LegalizeCommission Implementing Regulation (EU) 2022/996 of 14 June 2022 on rules to verify sustainability and greenhouse gas emissions saving criteria and low indirect land-use change-risk criteria (Text with EEA relevance)
The test shall include only those costs and yields that are directly related to the additionality measure investment. Normal operating costs of the entire farm shall therefore not be included in the analysis. The costs and revenues included in the test shall be related to the preparation, implementation, maintenance and decommissioning of the additionality measure that would not have been otherwise incurred.
Financial attractiveness arises from a business case in which the net present value (‘NPV’) (9) of the investment is positive, which means that the investment may be conducted by the economic operator itself. As a result, only measures for which the business case analysis is negative (without the inclusion of a premium) shall pass the financial additionality test and become eligible to be certified as low ILUC risk. Outcomes above zero (a positive NPV) may still be eligible only if they pass the non-financial barrier test.
Formula to calculate the NPV of an investment:
Where:
The parameters used in the NPV calculation shall be in line with the data included in the management plan.
The following parameters shall be included in the NPV calculation:
(a) estimate of additional biomass volume;
(b) feedstock sales price [currency/tonne]: (i) the feedstock sales price may be a single number extrapolated over the lifetime of the additional yield investment; (ii) this single number may be based on an average of actual historical feedstock sales values achieved by the economic operator. The average value shall be based on data for the same 3 years that the historical yield data used to set the dynamic yield baseline; (iii) in the event of introducing a new crop for which the economic operator does not have actual price data, this value may be based on price data from FAOSTAT (10);
(c) discount rate to be used: 3,5 % for high income countries (11) and 5,5 % for all other countries;
(d) lifetime of the investment: (i) a lifetime of up to 10 years shall be used in conformity with the lifetime of the low ILUC risk certification (baseline validity); (ii) in some cases, the maximum lifetime of the investment may be set at 25 years based on the typical lifetime of perennial crops (that is to say, oil palm tree, in the case of oil palm replanting);
(e) investment cost related to the additionality measure [CAPEX + OPEX].
2. Non-financial barrier test
The non-financial barrier analysis shall only cover non-financial project barriers that prevent the implementation of the additionality measures in case of no low ILUC risk certification. Any barrier whose cost can be estimated shall be included in the financial attractiveness analysis rather than in the non-financial barrier analysis.
The economic operator that plans the additionality measure is responsible for justifying the existence of non-financial barriers. The justification shall consist of a clear, verifiable description of the situation that prevents the uptake of the additionality measure. The economic operator shall provide all the necessary verifiable evidence to support the claim and demonstrate how low ILUC risk certification would ensure that the non-financial barrier is overcome.
The validity of the operator’s claim shall be assessed and validated by the baseline audit before issuing a low ILUC risk certificate.
C. Setting the dynamic yield baseline and calculation of the actual volume of low ILUC risk biomass
The dynamic yield baseline shall be set individually for each delineated plot based on the crop and the type or combination of additionality measures applied. Plot-specific historical crop yield data from at least the 3 years preceding the application of an additionality measure shall be used to calculate the starting point of the dynamic yield baseline. This shall be combined with a global crop-specific trend line for expected yields based on historical data of actual yields over the past decade, or longer if data is available. For perennial crops, the dynamic yield baseline also takes into account the yield curve over the lifetime of the crop.
1. Setting the dynamic yield baseline for annual crops
Where a farm rotates crops between fields and the crop whose yield will be increased (‘target crop’) has been planted in different fields on the same farm in previous years, two options are envisaged for gathering the historical yield data in order to calculate the dynamic yield baseline:
Option 1: The economic operator calculates an average of the yields for the 3 most recent years that the target crop was grown on the specific delineated plot prior to implementation of the additionality measure. As crops are grown in rotation, this may mean using data that is more than 5 years old.
Option 2: The economic operator calculates a weighted average of the yields of the 3 most recent years that the target crop was grown on the farm prior to implementation of the additionality measure, even if those yields were obtained from different plots of different sizes on the same farm.
If historical data for the 3 most recent years of crop yields is not available, whether inaccessible or not representative as per the auditor’s judgement, or if crop yield data is of insufficient quality, additional data may be obtained for earlier years or data from a neighbouring field growing the same crop under the same management plan. If 1 of the 3 years of historical data represents an exceptionally good or bad harvest (for example, discrepancy of 30 % or more compared to the other reference years), the outlier crop yield shall not be included in the calculation to avoid skewing the three-year average (12).
The auditor is responsible for determining a yield outlier, based on their expert judgement, experience on the ground and knowledge of the economic operator’s practices over the long term. The auditor is also obliged to evaluate whether the crop yield data is of insufficient quality to be included as part of the baseline and annual audits, and to then decide whether a crop yield needs to be excluded or not.
The slope of the dynamic yield baseline shall be taken as the slope of a straight trend line fitted for yield developments of the target crop over the previous 10 years or longer if data is available. It is based on global data and shall be derived from the FAOSTAT World+ data for the relevant crop. This shall be done at the start of the certification period, and the slope shall be valid for the 10-year baseline validity period of the low ILUC certification.
Table 2 shows the slope of the dynamic yield baseline for the most common biofuel feedstock crops. These values are obtained by fitting a trend line over 20 years of global crop data obtained from FAOSTAT.
| Crop | Barley | Maize | Oil palm fruit | Rapeseed | Soybean | Sugar beet | Sugar cane | Sunflower seed | Wheat |
|---|---|---|---|---|---|---|---|---|---|
| Slope-20 | 0,035 | 0,074 | 0,200 | 0,036 | 0,028 | 1,276 | 0,379 | 0,035 | 0,04 |
| Slope-20 is based on 2008-2017. |
For any crop in the table, the dynamic yield baseline is determined by taking the starting point (three-year average of historical yields prior to application of the additionality measure) and adding the global trend line (slope) from Table 2. The following formula shall be used, starting at the year the additionality measure is implemented:
DYBx = (starting point DYB) + (slope20)x
Where:
If the additionality measure is to replace the existing crop with a different (higher yielding) crop on a delineated plot, the counterfactual situation is the cultivation of the existing crop. The dynamic yield baseline shall be determined based on historical yield and trend line data for the existing crop.
The starting point of the baseline shall be the 3-year average of the crop yield obtained for the lower performing existing crop. The trend line is based on the global FAOSTAT trend line data for the existing crop (see Table 2). This approach shall only be used if it can be demonstrated that the better performing crop could be introduced due to changes in the biofuel market, as demonstrated in the additionality assessment.
2. Setting the dynamic yield baseline for perennial crops
Depending on the yield variation observed over the lifetime of different types of perennial crop, different methodological approaches shall be possible.
For palm trees, the following data may be used by economic operators of oil palm plantations when determining their dynamic yield baseline:
(a) the historical crop yields obtained prior to implementation of an additionality measure;
(b) the planting year of palm trees on the delineated plot of land and/or their age profile;
(c) the cultivars of palm trees on the delineated plot, if applicable;
(d) the area of land replanted each year on a plantation, if applicable.
That data is combined with a growth curve to determine the dynamic yield baseline. The key characteristic from the growth curve shall be the shape, not the magnitude of the yield.
The growth curve gives the shape and it needs to be combined with the historical yield data and age of the trees, as set out in points (a) and (b), to adjust the magnitude of the dynamic yield baseline curve to the specific plot.
The following three options are available for determining the dynamic yield baseline for palm trees.
For each option, the data required to set the dynamic yield baselines must include:
(a) Option 1a: Standard growth curve (i) 3 most recent years of historical crop yields for palm trees grown on the delineated plot; (ii) age of trees on the delineated plot/planting year;
(b) Option 1b: Economic operator provides growth curve (13) (i) 3 most recent years of historical crop yield for palm trees grown on the delineated plot; (ii) age of trees on the delineated plot/planting year; (iii) the cultivars of palm trees on the delineated plot; (iv) economic operator’s own reference growth curve.
(c) Option 2: Group certification approach (i) for the 3 most recent years, the total hectares and total yield in fresh fruit bunches (FFB) for palm trees grown on the delineated plot/plantation(s), producing palm as part of the group.
Options 1a and 1b apply where an additionality measure is taken on a stand of trees that are the same age, or if the age profile of the trees on the delineated plot(s) is known and does not remain constant year after year.
Option 2 may be applied when the age profile of the trees on the delineated plots is mixed and remains relatively constant year after year, that is to say in a group certification approach or if a consistent percentage of a plantation area is replanted each year, resulting in a constant age profile for the trees.
Option 2 shall not be used if more than 20 % of the volume in the group comes from the same plantation, or if more than 5 % of the total area in the group is being replanted in the same year. In that case, option 1a or b shall be used to determine the baseline.
The first option uses the shape of a pre-established ‘standard’ growth curve (based on existing scientific evidence) to determine the dynamic yield baseline for a delineated plot. The standard curve has been normalised and is shown in Figure 1 and Table 3 below.
The dynamic yield baseline is determined by using the 3 most recent years of historical crop yield data for the specific plot and the age of the palm trees when that yield was observed, and using the annual percentage yield change from the standard curve to form a ‘business-as-usual’ yield curve relevant to the specific plot.
| Years after planting | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Normalised yield | 0 | 0 | 0,147 | 0,336 | 0,641 | 0,833 | 0,916 | 0,968 | 0,996 | 1 | 0,999 | 0,980 | 0,965 |
| Years after planting | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | ≥ 26 (*1) |
| Normalised yield | 0,945 | 0,926 | 0,910 | 0,906 | 0,888 | 0,870 | 0,858 | 0,842 | 0,836 | 0,815 | 0,806 | 0,793 | 0,793 |
| (*1) After 25 years, the yield would be expected to continue to decline. However, as the typical lifetime of an oil palm tree is around 25 years, there is a lack of data to support the magnitude of the decline after 25 years. Therefore, a conservative approach is taken to assume that the yield curve would remain at the 25-year level. |
Option 1a involves the following methodological steps:
To determine the average historical crop yield, collect the three most recent historical crop yields observed on the delineated plot prior to implementation of the additionality measure, as well as the corresponding age of the trees when those yields were observed;
Calculate an average (mean) of the three historical crop yields;
Based on the age of the trees when the historical yield data is from, determine where this average historical crop yield shall be on the standard growth curve (e.g. if the yield data is from trees aged 7, 8 and 9 years, the average historical yield should be considered to be year 8);
To determine the next point of the dynamic yield baseline, multiply the average historical crop yield from step 2 by the corresponding calculated annual percentage change, derived from the standard growth curve (Table 4 below). Repeat this for each subsequent point to plot the dynamic yield baseline;
Table 4 Annual percentage change in yield derived from standard growth curve Years after planting 1 to 3 4 5 6 7 8 9 10 11 12 13 14 Annual percentage change - 128,0 % 90,6 % 30,0 % 10,0 % 5,6 % 2,9 % 0,4 % -0,1 % -1,9 % -1,6 % -2,0 % Years after planting 15 16 17 18 19 20 21 22 23 24 25 ≥ 26 (1) Annual percentage change -2,1 % -1,7 % -0,5 % -1,9 % -2,0 % -1,4 % -1,8 % -0,8 % -2,5 % -1,1 % -1,6 % 0 % (1) After 25 years, the yield would be expected to continue to decline. However, as the typical lifetime of an oil palm tree is around 25 years, there is a lack of data to support the magnitude of the decline after 25 years. Therefore, a conservative approach is taken to assume that the yield curve would remain at the 25-year level.
To incorporate the global yield trend in the dynamic yield baseline, apply the compound annual growth rate (CAGR) calculated from FAOSTAT World+ yield data (Table 5 below), to each point of the dynamic yield baseline to obtain the CAGR corrected dynamic yield baseline.
Table 5 Compound annual growth rate palm (20-year) Annual performance increase palm – business as usual 1,37 % Based on FAOSTAT World+ 2008-2017
This option may be used in exceptional cases, if the economic operator can demonstrate that option 1a is not appropriate for their specific case. In such a case, if the economic operator has an expected growth curve determined based on the available data of palm seedlings (that relates to their ‘business-as-usual’ scenario), that curve may be used as the basis for the dynamic yield baseline instead of using the standard growth curve. All steps described in Option 1a shall be followed, replacing the standard growth curve with the economic operator’s own curve. The economic operator shall therefore calculate the annual percentage change.
The plot-specific growth curve shall still be corrected for global yield development using the CAGR calculated FAOSTAT World+ yield data (Table 5).
In the case of group certification, or when a first gathering point or mill acts as the unit of certification, the dynamic yield baseline may be set using a similar ‘straight line’ dynamic yield baseline approach as used for annual crops. This approach may be used if a group manager, first gathering point or mill is seeking to certify a group that is taking the same additionality measure, and when the plantation or area supplying the mill contains a mix of ages of trees meaning that the annual yield supplying the mill has remained relatively constant.
To determine the dynamic yield baseline, the group manager needs to record the total plantation area (ha) supplying the mill and the total yield (fresh fruit bunches) that corresponds to that area in each of the last 3 years. This is used to determine the yearly yield per hectare for each of the last 3 years (in tonnes/ha). These data points are then averaged and used as the starting point for the dynamic yield baseline. The starting point is combined with the global trendline slope for oil palm from FAOSTAT World+ data (Table 2) to determine the dynamic yield baseline.
Sugar cane shall be treated as an annual crop when setting the dynamic yield baseline.
3. Setting the dynamic yield baseline for sequential cropping
If multi-cropping practices such as sequential cropping are used, the economic operators have three options to calculate the additional biomass:
Demonstrate that the second crop does not lower the yield of the main crop.
If the second crop lowers the yield of the main crop:
a. Determine a dynamic yield baseline for a system in which the main crop is the same each year; b. Determine a compensation factor for a system in which the main crop is different each year;
If an economic operator can demonstrate that the introduction of the second crop does not lower the yield of the main crop, the whole yield of the second crop can be claimed as additional biomass.
This may be demonstrated, for example, by comparison of the observed yield of the main crop before (3-year historical average) and after introduction of the second crop.
The dynamic yield baseline shall be based on the ‘business as usual’ situation for the delineated plot of land. When the main crop is the same each year, the baseline shall be determined based on at least the 3-year average historical yield of the main crop on that plot, combined with the global trend line for the main crop, as is done for annual crops.
This approach may also be used when the crop rotation follows a clearly defined rotation pattern that can be observed from historical data, which enables the business-as-usual situation to be clearly determined. In this case, it may be necessary to use data older than 3 years to determine the average historical yield of the main crop.
After implementation of sequential cropping, the net additional biomass shall be calculated as the difference between the total annual yield from the delineated plot of land (that is to say, the yield of the main crop plus the yield of the second crop) and the main crop dynamic yield baseline.
If the main and second crops are different feedstocks that produce a different combination of crop components (for example, oil, protein meal, starch, fibre), when the main crop and second crop yields are added together, the calculation shall be based on appropriate units of measurement to allow for the calculation of a single representative figure for the net additional biomass produced. Respectively, the methodology shall allow for an effective compensation of the biomass loss of the main crop. For example, the calculation can be done on a simple weight (tonnes) basis or an energy content basis (e.g. if the full second crop is used for energy, such as for biogas). The choice of methodology shall be justified by the economic operator and validated by the auditor.
When the main crop differs each year in the crop rotation and does not follow a regular pattern, the economic operator needs to assess any loss in yield of the main crop due to the second crop and to take it into account in the volume of additional biomass claimed.
The economic operator needs to compare the observed yield of the main crop after introduction of the second crop with the historical yield of the same (main) crop. That comparison may be done based on observed yields in neighbouring fields (e.g. if the same farm grows the same crops on rotation but in different fields), or on the basis of justified scientific literature that describes the impact of sequential cropping on those crops in that region.
The impact on yield of the main crop shall be translated into a compensation factor that shall be deducted from the volume of the second crop to calculate the additional biomass. As for Option 2a, the factor can be based on weight or energy content and shall allow for an effective compensation of the biomass loss of the main crop. The choice of methodology shall be justified by the economic operator and validated by the auditor.
4. Calculating additional biomass volume
After implementation of the additionality measure, the economic operator shall determine the volume of low ILUC risk biomass that can be claimed by comparing the actual crop yield achieved on the delineated plot with the dynamic yield baseline. The auditor must verify in the annual audit that the volume of additional biomass achieved is in line with the projections in the management plan, and seek justification if there are discrepancies of more than 20 % compared to the estimates in the management plan.
If certification is sought for an additionality measure applied in the past, the additional biomass yield may be calculated and recorded in the management plan. While this allows the actual volume of low ILUC risk biomass to be precisely calculated, low ILUC risk biomass may only be claimed after low ILUC risk certification has been awarded. Retrospective claims cannot be made for biomass supplied in the past.
To calculate the additional biomass volume, the economic operator must record the full crop yield from the delineated plot for each year, from the start of the implementation of the additionality measure. The economic operator must prove the link between the specific delineated plot and the crop yield achieved (tonne/ha).
If the harvested volume is only measured (weighed) at a first gathering point where products from multiple farms or plots arrive, then the documentation from the first gathering point may be used as proof of the harvested volume (yield) for the farms and plots involved.
A record of the business transaction between the economic operator and the first gathering point may be used as evidence, as long as the link back to the specific delineated plot can be proven. In this case, the first gathering point is responsible for collecting and recording the crop yield data. It shall record yields of biomass collected per farm (and if necessary, for a specified delineated plot on a farm) based on a template to be issued by the voluntary scheme.
In the case of group auditing and if the first gathering point acts as the group lead, it shall be responsible for recording yield data for all delineated plots.
To calculate the additional biomass volume, the crop yield data obtained for a given year shall be compared to the dynamic yield baseline. The additional biomass yield is equal to the difference between the crop yield observed and the yield projected by the dynamic yield baseline for the same year, multiplied by the surface area A (ha) of the delineated plot in question. This additional volume can then be claimed as low ILUC risk biomass.
Additional biomass = (Yx – DYBx) x A
Where:
D. Minimum content of the low ILUC risk certificate
Low ILUC-risk certificates must contain all the following information:
(a) contact details of main certified entity (company name and address, details of the designated point of contact);
(b) scope of certification (type of additionality measure and additionality test applied as well as type of economic operator (if they are small holders));
(c) longitude and latitude coordinates (for farms and plantations certified as single entities);
(d) list of sites under the scope of certification (name and address);
(e) total volume of biomass certified as low ILUC risk;
(f) contact details of the certification body (name and address) and logo;
(g) (unique) certificate number or code;
(h) place and date of issuance;
(i) certificate valid from/to dates (and date certified, if applicable);
(j) stamp and/or signature of issuing party.
ANNEX IX
| parameter: | GHG emission coefficient | Fossil energy input | ||||||
|---|---|---|---|---|---|---|---|---|
| unit: | gCO2,eq/g | gCO2/kg | gCH4/kg | gN2O/kg | gCO2-eq/kg | MJfossil/kg | ||
| Global warming potential | ||||||||
| CO2 | 1 | |||||||
| CH4 | 28 | |||||||
| N2O | 265 | |||||||
| Agro inputs: | ||||||||
| N-fertiliser (kg N) | ||||||||
| Ammonium nitrate (AN) | 2 671 | 6,9 | 2,1 | 3 469 | ||||
| Ammonium sulphate (AS) | 2 560 | 6,5 | 0,0 | 2 724 | ||||
| Ammonium nitrate sulphate (ANS) | 2 561 | 8,9 | 1,3 | 3 162 | ||||
| Anhydrous ammonia | 2 662 | 6,8 | 0,0 | 2 832 | ||||
| Calcium ammonium nitrate (CAN) | 2 863 | 7,3 | 2,1 | 3 670 | ||||
| Calcium nitrate (CN) | 2 653 | 7,0 | 5,1 | 4 348 | ||||
| Urea | 1 703 | 9,3 | 0,0 | 1 935 | ||||
| Urea ammonium nitrate (UAN) | 2 182 | 7,5 | 1,1 | 2 693 | ||||
| P2O5-fertiliser (kg P2O5) | ||||||||
| Triple superphosphate (TSP) | 517 | 0,9 | 0,0 | 544 | ||||
| Rock phosphate 21 %P2O5 23 %SO3 | 95 | 0,0 | 0,0 | 95 | ||||
| Mono ammonium phosphate (MAP) 11 %N 52 %P2O5 | 967 | 2,5 | 0,0 | 1 029 | ||||
| Di-Ammonium-Phosphate (DAP) 18 %N 46 %P2O5 | 1 459 | 3,7 | 0,0 | 1 552 | ||||
| K2O-fertiliser (kg K2O) | ||||||||
| Muriate of Potash (MOP) 60 %K2O | 409 | 0,17 | 0,0 | 413 | ||||
| Other fertilisers | ||||||||
| NPK 15-15-15 | 4 261 | 10,0 | 1,7 | 5 013 | ||||
| MgO (kg MgO) | 769 | 0,0 | 0,0 | 769 | ||||
| Sodium (Na) fertiliser (kg Na) | 1 620 | 0,0 | 0,0 | 1 620 | ||||
| Seeds- barley | 189,5 | 0,08 | 0,4001 | 310,6 | 3,23 | |||
| Seeds- eucalyptus cuttings | 0,0 | 0,00 | 0,0000 | 0,0 | ||||
| Seeds- maize | 189,5 | 0,08 | 0,4001 | 310,6 | 3,23 | |||
| Seeds- poplar cuttings | 0,0 | 0,00 | 0,0000 | 0,0 | ||||
| Seeds- rapeseed | 451,0 | 0,27 | 1,0024 | 756,5 | 8,33 | |||
| Seeds- rye | 191,0 | 0,08 | 0,4001 | 312,1 | 3,23 | |||
| Seeds- soy bean | 0,0 | 0,00 | 0,0000 | 0,0 | ||||
| Seeds- sugar beet | 2 363,0 | 1,37 | 4,2096 | 3 651,7 | 38,44 | |||
| Seeds- sugar cane | 4,97 | 0,00 | 0,0000 | 5,0 | 0,06 | |||
| Seeds- sunflower | 451,0 | 0,27 | 1,0024 | 756,5 | 8,33 | |||
| Seeds- triticale | 180,0 | 0,04 | 0,4000 | 300,2 | 3,00 | |||
| Seeds- wheat | 163,7 | 0,04 | 0,4000 | 283,9 | 2,76 | |||
| parameter: | GHG emission coefficient | Fossil energy input | ||||||
| --- | --- | --- | --- | --- | --- | --- | --- | |
| unit: | gCO2,eq/g | gCO2/kg | gCH4/kg | gN2O/kg | gCO2-eq/kg | MJfossil/kg | ||
| Residues (feedstock or input): | ||||||||
| Biogas digestate | 0,0 | 0,00 | 0,0000 | 0,0 | 0,00 | |||
| EFB compost (palm oil) | 0,0 | 0,00 | 0,0000 | 0,0 | 0,00 | |||
| Filter mud cake | 0,0 | 0,00 | 0,0000 | 0,0 | 0,00 | |||
| parameter: | GHG emission coefficient | Fossil energy input | Density | LHV (lower heating value) MJ/kg | ||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| unit: | gCO2/MJ | gCH4/MJ | gN2O/MJ | gCO2-eq/MJ | MJfossil/kg | MJfossil/MJ | kg/m3 | |
| Fuels- gases | ||||||||
| Natural gas (EU mix) | 66,00 | 0,0000 | - | 66,00 | 1,2000 | |||
| LPG | 66,30 | 0,0000 | 0,0000 | 66,31 | 1,2000 | |||
| Methane | ||||||||
| Fuels- liquids (also conversion inputs) | ||||||||
| Diesel | 95,1 | - | - | 95,10 | 1,2300 | 832 | ||
| Gasoline | 93,3 | - | - | 93,30 | 1,2000 | 745 | ||
| Heavy fuel oil | 94,2 | - | - | 94,20 | 1,1600 | 970 | ||
| Ethanol | 794 | |||||||
| Methanol | 97,08 | 0,0001 | 0,0000 | 97,09 | 1,7639 | 793 | ||
| DME | 670 | |||||||
| FAME | 890 | |||||||
| HVO | ||||||||
| PVO | 920 | |||||||
| Syn diesel (BtL) | 780 | |||||||
| Palm oil | 920 | |||||||
| Rapeseed oil | 920 | |||||||
| Soybean oil | 920 | |||||||
| Sunflower oil | 920 | |||||||
| parameter: | GHG emission coefficient | Fossil energy input | Density | LHV MJ/kg | ||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| unit: | gCO2/MJ | gCH4/MJ | gN2O/MJ | gCO2-eq/MJ | MJfossil/MJ | kg/m3 | (on a dry basis) | |
| Fuels- solids (also conversion inputs) | ||||||||
| Hard coal | 102,62 | 0,3854 | 0,0003 | 112,32 | 1,0909 | 26,5 | ||
| Lignite | 116,68 | 0,0014 | 0,0001 | 116,73 | 1,0149 | 9,2 | ||
| Wood chips | 155 | 19,0 | ||||||
| Wood pellets | 0,0080 | 650 | 19,0 | |||||
| parameter: | Density | LHV MJ/kg | ||||||
| --- | --- | --- | --- | |||||
| unit: | kg/m3 | (on a dry basis) | ||||||
| Fuels/feedstock/co-products/residues/wastes | ||||||||
| Agricultural residue bales | 18,0 | |||||||
| Animal fat (tallow) | 38,8 | |||||||
| Bagasse | 17,0 | |||||||
| Bagasse exit mill (dry) | 120 | 17,0 | ||||||
| Bagasse bales (dry) | 165 | 17,0 | ||||||
| Bagasse pellets (dry) | 650 | 17,0 | ||||||
| Barley | 17,0 | |||||||
| Biogasoline | 44,0 | |||||||
| Biowaste | 20,7 | |||||||
| DDGS (barley) | 17,8 | |||||||
| DDGS (maize) | 19,2 | |||||||
| DDGS (rye) | 17,8 | |||||||
| DDGS (triticale) | 18,0 | |||||||
| DDGS (wheat) | 18,1 | |||||||
| Eucalyptus (SRC) | 19,0 | |||||||
| Fatty acids | 37,0 | |||||||
| FFB | 24,0 | |||||||
| Forestry residues | 19,0 | |||||||
| Glycerol | 16,0 | |||||||
| Industry residues (wood) | 19,0 | |||||||
| Manure | 12,0 | |||||||
| Maize (grain only) | 17,3 | |||||||
| Maize whole crop | 16,9 | |||||||
| Palm kernel meal | 570 | 18,5 | ||||||
| Palm kernel oil | 37,0 | |||||||
| Poplar (SRC) | 19,0 | |||||||
| Rapeseed | 27,0 | |||||||
| Rapeseed oil cake | 18,4 | |||||||
| Rye | 17,1 | |||||||
| Sawdust | 19,0 | |||||||
| Soybeans | 23,0 | |||||||
| Soybean oil cake | 19,1 | |||||||
| Stemwood (Pine) | 19,0 | |||||||
| Straw | 17,2 | |||||||
| Straw bales | 125 | 17,2 | ||||||
| Straw chopped | 50 | 17,2 | ||||||
| Straw pellets | 600 | 17,2 | ||||||
| Sugar beet | 16,3 | |||||||
| Sugar beet pulp | 16,1 | |||||||
| Sugar cane | 19,6 | |||||||
| Sunflower seed | 27,2 | |||||||
| Sunflower oil cake | 18,2 | |||||||
| Triticale | 16,9 | |||||||
| Vinasse | 14,0 | |||||||
| Waste cooking oil | 37,0 | |||||||
| Wheat | 17,0 | |||||||
| Wheat straw | 17,2 | |||||||
| parameter: | GHG emission coefficient | Fossil energy input | LHV MJ/kg | |||||
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| unit: | gCO2/kg | gCH4/kg | (at 0 % water) | gCO2-eq/kg | gCO2/MJ | gCH4/MJ | gN2O/MJ | gCO2-eq/MJ |
| Conversion inputs | ||||||||
| Ammonia | 2 350,6 | 0,00 | 0,0022 | 2 351,3 | ||||
| Ammonium sulphate ((NH4)2SO4) | 420,9 | 1,29 | 0,0002 | 453,2 | ||||
| Antifoam (assumed to be propylene glycol) | 3 119,5 | 4,96 | 0,105 | 3 274,8 | ||||
| Alpha-amylase | 1 000,0 | 0,00 | 0,0000 | 1 000,0 | ||||
| Gluco-amylase | 7 500,0 | 0,00 | 0,0000 | 7 500,0 | ||||
| Calcium chloride (CaCl2) | 38,6 | 0,002 | 0,001 | 38,8 | ||||
| Cyclohexane | 723,0 | 0,00 | 0,0000 | 723,0 | ||||
| Diammonium phosphate (DAP) | 653,2 | 0,81 | 0,004 | 674,4 | ||||
| Fuller’s earth | 197,0 | 0,04 | 0,0063 | 199,8 | ||||
| n-Hexane | 80,08 | 0,0146 | 0,0003 | |||||
| Hydrochloric acid (HCl) | 977,1 | 2,91 | 0,0376 | 1 061,1 | ||||
| Lubricants | 947,0 | 0,00 | 0,0000 | 947,0 | ||||
| Magnesium sulphate (MgSO4) | 191,4 | 0,04 | -0,002 | 191,8 | ||||
| Monopotassium phosphate (KH2PO4) | 238,7 | 0,91 | 0,012 | 264,9 | ||||
| Nitrogen | 52,6 | 0,12 | 0,0024 | 56,4 | ||||
| Phosphoric acid (H3PO4) | 2 808,9 | 11,36 | 0,1067 | 3 124,7 | ||||
| Potassium hydroxide (KOH) | 403,0 | 0,40 | 0,0208 | 419,1 | ||||
| Pure CaO for processes | 1 188,5 | 0,10 | 0,0080 | 1 193,2 | ||||
| Sodium carbonate (Na2CO3) | 1 133,5 | 4,39 | 0,0060 | 1 245,1 | ||||
| Sodium chloride (NaCl) | 12,7 | 0,02 | 0,001 | 13,3 | ||||
| Sodium hydroxide (NaOH) | 485,5 | 1,45 | 0,0271 | 529,7 | ||||
| Sodium methoxide (Na(CH3O)) | 2 207,7 | 7,56 | 0,0965 | 2 425,5 | ||||
| SO2 | 52,0 | 0,03 | 0,001 | 53,3 | ||||
| Sulphuric acid (H2SO4) | 210,2 | 0,24 | 0,0046 | 217,5 | ||||
| Urea | 1 790,9 | 1,92 | 0,027 | 1 846,6 | ||||
| parameter: | Fuel Efficiency | Transport exhaust gas emissions | ||||||
| --- | --- | --- | --- | --- | ||||
| unit: | MJ/t.km | gCH4/t.km | gN2O/t.km | |||||
| Transport efficiencies – Trucks | ||||||||
| Truck (40 tonne) for dry product (Diesel) | 0,81 | 0,003 | 0,0015 | |||||
| Truck (40 tonne) for chips (and similar size dry product) (Diesel) | 0,84 | 0,004 | 0,0016 | |||||
| Truck (40 tonne) for liquids and pellets (Diesel) | 0,87 | 0,004 | 0,0016 | |||||
| Truck (40 tonne) for manure (Diesel) | 0,88 | 0,004 | 0,0016 | |||||
| Truck (40 tonne) for biowaste (Diesel) | 0,84 | 0,004 | 0,0016 | |||||
| Truck (40 tonne) for sugar cane transport | 1,37 | 0,001 | 0,0039 | |||||
| Truck (12 tonne) for FFB transport (Diesel) | 2,24 | 0,002 | 0,0015 | |||||
| Dumpster truck MB2213 for filter mud transport | 3,60 | 0,000 | 0,0000 | |||||
| Tanker truck MB2318 for vinasse transport | 2,16 | 0,000 | 0,0000 | |||||
| Tanker truck MB2318 for cane seed transport | 2,61 | 0,000 | 0,0000 | |||||
| Tanker truck with water cannons for vinasse transport | 0,94 | |||||||
| Transport efficiencies – Ships | ||||||||
| ‘Handymax’ bulk carrier (fuel oil) – Grains | 0,10 | |||||||
| ‘Handysize’ bulk carrier (fuel oil) – wood chips with bulk density 221 kg/m3 | 0,26 | |||||||
| ‘Supramax’ bulk carrier (fuel oil) – wood chips with bulk density 221 kg/m3 | 0,16 | |||||||
| ‘Handysize’ bulk carrier (fuel oil) – pellets with bulk density 650 kg/m3 | 0,10 | |||||||
| ‘Supramax’ bulk carrier (fuel oil) – pellets with bulk density 650 kg/m3 | 0,07 | |||||||
| ‘Handysize’ bulk carrier (fuel oil) – agri-residues with low bulk density (125 kg/m3) | 0,43 | |||||||
| ‘Supramax’ bulk carrier (fuel oil) – agri-residues with low bulk density (125 kg/m3) | 0,27 | |||||||
| ‘Handysize’ bulk carrier (fuel oil) – agri-residues with high bulk density (300 kg/m3) | 0,20 | |||||||
| ‘Supramax’ bulk carrier (fuel oil) – agri-residues with high bulk density (300 kg/m3) | 0,13 | |||||||
| ‘Handysize’ bulk carrier (fuel oil) – PKM | 0,13 | |||||||
| ‘Supramax’ bulk carrier (fuel oil) – PKM | 0,07 | |||||||
| Chemical/product tanker, 12,617 kt (fuel oil) | 0,12 | |||||||
| Chemical/product tanker, 15 kt (fuel oil) for ethanol transport | 0,17 | |||||||
| Chemical/product tanker, 15 kt (fuel oil) for FAME and HVO transport | 0,16 | |||||||
| Chemical/product tanker, 22,56 kt (fuel oil) | 0,10 | |||||||
| Inland bulk carrier, 8,8 kt (diesel) | 0,32 | 0,093 | 0,0004 | |||||
| Inland ship for oil transport, 1,2 kt (diesel) | 0,50 | 0,030 | ||||||
| Transport efficiencies – Pipeline and rail | ||||||||
| Local (10 km) pipeline | 0,00 | 0,000 | 0,0000 | |||||
| Freight train USA (diesel) | 0,25 | 0,005 | 0,0010 | |||||
| Rail (electric, MV) | 0,21 |
Carbon Intensity of electricity produced and consumed in the EU in 2019 [gCO2eq/kWh]
With upstream emissions, without emissions from construction
| CI net electricity production | CI used electricity HV | CI used electricity MV | CI used electricity LV | |
|---|---|---|---|---|
| Austria | 153 | 238 | 240 | 245 |
| Belgium | 204 | 214 | 215 | 219 |
| Bulgaria | 493 | 504 | 510 | 532 |
| Cyprus | 757 | 768 | 772 | 787 |
| Czechia | 518 | 526 | 531 | 549 |
| Germany | 389 | 386 | 388 | 398 |
| Denmark | 100 | 135 | 136 | 139 |
| Estonia | 654 | 468 | 471 | 485 |
| Greece | 577 | 585 | 590 | 610 |
| Spain | 245 | 248 | 251 | 263 |
| Finland | 105 | 127 | 128 | 130 |
| France | 74 | 81 | 82 | 86 |
| Croatia | 208 | 329 | 333 | 349 |
| Hungary | 277 | 307 | 310 | 322 |
| Ireland | 349 | 357 | 360 | 374 |
| Italy | 352 | 331 | 333 | 343 |
| Latvia | 203 | 312 | 315 | 325 |
| Lithuania | 79 | 291 | 294 | 305 |
| Luxembourg | 93 | 311 | 312 | 316 |
| Malta | 455 | 437 | 441 | 454 |
| Netherlands | 430 | 415 | 417 | 426 |
| Poland | 742 | 715 | 720 | 741 |
| Portugal | 268 | 282 | 285 | 299 |
| Romania | 388 | 421 | 427 | 454 |
| Slovakia | 168 | 316 | 319 | 329 |
| Slovenia | 269 | 281 | 283 | 291 |
| Sweden | 20 | 25 | 25 | 26 |
| EU27 | 288 | 295 | 298 | 308 |
| Iceland | 7 | 7 | 7 | 7 |
| Norway | 12 | 20 | 20 | 21 |
| Switzerland | 32 | 107 | 108 | 112 |
| United Kingdom | 271 | 277 | 280 | 292 |
| Albania | 0 | 302 | 308 | 332 |
| Bosnia Herzegovina | 799 | 766 | 776 | 818 |
| Kosovo | 1 099 | 1 067 | 1 097 | 1 224 |
| Moldova | 246 | 446 | 453 | 476 |
| Montenegro | 472 | 588 | 599 | 646 |
| North Macedonia | 794 | 760 | 774 | 831 |
| Serbia | 807 | 819 | 833 | 892 |
| Turkey | 487 | 508 | 516 | 546 |
| Belarus | 449 | 458 | 462 | 479 |
| Russia | 459 | 474 | 479 | 496 |
| Ukraine | 407 | 419 | 423 | 439 |
| parameter: | GHG emission coefficient | |||
| --- | --- | --- | --- | --- |
| unit: | gCH4/MJ | gN2O/MJ | gCO2-eq/MJ | |
| Emissions from machinery operations incl. chipping ( per MJ diesel) | ||||
| CH4 and N2O emissions from use of diesel (transport) | 0,0008 | 0,0032 | 0,97 | |
| CH4 and N2O emissions from use of diesel (forestry) | 0,0008 | 0,0032 | 0,97 | |
| CH4 and N2O emissions from use of diesel (agriculture) | 0,0013 | 0,0032 | 0,97 | |
| Emissions from boiler or CHP ( per MJ feedstock) | ||||
| CH4 and N2O emissions from agricultural residue boiler | 0,0017 | 0,0007 | 0,24 | |
| CH4 and N2O emissions from agricultural residue CHP | 0,0017 | 0,0007 | 0,24 | |
| CH4 and N2O emissions from bagasse boiler | 0,0025 | 0,0012 | 0,43 | |
| CH4 and N2O emissions from bagasse CHP | 0,0025 | 0,0012 | 0,43 | |
| CH4 and N2O emissions from biogas CHP gas engine | 0,3400 | 0,0014 | 8,92 | |
| CH4 and N2O emissions from biogas boiler | 0,0025 | 0,0010 | 0,36 | |
| CH4 and N2O emissions from hard coal CHP | 0,0018 | 0,0050 | 1,53 | |
| CH4 and N2O emissions from lignite CHP | 0,0007 | 0,0028 | 0,86 | |
| CH4 and N2O emissions from NG boiler | 0,0025 | 0,0010 | 0,36 | |
| CH4 and N2O emissions from NG CHP | 0,0042 | 0,0008 | 0,36 | |
| CH4 and N2O emissions from NG gas engine | 0,0030 | 0,0001 | 0,10 | |
| CH4 and N2O emissions from palm shells and fibres boiler | 0,0030 | 0,0040 | 1,27 | |
| CH4 and N2O emissions from palm shells and fibres CHP | 0,0030 | 0,0040 | 1,27 | |
| CH4 and N2O emissions from PKM boiler | 0,0017 | 0,0007 | 0,24 | |
| CH4 and N2O emissions from PKM CHP | 0,0017 | 0,0007 | 0,24 | |
| CH4 and N2O emissions from sawdust boiler | 0,0049 | 0,0010 | 0,41 | |
| CH4 and N2O emissions from straw pellet boiler | 0,0017 | 0,0007 | 0,24 | |
| CH4 and N2O emissions from straw pellet CHP | 0,0017 | 0,0007 | 0,24 | |
| CH4 and N2O emissions from wood chip boiler | 0,0049 | 0,0010 | 0,41 | |
| CH4 and N2O emissions from wood chip CHP | 0,0049 | 0,0010 | 0,41 | |
| CH4 and N2O emissions from wood pellet boiler | 0,0030 | 0,0006 | 0,25 | |
| CH4 and N2O emissions from wood pellet CHP | 0,0030 | 0,0006 | 0,25 | |
| CH4 and N2O emissions from liquid fuel boiler | 0,0009 | 0,0004 | 0,14 | |
| CH4 and N2O emissions from wood pellet co-combustion (fluidised bed coal-fired power plant) | 0,0010 | 0,0610 | 18,20 | |
| CH4 and N2O emissions from wood pellet co-combustion (pulverised coal-fired power plant) | 0,0009 | 0,0014 | 0,44 | |
| Emissions from digestate storage ( per MJ biogas) | ||||
| CH4 and N2O emissions from open biowaste digestate storage | 0,4930 | 0,0319 | 21,82 | |
| CH4 and N2O emissions from open maize digestate storage | 0,4422 | 0,0082 | 13,51 | |
| CH4 and N2O emissions from open manure digestate storage | 1,9917 | 0,0663 | 69,56 | |
| GHG emission coefficient | ||||
| --- | --- | --- | --- | --- |
| gCO2/kg | gCH4/kg | gN2O/kg | gCO2-eq/kg | |
| Manure methane credits ( per MJ biogas) | ||||
| CH4 and N2O emission credits for manure | ||||
| No emissions | 0,0 | 0,00 | 0,0000 |
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