantations.
GHG-PERFORMANCE JATROPHA BIODIESEL 14
3 Comparison of Jatropha to other crops
This chapter compares the GHG-performance of the Base Case Jatropha biodiesel
chain with the default GHG-performance of other biodiesel chains. Jatropha
outperforms the default values for all other first generation energy crops. This holds
true even if LUC (from tropical dry grassland) is included for Jatropha.
Sy
st
e
m
EC
RTFO
Without
LUC
Without
LUC
Inputs
Transportatio
n modus
Transportatio
n distance
Oil expeller
Base cases Chapter 2
Scenarios and comparison
Chapter 3 & 4
Methodology Throughout report
Best case
Optimal
Chapter 4 & 5
Comparison
other crops
Recomme
ndations
Yields
With LUC (IPCC Tier 1)
3.1 Jatropha b iod iese l compared to RTFO defau l ts
Under the default biodiesel production chains supplied by the RTFO2, Jatropha scores best
among the energy crops. Only Used Cooking Oil and tallow have a better GHG-
performance. Figure 6 displays the GHG-performance of Jatropha biodiesel (including
LUC) compared to the other crops.
2 The RTFO also provides defaults for Hydrotreated Vegetable Oils (HVO’s), these have not been
included as the GHG-performance of all these biodiesels increases equally regardless of the
feedstock.
GHG-PERFORMANCE JATROPHA BIODIESEL 15
0%
66%
85%
48%
10%
36%
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
90
100
110
RSO (UK) SBO
(Brazil)
PO
(Malaysia)
UCO &
Tallow
Jatropha
Oil
Diesel
GH
G p
erf
orm
an
ce
[kg
CO
2e
/ G
J f
uel]
Net total
Land use change
For PO: RPO refining
7 - Liquid fuel transport andstorage
6 - Conversion (esterification)
5 - Oil transport
4 - Conversion (crushing)
3 - Feedstock transport
2 - Drying and storage
1 - Crop production
Figure 6 . The GHG-per formance of Jatropha biodiesel compared to other
biodiesels under the RTFO. Some steps are indicated as
negat ive, s ince a subst i tut ion approach is used in these defau l ts .
The net total s of each crop are indicated in grey on the r ight .
The numbers at the top of the bar indicate the GHG-emiss ions
reduct ion compared to foss i l fuel .
Biodiesel from UCO and tallow scores best with 13 kgCO2e/GJ. Jatropha scores much
better than other crops, the next in line is palm oil with 45 kgCO2e/GJ compared to 29
kgCO2e/GJ for Jatropha biodiesel. Compared to fossil diesel, Jatropha biodiesel saves
66% of the GHG-emissions.
3.2 Jatropha b iod iese l compared to EC defau l ts
Figure 7 shows the comparison with the default values of biodiesel from other first
generation feedstocks as included in the EC’s proposal for a RED. Note that these are
default values from the proposed RED directive. These default values are not from the
RTFO. The default values of the RTFO may change if the RTFO has to adopt the EC
methodology for co-product treatment but this will not make them the same as the EC
default numbers. The reason for this is that the EC does not only differ from the RTFO
because of the co-product methodology but it also makes different assumptions on default
parameter values such as yields and fertiliser application rates.
Figure 7 displays two columns for palm oil as the EC provides a default value for if the oil
milling process is not specified and a default for a process in which methane emissions
are captured.
GHG-PERFORMANCE JATROPHA BIODIESEL 16
The EC only distinguishes three phases in the total GHG-performance: cultivation,
processing (both milling and transesterification) and transport and distribution. It is not
clear whether biofuel distribution is included in the scope of the EC default values - this is
not included in the RTFO and our analysis of Jatropha biodiesel. Remarkably soybean oil
has not been included in the draft directive.
68%
78%
53%
19%
53%
39%
0%
0
10
20
30
40
50
60
70
80
90
RSO Sunflower PO PO (no CH4
emissions)
Waste
vegetable or
animal oil
Jatropha Oil Diesel
GH
G p
erf
orm
an
ce
[kg
CO
2e / G
J f
ue
l]
GH
G s
av
ing
[%
]
Net total
Land use change
Transport and distribution
Processing
Cultivation
Figure 7 . The GHG-per formance of Jatropha biodiesel compared to other
biodiesels under the EC methodology.
Under the EC methodology, Jatropha ranks the same as under the RTFO leaving all other
crops behind, only waste vegetable and animal oils score better. Jatropha has a GHG-
performance of 28 kgCO2/GJ, whereas sunflower and palm oil (with methane capture)
have 41 kgCO2/GJ. Main difference between the methodologies is that the GHG-
performance of palm oil heavily depends on whether the process is specified or not. In the
EC proposal slightly higher default GHG-emissions are given for waste vegetable and
animal oil compared to the RTFO.
3.3 Understanding the di f ferences
The GHG-performance of the Base Case Jatropha biodiesel chain is better than the default
values of all first generation energy crops in both the RTFO and the draft RE-directive.
The main benefit for Jatropha lays in the very low emissions from cultivation, even if
Land Use Change from tropical dry grassland is taken into account. Using shell as an
energy source in the oil expeller provides another competitive advantage.
The results clearly illustrate the competitive advantage of Jatropha compared to other first
generation biodiesel energy crops in terms of GHG-performance. Assuming technical
suitability of JO, a market which values GHG-performance may lead to higher prices for
Jatropha biodiesel compared to biodiesel from other first generation energy crops.
GHG-PERFORMANCE JATROPHA BIODIESEL 17
Several remarks do need to be made here:
• We compared the GHG-performance of Jatropha based on real values with default
values of other crops. These default values for other crops have been set
conservatively to stimulate producers to report real values. In other words, by using
real values, suppliers of biodiesel from other energy crops may well be able to
improve their GHG-performance compared to the default values used here.
Nonetheless, Jatropha biodiesel performs very favourably compared to other energy
crops.
• In line with the above, a default value for Jatropha may be set at a more conservative
level than the Jatropha Base Case which we calculated. Currently no default value
exists in either the RTFO or draft directive. However, we expect a default value for
Jatropha to be included in the coming year in the RTFO and it will be interesting to
see at what level the default value will be set and what the difference is with both
other crops and the value for the Jatropha Base Case discussed here. Note that D1 will
be able to use the Jatropha Base Case number as long as it can demonstrate the inputs
used for the calculation as set out in the previous chapter.
• As discussed in section 5.1.3, the estimated electricity inputs for expelling that we
received from D1 are rather low. If these turn out to be higher, the GHG-performance
of Jatropha will deteriorate, resulting in a GHG-emission saving of 62% in stead of
66% in the Base Case (under the RTFO).
GHG-PERFORMANCE JATROPHA BIODIESEL 18
4 Impacts of Land Use Change
Land Use Change is the single most important parameter in the GHG-performance of
D1’s Jatropha biodiesel. If conservative defaults for LUC from the RTFO or EC are
used, this leads to higher GHG-emissions than for fossil diesel.
Both the RTFO and EC proposal allow producers to use more specific data on GHG-
emissions from LUC. The IPCC methodology (2006) is the internationally accepted
methodology for such calculations. Through the values it provides it is possible to make
a more specific calculation without the need for field carbon stock surveys.
Finally, actual data for the specific sites enables D1 to even claim an increase in carbon
stocks from LUC, which significantly improves the GHG-performance. Since
measuring actual carbon stocks was not included in the project, we show the potential
impact on the GHG-performance by using example data.
4.1 Using RTFO or EC defaul t values for Land Use
Change
In the Base Cases LUC has been taken into account by means of an IPCC Tier 1
calculation. How does this relate to the defaults provided in the RTFO and EC proposal?
We have analysed two types of land conversion: grassland to perennial cropland (Base
Case) and forestland to perennial cropland. We compare IPCC Tier 1 outcomes (Base
Case) with the RTFO and EC defaults, see Figure 8.
GHG-PERFORMANCE JATROPHA BIODIESEL 19
-183%
-285%
-105%
-224%
-183%
-5%
68%66%
Fossil diesel reference
3214
0
2,000
4,000
6,000
8,000
10,000
12,000
IPCC Tier 1
(economic
allocation)
IPCC Tier 1
(energetic
allocation)
RTFO default
(economic)
EC default
(energetic)
IPCC Tier 1
(economic
allocation)
IPCC Tier 1
(energetic
allocation)
RTFO default
(economic)
EC default
(energetic)
Grassland to Jatropha Forest to Jatropha
GH
G e
mis
sio
ns f
rom
LU
C [
kg
CO
2e/t
bio
die
sel]
8. Land UseChange
7. Biodieseltransport
6.Transesterification
5. Oil transport
4. Oil extraction
3. Seed transport
2. Drying andstorage
1. Jatrophacultivation
Fossil dieselreference
Figure 8 . The impact o f us ing defau l ts for LUC from the RTFO and EC
methodology compared to the Base Case IPCC Tier 1
calcu lat ions. Plus the di f ference in conversion f rom grass land or
forest land to perennial c ropland. For compar ison, the foss i l
diesel re ference GHG-per formance is indicated by the red l ine.
The following conclusions can be drawn from Figure 8:
• None of the defaults from either RTFO or EC could lead to a positive GHG balance
for Jatropha biodiesel. Only by using the more detailed IPCC methodology can LUC
be included without lowering the GHG-performance below that of fossil diesel.
• Conversion from forestland to perennial cropland always results in a negative GHG-
performance for Jatropha biodiesel, also if an IPCC Tier 1 approach is used.
The IPCC default numbers for carbons stocks in forest provide only a single number for
broadleaf forest in a certain region – based on full grown natural forest in that region. No
numbers are given for degraded forest for example. Therefore, as soon as an area is
classified as forest, the full carbon stock of full grown native forest in that region is taken
into account.
The above stressed the importance of the thresholds as used in the definition forest land.
The IPCC does not provide detailed definitions itself but refers to country-definitions of
forest – it does mention internationally accepted definitions such as those by FAO. As an
example, FAO uses a canopy cover threshold of only 10% - everything above that is
classified as forest. In situations where the threshold of the national forest definition are
only just exceeded, the actual carbon stock with be significantly smaller than the default
number given in IPCC (2006).
GHG-PERFORMANCE JATROPHA BIODIESEL 20
In these cases it is recommended to assess the actual carbon stock – this actual value can
then be used in the calculations in stead of the (much higher) default number.
The above discussion shows that default numbers for LUC-emissions from the RTFO and
EC can not be used. Therefore either an IPCC Tier (without field survey data) must be
used or a higher Tier (with field survey data). These are discussed below.
4.2 IPCC T ier 1 approach for LUC: the e f fect o f
c l imate zones
In the IPCC Tier 1 calculations for the Base Case, a tropical dry climate was assumed in
combination with grassland as the original land use. The climate zone and original
vegetation affects the carbon stocks such as the amount of carbon stored in above ground
biomass in forest or grassland. Figure 9 shows the effect of using a tropical moist and
tropical wet climate zone in the calculations. Tropical dry represents a large part of India
(see Annex C for an overview of climate zones) stretching from the mid to west side of
the country. Tropical wet can be found in the far east of India, near Bangladesh.
-297%
-169%
-105%
58%58%66%
Fossil diesel reference: 3214
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
13,000
Tropical dry Tropical moist Tropical wet Tropical dry Tropical moist Tropical wet
IPCC Tier 1 Grassland to Jatropha IPCC Tier 1 Forest to Jatropha
GH
G-e
mis
sio
ns f
rom
LU
C [
kg
CO
2/t
bio
die
se
l]
LUC emissions
Remaining production chain (RTFO)
Fossil diesel reference
Figure 9 . The impact of c l imate zone on total GHG-emissions from LUC and
the GHG-per formance of Jatropha biodiesel for two types of LUC.
For grasslands there is no difference between tropical moist and wet, both leading to a
decreased GHG-performance of 58% GHG-emission saving. Any forestland conversion is
highly disadvantageous to the GHG-performance, which is increasingly worse with moist
and wet climate zones.
Annex D shows the calculations behind this section per scenario.
GHG-PERFORMANCE JATROPHA BIODIESEL 21
4.3 Using s i te speci f i c data for Land Use Change
Perennial crops have an advantage above annual crops in the fact that they store carbon
during the plantation lifetime. The IPCC provides defaults for annual growth in carbon
stocks of perennial systems, but none are especially suited for Jatropha plantations. In this
section, we assess the impact of using site specific data. We assume that Jatropha crops
reach 20 kg fresh weight per tree at the end of the plantation life.
79%
58%
88%
66%
-600
-400
-200
0
200
400
600
800
1,000
1,200
1,400
1,600
Base case Carbon storage Base case Carbon storage
Tropical dry Tropical moist/wet
GH
G-e
mis
sio
ns f
rom
LU
C [
kg
CO
2/t
bio
die
sel]
LUC emissions
Remaining production chain (RTFO)
Net total
Figure 10. The ef fect of tak ing into account the carbon storage in biomass
of a perennial c rop. For al l c l imate zone thi s resul ts i s net
annual carbon capture, s ince the growth in h igher than the
carbon stock of the grassland pr ior to convers ion.
Figure 10 shows the effect of taking the carbon storage into account. In a tropical dry
grassland this leads to the carbon storage of 532 kgCO2 per tonne biodiesel (or 706 kg
CO2e per hectare). The net GHG-performance is then increased to 88% GHG-emission
saving. In a tropical moist and wet climate, the above ground biomass prior to LUC is
higher, so the effect of annual growth is relatively lower. Still 262 kg CO2e is captured per
tonne of biodiesel (or 348 kgCO2 per hectare), increasing the GHG-performance to 79%
saving. The 20 kg fresh weight per shrub is a conservative estimation and if also carbon
build in below ground biomass and soil are taken into account the effect could be larger
still.
Important to note is that an actual carbon measurement should be conducted in order to be
able to assess the carbon stock of a mature Jatropha plantation.
GHG-PERFORMANCE JATROPHA BIODIESEL 22
5 Opportunities to improve GHG-
performance
GHG-performance is most sensitive to a number of parameters which have been varied
in this chapter. Firstly, several changes are made to the Base Case scenario in order to
measure the effect on GHG-performance. Variations on cultivation inputs, LUC, and
production system parameters such as transportation modus are made. Secondly, a
sensitivity analysis is conducted on the impact of Jatropha yields, transportation
distances and oil yields on the GHG-performance of D1’s Jatropha biodiesel. Finally, a
best and a worst case scenario are constructed.
S
y
s
t
e
m
EC
RTFO
Without
LUC
Without
LUC
Inputs
Transportatio
n modus
Transportation distance
Oil expeller
Base cases Chapter 2
Scenarios and comparison
Chapter 3 & 4
Methodology Throughout report
Best case
Optimal
Chapter 4 & 5
Comparison
other crops
Recommendations
Yields
With LUC (IPCC Tier 1)
5.1 Changes to the product ion system
Several changes are made to the production systems assumptions and defaults in order to
quantify the effect on GHG-performance. Changes are modular in this section, in contrast
to the sensitivity analysis in which parameters are varied in a continuous way.
Successively changes are made to the cultivation inputs and yields, transportation modus
and oil expeller.
5.1.1 Cultivation inputs lower the GHG-performance
In the Base Case no fertiliser or other inputs are applied on the plantations. If D1 would
apply amounts of Urea, Phosphate or Lime this would affect the GHG-performance. In
our analysis of cultivation inputs it is assumed that any input will improve the seed yield
to 6.3 t/ha (provided by D1) which should cancel out (part of) the additional emissions
from cultivation inputs.
If seed yield would increase without any additional inputs, the GHG-performance would
increase by only 1%-pt. This is caused by the limited emissions in the cultivation phase
and from LUC. Figure 11 and Figure 12 show the effect of several inputs on the net total
GHG-performance (including LUC). It shows that:
GHG-PERFORMANCE JATROPHA BIODIESEL 23
� Applying 125 kg/ha/yr of Urea results in direct emissions and, much larger, indirect
soil emissions and lead to a significant worsening of the GHG-performance to 60%
GHG-emission saving.
� Applying 12.5 kg/ha/yr of Triple Super Phosphate results in almost no increase in
GHG-intensity.
� Applying 1200 kg/ha of Lime (once at plantation establishment) results is almost no
increase in GHG-intensity.
Concluding, by applying lime or TSP there is a net beneficial effect of higher yields. If
Urea is applied, this beneficial yield-effect is outbalanced by resulting GHG-emissions
from Urea production and soil emissions.
59%
66%66%
59%
66%
60%
67%67%
60%
67%
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
Base case Urea TSP Lime All
GH
G p
erf
orm
an
ce
[kg
CO
2e/t
bio
die
se
l]
Net total without yield increase
Net total with yield increase
Figure 11. The effect of di f fe ren t cu lt ivat ion inputs on overa l l GHG-
performance without (purple bars) and wi th (green bars) yie ld
improvements under the RTFO.
Under the EC methodology the effects are less severe: inputs lead to a relative smaller
increase in GHG-intensity. This is caused by a lower allocation factor for oil under the
energetic allocation of the EC because of which fewer of the increased emissions are
allocated to the oil.
The impact of TSP and lime on overall GHG-performance is negligible. However, the
application of nitrogen fertiliser has an important effect. If D1 is to apply Urea, this would
result in an increase of 233 kg CO2e per ton biodiesel. Still, Jatropha is performing better
than other default crops but it is getting closer to palm oil.
GHG-PERFORMANCE JATROPHA BIODIESEL 24
In conclusion, TSP, lime and probably also gypsum3 can be applied without any high risk,
but N fertiliser has a negative effect on the GHG-performance.
63%
67%67%
63%
68%
64%
68%68%
64%
68%
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
Base case Urea TSP Lime All
GH
G p
erf
orm
an
ce
[kg
CO
2e
/t b
iod
iesel]
Net total without yield increase
Net total with yield increase
Figure 12. The effect of di f fe ren t cu lt ivat ion inputs on overa l l GHG-
performance without (purple bars) and wi th (green bars) yie ld
improvements under the EC proposal .
5.1.2 Change in transportation modus could increase GHG-performance
Overland oil transport by rail
In the Base Case Jatropha oil is transported by truck from the oil expeller to the closest
harbour over a distance of 750 km. Since in general, diesel truck transport significantly
contributes to the GHG-performance of a biofuel, it is valuable to assess the impact of a
change in transport modus from truck to train. In India the fuel efficiency of trucks is
much lower than for trains: 1.94 MJ/t.km versus 0.19 MJ/t.km. This could lead to an
improvement of the GHG-performance by 111 kgCO2e/t biodiesel under the RTFO and
by 118 kgCO2e/t biodiesel under the EC proposal. The Base Case would then be improved
by 3%-pt under both methodologies, see Figure 13.
Oversea oil transport by ship
The RTFO assumes rather low fuel efficiency for international shipping, we have found
factors that are four times as low (mostly dependent on vessel capacity). Using more
efficient ships results in a significant improvement of 6%-pt under both methodologies,
see Figure 13.
3 No data on the GHG-intensity of gypsum was available.
GHG-PERFORMANCE JATROPHA BIODIESEL 25
74%
71%
68%
72%
69%
66%
0
100
200
300
400
500
600
700
800
900
1000
1100
Base case Rail transport Efficient ships Base case Rail transport Efficient ships
RTFO EC
GH
G p
erf
orm
an
ce
[kg
CO
2e/t
bio
die
sel]
8. Land Use Change
7. Biodiesel transport
6. Transesterification
5. Oil transport
4. Oil extraction
3. Seed transport
2. Drying and storage
1. Jatropha cultivation
Figure 13. The effects of rai l transport in stead of t ruck t ransport , and the
use of more e f f ic ient sh ips on overal l GHG-per formance.
5.1.3 Variations on the oil expeller
Figure 14 shows the impact on GHG-performance of several variations to the oil expeller,
i.e. using solvent extraction, facing higher electricity inputs and using a mobile expeller.
69%
65%
68%68%68%
62%
67%66%
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
Base case Solvent
extraction
Solvent +
>kWh's
Mobile
expeller
Base case Solvent
extraction
Solvent +
>kWh's
Mobile
expeller
RTFO EC
GH
G p
erf
orm
an
ce
[kg
CO
2e/t
bio
die
se
l]
8. Land Use Change
7. Biodiesel transport
6. Transesterification
5. Oil transport
4. Oil extraction
3. Seed transport
2. Drying and storage
1. Jatropha cultivation
Figure 14. The effect of var ious var iat ions on the oi l expel ler: using solvent
extract ion, facing h igher e lectr ic i ty inputs and the use of a
mobi le expel le r.
GHG-PERFORMANCE JATROPHA BIODIESEL 26
Solvent extraction
Currently the oil is extracted by using a mechanical expeller. The process requires only
electricity inputs as the shells are being burnt for steam generation. The current oil yield is
0.25 ton toil per ton seed. If solvent extraction is used, we have assumed that the
electricity inputs remain the same and shells are still sufficient to generate enough steam.
Solvent has to be added, but the GHG impact of this is zero in the RTFO. Oil yield
increases to 0.30 ton oil per ton seed if solvent extraction is used.
The higher yield from solvent extraction has only a small positive impact on the GHG-
performance which is increased with 1 %-pt resulting in 1055 kgCO2e/t biodiesel. The
effect is only small because of the low emissions up until the oil expelling. Dividing these
emissions over more oil therefore yields only little benefit.
In the sensitivity analysis we have also varied the oil yield and assessed the impact on
GHG-performance, assuming that oil yield could improve in the future regardless of the
use of a different technology.
Electricity inputs
The 6 kWh per tonne of oil electricity inputs for oil-extraction seems rather low compared
to an input of 410 kWh per tonne of oil for soy bean oil extraction (also using hexane
solvent extraction). We have run a scenario in which we have set the electricity input per
tonne of crushed Jatropha kernel equal to the RTFO default value for soybean crushing.
This leads to an electricity input of 228 kWh per tonne Jatropha oil. This lead to an
increase in the GHG-intensity to 1215 kgCO2e/t biodiesel in the RTFO and 1128
kgCO2e/t biodiesel under the EC (assuming higher oil yields as well). The smaller effect
under the EC methodology is due to the lower allocation factor for oil in this
methodology. These results indicate the importance of verifying the electricity inputs for
Jatropha oil expelling.
Mobile expeller
The use of a mobile oil expeller has several advantages: seedcake can directly be returned
to the farmers/plantation and seed transport is lowered. Oil yield is not likely to increase
with this small capacity and electricity inputs have been assumed to resemble the Base
Case.
In stead of transporting seed, now oil is transported which lowers the GHG-intensity of
this phase by 75% (direct result of the 0.25 ton oil yield). Overall this improves GHG-
performance with 64 kgCO2e/t biodiesel in the RTFO and 38 kgCO2e/t biodiesel in the
EC.
GHG-PERFORMANCE JATROPHA BIODIESEL 27
5.2 Sensi t iv i ty analys i s
5.2.1 Parameter variations
The impact of sensitive parameters is assessed on the GHG-performance of Jatropha
biodiesel. The varied parameters are:
• Seed yield;
• Transportation distance (land and freight);
• Oil yield.
For the RTFO results we have added variations on the price of:
• Seedcake;
• Jatropha oil;
• Biodiesel and glycerine price .
These prices affect the (economic) allocation factor. This is not the case in the EC results
since its allocation factor is based on energy contents.
Parameters have been varied with values that are either provided by D1 or by Ecofys
based on our view of how parameters could realistically change in the future ( Table 4).
Table 4. Summary on the var ied parameters and the explanat ions/sources
behind. Bi odiesel and g lycer ine pr ices have on ly been varied in
the RTFO Base Case.
Parameter Low Base case
High Unit Source / remark
Seed yield 3.0 4.5 6.3 t seed /ha D1: higher agronomy Transport dist. oil (land) 250 750 1,500 Km Different harbour
Transport dist. oil (ship) 12,000 14,500 16,000 Km Different harbour Oil yield 0.20 0.25 0.30 t oil /t seed Solvent extraction Jatropha oil price 350 427 550 GBP/t Increased demand Seedcake price 20 36 144 GBP/t D1: use as feed
Biodiesel price (RTFO) 300 340 700 GBP/t FO Licht 2007 Glycerine price (RTFO) 300 345 700 GBP/t Oleonline 2007
Figure 15 and Figure 16 show spider diagrams in which the relative change to a parameter
is shown on the x-axis and the effect on GHG-performance on the y-axis. Although the
sensitivity analysis is a helpful tool, the likelihood of variation is often more important.
The parameter variation given on the x-axis corresponds with realistic values.
5.2.2 General observations
Overall the graphs show that the GHG-performance is highly sensitive to the shipment of
oil. This is caused by the large distances. Although highly sensitive, it is more important
to look at realistic ranges. This indicates that oil transport will not exceed 16,000km, and
therefore the impact is still limited compared to for instance oil transport over land. Here
the ranges are much larger, which makes the overall impact to GHG-performance larger
despite the fact that the curve is less steep.
GHG-PERFORMANCE JATROPHA BIODIESEL 28
In general the GHG-performance is sensitive for oil and seed yield, but not as high as one
would expect. This is caused by the few GHG-emissions resulting from the cultivation,
transport and extraction phase compared to emissions from oil transport and
transesterification. For the results from the RTFO, glycerine and biodiesel prices and
especially the ratio between these, could affect GHG-performance significantly in the
future – this allocation factor affects the emissions from all steps up until and including
transesterification. The seedcake and Jatropha-oil price have only a small effect on the
GHG-performance – this allocation factor affects emissions of only the steps up until and
including oil expelling.
Yields: 3 - 6.3 t/ha
Oil transport (land) 250-1500km
Oil expelling: 0.25 - 0.30 t/t
Oil transport (ship) 12000-
16000km
Biodiesel price: 300-700 GBP/t
Glycerine price: 300-700 GBP/t
Seedcake price: 20-144 GBP/t
Oil price: 350-550 GBP/t
950
1000
1050
1100
1150
1200
1250
25% 50% 75% 100% 125% 150% 175% 200%
GH
G p
erf
orm
an
ce
[kg
CO
2e/t
bio
die
se
l]
60.0%
61.7%
63.3%
65.0%
66.7%
68.3%
70.0%
GH
G s
av
ing
[%
]
Figure 15. Spider diagram for several parameters wi th the RTFO methodology.
GHG-PERFORMANCE JATROPHA BIODIESEL 29
Yields: 3 - 6.3 t/ha
Oil transport (land) 250-1500km
Oil expelling: 0.25 - 0.30 t/t
Oil transport (ship) 12000-
16000km
950
1000
1050
1100
1150
1200
1250
25% 50% 75% 100% 125% 150% 175% 200%
GH
G p
erf
orm
an
ce
[k
gC
O2
2/t
bio
die
sel]
60.0%
61.7%
63.3%
65.0%
66.7%
68.3%
70.0%
GH
G s
av
ing
[%
]
Figure 16. Spider diagram wi th the EC methodology.
More sensitive for oil yield than for seed yield
Higher seed yield results in relatively less GHG-emissions attributed to cultivation and
Land Use Change. Whereas higher oil yield additionally leads to lower seed transport
emissions and emissions associated with oil extraction, making the GHG-performance
more sensitive for oil yield. The overall effect of seed yield is however larger, as the range
in expected seed yield in wider than for oil yield.
More sensitive for oil transport per ship than by truck
Oil shipping distance is a decisive parameter and affects GHG-performance to a large
extent. In the base case oil is transported over 14,500 km, so any percentual increase leads
to a significant effect. Transport by truck leads to minor increase per percentage
parameter variation, but overall there is high sensitiveness as the expected variations in
truck transport distance are very large.
Glycerine and biodiesel prices affect GHG-performance under the RTFO
Higher prices for glycerine and biodiesel increase or lower the GHG-performance
respectively, since it affects the allocation factor that is based on market values under the
RTFO. In terms of percentage the effects are minor, but as prices are highly volatile
overall effects on the GHG-performance are significant, ranging from 61% to 68% GHG-
emission saving.
As indicated in Section 3.2, the RTFO assumes different prices for biodiesel and glycerine
than are currently applicable. Prices of biodiesel are now generally higher (FO Licht,
December 2007; Oleonline 2007). Glycerine prices are expected to decline in the future as
biodiesel production is increasing. This would lead to overall worse GHG-performance.
GHG-PERFORMANCE JATROPHA BIODIESEL 30
Upgrading of seedcake and fluctuating Jatropha oil prices play a minor role
The value of seedcake can be increased by enabling its usage as animal feed. Under the
RTFO, values for seedcake (and Jatropha oil) determine the allocation factors that impacts
the GHG-performance. Overall, the impact is low. Both by percentage of increase and by
taking into account the total expected range, there is little incentive to upgrade the
seedcake. Jatropha oil price has a similar low, but inverse, effect on GHG-performance
under the RTFO.
5.3 Worst and best case scenar io
The findings of the what-if scenarios and sensitivity analysis can be combined into a
worst- and best-case scenario, with the parameters displayed in Table 5. Outcomes are
displayed in Figure 17.
Table 5. Input parameters for the worst and the best case scenar io.
Parameter Worst case Best case Unit
Seed yield 3 6.3 t/ha Cultivation inputs All Zero -
Land Use Change (a Grassland Grassland - Oil transport land 1,500 250 Km Oil transport land- modus Truck Rail - Oil transport sea 16,000 12,000 Km
Glycerine price (RTFO only) 300 700 GBP/t Biodiesel price (RTFO only) 700 300 GBP/t
a) Land Use Change has not been varied, as this would impacts the results that much that other influenced are not visible. But in general we have shown that LUC is the single most important factor in the GHG performance of energy crop based biofuels with potentially large positive and negative effects.
RTFO
A worst case scenario results in a 14%-pt reduced GHG performance of 51%. Especially
the cultivation phase and the oil transport highly contribute to this overall decline in
performance. A best case scenario results in a 9%-pt increase in GHG-performance to
75%.
EC
A worst case scenario results in a 10%-pt reduced of 58%. A best case scenario results in
a 6%-pt increase in GHG-performance to 74%.
GHG-PERFORMANCE JATROPHA BIODIESEL 31
66%
51%
75%
68%
58%
74%
0
200
400
600
800
1000
1200
1400
1600
Base case Worst case Best case Base case Worst case Best case
RTFO EC
GH
G p
erf
orm
an
ce [
kg
CO
2e
/t b
iod
iese
l]
8. Land Use Change
7. Biodiesel transport
6. Transesterification
5. Oil transport
4. Oil extraction
3. Seed transport
2. Drying and storage
1. Jatropha cultivation
Figure 17. GHG-per formance of the Base Case, worst case and best case.
GHG-PERFORMANCE JATROPHA BIODIESEL 32
6 Conclusions and discussion
6.1 GHG-performance Jatropha versus other crops
For the Indian production chain analysed in this project, Jatropha biodiesel saves 66% and
68% of the GHG-emissions under the RTFO and EC respectively. Among all first
generation biodiesel energy crops, Jatropha scores best - even when taking into account
the resulting emissions from Land Use Change.
Main advantages are the zero-input cultivation of Jatropha, which especially avoids
nitrogen fertiliser related emissions. Furthermore the shells are used for steam generation
in the oil extraction phase, which significantly reduces fossil energy inputs. On the down
side, emissions from Jatropha oil transport are considerably higher than for many other
crops due to the relatively long transportation distances.
The comparison is based on default values given in either the RTFO or EC draft directive.
These default values do not necessarily represent the values that will actually be reported
for these alternative feedstocks. Just as for Jatropha, producers can use real data which
would lead to a better GHG-performance. Nonetheless, Jatropha biodiesel performs very
favourably compared to other energy crops.
6.2 Performance under RTFO versus EC methodology
If the UK is to adopt the EC directive this would results in a slightly improved GHG-
performance of D1’s Jatropha biodiesel. Under the RTFO (allocation by market value)
Jatropha biodiesel saves 66% whereas under the EC proposal (allocation by energy
content) Jatropha saves 68% of the GHG-emissions compared to fossil diesel.
The difference in the results is caused by a different allocation method to calculate GHG-
emissions associated with the co-products. The allocation of seedcake is more beneficial
under the EC, as its energy content is relative high compared to its current market value.
This leads to much less emissions from LUC, seed transport and oil extraction attributed
to the Jatropha oil (49% in stead of 86% under the RTFO). Although the allocation of
transesterification co-products under the EC-methodology is less beneficial this does not
outbalance its positive effect of the seedcake allocation.
Although the overall differences are small, Jatropha scores slightly better under the EC
proposal. Future upgrading of the seedcake is not rewarded under the EC as it only
considers the (fixed) energy content.
GHG-PERFORMANCE JATROPHA BIODIESEL 33
The use of the EC methodology will also impact all other (default) chains reported under
RTFO. Given the scope of the study, this has not been quantified.
6.3 Areas for improvement
Besides making optimal use of the carbon storage of Jatropha plantations, several
improvements can be made that increase the GHG-performance of Jatropha biodiesel, see
Table 6.
Table 6. Measures that improve GHG-per formance Jatropha biodiese l .
Measure Result to GHG-emission saving RTFO EC
1. Use large efficient ships for international oil transport + 5.8%-pt + 6.7%-pt 2. Oil transport by rail + 3.5%-pt + 3.7%-pt
3. Mobile oil expeller + 2.0%-pt + 1.2%-pt 4. 4x higher seedcake value + 2.4%-pt + 0%-pt 5. Higher yields (by TSP/lime) + 1.4%-pt + 0.9%-pt 6. Solvent oil extraction + 1.2%-pt + 0.4%-pt
7. Site specific carbon stock measurement Approximately + 22%-pt
The choice for oil shipment in large crude carriers is one of the controllable measures that
contributes to a better GHG-performance. Although it seems theoretical, if D1 is able to
control this, GHG-emission saving could be increased to 72% under the RTFO and 74%
under the EC proposal.
Other measures such as solvent extraction and the introduction of a mobile expeller
provide smaller improvements, up to 3.5 percentage points.
Land Use Change can have a significant positive contribution to GHG-performance if real
carbon measurements are used to take into account the annual built up carbon in the
Jatropha plantation. For a tropical dry grassland conversion and above ground biomass of
20 kg dry weight per tree, GHG-performance could be increased to 88% savings.
6.4 Land Use Change
Land Use Change is by far the most important parameter for D1’s Jatropha plantations.
Emissions from Land Use Change must be included as most plantations are established
after to the dates provided in the RTFO and EC proposal. The most important
conclusions with respect to direct LUC are:
• Any default for LUC provided in the RTFO and EC proposal ruins the GHG-
performance.
• Any conversion from forestland results in more GHG-emissions compared to fossil
diesel in the IPCC Tier 1 approach.
• Under the IPCC Tier 1 approach, grassland conversion in a tropical dry, moist or wet
climate (representing the major part of the plantations in India) results is a minor
decrease in GHG-performance, but still saves GHG-emissions compared to fossil
diesel.
• Land use conversion could result in a positive contribution to GHG-performance if
the actual increase in carbon stocks is quantified.
GHG-PERFORMANCE JATROPHA BIODIESEL 34
This requires actual carbon stock measurements of the plantation and preferable also of
the original land cover. In some cases this results in an increase in carbon storage. This is
the main advantage that perennial crops have and should be addressed in detail. Actual
field measurements are also of importance in those cases in which national forest
thresholds are just exceeded.
GHG-PERFORMANCE JATROPHA BIODIESEL 35
References
E4Tech 2008: Carbon Reporting within the Renewable Transport Fuel Obligation –
Methodology.
EC 2008: Proposal for a directive of the Parliament and the Council on the promotion of
the use of energy from renewable sources. Version 15.4, 23 January 2008.
FO Licht, 2007: World Biodiesel Price Report. Vol 1, No 47, 6 December 2007.
FO Licht, 2008: World Biodiesel Price Report. Vol 2, No 18, 8 May 2008-06-11
FO Licht, 2008: World Ethanol and Biofuels Report. Vol 6, No 13, 10 March 2008
FO Licht, 2008: European Ethanol Price Report. Vol 4, No 1, 31 March 2008
IEA 2007: Energy statistics
IPCC 2006: IPCC Guidelines for National Greenhouse Gas Inventories
Oleonline, 2007: Glycerine Market Report. Nr 79, 15 December, 2007.
GHG-PERFORMANCE JATROPHA BIODIESEL 36
Annex A Key dif ferences between the
RTFO and EC proposal
This Annex summarises the key differences between the RTFO methodology and the
methodology as proposed in the EC proposal. Main differences between the two
methodologies are the way co-products are dealt with, default values and Land Use
Change parameters.
Status of the RTFO and draft EC directive
In January 2008, the RFA issued the technical guidance for carbon and sustainability
reporting within the RTFO. As of April 2008 the RTFO came into force, requiring 2.5%
of all road fuels to come from biofuels. Transport fuel suppliers can comply with the rules
by supplying the relevant amount of biofuel themselves, purchasing certificates from
another transport fuel supplier, or by paying a ‘buy out' price in respect of some or all of
their obligation.
Also in January 2008, the European Commission (EC) released its draft directive on the
promotion of the use of energy from renewable sources. This document includes default
values for several biofuel production chains as well as a methodology that treats co-
products by means of allocation by energy content. While still uncertain, it is expected the
directive will be finished in 2009 and will enter into force in 2010.
Calculating GHG-emissions associated with co-products
The impact of co-products must be taken into account when calculating the carbon
performance of a biofuel. Multiple approaches are used to deal with co-products:
� Substitution: this is based on the principle that a biofuel should be attributed with
any consequences (i.e. increased or avoided GHG-emissions) of an increase in
demand of any (co-) product. Any impact that a co-product has on GHG-emissions
should be included within the boundaries of the biofuels’ carbon performance.
� Allocation: each co-product is partially responsible for the environmental impacts
which have occurred up to this point and should be allocated a portion of these
impacts. The allocation can be carried out on the basis of a range of characteristics
of the co-product, the relevant methods are given below:
o Market value.
o Energy content.
The reader is directed to E4Tech (2008) for further explanations and discussions on co-
products treatment.
GHG-PERFORMANCE JATROPHA BIODIESEL 37
The approach taken in the RTFO depends on the co-products and its use. In principle, co-
products must be accounted for by using the substitution approach where possible. Where
there is no sufficient data to undertake the substitution approach, the co-products must be
accounted for by using allocation by market value. For default fuel chains in the RTFO it
has already been indicated how to address co-products and fixed credits have been
determined for most of the different uses of the co-products. Jatropha biodiesel is not
included in the RTFO default chains and no default uses of the main co-products are
provided. Therefore, allocation by market value is the preferred approach, since detailed
information on the co-products usage is lacking.
In the EC proposal all co-products are dealt with by allocation on energy content.
Defaults
The RTFO calculation methodology uses default values that provide estimates of the
carbon intensity of different fuel chains. This enables suppliers with specific information
about their supply chain to provide additional qualitative or quantitative data to improve
the accuracy of the calculation. High level default values (where little is known about the
origin of the supply chain) represent conservative GHG-emission savings; but typical
default factors (where the calculation includes more detailed information) are less
conservative in order to encourage the supply of information. This is illustrated in the
figure below.
The EC directives in principle proposes the same approach but provides default values at
only one level,
GHG-PERFORMANCE JATROPHA BIODIESEL 38
Land Use Change
Where information on previous land use has been supplied the calculation includes the
effect on overall GHG-emission savings. RTFO default values for specific Land Use
Changes are based on IPCC guidelines and are specified per climate region, crop type and
vegetation type before conversion. For all plantations established after November 30,
2005 emissions from Land Use Change must be included.
In the EC directive, only one single default value for emissions from LUC is given
without any differentiation between regions, crop types or original vegetation. The
reference date in the proposal is set at January 2008.
Both the RTFO and EC-proposal allow for the use of more detailed data based on IPCC
methodologies.
GHG-PERFORMANCE JATROPHA BIODIESEL 39
Annex B Jatropha yield
Base case yields
D1 Oils provided us with Jatropha seed yields for various stages in the plantation lifetime.
An average yield figure has been used to calculate the GHG-performance of Jatropha
biodiesel. Yield has been averaged over the plantation lifetime, in which only harvested
yield is considered. This is in line with FAO statistics which also give average yields for
‘harvested area’. We assume a plantation lifetime of 20 years, excluding the first three
unproductive years.
Jatropha yields no seeds in the first three years. Only in year 4 yields start and grow until
year 9, when yields remain constant until year 23.
Year Yr 1-3 Yr 4 Yr 5 Yr 6 Yr 7 Yr 8 Yr 9-23
Yield 0 1.875 2.5 3.125 3.75 4.375 5 tseed/ha
Average harvested yield is then: 4.53 t/ha.
High yields
A higher yield series has been provided by D1 Oils as well to represent better agronomy
figures. These yields are indicated below.
Year Yr 1-3 Yr 4 Yr 5 Yr 6 Yr 7 Yr 8 Yr 9-23
Yield 0 2.5 3.75 5 5.625 6.25 6.875 tseed/ha
Average harvested yields equals 6.31 t/ha.
Average Base case
yield
Average Improved
higher yield
0
1
2
3
4
5
6
7
8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Years
Ja
tro
ph
a s
ee
d y
ield
[to
n/h
a]
Base case yields
Improved higher yields
GHG-PERFORMANCE JATROPHA BIODIESEL 40
Annex C IPCC definitions
Land use categor ies (IPCC, 1996)
� Forest Land
This category includes all land with woody vegetation consistent with thresholds used to
define Forest Land in the national greenhouse gas inventory. It also includes systems with
a vegetation structure that currently fall below, but in situ could potentially reach the
threshold values used by a country to define the Forest Land category.
� Cropland
This category includes cropped land, including rice fields, and agro-forestry systems
where the vegetation structure falls below the thresholds used for the Forest Land
category.
� Grassland
This category includes rangelands and pasture land that are not considered Cropland. It
also includes systems with woody vegetation and other non-grass vegetation such as herbs
and brushes that fall below the threshold values used in the Forest Land category. The
category also includes all grassland from wild lands to recreational areas as well as
agricultural and silvi-pastural systems, consistent with national definitions.
� Wetlands
This category includes areas of peat extraction and land that is covered or saturated by
water for all or part of the year (e.g., peatlands) and that does not fall into the Forest Land,
Cropland, Grassland or Settlements categories. It includes reservoirs as a managed sub-
division and natural rivers and lakes as unmanaged sub-divisions.
� Settlements
This category includes all developed land, including transportation infrastructure and
human settlements of any size, unless they are already included under other categories.
This should be consistent with national definitions.
� Other Land
This category includes bare soil, rock, ice, and all land areas that do not fall into any of
the other five categories. It allows the total of identified land areas to match the national
area, where data are available. If data are available, countries are encouraged to classify
unmanaged lands by the above land-use categories (e.g., into Unmanaged Forest Land,
Unmanaged Grassland, and Unmanaged Wetlands).
GHG-PERFORMANCE JATROPHA BIODIESEL 43
Annex D LUC calculations IPCC Tier 1
Total- 1.15 t C
=
+ 0.211tCO2/ha/y
Above ground biomass- 1.15 t C
Below ground biomass+ 0 t C
Dead Organic Matter (DOM)+ 0 t C
Soil carbon+ 0 t C
Annual C build up (growth) 0 t CAssume
Annual C losses 0 t C
C before LUC 1.15 t CIPCC
C after LUC 0 t CBy definition
DOM / litter stock under old LU+ 0 t CGrassland
DOM / litter stock under new LU:+ 0 t CAssume
Change in carbon stock in mineral soils+ 0 t C
No change (management factors for perennials ≠Tier 1)
N2O emissions from mineralised N(result of carbon stock change, so effect under Tier 1)
Delta built up+ 0 t C
Delta biomass- 1.15 t C
Not included in Tier 1 approach
(In-)Direct N2O emission factor0.01 + 0.00225
Amount of NDependent on change in management
Reference carbon stock +34.5 t C
IPCC
Management factorsNo change after LUC All: 1
From Grassland to perennial Jatropha plantations (tropical dry climate)
Total-69 t C
=
+ 12.6tCO2/ha/y
Above ground biomass- 65 t C
Below ground biomass+ 0 t C
Dead Organic Matter (DOM)- 3.65 t C
Soil carbon+ 0 t C
Annual C losses 0 t C
C before LUC 130 t biomassIPCC
C after LUC 0 t CBy definition
DOM / litter stock under old LU- 3.65 t C
IPCC Forest land
DOM / litter stock under new LU:+ 0 t CAssume
Delta growth+ 0 t C
Delta biomass- 65 t C
Not included in Tier 1 approach
(In-)Direct N2O emission factor
0.01 + 0.00225
Reference carbon stock +34.5 t CIPCC
Management factorsNo change after LUC All: 1
From Forest to perennial Jatropha plantations (tropical dry climate)
Amount of NDependent on change in management
Change in carbon stock in mineral soils+ 0 t C
No change (management factors for perennials ≠Tier 1)
N2O emissions from mineralised N(result of carbon stock change, so effect under Tier 1)
Annual C build up (growth) 0 t CAssume
GHG-PERFORMANCE JATROPHA BIODIESEL 44
Total- 3.1 t C
=
+ 0.57tCO2/ha/y
Above ground biomass+ 3.1 t C
Below ground biomass+ 0 t C
Dead Organic Matter (DOM)+ 0 t C
Soil carbon+ 0 t C
Annual C losses 0 t C
C before LUC 3.1 t CIPCC
C after LUC 0 t CBy definition
DOM / litter stock under old LU+ 0 t CGrassland
DOM / litter stock under new LU:+ 0 t CAssume
Delta growth+ 0 t C
Delta biomass- 3.1 t C
Not included in Tier 1 approach
(In-)Direct N2O emission factor0.01 + 0.00225
Reference carbon stock +55 t C
IPCC
Management factorsNo change after LUC All: 1
From Grassland to perennial Jatropha plantations (tropical moist climate)
Amount of NDependent on change in management
Change in carbon stock in mineral soils+ 0 t C
No change (management factors for perennials ≠Tier 1)
N2O emissions from mineralised N(result of carbon stock change, so effect under Tier 1)
Annual C build up (growth) 0 t CAssume
Total-90 t C
=
+ 16.3tCO2/ha/y
Above ground biomass- 90 t C
Below ground biomass+ 0 t C
Dead Organic Matter (DOM)- 3.65 t C
Soil carbon+ 0 t C
Annual C losses 0 t C
C before LUC 180 t biomassIPCC
C after LUC 0 t CBy definition
DOM / litter stock under old LU- 3.65 t C
IPCC Forest land
DOM / litter stock under new LU:+ 0 t CAssume
Delta growth+ 0 t C
Delta biomass- 90 t C
Not included in Tier 1 approach
(In-)Direct N2O emission factor0.01 + 0.00225
Reference carbon stock + 55 t C
IPCC
Management factorsNo change after LUC All: 1
From Forest to perennial Jatropha plantations (tropical moist climate)
Amount of NDependent on change in management
Change in carbon stock in mineral soils+ 0 t C
No change (management factors for perennials ≠Tier 1)
N2O emissions from mineralised N(result of carbon stock change, so effect under Tier 1)
Annual C build up (growth) 0 t CAssume