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)

Type Implementing Regulation
Publication 2022-06-27
Last updated 2025-02-24
State In force
Department European Commission, ENER
Source EUR-Lex
articles 29
Reform history JSON API
    1. 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.
    1. 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:

    1. Demonstrate that the second crop does not lower the yield of the main crop.
    1. 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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