Commission 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)
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- 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 [^E0016] 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.
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- 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
Option 1b: Economic operator provides the growth curve
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).
Option 2: Group certification approach
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:
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- Demonstrate that the second crop does not lower the yield of the main crop.
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- 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;
Option 1. Demonstrate that the second crop does not lower the yield of the main crop
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.
Option 2a. Determine a dynamic yield baseline for a system in which the main crop is the same each year
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.
Option 2b. Determine a compensation factor for a system in which the main crop is different each year
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:
| Yx | = | Observed yield in year x (in tonne/ha/yr) |
| --- | --- | --- |
| DYBx | = | Dynamic yield baseline in year x (in tonne/ha/yr) |
| --- | --- | --- |
| A | = | Surface area of delineated plot (ha) |
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
STANDARD VALUES OF EMISSIONS FACTORS
| parameter: | parameter: | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient | Fossil energy input | ||
|---|---|---|---|---|---|---|---|---|
| unit: | unit: | gCO2,eq/g | gCO2/kg | gCH4/kg | gN2O/kg | gCO2-eq/kg | MJfossil/kg | |
| Global warming potential | Global warming potential | Global warming potential | ||||||
| CO2 | 1 | |||||||
| CH4 | 28 | |||||||
| N2O | 265 | |||||||
| Agro inputs: | Agro inputs: | Agro inputs: | ||||||
| N-fertiliser (kg N) | N-fertiliser (kg N) | N-fertiliser (kg N) | ||||||
| Ammonium nitrate (AN) | Ammonium nitrate (AN) | 2 671 | 6,9 | 2,1 | 3 469 | |||
| Ammonium sulphate (AS) | Ammonium sulphate (AS) | 2 560 | 6,5 | 0,0 | 2 724 | |||
| Ammonium nitrate sulphate (ANS) | Ammonium nitrate sulphate (ANS) | 2 561 | 8,9 | 1,3 | 3 162 | |||
| Anhydrous ammonia | Anhydrous ammonia | 2 662 | 6,8 | 0,0 | 2 832 | |||
| Calcium ammonium nitrate (CAN) | Calcium ammonium nitrate (CAN) | 2 863 | 7,3 | 2,1 | 3 670 | |||
| Calcium nitrate (CN) | Calcium nitrate (CN) | 2 653 | 7,0 | 5,1 | 4 348 | |||
| Urea | Urea | 1 703 | 9,3 | 0,0 | 1 935 | |||
| Urea ammonium nitrate (UAN) | Urea ammonium nitrate (UAN) | 2 182 | 7,5 | 1,1 | 2 693 | |||
| P2O5-fertiliser (kg P2O5) | P2O5-fertiliser (kg P2O5) | P2O5-fertiliser (kg P2O5) | ||||||
| Triple superphosphate (TSP) | Triple superphosphate (TSP) | 517 | 0,9 | 0,0 | 544 | |||
| Rock phosphate 21 %P2O5 23 %SO3 | Rock phosphate 21 %P2O5 23 %SO3 | 95 | 0,0 | 0,0 | 95 | |||
| Mono ammonium phosphate (MAP) 11 %N 52 %P2O5 | Mono ammonium phosphate (MAP) 11 %N 52 %P2O5 | 967 | 2,5 | 0,0 | 1 029 | |||
| Di-Ammonium-Phosphate (DAP) 18 %N 46 %P2O5 | Di-Ammonium-Phosphate (DAP) 18 %N 46 %P2O5 | 1 459 | 3,7 | 0,0 | 1 552 | |||
| K2O-fertiliser (kg K2O) | K2O-fertiliser (kg K2O) | K2O-fertiliser (kg K2O) | ||||||
| Muriate of Potash (MOP) 60 %K2O | Muriate of Potash (MOP) 60 %K2O | 409 | 0,17 | 0,0 | 413 | |||
| Other fertilisers | Other fertilisers | Other fertilisers | ||||||
| NPK 15-15-15 | NPK 15-15-15 | 4 261 | 10,0 | 1,7 | 5 013 | |||
| MgO (kg MgO) | MgO (kg MgO) | 769 | 0,0 | 0,0 | 769 | |||
| Sodium (Na) fertiliser (kg Na) | Sodium (Na) fertiliser (kg Na) | 1 620 | 0,0 | 0,0 | 1 620 | |||
| Seeds- barley | Seeds- barley | 189,5 | 0,08 | 0,4001 | 310,6 | 3,23 | ||
| Seeds- eucalyptus cuttings | Seeds- eucalyptus cuttings | 0,0 | 0,00 | 0,0000 | 0,0 | |||
| Seeds- maize | Seeds- maize | 189,5 | 0,08 | 0,4001 | 310,6 | 3,23 | ||
| Seeds- poplar cuttings | Seeds- poplar cuttings | 0,0 | 0,00 | 0,0000 | 0,0 | |||
| Seeds- rapeseed | Seeds- rapeseed | 451,0 | 0,27 | 1,0024 | 756,5 | 8,33 | ||
| Seeds- rye | Seeds- rye | 191,0 | 0,08 | 0,4001 | 312,1 | 3,23 | ||
| Seeds- soy bean | Seeds- soy bean | 0,0 | 0,00 | 0,0000 | 0,0 | |||
| Seeds- sugar beet | Seeds- sugar beet | 2 363,0 | 1,37 | 4,2096 | 3 651,7 | 38,44 | ||
| Seeds- sugar cane | Seeds- sugar cane | 4,97 | 0,00 | 0,0000 | 5,0 | 0,06 | ||
| Seeds- sunflower | Seeds- sunflower | 451,0 | 0,27 | 1,0024 | 756,5 | 8,33 | ||
| Seeds- triticale | Seeds- triticale | 180,0 | 0,04 | 0,4000 | 300,2 | 3,00 | ||
| Seeds- wheat | Seeds- wheat | 163,7 | 0,04 | 0,4000 | 283,9 | 2,76 | ||
| parameter: | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient | 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): | 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 | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient | Fossil energy input | Fossil energy input | Density | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| unit: | gCO2/MJ | gCH4/MJ | gN2O/MJ | gCO2-eq/MJ | MJfossil/kg | MJfossil/MJ | kg/m3 | |
| Fuels- gases | 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) | 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 | GHG emission coefficient | GHG emission coefficient | 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) | 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: | parameter: | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient | GHG emission coefficient |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| unit: | unit: | gCO2/kg | gCH4/kg | (at 0 % water) | gCO2-eq/kg | gCO2/MJ | gCH4/MJ | gN2O/MJ |
| Conversion inputs | 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: | parameter: | Fuel Efficiency | Transport exhaust gas emissions | Transport exhaust gas emissions | ||||
| --- | --- | --- | --- | --- | ||||
| unit: | unit: | MJ/t.km | gCH4/t.km | gN2O/t.km | ||||
| Transport efficiencies – Trucks | 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 | 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 | 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 | GHG emission coefficient | GHG emission coefficient | |
| --- | --- | --- | --- | --- |
| unit: | gCH4/MJ | gN2O/MJ | gCO2-eq/MJ | |
| Emissions from machinery operations incl. chipping ( per MJ diesel) | 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) | 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) | 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 | GHG emission coefficient | GHG emission coefficient | ||
| --- | --- | --- | --- | --- |
| gCO2/kg | gCH4/kg | gN2O/kg | ||
| Manure methane credits ( per MJ biogas) | Manure methane credits ( per MJ biogas) | |||
| CH4 and N2O emission credits for manure | ||||
| No emissions | 0,0 | 0,00 | 0,0000 |
[^E0001]: Commission Regulation (EU) No 1307/2014 of 8 December 2014 on defining the criteria and geographic ranges of highly biodiverse grassland for the purposes of Article 7b(3)(c) of Directive 98/70/EC of the European Parliament and of the Council relating to the quality of petrol and diesel fuels and Article 17(3)(c) of Directive 2009/28/EC of the European Parliament and of the Council on the promotion of the use of energy from renewable sources (OJ L 351, 9.12.2014, p. 3).
[^E0002]: Directive (EU) 2019/1937 of the European Parliament and of the Council of 23 October 2019 on the protection of persons who report breaches of Union law (OJ L 305, 26.11.2019, p. 17).
[^E0003]: Directive 2009/31/EC of the European Parliament and of the Council of 23 April 2009 on the geological storage of carbon dioxide and amending Council Directive 85/337/EEC, European Parliament and Council Directives 2000/60/EC, 2001/80/EC, 2004/35/EC, 2006/12/EC, 2008/1/EC and Regulation (EC) No 1013/2006 (OJ L 140, 5.6.2009, p. 114).
[^E0004]: ‘Pesticides’ means all plant protection products, including herbicides, insecticides, fungicides, etc.
[^E0005]: IPCC (2006), Vol. 4, Chapter 11: N2O emissions from managed soils, and CO2 emissions from lime and urea application.
[^E0006]: Data source: JRC report ‘Definition of input data to assess GHG default emissions from biofuels in EU legislation’ JRC 2019 (EUR 28349 EN). https://op.europa.eu/en/publication-detail/-/publication/7d6dd4ba-720a-11e9-9f05-01aa75ed71a1
[^E0007]: Nomenclature of territorial units for statistics.
[^E0008]: If applying for group certification, the application must include the name and contact details of the group manager and the name, contact details and locations of the farms/plantations that are part of the group.
[^E0010]: NPV is the difference between the present value of cash inflows and the present value of cash outflows over a period of time. NPV is used in capital budgeting and investment planning to analyse the profitability of a future investment or project. Source: https://www.investopedia.com/terms/n/npv.asp
[^E0011]: FAOSTAT producer prices. Source: http://www.fao.org/faostat/en/#data/PP
[^E0012]: OECD countries
[^E0013]: In line with Article 2(7) of Delegated Regulation (EU) 2019/807, yield fluctuations should be excluded.
[^E0014]: To use this option, economic operators have to show that the correlation between the standard growth curve and their baseline growth curve is less than 0,8.
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