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general:projections:calculation_documentation [2025/04/03 13:01] – eisoldgeneral:projections:calculation_documentation [2025/04/24 11:12] (current) – eisold
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 In the scenario “with measures” (WM), for the majority of the emission sources in the sectors 1.A.1 (energy industry), 1.A.2 (manufacturing industry), 1.A.4 (other combustion systems), 1.A.5 (military), 1.B (diffuse emissions from fuels), 2 (industrial processes) and 5 (waste and wastewater treatment) projected development of the activity rates is based on the with existing measures scenario (MMS=WEM) of the ‘Treibhausgas-Projektion 2024 für Deutschland’. The MMS of the 2024 GHG emission projections for Germany includes all climate protection-relevant measures and instruments adopted by July 31, 2023. In the scenario “with measures” (WM), for the majority of the emission sources in the sectors 1.A.1 (energy industry), 1.A.2 (manufacturing industry), 1.A.4 (other combustion systems), 1.A.5 (military), 1.B (diffuse emissions from fuels), 2 (industrial processes) and 5 (waste and wastewater treatment) projected development of the activity rates is based on the with existing measures scenario (MMS=WEM) of the ‘Treibhausgas-Projektion 2024 für Deutschland’. The MMS of the 2024 GHG emission projections for Germany includes all climate protection-relevant measures and instruments adopted by July 31, 2023.
  
-In contrast to this comprehensive projection of activity data, transport emissions are calculated using the TREMOD model ("Transport Emission Model"). To estimate the future development of transport-related energy consumption and emissions, a trend scenario up to 2050 was developed, which is updated annually. Version 6.53 of TREMOD formed the basis for the current emission projections (see Allekotte et al., 2024((https://www.umweltbundesamt.de/publikationen/aktualisierung-tremodtremod-mm-ermittlung-der))). Therefore, road transport measures from the WAM scenario of the German NAPCP 2023((https://iir.umweltbundesamt.de/2023/general/projections/wam-scenario)), including the expansion of the truck toll system and a package of measures to promote electromobility, which have since been implemented, become part of the trend scenario. Beyond the trend scenario, findings from ongoing work for the upcoming HBEFA 5.1 update (Handbook of Emission Factors for Road Transport((https://www.hbefa.net/))), such as an increase in the implied emission factors for trucks and coaches with Euro V and VI due to defective or manipulated exhaust aftertreatment systems were considered((https://ermes-group.eu/sites/default/files/2024-12/1.2_Hausberger.pdf)) in the current WM scenario. In addition, the introduction of Euro 7 on the basis of Regulation (EU) 2024/1257 was considered in the WM scenario, leading to further decline of implied emission factors of the fleet, especially beyond 2030. Those additional assumptions are documented in Allekotte et al. (2025)((not yet published, (Link will be added as soon as the report is published.))). Furthermore, assumptions about emissions from road abrasion as well as tyre and brake wear from electrically driven mileage were included according to EMEP/EEA air pollutant emission inventory guidebook 2023((https://www.eea.europa.eu/en/analysis/publications/emep-eea-guidebook-2023)). In order to reflect the Euro 7 Regulation also regarding particle emissions from tyre and brake wear the emission factors of the historical emissions were further adjusted for the emission projections.+In contrast to this comprehensive projection of activity data, transport emissions are calculated using the TREMOD model ("Transport Emission Model"). To estimate the future development of transport-related energy consumption and emissions, a trend scenario up to 2050 was developed, which is updated annually. Version 6.53 of TREMOD formed the basis for the current emission projections (see Allekotte et al., 2024((https://www.umweltbundesamt.de/publikationen/aktualisierung-tremodtremod-mm-ermittlung-der))). Therefore, road transport measures from the WAM scenario of the German NAPCP 2023((https://iir.umweltbundesamt.de/2023/general/projections/wam-scenario)), including the expansion of the truck toll system and a package of measures to promote electromobility, which have since been implemented, become part of the trend scenario. Beyond the trend scenario, findings from ongoing work for the upcoming HBEFA 5.1 update (Handbook of Emission Factors for Road Transport((https://www.hbefa.net/))), such as an increase in the implied emission factors for trucks and coaches with Euro V and VI due to defective or manipulated exhaust aftertreatment systems were considered((https://ermes-group.eu/sites/default/files/2024-12/1.2_Hausberger.pdf)) in the current WM scenario. In addition, the introduction of Euro 7 on the basis of Regulation (EU) 2024/1257 was considered in the WM scenario, leading to further decline of implied emission factors of the fleet, especially beyond 2030. Those additional assumptions are documented in Allekotte et al. (2025)((not yet published, (Link will be added as soon as the report is published.) )). Furthermore, assumptions about emissions from road abrasion as well as tyre and brake wear from electrically driven mileage were included according to EMEP/EEA air pollutant emission inventory guidebook 2023((https://www.eea.europa.eu/en/analysis/publications/emep-eea-guidebook-2023)). In order to reflect the Euro 7 Regulation also regarding particle emissions from tyre and brake wear the emission factors of the historical emissions were further adjusted for the emission projections.
  
 The projection for the agricultural sector (NFR 3) was created by the Thünen Institute (TI) using the py-GAS-EM reporting model twofold, once based on the inventory submission 2024 and the MMS (WEM, with existing measures) of the “Treibhausgas-Projektionen 2024 für Deutschland” and once based on the current inventory submission 2025 and the MMS (WEM, with existing measures) of the “Treibhausgas-Projektionen 2025 für Deutschland”((see chapter 6 for agriculture: https://www.umweltbundesamt.de/sites/default/files/medien/11850/publikationen/projektionen-2025-zentrale-annahmen.pdf)). For both projections, the most important input data for the calculation of the agricultural emissions (animal numbers, animal performance, mineral fertilizer use) were derived for the first time using the CAPRI model, based on the current Thünen-Baseline 2024-2034 (2024)((https://literatur.thuenen.de/digbib_extern/dn068888.pdf)). Further assumptions for the sector agriculture were assumed as in the WM scenario of the German NAPCP 2023 and are described below for the year 2030. The projection for the agricultural sector (NFR 3) was created by the Thünen Institute (TI) using the py-GAS-EM reporting model twofold, once based on the inventory submission 2024 and the MMS (WEM, with existing measures) of the “Treibhausgas-Projektionen 2024 für Deutschland” and once based on the current inventory submission 2025 and the MMS (WEM, with existing measures) of the “Treibhausgas-Projektionen 2025 für Deutschland”((see chapter 6 for agriculture: https://www.umweltbundesamt.de/sites/default/files/medien/11850/publikationen/projektionen-2025-zentrale-annahmen.pdf)). For both projections, the most important input data for the calculation of the agricultural emissions (animal numbers, animal performance, mineral fertilizer use) were derived for the first time using the CAPRI model, based on the current Thünen-Baseline 2024-2034 (2024)((https://literatur.thuenen.de/digbib_extern/dn068888.pdf)). Further assumptions for the sector agriculture were assumed as in the WM scenario of the German NAPCP 2023 and are described below for the year 2030.
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 The NMVOC emissions from NFR sector 2.D.3, which includes emissions resulting from the use of solvents and solvent-containing products, as well as their manufacturing, are not calculated using activity rates and emission factors within the inventory. Instead, a separate model developed and expanded steadily over the past 15 years primarily by the Institute for Environmental Strategies (Ökopol GmbH) is utilized to calculate these emissions, and the results are imported into the inventory database. This model also provides emission projections based on economic forecasts specific to certain branches of industry. These economic projections were last updated for the emissions projections reported in 2023 using the Prognos report (2019) titled “Deutschland Report 2025 | 2035 | 2045”. The methodology for updating the NMVOC inventory and projections is detailed in Zimmermann and Memelink (2023((https://www.umweltbundesamt.de/publikationen/aktualisierung-des-deutschen-inventars-fuer-nmvoc-0))). The NMVOC emissions from NFR sector 2.D.3, which includes emissions resulting from the use of solvents and solvent-containing products, as well as their manufacturing, are not calculated using activity rates and emission factors within the inventory. Instead, a separate model developed and expanded steadily over the past 15 years primarily by the Institute for Environmental Strategies (Ökopol GmbH) is utilized to calculate these emissions, and the results are imported into the inventory database. This model also provides emission projections based on economic forecasts specific to certain branches of industry. These economic projections were last updated for the emissions projections reported in 2023 using the Prognos report (2019) titled “Deutschland Report 2025 | 2035 | 2045”. The methodology for updating the NMVOC inventory and projections is detailed in Zimmermann and Memelink (2023((https://www.umweltbundesamt.de/publikationen/aktualisierung-des-deutschen-inventars-fuer-nmvoc-0))).
  
-In a more recent project, Zimmermann et al. (2025((not yet published (Link will be added as soon as the report is published.) ))) conducted a comprehensive review of the previous methods used for projecting NMVOC emissions in the sectors of printing (NFR 2.D.3.h) and coating applications (NFR 2.D.3.d). They developed new projection methods for the years 2025 to 2050. The updates made for the individual SNAP codes can be summarised as follows:+In a more recent project, Zimmermann et al. (2025((https://www.umweltbundesamt.de/publikationen/nmvoc-projektionen))) conducted a comprehensive review of the previous methods used for projecting NMVOC emissions in the sectors of printing (NFR 2.D.3.h) and coating applications (NFR 2.D.3.d). They developed new projection methods for the years 2025 to 2050. The updates made for the individual SNAP codes can be summarised as follows:
  
 SNAP 60101 demonstrated a clear correlation with automobile production. A forecast for production in Germany has been established based on EU production forecasts and used in the emission projections. For SNAP 60102, emissions showed a correlation with vehicle fleet size from 2015 onward. Forecast data on the vehicle population was taken from the study (Adolf et al., 2014((https://www.prognos.com/sites/default/files/2021-01/140900_prognos_shell_studie_pkw-szenarien2040.pdf ))) and used for the projection. SNAP 60103 showed that employee numbers and annual construction output correlated with emissions from 2018 on. Projections have been implemented using data from BMAS (2021((https://www.bmas.de/SharedDocs/Downloads/DE/Publikationen/Forschungsberichte/fb526-3-aktualisierte-bmas-prognose-digitalisierte-arbeitswelt.pdf?__blob=publicationFile&v=2))) and the Prognos report (2019). For SNAP 60104, a correction factor to the "population development" indicator has been applied to update projections. SNAP 60105 showed a significant correlation with the completion of commercial buildings. Due to the lack of specific forecasts for this indicator, automotive production data (from SNAP 60101) has been used as an alternative. SNAP 60106 faced challenges in identifying suitable indices for emissions. A weak correlation with boat and shipbuilding turnover has been noted and additional corrections to enhance projection accuracy have been applied. In the case of SNAP 60107, no meaningful correlations were established, prompting to create emission projections based on trend extrapolations. Future investigations into specific SNAP areas will yield better methodologies. SNAP 60108 required careful analysis of several sub-areas. No correlation of the emissions to economic (or other) variables could be determined for several sub-areas, which is why some of the emissions were updated using trend progression. In the future, it may be useful to take a closer look at the individual areas of the SNAP code that are particularly relevant to emissions and the developments taking place in these areas to identify suitable alternative approaches for creating projections. No correlation could be established between SNAP 60109 and the indices used to date. However, with the "Other expenditure, inflation-adjusted" index, an alternative suitable forecast index could be used for the projection. SNAP 60101 demonstrated a clear correlation with automobile production. A forecast for production in Germany has been established based on EU production forecasts and used in the emission projections. For SNAP 60102, emissions showed a correlation with vehicle fleet size from 2015 onward. Forecast data on the vehicle population was taken from the study (Adolf et al., 2014((https://www.prognos.com/sites/default/files/2021-01/140900_prognos_shell_studie_pkw-szenarien2040.pdf ))) and used for the projection. SNAP 60103 showed that employee numbers and annual construction output correlated with emissions from 2018 on. Projections have been implemented using data from BMAS (2021((https://www.bmas.de/SharedDocs/Downloads/DE/Publikationen/Forschungsberichte/fb526-3-aktualisierte-bmas-prognose-digitalisierte-arbeitswelt.pdf?__blob=publicationFile&v=2))) and the Prognos report (2019). For SNAP 60104, a correction factor to the "population development" indicator has been applied to update projections. SNAP 60105 showed a significant correlation with the completion of commercial buildings. Due to the lack of specific forecasts for this indicator, automotive production data (from SNAP 60101) has been used as an alternative. SNAP 60106 faced challenges in identifying suitable indices for emissions. A weak correlation with boat and shipbuilding turnover has been noted and additional corrections to enhance projection accuracy have been applied. In the case of SNAP 60107, no meaningful correlations were established, prompting to create emission projections based on trend extrapolations. Future investigations into specific SNAP areas will yield better methodologies. SNAP 60108 required careful analysis of several sub-areas. No correlation of the emissions to economic (or other) variables could be determined for several sub-areas, which is why some of the emissions were updated using trend progression. In the future, it may be useful to take a closer look at the individual areas of the SNAP code that are particularly relevant to emissions and the developments taking place in these areas to identify suitable alternative approaches for creating projections. No correlation could be established between SNAP 60109 and the indices used to date. However, with the "Other expenditure, inflation-adjusted" index, an alternative suitable forecast index could be used for the projection.