Support to the development of the fourth clean air outlook : final report.
Date: 2025-03-03
Institutions: Directorate-General for Environment
Subjects: air quality, atmospheric pollutant, climate change policy, environmental impact, environmental monitoring, pollution control, reduction of gas emissions, report, sustainable development

Support to the development of the 
fourth Clean Air Outlook 

 Final Report 

Written by IIASA, EMRC, MET Norway, 
TNO, e-misia, RIVM, Logika Group 
February - 2025 

National InstituteforPublic Health and the EnvironmentMinistryof Health, Welfare and SportNational InstituteforPublic Health and the EnvironmentMinistryof Health, Welfare and Sport 
EUROPEAN COMMISSION 

Directorate-General for Environment 
Directorate C — Zero Pollution 
Unit C.3 — Clean Air & Urban Policy 

Contact:  Bettina Kretschmer 

E-mail:   Bettina.KRETSCHMER@ec.europe.eu 

European Commission 
B-1049 Brussels 

 
 
EUROPEAN COMMISSION 

Support to the development of the 
fourth Clean Air Outlook 
 Final Report 

2025 

EN 

Directorate-General for Environment 

Manuscript completed in February 2025 

1st edition 

This document has been prepared for the European Commission however it reflects the views only of the 
authors, and the European Commission is not liable for any consequence stemming from the reuse of this 
publication. 

Luxembourg: Publications Office of the European Union, 2025 

© European Union, 2025 

The reuse policy of European Commission documents is implemented by Commission Decision 
2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). 
Unless otherwise noted, the reuse of this document is authorised under a Creative Commons Attribution 
4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that 
reuse is allowed provided appropriate credit is given and any changes are indicated. 

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Final 

Chris Whall (Director) 

EcoMetrics Research and Consulting (E

Netherlands Organisation for Applied Scientific Research

1 

National InstituteforPublic Health and the EnvironmentMinistryof Health, Welfare and SportNational InstituteforPublic Health and the EnvironmentMinistryof Health, Welfare and Sport 
      
      
 
 
 
 
 
 
 
      
 
Contents 

1.1  This report ......................................................................................................................................... 18 

1.2  Purpose of the Service Request ......................................................................................................... 18 

1.2.1  Introduction 

1.2.2  Scope of the study 

18 

18 

1.3  Policy context .................................................................................................................................... 19 

2.1  Overview ............................................................................................................................................ 21 

2.2  Approach ........................................................................................................................................... 21 

2.3  Update of impact and benefit assessment modelling ....................................................................... 23 

2.3.1  Concentration-response functions: Mortality 

2.3.1  Concentration-response functions: Morbidity 

2.3.2  Data on population and health 

2.3.3  Valuation of health impacts 

2.3.4  Static and dynamic analysis of health impacts 

2.3.5  Non-health impacts 

23 

25 

29 

29 

30 

31 

3.1  Introduction ....................................................................................................................................... 32 

3.2  Update of the GAINS model databases ............................................................................................. 32 

3.3  Development of the Baseline scenario .............................................................................................. 32 

3.4  Consultation of the Baseline with EU Member States ...................................................................... 34 

3.4.1  Introduction 

3.4.2  Approach 

34 

34 

4.1  Introduction ....................................................................................................................................... 35 

4.2  Approach ........................................................................................................................................... 35 

4.3  Development of policy and sensitivity scenarios .............................................................................. 35 

4.3.1  Key policy scenarios 

36 

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4.3.2  Inclusion of condensable PM 

4.3.3  Sensitivity to global reductions of methane and other ozone precursors 

37 

41 

4.4  Emissions and air quality under the scenarios .................................................................................. 42 

4.4.1  Evolution of emissions 

4.4.2  Compliance with the NEC Directive objectives 

4.4.3  Indicators for exposure to PM2.5 

4.4.4  Indicators for exposure to ground-level ozone 

4.4.5  Indicators for exposure to NO2 

4.4.6  Ecosystem impact indicators 

4.4.7  Condensable PM sensitivity 

4.4.8  Sensitivity to methane emission reductions 

42 

48 

52 

58 

60 

64 

69 

70 

4.5  Cross-border effects of pollution and pollution reduction ............................................................... 75 

4.5.1  Import-export budgets 

4.5.2  Origin of PM2.5 in each Member State 

75 

79 

5.1  Introduction ....................................................................................................................................... 85 

5.2  Approach ........................................................................................................................................... 85 

5.3  Costs of the measures at the EU level ............................................................................................... 85 

5.4  Benefits analysis with the ALPHA-RiskPoll model ............................................................................. 86 

5.4.1  Economic value of health impacts 

5.4.2  Non-health effects 

5.4.3  Cost-benefit analysis 

86 

89 

91 

5.5  Macro-economic impact analysis ...................................................................................................... 94 

A1.1 Structure of the Annex .................................................................................................................... 104 

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A2.1 Approach ......................................................................................................................................... 104 

A2.2 Update of the GAINS model databases ........................................................................................... 106 

A2.3 Comparison of GAINS and Member States reported emissions ..................................................... 109 

A3.1 Objectives ........................................................................................................................................ 113 

A3.2 Approach ......................................................................................................................................... 113 

A3.3 Preparation and organization of the consultation meetings .......................................................... 114 

A3.4 Documentation and reporting ......................................................................................................... 115 

A4.1 Health impact indicators ................................................................................................................. 198 

A4.2 Ecosystem impact indicators ........................................................................................................... 204 

A4.3 Analysis of measures to achieve ERCs ............................................................................................. 205 

A4.4 Sensitivity to condensable PM ........................................................................................................ 210 

A5.1 Health damage ................................................................................................................................ 217 

A5.2 Economic value of non-health impacts ........................................................................................... 224 

A5.3 Health impacts of PM2,5 and NO2 above WHO Air Quality Guidelines ............................................ 229 

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Tables and figures 
Table 2-1: All-cause mortality functions as relative risk (RR) per 10 µg/m3 adopted for CAO4. ...................... 24 

Table 2-2: Comparison of relative estimates (with 95% confidence intervals) per 10 ug/m3 from recent PM2.5 
and NO2 mortality studies. ................................................................................................................................ 24 

Table 2-3: Tiering of functions in CAO3 and CAO4. ........................................................................................... 26 

Table 2-4: Functions used for analysis of impacts of PM exposure. Normal text: functions used in CAO4 
analysis. Grey italic text: functions from CAO3 that have been replaced with updated estimates from 
EMAPEC, shown for comparison. ...................................................................................................................... 27 

Table 2-5: Functions used for analysis of impacts of NO2 exposure. Normal text: functions used in CAO4 
analysis. Italic text: functions from CAO3 that have been replaced with updated estimates from EMAPEC, 
shown for comparison. ...................................................................................................................................... 28 

Table 2-6: Functions used for analysis of impacts of O3 exposure. EMAPEC did not consider O3 and so this list 
is the same as used for CAO3. ........................................................................................................................... 28 

Table 2-7: Values applied to estimates of health impacts (all units €, 2015 prices). Source: as per sources 
referred to in the technical support report to CAO3 (Klimont et al. 2022) , Annex Section 5.3, unless 
otherwise noted. ............................................................................................................................................... 29 

Table 4-1: Overview of the developed policy and sensitivity scenarios ........................................................... 36 

Table 4-2: EU countries where condensables are not included in the 2023 reporting ..................................... 39 

Table 4-3: Setup of ozone precursor sensitivity scenarios ................................................................................ 41 

Table 4-4: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios for 
SO2. 

48 

Table 4-5: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios for 
NOx. Highlighted cells indicate non-attainment of the 2030 ERCs. .................................................................. 49 

Table 4-6: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios for 
PM2.5. Highlighted cells indicate non-attainment of the 2030 ERCs. ................................................................ 50 

Table 4-7: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios for 
NH3. Highlighted cells indicate non-attainment of the 2030 ERCs. ................................................................... 51 

Table 4-8: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios for 
NMVOC. Highlighted cells indicate non-attainment of the 2030 ERCs. ............................................................ 52 

Table 4-9: Cases of premature death attributable to the exposure to PM2.5 in the EU; thousand cases per 
year, using constant 2010 population data. ...................................................................................................... 57 

Table 4-10: Population-weighted PM2.5 concentrations in the EU, incl. and excl. natural sources. ................. 58 

Table 4-11: Ground-level ozone-related health impact indicators for the EU. ................................................. 60 

Table 4-12: Cases of premature death attributable to the exposure to NO2 in the EU; thousand cases per 
year, using constant 2010 population data. ...................................................................................................... 63 

Table 4-13: Population-weighted NO2 concentrations in the EU; µg/m3. ......................................................... 63 

Table 4-14: Ecosystem area in the EU where the critical loads for acidification are exceeded. ....................... 65 

Table 4-15: Ecosystem area in the EU where the critical loads for eutrophication are exceeded. .................. 66 

Table 5-1: Total air pollution control costs for the EU for analysed scenarios. Units: million €/year. ............. 86 

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Table 5-2: Additional air pollution control costs over the Baseline for the EU for analysed scenarios.  Units: 
million €/year. 86 

Table 5-3: Economic value of health impacts linked to PM2.5, NO2 and O3 by scenario accounting for 
sensitivity to the approach used for mortality valuation (VOLY, VSL) and testing sensitivity to the inclusion of 
NO2 impacts. Units €million/year, 2015 prices. Excludes Tier 3 impacts. ......................................................... 89 

Table 5-4: Economic valuation of air pollution impacts to materials for selected scenarios for the EU. Units: 
€million/year (2015 prices). .............................................................................................................................. 90 

Table 5-5: Economic valuation of air pollution impacts to crops for selected scenarios for the EU. Units: 
€million/year (2015 prices). .............................................................................................................................. 90 

Table 5-6: Economic valuation of air pollution impacts to forests for selected scenarios for the EU. Units: 
€million/year (2015 prices). .............................................................................................................................. 90 

Table 5-7: Economic valuation of air pollution impacts to ecosystems for selected scenarios for the EU. Units: 
€million/year (2015 prices). .............................................................................................................................. 90 

Table 5-8: Additional costs above the Baseline. Units €million/year. ............................................................... 91 

Table 5-9: Benefits from reduced health and non-health damage relative to the CAO4 Baseline for the EU. 
Units: €M/year, 2015 prices. Tier 3 health effects excluded. ........................................................................... 92 

Table 5-10: Net benefits from reduced health and non-health damage relative to the CAO4 Baseline for the 
EU. Units: €M/year, 2015 prices. Tier 3 health effects excluded. ..................................................................... 93 

Table 5-11: Benefit-cost ratios by scenario for the EU. Tier 3 health effects excluded. ................................... 93 

Table 5-12. Macro-economic impacts in 2030, 2040 and 2050, considering the full concentration range (no 
cut-off); % GDP change compared to the respective baseline; except ‘Benefit/cost ratio’ that shows the ratio 
of benefits to costs. Source: JRC-GEM-E3 model .............................................................................................. 95 

Table 5-13. Sector-specific market impacts compared to respective baseline (% change in output), in 2030, 
2040 and 2050, considering the full concentration range (no cut-off) and work loss days (WLD) based on 
Ostro (1987); Source: JRC-GEM-E3 model. ....................................................................................................... 96 

Table 5-14. Sector-specific market impacts compared to respective baseline (% change in output), in 2030, 
2040 and 2050, considering the full concentration range (no cut-off) and work loss days (WLD) based on 
Ostro (1987) with crop yield gains; Source: JRC-GEM-E3 model. ..................................................................... 97 

Table 5-15. Sector-specific market impacts compared to respective baseline (% change in output), in 2030 
and 2050, considering the full concentration range (no cut-off) and recent empirical evidence on impact on 
labour productivity following Dechezleprêtre et al (2019); Source: JRC-GEM-E3 model. ................................ 97 

Table 5-16. Sector-specific market impacts compared to respective baseline (% change in output ), in 2030, 
2040 and 2050, considering the full concentration range (no cut-off) and benefits through labour 
productivity following Dechezleprêtre et al (2019) and crop yield gains; Source: JRC-GEM-E3 model. .......... 98 

Table 8-1: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 115 

Table 8-2: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 117 

Table 8-3: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 119 

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Table 8-4: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 121 

Table 8-5: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 123 

Table 8-6: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 125 

Table 8-7: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 127 

Table 8-8: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 129 

Table 8-9: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 131 

Table 8-10: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 133 

Table 8-11: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 135 

Table 8-12: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 137 

Table 8-13: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 139 

Table 8-14: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 141 

Table 8-15: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 143 

Table 8-16: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 145 

Table 8-17: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 147 

Table 8-18: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
preliminary GAINS estimates for 2005, 2015, and 2020. ................................................................................ 149 

Table 8-19: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 151 

Table 8-20: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 153 

Table 8-21: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 155 

Table 8-22: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 157 

Table 8-23: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 159 

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Table 8-24: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 161 

Table 8-25: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 163 

Table 8-26: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 165 

Table 8-27: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 
GAINS estimates for 2005, 2015, and 2020..................................................................................................... 167 

Table 8-28: Years of life lost (YOLLs) attributable to the total exposure to PM2.5 in the EU; million years lost 
using constant 2010 population data, for all analysed scenarios. .................................................................. 199 

Table 8-29: Years of life lost (YLLs), attributable to the total exposure to PM2.5, in a specific year in the EU for 
all analysed scenarios; [thousand years/year]. ............................................................................................... 199 

Table 8-30: Loss in statistical life expectancy, attributable to the exposure to total PM2.5 in the EU [months].

200 

Table 8-31: Cases of premature death attributable to the exposure to total PM2.5 above 5 µg/m3 in the EU; 
thousand cases per year, using constant 2010 population data. .................................................................... 201 

Table 8-32: Population-weighted PM2.5 concentrations in the EU; µg/m3, including all sources but considering 
a cut-off of 5 µg/m3. ........................................................................................................................................ 202 

Table 8-33: Population-weighted NO2 concentrations in the EU; µg/m3, including all sources but considering a 
cut-off of 10 µg/m3. ......................................................................................................................................... 202 

Table 8-34: Cases of premature death attributable to the exposure to NO2 (all sources) above 10 µg/m3 in 
the EU; thousand cases per year, using constant 2010 population data. ....................................................... 203 

Table 8-35: Nature2000 nature protection areas with nitrogen deposition exceeding CLs for eutrophication.

204 

Table 8-36: Model setup, acronyms and emission scenarios. ......................................................................... 211 

Table 8-37: Health impacts (mortality and morbidity) under the CAO4 Baseline by Member State, 2005-2050, 
€million (2015) per year. Mortality valued using the VOLY and all NO2 functions included. Tier 3 morbidity 
only included in last row. ................................................................................................................................ 217 

Table 8-38: Health impacts (mortality and morbidity) under the CAO4 Baseline by Member State, 2005-2050, 
€million (2015) per year. Mortality valued using the VSL and all NO2 functions included. Tier 3 morbidity only 
included in last row. ........................................................................................................................................ 218 

Table 8-39: Health impacts (mortality and morbidity) under the CAO4 Baseline by country, 2005-2050, 
€million (2015) per year. Mortality valued using the VOLY with NO2 functions excluded where there is 
potential for double counting. Tier 3 morbidity only included in last row. .................................................... 219 

Table 8-40: Health impacts (mortality and morbidity) under the CAO4 Baseline by country, 2005-2050, 
€million (2015) per year. Mortality valued using the VSL with NO2 functions excluded where there is 
potential for double counting. Tier 3 morbidity only included in last row. .................................................... 220 

Table 8-41: Aggregated health impacts for the EU27 per year under the CAO4 Baseline Scenario, 2005-2050. 
Units: Thousand life years lost, cases, etc. ...................................................................................................... 221 

Table 8-42: Value of health impacts for the EU27 per year under the CAO4 Baseline Scenario, 2005-2050. 
Figures in italics represent sensitivity cases. Units: €M per year. ................................................................... 222 

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Table 8-43: Value of morbidity health impacts for the EU27 per year under the CAO3 and CAO4 Baseline 
Scenario, 2030, where response functions have changed between CAO3 and CAO4 reflecting new evidence. 
Units: €M per year. .......................................................................................................................................... 223 

Table 8-44: Value of the damage to materials under the CAO4 Baseline by country, 2005-2050, €million 
(2015) per year. ............................................................................................................................................... 224 

Table 8-45: Value of the damage to crops under the CAO4 Baseline by country, 2005-2050, €million (2015) 
per year. 

225 

Table 8-46: Value of the damage to forest production and carbon sequestration under the CAO4 Baseline by 
country, 2005-2050, €million (2015) per year. ............................................................................................... 226 

Table 8-47: Lower bound estimate of the value of the damage to ecosystems under the CAO4 Baseline by 
country, 2005-2050, €million (2015) per year. ............................................................................................... 227 

Table 8-48: Upper bound estimate of the value of the damage to ecosystems under the CAO4 Baseline by 
country, 2005-2050, €million (2015) per year. ............................................................................................... 228 

Table 8-49: Economic value of health impacts linked to PM2.5, NO2 and O3 by scenario accounting for 
sensitivity to the approach used for mortality valuation (VOLY, VSL), to the inclusion of NO2 impacts and to 
use of the WHO Air Quality Guidelines as cut-off points for analysis. Units € million/year, 2015 prices. 
Excludes Tier 3 impacts. .................................................................................................................................. 229 

Table 8-50: Benefits from reduced PM2.5, NO2 and O3 damage relative to the CAO4 Baseline for the EU27. 
Units: € million/year, 2015 prices. Tier 3 health effects excluded. WHO Air Quality Guidelines used as cut-off 
points for analysis. ........................................................................................................................................... 230 

Table 8-51: Net benefits from reduced PM2.5, NO2 and O3 damage relative to the CAO4 Baseline for the EU27. 
Units: €M/year, 2015 prices. Tier 3 health effects excluded. WHO Air Quality Guidelines used as cut-off 
points for analysis. ........................................................................................................................................... 231 

Table 8-52: Benefit-cost ratios by scenario for the EU27. Tier 3 health effects excluded. ............................. 231 

Figure 2-1: Analysis of ozone attributable deaths using static and dynamic population ................................. 30 

Figure 4-1: Comparison of PM2.5 emissions [kt/year] for small combustion for 2018 (Simpson et al., 2022) .. 38 

Figure 4-2: Comparison of Ref2 scenarios to reported 2019 emissions, based on 2023 submission [kt PM2.5] 40 

Figure 4-3: Comparison of Ref2 scenario (HighEF) to reported emissions for 2019-2021, based on the 2023 
submissions [kt PM2.5] ....................................................................................................................................... 40 

Figure 4-4: Emissions of SO2 in the baseline and selected scenarios, EU total. ................................................ 43 

Figure 4-5: Emissions of NOX (as NO2) in the baseline and selected scenarios, EU total. ................................. 44 

Figure 4-6: Emissions of PM2.5 in the baseline and selected scenarios, EU total. ............................................. 44 

Figure 4-7: Emissions of NH3 in the baseline and selected scenarios, EU total. ............................................... 45 

Figure 4-8: Emissions of NMVOC in the baseline and selected scenarios, EU total. ......................................... 45 

Figure 4-9: Emissions of black carbon (BC) in the baseline and selected scenarios, EU total. .......................... 46 

Figure 4-10: Emissions of CH4 in the baseline and selected scenarios, EU total. .............................................. 47 

Figure 4-11: Annual mean concentrations of PM2.5 (including natural sources) in 2005, 2025, compliance with 
ERCs (within feasibility) and MTFR cases for 2030 and 2050. ........................................................................... 54 

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Figure 4-12: Population in the EU exposed to different concentrations of PM2.5 for selected key scenarios 
(total PM2.5 including natural sources). ............................................................................................................. 55 

Figure 4-13: Share of population in the EU and MS exposed to different levels of PM2.5 concentrations in 
2005, currently (2025), and in 2030 in selected scenarios (total PM2.5 including natural sources). ................. 56 

Figure 4-14: Comparison of the cases of premature deaths attributable to the exposure to total 
anthropogenic PM2.5 in the EU, for the analysed scenarios. The marked 55% reduction of 2005 premature 
deaths refers to the ZPAP target. ...................................................................................................................... 57 

Figure 4-15: Cases of premature deaths attributable to the exposure to ground-level ozone in the EU. ....... 59 

Figure 4-16: Population-weighted SOMO35 indicator (ppb days) in the EU. ................................................... 60 

Figure 4-17: Population in the EU exposed to different concentrations of NO2 for selected key scenarios. ... 62 

Figure 4-18: Comparison of the cases of premature deaths attributable to the exposure to total NO2 
(including all sources) in the EU, for the analysed scenarios. ........................................................................... 63 

Figure 4-19: Ecosystem area in the EU where the critical loads for acidification are exceeded. ..................... 65 

Figure 4-20: Ecosystem area in the EU where the critical loads for eutrophication are exceeded. The marked 
25% reduction of 2005 area with N deposition exceeding CLs refers to the ZPAP target. ............................... 66 

Figure 4-21: Distribution of further NH3 reductions in the ERCs compliant and ZPAP scenario, compared to 
Baseline, in 2030................................................................................................................................................ 68 

Figure 4-22: All-site comparison of modelled OC (in PM2.5) versus observations, 2019, non-volatile tests. .... 69 

Figure 4-23: Spatial distribution of ozone concentrations for the 26th highest MD8M (two top rows) and for 
the 19th highest MD8M. Two mitigation cases for 2050 assume MTFR for CH4 and either CLE (Global MTFR – 
only CH4) or MTFR (Global MTFR – AP+CH4) for non-methane precursors. ...................................................... 71 

Figure 4-24: Spatial distribution of peak season average MD8M ozone concentrations. Two mitigation cases 
for 2050 assume MTFR for CH4 and either CLE (Global MTFR – only CH4) or MTFR (Global MTFR – AP+CH4) for 
non-methane precursors. .................................................................................................................................. 72 

Figure 4-25: Spatial distribution of AOT40 indicator for 2050. Two mitigation cases for 2050 assume MTFR for 
CH4 and either CLE (Global MTFR – only CH4) or MTFR (Global MTFR – AP+CH4) for non-methane precursors.

73 

Figure 4-26: Assessment of compliance with the current and proposed ozone legislation, showing 26th 
highest MD8M (top row) and for the 19th highest MD8M (bottom row) for the Baseline and three MTFR 
cases for 2030, 2040, 2050. ............................................................................................................................... 74 

Figure 4-27: Origin of population-weighted PM2.5 background concentrations in each Member State 
computed for 2005 in µg/m3 [top] and as percent of total background concentrations [bottom]. ................. 76 

Figure 4-28: Origin of population-weighted PM2.5 background concentrations in each Member State 
computed for 2020 in µg/m3 [top] and as percent of total background concentrations [bottom]. ................. 77 

Figure 4-29: Origin of population-weighted PM2.5 background concentrations in each Member State 
computed for 2030 Baseline scenario in µg/m3 [top] and as percent of total background concentrations 
[bottom]. 

78 

Figure 4-30: Origin of population-weighted PM2.5 background concentrations in each Member State 
computed for 2050 Baseline scenario in µg/m3 [top] and as percent of total background concentrations 
[bottom]. 

79 

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Figure 4-31: Origins of ambient background concentrations of PM2.5 (population-weighted) in each Member 
State in the period 2005 to 2050 and the contribution to the changes during respective periods (bars 2, 4, 6). 
The category ‘Non-EU’ includes West Balkan, Belarus, Norway, the European part of Russia, Switzerland, 
Turkey, the UK, the Ukraine, and international shipping. ................................................................................. 80 

Figure 5-1: Economic value of health impacts of air pollutants for the EU under the Baseline scenario, 
showing the split between impacts quantified according to the three tiers of analysis (see text). Mortality is 
valued using the VOLY (top) and VSL (bottom). Figures on the left-hand side include all NO2 impacts, whilst 
those on the right-hand side exclude NO2 effects where there is a risk of double counting impacts quantified 
against PM2.5 exposure. Units: €billion/year. Note the difference in scales for the upper and lower figures.  87 

Figure 5-2: Value of damage due to PM2.5, O3 and NO2 exposure for the EU under the Baseline scenario. 
Mortality is valued using the VOLY. Units: €Billion per year. Tier 3 effects (dementia and diabetes) not 
included. 

88 

Figure 5-3. Macro-economic market effects of clean air scenarios in the EU considering the full concentration 
range (no cut-off). Benefits include labour productivity losses following Dechezleprêtre et al (2019) and crop 
yield gains. % GDP change relative to baseline. Source: JRC-GEM-E3 model. .................................................. 96 

Figure 8-1: Change in 2023 Member States submission of SO2 emissions for 2005 and 2015, compared to 
2021 submission. ............................................................................................................................................. 106 

Figure 8-2: Change in 2023 Member States submission of NOx emissions for 2005 and 2015, compared to 
2021 submission. ............................................................................................................................................. 107 

Figure 8-3: Change in 2023 Member States submission of PM2.5 emissions for 2005 and 2015, compared to 
2021 submission. ............................................................................................................................................. 107 

Figure 8-4: Change in 2023 Member States submission of NH3 emissions for 2005 and 2015, compared to 
2021 submission. ............................................................................................................................................. 108 

Figure 8-5: Change in 2023 Member States submission of NMVOC emissions for 2005 and 2015, compared to 
2021 submission. ............................................................................................................................................. 108 

Figure 8-6: Difference between GAINS SO2 emissions for 2005 and 2020 and the 2023 Member States 
submissions  110 

Figure 8-7: Difference between GAINS NOx emissions for 2005 and 2020 and the 2023 Member States 
submissions  110 

Figure 8-8: Difference between GAINS PM2.5 emissions for 2005 and 2020 and the 2023 Member States 
submissions  111 

Figure 8-9: Difference between GAINS NH3 emissions for 2005 and 2020 and the 2023 Member States 
submissions  112 

Figure 8-10: Difference between GAINS NMVOC emissions for 2005 and 2020 and the 2023 Member States 
submissions  112 

Figure 8-11: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 116 

Figure 8-12: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 116 

Figure 8-13: Comparison of the 2023 MS inventory submission PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 117 

11 

 
      
      
 
Figure 8-14: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 118 

Figure 8-15: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 118 

Figure 8-16: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 119 

Figure 8-17: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 120 

Figure 8-18: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 120 

Figure 8-19: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 121 

Figure 8-20: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 122 

Figure 8-21: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the preliminary GAINS estimates for 2005, 2015, and 2020. ........... 122 

Figure 8-22: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 123 

Figure 8-23: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 124 

Figure 8-24: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 124 

Figure 8-25: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 125 

Figure 8-26: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 126 

Figure 8-27: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 126 

Figure 8-28: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 127 

Figure 8-29: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 128 

Figure 8-30: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 128 

Figure 8-31: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 129 

Figure 8-32: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 130 

Figure 8-33: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 130 

12 

 
      
      
 
Figure 8-34: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 131 

Figure 8-35: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 132 

Figure 8-36: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 132 

Figure 8-37: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 133 

Figure 8-38: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 134 

Figure 8-39: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 134 

Figure 8-40: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 135 

Figure 8-41: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 136 

Figure 8-42: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 136 

Figure 8-43: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 137 

Figure 8-44: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 138 

Figure 8-45: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. ............................... 138 

Figure 8-46: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 139 

Figure 8-47: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 140 

Figure 8-48: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 140 

Figure 8-49: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 141 

Figure 8-50: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 142 

Figure 8-51: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 142 

Figure 8-52: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 143 

Figure 8-53: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 144 

13 

 
      
      
 
Figure 8-54: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 144 

Figure 8-55: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 145 

Figure 8-56: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 146 

Figure 8-57: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 146 

Figure 8-58: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 147 

Figure 8-59: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 148 

Figure 8-60: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 148 

Figure 8-61: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 149 

Figure 8-62: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 150 

Figure 8-63: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 150 

Figure 8-64: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 151 

Figure 8-65: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 152 

Figure 8-66: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 152 

Figure 8-67: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 153 

Figure 8-68: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 154 

Figure 8-69: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 154 

Figure 8-70: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 155 

Figure 8-71: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 156 

Figure 8-72: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 156 

Figure 8-73: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 157 

14 

 
      
      
 
Figure 8-74: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 158 

Figure 8-75: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 158 

Figure 8-76: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 159 

Figure 8-77: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 160 

Figure 8-78: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 160 

Figure 8-79: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 161 

Figure 8-80: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 162 

Figure 8-81: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 162 

Figure 8-82: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the preliminary GAINS estimates for 2005, 2015, and 2020. ................................................... 163 

Figure 8-83: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 164 

Figure 8-84: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 164 

Figure 8-85: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 165 

Figure 8-86: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 166 

Figure 8-87: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 166 

Figure 8-88: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 167 

Figure 8-89: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 
emission sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................... 168 

Figure 8-90: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 ................................ 168 

Figure 8-91: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic emission 
sources) with the GAINS estimates for 2005, 2015, and 2020. ....................................................................... 169 

Figure 8-92: Years of life lost (YOLLs) attributable to the exposure to anthropogenic PM2.5 in the EU. ........ 198 

Figure 8-93: Years of life lost (YLL), attributable to the exposure to total PM2.5, in a specific year in the EU. 199 

Figure 8-94: Loss in statistical life expectancy, attributable to the exposure to total PM2.5 in the EU. .......... 200 

15 

 
      
      
 
Figure 8-95: Comparison of the cases of premature deaths attributable to the exposure to total PM2.5 
concentrations above 5 µg/m3 in the EU, for the analysed scenarios. ........................................................... 201 

Figure 8-96: Comparison of the cases of premature deaths attributable to the exposure to NO2 
concentrations above 10 µg/m3 (including all sources) in the EU, for the analysed scenarios. ..................... 203 

Figure 8-97: Nature2000 nature protection area in the EU with nitrogen exceeding critical loads for 
eutrophication. The marked 25% reduction of 2005 area with N deposition exceeding CLs refers to the ZPAP 
target for all ecosystems – for comparison only. ............................................................................................ 204 

Figure 8-98: Distribution of further SO2 reductions in the ERCs compliant and ZPAP scenario, compared to 
Baseline, in 2030.............................................................................................................................................. 206 

Figure 8-99: Distribution of further NOx reductions in the ERCs compliant and ZPAP scenario, compared to 
Baseline, in 2030.............................................................................................................................................. 207 

Figure 8-100: Distribution of further PM2.5 reductions in the ERCs compliant and ZPAP scenario, compared to 
Baseline, in 2030.............................................................................................................................................. 208 

Figure 8-101: Distribution of further NMVOC reductions in the ERCs compliant and ZPAP scenario, compared 
to Baseline, in 2030. ........................................................................................................................................ 209 

Figure 8-102: All-site comparison of modelled OC (in PM2.5) versus observations, 2019, non-volatile tests (see 
above for model scenarios; the ‘32’ refers to the model resolution used, 0.3×0.2 degrees). ........................ 211 

Figure 8-103: All-site comparison of modelled OC (in PM2.5) versus observations, 2019, NV, SV and SIV tests.

212 

Figure 8-104: Normalized mean bias (NMB, %), all sites, 2019 (from monthly data). .................................... 213 

Figure 8-105: Mean spatial correlation (Rspace), all sites, 2019 (from monthly data). (Note: R-values from 0.5 
to 1.0.) 

213 

Figure 8-106: Median temporal correlation (Rtemp), all sites, 2019 (from daily data). (Note: R-values from 0.5 
to 1.0.) 

214 

Figure 8-107: All-site comparison of NV-C modelled OC (in PM2.5) versus observations, 2019, 2015 emissions 
vs 2020 emissions ............................................................................................................................................ 214 

Figure 8-108: EMEP results for OC in PM2.5 with NV-C, NV-H and SIV-H runs, and NO2, meteorological data for 
2019 was used in simulations. ......................................................................................................................... 215 

16 

 
      
      
 
 
 
 
 
List of acronyms 

AAQD 
ALRI  
BAT 
BAT-AEL 
BC 
CLE 
CAO  
CH4  
COPD 
CO2  
EEA 
ELAPSE  
EMAPEC  
EMEP 

Ambient Air Quality Directives 
Asthma in adults and acute Lower respiratory Infection  
Best Available Techniques 
Best Available Techniques-Associated Emissions Levels 
Black carbon 
Current legislation 
Clean Air Outlook 
Methane 
Chronic Obstructive Pulmonary Disease 
Carbon dioxide 
European Environment Agency 
Effects of Low-level Air Pollution: a Study in Europe 
Estimating the Morbidity from Air Pollution and its Economic Consequences 
European Monitoring and Evaluation Program of the Convention on Long-range Transboundary 
Air Pollution 
Emission Reduction Commitment of the NEC directive 
European Union 
Greenhouse gas - Air pollution Interactions and Synergies model 
Gross domestic product 
‘Nomenclature For Reporting’ of emission inventories to EMEP/EEA 
Health Response to Air Pollutants in Europe 2 
Industrial Emissions Directive 
International Institute for Applied Systems Analysis 
Informative Inventory Reports 
kilotons = 103 tons 
Maximum technically feasible emission reductions 
National Air Pollution Control Programme 

ERC 
EU  
GAINS 
GDP 
GNFR 
HRAPIE2 
IED 
IIASA  
IIR 
kt  
MTFR 
NAPCP 
NEC Directive   National Emission reduction Commitments Directive 
NH3 
NMVOC  
NOx  
NO2  
O3  
PaMs 
PJ  
PM2.5 
PRIMES 
SECA 
SO2  
VOLY 
VSL 
WHO 
YOLLs / YLLs 
ZPAP 

Ammonia 
Non methane volatile organic compounds 
Nitrogen oxides 
Nitrogen dioxide  
Ozone 
Policies and Measures 
Petajoule = 1015 joule 
Fine particles with an aerodynamic diameter of less than 2.5 µm 
Energy Systems Model of the National Technical University of Athens 
Sulphur Emission Control Area 
Sulphur dioxide 
Value of a Life Year 
Value of Statistical Life 
World Health Organization 
Years of Life Lost 
Zero Pollution Action Plan 

17 

 
      
      
 
 
Introduction 

1.1 

This report 

This  is  the  final  report  for  the  Specific  Contract  No  090202/2023/906366/SER/ENV.C.3  –  “Support  to  the 
development of the fourth Clean Air Outlook”. The project was led by IIASA with support from the following 
organisations: Norwegian Meteorological Institute (Met.no), EMRC, TNO, e:misia, Logika Group, RIVM. The 
macro-economic  analysis  (chapter  5.5)  was  undertaken  by  staff  from  the  Joint  Research  Centre  of  the 
European Commission in Seville. 

1.2 

Purpose of the Service Request 

1.2.1 

Introduction 

The overall objective for this service request was to provide the underlying analysis to support the preparation 
of the Commission’s fourth Clean Air Outlook report. Alongside this, the outputs will also help to inform the 
Commission’s review of the NEC Directive due in 2025. In line with the service request, the analysis undertaken 
as part of this contract should help to address a series of research questions:  

•  To what extent will the national emission reduction commitments (ERC) set in the NEC Directive be 
achieved for the periods 2020-29 and 2030 onwards with the implementation of the existing and 
proposed EU and national legislation? 

•  To what extent will the 2030 target of the Zero Pollution Action Plan to “reduce by more than 55% 
compared to 2005 the health impacts (premature deaths) of air pollution” be achieved with the 
implementation of the existing and proposed EU and national legislation? 

•  To what extent will the 2030 target of the Zero Pollution Action Plan to “reduce by 25% compared to 

2005 the EU ecosystems where air pollution threatens biodiversity” be achieved with the implementation 
of the existing and proposed EU and national legislation? 

•  What are the most effective and efficient measures per Member State to reduce ammonia emissions so 

as to achieve their ammonia emission reduction commitments for the periods 2020-29 and 2030 onwards 
and the Zero Pollution Action Plan ecosystem related target? 

•  How has the implementation of the NEC Directive influenced air pollutant emissions in recent and current 

years as opposed to source legislation at EU or national level? 

As with previous iterations of the Clean Air Outlook, the engagement with, and involvement of, Member State 
experts has been critical for ensuring the analysis is robust and reflective of the situation across the EU.  

1.2.2 

Scope of the study 

In order to deliver on the objectives outlined above, the project covers the following activities: 

•  Up-to-date modelling framework for the analysis.  

•  Updated baseline reflecting the latest policy developments and sectoral changes.  

•  Direct engagement with the Member States on the baseline assumptions.  

•  Development and modelling of a series of policy scenarios and further analysis of the outputs.  

•  Assessment of the costs and benefits associated with each scenario.  

18 

 
      
      
 
 
 
The geographical scope of the analysis is each EU Member State as well as the EU as a whole. The analysis 
considers transboundary pollution within the EU and to and from non-EU neighbouring countries. 

The  temporal  scope  of  the  analysis  includes  2005  as  the  base  year  (as  set  out  in  the  NEC  Directive)  and 
modelling for the years 2025, 2030, 2040, and 2050. 

Finally, as set out in the terms of reference, the analysis is expected to be consistent with the following:  

•  The analysis undertaken in support of the third Clean Air Outlook (CAO3) (Klimont et al. 2022). 

•  Member States’ air pollutant emission inventories and projections submitted in 2023. 

•  Member States’ National Air Pollution Control Programmes (NAPCPs) & Policies and Measures (PaMs). 

•  The analysis developed in support of the most recent REPowerEU package or other impact assessments 

developed in support of climate and energy policies (2040 climate target analysis). 

•  Analysis or modelling work done as part of the review of the amended Gothenburg Protocol to the Air 

Convention, where relevant and appropriate. 

• 

• 

• 

Latest reviewed critical loads available, as developed by the Air Convention EMEP-Working Group on 
Effects. 

Latest WHO air quality guidelines 

Latest reviewed HRAPIE health impacts values or alternative datasets duly justified. 

1.3 

Policy context 

The previous three Clean Air Outlook reports1 presented results on the prospects of achieving the national 
emission  reduction  commitments  for  five  air  pollutants  (sulphur  dioxide,  nitrogen  oxides,  non-methane 
volatile organic compounds, ammonia and fine particulate matter) as set in Directive (EU) 2016/22842 on the 
reduction of national emissions of certain atmospheric pollutants (also known as the “NEC Directive”). They 
also presented the related air quality, health, ecosystems and economic impacts, as well as other elements 
such as transboundary pollution and synergies with climate and energy policies. 

In  May  2021,  the  Commission  adopted  its  Zero  Pollution  Action  Plan  (COM(2021)  400  final)3,  including  a 
corresponding monitoring and outlook framework (SWD(2021) 141 final)4, as per the European Green Deal. 
The Zero Pollution Action Plan sets out two clean air related targets, to reduce health and ecosystem impacts 
linked to air pollution. In October 2022, the Commission adopted a proposal (COM(2022) 542 final/2)5 to align 
the EU’s air quality standards set in the Ambient Air Quality Directives more closely with the 2021 WHO air 
quality guidelines  (WHO 2021). The  impact  assessment underpinning this proposal was developed in close 
coordination with the third Clean Air Outlook.  

The fourth Clean Air Outlook updates the policy baseline assumptions to bring them in line with latest political 
and legislative developments. This has involved updating the baseline to include the “Fit for 55” package that 
is adopted by Council and Parliament and legislative proposals on other source legislation, such as on industrial 
emissions and vehicle emission standards, as well as the energy market measures to speed up the clean energy 

1 COM(2018)446, COM(2021)3 and COM(2022)673 final; https://environment.ec.europa.eu/topics/air/clean-air-
outlook_en  
2 https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016L2284&from=EN  
3 https://environment.ec.europa.eu/strategy/zero-pollution-action-plan_en  
4 https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A52021SC0141  
5 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2022%3A542%3AFIN  

19 

 
      
      
 
 
 
transition and end Europe’s dependency on gas, oil and coal imports from Russia (REPowerEU). Furthermore, 
new  modelling  assessing  the  impacts  of  different  options  to  reach  a  2040  climate  target6  in  line  with  the 
European Climate Law has been reflected (Regulation (EU) 2021/1119)7. The impact of climate and energy 
measures on air quality, health and ecosystems is analysed. 

Scenarios to support the fourth Clean Air Outlook show the prospects of achieving national emission reduction 
commitments  for  air  pollutants  as  set  in  the  NEC  Directive.  Scenario  results  are  compared with  the  policy 
targets for air quality, health and ecosystem protection that are formulated in the European Green Deal, the 
Zero Pollution Action Plan and the air quality standards of the revised Ambient Air Quality Directive (AAQD)8.  

The  fourth Clean  Air  Outlook  complements  the  second  NEC Directive  implementation  report,  due  by  April 
2024, in order to feed into the review of the NEC Directive, due by 2025. 

The study includes the costs and benefits of policy packages including potential macro-economic impacts. 

6 https://climate.ec.europa.eu/eu-action/climate-strategies-targets/2040-climate-target_en  
7 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32021R1119  
8 Directive (EU) 2024/2881 of the European Parliament and of the Council of 23 October 2024 on ambient air quality 
and cleaner air for Europe (recast). https://eur-lex.europa.eu/eli/dir/2024/2881/oj  

20 

 
      
      
 
 
  Approach and methodology 

2.1  Overview 

Five main tasks were addressed in the Service Request: 

•  Task 1: Provide an up-to-date modelling framework and a Baseline scenario: Task 1 establishes an 

integrated assessment modelling framework that is based on latest scientific understanding and best 
available data reflecting most recent Member State’s emission submissions for use in all following 
analysis. The developed Baseline scenario includes all relevant EU legislation proposed by the 
Commission or adopted by the co-legislators since the CAO3 as well as updates considering latest 
national communication on policy implementation and plans of Member States.  

•  Task 2: Consult the Baseline with the Member States: Under this task, we presented, discussed, and 

validated the assumptions and key results of the Baseline scenario developed in Task 1 with the Member 
States. As a result of consultations with the Member State, the assumptions used in the Baseline have 
been complemented or modified accordingly, where necessary. 

•  Task 3: Develop, run, and analyse policy scenarios: Under Task 3, a set of scenarios (in addition to the 
Baseline) has been developed and implemented in GAINS (see 2.2, below), and emissions as well as air 
quality indicators quantified. Scenarios are designed to explore and illustrate the air quality, health and 
ecosystem impacts, and co-benefits of recently proposed legislation aligning the climate and pollution 
targets as well as the likely scope for further mitigation to achieve increasing ambition of air quality 
targets. Additional analysis explores the sensitivity to the choice of emission factors (measurement 
method) for condensable PM and analysis of the impacts of EU and non-EU mitigation measures 
addressing methane on ozone concentrations in the EU, including target values specified in the AAQD. 

•  Task 4: Assess the costs and benefits of air pollution reduction: Under Task 4 we have quantified cost of 
air pollution, and benefits of reductions, now and in the future for each of the scenarios developed in 
Task 3. The assessment has been performed with updated modelling framework where most recent data 
and methodological updates drawing on the EMAPEC outcomes have been considered to the possible 
extent. Originally planned updates for mortality drawing on HRAPIE-2 outcomes have not been possible 
since the results of that study are not yet available. 

•  Task 5: Ad-hoc support to inform the review of the NEC Directive: Under this task, upon request, we 

provide the Commission with further analysis and comparison of the results of this service request with 
the previous Outlooks as well as of Member States’ reported air pollutant emissions for recent years, in 
view of identifying the role of policies and measures resulting from the implementation of the NEC 
Directive versus other (national or EU level) source control policies. 

2.2 

Approach  

While  maintaining  the  consistency  of  the  modelling  approach  applied  in  the  CAO3  and  in  the  impact 
assessment for the revision of the EU AAQD9, the modelling framework has been updated considering latest 
improvements and recalculations of reported historic emissions by Member States, in particular for 2005, the 
base year for the NEC Directive, as well as 2020, and is consistent with the recently updated EMEP/EEA air 
pollutant emission inventory guidebook (EEA 2023). Special attention has been paid to condensable and non-

9 https://environment.ec.europa.eu/topics/air/air-quality/revision-ambient-air-quality-directives_en  

21 

 
      
      
 
 
 
condensable  parts  of  particulate  matter  emissions,  identifying  in  which  Member  States  and  sectors  the 
condensable part is included in the national inventories. 

IIASA  employs  its  Greenhouse  gas  –  Air  pollution  Interactions  and  Synergies  (GAINS)  model10  to  calculate 
emissions of air pollutants as well as ambient concentrations of PM2.5 and NO2 based on a linearized version 
of the EMEP atmospheric chemistry transport model11; the same approach and model version was used in 
CAO3. Ozone (O3) concentrations were calculated with the EMEP model, which is different from CAO3 where 
the  GAINS  model  with  its  linear  approach  was  applied;  a  summary  of  the  key  differences  between  the 
approaches is provided in Box 1. 

Box 1: Summary of major differences for ozone modelling between CAO3 and CAO4 

The EMEP model was used to calculate ozone concentrations in CAO4. This leads to differences compared to 
CAO3 where the linearized GAINS approach was applied but assures consistency for Baseline and policy scenario 
for the EU as well as sensitivity cases to global changes of methane and non-methane ozone precursors. The 
approach implemented in GAINS does not  consider changes in boundary initial conditions and therefore such 
sensitivity cases cannot be evaluated with GAINS. Here a summary of key differences  in approaches and their 
impacts: 

-  Model resolution: EMEP calculates concentrations at 0.1 x 0.1 leading to higher population exposure 

- 

than in GAINS where calculation in CAO3 was at 0.3 x 0.2 spatial resolution,  
Boundary Conditions (inflow from outside Europe): The EMEP model considers dynamic (scenario year 
dependent) boundary conditions while they are constant in the source receptor (SR) runs for the GAINS 
model. Since O3 from boundary conditions (hemispheric ozone) increased from 2005 to 2020 it has been 
counteracting  the  effects  of decreases  in  European  emissions  and  thus  leads  to  slower  reductions  in 
ambient O3 concentrations over time, 

-  Ozone  non-linearity:  The  impact  of  titration  (availability  of  NOx  to  destroy  ozone  in  areas  of  high 
population density and/or with high traffic density) and particularly its change over time cannot be well 
represented  in  a  linear  GAINS  approach.  Consequently,  GAINS  would  tend  to  overestimate  ozone  in 
some highly populated areas, especially in the earlier years when NOx emissions are still relatively high 
– this is visible for 2005 when comparing CAO3 and CAO4 results,  
An  updated  chemistry  scheme  implemented  in  the  EMEP model  after  the  generation  of  the  transfer 
coefficients for GAINS used in CAO3, leading to higher ozone production rates, and 

- 

-  Different  meteorological conditions assumed in both models: GAINS SR rely on five-year (2016-2020) 
average meteorological conditions, while EMEP used 2013-2017 average. This last factor, however, had 
very small impact on the overall results. 

Overall, the impact of different approach translates into CAO4 showing lower SOMO35 in 2005 and higher in 
future years, with 2020 being very similar. This translates in similar way to assessment of premature deaths. 

The baseline scenario for this work is an update of the CAO3 baseline incorporating all relevant EU legislation 
proposed by the  Commission or adopted by the  co-legislators since  the CAO3 analysis was  undertaken. In 
particular  this  relates  to  the  climate  and  energy  legislation,  reflecting  latest  political  agreements  on  the 
legislative initiatives part of the Fit for 55 package as well as of the REPowerEU initiatives, and most recent 
developments in relevant source legislation, e.g., Euro 7, revision of the Industrial Emissions Directive (IED), 
etc.  The  fourth  Clean  Air Outlook  (CAO4)  furthermore  builds  upon  the  modelling  assessing  the  impacts of 

10 See Amann et al. (2011) for a general description and the GAINS release notes 
https://gains.iiasa.ac.at/gains/download/release_notes.pdf?version=4.03 for the latest updates.  
11 See Simpson et al. (2012) for a general description and Simpson et al. (2023) for the latest updates. 

22 

 
      
      
 
 
 
different options to reach a 2040 climate target, as mentioned above. More details are provided in section 3.3 
and in the Annex section 2.1. 

Ambient  air  quality,  health  and  environmental  impacts  are  assessed  with  the  GAINS  model,  although  for 
ozone, the results of the EMEP model are used. GAINS calculates premature deaths from exposure to PM2.5 
and exceedance of critical loads for acidification and eutrophication due to deposition of sulphur and nitrogen. 
For the latter, the latest database of critical loads (CLs) applied within the Air Convention is used (approved by 
the Executive Body of the Convention in 2021), which is consistent with the CAO3 study. For ozone, health 
impacts are calculated by EMRC making use of the results of the EMEP chemical transport model runs for the 
baseline and respective policy scenarios. Monetary evaluation of health and other benefits is performed with 
the  ALPHA-RiskPoll  model,  based  on  the  GAINS  outputs.  Overall,  the  impact  assessment  methodology  is 
consistent with the one used in the CAO3 but includes updates taking into account most recent advancements, 
including  the EMAPEC  (Estimating the Morbidity from Air Pollution and its Economic Consequences)  study 
results for morbidity (Forastiere et al., 2024). A detailed discussion of assumptions is provided in Section 2.3  
and summarized in Box 2. 

2.3  Update of impact and benefit assessment modelling 

This section provides a discussion and documentation of updates to the modelling of impacts and benefits 
with key developments summarized in Box 2. 

Box 2: Summary of major method updates for health impact assessment and valuation 

Morbidity functions for PM2.5 and NO2 have been updated since CAO3 using results from the EMAPEC (Estimating 
the Morbidity from Air Pollution and its Economic Costs) study coordinated by WHO and published in summer 
2024. These cover updated functions for ischaemic heart disease, chronic obstructive pulmonary disease (COPD), 
stroke, diabetes, acute lower respiratory infections and new incidence of asthma, and an additional function for 
dementia. Incidence data for morbidity effects has also been reviewed and updated using estimates from Global 
Burden of Disease initiative. Valuation data were reviewed for all quantified effects, but no changes made. 

In October 2024, a second study coordinated by WHO, HRAPIE2 (Health Response to Air Pollutants in Europe 2) 
released papers considering mortality functions for PM2.5, NO2 and O3. Whilst these were published too late for 
inclusion here, a commentary is provided on their implications for analysis. 

For impacts to materials, crops, forests and ecosystems, the methods remain the same as in the CAO3. 

The changes to morbidity assessment alter their contribution to total damage (with mortality valued using the 
VOLY and including only Tier 1 and Tier 2 effects – see Table 2-3) from a range of 25% to 31%  in CAO3 to 18% to 
27%  in  CAO4  baseline  leading  to  a  slight  reduction  in  the  estimated  economic  benefits  of  pollution  control 
measures. However, CAO4 analysis also shows that including Tier 3 effects would increase the contribution from 
morbidity to between 37% and 58%. The ranges reflect population ageing with the lower bound being for 2005 
and the upper bound for 2050. 

2.3.1  Concentration-response functions: Mortality 

Mortality functions have been adopted for PM2.5, NO2 and O3, drawing on findings from the WHO 2021 Air 
Quality Guidelines (WHO 2021) and are reported in Table 2-1. As previously, estimates of mortality can be 
calculated  in  terms  of  both  life  years  lost  or  deaths  for  the  economic  assessment:  these  are  alternative 
positions for valuation for which the results are not additive. 

23 

 
      
      
 
 
 
 
Table 2-1: All-cause mortality functions as relative risk (RR) per 10 µg/m3 adopted for CAO4. 

Poll. 
PM2.5 

NO2 
O3 

Effect 
Adults, chronic exposure 
Infant (1-12 months) mortality 

Adults, chronic exposure 

Adults, chronic exposure 

RR 
1.08 
1.04 

1.02 

1.01 

Source 
Chen and Hoek (2020) 
Woodruff et al. (1997) 

Huangfu and Atkinson (2020) 

Huangfu and Atkinson (2020) 

Three  important  papers  were  published  during  the  writing-up  phase  of  this  work  but  after  completion  of 
analysis. Two papers were published from the ongoing update to the Health Risks of Air Pollution in Europe 
(HRAPIE2) assessment coordinated by WHO: Orellano et al. (2024) dealing with effects of chronic exposure to 
PM2.5 and mortality, and Kasdagli et al. (2024) addressing effects of chronic exposure to NO2 and O3. The third 
paper (Chen et al. 2024) investigated the use of ‘two-pollutant models’ to consider the effect of PM2.5 and NO2 
combined, recognising the potential for double counting of impacts when using epidemiological methods for 
pollutants whose concentrations are correlated as a result of them sharing some common sources (e.g. traffic, 
and  burning  of  solid  and  liquid  fuels  more  generally).  Relative  risks  for  PM2.5  and  NO2  linked  to  all-cause 
mortality from these studies are compared with the functions used here from WHO (2021) in Table 2-2. 

Table 2-2: Comparison of relative estimates (with 95% confidence intervals) per 10 ug/m3 from recent 

PM2.5 and NO2 mortality studies. 

Study 
WHO (2021) (used here) 

Orellano et al (2024) 

Kasdagli et al (2024) 

Chen et al (2024) 1 pollutant models 

Chen et al (2024) 2 pollutant models 

PM2.5 
1.08 (1.06-1.09) 

1.095 (1.064-1.127) 

1.106 (1.068-1.142) 

1.070 (1.028-1.104) 

NO2 
1.02 (1.01-1.04) 

1.05 (1.05-1.07) 

1.032 (1.014-1.049) 

1.024 (1.000-1.049) 

From the one-pollutant models our estimates from WHO (2021) are lower for both PM2.5 and NO2 than the 
results from Orellano et al (2024), Kasdagli et al (2024), and Chen et al (2024). However, the two-pollutant 
modelling from Chen et al (2024) shows our estimates to be higher for PM2.5 and lower for NO2. In the light of 
the evidence from the two-pollutant modelling of Chen et al (2024), it is concluded that our continued use of 
information from WHO (2021) and the sensitivity analysis including/excluding NO2 effects with potential for 
double counting against PM2.5 effects, remains reasonable. It is noted that the results of the Orellano et al 
(2024) and Kasdagli et al (2024) papers, although published from work coordinated by WHO, will be further 
discussed in the HRAPIE2 assessment and do not represent recommendations of WHO at the present time. 

Association of PM2.5 exposure with post-neonatal mortality was not assessed by Orellano et al (2024) because 
only two studies (Son et al. 2011; Liao et al. 2022), in India and South Korea, respectively) reported estimates. 
However,  both  were  positive  and  significant,  supporting  quantification  of  the  endpoint.  A  function  from 
Woodruff et al. (1997) is retained from CAO3 for this endpoint. Given the limited number of papers available 
on this endpoint, it is subject to potentially large uncertainty taken in isolation. However, very low rates of 
infant mortality in the EU lead to it making only a very small contribution to overall impacts and benefits. The 
inclusion of impacts to children (including also functions for asthma, below) is important in demonstrating that 
pollution affects people throughout the life course. 

The situation for ozone is more complex. WHO (2021) concluded on a function for a relative risk of 1.01 (1.00-
1.02)  per  10 ug/m3  increase  in  peak  season  average  of  daily  1-hour  maximum  ozone  for  all-cause  (non-
accidental) mortality from Huangfu and Atkinson (2020). The ELAPSE study (Brunekreef et al. 2021) found no 
significant relationships between long-term ozone exposure and mortality from a series of European cohorts. 

24 

 
      
      
 
 
 
 
 
 
Kasdagli et al (2024) found no significant relationship for peak-season ozone with all-cause, respiratory, COPD 
or  ALRI  (acute  lower  respiratory  symptom)  mortality,  or  for  annual  ozone  exposure  with  all-cause  or  ALRI 
mortality. They did, however, find significant relationships with annual ozone and respiratory mortality (1.05, 
1.02-1.08) and COPD mortality (1.06, 1.03-1.08). There is more consistent evidence supporting the inclusion 
of impacts linked to short-term exposure (Orellano et al. 2020; WHO 2021), but a long-term impact cannot be 
ruled out. The decision was taken here to retain the risk estimate for all-cause mortality from Huangfu and 
Atkinson (2020) as adopted in WHO (2021), but apply it to the SOMO35 exposure indicator, consistent with 
the  approach  for  CAO3.  Given  high  variability  in  the  conclusions  of  work  on  chronic  impacts  of  ozone,  a 
conservative approach has been taken to valuation of ozone mortality, in line with earlier quantification of 
effects of short-term exposure (and in line with the approach taken in CAO3), where it is assumed that ozone 
deaths are equivalent to the loss of 1 year of life expectancy per death. It is possible that this undervalues 
ozone health impacts. 

An alternative source of updated functions for both mortality and morbidity would be the ELAPSE (Effects of 
Low-level  Air  Pollution:  a  Study  in  Europe)  study  (Brunekreef  et  al.  2021).  ELAPSE  focused  on  European 
cohorts,  and  hence  may  be  considered  by  some  to  be  more  appropriate  for  application  in  a  European 
assessment. However, ELAPSE data are only from a selection of affluent countries in Europe: Austria, Belgium, 
Denmark, France, Germany, Italy, the Netherlands and Sweden, so relevance to other parts of Europe might 
be  questioned.  EMAPEC  is  preferred  here  as  it  considers  eligible  studies  (those  meeting  quality  criteria) 
globally, and by doing so draws on a larger amount of data. The approach of preferring studies that consider 
the global literature was also adopted in earlier CAO reports. 

Some  analysts  prefer  to  use  cause-specific  functions  for  quantification  of  pollutant  impacts  on  mortality. 
Reasons  given  include  that  part  of  the  all-cause  estimate  will  feature  causes  unrelated  to  air  pollutant 
exposure, there is a desire to link mortality to specific types of impact and there is some assumption that the 
use of cause-specific functions will generate more accurate results. The accuracy argument seems reasonable 
in regions where few air pollution epidemiology studies have been carried out and where the dominant causes 
of death differ to those where there has been extensive epidemiological research. However, this argument is 
weaker in the context of CAO4 given that epidemiological studies have been carried out on air pollution in 
many  European  countries.  The  finding  by  Hegelund  et  al.  (2024)  that  PM2.5  and  NO2  were  both  positively 
correlated with the onset of more than 700 health conditions in Denmark, 80% of registered health conditions, 
also supports the use of all-cause functions. A further factor is that it has been noted previously that there is 
an  unexpected  level  of  variation  in  attribution  of  mortality  to  causes  between  countries  in  Europe.  The 
decision has therefore been taken to continue using all-cause functions to quantify mortality for CAO4.  

2.3.1  Concentration-response functions: Morbidity 

Response functions for morbidity are shown in Table 2-4 (PM2.5), Table 2-5 (NO2) and Table 2-6 (O3). Since 
CAO3, research by Forastiere et al. (2024) under the EMAPEC (Estimating the Morbidity from Air Pollution and 
its Economic Consequences) project12 coordinated by WHO has provided new response functions for a series 
of  chronic  health  conditions.  The  EMAPEC  functions  are  derived  from  a  global  assessment  of  systematic 
reviews, of which only those that met high quality standards were used to inform the final recommendations. 
The EMAPEC functions have been adopted for CAO4, replacing many of those used in CAO3.  

The EMAPEC functions applying to adults are all standardised for application above the age of 30, except for 
asthma. For CAO3, analysis of the same conditions included different age groups depending on the function 
used, in some  cases covering all ages,  in others, above 20 or 27 years, but  consistently below the 30-year 
boundary used in EMAPEC. The impact on results will be small given that the conditions referred to (chronic 
bronchitis, stroke, lung cancer, Type 2 diabetes and ischaemic heart disease events) are far more prevalent in 

12 https://www.who.int/activities/estimating-the-morbidity-from-air-pollution-and-its-economic-costs  

25 

 
      
      
 
 
 
older age groups. The EMAPEC recommendations for asthma cover all age groups (0 to 18 years, and 19 years 
and over). Data on incidence of the conditions brought in through the EMAPEC analysis has been taken from 
Global Burden of Disease (https://www.healthdata.org/research-analysis/gbd). 

Two effects were not recommended for quantification by EMAPEC, atrial fibrillation and Parkinson’s disease, 
with  evidence  considered  insufficient  for  risk  assessment.  Two  functions,  for  dementia  and  ASD  (autism 
spectrum disorder), which have the largest RRs of all outcomes, were marked by Forastiere et al (2024) as 
having applicability over a restricted range, not more than 10 µg/m3, and should be regarded as having added 
uncertainty.  ASD,  however,  has  not  been  included  in  the  estimates  made  here  given  data  constraints  on 
incidence and valuation. Hypertension was also not quantified for CAO4 given potential for significant overlap 
with ischaemic heart disease and stroke. Similarly, some other effects included for CAO3 were omitted through 
concerns over the potential for double counting (e.g. asthma symptoms, given inclusion of new incidence of 
asthma). 

For CAO3, health impacts were arranged into ‘Tiers’ of varying confidence, based on whether or not functions 
had been evaluated by reviews conducted for WHO. Mortality functions taken from WHO (2021) formed Tier 
1 (highest confidence). A selection of mainly short-term impacts formed Tier 2 based on HRAPIE (WHO 2013) 
recommendations, whilst long-term morbidity impacts from non-WHO literature review were included as Tier 
3. For CAO4 the Tiers again indicate variation in confidence, though the contents of each Tier differ to before, 
reflecting advances in the knowledge base. Tier 1 now includes both mortality (again from WHO, 2021) and 
those long-term impacts on morbidity given an ‘A’ rating by Forastiere et al (2024). Tier 2 includes a truncated 
account of morbidity recommendations from WHO (2013) eliminating a function for chronic bronchitis as this 
would double count against COPD (now in Tier 1). Tier 3 includes PM2.5 impacts on dementia and diabetes 
which were given a ‘B+’ rating, indicating lower confidence, by Forastiere et al (2024). The Tiering systems for 
CAO3 and CAO4 are summarised in Table 2-3. 

Results reported in this report (main text) exclude the Tier 3 results as they reflect a lower level of confidence 
than the Tier 1 and 2 results. For CAO3 main text all tiers were included, as the difference in confidence across 
the 3 Tiers was not considered to be so pronounced. Results for dementia (CAO4/Tier3) are particularly large 
and merit further evaluation by expert groups, explaining the cautious approach adopted here of excluding 
them from the main text results. 

Table 2-3: Tiering of functions in CAO3 and CAO4. 

Tier 1 

All mortality from WHO (2021). 

CAO3 

Tier 2 

Tier 3 

Short-term morbidity functions and PM2.5/ 
chronic bronchitis from WHO (2013). 
Long-term morbidity functions introduced 
for CAO3 but without WHO review. 

CAO4 
All mortality from WHO (2021). Long-term morbidity from Forastiere et al 
(2024) where functions were given an ‘A’ rating. 
Short-term morbidity functions carried over from WHO (2013) / CAO3. 
Chronic bronchitis excluded though COPD (which includes it) is now Tier 1. 
Long-term morbidity from Forastiere et al (2024) for PM2.5 dementia and 
diabetes (‘B+’ rating). 

NO2 functions were distinguished between those quantifying an impact not addressed for PM2.5 (Asthma in 
adults  and  acute  Lower  Respiratory  Infection/ALRI)  and  those  quantifying  effects  also  quantified  for PM2.5 
where there was considered significant risk of double counting.  This allowed for a sensitivity analysis, whereby 
the potentially duplicated NO2 effect would, respectively, be added to or omitted from totals to give a range 
for the total impact. 

26 

 
      
      
 
 
 
Table 2-4: Functions used for analysis of impacts of PM exposure. Normal text: functions used in CAO4 analysis. Grey italic text: functions from CAO3 

that have been replaced with updated estimates from EMAPEC, shown for comparison. 

Impact 
Life years lost, deaths 
Premature deaths 
Cases 
Cases 
Cases 
Cases 
Cases 
Cases 
Cases 
Cases 
Cases 
Cases 
Cases 
Cases 

Health impact 
Chronic Mortality (30yr +) 
Infant Mortality (0-1yr)  
Chronic Bronchitis (27yr +) 
COPD (>30) 
Stroke (all ages) 
Stroke (30+) 
Lung cancer (>20) 
Lung cancer (>30) 
Type 2 diabetes (all ages) 
Type 2 diabetes (>30) 
Myocardial infarction (all ages) 
IHD events (>30) 
Asthma (new incidence, 0-15) 
Asthma (new incidence, 0-18) 
Morbidity effects from EMAPEC not previously addressed in CAO studies 
Dementia (60+) (restricted applicability) 
Morbidity effects of short-term exposure not covered in EMAPEC 
Bronchitis in children aged 6 to 12 
Respiratory Hospital Admissions (All ages) 
Cardiac Hospital Admissions All ages) 
Restricted Activity Days (all ages) 
Asthma symptom days (children 5-19yr) 
Lost working days (15-64 years) 

Cases 
Cases 
Cases 
Days 
Days 
Days 

Cases 

Pollutant 
PM2.5 
PM10 
PM10 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 

PM2.5 

PM10 
PM2.5 
PM2.5 
PM2.5 
PM10 
PM2.5 

Tier 
1 
2 

1 

1 

1 

3 

1 

1 

3 

2 
2 
2 
2 

2 

Relative risk  Source 

Forastiere et al (2024) 

1.08  CAO3 (WHO 2021) 
1.04  CAO3 (Woodruff et al. 1997) 
1.12  CAO3 (Abbey et al. 1995; Schindler et al. 2012),  
1.18 
1.13  CAO3 (Alexeeff et al. 2021) 
Forastiere et al 2024 
1.16 
1.09  CAO3 (Hamra et al. 2014) 
1.16 
1.10  CAO3 (Yang et al. 2020) 
1.10 
Forastiere et al 2024 
1.08  CAO3 (Alexeeff et al. 2021) 
1.13 
Forastiere et al 2024 
1.03  CAO3 (Khreis et al. 2017) 
1.34 

Forastiere et al 2024 

Forastiere et al 2024 

1.46 

Forastiere et al 2024 

1.08  CAO3 (Hoek et al. 2012) 

1.019  CAO3 (APED studies [WHO, 2013]) 
1.0091  CAO3 (APED studies [WHO, 2013]) 

1.047  CAO3 (Ostro 1987) 
1.028  CAO3 
1.046  CAO3 (Ostro, 1987) 

COPD = Chronic Obstructive Pulmonary Disease, IHD = Ischaemic Heart Disease, APED = Air Pollution Epidemiology Database 

27 

 
      
      
 
 
 
 
 
 
 
  
 
  
  
 
 
 
 
 
 
 
 
Table 2-5: Functions used for analysis of impacts of NO2 exposure. Normal text: functions used in CAO4 analysis. Italic text: functions from CAO3 that 

have been replaced with updated estimates from EMAPEC, shown for comparison. 

Health impact 
Chronic Mortality (30yr +) 
Asthma in children (0-18) 
Asthma, (new incidence 30-75) 
Asthma in adults (19+) 
ALRI in children (0-12) 
Morbidity effects of long-term exposure not covered in EMAPEC 
Stroke (40-89) 
Morbidity effects of short-term exposure not covered in EMAPEC 
Bronchitis in asthmatic children (5 to 14) 
Respiratory Hospital Admissions (All ages) 

Cases 

Cases 
Cases 

Impact 
Life years lost, deaths 
Cases 
Cases 
Cases 
Cases 

Pollutant 
NO2 
NO2 
NO2 
NO2 
NO2 

NO2 

NO2 
NO2 

Tier 
1* 
1* 

1 
1 

1* 

2* 
2* 

Relative risk  Source 

1.02  CAO3 (Huangfu and Atkinson 2020; WHO 2021) 
1.10 
Forastiere et al (2024) 
1.17  CAO3 (Brunekreef et al, 2021) 
Forastiere et al (2024) 
1.10 
Forastiere et al (2024) 
1.09 

1.08  CAO3 (Brunekreef et al (2021) 

1.021  CAO3 (McConnell et al. 2003) 
1.018  CAO3 (WHO, 2013) 

ALRI = Acute Lower Respiratory Infection;* Impacts also addressed for PM2.5, that are omitted for a sensitivity case targeted at avoiding potential for double counting 

Table 2-6: Functions used for analysis of impacts of O3 exposure. EMAPEC did not consider O3 and so this list is the same as used for CAO3. 

Health impact 
Chronic Mortality (30yr +) 

Impact 
Life years lost, deaths 

Pollutant 
O3 

Tier 
1 

Relative risk  Source 

1.01  CAO3 (WHO, 2021, following Huangfu and 

Atkinson, 2020) 

Morbidity effects of short-term exposure not covered in EMAPEC 
Respiratory hospital admissions (>64 years) 
Cardiovascular hospital admissions (>64 years) 

Cases 
Cases 

O3 
O3 

2 
2 

1.0044  CAO3 (WHO, 2013) 
1.0089  CAO3 (WHO, 2013) 

28 

 
      
      
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
2.3.2  Data on population and health  

Population and mortality projections have been taken from EUROPOP2023, from Eurostat13. Disease incidence 
data have been taken from WHO databases and Global Burden of Disease. 

2.3.3  Valuation of health impacts 

The  valuation  estimates  adopted  here  are  shown  in  Table  2-7.  Further  commentary  is  provided  in  the 
appendix, with some evidence suggesting that the values shown here for morbidity are conservative14. The 
high valuation for dementia stands out in the table, with more than half of the estimate linked to health and 
social care costs. The other major component is lost utility, reflecting a significant decline in quality of life that 
can persist for a number of years. 

Table 2-7: Values applied to estimates of health impacts (all units €, 2015 prices). Source: as per sources 

referred to in the technical support report to CAO3 (Klimont et al. 2022) , Annex Section 5.3, 
unless otherwise noted. 

Impact 
Mortality 
    Mortality – life years lost 
    Mortality – deaths 

Morbidity 
    ALRI 
    Asthma in children 
    Asthma in adults 
    Bronchitis in children 
    Cardiac hospital admissions 
    COPD 
    Dementia 
    IHD events 
    Lost working days 
    Lung cancer 
    Minor restricted activity days 
    Respiratory hospital admissions 
    Restricted activity days 
    Stroke 
    Type 2 diabetes 

                        Value (€, 2015) 

94,660 

3.6 million (adults) 
5.5 million (infants) 

490 (Walton et al, 2024)15 

6,927 
6,927 
358 
5,900 
63,800 
490,000 (Walton et al, 2024) 
33,559 
155 
29,832 
48 
4,800 
131 
98,113 
21,194 

13 
https://ec.europa.eu/eurostat/cache/metadata/en/proj_23n_esms.htm#:~:text=EUROPOP2023%20are%20the%20late
st%20Eurostat,horizon%20from%202022%20to%202100. 
14 The adult VSL of €3.6 million originates from an OECD study (OECD 2012). OECD is currently updating the earlier 
study, with results due to be published in early 2025; https://www.oecd.org/en/topics/sub-issues/environmental-cost-
benefit-analysis-and-valuation/valuation-of-statistical-life.html 
15 Walton, H. et al (2024, submitted for publication) How can the UK’s public health and economic burden from air 
pollution in 2030/40 and 2050 be reduced by the early introduction of the 2050 net-zero climate policies? 

29 

 
      
      
 
 
 
 
 
 
 
2.3.4 

Static and dynamic analysis of health impacts 

Quantification  of  health  impacts  with  the  GAINS  model  is  focused  on  the  population  structure  in  2010, 
providing a static perspective of the risk faced. This is relevant for analysing the effects of changes in emissions 
only  and  also  allows  an  assessment  of  attainment  of  the  ZPAP  target.  The  CBA,  in  contrast,  accounts  for 
changes in population over time, taking a dynamic perspective accounting for population change. This can be 
illustrated using the  example of mortality linked to ozone exposure  (expressed using the SOMO35 metric) 
under  the  baseline  scenario.  Under  this  scenario,  SOMO35  falls  over  time  (Figure  2-1a).  Assuming  a  static 
population (here fixed at 2010), deaths linked to ozone also (naturally) fall over time (Figure 2-1c). However, 
the number of deaths in the population is not constant (Figure 2-1b). For the first half of the 21st century the 
Eurostat EUROPOP2023 forecast indicates that the annual number of deaths in the EU will increase as the mid-
20th century baby boom works its way through the population. Change year on year is not smooth: Figure 2-1b 
shows, particularly, the peak from the COVID pandemic in 2020. Combining these data with declining ozone 
concentrations leads to a more complex pattern in estimated ozone deaths (Figure 2-1d), peaking in 2020, 
reaching a low point in 2030 before increasing again to 2050. 

Figure 2-1: Analysis of ozone attributable deaths using static and dynamic population   

c) Ozone attributable deaths: static (2010) population 

d) Ozone attributable deaths: 
dynamic population 

Both  estimates  are  useful,  but  in  different  ways.  The  ‘static’  estimate  demonstrates  better  how  risk  from 
exposure to ozone changes year on year. This has advantages for optimisation within the GAINS model as the 
impact is not affected by a factor (population change) over which GAINS has no control. The static result also 
provides a clear basis for considering how individual risk from ozone exposure changes. In the example, the 
static  analysis  (Figure  2-1c)  indicates  an  18%  reduction  in  individual  risk  of mortality  from  ozone  over  the 
period 2005 to 2050, whilst the dynamic analysis indicates only a 3% change (Figure 2-1d). Results comparing 
2005 with 2020, or 2030 with 2050 accounting for population dynamics could suggest that no progress was 
being made with ozone when the modelling of ozone (Figure 2-1a) indicates otherwise. 

30 

 2,000 2,200 2,400 2,600 2,800 3,000 3,2002005201020152020202520302035204020452050Ozone SOMO35 ppb.daysa) Ozone concentration - 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,0002005201020152020202520302035204020452050Deaths/year EU27b) TotalEU27 deaths - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000200520202025203020402050Ozone deaths per yearc) Deaths: static (2010) population - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000200520202025203020402050Ozone deaths per yeard) Deaths:dynamic population 
      
      
 
 
The ‘dynamic’ estimate, in contrast is better suited for use in the economic analysis. The costs of controlling 
emissions  in the  future  are  partly  a  function  of  population change  as this  influences  demand  for  polluting 
activities in the future and hence the costs of pollution control. It would be inconsistent for the CBA to use an 
estimate of impact/benefit based on a static population for comparison with costs for which account is taken 
of population change via demand forecasting. 

2.3.5  Non-health impacts 

The following non-health impacts have been quantified: 

• 

• 

• 

• 

Impacts of O3 on crop production  

Impacts of O3 on wood production 

Impacts of O3 on carbon sequestration 

Impacts of N deposition on terrestrial ecosystems 

•  Effects of acid gases on building materials 

In all cases the methods used are the same as adopted in CAO2 and CAO3. 

31 

 
      
      
 
 
 
  An up-to-date Baseline scenario for Europe 

3.1 

Introduction 

The objective of this task is to develop a Baseline scenario that includes updates considering all relevant EU 
legislation proposed by the Commission or adopted by the co-legislators since the CAO3. 

For  this  purpose,  updates  to  the  GAINS  model  database  have  been  made,  described  in  Section  3.2. 
Assumptions for the Baseline scenario are described in Section 3.3. The Baseline scenario was discussed with 
EU Member States in bilateral consultations, as summarized in Section 3.4. 

3.2  Update of the GAINS model databases 

New methods for emission estimation and newly assessed or developed emission factors are reflected in the 
regularly updated EMEP/EEA Guidebook (latest release in October 2023)16 which supports Member States in 
estimating their national emissions. As shown and discussed in previous Clean Air Outlooks, several Member 
States  recalculate  their  historic  emission  inventories  (including  those  for  2005)  leading  sometimes  to 
substantial  changes  when  compared  to  data  reported  earlier,  which  in  turn  has  implications  for  the 
achievement  of  the  NEC  Directive  emission  reduction  requirements  in  2030.  Beyond  updated  methods, 
revised estimates reflect corrections of calculation errors, revision of activity data and/or emissions factors, 
and implementation of recommendations from reviews of respective submissions. The Member States have 
continued  to  update  the  emission  estimates  since  2021  submission  and  IIASA  has  reviewed  the  major 
recalculations  included  in  the  2023  submission,  as well  as  their  impact  on  the GAINS  model estimates  for 
historical years and the baseline scenario.  

Compared to the GAINS model version used in the CAO3 study, updates were made to PM emission factors 
for wood and coal use in the residential sector in order to improve consistency of accounting for condensable 
PM. This is specifically the case for Austria, Estonia, Germany where updated emission factors in GAINS are 
consistent with the latest EMEP/EEA Guidebook. This means they are much larger than emission factors used 
by these Member States in their submissions, since these three countries do not account for the condensable 
fraction  when  reporting  PM.  On  the  other  hand,  some  countries  reported  new  updated  emissions  from 
residential  sector,  considering  condensable  PM  and  revised  (in  information)  structure  of  combustion 
installations which lead to large increase; the most pronounced changes are for France and Poland. 

Section 2.2 in the Annex provides a comparison of changes in the national emission reporting in 2021 (used in 
CAO3) and 2023 (used in CAO4) for the year 2005 and 2015 as well as comparison of GAINS and Member State 
reported emissions for 2005 and 2020. While most of the updates and revision result in changes smaller than 
10%,  there  are  several  cases  for  each  pollutant  where  differences  are  very  significant  also  illustrating  a 
challenge modelling teams face in addressing such updates. These updates were subject of exchange between 
IIASA  and  national  experts  during  Member  State  consultations  (Task  2;  for  detailed  account  and  country-
sector-level comparison see Section 3 in the Annex).  

3.3 

Development of the Baseline scenario  

The Baseline scenario for CAO4 is an update of the Baseline developed for the CAO3. It relies on the latest 
available expectations on future development trends and on the most recent climate, energy, transport, and 
agricultural policies, including the most recent REPowerEU package. The energy projections have been used 
before for the analysis of different options for the 2040 Climate Target (CT). The “S3” scenario from the 2040 

16 https://www.eea.europa.eu/publications/emep-eea-guidebook-2023 

32 

 
      
      
 
 
CT Impact Assessment17, which illustrates a pathway to meet the greenhouse gas emission reductions of the 
preferred target option in that impact assessment, was the starting point for the development of the CAO4 
Baseline. The source of these projections are scenarios developed with the PRIMES energy model18 within the 
EUCLIMIT-6 project19 in 2023 and the CAO4 Baseline uses the reference case from that project. 

To  capture  the  inflow  of  pollution  from  neighbouring  countries  into  the  EU,  IIASA  updated  the  baseline 
projections for non-EU countries. For Norway and Switzerland, projections that have been developed in the 
context of the EU-CLIMIT projects were used. For other non-EU countries in Europe, the activity projections 
relying on results of the EU funded EUCLIMIT-9EAST project20 (includes Western Balkan, Republic of Moldova, 
Ukraine,  and  Georgia)  have  been  updated  considering  the  results  of  the  recently  completed  project  for 
Republic of Moldova (led by IIASA and funded by UNECE) and the work for Western Balkan (EU funded project 
EU4Green  where  IIASA  led  work  on  modelling  future  emissions  and  mitigation).  In  both  projects  bilateral 
consultations with national experts have taken place allowing to improve, to some extent, estimates for the 
past years and consequently also projections, since revised and up to date schedules for implementation of 
various  policies  have  been  communicated.  Additionally,  several  other  EECCA  countries  (Armenia,  Georgia, 
Kazakhstan, Ukraine) took part in a meeting at IIASA in November 2023 discussing national air pollution, air 
quality measurements and policies. The exchange provided input to GAINS databases (fuel quality, structure 
of transport sources, residential fuel use information, availability of air quality measurement data) improving 
data and enabling better validation of atmospheric calculations. For the remaining countries, the projections 
that have been developed and published by the International Energy Agency (IEA) in its World Energy Outlook 
2023 (IEA 2023) are used.  

The Baseline reflects the final Euro 7 Regulation21. Compared to CAO3, the CAO4 Baseline includes updated 
implementation of the revision of the IED. The revised IED (Directive 2010/75/EU as amended by Directive 
2024/1785)22 was published in the Official Journal of the EU on 15 July 2024 and entered into force on 4 August 
2024. The revised IED will lead to a greater emphasis on, and application of, the lower end of the emission 
levels  associated  with  the  BAT  (BAT-AEL  ranges)  included  within  the  BAT  Conclusions.  The  CAO4  Baseline 
assumes, as a minimum, compliance with the upper end of the BAT-AELs and the revised IED will likely lead to 
further reductions although it is not currently possible to estimate the extent of that further reduction. 

In the CAO3, the  Commission’s  proposal for a revised IED  for agriculture was  integrated into the  Baseline, 
meaning cattle, pig and poultry farms with more than 150 livestock units (LSU) were covered by BAT measures 
as defined under the IED. However, since the European Parliament has adopted amendments to the original 
proposal, the implementation of the revised IED in GAINS has been reassessed. The revised IED now sets the 
threshold for the inclusion of poultry and pig farms to 280 LSU for poultry (300 LSU for laying hens) and 350 
for pigs or 380 LSU for mixed pig and poultry farms. An exemption is made for organic or low-density pig farms. 
Cattle farms are  excluded with a  reassessment of  also addressing emissions from cattle farms  planned for 
2026  23.  As in CAO3, the  EUROSTAT farm structure survey  is  used to  evaluate  the  individual impact  of the 
implementation of the IED on each Member State using newly available data for 2020 (EUROSTAT, 202224), 
unless more detailed country level data was received. This information has been discussed with the national 

17 SWD(2024) 63 final 
18 https://e3modelling.com/modelling-tools/primes/ 
19 Service Contract 340201/2019/813567/CLIMA.C.1   
20 Service Contract ENER/A3/SER/2019-563/SI2.840866 - ENER/2020/OP/0005; Extension of the EU Energy and Climate 
Modelling Capacity to include the Energy Community and its Nine Contracting Parties 
21 Regulation - 2024/1257 - EN - EUR-Lex   
22 The revised IED (Industrial and Livestock Rearing Emissions Directive (IED 2.0)): 
https://environment.ec.europa.eu/topics/industrial-emissions-and-safety/industrial-and-livestock-rearing-emissions-
directive-ied-20_en  
23 Item10-IED_provisionalagreement_20220104COD_EN.pdf (europa.eu) 
24 https://ec.europa.eu/eurostat/databrowser/view/ef_lsk_main/default/table?lang=en  

33 

 
      
      
 
 
experts  during  consultations  (Task  2,  see  Section  3.4)  to  review  our  assessment  and  implementation  of 
structural  changes  as  well  as  policies  in  the  GAINS  model,  also  ensuring  that  any  more  stringent  national 
policies are reflected accordingly in the Baseline.  

Another key element for inclusion in the Baseline has been the information provided by Member States (since 
the  analysis  done  for  the  third  Clean  Air  Outlook)  in  the  latest  versions  of  their  air  pollutant  emission 
projections  and  NAPCPs  (primarily  related  to  additional  Policies  and  Measures).  While,  based  on  our 
experience, the provided projections often lack quantitative assessment of impact of policies to be able to 
make changes to the baseline scenario, the information from the NAPCPs25 and PaM tool has been used to 
help inform discussions with the Member States in Task 2  and jointly conclude about potential changes to 
assumptions about policy implementation, if needed. 

The potential impact of the EU Urban Mobility Framework26 initiative that aims to make urban mobility more 
sustainable, smart, and healthy has not been considered in the CAO4 Baseline. The PRIMES energy system 
model,  which  provides  the  energy  sector  projections  for  the  Clean  Air  Outlook,  has  limitations  to  provide 
spatially explicit output reflecting the impact of such an incentive on urban transport and its emissions.  

3.4 

Consultation of the Baseline with EU Member States  

3.4.1 

Introduction 

The purpose of this task is to present, discuss, and validate the assumptions and key results of the Baseline 
scenario with the Member States. As a result of consultations with the Member States, the assumptions used 
in the Baseline have been complemented or modified accordingly, where necessary. 

3.4.2  Approach 

The consultations were organized as half-day online meetings with experts representing each Member State 
to share the GAINS model Baseline scenario developed for this assignment and to discuss specific national 
features included in the Baseline and the modelling framework. Key elements discussed during consultations 
included  policies  and  measures  implemented  and  planned  in  the  Member  State,  methodological  features 
which have impact on emission inventories (including condensable PM), discussion of discrepancies between 
GAINS and the national inventories as well as projections that remain after development and implementation 
of the Baseline in GAINS.  

The  summary  of  introduced  changes,  in  responses  to  inputs  from  Member  States  and  discussions  at  the 
consultation meetings,  as well  as the  final  revised  Baseline  were  presented  at  the online  meeting  with all 
Member States and the Commission.  

The detailed country comparisons and discussion of remaining discrepancies is provided in the Annex to this 
report (see Section 3 in Annex). 

25 https://ec.europa.eu/environment/air/reduction/NAPCP.htm  
26 EU Urban Mobility Framework (COM(2021) 811) 

34 

 
      
      
 
 
 
Policy scenarios and their consequences for emissions 
and ambient air quality 

4.1 

Introduction 

In  this  section,  we  develop  and  analyse  a  set  of  scenarios  (in  addition  to  the  Baseline)  exploring  further 
mitigation potential and cost-optimal achievement of air quality and emission reduction targets. The impact 
of systematic inclusion of condensable particles as well as methane reduction on achievement of EU air quality 
targets is assessed via sensitivity analysis. For each scenario, the air quality, health and ecosystem impacts and 
co-benefits are shown. 

4.2 

Approach 

Five key policy scenarios were developed. Additional sensitivity analysis was performed, including two cases 
where emission factors representing varying measurement methods of condensable particulate matter, and 
further scenario variants analysing impacts of the EU vs non-EU reduction of methane on ozone concentrations 
and compliance with legislation (Table 4-1). 

The scenarios were developed and implemented in the GAINS model, which is used to calculate emissions of 
all air pollutants as well as PM2.5 and NO2 concentrations, health, and ecosystem impacts. GAINS was operated 
in simulation mode for the Baseline scenario (and its sensitivity cases) as well as in optimization mode for the 
other scenarios. The simulation of ozone concentrations and impact of methane emission changes in the EU 
and outside was performed with the global EMEP model by Met.no. IIASA developed and provided respective 
global emission fields for methane and non-methane ozone precursors and calculated health and ecosystem 
impacts from gridded concentration output received from the EMEP model.  

4.3 

Development of policy and sensitivity scenarios 

The scenarios and sensitivity cases are summarized in Table 4-1. The scenarios were developed for the period 
2005-205027  and  analysed  for  years  2005,  2020,  2025,  2030,  2040  and  2050,  depending  on  the  scenario. 
Sensitivity cases are variants of the Baseline, where specific elements are altered (emission factors for the 
condensable fraction of PM, methane emissions outside Europe), while all other scenario elements are kept 
unchanged, so that the effect of this particular alteration can be quantified. 

27 For years prior to 2020, only the (statistical) Baseline scenario and its sensitivity cases were developed. For the year 
2020, also the optimized ERC scenario was developed. All other scenarios are only developed for future years. 

35 

 
      
      
 
 
 
Table 4-1: Overview of the developed policy and sensitivity scenarios 

Scenario name 
Baseline 

Scenario description 
Consistent with the projected evolution of the energy system in the 2040 
climate target impact assessment28. It includes the latest EU and national 
legislation and plans.  

Results for: 
2005 - 2050 

MTFR 

ZPAP 

ERC 

Maximum Technically Feasible Reduction (MTFR) scenario developed for the 
Baseline scenario. 

2030 – 2050;  
(2020, 2025) (a) 

A Zero Pollution Action Plan (ZPAP) scenario that allows to fulfil the target 
related to the ecosystem impacts of air pollution from the ZPAP(b); this is an 
optimized scenario.   
Optimized scenarios where the model would be forced to reach a situation 
where all MS meet all their 2020-29 emission reduction commitments (ERCs) 
in 2020 and 2025, and ERCs 2030 in 2030 and beyond to evaluate impacts 
compared to the baseline, and measures required to achieve compliance, 
providing results to inform the 2025 review of the NEC Directive. 

2030 

2020 - 2050 

ERC+ZPAP 

Optimized scenario where both, ZPAP targets as well as compliance with ERCs 
in 2030 are assured. 

2030 

Sensitivity cases to Baseline 

Ref2_filter 

High_C 

Sensitivity case where only filterable PM from combustion sources is 
measured, i.e., excluding the condensable fraction. Emission factors originate 
from TNO Ref2 set.  

Sensitivity case for condensable PM where ‘high’ emission factors originating 
from the study supported by Nordic Council of Ministers (Simpson et al. 
2022b) are applied consistently to all countries.  

Global MTFR 
(CH4 only) 

A scenario implementing maximum technically feasible reductions on 
methane (CH4) emission sources globally, while retaining Baseline on other 
ozone precursors (NOx, VOC, CO). 

2005 - 2050 

2005 - 2050 

2040, 2050 

Sensitivity case to MTFR 

Global MTFR 
(AP+CH4)  

A scenario implementing maximum technically feasible reductions on all 
ozone precursors (NOx, VOC, CO, CH4) globally. 

2040, 2050 

(a) The MTFR for 2020 and 2025 were calculated to develop the optimized scenarios achieving respective ERCs in 2020 and 2025. 
(b) According to the analysis with the GAINS model, the ZPAP target on reducing premature deaths from air pollution is met in the 
baseline. 

4.3.1  Key policy scenarios 

The Baseline scenario assumptions have been discussed in Section 3.3; it creates a starting point for all other 
scenarios, i.e., all remaining policy scenarios are built/further developed starting from assumptions and setup 
of the Baseline scenario. 

The  MTFR  (Maximum  Technically  Feasible  Reduction)  scenario  implies  the  application  of  best  available 
emission control technologies (conversely, achieving highest emission reductions) defined in the GAINS model 
considering achievable reduction rates and technological application limits. The latter refer to limitations due 
to  the  age  structure  of  installations,  their  lifetimes,  and  capacity  for  uptake  of  mitigation  technologies 
appropriate for a given source. Effectively, it means that over time more and more capacity can be controlled 
with low emission techniques  and the  pace  and  extent  of penetration will be sector-  and country-specific. 
While  such  technological  constraints  are  taken  into  account,  no  financial  constraints  are  assumed  limiting 

28 More specifically, it is consistent with the ‘S3 scenario’ of that impact assessment, which illustrates a pathway to meet 
the greenhouse gas emission reductions of the preferred target option. 

36 

 
      
      
 
 
installation or operation of any of the measures. Such a definition was also applied for the development of 
MTFR scenarios within the AAQD impact assessment as well as the CAO3. 

For non-EU countries, current legislation was assumed in all scenarios. Energy projections in non-EU countries 
are  consistent  with  recently  completed  EU  projects;  in  particular  for  West  Balkan,  the  Baseline  contains 
climate mitigation action comparable to EU MIX55, as used in the EU4Green West Balkan project. 

The ZPAP (Zero Pollution Action Plan) scenario is developed to address achievement of the ZPAP targets29 for 
2030. Since, according to GAINS model assessment, the health-related target is achieved in the Baseline, the 
focus is to achieve the target related to the ecosystem impacts of air pollution. Development of this scenario 
involved  additional  optimization  runs  with  the  GAINS  model  to  identify  a  cost-effective  solution  achieving 
these objectives, provided they are feasible.  

The ERC scenario was developed as a cost optimal scenario, where all MS meet all their 2020-29 emission 
reduction commitments in 2020 and 2025 (ERC-20 and ERC-25, respectively), to evaluate impacts compared 
to  the  baseline,  providing  results  to  inform  the  2025  review  of  the  NEC  Directive.  A  retrospective  cost 
optimization  in  GAINS  is  a  rather  unusual  exercise  as  the  constraints  regarding  applicability  limits  for 
technologies  depend  on  the  time  perspective  (e.g.,  constraints  for  2030  are  stricter  than  for  2040).  The 
assumptions  on  applicability  (of  measures  beyond  current  legislation)  constraints  for  the  2020  and  2025 
optimization are set from a 2015 perspective. Furthermore, the ERC scenarios where compliance of the ERC 
for 2030 is assured were developed for 2030, 2040, and 2050. 

The ERC+ZPAP scenario: Additionally, IIASA has developed a scenario for 2030 where both ZPAP and ERCs 
compliance  is assured, provided attainment of both is feasible.  This scenario is motivated by the  fact  that 
achieving  ZPAP  objectives  does  not  assure  compliance  with  ERCs  and  vice  versa,  achieving  ERCs  does  not 
assure that all objectives of ZPAP are reached.  

4.3.2 

Inclusion of condensable PM 

As shown in the CAO3, and also demonstrated in Figure 4-1 (from Simpson et al., 2022) below, the inclusion 
of  the  condensable  fraction  of  PM  can  change  PM  emission  estimates  substantially,  particularly  for  small 
combustion sources. Figure 4-1 shows the PM2.5 emissions for small combustion for the year 2018 for cases 
excluding  condensables  (Typical_solid),  including  condensables  (Typical_total),  and  an  alternative  case 
assuming higher emission factors (HighEF_total).  

In recent years, the reporting of emissions with condensables has been improving. For several sectors, such 
as transport, condensables have long been included due to the established measurement methods and the 
availability  of  default  emissions  factors  (e.g.,  those  provided  in  the  Guidebook).  However,  in  the  case  of 
residential  combustion  of solid  fuels  (GNFR  C), which  is  typically  a  key  source  of  primary  PM2.5  emissions, 
reporting  has  been  more  inconsistent.  This  stems  from  challenges  like  a  lack  of  data,  differences  in  the 
methods used etc. 

29 https://ec.europa.eu/environment/strategy/zero-pollution-action-plan_en  

37 

 
      
      
 
 
 
Figure 4-1: Comparison of PM2.5 emissions [kt/year] for small combustion for 2018 (Simpson et al., 2022) 

The  inclusion  of  condensables  in  official  reporting  has  progressively  improved,  especially  following  the 
introduction of a set of emission factors in  the Guidebook based on the work of Denier van der Gon et al. 
(2015).  Despite  this  progress,  some  inconsistencies  in  reporting  still  persist  due  to,  for example,  (i)  use  of 
different emission factors Tiers from the Guidebook, where Tier 1 default factors account only for fuel type 
and not for type of combustion device, (ii) lack of data about combustion type structure and its development 
over  time,  (iii)  quality  of  data  or assumptions  how  to  estimate  fuel  use  and  combustion  practices,  etc.  To 
identify possible inconsistencies with respect to emission factors used, we have reviewed the 2023 reported 
emissions submissions using the following approach: 

• 

Identified EU Member States that use Ref2 as a data source for PM2.5 emissions in GNFR C for the EMEP 
status runs in 202330. 

•  Checked the descriptions of the Emission Factors (EFs) for GNFR C in the Informative Inventory Report 

(IIR) and any mention of condensables. 

•  When the inclusion of condensables was unclear, the implied emission factor was calculated based on 
NFR data. If the EF was less than approximately 100 g/GJ, it was assumed that condensables were not 
included, as the implied EF is expected to range between 500 and 800 g/GJ for traditional appliances 
when condensables are included31. Since the more advanced stoves have lower emission factors as 
illustrated in Simpson et al. (2022), the 100 g/GJ implied emission factor has been chosen as a safe limit 
below which condensables can be assumed to be excluded. 

The analysis, as well as Baseline consultations with Member States, revealed that five countries (Table 4-2) 
have not incorporated condensables for residential (stationary) small combustion. 

30 https://emep.int/publ/reports/2023/EMEP_Status_Report_1_2023.pdf 

38 

 
      
      
 
 
 
Table 4-2: EU countries where condensables are not included in the 2023 reporting 

Country 
Austria 

Estonia 

Germany 

Explanation 
The IIR is not clearly explaining the inclusion, however, national experts confirmed during Baseline 
consultations that the condensables are not included. Furthermore, the assessment of implied EFs 
shows they are < 100 g/GJ, indicating that condensables are likely not included. 
IIR explicitly mentions that condensables are excluded (based on input of measurement team). 

The IIR does not provide explicit information if condensables are included. However, during 
Baseline consultations, national experts confirmed that the submission did not include explicitly 
condensables. Furthermore, the assessment of implied EFs shows they are rather small and 
therefore condensables are likely not included. 

Lithuania 

IIR explicitly mentions that PM2.5 condensables are not included in the national inventory.  

Luxembourg 

The IIR does not provide explicit information if condensables are included. However, during 
Baseline consultations, national experts confirmed that the national inventory did not include 
explicitly condensables. Assessment of implied EFs shows they are too small and therefore 
confirming that the condensables are likely not included. 

In the Baseline, PM emission factors in the GAINS model are country specific and assume that condensable 
fraction is included, also for the five countries listed above (Table 4-2). The assumptions in GAINS have been 
also communicated to the Member States during the consultations that IIASA held with them (Section 3.4). A 
simple comparison of GAINS emission factors in CAO4, CAO3, vs national reporting is not directly possible, 
owing  to  different  resolution  of  the  model  and  the  guidebook/national  methods  of  estimating  emissions. 
However, as illustrated in Section 3 of the Annex, GAINS and nationally reported residential sector emissions 
compare well for all countries that report condensable PM and differences for the remaining few (as listed 
above) were discussed and acknowledged by the Member States experts. 

Comparing Ref2 scenarios with reported emissions 
Reported emissions of PM2.5 for GNFR C in 2019 were compared with the outcomes of the 2019 Ref2 scenarios 
for all Member States31. The Ideal and Typical scenarios are both based on the median of the full range of 
emission factors collected when developing Ref2, whereas the HighEF scenario is based on the median of the 
top 5 (or top 3 when less than 10 values were identified) emission factors found in the literature search. Both, 
the  Typical  and  HighEF  scenarios  include  “bad  combustion”  practices,  such  as  burning  wet  wood,  which 
increase emission factors (Simpson et al. 2022b).  

The analysis of Figure 4-2 shows a diverse picture, with some countries with reported emissions lying between 
the Ideal and Typical scenarios, and thus lower than the Typical values that incorporate the impact of human 
behaviour and “bad combustion”, and some, such as Poland and France, with higher reported emissions, that 
come closer to the HighEF scenario. These variations highlight the lack of a consistent approach to incorporate 
condensables in the small combustion sector across Member States. As for the Member States that do not 
include condensables at all (as shown in Table 4-2), the EFs are much lower, even lower than the EFs based on 
the Ideal scenario, as can be expected. 

31 Based on the 2023 submission (see https://www.ceip.at/webdab-emission-database/reported-emissiondata) extract 
reported small combustion emissions 

39 

 
      
      
 
 
 
 
Figure 4-2: Comparison of Ref2 scenarios to reported 2019 emissions, based on 2023 submission [kt PM2.5]  

The analysis of Figure 4-3 indicates that for all Member States, reported emissions for 2019 to 2021 are lower 
than the HighEF scenario, with some countries relatively lower than others. The variation of emissions from 
year to year is relatively limited. It can be attributed to a combination of changing activity data (meteorological 
variation combined with changes in the fuel mix) and emission factors (installation renewal). When drawing 
these comparisons, it is also important to keep in mind the impact of country-specific issues on emissions in 
this sector. Additionally, since 2020 and 2021 coincide with the COVID-19 pandemic, it is challenging to assess 
the extent to which the pandemic may have influenced emissions from small combustion sources.  

 Figure 4-3: Comparison of Ref2 scenario (HighEF) to reported emissions for 2019-2021, based on the 2023 

submissions [kt PM2.5] 

40 

 
      
      
 
 
 
Scenarios 
To  address  the  uncertainties  and  analyse  impact  of various  assumptions  (emission  factors)  for PM EFs  for 
residential sector, we explore the impact on compliance and assess which of the assumption leads to improved 
representation of measured particles in the ambient air. For that purpose, we use the following assumptions 
about  the  condensable  fraction  of  PM  in  the  two  analysed  scenarios  (these  are  sensitivity  cases  to  the 
Baseline):  

•  Ref2_Filter: We assume that the condensables in EFs for small combustion are fully excluded. The 
emission factor set relies on the fuel and combustion technology specific set of Ref2 EFs without 
condensables, as reported in Simpson et al. (2022) 

•  High_C: The second sensitivity scenario considers applying higher emission factors for condensables. 

These will be based on the high emission factor set from Simpson et al. (2022), HighEF_total, combined 
with national inventory information, if found necessary. Recent inventory submissions (2022/2023) by 
e.g., Poland and France suggested that the base scenario used in the Ref2 study might underestimate 
actual emissions and some national emission factors might be even higher, i.e., close to those reported 
as HighEF_total estimated in the Simpson et al. (2022). Since the recently reported emission factors for 
these two countries were similar to HighEF_total, the scenario is relying on the consistent set of high 
emission factors as reported in the Simpson et al. (2022).  

The results of these two sensitivity cases are compared against the Baseline scenario in view of achieving (in 
2025 and 2030) the NEC Directive emission reduction commitments for PM2.5 per Member State. This way, the 
sensitivity of the  condensables  issue with regard to meeting the reduction commitments can be  assessed. 
Furthermore, the results will be evaluated against the newly available ambient measurements with the EMEP 
model.  

4.3.3 

Sensitivity to global reductions of methane and other ozone precursors 

Sensitivity  to  changes  in  CH4  emissions  outside  Europe:  Background  concentrations  of  ozone  are  to  some 
degree determined by emissions of methane as an ozone precursor. Due to the long lifetime of CH4 in the 
atmosphere, this contribution is non-local and thus strongly influenced by emissions outside the EU. Sensitivity 
cases  were  constructed  testing  different  assumptions  for  global  CH4  emissions,  drawing  from  scenarios 
developed  in  the  context  of  the  Gothenburg  Protocol  review.  Separate  reduction  cases  of  only  CH4  and 
combined with other precursors (NOx, VOCs, CO) were analysed. The configurations of sensitivity scenarios 
are  shown  in  Table  4-3.  The  global  MTFR  mitigation  case  is  very  ambitious  and  assumes  50 %  global  CH4 
emission reductions and even higher reductions of the non-methane ozone precursors (NOx, NMVOC, CO) in 
2050. The high reduction of non-methane ozone precursors at the global level is driven by the still available 
mitigation potential for non-EU countries, which is much higher compared to the EU (at the EU level, the MTFR 
case yields  about  20-30% reduction  beyond the Baseline) owing to existing policies  and underlying energy 
scenario where strong decline of fossil fuels is projected.  

Table 4-3: Setup of ozone precursor sensitivity scenarios 

Scenario 
Global  MTFR 
only) 

Global 
(AP+CH4) 

(CH4 

MTFR 

Region 
EU 
Non-EU/World 
EU 
Non-EU/World 

CLE 

CH4 

MTFR 
√ 
√ 
√ 
√ 

NOx, VOC, CO 
MTFR 

CLE 
√ 
√ 

√ 
√ 

41 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
4.4 

Emissions and air quality under the scenarios 

For each of the scenarios (Baseline and policy scenarios) the modelling results are provided and analysed for 
each  Member  State,  for  the  EU  as  a  whole  and  for  at  least  the  years  2025,  2030,  2040  and  2050.  The 
description  includes  an  explanation  of  key  trends,  differences  of  results  between  scenarios,  and  brief 
statement as to how CAO4 results compare to the CAO3.  

4.4.1  Evolution of emissions 

This section provides estimates of emissions of key air pollutants, including SO2, NOx, NMVOC, NH3 PM2.5, Black 
Carbon (BC), and methane (CH4) for selected scenarios. The estimates are shown here as EU total, distributed 
by major source categories (aggregates of GNFR sectors).  

Figure  4-4  through  Figure  4-9  show  the  air  pollutant  emissions  in  the  EU  for  the  Baseline  and  selected 
scenarios, including MTFR (reflecting on the scope for further mitigation in 2030 and beyond) and scenarios 
that assure compliance with the ZPAP objectives as well as achieve the ERCs in the period 2020 through 2050.  

At the EU level, emissions of all six pollutants have declined since 2005 by about 40% for PM2.5 and NMVOC, 
over  50%  for  BC,  nearly  60%  for  NOx  and  about  85%  for  SO2.  However,  NH3  declined  by  only  9%.  Key 
contributors  to this  reduction  are  the power, industry,  and  transport  sectors  for SO2, NOx, while  for  PM2.5 
residential sector is more important. For BC, over 70% of the achieved reduction originates from transport and 
about  20%  from  residential  combustion.  Further  reductions  are  expected  for  most  species  owing  to  the 
continued renewal of installations that need to comply with more stringent emission limit values as well as 
envisaged decarbonization, consistent with the objectives of the European Green Deal.   

Compared to 2005, emissions are expected to decline in the Baseline by 50 to 90% (depending on species) by 
2030, except ammonia, which decline only by 11%, i.e., only 2 percentage points from 2025 level estimated in 
GAINS32. Overall, the key contribution to emission reduction comes from improved compliance with the IED, 
successful  transition  and  implementation  of  policies  in  the  transport  sector,  and  an  assumed  decline  in 
fuelwood use for residential heating. These transformations will have little impact on ammonia emissions and 
since  the  revised  IED  does  not  bring  a  significant  increase  in  coverage  and  stringency,  and  other  new 
environmental policies for agriculture are scarce, the expected change is rather small. 

Despite significant success of current legislation, including the envisaged impacts of the EU climate policy, and 
an associated strong reduction of air pollutants, there are still technical means of reducing emissions further. 
This is demonstrated in the MTFR scenario as well as scenarios assuming full compliance with ZPAP objectives 
and  ERCs.  The  GAINS  model  estimates  that  the  MTFR  scenario  could  bring,  EU-wide,  over  40%  additional 
mitigation potential (compared to baseline) for SO2 in 2030 and 2050, 25-30% for PM2.5 (nearly 38% for BC), 
over 20% for NMVOC and NOx, and about 23% and 27% for NH3 by 2030 and 2050, respectively. It is interesting 
to  note  that  these  estimates  indicate  a  very  similar  further  mitigation  potential  for  all  species,  even  for 
ammonia, that typically had a much smaller mitigation scope. 

As  evaluated  further  in  the  report,  several  Member  States  have  not  (in  the  past)  and  are  not  expected 
(according  to  GAINS  calculation  in  the  baseline)  to  be  in  compliance  with  the  ERCs  for  some  pollutants, 
especially  NH3.  At  EU  level,  the  ZPAP  objective  for  eutrophication  is  missed.  The  additional  reductions  to 
achieve compliance  are typically  not large  from the perspective of the  EU as a whole,  as illustrated in the 

32 This estimated decrease in GAINS is lower than the value given in the EEA Briefing which is based on Member States’ 
reported emission inventories (https://www.eea.europa.eu/publications/national-emission-reduction-commitments-
directive-2024) due to a lower estimate in GAINS for 2005. For 2020 the EEA and GAINS estimates agree well, whereas 
the number for 2022  by EEA based on Member States’ reports EEA is lower than GAINS in 2025. 

42 

 
      
      
 
 
 
following figures. The exception is NH3, for which more than half of the identified mitigation potential (at the 
EU level) would need to be mobilized to achieve ZPAP objectives. 

Overall, the trends of all air pollutants mirror those assessed in CAO3. However, there are some systematic 
differences,  which  are  either  driven  by  updates  of  methodology,  activity  development  outlook,  or  policy 
implementation. These include, for example:  

- 

slightly higher NOx due to updates for soil NOx,  

-  updated emission factors, structure and outlook for residential sector driving up PM and BC, although 

further mitigation potential, especially in the longer term, remains pretty much the same,  

- 

and higher 2030 NH3 emissions since ambition of IED has been reduced.  

Current estimates and trends for SO2 and NMVOC are pretty much the same as CAO3. 

Figure 4-4: Emissions of SO2 in the baseline and selected scenarios, EU total. 

43 

 
      
      
 
 
 
 
Figure 4-5: Emissions of NOX (as NO2) in the baseline and selected scenarios, EU total. 

Figure 4-6: Emissions of PM2.5 in the baseline and selected scenarios, EU total. 

44 

 
      
      
 
 
 
 
 
 
Figure 4-7: Emissions of NH3 in the baseline and selected scenarios, EU total. 

Figure 4-8: Emissions of NMVOC in the baseline and selected scenarios, EU total. 

45 

 
      
      
 
 
 
 
 
Figure 4-9: Emissions of black carbon (BC) in the baseline and selected scenarios, EU total. 

Methane emissions for the developed scenarios have also been estimated (Figure 4-10). Emissions and future 
outlook  are  very similar to  those  presented in CAO3. Since  2005, emissions declined by over 30%  and are 
expected to decline further reaching about 37% lower levels than estimated for 2005 by 2030.  Compared to 
2020, the expected reduction by 2030 is about 21%. Notably, the GAINS model underestimates the benefits 
of  measures  introduced  in  agriculture  to  achieve  ZPAP  targets  (the  impact  of  NH3  dedicated  housing  and 
storage measures on CH4 emissions is not considered in the current model formulation). By 2040,  a reduction 
of 31% is estimated, compared to 2020, with key reductions from progressing decarbonization of economy 
and benefits of existing waste sector policies.  

46 

 
      
      
 
 
 
Figure 4-10: Emissions of CH4 in the baseline and selected scenarios, EU total. 

47 

 
      
      
 
 
 
 
4.4.2  Compliance with the NEC Directive objectives 

The following tables summarize for all NEC Directive pollutants and EU countries, the compliance with the NEC 
Directive ERCs for 2030 and 2050 for selected scenarios. The Annex (Section 6) provides analysis of compliance 
with  the  NEC  Directive  ERCs  for  2020  and  2025  as  well  as  assessment  of  the  potential  and  feasibility  for 
achieving them applying GAINS optimization to the 2020 and 2025 emissions. 
For SO2, the Baseline projections indicate that all countries will be in compliance in 2030 and beyond (Table 
4-4).  

Table 4-4: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios 

for SO2.  

Country 
Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Rep. 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
EU 

2030 ERC 
41% 
66% 
88% 
83% 
93% 
66% 
59% 
68% 
34% 
77% 
58% 
88% 
73% 
85% 
71% 
46% 
60% 
50% 
95% 
53% 
70% 
83% 
88% 
82% 
92% 
88% 
22% 
78% 

2030 

Baseline 

ZPAP 

72% 
83% 
95% 
92% 
96% 
86% 
73% 
98% 
82% 
85% 
67% 
97% 
82% 
94% 
85% 
73% 
76% 
71% 
99% 
80% 
85% 
91% 
93% 
89% 
93% 
95% 
68% 
89% 

72% 
83% 
95% 
92% 
96% 
86% 
73% 
98% 
82% 
85% 
67% 
97% 
82% 
94% 
85% 
73% 
76% 
71% 
99% 
80% 
85% 
91% 
93% 
89% 
93% 
95% 
68% 
89% 

ERC 

72% 
83% 
95% 
92% 
96% 
86% 
73% 
98% 
82% 
85% 
67% 
97% 
82% 
94% 
85% 
73% 
76% 
71% 
99% 
80% 
85% 
91% 
93% 
89% 
93% 
95% 
68% 
89% 

MTFR 

Baseline 

76% 
90% 
98% 
96% 
99% 
89% 
78% 
99% 
89% 
88% 
81% 
99% 
88% 
96% 
94% 
77% 
90% 
87% 
100% 
88% 
91% 
96% 
97% 
97% 
95% 
98% 
69% 
94% 

81% 
87% 
98% 
93% 
97% 
95% 
78% 
99% 
87% 
89% 
82% 
98% 
87% 
96% 
90% 
74% 
86% 
76% 
100% 
83% 
95% 
94% 
96% 
91% 
97% 
96% 
77% 
94% 

2050 
ERC 

81% 
87% 
98% 
93% 
97% 
95% 
78% 
99% 
87% 
89% 
82% 
98% 
87% 
96% 
90% 
74% 
86% 
76% 
100% 
83% 
95% 
94% 
96% 
91% 
97% 
96% 
77% 
94% 

MTFR 

83% 
95% 
100% 
97% 
99% 
97% 
82% 
100% 
92% 
93% 
90% 
99% 
90% 
98% 
95% 
78% 
94% 
90% 
100% 
90% 
98% 
97% 
99% 
97% 
99% 
98% 
77% 
97% 

48 

 
      
      
 
 
 
 
 
 
 
For  NOx,  emissions  of  two  countries  were  estimated  above  the  ERCs  for  2030,  while  all  comply  by  2050. 
Inclusion of soil NOx would lead to much more severe non-compliance (eight countries) with 2030 ERCs in 
2030, but not in 2050 (Table 4-5). 

Table 4-5: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios 

for NOx. Highlighted cells indicate non-attainment of the 2030 ERCs. 

Country 

2030 ERC  Baseline 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Rep. 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
EU 

69% 
59% 
58% 
57% 
55% 
64% 
68% 
30% 
47% 
69% 
65% 
55% 
66% 
69% 
65% 
34% 
51% 
83% 
79% 
61% 
39% 
63% 
60% 
50% 
65% 
62% 
66% 
61% 

78% 
79% 
69% 
71% 
76% 
72% 
75% 
72% 
70% 
75% 
71% 
80% 
67% 
75% 
77% 
55% 
57% 
89% 
75% 
78% 
61% 
74% 
56% 
66% 
70% 
79% 
72% 
73% 

2030 

Baseline+
Soil NOx 
74% 
74% 
58% 
65% 
73% 
64% 
65% 
63% 
66% 
68% 
66% 
77% 
55% 
61% 
74% 
44% 
43% 
88% 
74% 
73% 
54% 
72% 
48% 
59% 
67% 
75% 
65% 
68% 

ZPAP 

ERC 

MTFR 

Baseline 

80% 
80% 
73% 
73% 
77% 
75% 
76% 
75% 
76% 
76% 
74% 
81% 
70% 
76% 
79% 
62% 
61% 
91% 
76% 
79% 
64% 
78% 
65% 
69% 
73% 
81% 
75% 
76% 

78% 
79% 
70% 
71% 
76% 
74% 
75% 
72% 
70% 
75% 
72% 
80% 
72% 
75% 
77% 
56% 
57% 
90% 
79% 
78% 
61% 
75% 
66% 
66% 
73% 
80% 
72% 
74% 

84% 
82% 
80% 
82% 
85% 
81% 
79% 
78% 
78% 
78% 
77% 
85% 
78% 
81% 
82% 
67% 
70% 
93% 
80% 
82% 
73% 
84% 
75% 
81% 
76% 
86% 
80% 
80% 

91% 
92% 
92% 
86% 
87% 
90% 
89% 
89% 
86% 
92% 
88% 
90% 
89% 
90% 
90% 
79% 
88% 
95% 
91% 
89% 
87% 
89% 
85% 
83% 
92% 
89% 
86% 
89% 

2050 

Baseline+
Soil NOx 
86% 
86% 
79% 
80% 
84% 
82% 
79% 
80% 
81% 
84% 
83% 
87% 
75% 
74% 
86% 
66% 
73% 
94% 
90% 
83% 
78% 
86% 
73% 
76% 
88% 
85% 
79% 
83% 

ERC 

MTFR 

91% 
92% 
93% 
86% 
88% 
91% 
89% 
90% 
86% 
92% 
89% 
90% 
89% 
91% 
90% 
79% 
88% 
95% 
91% 
89% 
87% 
89% 
85% 
83% 
92% 
89% 
86% 
89% 

95% 
94% 
96% 
95% 
95% 
96% 
91% 
93% 
90% 
95% 
92% 
95% 
93% 
95% 
94% 
88% 
94% 
98% 
93% 
92% 
93% 
95% 
93% 
94% 
95% 
93% 
91% 
93% 

49 

 
      
      
 
 
 
 
 
Nearly a third of Member States might  be missing on compliance with the PM2.5  ERCs  in 2030  (Table 4-6), 
although several of these instances are rather small according to GAINS calculations, i.e., Cyprus, Denmark, 
Portugal, within 1% of ERCs. There exists mitigation potential that could be unlocked to achieve compliance. 
Compared to CAO3, the number of Member States, for which non-compliance with ERCs in 2030 is estimated, 
doubled, i.e. from 4 in CAO3 to 8 in CAO4. However, for Cyprus and Portugal the  non-compliance is small, 
while  for  Czechia  and  Romania  the  difference  is  larger  and  driven  by  revised  assessment  of  emissions  for 
residential sector (Czechia and Romania) and also open burning of agricultural residue (Romania). The updates 
reflect new information and methods applied and are reflected in both the national inventories and GAINS for 
2005 and 2030.  

Table 4-6: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios 

for PM2.5. Highlighted cells indicate non-attainment of the 2030 ERCs. 

Country 

2030 ERC 

Baseline 

Ref2_filter 

High_C 

2030 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Rep. 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
EU 

46% 
39% 
41% 
55% 
70% 
60% 
55% 
41% 
34% 
57% 
43% 
50% 
55% 
41% 
40% 
43% 
36% 
40% 
50% 
45% 
58% 
53% 
58% 
49% 
60% 
50% 
19% 
50% 

66% 
62% 
65% 
74% 
69% 
54% 
55% 
75% 
59% 
63% 
51% 
70% 
43% 
73% 
54% 
77% 
62% 
71% 
73% 
50% 
60% 
52% 
39% 
76% 
41% 
46% 
69% 
57% 

63% 
58% 
67% 
66% 
70% 
54% 
53% 
71% 
63% 
55% 
50% 
68% 
48% 
68% 
56% 
67% 
62% 
69% 
74% 
52% 
59% 
48% 
40% 
74% 
46% 
43% 
64% 
55% 

66% 
63% 
61% 
77% 
69% 
43% 
53% 
74% 
49% 
72% 
51% 
72% 
38% 
74% 
48% 
77% 
64% 
71% 
71% 
46% 
61% 
56% 
32% 
71% 
40% 
45% 
80% 
57% 

ERC 

66% 
62% 
65% 
75% 
70% 
60% 
55% 
75% 
59% 
63% 
51% 
70% 
55% 
74% 
54% 
77% 
62% 
71% 
77% 
50% 
60% 
53% 
58% 
76% 
60% 
50% 
69% 
60% 

MTFR 

Baseline 

77% 
72% 
75% 
83% 
81% 
66% 
68% 
84% 
71% 
73% 
62% 
78% 
57% 
80% 
63% 
83% 
76% 
74% 
79% 
61% 
70% 
74% 
61% 
82% 
61% 
58% 
72% 
68% 

79% 
70% 
84% 
88% 
69% 
84% 
66% 
85% 
72% 
75% 
61% 
77% 
75% 
81% 
70% 
88% 
83% 
75% 
74% 
54% 
84% 
67% 
76% 
86% 
79% 
55% 
70% 
74% 

2050 

ERC 

79% 
70% 
84% 
90% 
70% 
84% 
66% 
85% 
72% 
75% 
61% 
77% 
76% 
81% 
70% 
88% 
83% 
75% 
75% 
54% 
85% 
67% 
76% 
86% 
79% 
55% 
70% 
74% 

MTFR 

84% 
78% 
90% 
94% 
80% 
90% 
75% 
91% 
81% 
81% 
68% 
83% 
83% 
86% 
79% 
92% 
89% 
77% 
80% 
64% 
90% 
84% 
91% 
90% 
89% 
65% 
73% 
82% 

For  PM2.5,  the  comparison  of  attainment  of  ERCs  also  includes  the  two  sensitivity  scenarios  where  only 
filterable  PM  is  included  (Ref2_filter)  and  the  scenario  where  high  emission  factors  were  used  including 
condensables and bad combustion practices (High_C). Somewhat surprisingly, different variants do not change 
much the compliance issues. In fact, the case where high emission factors are used shows in some cases better 
ERC compliance; this is likely because the transformation towards cleaner residential heating installations in 

50 

 
      
      
 
 
 
the baseline has the strongest impact on the very high condensable fraction of emissions which are reduced 
more efficiently than the solid fraction. 

As indicated earlier, for NH3, most Member States are estimated to be in non-compliance in 2030 as well as in 
2050,  under  baseline  assumptions  (Table  4-7).  Compliance  with  the  IED  and  other  currently  implemented 
policies do not assure achieving necessary reductions in emissions. Interestingly, achievement of the ZPAP 
objective  for  eutrophication,  shown  here  as  a  cost-optimal  scenario,  still  leaves  several  Member  States  in 
projected  non-compliance,  though  the  situation  would  be  much  improved  compared  to  the  baseline.  A 
dedicated scenario (ERC) shows it is feasible to meet the ammonia ERC in all Member States, apart from the 
Netherlands where GAINS does not identify enough mitigation potential to reach the ERC in 2030 or 2050 with 
technical  measures.  It  appears  that  technical  measures,  which  are  already  largely  employed  in  the 
Netherlands,  including  application  of  measures  on  farms  that  are  smaller  than  IED  thresholds,  are  not 
sufficient.  According  to  Dutch  experts  (from  discussion  during  consultations),  achievement  of  ERCs  would 
necessitate structural changes in the agriculture system or behavioural changes which could help to achieve 
necessary reductions. 

Table 4-7: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios 

for NH3. Highlighted cells indicate non-attainment of the 2030 ERCs. 

Country 

2030 ERC 

Baseline 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Rep. 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
EU 

12% 
13% 
12% 
25% 
20% 
22% 
24% 
1% 
20% 
13% 
29% 
10% 
32% 
5% 
16% 
1% 
10% 
22% 
24% 
21% 
17% 
15% 
25% 
30% 
15% 
16% 
17% 
19% 

11% 
10% 
6% 
11% 
17% 
9% 
20% 
2% 
24% 
8% 
16% 
15% 
11% 
-7% 
17% 
-12% 
15% 
8% 
26% 
12% 
5% 
3% 
20% 
22% 
10% 
8% 
14% 
11% 

2030 

ZPAP 

24% 
19% 
30% 
33% 
27% 
19% 
36% 
13% 
33% 
20% 
25% 
25% 
25% 
2% 
28% 
-1% 
23% 
20% 
40% 
13% 
27% 
17% 
29% 
42% 
20% 
23% 
26% 
24% 

MTFR 

Baseline 

34% 
25% 
43% 
48% 
32% 
22% 
38% 
18% 
37% 
28% 
31% 
32% 
34% 
11% 
35% 
4% 
28% 
23% 
45% 
14% 
39% 
26% 
33% 
45% 
31% 
39% 
34% 
32% 

16% 
11% 
7% 
11% 
21% 
16% 
25% 
5% 
25% 
12% 
20% 
22% 
18% 
-5% 
22% 
-9% 
20% 
16% 
27% 
12% 
0% 
14% 
24% 
28% 
18% 
12% 
13% 
14% 

2050 

ERC 

16% 
13% 
12% 
25% 
21% 
22% 
25% 
5% 
25% 
13% 
29% 
22% 
32% 
5% 
22% 
1% 
20% 
22% 
27% 
13% 
17% 
15% 
25% 
30% 
18% 
16% 
17% 
19% 

MTFR 

40% 
30% 
49% 
53% 
38% 
29% 
45% 
27% 
39% 
36% 
39% 
41% 
41% 
15% 
41% 
10% 
36% 
32% 
51% 
14% 
39% 
41% 
38% 
50% 
43% 
43% 
37% 
38% 

ERC 

12% 
13% 
12% 
25% 
20% 
22% 
24% 
2% 
25% 
13% 
29% 
15% 
32% 
5% 
17% 
1% 
15% 
22% 
26% 
13% 
17% 
15% 
25% 
30% 
15% 
16% 
17% 
18% 

51 

 
      
      
 
 
 
 
Table 4-8: Comparison of % reductions achieved in 2030 and 2050, relative to 2005, in selected scenarios 

for NMVOC. Highlighted cells indicate non-attainment of the 2030 ERCs. 

Country 

2030 ERC 

Baseline 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Rep. 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
EU 

36% 
35% 
42% 
48% 
50% 
50% 
37% 
28% 
48% 
52% 
28% 
62% 
58% 
32% 
46% 
38% 
47% 
42% 
27% 
15% 
26% 
38% 
45% 
32% 
53% 
39% 
36% 
41% 

58% 
57% 
50% 
69% 
69% 
60% 
62% 
59% 
58% 
63% 
43% 
75% 
56% 
38% 
54% 
52% 
46% 
45% 
50% 
36% 
43% 
49% 
49% 
54% 
49% 
40% 
51% 
52% 

2030 

Baseline+
Agr VOC 
46% 
50% 
45% 
65% 
63% 
56% 
47% 
52% 
54% 
55% 
37% 
72% 
49% 
23% 
48% 
48% 
43% 
34% 
47% 
25% 
38% 
44% 
46% 
53% 
45% 
35% 
45% 
46% 

ZPAP 

ERC  MTFR 

Baseline 

58% 
58% 
53% 
73% 
71% 
62% 
66% 
61% 
59% 
63% 
46% 
77% 
58% 
39% 
55% 
53% 
51% 
46% 
53% 
37% 
47% 
53% 
57% 
57% 
51% 
41% 
53% 
54% 

58% 
57% 
50% 
69% 
69% 
64% 
62% 
60% 
58% 
63% 
43% 
75% 
61% 
38% 
54% 
53% 
47% 
45% 
52% 
36% 
43% 
50% 
59% 
54% 
57% 
41% 
51% 
53% 

67% 
63% 
63% 
82% 
77% 
72% 
77% 
72% 
70% 
71% 
61% 
83% 
67% 
54% 
63% 
70% 
61% 
61% 
64% 
47% 
60% 
63% 
67% 
70% 
65% 
54% 
60% 
65% 

67% 
66% 
69% 
78% 
77% 
80% 
67% 
63% 
72% 
70% 
51% 
82% 
72% 
40% 
64% 
65% 
70% 
55% 
63% 
47% 
59% 
61% 
75% 
67% 
68% 
48% 
60% 
63% 

2050 

Baseline+
Agr VOC 
52% 
57% 
63% 
72% 
71% 
76% 
52% 
57% 
65% 
62% 
44% 
79% 
61% 
24% 
57% 
59% 
62% 
43% 
59% 
34% 
51% 
55% 
68% 
65% 
62% 
42% 
51% 
55% 

ERC 

MTFR 

67% 
66% 
69% 
79% 
77% 
80% 
67% 
63% 
72% 
70% 
51% 
82% 
72% 
40% 
64% 
65% 
70% 
55% 
63% 
47% 
59% 
61% 
75% 
67% 
68% 
48% 
60% 
63% 

75% 
72% 
79% 
90% 
84% 
88% 
82% 
76% 
80% 
76% 
69% 
89% 
79% 
59% 
74% 
83% 
82% 
70% 
75% 
57% 
75% 
72% 
87% 
82% 
79% 
61% 
68% 
74% 

4.4.3 

Indicators for exposure to PM2.5 

Ambient concentrations of PM2.5 are modelled in GAINS using linear transfer coefficients to translate emissions 
of  PM  and  precursor  gases  into  ambient  concentrations  of  PM2.5  at  a  resolution  of  0.1°.  This  includes  a 
downscaling  for PPM  based  on  the  uEMEP model which  calculates  the  additional  increment  in  population 
exposure arising from fine-scale (250×250m) local variation of PM2.5 concentrations (Denby et al. 2024). In 
addition  to  the  contributions  from  anthropogenic  sources,  concentrations  of  natural  dust  and  sea  salt 
modelled with the EMEP CTM are available in the model and are added for the purpose of producing maps of 
total PM2.5. However, they are not included in the health impact calculations. 

Consistent  with  CAO3,  the  GAINS  model  estimates  health  impact  indicators  following  the  health  impact 
assessment methodology that has been recommended for Europe by the World Health Organization (WHO) 
in the HRAPIE project (WHO 2013) with updated concentration-response functions for PM2.5 (Chen and Hoek 
2020). The model quantifies risks from long-term exposure to PM2.5 

• 

for all-cause mortality, 

52 

 
      
      
 
 
 
 
• 

considering only population above 30 years age, 

•  employing long-term (annual mean) PM2.5 levels that are typical for the exposure of a given population 

(e.g., PM2.5 levels measured at urban background stations, but not at hot spots),  

•  applying a linear exposure-response function to all PM2.5 caused by anthropogenic sources, and 

•  excluding exposure from natural sources of PM2.5 (e.g., soil dust) in the assessment33. 

The systematic review undertaken for the 2021 WHO  Air Quality Guidelines  (Chen and Hoek 2020), which 
scrutinized a much wider range of epidemiological evidence than in 2013, confirmed the earlier findings of the 
HRAPIE  project,  suggesting  that  a  linear  relationship  between  concentration  and  relative  risk  for  all-cause 
mortality from PM2.5 gives the best fit. The recommended value for the risk ratio was increased to 1.08 per 
10 µg/m3 compared to 1.06 per 10 µg/m3 found by HRAPIE. 

Consistent  with  the  previous  Clean  Air  Outlooks,  GAINS  retains  the  principle  to  account  –  in  its  standard 
calculations presented in the main report – for all anthropogenic PM2.5 for calculating health impacts. This is 
due  to  there  being  no  conclusive  evidence  for  a  lower  limit of PM2.5  toxicity (or  ‘cut-off’).  Natural  PM2.5  is 
excluded from the assessment, following the argument that natural PM2.5 cannot be (significantly) influenced 
by policy measures. Thus, the local level of natural background PM2.5 is used as a lower cut-off for the risk 
calculation.  

Other assessments, notably the Impact Assessment for the AAQD, and the EEA Air Quality in Europe reports 
have employed a different approach and used the WHO Air Quality Guideline level (5 µgm-3) as lower cutoff 
for health risk calculations, leading to lower total burden estimates. In order to allow for better comparability 
of the CAO4 results, a sensitivity analysis quantifying mortality for total PM2.5 concentrations (including natural 
sources) above 5 µgm-3 is presented in the Annex (section 4.1.2). The GAINS analysis computes the following 
indicators for health impacts from PM2.5: 

•  Mean population exposure to PM2.5 in each Member State. 

•  Annual cases of premature deaths attributable to exposure to PM2.5 are estimated assuming 2010 (i.e. 
static) population data, also for the future. Thereby, future demographic changes do not affect the 
distribution of mitigation efforts among Member States; this is also consistent with how the ZPAP 
targets were defined. However, the use of constant 2010 figures introduce inaccuracies in the 
assessment of future health benefits, which scale with the change in total population. Thus, the 
accompanying assessment monetizing the economic and health benefits from the policy scenarios 
employs dynamic population data that are projected for the different Member States. This is in line with 
the approach used in the previous Clean Air Outlooks.  

GAINS also provides other indicators, specifically the loss of statistical life expectancy and Years of Life Lost 
(YOLLs, YLL). The definition of these indicators and respective results are reported in the section 4.1 of the 
Annex. 

Figure 4-11 shows annual mean concentrations of PM2.5 calculated with the GAINS model for the period 2005 
to  2050  for  selected  scenarios.  In  2005,  there  were  several  regions  in  the  EU  not  complying  with  the  EU 
legislation limit values (25 µg/m3), but the concentrations keep declining and by 2030, there are very few areas 
exceeding 25 µg/m3 (estimated less than 0.3 % of the EU population, Figure 4-12), with a further decline by 
2050 when no areas exceeding 20 µg/m3 are seen. At the same time, in 2030 and even in 2050, large areas 
(and populations, see Figure 4-12) are exposed to levels well above the current WHO air quality guideline of 
5 µg/m3.  

33 While natural sources are excluded from the health impact assessment, they are included in figures showing total 
PM2.5 concentrations (maps, exposure distribution). 

53 

 
      
      
 
 
Figure 4-11: Annual mean concentrations of PM2.5 (including natural sources) in 2005, 2025, compliance 

with ERCs (within feasibility) and MTFR cases for 2030 and 2050.  

Figure 4-12 illustrates the impact of policies included in the Baseline and selected scenarios on the population 
exposed to various levels of PM2.5. In 2005, less than 3 million people (or less than 1% of the EU population) 
enjoyed clean air (defined as per 2021 WHO recommendations) and a much larger number (about 66 million) 
was exposed to PM2.5 levels exceeding the EU limit values at the time (25 µg/m3). It is estimated that since 
2005, air quality has improved significantly (illustrated by calculation for 2025), reducing exposure to levels 
above 15 µg/m3. Close to two thirds of the EU population is exposed to levels below the WHO Interim Target 
4 of 10 µg/m3 (which is the new limit value introduced by the revised AAQD for 2030). 

As discussed earlier, concentrations drop owing to successful introduction of air quality and decarbonization 
policies,  although  in  2030 nearly  15%  of  population would still  be  exposed  to  levels  above  10  µg/m3  if  no 
further action is taken (but with all current legislation effectively enforced, including Fit for 55 package). Only 

54 

 
      
      
 
 
13% of EU population (or about 60 million persons) would live in areas where concentrations would be below 
the  2021  WHO  recommendation  (5 µg/m3).  This  number  would  increase  to  70  million  (or  16%  of  EU 
population) assuming full compliance with the NEC Directive emission reduction commitments and the ZPAP 
targets in 2030. Implementation of the MTFR scenario could double the number of people exposed to levels 
below 5 µg/m3 and reduce the number of those exposed to levels above 10 µg/m3 also by nearly half to about 
7% of total EU, compared to the Baseline.  

Significant  further  improvements  are  expected  by  2050,  where  in  the  Baseline  more  than  half  of  the  EU 
population (or nearly 220 million) would be exposed to levels below 5 µg/m3 and only about 3% to levels above 
10 µg/m3. Further potential exists, as shown in the MTFR case for 2050. 

Overall, the results for 2005, Baseline, and MTFR scenarios are comparable to CAO3 results, although for 2030, 
the CAO4 indicates that less population will be enjoying concentrations below 5 µg/m3 in the Baseline as well 
as in the MTFR. For the Baseline, this is the result of higher PM2.5 emissions from residential combustion sector 
in  CAO4  (driven  by  slightly  higher  consumption  of  solid  fuels,  i.e.,  coal  and  biomass,  revised  data  and 
assumptions on structure of installations, and consistent inclusion of condensable PM for all Member States) 
and lower ambition (compared to CAO3) for IED in agriculture since ammonia is a PM precursor. For the MTFR, 
major reason is a slightly less optimistic outlook for the feasible pace of upgrade of the solid fuel heating stoves 
and boilers in the residential sector. 

Figure 4-12: Population in the EU exposed to different concentrations of PM2.5 for selected key scenarios 

(total PM2.5 including natural sources).  

The  picture  is  less  homogenous  for  individual  countries  (Figure  4-13),  foremost  in  2005,  where  large 
differences in exposure distribution are visible (as was evident from the concentration maps), but also in pace 
of improvement over time. The strong decline in emissions of precursors of PM2.5 in the last decades, especially 
in power plants, industry, and transport, is clearly visible in exposure change since 2005 for all EU countries. 
Currently, only  small shares  of  population  in  some  countries  are  exposed  to  PM2.5  levels  above  25  µg/m3. 
However,  there  are  also  only  a  few  countries  (Estonia,  Finland,  Ireland,  Sweden)  where  a  majority  of  the 

55 

 
      
      
 
 
 
 
population  lives  in  areas  with  concentration  levels  consistent  with  the  WHO  AQG  of  5 µg/m3.  Assuming 
effective implementation of existing and planned policies, concentrations are estimated to fall further in all 
countries in the Baseline scenario. While small number of people will face pollution levels in excess of 10-
20 µg/m3, most of the population in most of the EU countries will be still exposed to levels beyond 5 µg/m3. 

While there is scope for improvement in all Member States, in some countries, i.e., Malta, Italy, Romania, and 
Greece,  GAINS  results show  that  over  10%  of  population would  be  exposed  in  2030  to PM2.5  levels  above 
10 µg/m3 even in the MTFR case, which in these countries does not allow to improve the exposure situation 
much  beyond  the  Baseline.  The  reasons  differ  by  country  –  for  example  Malta  has  high  natural  dust 
background  levels,  while  Italy  has  high  anthropogenic  emissions  in  some  regions  (e.g.,  in Po-Valley) which 
cannot be eliminated by 2030 in GAINS optimisation due to model-built limitations on implementation rates 
of  technologies  and  stock  turnover.  The  distribution  of  exposure  for  the  scenario  where  2030  ERCs  are 
achieved  for  all  Member  States  and  all  PM  precursors  (within  feasibility)  shows  slight  improvement  over 
Baseline  at  the  EU  level,  with  more  pronounced  improvements  in  some  Member  States  such  as  Romania, 
Slovenia, Bulgaria. While the MTFR scenario, in 2030, shows further potential, it is not very large, indicating 
that  there  is  only  limited  further  technological  potential  available  within  a  rather  short  time  (less  than  a 
decade).  As  discussed  earlier,  further  improvement  is  expected  and  possible,  introducing  also  further 
structural changes in energy system and agriculture, however, in the longer term. 

Figure 4-13: Share of population in the EU and MS exposed to different levels of PM2.5 concentrations in 
2005, currently (2025), and in 2030 in selected scenarios (total PM2.5 including natural sources).  

Figure 4-14 and Table 4-9 summarize the estimates of premature mortality from PM2.5 using static population 
assumptions (constant population in 2010). The comparison shows for Baseline and ERC scenarios a decline in 
premature deaths (related to 2005) of about 62-75% in 2030 and 2050, and up to a 79% reduction by 2050 in 
the  MTFR  scenario.  The  ZPAP  target of  reducing  premature  deaths  by  55%  in  2030,  compared  to  2005,  is 
achieved in the  Baseline. This result  demonstrates  the  impact  and air quality benefits of the clean air and 
decarbonisation policies included in the Baseline, provided they are fully implemented.   

56 

 
      
      
 
 
 
A shown in Section 4.4.2, several Member States were not in compliance with ERCs for NEC Directive pollutants 
in  2020  and  beyond,  especially  for  NH3,  but  in  earlier  years  also  for  other  precursors  of  ambient  PM2.5.  A 
scenario was  developed to estimate the  benefit  of full compliance  with the ERCs in 2020, 2025, 2030 and 
beyond.  This  scenario  is  shown  in  Figure  4-14  as  ERC  assuring  compliance  with  2020  ERC  for  2020,  linear 
interpolation  between  2020  and  2030  ERCs  for  2025,  and  2030  ERCs  for  2030;  and  as  ERC  2020  where 
compliance with 2020 ERCs (rather than with the linear interpolation) for 2025 is enforced. The results indicate 
that in the period 2020 to 2030, about 1.6-3.3% of premature deaths (5,000 to 7,000, depending on the year) 
could have been avoided, except for 2025 in the ERC 2020 case where the benefit would have been small 
(about 1,000).  

Achieving ZPAP targets for eutrophication would result in the additional benefit (compared to the ERC scenario 
in 2030) of about 1400 avoided premature deaths while assuring also compliance with ERCs (ERC+ZPAP) could 
avoid nearly 5000 additional premature deaths, compared to the ERC (Table 4-9). 

Figure 4-14: Comparison of the cases of premature deaths attributable to the exposure to total 

anthropogenic PM2.5 in the EU, for the analysed scenarios. The marked 55% reduction of 2005 
premature deaths refers to the ZPAP target. 

Table 4-9: Cases of premature death attributable to the exposure to PM2.5 in the EU; thousand cases per 

year, using constant 2010 population data. 

2005 

2010 

2015 

2020 

2025 

2030 

2035 

2040 

2045 

2050 

507 

581 

Premature deaths attributable to the exposure to total anthropogenic PM2.5 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

284 
279 
283 

329 
323 

412 

221 
213 

212 
209 
183 

182 

153 

163 
160 

147 
145 

134 

125 

57 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4-10: Population-weighted PM2.5 concentrations in the EU, incl. and excl. natural sources. 

6.3 

16.2 

18.5 

2035 

2030 

2025 

2010 

2005 

2020 

7.4 
7.2 

10.7 
10.5 

9.3 
9.2 
9.3 

2040 
2015 
Population-weighted PM2.5 concentrations considering anthropogenic and natural sources (µg/m3) 
5.7 
Baseline 
13.3 
ERC  
5.6 
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 
Population-weighted PM2.5 concentrations considering anthropogenic only (µg/m3) 
Baseline 
15.5 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

6.5 
6.4 
5.6 

8.7 
8.5 
8.6 

7.1 
7.
6.3 

10.0 
9.8 

6.7 
6.5 

5.0 
4.9 

12.6 

17.8 

5.6 

4.8 

4.1 

1 

2045 

2050 

5.4 

4.7 

5.2 
5.2 

4.6 

4.5 
4.5 

3.9 

Further results for health indicators are provided in the section 4.1 of the Annex. 

4.4.4 

Indicators for exposure to ground-level ozone 

Following the recommendation of the WHO/HRAPIE project, two impact indicators for ground-level ozone are 
provided: 

•  Annual cases of premature deaths attributable to ozone exposure (using constant 2010 population 

data), and 

•  The SOMO35 exposure indicator, calculated as the sum of the daily exceedances of maximum 8-hour 
ozone  concentrations  over  35  ppb  threshold,  summed  over  the  whole  year.  SOMO35  indicator  is 
calculated  with  a  spatial  resolution  of  0.1×0.1°  longitude/latitude  and  then  multiplied  by  the 
population exposed in each of the grids.  

Contrary to CAO3 report, the calculation of ozone concentrations has not been done with the GAINS model 
but with the EMEP model (as discussed earlier in the method section). The results for the Baseline and other 
scenarios for which EMEP model calculated ozone concentrations, have been prepared for the ALPHA-RiskPoll 
model, which was used to calculate ozone impacts, specifically premature deaths as shown in Figure 4-15 and 
Table 4-11. There is a steady reduction in estimated premature deaths reaching about 14% by 2030 in the 
Baseline, compared to 2005. The other scenarios, and even MTFR, do not achieve more than 18% reduction 
in premature deaths. These changes are similar to the trends calculated for SOMO35.  

Compared to CAO3, the overall downward trends in SOMO35 and premature deaths are less pronounced than 
in CAO3, as a result of using EMEP model calculations of ozone in CAO4 rather than GAINS estimates. In CAO4, 
SOMO35 in future years is about 30% higher than in CAO3 (the same applies to premature death estimates), 
while  for  2005  the  CAO4  estimate  of  SOMO35  is  lower  than  CAO3  by  nearly  20%.  These  differences  are 
primarily due to the interplay of three factors: 

(i) 

An updated chemistry scheme implemented in the EMEP model after the generation of the transfer 
coefficients for GAINS used in CAO3, leading to higher ozone production rates and thus a (more or less 
constant) offset, 

58 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(ii) 

(iii) 

the assumptions about boundary conditions (inflow from the rest of the world) which are dynamic 
(changing  over  time)  in  the  EMEP  model  simulations  for  CAO4  but  were  assumed  constant  in  the 
source receptor runs for GAINS. Because O3 inflow from the rest of the world increased from 2005 to 
2020, the decrease in European O3 between 2005 and 2020 is smaller in CAO4 than in CAO3.  

the impact of non-linearity in ozone chemistry which is not reflected in the linear GAINS approach; 
this is most relevant for 2005 where the atmospheric composition is rather different from the 2030 
baseline case for which the GAINS atmospheric coefficients were developed.  In particular, titration 
(destruction of ozone by NO in the vicinity of strong NOx emission sources like cities) and its change 
over time cannot be represented well in a linear approach – the impact being that GAINS would tend 
to overestimate ozone in some highly populated areas, in the earlier years when NOx emissions are 
still relatively high, facilitating titration. 

Figure 4-15: Cases of premature deaths attributable to the exposure to ground-level ozone in the EU.   

59 

 
      
      
 
 
Figure 4-16: Population-weighted SOMO35 indicator (ppb days) in the EU.   

Table 4-11: Ground-level ozone-related health impact indicators for the EU.  

2005 

2010 

2015 

2020 

2025 

2030 

2035 

2040 

2045 

2050 

62.7 

66.8 

75.7 

68.4 

65.1 
64.5 

Cases of premature deaths attributable to the exposure to ground-level ozone (thousand cases per year) 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 
Population weighted SOMO35 (ppb.days) 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2766 
2765 
2666 

64.3 
64.3 
62.0 

2801 
2775 

2552 

3248 

2937 

2871 

2710 

59.1 

62.3 

58.6 

2694 

2536 

4.4.5 

Indicators for exposure to NO2 

Exposure to NO2 has been associated with mortality and morbidity from a range of respiratory diseases. The 
systematic review undertaken for the 2021 WHO Air Quality Guidelines (Huangfu and Atkinson 2020) found 
evidence for a linear relationship between NO2 concentration all-cause mortality with a relative risk of 1.02 
per 10 µgm-3. The calculation of NO2 concentrations in GAINS relies on the atmospheric transfer coefficients 
that include downscaling, which approximates the calculations of the uEMEP model at 250m resolution (this 
update of the GAINS model, compared to CAO2, was discussed and applied already in the CAO3 work). 

The 2021 WHO Air Quality Guideline has been set at 10 µg/m3 annual mean NO2, a value which is currently 
still exceeded widely in Europe (Figure 4-17). The guideline represents the lower limit of conclusive evidence 

60 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
but does not exclude health impacts below this value.  For NO2, the analysis presented below calculates health 
impacts considering the full concentration range without any cut-off. As a sensitivity analysis, results with a 
cut-off at the WHO Air Quality Guideline level are presented in the Annex. 

The GAINS analysis computes the following indicators for health impacts from NO2: 

•  Mean population exposure to NO2 in each Member State. 

•  Annual cases of premature deaths attributable to exposure to NO2 are estimated in a consistent manner 
with the approach used for PM2.5. The numbers of attributable deaths from NO2 and from PM2.5 are 
calculated independently and are not additive due to the potential overlap of effects from both 
pollutants (more detailed discussion of that aspect has been provided in the Annex of the support to 
CAO3 report34 (Klimont et al., 2022)). 

Population exposure distribution has been estimated for 2005, current situation and future scenarios and is 
summarized in Figure 4-17.  

In  2005,  over  30%  of  the  EU  population  (or  about  135  million)  were  exposed  to  annual  mean  NO2 
concentrations  below  the  current  WHO  air  quality  guideline  value  of  10  µg/m3,  however,  about  20%  of 
population  (or  nearly  90  million  people)  experiences  levels  above  the  current  limit  value  of  40  µg/m3. 
According to GAINS estimates, this has improved, reducing current exposure to levels above the current limit 
value to about 3% of population (or about 15 million) while nearly 60% are exposed to levels below WHO 
guidance in 2025.  

Effective implementation of current and proposed policies (as assumed in the Baseline) is expected to bring 
significant further improvements leading to near elimination of exceedances of current limit values (remaining 
at about 1% of population in 2030 and less than 0.1% in 2050) and increasing population share exposed to 
levels below WHO AQG to  about  72%  and 97%  in 2030 and 2050, respectively.  Compliance with the ZPAP 
objectives as well as with the ERCs would result in additional reduction of exposure, bringing further 13 million 
people within exposure consistent with the WHO AQG by 2030. Some further potential to reduce emissions 
and consequently exposure is estimated for the MTFR. The MTFR scenario allows to slightly reduce exposure 
to concentrations above 40 µg/m3 and a more pronounced change for increasing share of population enjoying 
concentrations below WHO guideline, i.e., to nearly 80% in 2030 and over 98%, or 417 million, by 2050.  

These exposure distributions are comparable to the CAO3 results with a slightly larger share of population 
exposed  to  levels  above  10  µg/m3  in  2030,  but  a  more  optimistic  outlook  towards  2050  where  even  less 
population is exposed to levels above the WHO AQG. 

34 https://environment.ec.europa.eu/topics/air/clean-air-outlook_en 

61 

 
      
      
 
 
Figure 4-17: Population in the EU exposed to different concentrations of NO2 for selected key scenarios.  

Compared to 2005, the number of premature deaths attributable to exposure to NO2 is expected to decline in 
the  Baseline  by about 65% and 85%, considering total NO2  from anthropogenic sources  (Figure  4-18,Table 
4-12). As discussed above for population exposure, the further mitigation potential (MTFR) exists and brings 
further improvement especially with respect to reduction of premature deaths attributable to exposure above 
10 µg/m3 (see Annex). In relative terms, compared to Baseline, further reductions could be in order of 17-37%, 
depending on the time period with declining additional benefits in the longer-term horizon, owing to strong 
decarbonization in the Baseline scenario.  

Similarly  to  the  assessment  for  population  exposure,  these  results  are  comparable  to  CAO3  with  higher 
number of premature deaths estimated for 2030, but a slightly more positive long-term outlook. The slight 
increase in 2030 (compared to CAO3) is likely linked to the revised estimates of soil NOx emissions and lesser 
impact of Euro 7 in 2030. 

62 

 
      
      
 
 
 
Figure 4-18: Comparison of the cases of premature deaths attributable to the exposure to total NO2 

(including all sources) in the EU, for the analysed scenarios.    

Table 4-12: Cases of premature death attributable to the exposure to NO2 in the EU; thousand cases per 

year, using constant 2010 population data. 

2025 

2020 

2005 

2010 

194.3 

2040 
2015 
Premature deaths attributable to the exposure to total NO2 originating from all sources, no cut-off 
37.0 
151.1 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

94.3 
92.8 
94.0 

62.9 
62.9 
56.5 

111.7 
110.2 

68.0 
66.1 

2030 

2035 

29.8 

2045 

2050 

29.6 
29.4 

24.3 

Table 4-13: Population-weighted NO2 concentrations in the EU; µg/m3. 

24.4 

2015 

2025 

2020 

2005 

2010 
Population-weighted total NO2 concentrations, no cut-off, all sources (µg/m3) 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

11.7 
11.5 
11.7 

13.9 
13.7 

18.9 

8.4 
8.2 

7.8 
7.8 
7.0 

2030 

2035 

2040 

2045 

2050 

4.6 

3.7 
3.7 

3.7 

3.0 

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4.4.6  Ecosystem impact indicators 

Consistent  with  the  previous  Clean  Air  Outlook  reports,  this  section  provides  evolution  of  the  selected 
ecosystem impacts indicators for the key scenarios. The assessment relies on the most recent critical load (CL) 
data on eutrophication and acidification35 (updated in 2021 and used in CAO3 study). This dataset relies on 
the  submissions  by  the  Member  States  and  non-EU  countries  that  were  evaluated  and  accepted  by  the 
Coordination Centre for Effects (CCE) of the Working Group on Effects (WGE) under the Air Convention (UNECE 
LRTAP Convention).  

For calculating ozone concentrations, AOT40 (accumulated ozone exposure above a threshold of 40 ppb), and 
phytotoxic ozone doses (PODs), the EMEP chemical transport model was run for Europe on a resolution of 0.1° 
× 0.1°, using the relevant emission scenarios as input. These results were used to calculate ozone effects on 
ecosystems as well as effects on agricultural yields. 

In particular, the following indicators are presented here:  

•  The total ecosystems area in the EU where acid deposition exceeds the critical loads for acidification (in 

km2 and as percentage of the total ecosystem area in the EU). 

•  The total ecosystems area in the EU where nitrogen deposition exceeds the critical loads for 

eutrophication (in km2 and as percentage of the total ecosystem area in the EU). 

•  The total area of Natura2000 ecosystem protection zones where N deposition exceeds CL for 

eutrophication. 

•  The forest area at risk from ozone quantified as the area with AOT40 levels exceeding the critical 

exposure level of 10,000 µg/m3 h, as defined in the Manual on Mapping Critical Levels for Vegetation36 
by the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops 
(ICP-Vegetation) of the UNECE Air Convention’s Working Group on Effects. Effects on forest yield and 
carbon sequestration have been calculated with the ALPHA-RiskPoll model using POD. These are shown 
only for the Baseline and methane sensitivity scenarios for 2040 and 2050; see section 4.4.8. 

•  Effects on agricultural yields have been calculated from PODs suitable for integrated assessment 

modelling (i.e., POD3IAM metrics, see Simpson et al. (2022a)). The POD values will be combined with 
spatially resolved data on crop yields and exposure-yield relationships from the literature to estimate 
yield losses at least for wheat. 

Ecosystem impact assessment shows a significant improvement in the Baseline with respect to exceedance of 
CL for acidification (Figure 4-19, Table 4-14) but much less improvement for eutrophication (Figure 4-20, Table 
4-15). More detail for the protection of Natura2000 areas is shown in the Annex and the results for ozone 
impacts on vegetation (via the AOT40 indicator) are reported in Section 4.4.8. 

The share of ecosystems with excess acid deposition drops by 2030 to below 3% of ecosystem area in the EU 
in the Baseline; compared to 2005 the affected area is reduced by over 80% and declines further to below 2% 
in 2050 (Table 4-14). Scenarios aiming at achievement of goals outlined by ZPAP for reducing eutrophication 
and MTFR bring further reduction in excess deposition resulting in still significant relative improvements (25 
to 45%, compared to the level in the Baseline), although the overall level of excess deposition is already rather 
low across most areas. 

These results are very similar to the assessment presented in CAO3.  

35 For natural terrestrial and freshwater ecosystems 
36 https://unece.org/fileadmin/DAM/env/documents/2017/AIR/EMEP/Final__new_Chapter_3_v2__August_2017_.pdf  

64 

 
      
      
 
 
Figure 4-19: Ecosystem area in the EU where the critical loads for acidification are exceeded.   

Table 4-14: Ecosystem area in the EU where the critical loads for acidification are exceeded. 

2005 

2010 

2015 

2020 

2025 

2030 

2035 

2040 

2045 

2050 

32.3 
27.4 

36.8 

155.1 

195.1 

260.3 

49.5 
41.7 

95.6 
93.4 

73.5 
69.3 
73.2 

Ecosystem area with acid deposition exceeding critical loads for acidification (1000 km2) 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 
Ecosystem area with acid deposition exceeding critical loads for acidification (% of total ecosystem area) 
9.0 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

38.5 
37.5 
27.4 

2.2 
2.2 
1.6 

4.3 
4.0 
4.3 

5.6 
5.4 

2.9 
2.4 

15.1 

11.4 

2.1 

17.9 

1.9 
1.6 

1.0 

30.0 

28.8 
25.0 

1.7 

16.4 

1.7 
1.5 

1.0 

The status and progress in reducing eutrophication is much less optimistic than that for acidification. Nearly 
70%  of  ecosystems  remain  affected  by  eutrophication  in  2030.  While  this  is  a  reduction  by  about  19 %, 
compared to 2005, it comes short of the ZPAP target of 25% (Figure 4-20, Table 4-15). The 25 % reduction, 
compared  to  2005,  of  the  EU  ecosystems  area  with  nitrogen  deposition  exceeding  the  critical  loads  for 
eutrophication is achieved by 2030 in the ZPAP and MTFR scenarios, where for the latter a reduction of 31 % 
are estimated by 2030. At the same time, even in the MTFR case, only about 41 % of the EU ecosystems area 
are  protected  from  eutrophication  in  2030.  In  a  longer  term,  further  improvements  are  achieved  in  all 
scenarios, however, even in the MTFR case for 2050, about 52 % of ecosystem area remains unprotected from 
eutrophication.  Similar,  although  slightly  more  optimistic,  outlook  is  assessed  for  Nature2000  nature 
protection areas where in the Baseline a 23 % reduction is achieved, close to the 25 % target. In the ZPAP 
scenario that reduction is nearly 30 %; for detailed results see section 4.2 of the Annex. 

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Overall, the CAO4 outlook is similar to the assessment in CAO3, however, for 2030 as well as in the longer 
term, the Baseline shows 1.5-3 % (or up to 50,000 km2) more area where CLs for eutrophication are exceeded. 
Also the MTFR reduction potential for 2030 is few percent points lower than in CAO3, which is partly due to 
the reduced ambition of IED defining a slightly different Baseline; the difference for MTFR becomes smaller in 
2050. 

Figure 4-20: Ecosystem area in the EU where the critical loads for eutrophication are exceeded. The 

marked 25% reduction of 2005 area with N deposition exceeding CLs refers to the ZPAP target.   

Table 4-15: Ecosystem area in the EU where the critical loads for eutrophication are exceeded. 

2005 

2010 

2015 

2020 

2025 

2030 

2035 

2040 

2045 

2050 

964.7 

1167.
7 

1200.
3 

1239.
0 

939.1 
904.4 

1003.
0 
968.4 

1089.
4 
1082.
2 

1056.
3 
1041.
5 
1053.
6 

Ecosystem area with nitrogen deposition exceeding critical loads for eutrophication (1000 km2) 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 
Ecosystem area with nitrogen deposition exceeding critical loads for eutrophication (% of all ecosystems) 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

931.0 
928.9 
856.3 

64.4 
64.3 
59.2 

73.1 
72.0 
72.9 

75.4 
74.9 

69.4 
67.0 

65.0 
62.6 

769.8 

66.7 

85.7 

83.0 

80.8 

53.2 

921.7 

63.8 

914.2 
889.6 

748.7 

63.2 
61.5 

51.8 

To  assess  further  steps  necessary  to  achieve  the  EU  ZPAP  ecosystem  target,  a  dedicated  scenario  was 
developed where a cost-effective set of measures results from the  GAINS model optimisation to reach the 
25% reduction of ecosystems area where CLs for eutrophication are exceeded by 2030. The distribution of 

66 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
mitigation efforts, compared to the Baseline, by source category and Member State are shown in Figure 4-21. 
The overall NH3 reduction at the EU level, is estimated at about 14% compared to the Baseline, with majority 
achieved  via  measures  addressing  emissions  from  cattle,  followed  by  measures  towards  mitigation  of 
emissions from application of mineral fertilizers, breeding of pigs, and poultry. Among these four groups, most 
of the emission reduction is associated with measures addressing on-field application of organic manures and 
mineral fertilizers,  which jointly provide over 70% of total mitigation achieved in the  ZPAP  scenario, while 
about 25% reduction is achieved by low emission animal housing and improved storage of manures.  

Since exceedance of critical loads for eutrophication is rather widespread,  all of the Member States would 
need to introduce further measures to reduce ammonia emissions (Figure 4-21), typically (21 Member States) 
providing  between  8-15%  NH3  reduction,  with  few  exceptions  (5 Member  States)  where  more  ambition is 
needed to meet ZPAP target and reductions vary from 16% to 24%.  These additional reductions represent 
more than half of the total mitigation potential identified in the MTFR scenario for 2030 at the EU level. In 
fact, only two countries would need to mobilize less than 50% of their mitigation potential estimated for the 
MTFR case, while most (21 MS) need to exploit 50-70% of that potential.  

We  have  also  analysed  the  measures  needed  to  comply  with  the  ERCs  in  2030,  since  many  countries  are 
estimated to head towards no compliance (see section 4.4.2, Figure 4-7). This analysis shows that, while the 
ZPAP  objectives  are  not  achieved  (compare  the  combination  of  both  targets  shown  also  in  Figure  4-21  as 
ERC+ZPAP scenario) in the ERC case, many Member States need to pursue further mitigation with the same 
type of cost optimal set of measures as calculated for ZPAP case. 

Finally, the enforcement of ZPAP targets would also result in some additional mitigation of other pollutants 
than ammonia, especially NOx, but there is also small feedback on other pollutants; this is shown in the Annex 
(Section 4.3). That section shows also mitigation necessary to achieve ERCs, and full compliance with both 
ERCs and ZPAP (ERC+ZPAP scenario) for all pollutants across EU Member States by key categories of measures.  

67 

 
      
      
 
 
Figure 4-21: Distribution of further NH3 reductions in the ERCs compliant and ZPAP scenario, compared to 

Baseline, in 2030.   

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4.4.7  Condensable PM sensitivity 

The quantitative and qualitative analysis, for each Member State, of the impacts that various consideration of 
the condensable PM could have on the achievement of the NEC Directive reduction commitments has been 
shown in Section 4.4.2. However, both the amount and characteristics of the condensable compounds are 
very  uncertain.  A  further  sensitivity  has  been  conducted.  In  these  additional  tests,  both  the  amount  of 
emissions, and the assumptions concerning volatility have been varied, and the results have been compared 
to the latest available data for ambient measurements of organic carbon, in order to determine the best set 
of assumptions and to validate the emission factors used in GAINS in the Baseline and the policy scenarios. 

Due to the  complexity of the  analysis  and  the  need for  dedicated EMEP model simulations, the analysis  is 
limited to the two defined Baseline sensitivity scenarios, as described in Section 4.3.2.  

IIASA and TNO developed the emission factor sets and implemented these in GAINS, producing new sets of 
chemically speciated particle emissions that were gridded, speciated (distinguishing BC and OC as well as size 
distribution) and used in the EMEP model. Beyond the central emission factors set that is further referred to 
as  ‘GAINS_EF’  and  represents  emission  factors  used  in  the  Baseline  and  policy  scenarios  in  CAO4,  the 
‘Ref2_Filter’ and ‘High_C’ sets representing filterable PM only and high EFs with condensable PM, respectively, 
were used in this analysis (see also section 4.3 and Table 4-1). 

Figure 4-22 shows the model agreement with OC (in PM2.5) measurements in 2019, averaged across all sites, 
for the three model runs using respective emission factors and assuming that all condensable PM emissions 
are non-volatile (for further details see Section 4.4 in the Annex). Of these three runs, only the central estimate 
(GAINS_EF) shows a satisfactory agreement with the observations. This is expected since the Ref2_Filter case 
omits the known condensable PM contributions, and the High_C case assumes both high emission factors and 
that condensable PM are non-volatile.  

Figure 4-22: All-site comparison of modelled OC (in PM2.5) versus observations, 2019, non-volatile tests.   

The results of the analysis show that OC modelling is very sensitive to the emission factors used, and to the 
assumptions  concerning  volatility  used  in  the  modelling  (see  Annex,  Section  4.4,  for  more  discussion). 
Although the results for the default non-volatile setup with central emission factors (GAINS_EF) were not quite 
as good as the more complex setups with higher emission factors and volatility assumptions, the results were 
rather similar and compared well with observations in any case. Although we know that the assumption that 
condensable PM  is non-volatile  is  incorrect,  it is also the  simplest assumption we can make,  and both the 

69 

 
      
      
 
 
model results presented here, and other studies (see Simpson et al, 2022, for references) confirm that such 
an approach generates OC levels consistent with much more complex approaches.  

Thus, for EMEP regional scale modelling the non-volatile approach with central emission factors still seems to 
represent the best  approach.  The high case  (High_C)  consistently overestimates measured concentrations, 
while  using  emission  factors  representing  filterable  PM  only  (Ref2_Filter)  shows  rather  consistent,  and 
sometimes significant, underestimation. These results show that the emission factors (including condensable 
PM) used in GAINS and currently recommended by the EEA/EMEP Guidebook as well used by most Member 
States in their reporting, appear to represent well the PM emissions.  

Further details and additional results of modelling where various assumptions about volatility as well as about 
total emissions (2015 vs 2020) are made, are presented in the Annex (Section 4.4). 

4.4.8 

Sensitivity to methane emission reductions 

The purpose of this sensitivity analysis is to assess how various levels of methane emission reduction (in the 
EU and / or through reducing emissions arriving from outside the EU) would contribute to the fulfilment of the 
air quality standard set for ozone as envisaged in the revised Ambient Air Quality Directive37. The model runs 
estimated the maximum daily 8-hour mean (MD8M) and AOT40. These simulations also included assumptions 
on the projected emissions of ozone precursors other than methane, as specified in Section 4.3.3. The model 
runs were performed for the period 2005 to 2050, but the specific simulation of methane mitigation impacts 
was estimated for the years 2040 and 2050.  

The atmospheric calculations were done with the EMEP MSC-W chemical transport model (also used in the 
impact assessment for the revision of the Ambient Air Quality Directive). The analysis involves global model 
simulations  combining  global  scenarios  developed  by  IIASA  within  the  discussion  of  the  revision  of  the 
Gothenburg Protocol and updating them with the latest Baseline from this project for the EU and EU4Green 
project outcomes for the West Balkan.  

Concentrations  of  CH4  in  ambient  air  were  estimated  using  a  reduced  complexity  Earth  System  Model 
emulator, as described in detail in van Caspel et al. (2024), assuming a uniform distribution of CH4 over the 
globe.  These  CH4  concentrations  were  implemented  in  the  global  EMEP  model  and  the  effect  on  the 
background levels of ozone was estimated, as well as the impact on ozone on European scale.  

The outcomes from this modelling work are shown for indicators relevant for assessing compliance with the 
ozone target values in the current AAQD as well as the revised version which will apply from 2030. For the 
protection of human health, the  AAQD specifies a target value of 120µgm-3  for MD8M, which is not to be 
exceeded for more than 25 days (current AAQD) / 18 days (revised AAQD) per year (averaged over 3 years). 
For  the  protection  of  the  environment,  the  Directive  specifies  a  target  value  for  AOT40  of  18,000  µgm-3h 
(averaged for May to July over 5 years), and a long-term objective of 6,000 µgm-3h. 

Figure 4-23 shows how the 26th highest MD8M concentration, indicating compliance with the current target 
value, changes from 2005 in the Baseline up to 2050, and then impacts of the two Global MTFR mitigation 
cases  for 2050: in both cases  methane  is mitigated globally  while the non-methane  ozone  precursors are 
either at baseline policy  (“only CH4”) or are also controlled with maximum mitigation potential (“AP+CH4”) 
globally, not only in the EU. The last row provides the 19th highest MD8M estimated for the mitigation cases 
for 2050, indicating achievement of the revised AAQD target value. The results for both metrics are very similar 
although naturally, the ‘26th highest’ indicator shows less exceedances.  

The current policies are expected to bring improved compliance (all blue and white areas, i.e., below indicator 
value of 120 µg/m3) in most regions of the EU but hot spots in Italy, Portugal, Greece, parts of Spain, and parts 

37 Directive (EU) 2024/2881 

70 

 
      
      
 
 
 
of  Eastern  Europe  remain.  The  mitigation  cases  bring  some  further  reductions  but  not  allowing  for  full 
compliance,  independent  if  only  methane  or  both  methane  and  non-methane  ozone  precursors  are 
controlled. The response is stronger for additional reduction of non-methane ozone precursors (NOx, NMVOC, 
CO) on top of the additional methane mitigation, i.e., ‘Global MTFR (AP+CH4)’ case vs ‘Global MTFR (only CH4)’ 
(Figure 4-23).  

Figure 4-23: Spatial distribution of ozone concentrations for the 26th highest MD8M (two top rows) and for 
the 19th highest MD8M. Two mitigation cases for 2050 assume MTFR for CH4 and either CLE 
(Global MTFR – only CH4) or MTFR (Global MTFR – AP+CH4) for non-methane precursors. 

The impact of methane mitigation in Europe alone has been also analysed (not shown) but it is very small, 
contributing only about 1% of the overall reduction. This is not surprising since the baseline already shows a 
decline in CH4 emissions and further potential is rather small when considering technical measures only. In 
order to reduce methane emissions further, a set of transformational policies, e.g., dietary changes, would be 
needed as agriculture remains the largest source and among the most challenging to control. 

The analysed sensitivity cases are further illustrated in Figure 4-24, showing the spatial distribution of peak 
season average MD8M for 2005, 2025, 2050 Baseline and the two mitigation cases for 2050 described above. 

71 

 
      
      
 
 
Although not used in the health impact calculations in this report, peak season ozone (defined as the average 
MD8M over the highest consecutive six months) is an exposure metric associated with mortality from long-
term exposure to ozone (Huangfu and Atkinson, 2020). The most significant change in peak-season ozone is 
estimated in the Baseline scenario where, especially in Southern Europe, peak season concentrations decline 
strongly by 2050. Further mitigation of methane (Global MTFR – only CH4) and other ozone precursors (Global 
MTFR – AP+CH4) brings further important benefits, including for northern and central Europe. The impact of 
the latter seems more pronounced and in this case most of the areas (except Po Valley) are below 90 ug/m3.  

A similar response is seen for AOT40 (Figure 4-25) where mitigation of methane beyond Baseline results in 
reduction of AOT40 indicator but rather limited impact on compliance with the target value of 18,000µgm-3h, 
leaving parts of Spain, Italy, Greece, Bulgaria in non-compliance (dark brown), while near compliance appears 
to be achieved in several central and eastern EU countries. The big change comes with the further and global 
mitigation of non-methane ozone precursors (Global MTFR – AP+CH4) where most of EU, with exception of Po 
Valley  and  southern  Romania,  would  be  in  compliance.  This  scenario  brings  also  markable  improvement 
outside EU.  Still,  even in this most ambitious sensitivity case,  there  is widespread non-achievement of the 
much stricter long-term objective for the protection of vegetation of 6,000 µgm-3h specified in the revised 
AAQD. 

Figure 4-24: Spatial distribution of peak season average MD8M ozone concentrations. Two mitigation 
cases for 2050 assume MTFR for CH4 and either CLE (Global MTFR – only CH4) or MTFR (Global 
MTFR – AP+CH4) for non-methane precursors. 

72 

 
      
      
 
 
Figure 4-25: Spatial distribution of AOT40 indicator for 2050. Two mitigation cases for 2050 assume MTFR 
for CH4 and either CLE (Global MTFR – only CH4) or MTFR (Global MTFR – AP+CH4) for non-
methane precursors. 

Finally, Figure 4-26 provides a comparison of compliance with the current (26th highest MD8M) and revised 
(19th  highest  MD8M)  target  values  for  MD8M  at  the  monitoring  stations  in  the  EU.  We  compare  baseline 
scenario (missing for 203038), MTFR case (NOx, VOC, CO are further controlled but only in the EU), and the two 
methane sensitivity cases as described above.  

As expected, the compliance continues to improve over time and with added mitigation in the MTFR cases. 
The improvement for the case when global methane mitigation is added (yellow line) over the EU MTFR case 
is comparable to the change observed (for 2050) between the Baseline and MTFR case (only EU non methane 
ozone precursors are controlled further), i.e., the difference between the blue and the red line. A much larger 
benefit is expected from mitigation of NOx, VOC, and CO globally (violet line); it is not so large for 2030 (short 
time  frame  to  implement  measures)  but  very  significant  for  2040,  bringing  a  large  number  of  additional 
stations in compliance with the target value compared to the scenario variants of acting in the EU only (MTFR) 
and of reducing only methane globally. The impact in 2050 is also large, although somehow smaller than in 
2040 owing to continued reductions of emissions in many regions and growing response from the reduction 
of methane. 

38 The respective 26th and 19th MD8M indicators were not calculated with the EMEP model for the Baseline 2030 as the 
focus of the methane sensitivity runs was set on 2040 and 2050. 

73 

 
      
      
 
 
 
Figure 4-26: Assessment of compliance with the current and proposed ozone legislation, showing 26th 

highest MD8M (top row) and for the 19th highest MD8M (bottom row) for the Baseline and three 
MTFR cases for 2030, 2040, 2050. 

74 

 
      
      
 
 
 
4.5 

Cross-border effects of pollution and pollution reduction  

IIASA has employed its GAINS model to estimate  in all EU Member States the contributions from different 
source regions (same country, other EU Member States, non-EU) to ambient PM2.5 concentrations, and to the 
changes in PM2.5 concentrations between different scenario years. Although the presentation of results does 
not  highlight  the  Western  Balkans  explicitly  (included  in  the  non-EU  region  in  the  following  Figures),  in 
neighbouring regions the non-EU contribution can be linked to the developments in the Western Balkan as a 
dominating contributor. This can be seen in Figure 4-31 for e.g. Croatia and Hungary, where after 2020 more 
significant  PM2.5  concentration  reductions  occur.  These  are  driven  by  the  expected  decline  in  precursor 
emissions  (especially  in  the  power  sector)  in  the  Western  Balkan  countries’  baselines,  assuming  effective 
implementation of emission controls and strong decline in coal use, consistent with decarbonization policy 
and other air pollution regulations.  

4.5.1 

Import-export budgets 

A series of figures in this section illustrates first the contribution of domestic pollution sources as opposed to 
those from other sources (other Member States, non-EU countries and natural sources) to the population-
weighted mean concentration of PM2.5 in 2005 in a given Member State. The results are presented as back-to-
back tables showing absolute values (µg/m3) and in relative terms (% of total concentration). These so-called 
‘import-export’ matrices  illustrate  how  the  contributions change  over  time  in the  Baseline  scenario (2015, 
2030, and 2050 are shown) where, owing to the structure of pollution sources and economic development, 
the progress in reduction of particular species might vary, affecting pollutant concentrations in the country 
itself and in its neighbours. The  impact  depends on both primary PM precursors (PM2.5) and precursors of 
secondary PM, including SO2, NOx, NH3, NMVOC, and their reduction will vary from measure to measure, i.e., 
flue gas desulphurization on a power plant, new efficient stove, new animal house, etc. will reduce different 
pollutants and to a varying degree.  

Such matrices can inform about the implications of different local and international policy showing how the 
contribution from different regions (or even sectors, although not shown here) could change. For example, 
the  results  presented  here  for  the  Baseline  scenario  show  how,  in  several  Member  State  where  the  local 
contribution dominated in 2005-2020 (e.g. Bulgaria, France, Italy, Poland, Portugal, Spain), over time their own 
contribution drops by about half because of implementation of national and EU policies. Some other countries, 
especially those with lower concentrations and away from larger industrialized Member States, like Finland or 
Sweden, see much smaller improvements in PM2.5 concentrations. One rather common feature is that, owing 
to continued reduction of Member State emissions in the Baseline scenario, the role of emissions from the 
non-EU countries as well as from natural sources becomes more prominent. 

75 

 
      
      
 
 
 
Figure 4-27: Origin of population-weighted PM2.5 background concentrations in each Member State 

computed for 2005 in µg/m3 [top] and as percent of total background concentrations [bottom].   

76 

ATBEBGHRCYCZDKEEFIFRDEGRHUIEITLVLTLUMTNLPLPTROSKSIESSEnon-EU27ShippingNatural + AfricaTotalAT7.760.080.090.370.000.910.010.000.000.421.650.000.340.000.680.000.000.000.000.061.000.000.250.250.550.020.001.200.071.1716.9BE0.058.730.000.000.000.190.050.000.004.082.580.000.000.100.050.000.000.190.001.540.330.000.000.000.000.100.012.711.420.5422.7BG0.050.0014.170.050.000.120.000.000.000.050.150.600.100.000.120.000.000.000.000.000.430.001.770.060.010.010.003.710.152.1423.7HR0.520.040.2811.830.000.570.000.000.000.210.600.020.500.001.490.000.000.000.000.030.970.000.570.170.590.050.003.790.251.6324.1CY0.000.000.310.001.860.000.000.000.000.010.020.490.000.000.130.000.000.000.000.000.050.000.090.000.000.000.004.090.885.9813.9CZ0.630.150.120.200.009.530.040.000.000.542.300.000.340.000.220.000.000.010.000.132.790.000.350.420.160.020.011.360.130.8920.3DK0.030.280.000.000.000.213.650.000.030.461.630.000.020.050.010.010.020.010.000.471.040.000.030.020.000.030.241.651.380.9212.2EE0.010.030.000.000.000.060.063.560.300.060.220.000.010.000.000.220.120.000.000.040.620.000.060.010.000.000.121.040.661.158.4FI0.000.020.000.000.000.030.040.122.960.040.130.000.000.000.000.050.040.000.000.020.310.000.020.000.000.000.140.610.371.126.0FR0.060.570.000.010.000.120.010.000.0010.820.990.000.000.050.270.000.000.050.000.250.170.010.000.000.010.280.001.290.691.1516.8DE0.340.790.000.010.000.690.080.000.001.557.510.000.030.020.100.000.000.090.000.820.880.000.030.030.010.060.021.430.510.6815.7GR0.010.002.850.020.000.060.000.000.000.050.0817.170.030.000.350.000.000.000.000.000.230.000.470.020.000.090.003.170.783.7329.1HU0.550.060.480.620.000.880.010.000.000.240.780.026.460.000.510.000.000.000.000.042.040.001.790.940.210.010.002.940.091.1819.9IE0.000.090.000.000.000.020.030.000.000.330.190.000.003.330.000.000.000.000.000.100.010.000.000.000.000.030.012.130.531.178.0IT0.240.010.080.260.000.140.000.000.000.500.260.020.040.0018.340.000.000.000.000.000.250.000.100.030.300.150.000.740.812.4124.7LV0.020.050.010.000.000.120.080.130.120.090.390.000.020.000.0110.280.510.000.000.071.300.000.100.040.000.000.111.370.371.1516.3LT0.030.070.050.010.000.190.110.070.070.130.570.000.050.000.030.154.090.000.000.092.360.000.150.060.000.000.091.900.341.0911.7LU0.121.790.000.000.000.290.020.000.004.243.690.000.010.020.090.000.004.840.000.520.340.000.000.010.000.090.001.340.480.5618.5MT0.000.000.210.040.000.050.000.000.000.390.090.100.010.002.120.000.000.002.180.000.120.000.050.000.000.260.000.602.787.816.8NL0.052.520.000.000.000.240.100.000.002.243.250.000.000.110.040.000.000.100.006.170.520.000.000.010.000.090.023.011.680.5820.7PL0.140.120.110.080.001.140.100.000.020.291.480.000.200.000.120.010.040.000.000.1313.810.000.350.330.050.010.041.580.260.9421.4PT0.000.010.000.000.000.000.000.000.000.180.040.000.000.000.010.000.000.000.000.000.007.360.000.000.001.370.000.090.852.4312.3RO0.070.012.180.060.000.180.000.000.000.070.240.120.250.000.130.000.000.000.000.010.930.0013.050.120.010.000.003.310.111.7122.6SK0.510.060.270.280.001.330.020.000.000.250.870.011.180.000.320.000.000.000.000.053.130.001.016.000.160.010.001.930.071.0518.5SI1.280.030.111.680.000.490.000.000.000.250.700.000.250.003.730.000.000.000.000.030.760.000.310.1110.460.040.001.260.261.2423.0ES0.000.020.000.000.000.000.000.000.000.520.080.000.000.000.170.000.000.000.000.010.020.320.000.000.0010.330.000.1212.5615.2SE0.010.080.000.000.000.080.350.020.070.160.510.000.010.010.000.020.030.000.000.130.620.000.030.010.000.012.540.870.881.087.5Source regionsReceptor regionsATBEBGHRCYCZDKEEFIFRDEGRHUIEITLVLTLUMTNLPLPTROSKSIESSEnon-EU27ShippingNatural + AfricaAT46%0%1%2%0%5%0%0%0%2%10%0%2%0%4%0%0%0%0%0%6%0%1%1%3%0%0%7%0%7%BE0%39%0%0%0%1%0%0%0%18%11%0%0%0%0%0%0%1%0%7%1%0%0%0%0%0%0%12%6%2%BG0%0%60%0%0%1%0%0%0%0%1%3%0%0%1%0%0%0%0%0%2%0%7%0%0%0%0%16%1%9%HR2%0%1%49%0%2%0%0%0%1%2%0%2%0%6%0%0%0%0%0%4%0%2%1%2%0%0%16%1%7%CY0%0%2%0%13%0%0%0%0%0%0%4%0%0%1%0%0%0%0%0%0%0%1%0%0%0%0%29%6%43%CZ3%1%1%1%0%47%0%0%0%3%11%0%2%0%1%0%0%0%0%1%14%0%2%2%1%0%0%7%1%4%DK0%2%0%0%0%2%30%0%0%4%13%0%0%0%0%0%0%0%0%4%9%0%0%0%0%0%2%14%11%8%EE0%0%0%0%0%1%1%43%4%1%3%0%0%0%0%3%1%0%0%0%7%0%1%0%0%0%1%12%8%14%FI0%0%0%0%0%0%1%2%49%1%2%0%0%0%0%1%1%0%0%0%5%0%0%0%0%0%2%10%6%19%FR0%3%0%0%0%1%0%0%0%64%6%0%0%0%2%0%0%0%0%1%1%0%0%0%0%2%0%8%4%7%DE2%5%0%0%0%4%1%0%0%10%48%0%0%0%1%0%0%1%0%5%6%0%0%0%0%0%0%9%3%4%GR0%0%10%0%0%0%0%0%0%0%0%59%0%0%1%0%0%0%0%0%1%0%2%0%0%0%0%11%3%13%HU3%0%2%3%0%4%0%0%0%1%4%0%33%0%3%0%0%0%0%0%10%0%9%5%1%0%0%15%0%6%IE0%1%0%0%0%0%0%0%0%4%2%0%0%42%0%0%0%0%0%1%0%0%0%0%0%0%0%27%7%15%IT1%0%0%1%0%1%0%0%0%2%1%0%0%0%74%0%0%0%0%0%1%0%0%0%1%1%0%3%3%10%LV0%0%0%0%0%1%0%1%1%1%2%0%0%0%0%63%3%0%0%0%8%0%1%0%0%0%1%8%2%7%LT0%1%0%0%0%2%1%1%1%1%5%0%0%0%0%1%35%0%0%1%20%0%1%1%0%0%1%16%3%9%LU1%10%0%0%0%2%0%0%0%23%20%0%0%0%0%0%0%26%0%3%2%0%0%0%0%0%0%7%3%3%MT0%0%1%0%0%0%0%0%0%2%1%1%0%0%13%0%0%0%13%0%1%0%0%0%0%2%0%4%17%46%NL0%12%0%0%0%1%0%0%0%11%16%0%0%1%0%0%0%0%0%30%3%0%0%0%0%0%0%15%8%3%PL1%1%1%0%0%5%0%0%0%1%7%0%1%0%1%0%0%0%0%1%65%0%2%2%0%0%0%7%1%4%PT0%0%0%0%0%0%0%0%0%1%0%0%0%0%0%0%0%0%0%0%0%60%0%0%0%11%0%1%7%20%RO0%0%10%0%0%1%0%0%0%0%1%1%1%0%1%0%0%0%0%0%4%0%58%1%0%0%0%15%0%8%SK3%0%1%2%0%7%0%0%0%1%5%0%6%0%2%0%0%0%0%0%17%0%5%32%1%0%0%10%0%6%SI6%0%0%7%0%2%0%0%0%1%3%0%1%0%16%0%0%0%0%0%3%0%1%0%45%0%0%5%1%5%ES0%0%0%0%0%0%0%0%0%3%1%0%0%0%1%0%0%0%0%0%0%2%0%0%0%68%0%1%7%17%SE0%1%0%0%0%1%5%0%1%2%7%0%0%0%0%0%0%0%0%2%8%0%0%0%0%0%34%12%12%14%Source regionsReceptor regions 
      
      
 
 
 
 
Figure 4-28: Origin of population-weighted PM2.5 background concentrations in each Member State 

computed for 2020 in µg/m3 [top] and as percent of total background concentrations [bottom].   

77 

ATBEBGHRCYCZDKEEFIFRDEGRHUIEITLVLTLUMTNLPLPTROSKSIESSEnon-EU27ShippingNatural + AfricaTotalAT4.290.030.010.160.000.520.000.000.000.170.990.000.270.000.290.000.000.000.000.030.490.000.070.110.200.010.000.880.061.179.8BE0.033.660.000.000.000.090.020.000.001.741.590.000.000.050.020.000.000.040.000.770.180.000.000.000.000.050.001.010.660.5410.5BG0.030.007.190.030.000.060.000.000.000.020.090.180.080.000.050.000.000.000.000.000.210.000.750.020.000.000.002.830.072.1413.8HR0.300.010.046.450.000.290.000.000.000.080.350.010.380.000.650.000.000.000.000.010.460.000.150.080.300.020.003.310.121.6314.6CY0.000.000.030.000.790.000.000.000.000.010.010.110.000.000.040.000.000.000.000.000.020.000.020.000.000.000.002.990.375.9810.4CZ0.360.050.020.080.006.990.020.000.000.211.360.000.250.000.090.000.000.000.000.061.570.000.090.210.050.010.000.940.090.8913.3DK0.020.110.000.000.000.111.870.000.010.201.030.000.020.020.000.010.010.000.000.230.620.000.010.010.000.020.140.670.680.926.7EE0.000.010.000.000.000.030.032.340.140.020.140.000.010.000.000.130.080.000.000.020.370.000.020.010.000.000.080.710.191.155.5FI0.000.010.000.000.000.020.020.051.440.010.080.000.000.000.000.030.030.000.000.010.190.000.010.000.000.000.080.400.111.123.6FR0.030.230.000.000.000.060.010.000.004.930.610.000.000.020.120.000.000.010.000.120.090.000.000.000.000.130.000.520.331.158.4DE0.190.290.000.010.000.340.040.000.000.624.600.000.020.010.040.000.000.020.000.390.490.000.010.020.000.030.010.610.290.688.7GR0.000.000.320.010.000.030.000.000.000.020.056.340.020.000.130.000.000.000.000.000.100.000.130.010.000.010.002.380.313.7313.6HU0.320.020.080.310.000.470.000.000.000.100.460.015.620.000.220.000.000.000.000.021.040.000.670.480.090.000.002.380.061.1813.5IE0.000.040.000.000.000.010.010.000.000.150.120.000.001.730.000.000.000.000.000.050.010.000.000.000.000.020.000.800.281.174.4IT0.130.000.010.090.000.070.000.000.000.190.160.000.030.009.320.000.000.000.000.000.110.000.020.010.110.070.000.630.412.4113.8LV0.010.020.000.000.000.060.050.040.050.040.240.000.020.000.014.950.350.000.000.030.810.000.040.020.000.000.070.910.151.159.0LT0.020.020.010.010.000.100.060.020.030.050.340.000.040.000.020.092.790.000.000.041.530.000.060.030.000.000.061.280.161.097.9LU0.070.690.000.000.000.150.010.000.001.872.210.000.010.010.040.000.001.340.000.250.180.000.000.010.000.040.000.500.250.568.2MT0.000.000.020.010.000.030.000.000.000.160.050.020.000.000.750.000.000.001.490.000.050.000.010.000.000.130.000.551.297.8012.4NL0.031.010.000.000.000.110.050.000.000.962.050.000.000.050.020.000.000.020.003.010.280.000.000.000.000.040.011.130.820.5810.2PL0.080.040.010.040.000.650.050.000.010.120.880.000.150.000.050.010.030.000.000.0611.310.000.110.160.020.000.021.000.140.9415.9PT0.000.000.000.000.000.000.000.000.000.070.020.000.000.000.000.000.000.000.000.000.004.180.000.000.000.680.000.030.352.437.8RO0.040.000.500.030.000.090.000.000.000.030.140.030.190.000.060.000.000.000.000.000.470.009.090.050.010.000.002.310.061.7114.8SK0.300.020.040.130.000.770.010.000.000.100.520.000.930.000.140.000.000.000.000.021.680.000.343.300.060.000.001.470.051.0510.9SI0.710.010.010.700.000.250.000.000.000.100.410.000.190.001.590.000.000.000.000.010.360.000.070.045.720.020.001.040.151.2412.6ES0.000.010.000.000.000.000.000.000.000.220.050.000.000.000.060.000.000.000.000.000.010.120.000.000.005.330.000.050.432.568.8SE0.010.030.000.000.000.040.190.010.030.070.320.000.010.010.000.010.020.000.000.060.380.000.010.010.000.001.510.400.301.084.5Source regionsReceptor regionsATBEBGHRCYCZDKEEFIFRDEGRHUIEITLVLTLUMTNLPLPTROSKSIESSEnon-EU27ShippingNatural + AfricaAT44%0%0%2%0%5%0%0%0%2%10%0%3%0%3%0%0%0%0%0%5%0%1%1%2%0%0%9%1%12%BE0%35%0%0%0%1%0%0%0%17%15%0%0%0%0%0%0%0%0%7%2%0%0%0%0%0%0%10%6%5%BG0%0%52%0%0%0%0%0%0%0%1%1%1%0%0%0%0%0%0%0%2%0%5%0%0%0%0%21%1%16%HR2%0%0%44%0%2%0%0%0%1%2%0%3%0%4%0%0%0%0%0%3%0%1%1%2%0%0%23%1%11%CY0%0%0%0%8%0%0%0%0%0%0%1%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%29%4%58%CZ3%0%0%1%0%52%0%0%0%2%10%0%2%0%1%0%0%0%0%0%12%0%1%2%0%0%0%7%1%7%DK0%2%0%0%0%2%28%0%0%3%15%0%0%0%0%0%0%0%0%3%9%0%0%0%0%0%2%10%10%14%EE0%0%0%0%0%1%1%43%3%0%3%0%0%0%0%2%1%0%0%0%7%0%0%0%0%0%1%13%3%21%FI0%0%0%0%0%1%1%1%40%0%2%0%0%0%0%1%1%0%0%0%5%0%0%0%0%0%2%11%3%31%FR0%3%0%0%0%1%0%0%0%59%7%0%0%0%1%0%0%0%0%1%1%0%0%0%0%2%0%6%4%14%DE2%3%0%0%0%4%0%0%0%7%53%0%0%0%0%0%0%0%0%4%6%0%0%0%0%0%0%7%3%8%GR0%0%2%0%0%0%0%0%0%0%0%47%0%0%1%0%0%0%0%0%1%0%1%0%0%0%0%18%2%27%HU2%0%1%2%0%3%0%0%0%1%3%0%42%0%2%0%0%0%0%0%8%0%5%4%1%0%0%18%0%9%IE0%1%0%0%0%0%0%0%0%3%3%0%0%39%0%0%0%0%0%1%0%0%0%0%0%0%0%18%6%27%IT1%0%0%1%0%1%0%0%0%1%1%0%0%0%68%0%0%0%0%0%1%0%0%0%1%1%0%5%3%18%LV0%0%0%0%0%1%1%0%1%0%3%0%0%0%0%55%4%0%0%0%9%0%0%0%0%0%1%10%2%13%LT0%0%0%0%0%1%1%0%0%1%4%0%1%0%0%1%36%0%0%1%19%0%1%0%0%0%1%16%2%14%LU1%8%0%0%0%2%0%0%0%23%27%0%0%0%0%0%0%16%0%3%2%0%0%0%0%0%0%6%3%7%MT0%0%0%0%0%0%0%0%0%1%0%0%0%0%6%0%0%0%12%0%0%0%0%0%0%1%0%4%10%63%NL0%10%0%0%0%1%0%0%0%9%20%0%0%0%0%0%0%0%0%30%3%0%0%0%0%0%0%11%8%6%PL1%0%0%0%0%4%0%0%0%1%6%0%1%0%0%0%0%0%0%0%71%0%1%1%0%0%0%6%1%6%PT0%0%0%0%0%0%0%0%0%1%0%0%0%0%0%0%0%0%0%0%0%54%0%0%0%9%0%0%5%31%RO0%0%3%0%0%1%0%0%0%0%1%0%1%0%0%0%0%0%0%0%3%0%61%0%0%0%0%16%0%12%SK3%0%0%1%0%7%0%0%0%1%5%0%9%0%1%0%0%0%0%0%15%0%3%30%1%0%0%13%0%10%SI6%0%0%6%0%2%0%0%0%1%3%0%2%0%13%0%0%0%0%0%3%0%1%0%45%0%0%8%1%10%ES0%0%0%0%0%0%0%0%0%2%1%0%0%0%1%0%0%0%0%0%0%1%0%0%0%60%0%1%5%29%SE0%1%0%0%0%1%4%0%1%2%7%0%0%0%0%0%0%0%0%1%8%0%0%0%0%0%34%9%7%24%Source regionsReceptor regions 
      
      
 
 
 
 
Figure 4-29: Origin of population-weighted PM2.5 background concentrations in each Member State 

computed for 2030 Baseline scenario in µg/m3 [top] and as percent of total background 
concentrations [bottom].   

78 

ATBEBGHRCYCZDKEEFIFRDEGRHUIEITLVLTLUMTNLPLPTROSKSIESSEnon-EU27ShippingNatural + 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regionsSource regions 
      
      
 
 
 
 
Figure 4-30: Origin of population-weighted PM2.5 background concentrations in each Member State 

computed for 2050 Baseline scenario in µg/m3 [top] and as percent of total background 
concentrations [bottom].   

4.5.2  Origin of PM2.5 in each Member State 

Another  way  of  revealing  the  importance  of  the  joint  action  and  international  collaboration  in  air  quality 
management, including benefits of the coordinated EU policy stimulating emission reductions, is to show the 
origin of contribution to background PM2.5 in each Member State and over time.  

The charts in Figure 4-31 show for each Member State the contributions to population-weighted PM2.5 and 
their change over time (for four selected years: 2005, 2020 and 2030 and 2050 Baseline scenario). They show 
for each Member State own contribution (always in black), six largest contributors from the EU, and always 
(and in the same colours) contribution from natural, non-EU, and other-EU sources.  

For many Member States, their own domestic reductions represent often about 50% or more of the reduction 
of PM2.5 concentrations. An important contribution driven by measures introduced across the EU highlights 
the importance of coordinated action and for some Member States these reductions dominate the estimated 
reduction  of  PM2.5  concentrations.  Similarly  to  the  observation  made  in  the  previous  section,  and 
unsurprisingly, the role of sources outside the EU increases over time and their importance varies from country 

79 

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regionsSource regions 
      
      
 
 
 
to  country.  However,  compared  to  CAO3,  the  non-EU  contribution  in  absolute  terms  is  lower  owing  to 
consideration  (in  CAO4  Baseline)  of  the  results  of  the  EU4Green  study  for  West  Balkan,  which  include 
decarbonization  policy  and  effective  implementation  of  air  pollution  legislation  in  power,  industry,  and 
transport sectors.  

This analysis also shows that over time, some countries or regions contribute to an increase of concentrations 
in a given time  period, however,  this is always more than compensated by remaining reductions and so  a 
declining  trend  in  concentrations  is  observed  throughout  the  whole  time  horizon.  In  such  cases,  the  bars 
illustrating  differences  between  scenario  years  are  broken  into  increases  and  decreases.  These  examples 
include Finland, Malta, Italy, Portugal, Spain, Sweden where typically the non-EU contribution leads to a slight 
increase  in  the  period  2020-30  or  2030-2050.  Finally,  some  countries  have  very  different  structure  of 
contributing sources and natural sources and shipping dominate39. 

Figure 4-31: Origins of ambient background concentrations of PM2.5 (population-weighted) in each 

Member State in the period 2005 to 2050 and the contribution to the changes during respective 
periods (bars 2, 4, 6). The category ‘Non-EU’ includes West Balkan, Belarus, Norway, the 
European part of Russia, Switzerland, Turkey, the UK, the Ukraine, and international shipping.   

39 Note that in this figure, shipping is included in the category ‘non-EU’, but the explicit contribution of shipping can be 
read from the tables in the previous section.  

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Costs of air pollution and the benefits of air pollution 
reduction  

5.1 

Introduction 

This chapter analyses mitigation costs, impact costs of air pollution, and quantifies benefits of air pollution 
reduction,  for  the  scenarios  developed  in  in  this  project  (see  Section  4.3),  except  the  condensable  and 
methane sensitivity scenarios.  

5.2 

Approach 

Costs of air pollution control measures are calculated with the GAINS model for each scenario and presented 
in Section 5.3. 

Impact costs are estimated in Section 5.4. The valuation methods used in the assessment (GAINS and ALPHA-
RiskPoll models) are consistent with the ones used in the third Clean Air Outlook. However, the methods have 
been updated (see Section 2.3). 

The following aspects are monetised for the core scenarios described in Section 4.3.1: 

•  Cost of air pollution in 2020, 2025, 2030, 2040, 2050, comprising market costs (i.e. healthcare costs, lost 
workdays costs, lost crops costs) and non-market costs of mortality and morbidity and, to the extent 
possible, costs of lost ecosystem services; 

•  The market and non-market costs of pollution are put in perspective with the cost of the measures to 
reduce pollution that would be put in place under the various scenarios, for 2020, 2025, 2030, 2040, 
2050. 

Subsequent analysis of the macro-economic impact of air pollution and the costs of control measures has been 
performed by the Commission’s Joint Research Centre with the Computable General Equilibrium (CGE) Model 
JRC-GEM-E340  using  outputs  from  this  analysis,  in  particular  costs  and  benefits  of  the  analysed  scenarios 
(Section 5.5). The analysis addresses, for each scenario, the impact on the EU GDP, employment, and trade 
flows, broken down by sector. 

Results presented in this Section account for the full change in pollutant concentrations as a consequence of 
the ERC, ZPAP, ERC+ZPAP and MTFR scenarios (see Section 4.3). Section 5 of the Annex provides further results 
including  cost-benefit  analysis  where  cut-off  points  at  the  level  of  WHO  Global  Air  Quality  Guidelines  are 
applied to quantification of effects of PM2.5 and NO2. 

5.3 

Costs of the measures at the EU level  

The cost of air pollution control measures calculated by the GAINS model for the key analysed scenarios are 
shown in Table 5-1 and Table 5-2. According to the GAINS calculation, the Baseline costs decline after 2025 
and by 2050 could represent less than half of the current cost. Also, the distribution across sectors will change, 
with  reductions expected  in power plants  and transport  sector owing to decarbonization (GAINS does  not 
consider costs of structural changes but only costs for air pollution control measures) and therefore lower 
demand for controlled capacity, including fossil fuel power plants or vehicles.  

40 https://joint-research-centre.ec.europa.eu/scientific-tools-and-databases/jrc-gem-e3-model/overview-jrc-gem-e3-
model_en   

85 

 
      
      
 
 
 
The additional costs for the mitigation (Table 5-2) necessary to achieve the ERCs in 2020 and 2025 are very 
low compared to the costs of current legislation, i.e., only in order of 0.03-0.19 % of the Baseline costs in these 
years, but they increase to about 0.75 % of Baseline (or over 550 million €/year) for the attainment of the ERC 
in 2030. The initial lower costs are linked to typically smaller reductions needed and also potential for rather 
cheap measures,  including  strict  enforcement  of open  burning  bans,  elimination  of  high-emitting  vehicles, 
improved efficiency of applying mineral fertilizers. Costs grow in the future to achieve the ERCs in 2030 and 
are comparable to the costs estimated to achieve the ZPAP objectives for ecosystems, i.e., about 400 million 
€/year, which represent about 0.55 % of the Baseline costs. 

Applying all technical measures defined in the GAINS model (the MTFR scenarios) leads to a large increase in 
abatement costs. For 2030, the additional costs of the MTFR over the Baseline scenario (Table 5-2) are in the 
order of 23 billion €/year and represent over 30% of the Baseline costs, while for 2050 this difference is lower 
in  absolute  terms,  estimated  at  about  20  billion  €/year,  representing  nearly  70  %  of  the  respective  total 
Baseline costs in 2050. 

Table 5-1: Total air pollution control costs for the EU for analysed scenarios. Units: million €/year. 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2005 
44338 

2010 
55154 

2015 
67772 

2020 
67928 
68054 

2025 
79853 
79931 
79897 

2030 
73249 
73806 

73648 
74047 
96262 

2035 
60405 

2040 
44263 
44418 

2045 
34361 

2050 
28992 
29118 

64875 

48886 

Table 5-2: Additional air pollution control costs over the Baseline for the EU for analysed scenarios.  

Units: million €/year. 

2005 

2010 

2015 

2020 

2025 

2030 

2035 

2040 

2045 

2050 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

0.0 

0.0 

0.0 

0.0 
0.126 

0.0 
78 
26 

0.0 
556 

399 
798 
23013 

0.0 

0.0 
155 

0.0 

0.0 
126 

20611 

19894 

5.4 

Benefits analysis with the ALPHA-RiskPoll model 

5.4.1  Economic value of health impacts  

Figure 5-1 shows annual health damage in €billion/year for the CAO4 Baseline scenario from 2005 to 2050. 
Impacts are split into three ‘tiers’, as described in Section 2.3.1. Tier 1 is based on mortality response functions 
from the WHO Air Quality Guidelines (WHO 2021) and morbidity effects from long-term exposure from the 
EMAPEC study also coordinated by WHO (Forastiere et al. 2024). Tier 2 includes additional functions, largely 
for  effects  of  short-term  exposure,  that  were  included  in  CAO3  (the  remit  of  EMAPEC  did  not  extend  to 
assessment of effects of short-term exposure). Tier 3 includes two further long-term exposure effects from 
EMAPEC, concerning type 2 diabetes and dementia, both of which were given a lower confidence rating by 
Forastiere et al. Given higher uncertainty the Tier 3 effects are not included in the analysis that follows Figure 
5-1. 

86 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  5-1  accounts  for  sensitivity  to  two  factors,  the  first  concerns  the  approach  to  mortality  valuation, 
whether  using  the  value  of  a  life  year  (VOLY)  or  the  value  of  a  statistical  life  (VSL),  noting  that  for  ozone 
mortality only the VOLY is applied given uncertainties in the modelling of the impact. The second sensitivity 
concerns the extent to which NO2 effects are included. A simple approach is taken here, showing results with 
and without NO2 impacts where there is potential for effects to be double counted against the quantification 
of PM2.5 effects. The left-hand side of the figure includes all modelled NO2 effects (mortality from long term 
exposure, asthma in children and adults, acute lower respiratory infections (ALRI), bronchitis in children and 
respiratory hospital admissions), and the right-hand side of the figure only includes asthma in adults and ALRI 
(the  other  impacts  being  covered  also  for  PM2.5).  This  approach  provides  a  range  within  which  the  true 
estimate  of  the  combined  effect  of  PM2.5  and  NO2  impacts  should  lie.  Note  that  this  approach  is  slightly 
different from CAO3 where all effects of NO2 were either taken on board, or completely ignored (sensitivity 
case). In CAO4 we attempt to decrease the uncertainty range by excluding only those outcomes where there 
is a risk for double counting. 

Figure 5-1: Economic value of health impacts of air pollutants for the EU under the Baseline scenario, 

showing the split between impacts quantified according to the three tiers of analysis (see text). 
Mortality is valued using the VOLY (top) and VSL (bottom). Figures on the left-hand side include 
all NO2 impacts, whilst those on the right-hand side exclude NO2 effects where there is a risk of 
double counting impacts quantified against PM2.5 exposure. Units: €billion/year. Note the 
difference in scales for the upper and lower figures.   

Figure 5-2 provides total estimates of impact (mortality and morbidity) for PM2.5, NO2 and O3, corresponding 
to the upper left panel of Figure 5-1 but excluding Tier 3 effects (a similar graph including Tier 3 is included in 
the Annex).  For each pollutant total damage is shown, ignoring potential for double counting when impacts 
are  aggregated  across  the  pollutants.  Health  costs  are  dominated  throughout  by  the  impacts  quantified 
against  PM2.5  exposure,  followed  by  NO2  and  then  O3.  The  results  for  ozone  are  very  sensitive  to  the 
quantification and valuation of mortality impacts given that only  acute  exposure on  hospital admissions is 
included for morbidity. Over the period 2005-2050 there is a 77% decline in damage linked to PM2.5, an 88% 

87 

 
      
      
 
 
decline for NO2 but only a 2% decline for ozone. The result for ozone is strongly influenced by demographic 
changes: results above (Table 4-11, based on a static population) indicate a larger change, reflecting reduction 
in individual risk as ozone levels fall.  A conservative position (tending to lower bound estimates) is adopted 
here for consistency with earlier analysis and recognising uncertainties in recent meta-analyses of ozone and 
mortality  where  conclusions  have  varied  with  respect  to  both  cause  and  exposure  metrics  (WHO,  2021; 
Kasdagli et al, 2024). 

Figure 5-2: Value of damage due to PM2.5, O3 and NO2 exposure for the EU under the Baseline scenario. 

Mortality is valued using the VOLY. Units: €Billion per year. Tier 3 effects (dementia and diabetes) 
not included. 

Table 5-3 shows the total economic value of health impacts by scenario over time, excluding Tier 3 effects. 
The table again shows sensitivity to the approach used for mortality valuation (VOLY – value of a life year, or 
VSL – value of statistical life) and to potential double counting of impacts when using functions for both PM2.5 
and NO2. Results for each Member State and Baseline year are included in the Annex Section 5.1. 

88 

 
      
      
 
 
 
 
 
 
Table 5-3: Economic value of health impacts linked to PM2.5, NO2 and O3 by scenario accounting for 

sensitivity to the approach used for mortality valuation (VOLY, VSL) and testing sensitivity to the 
inclusion of NO2 impacts. Units €million/year, 2015 prices. Excludes Tier 3 impacts. 

2020 

2025 

2030 

2040 

2050 

454,652 

579,322 

1,508,157 

2,001,029 

Including all NO2 functions 
Mortality valued using VOLY  
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR  
Including NO2 functions only for adult asthma and child ALRI morbidity 
Mortality valued using VOLY  
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR  
Mortality valued using VSL  
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR  

1,529,474 

1,165,193 

468,774 

371,981 

348,284 
339,043 
334,216 
328,872 
295,384 

1,202,636 
1,171,200 
1,152,837 
1,133,403 
1,022,348 

290,323 
282,601 
280,426 
277,172 
246,933 

949,764 
924,948 
918,124 
907,518 
810,913 

245,746 

213,476 

207,890 

185,782 

917,127 

870,226 

775,464 

757,123 

215,939 

190,751 

183,697 

167,088 

771,278 

745,366 

657,145 

654,418 

5.4.2  Non-health effects  

Damage to building materials was extensively researched in the 1980s and 1990s. Although research in the 
area continues, it is less active now than previously. Damage values per unit emission for SO2 and NOx have 
been taken from an earlier EC research project, CASES. 

Damage to crops is assessed using the methods developed in the ECLAIRE study  (Holland et al. 2015). This 
includes  impacts  of  ozone  on  all  crops  grown  in  Europe.  It  does  not,  however,  include  impacts  on  the 
production of meat and animal products (milk, cheese, wool, honey, etc.) via impacts on grass production. 
Impacts  to  forests  are  also  quantified  using  methods  from  ECLAIRE,  accounting  for  reduced  production  of 
wood and reduced carbon sequestration linked to ozone exposure. 

Economic analysis of ecosystem damage is also based on the ECLAIRE study. It assesses impacts related to 
terrestrial  ecosystems  only.  ECLAIRE  used  three  methods  to  estimate  damage  costs  for  ecosystems,  a 
willingness to pay (WTP) approach based on the results of research by  Christie et al. (2006, 2011; 2012), a 
repair  cost  approach  described  by  Ott  et  al.  (2006)  and  a  ‘regulatory  revealed  preference’  approach 
(developed in the ECLAIRE study). The focus of analysis is on exceedance of the critical load for nitrogen in 
Natura 2000 sites, with valuation applied to the area subject to critical loads exceedance. No account was 
taken of  exceedance  of  the  critical  load  for  acidification,  because the  area concerned  is  far  less than  that 
affected by eutrophication and there is potential for double counting if results for both effects are combined. 
The WTP based approach, representing WTP of the public for ecosystem protection, is adopted here as the 
preferred option because it is most consistent with the method used for other impacts assessed in this report. 
Uncertainties in the methods used in ECLAIRE led to the use of a factor 3 variation between low and high 

89 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
estimates of damage. The economic cost of impacts to materials, crops, forests and ecosystems is shown in 
Table 5-4, Table 5-5, Table 5-6, and Table 5-7, respectively. 

Table 5-4: Economic valuation of air pollution impacts to materials for selected scenarios for the EU. Units: 

€million/year (2015 prices). 

Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

2020 
1,231  

2025 
1,011  

2030 
742 
730 
714 
714 
523 

2040 
445 

2050 
384 

289 

246 

Table 5-5: Economic valuation of air pollution impacts to crops for selected scenarios for the EU. Units: 

€million/year (2015 prices). 

Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

2020 
13,840 

2025 
13,481 

2030 
12,948 
12,867 
12,818 
12,815 
12,545 

2040 
12,220 

2050 
12,087 

11,848 

11,750 

Table 5-6: Economic valuation of air pollution impacts to forests for selected scenarios for the EU. Units: 

€million/year (2015 prices). 

Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

2020 
18,768 

2025 
18,435 

2030 
17,927 
17,852 
17,799 
17,798 
17,566 

2040 
41,504 

2050 
41,156 

40,686 

40,405 

Table 5-7: Economic valuation of air pollution impacts to ecosystems for selected scenarios for the EU. 

Units: €million/year (2015 prices). 

2020 

2025 

2030 

2040 

2050 

Ecosystem damage - Low 
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Ecosystem damage - High 
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

4,072 

3,914 

12,217 

11,741 

3,675 
3,506 
3,359 
3,353 
3,023 

11,024 
10,518 
10,077 
10,058 
9,070 

3,396 

3,293 

2,642 

2,560 

10,188 

9,880 

7,926 

7,679 

90 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The large increase in the value of impacts to forests between 2030 and 2040 is attributable to the change in 
carbon values for future years recommended in the DG MOVE Handbook on External Costs of Transport (CE 
Delft et al. 2020). This recommends values of €100/t CO2 up to 2030 and €267/t CO2 from 2040 to 2060. This 
change has limited effect on the cost-benefit analysis below, given that there are only small changes in forest 
exposure to ozone across the scenarios in any year. 

5.4.3  Cost-benefit analysis 

This section provides cost-benefit analysis (CBA) for total change in the benefits of the non-Baseline scenarios. 
Incremental cost data for scenarios above Baseline, calculated with the GAINS model, are shown in Table 5-8. 
Summary of the total EU costs for the Baseline and analysed scenarios were shown in Table 5-1 and Table 5-2.  

Table 5-8: Additional costs above the Baseline. Units €million/year. 

2030 

Country 
Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Rep. 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
EU 

ERC 
- 
0.5 
1.0 
5.2 
- 
17.4 
0.8 
0.0 
0.0 
7.7 
225.6 
0.0 
82.1 
17.2 
0.1 
1.7 
- 
1.7 
1.8 
1.5 
19.3 
6.5 
117.2 
0.8 
34.8 
13.4 
0.3 
556.4 

ZPAP 
0.2 
9.1 
15.1 
10.2 
0.5 
2.3 
8.3 
2.1 
3.4 
102.4 
52.9 
1.5 
8.4 
8.8 
5.1 
1.5 
0.4 
0.5 
0.0 
2.6 
72.6 
11.8 
19.6 
8.2 
0.7 
45.0 
5.5 
398.7 

ERC+ZPAP 
0.2 
6.8 
14.9 
9.3 
0.5 
17.5 
4.7 
2.0 
3.4 
97.1 
226.7 
1.5 
82.2 
17.5 
5.1 
2.0 
0.1 
1.7 
1.8 
2.8 
69.2 
11.7 
127.5 
4.7 
35.3 
46.1 
5.5 
797.9 

MTFR 
423 
363 
336 
318 
49 
721 
329 
144 
572 
3,308 
4,672 
780 
426 
318 
2,003 
171 
212 
36 
13 
558 
2,664 
465 
988 
311 
117 
2,323 
391 
23,013 

2040 
MTFR 
441 
407 
288 
260 
40 
625 
304 
139 
516 
3,104 
3,704 
621 
451 
342 
1,836 
174 
162 
39 
12 
563 
2,333 
435 
916 
304 
140 
2,104 
351 
20,611 

2050 
MTFR 
420 
429 
221 
234 
42 
590 
298 
130 
378 
2,913 
3,688 
625 
458 
338 
1,830 
166 
153 
41 
9 
571 
2,268 
416 
782 
306 
119 
2,107 
362 
19,894 

Benefits are assessed by combining the results on impacts from the previous section (health, crops, forest, 
materials and ecosystems) for each scenario relative to the CAO4 Baseline in the appropriate year (2030 or 
2050). Results are shown in Table 5-9, which includes the sensitivity cases considered above. VOLY- and VSL-

91 

 
      
      
 
 
 
 
based  estimates  for  health  benefit  are  combined  with  the  Low  and  High  estimates  for  ecosystems, 
respectively, to show the range of overall estimated benefits.  

Results  show  that  benefits  by  scenario  increase  in  order  from  ERC  to  ZPAP,  to  ERC+ZPAP  to  MTFR.  Going 
forward in time, the annual benefits of the MTFR scenario are reduced, largely reflecting action accounted for 
in the Baseline. 

Table 5-9: Benefits from reduced health and non-health damage relative to the CAO4 Baseline for the EU. 

Units: €M/year, 2015 prices. Tier 3 health effects excluded. 

2030 

9,578 
14,670 
20,024 
54,535 

Including all NO2 functions 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Including NO2 functions only for adult asthma and child ALRI morbidity 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

25,490 
32,873 
43,502 
141,788 

32,110 
51,032 
70,489 
183,225 

8,059 
10,499 
13,763 
45,025 

2040 

2050 

39,956 

29,653 

145,271 

116,530 

34,342 

25,622 

117,741 

94,375 

Net benefits (benefits – costs) are shown in Table 5-10 and benefit-cost ratios (net benefits divided by net 
costs, taken from Table 5-2) in Table 5-11. In all cases results demonstrate a strong excess of benefit over 
further control costs. Analysis accounting for the use of cut-off points for PM2.5 and NO2 set at the WHO Global 
Air Quality Guidelines is presented in Section 5.3 of the Annex. Results there show broadly similar trends. The 
effect of the cut-off points in reducing estimated damage is modest for 2030, but grows over time as increasing 
areas are forecast to meet the WHO Guidelines. In all cases except MTFR in 2040 and 2050 with mortality 
valued with the VOLY, results demonstrate a strong excess of benefit over further control costs. Proportionally, 
impacts on the benefit-cost ratios through the application of the cut-off points increase over time, as the area 
subject to their exceedance shrinks and estimated benefits fall. 

Care  is  needed  in  interpretation  of  the  results  for  the  MTFR  scenarios  as  they  could  be  interpreted  as 
demonstrating a net benefit for all measures included for MTFR. In reality, some of the measures included in 
the cost curve will be cost-efficient whilst some others are extremely expensive per unit emission abated, and 
on their own may not generate a net benefit either across the EU or in all Member States. This is suggested by 
the decline in benefit-cost ratios when moving down through scenarios to the MTFR. More detailed analysis, 
involving a series of additional scenarios would be needed to test the costs and benefits of the increasingly 
less cost-effective measures towards the upper end of the cost curve. 

92 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5-10: Net benefits from reduced health and non-health damage relative to the CAO4 Baseline for the 

EU. Units: €M/year, 2015 prices. Tier 3 health effects excluded. 

2030 

9,022 
14,271 
19,226 
31,522 

Including all NO2 functions 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Including NO2 functions only for adult asthma and child ALRI morbidity 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

24,933 
32,474 
42,704 
118,775 

31,554 
50,633 
69,691 
160,212 

7,503 
10,100 
12,965 
22,012 

2040 

2050 

19,344 

9,758 

124,660 

96,636 

13,731 

5,727 

97,129 

74,481 

Table 5-11: Benefit-cost ratios by scenario for the EU. Tier 3 health effects excluded. 

2030 

17 
37 
25 
2.4 

Including all NO2 functions 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Including NO2 functions only for adult asthma and child ALRI morbidity 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

58 
128 
88 
8.0 

14 
26 
17 
2.0 

46 
82 
55 
6.2 

2040 

2050 

1.9 

7.0 

1.7 

5.7 

1.5 

5.9 

1.3 

4.7 

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5.5  Macro-economic impact analysis 

This section integrates the air pollution control costs (Section 5.3) and the corresponding benefits of clean air 
(Section 5.4) into the broader, economy-wide framework as captured by the JRC-GEM-E3 model at member 
state level. A suitable data interface between the GAINS  and JRC-GEM-E3 models has been developed and 
successfully applied in the previous Clean Air Outlook studies. Results of the GAINS model, including cost data 
at  the  agreed  sectoral  level  suitable  for  the  JRC-GEM-E3  analysis  as  well  as  population  weighted 
concentrations of PM2.5 for the analysed scenarios were provided to the JRC team. In addition, results of the 
cost benefit analysis with the ALPHA-RiskPoll model has been completed and Work Lost Days and crop yields 
data provided to JRC. 

For  the  analysis,  the  JRC-GEM-E3  model  represents  abatement  costs  as  additional  intermediate  inputs 
required in the production process. With respect to the benefits, two different methods are used. i) macro-
economic  gains  from  improved  productivity  by  0.80%  per  1  μgm-3
  decrease  in  the  concentration  of  fine 
particulate matter (Dechezleprêtre et al. 2019), ii) macro-economic gains from changes in labour supply based 
on lost work days estimates from Alpha-RiskPoll Framework (Ostro, 1987). In addition, it includes to the two 
above mentioned market benefits, gains from changes in total factor productivity of the crop sector based on 
changes in crop yield, available from the Alpha-RiskPoll model.  

The time periods estimated varies across the scenarios. The macro-economic effects of the MTFR scenario 
have been estimated for 2030, 2040 and 2050. For the ERC scenario, work lost days and crop yield estimates 
were not available in 2040 and 2050 due to no ozone data. Therefore, the macro-economic effects of the ERC, 
ZPAP and ERC+ZPAP scenarios are only available in 2030.  

The time periods estimated varies across the scenarios. The macro-economic effects of the MTFR scenario 
have been estimated for 2030, 2040 and 2050. For the ERC scenario, work lost days and crop yield estimates 
were not available in 2040 and 2050 due to no ozone data. Therefore, the macro-economic effects of the ERC, 
ZPAP and ZPAP+ERC scenarios are only available in 2030.  

Table 5-12 summarises the macro-economic GDP impacts and Table 5-13, Table 5-14, Table 5-15, and Table 
5-16 the output effects for specific sectors when considering additional abatement costs (compared to the 
baseline)  and  two  different  measures  of  ‘market’  benefits.  These  two  measures  of  benefits  consider  the 
following; i) avoided lost work days and increased crop yields (Table 5-12, Table 5-13, Table 5-14) and ii) labour 
productivity and increased crop yields (Table 5-12, Table 5-15, Table 5-16) as discussed above. 

Air pollution control costs differ across scenarios and sectors, reflecting the mitigation strategy. The MTFR 
scenario has an ambitious pollution control that requires additional investment in agriculture, industry and 
the residential sector in particular, while the scenarios targeting the ERC and ZPAP mainly increase cost for 
agriculture due to additional reductions of NH3 emissions in these scenarios. Higher pollution control costs in 
MTFR scenario also imply larger benefits compared to the other scenarios. Nevertheless, the net effect (costs 
and benefits) in the MTFR can be negative (Ostro 1987) or positive (Dechezleprêtre et al. 2019) depending on 
the method used to estimate benefits. On the other hand, in 2030, the ZPAP scenario indicates positive net 
effects irrespective of the methodology used to estimate benefits.   

Figure 5-3 provides the summary of macro-economic impacts on the economy showing that when the benefits 
through  the  labour  productivity  and  crop  yield  gains  (drawing  on  the  recent  empirical  evidence)  are 
considered, the benefits offset the costs. In the MTFR scenario, the EU aggregate GDP would increase, by up 
to 0.243% in 2030, to 0.187% in 2040 and 0.146% in 2050 compared to the baseline. 

94 

 
      
      
 
 
Table 5-12. Macro-economic impacts in 2030, 2040 and 2050, considering the full concentration range (no 

cut-off); % GDP change compared to the respective baseline; except ‘Benefit/cost ratio’ that 
shows the ratio of benefits to costs. Source: JRC-GEM-E3 model 

2030 

2040 

2050 

Scenario 

Costs 

Benefits 

Net 
effect 

Benefit
/ cost 
ratio 

Costs 

Benefits 

Net 
effect 

Benefit
/ cost 
ratio 

Costs 

Benefits 

Net 
effect 

Benefit
/ cost 
ratio 

MTFR 

ERC 

ZPAP 

ERC+ZPAP 

MTFR 

ERC 

ZPAP 

ERC+ZPAP 

MTFR 

ERC 

ZPAP 

ERC+ZPAP 

MTFR 

ERC 

ZPAP 

ERC+ZPAP 

-0.154 

-0.003 

-0.002 

-0.004 

-0.154 

-0.003 

-0.002 

-0.004 

-0.154 

-0.003 

-0.002 

-0.004 

-0.154 

-0.003 

-0.002 

-0.004 

Considering benefits through work days lost (WLD) based on Ostro (1987) 

0.012 

0.002 

0.003 

0.003 

-0.142 

-0.002 

0.001 

-0.001 

0.080 

-0.119 

0.010 

-0.109 

0.080 

-0.098 

0.008 

-0.091 

0.078 

0.531 

1.433 

0.748 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

Considering benefits through work days lost (WLD) based on Ostro (1987) and crop yield gains 

0.015 

0.002 

0.004 

0.004 

0.394 

0.056 

0.092 

0.109 

-0.139 

-0.001 

0.002 

0.000 

0.099 

-0.119 

0.012 

-0.107 

0.101 

-0.098 

0.010 

-0.089 

0.099 

0.691 

1.908 

0.964 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

Considering benefits through labour productivity using Dechezleprêtre et al (2019) 

0.240 

0.052 

0.090 

0.104 

2.6 

-0.119 

0.303 

0.185 

2.553 

-0.098 

0.242 

0.144 

2.5 

17.0 

46.2 

24.4 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

Considering benefits through labour productivity using Dechezleprêtre et al (2019) and crop yield gains 

0.397 

0.056 

0.093 

0.110 

0.243 

0.053 

0.091 

0.105 

2.6 

-0.119 

0.306 

0.187 

2.571 

-0.098 

0.244 

0.146 

2.5 

17.2 

46.6 

24.6 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

n.a 

The  impacts  on  sectoral  output  are  available  in  Table  5-13  to  Table  5-16.  The  scenarios  ZPAP,  ERC  and 
ERC+ZPAP are only considered in 2030. The power sector, industries and services are in all scenarios equal or 
better off compared to the change in economic activity at the aggregate level (Table 5-13, Table 5-14, Table 
5-15, Table 5-16). The power sector benefits in all scenarios, and is the sector that benefits most in the MFTR 
scenario when only introducing abatement costs into the model. This is an effect that arises within the CGE 
model, as the fossil fuel sectors bear some of the abatement costs, there is a substitution from fossil fuels to 
cleaner electricity. The crops sector increases output compared to the Baseline in the ZPAP, ERC and ERC+ZPAP 
scenarios when including crop yield gains (Table 5-13, Table 5-16). The livestock sector faces the largest output 
reduction due to the abatement costs (except in the ZPAP scenario, where the output drop is higher for crops). 
The livestock and fossil fuel sectors increase outputs compared to the  Baseline in the ZPAP scenario when 
benefits are estimated according to Dechezleprêtre et al (Table 5-15, Table 5-16). 

95 

 
      
      
 
  
  
  
  
  
 
Figure 5-3. Macro-economic market effects of clean air scenarios in the EU considering the full 
concentration range (no cut-off). Benefits include labour productivity losses following 
Dechezleprêtre et al (2019) and crop yield gains. % GDP change relative to baseline. Source: JRC-
GEM-E3 model. 

Table 5-13. Sector-specific market impacts compared to respective baseline (% change in output)41, in 
2030, 2040 and 2050, considering the full concentration range (no cut-off) and work loss days 
(WLD) based on Ostro (1987); Source: JRC-GEM-E3 model. 

Costs 

-0.718 
-2.143 
-0.337 
0.124 
-0.013 
0.004 
-0.154 

Costs 

-0.725 
-2.320 
-0.213 
0.125 
-0.004 
0.012 
-0.119 

MTFR 
Costs & benefits 
-0.697 
-2.127 
-0.325 
0.135 
0.003 
0.016 
-0.142 

2040 
MTFR 
Costs & benefits 
-0.708 
-2.307 
-0.205 
0.134 
0.008 
0.021 
-0.109 

Costs 
-0.037 
-0.154 
-0.004 
0.001 
0.005 
0.000 
-0.003 

Costs 
-0.638 
-2.245 
-0.146 
0.134 
-0.008 
0.014 
-0.098 

Crops 
Livestock 
Fossil fuels 
Power sector 
Industry 
Services 
Memo: GDP 

Crops 
Livestock 
Fossil fuels 
Power sector 
Industry 
Services 
Memo: GDP 

WLD based on Ostro (1987) 
2030 

ERC 

Costs & benefits 

Costs 

-0.034 
-0.152 
-0.002 
0.002 
0.008 
0.002 
-0.002 

-0.127 
-0.100 
0.001 
0.002 
0.006 
0.001 
-0.002 

ZPAP 
Costs & benefits 
-0.122 
-0.097 
0.003 
0.005 
0.010 
0.004 
0.001 

ERC+ZPAP 

Costs 

Costs & benefits 

-0.137 
-0.192 
-0.003 
0.002 
0.009 
0.001 
-0.004 

-0.131 
-0.187 
0.000 
0.005 
0.014 
0.004 
-0.001 

2050 
MTFR 

Costs & benefits 

-0.625 
-2.235 
-0.140 
0.141 
0.002 
0.021 
-0.091 

41 Note: All results are expressed in percent difference relative to the respective baseline. In each column, the “Costs” column 
indicate the impacts of the cost of the pollution abatement measures, when benefits from clean air are not considered. The “Cost & 
benefits” column includes market benefits from clean air measures, in terms of enhanced worker productivity and costs, therefore 
this column represents a net effect. Non-market benefits of clean air and costs of climate policy are not included. 

96 

 
      
      
 
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5-14. Sector-specific market impacts compared to respective baseline (% change in output)41, in 
2030, 2040 and 2050, considering the full concentration range (no cut-off) and work loss days 
(WLD) based on Ostro (1987) with crop yield gains; Source: JRC-GEM-E3 model. 

WLD based on Ostro (1987) and crop yield gains 
2030 

Costs 

-0.718 
-2.143 
-0.337 
0.124 
-0.013 
0.004 
-0.154 

Costs 

-0.725 
-2.320 
-0.213 
0.125 
-0.004 
0.012 
-0.119 

MTFR 
Costs & benefits 
-0.175 
-2.096 
-0.333 
0.129 
-0.010 
0.013 
-0.139 

2040 
MTFR 
Costs & benefits 
-0.195 
-2.279 
-0.211 
0.129 
-0.006 
0.018 
-0.107 

Costs 
-0.037 
-0.154 
-0.004 
0.001 
0.005 
0.000 
-0.003 

Costs 
-0.638 
-2.245 
-0.146 
0.134 
-0.008 
0.014 
-0.098 

Crops 
Livestock 
Fossil fuels 
Power sector 
Industry 
Services 
Memo: GDP 

Crops 
Livestock 
Fossil fuels 
Power sector 
Industry 
Services 
Memo: GDP 

ERC 

Costs & benefits 

Costs 

0.050 
-0.146 
-0.003 
0.001 
0.006 
0.001 
-0.001 

-0.127 
-0.100 
0.001 
0.002 
0.006 
0.001 
-0.002 

ZPAP 
Costs & benefits 
0.043 
-0.088 
0.002 
0.002 
0.006 
0.003 
0.002 

ERC+ZPAP 

Costs 

Costs & benefits 

-0.137 
-0.192 
-0.003 
0.002 
0.009 
0.001 
-0.004 

0.036 
-0.178 
-0.002 
0.003 
0.010 
0.003 
0.000 

2050 
MTFR 

Costs & benefits 

-0.156 
-2.209 
-0.146 
0.136 
-0.010 
0.017 
-0.089 

Table 5-15. Sector-specific market impacts compared to respective baseline (% change in output)41, in 2030 

and 2050, considering the full concentration range (no cut-off) and recent empirical evidence on 
impact on labour productivity following Dechezleprêtre et al (2019); Source: JRC-GEM-E3 model. 

Labour productivity based on Dechezleprêtre et al. (2019) 
2030 

Costs 

-0.718 
-2.143 
-0.337 
0.124 
-0.013 
0.004 
-0.154 

Costs 

-0.725 
-2.320 
-0.213 
0.125 
-0.004 
0.012 
-0.119 

MTFR 
Costs & benefits 
-0.007 
-1.593 
0.025 
0.516 
0.481 
0.364 
0.240 

2040 
MTFR 
Costs & benefits 
-0.174 
-1.892 
0.059 
0.429 
0.381 
0.288 
0.185 

Costs 
-0.037 
-0.154 
-0.004 
0.001 
0.005 
0.000 
-0.003 

Costs 
-0.638 
-2.245 
-0.146 
0.134 
-0.008 
0.014 
-0.098 

Crops 
Livestock 
Fossil fuels 
Power sector 
Industry 
Services 
Memo: GDP 

Crops 
Livestock 
Fossil fuels 
Power sector 
Industry 
Services 
Memo: GDP 

ERC 

Costs & benefits 

Costs 

0.081 
-0.072 
0.058 
0.059 
0.078 
0.049 
0.052 

-0.127 
-0.100 
0.001 
0.002 
0.006 
0.001 
-0.002 

ZPAP 
Costs & benefits 
0.037 
0.028 
0.082 
0.094 
0.119 
0.085 
0.090 

ERC+ZPAP 

Costs 

Costs & benefits 

-0.137 
-0.192 
-0.003 
0.002 
0.009 
0.001 
-0.004 

0.074 
-0.035 
0.103 
0.115 
0.146 
0.099 
0.104 

2050 
MTFR 

Costs & benefits 

-0.202 
-1.904 
0.055 
0.369 
0.297 
0.235 
0.144 

97 

 
      
      
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5-16. Sector-specific market impacts compared to respective baseline (% change in output )41, in 

2030, 2040 and 2050, considering the full concentration range (no cut-off) and benefits through 
labour productivity following Dechezleprêtre et al (2019) and crop yield gains; Source: JRC-GEM-
E3 model. 

Labour productivity based on Dechezleprêtre et al. (2019) and crop yields gains 
2030 

Costs 

-0.718 
-2.143 
-0.337 
0.124 
-0.013 
0.004 
-0.154 

Costs 

-0.725 
-2.320 
-0.213 
0.125 
-0.004 
0.012 
-0.119 

MTFR 
Costs & benefits 
0.523 
-1.569 
0.017 
0.510 
0.468 
0.361 
0.243 

2040 
MTFR 
Costs & benefits 
0.342 
-1.869 
0.053 
0.424 
0.367 
0.284 
0.187 

Costs 
-0.037 
-0.154 
-0.004 
0.001 
0.005 
0.000 
-0.003 

Costs 
-0.638 
-2.245 
-0.146 
0.134 
-0.008 
0.014 
-0.098 

Crops 
Livestock 
Fossil fuels 
Power sector 
Industry 
Services 
Memo: GDP 

Crops 
Livestock 
Fossil fuels 
Power sector 
Industry 
Services 
Memo: GDP 

ERC 

Costs & benefits 

Costs 

0.160 
-0.068 
0.057 
0.058 
0.076 
0.049 
0.053 

-0.127 
-0.100 
0.001 
0.002 
0.006 
0.001 
-0.002 

ZPAP 
Costs & benefits 
0.204 
0.035 
0.081 
0.092 
0.115 
0.084 
0.091 

ERC+ZPAP 

Costs 

Costs & benefits 

-0.137 
-0.192 
-0.003 
0.002 
0.009 
0.001 
-0.004 

0.239 
-0.029 
0.102 
0.113 
0.142 
0.099 
0.105 

2050 
MTFR 

Costs & benefits 

0.275 
-1.882 
0.049 
0.365 
0.285 
0.232 
0.146 

98 

 
      
      
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Key findings 

This report presents a set of model calculations which explore the likely future development of air pollutant 
emissions and air quality in the European Union and assesses health and environmental impacts, as well as 
costs and benefits associated with further pollution mitigation. To inform and support the development of the 
Fourth Clean Air Outlook report of the European Commission, the analysis incorporates recently proposed EU 
policies as well as the latest information on air pollutant emission inventories and projections and pollution 
control policies  that  have been reported by the Member States  in their official inventory  submissions and 
discussed in bilateral consultations between IIASA and Member States.  

The modelling framework and approach used in the analysis is consistent with the work on the Third Clean Air 
Outlook. However, it considers updates to methodologies applied for health impact assessment and valuation 
of benefits as well as a refined modelling of ozone concentrations enabling analysis of ozone sensitivity to 
changes in global emissions of ozone precursors, including methane. 

Analysis  of  the  baseline  scenario,  which  assumes  full  compliance  with  the  current  and  proposed  EU  and 
national legislation, shows that only four Member States (down from five in CAO3) would fulfil all NEC Directive 
reduction commitments (i.e. sufficient reduction for each of the 5 pollutants) in 2030. Reduced ambition of 
the revised IED for agriculture (compared to the assumptions from the revision proposal included in the CAO3) 
results in higher ammonia emissions in CAO4, compared to CAO3, in 2030, leading to lower compliance with 
ERCs  for  ammonia.  In  the  CAO4  Baseline  scenario  only  6  Member  States  are  estimated  to  comply  with 
ammonia ERCs by 2030. While for SO2, full compliance is expected and only few Member States could exceed 
ERCs for NOx, and NMVOC, there are 8 Member States where ERCs for PM2.5 would not be achieved under the 
Baseline – this is twice as many as in CAO3. While for two of the countries likely non-compliance is within 1% 
of the ERCs, for the other two Member States, revision of the national and GAINS estimates for residential 
combustion and agricultural waste burning results in changes of 2005 and 2030 emissions leading to increased 
likelihood of missing the ERC. The analysis has also shown that while the air pollution-related ZPAP target on 
reducing  premature  deaths  would  be  achieved  in  the  baseline  scenario,  the  ZPAP  target  for  reducing 
eutrophication would require further measures, primarily in agriculture.  

The status of inclusion of condensable PM in the inventories and further evaluation of various emission factors 
has  been  addressed  in  sensitivity  scenarios  concluding  that  the  currently  recommended  (EEA/EMEP 
Guidebook) and already extensively used emission factors by Member States, appear to provide a satisfactory 
assessment of emissions assuring a fair representation of observations.  

Increasing over time the clean air and climate ambition, by implementing and enforcing proposed and agreed 
policies, would continue to drive significant reductions of air pollutant emissions and hence reduce exposure 
to PM2.5 and NO2 in the Baseline. However, in 2030, nearly 87% of the EU population are projected to still be 
exposed to PM2.5 levels above the WHO guidelines (and about 28 % for NO2) in the Baseline case. The CAO4 
outlook is slightly less optimistic than the results of the analysis for the Baseline scenario in CAO3, where each 
of the health indicators shown above was better by a few percentage points. For PM2.5, the major reasons 
include higher emissions from residential combustion sector in CAO4 (driven by slightly higher consumption 
of solid fuels, i.e., coal and biomass, until 2030, revised data and assumptions on structure of installations, and 
a more consistent inclusion of condensable) and less optimistic outlook for ammonia emissions (precursor of 
PM). While the ZPAP target for reduction of premature deaths (reduction by 55% by 2030, compared to 2005) 
is achieved in the baseline, this still leaves in 2030 over 220 000 premature deaths due to exposure to all PM2.5 
in the EU (including below WHO guideline levels). The exposure is expected to decline towards 2050, but at 
the  same  time  there  are  proven  measures  to  reduce  emissions  further  and  earlier,  and  those  should  be 
implemented. For NO2, the revised estimate of soil NOx and lesser impact of Euro 7 by 2030 are key reasons, 
but the difference is not large and in the long term (by 2050) the indicators for NO2 are better than in CAO3. 

99 

 
      
      
 
 
Compliance with ZPAP targets and ERCs would bring important benefits following emissions decline, reducing 
premature  mortality  from  PM2.5,  NO2  and  ozone  by  2-8%  and  impact  on  ecosystems  from  7%  for 
eutrophication  to  over  20%  for  acidification  (relative  to  Baseline  in  2030),  at  a  moderate  additional  (to 
Baseline)  costs  of  less  than  0.5  billion €/year  (or  up  to  about  0.75%  of the estimated  Baseline  cost  for  air 
pollution  controls  in  2030).  Further  potential  exists  bringing  obvious  larger  benefits,  but  the  cost  increase 
rapidly  and  MTFR  costs  are  in  order  of  23 billion €/year  in  2030,  declining  in  the  longer  term  due  to 
decarbonization and lesser need for flue gas cleaning investments. If all technically available measures are put 
in place, the analysis conducted for this report clearly indicates net benefits from additional actions towards 
cleaner air, with positive macro-economic implications in the near- and long-term for all clean air scenarios, 
when labour productivity effects are evaluated following Dechezleprêtre et al (2019) .  

The changes to morbidity assessment alter their contribution to total damage (with mortality valued using the 
VOLY) from a range of 25% to 31% in CAO3 to 18% to 27% in CAO4 baseline leading to a slight reduction in the 
estimated economic benefits of pollution control measures. However, CAO4 analysis also shows that including 
updated  long-term  morbidity  functions  for  dementia  and  diabetes  would  increase  the  contribution  from 
morbidity to between 37% and 58%. The ranges reflect population ageing with the lower bound being for 2005 
and the upper bound for 2050.  

Owing to the atmospheric lifetime of PM2.5 from hours to several days, the PM2.5 concentration at any given 
location originates from a large area, including outside of national borders. For each Member State, the study 
quantifies the contributions of other countries to national PM2.5  exposure,  as well as the destinations of a 
given Member State’s ‘exported’ pollution. It is found that while  domestic sources are the main sources of 
pollution in most Member States, a significant contribution to PM2.5 background concentration is generated 
in other Member States. Consistent with what was shown in CAO3, full implementation of the NEC Directive 
will lead to improvements in European air quality within and beyond national borders. Over time, the share of 
pollution  from  within  the  EU  is  projected  to  fall,  increasing  the  relative  importance  of  non-EU  sources. 
However, compared to CAO3, the non-EU contribution is lower owing to recent commitments of, for example, 
West Balkan countries to decarbonization policies and improved effectiveness of implementation of policies 
in power and industry sectors. This highlights once more the case for internationally coordinated policy action 
for clean air in view of meeting more stringent air quality standards going forward.   

The influence of transboundary sources on air quality in the EU is even larger for ozone. One sensitivity case 
analysed  the  role  of  reductions  in  global  emissions  of  methane  and  non-methane  ozone  precursors  for 
ambient ozone concentrations in Europe. It shows that global emission trends of CH4, NOx and NMVOCs will 
influence ground-level ozone in Europe significantly; ambitious action outside of the EU would help to achieve 
compliance  with  recently  adopted  legislation  on  ambient  air  quality  targets  in  the  EU.  Methane  emission 
reductions in the EU only, on the other hand, have a very limited potential to influence ground level ozone in 
Europe. 

100 

 
      
      
 
 
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  Annex 

A1. Introduction 

This is the Annex to the main report of the Specific Contract No 090202/2023/906366/SER/ENV.C.3 – “Support 
to the development of the fourth Clean Air Outlook”. It provides additional information for various tasks that 
were developed during the course of work. The material provided here shows several extended outputs for 
both scenarios and years assuring full transparency of the work done during this service contract. 

A1.1  Structure of the Annex  

Major elements include full documentation of the comparison of historical emissions estimated in GAINS and 
reported by Member States (Section 2), more details on the model updates (Section 2), detailed sectoral and 
national  data  and  GAINS  estimates  for  past  and  future  emissions,  including  comparison  to  the  national 
projections which have been performed during the consultations with the Member States and development 
of  the  Baseline  emission  scenario  (Section  3).  Furthermore,  additional  details  for  the  analysed  policy  and 
sensitivity scenarios are provided in Section 4 and 5. 

A2. Updating modelling framework and a Baseline 

scenario 

This section provides additional documentation of the updates to the GAINS database, applied methods, and 
development of the Baseline scenario. Some parts of the text below are the same as in the main report where 
only summary of key updates is provided. 

A2.1  Approach 

While  maintaining  the  consistency  of  the  modelling  approach  applied  in  the  CAO342  and  IA  AAQD43,  the 
modelling  framework  has  been  updated  considering  latest  improvements  and  recalculations  of  reported 
historic emissions by Member States (MS), in particular NEC base year 2005 as well as 2020, and is consistent 
with the recently updated EMEP/EEA air pollutant emission inventory guidebook44. Special attention has been 
paid to condensable and non-condensable part of particulate matter emissions, identifying which Member 
States and sectors the condensable part is included in the national inventories. 

IIASA employs its Greenhouse gas – Air pollution Interactions and Synergies (GAINS) model. GAINS is a fully-
fledged integrated assessment model which traces the entire causal chain from the drivers of air pollution 
emissions to their impacts (Amann et al. 2011). The GAINS model is calculating emissions of key air pollutants 
(SO2, NOx, NMVOC, NH3, PM2.5 (including black and organic carbon)) as well as GHGs, including methane (CH4). 
Atmospheric  calculations  in  GAINS  are  based  on  a  linearized  version  of  the  EMEP  atmospheric  chemistry 
transport model. Together with the uEMEP extension, the EMEP model is capable of linking air pollution on 
urban to regional to global scales and quantify ambient pollution levels at very high resolution (Denby et al. 

42 https://environment.ec.europa.eu/topics/air/clean-air-outlook_en  
43 https://environment.ec.europa.eu/topics/air/air-quality/revision-ambient-air-quality-directives_en  
44 https://www.eea.europa.eu/publications/emep-eea-guidebook-2023  

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2020). During the CAO3 project as well as work on the review of the Gothenburg Protocol to the UNECE LRTAP 
Convention (2021-2022) the  GAINS atmospheric calculations have  been updated and harmonized with the 
EMEP/uEMEP models and no substantial updates are envisaged within this work. As before, five-year average 
meteorological conditions (2016-2020) are used for the atmospheric calculations. 

Health and environmental impacts are assessed with the GAINS model, although for ozone, the results of the 
EMEP model are used. GAINS provides health impact from exposure to PM2.5 and exceedance of critical loads 
for  acidification  and  eutrophication  due  to  deposition  of  sulphur  and  nitrogen.  For  the  latter,  the  latest 
database of critical loads (CLs) applied within the Air Convention is used. This 2021 dataset has been reviewed 
by the Coordination Centre for Effects (CCE) of the Working Group on Effects and approved by the Executive 
Body of the Convention and has been implemented in the GAINS model during the CAO3 study. For ozone, 
health impacts are calculated by IIASA making use of the results of the EMEP chemical transport model runs 
for  the  baseline  and  respective  policy  scenarios.  Monetary  evaluation  of  health  and  other  benefits  is 
performed  with  the  ALPHA-RiskPoll  model,  based  on  the  GAINS  outputs.  Overall,  the  impact  assessment 
methodology is consistent with the one used in the CAO3 but includes updates taking into account most recent 
advancements, including the EMAPEC study results; HRAPIE-2 results have not become available on time and 
therefore cannot be included. 

The baseline scenario for this work is an update of the CAO3 baseline incorporating all relevant EU legislation 
proposed by the Commission or adopted by the co-legislators since the CAO3 analysis was undertaken. This 
relates in particular to: 

•  Climate and energy legislation, reflecting latest political agreements on the legislative initiatives part 
of the Fit for 55 package as well as of the REPowerEU initiatives, incorporating in particular details 
related to projected solid fuel use development  and other climate or energy measures that would 
have an important impact (positive or negative) on air pollution; 

•  The  most  recent  developments  in  other  relevant  source  legislation,  reflecting  latest  political 

agreements on relevant legislative initiatives, notably on: 

o  updated emission standards for vehicles (Euro 7), 

o  CO2 standards for light- and heavy-duty vehicles, 

o 

the revised Industrial Emissions Directive 

Since revision of the Ecodesign rules is still ongoing, no changes have been introduced for residential 
solid fuel stoves and boilers and the CAO4 Baseline remains consistent with the CAO3 assumptions. 
Consideration of Commission initiatives on urban mobility45 in the modelling framework was discussed 
during inception phase but owing to lack of respective data and modelling inputs form the PRIMES 
model an agreement was reached not to pursue development of such sensitivity scenarios within this 
assignment; 

•  The information provided by MSs in their latest submitted air pollutant emission projections, NAPCPs 

and PaMs updates since the analysis done for the third Clean Air Outlook; 

•  The information provided by MSs on agricultural measures put in place, not only from their NAPCPs 

but also, where available, from their latest rural development plans and CAP strategic plans; 

•  Any other relevant developments, including for instance maritime emissions regulations. 

45 EU Urban Mobility Framework (COM(2021)811 

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A2.2  Update of the GAINS model databases 

New methods for emission estimation and newly assessed or developed emission factors are reflected in the 
regularly updated EMEP/EEA Guidebook (latest release in October 2023)46 which is supporting the Member 
States to estimate their national emissions. As shown and discussed in previous Clean Air Outlooks, several 
Member States recalculate their historic emission inventories (including those for 2005) leading sometimes to 
substantial  changes  when  compared  to  data  reported  earlier  which  in  turn  had  implications  on  the 
achievement  of  the  NECD  emission  reduction  requirements  in  2030.  Beyond  updated  methods,  revised 
estimates  reflect  corrections  of  calculation  errors,  revision  of  activity  data  and/or  emissions  factors, 
implementation  of  recommendations  from  reviews  of  respective  submissions.  The  Member  States  have 
continued  to  update  the  emission  estimates  since  2021  submission  and  IIASA  has  reviewed  the  major 
recalculations  included  in  the  2023  submission,  as well  as  their  impact  on  the GAINS  model estimates  for 
historical years and the baseline scenario.  

Compared to the GAINS model version used in the CAO3 study, updates were made to PM emission factors 
for wood and coal use in residential sector in order to improve consistency of accounting for condensable PM. 
This  is  specifically  the  case  for  Austria,  Estonia,  Germany  where  updated  emission  factors  in  GAINS  are 
consistent  with  the  latest  EMEP/EEA  Guidebook  and  therefore  much  larger  that  emission  factors  used  by 
Member States in their submissions since these three countries do not account for condensable PM when 
reporting PM. 

The following figures provide  a comparison of changes  in the national emission reporting in 2021 (used in 
CAO3) and 2023 (used in CAO4) for the year 2005 and 2015; in further sections a comparison is provided for 
2020 but this has not been possible for the 2021 submissions, which did not include, or only very preliminary, 
estimates  for  that  year.  The  percentage  change  shows  the  change  in  2023  reporting  compared  the  2021 
submission for respective year. While most of the updates and revision result in changes smaller than 10%, 
there are several cases for each pollutant where differences are very significant also illustrating a challenge 
modelling teams face in addressing such updates. These updates were subject of exchange between IIASA and 
national experts during Member States consultations. 

Figure 8-1: Change in 2023 Member States submission of SO2 emissions for 2005 and 2015, compared to 

2021 submission.   

46 https://www.eea.europa.eu/publications/emep-eea-guidebook-2023 

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Figure 8-2: Change in 2023 Member States submission of NOx emissions for 2005 and 2015, compared to 

2021 submission.   

Revisions for emissions of PM2.5 are most significant, at least for some countries (Figure 8-3). An about factor 
two  increase  in  reported emissions  by  Czechia  and Poland  and  a very  large  increase  for  France  are  linked 
primarily to inclusion of condensable PM in their 2023 submissions.  

Figure 8-3: Change in 2023 Member States submission of PM2.5 emissions for 2005 and 2015, compared to 

2021 submission.   

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Figure 8-4: Change in 2023 Member States submission of NH3 emissions for 2005 and 2015, compared to 

2021 submission.   

Figure 8-5: Change in 2023 Member States submission of NMVOC emissions for 2005 and 2015, compared 

to 2021 submission.   

IIASA has compiled an updated dataset of SO2, NOx, VOC, NH3 and PM2.5 emissions for all 27 EU Member States.  
The  analysis  has  been  relying  on  the  NECD  inventory  (and  projections)  submission  in  2023  available  at: 
ReportekEngine (europa.eu), focusing on the most relevant recalculations for the years 2005 through 2020 to 
assure the trends in the GAINS model are aligned with those in the inventories. The analysis and comparison 
of  the  national  data  submissions  under  the  NEC  Directive  has  been  complemented  by  methodological 
information provided in the Informative Inventory Reports (IIRs) submitted along the national emission data 
as well as resubmissions of 2023 datasets in the second half of 2023, following the inventory review process.  

Special attention has been paid to the condensable part of particulate matter emissions, identifying for which 
Member States and sectors the condensable part is included in the national inventories and which measuring 
method is used, pointing out any changes compared to the situation at the time the CAO3 was prepared. This 

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is based on an analysis of most recent Member States emission inventory submissions that was available at 
the start of the project (2023 submission and respective IIR) and expert knowledge from development of an 
independent estimate consistently including condensable PM (the “Ref2” emission inventory) by TNO in 2022. 
The results of the CLRTAP Stage 3 review carried out in 2022 were also considered in this assessment. That 
review  analysed  submission  of  all  Parties  in  the  CLRTAP  region  in  detail  for  the  small  combustion  sector, 
looking at various aspects including if and to what extent condensables are included in their PM emissions. 
The results of this assessment are synthesized and reported in a table by country, highlighting if and to what 
extent condensables are included. 

During  the  CAO3  study,  several  updates  to  road  transport  emission  factors  and  fleet  characteristics  were 
made,  drawing  on  the  results  of  remote  sensing  measurements  across  Europe47,  data  from  SIBYL  model, 
discussions with national experts during CAO3 consultations as well as the proposal for the Euro 7 standard; 
the latter was based on preliminary data. In December 2023, the European Parliament and the Council reached 
an agreement on the Euro 7 Regulation48. The upcoming Euro 7 emission standard for road vehicles has been 
underpinned  by  an  impact  assessment  study  that  the  European  Commission  assigned  to  the  CLOVE 
consortium49 led by EMISIA (and Aristotle University of Thessaloniki) examining different policy options and 
scenarios.  In the framework of that study, road transport activity data and emission factors were reviewed 
and updated. The results have been adjusted based on the recent final Euro 7 agreement and used to review 
and update the GAINS model emission and cost characteristics for road transport sector.  

Latest developments and updates on emission factors for shipping activities are also available through the 
H2020 EMERGE50 project (nearing finalization) and the recently completed SCIPPER51 project. Within SCIPPER, 
a new set of emission factors was developed based on both literature and measurements. The emission factors 
had  been  distinguished  according  to  engine  and  fuel  type  and  concern  the  main  gaseous  pollutants  and 
greenhouse gases as well as energy consumption. 

A2.3  Comparison of GAINS and Member States reported emissions  

This  section  documents  the  comparison of  air  pollutant  emission calculation  in  the  GAINS model  with the 
Member States submission in 2023. The objective has been to arrive at the differences at the national level 
not larger than about 5% for SO2, NOx, NH3, and 10-20% for NMVOC and PM2.5 that are burdened with larger 
uncertainties with respect to activity data beyond emission factor. Differences larger than 5% are marked in 
orange  in  the  tables  shown  in  Section  3.4  where  detailed  national  total  and  sectoral  data  is  shown  and 
discussed.   

The following figures summarize the status of how current GAINS calculation of emissions for 2005 and 2020 
compare to the national submissions in 2023; note that in previous section national submissions of 2021 and 
2023 are compared for 2005 and 2015, rather than 2020 since the 2021 submission have not included, or only 
very preliminary, estimate for 2020. On each of the figures, the 5% (larger for NMVOC and PM2.5) difference 
range  is  marked  in  green  showing  larger  discrepancies  or  differences.  Note,  that  here  we  compare  totals 
including NEC Directive sectors and so agricultural emissions of NOx and NMVOC are not considered, even 
though  reported  by  Member  States  and  calculated  in  GAINS  as  is  demonstrated  in  the  country-specific 
comparisons in Section 3.4.  

47 CARES Project; H2020 Grant Agreement No. 814966. www.cares-project.eu 
48 Final text of the Euro 7 agreement https://data.consilium.europa.eu/doc/document/ST-16960-2023-REV-1/en/pdf 
49 DG GROW, Framework Contract 688/PP/2018/FC  
50 EMERGE - Evaluation, control and mitigation of the Environmental impacts of shipping emissions. H2020 Grant 
Agreement No. 874990. emerge-h2020.eu 
51 SCIPPER -: Shipping Contributions to Inland Pollution Push for the Enforcement of Regulations. H2020 Project under 
Grant Agreement Nr.814893. www.scipper-project.eu 

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One common feature for all pollutants is that for 2020 differences are much larger than for 2005. This is not 
surprising since there is a longer history of reporting data for 2005 and since this was the ‘COVID year’ some 
of  the  statistical  information  and  especially  sectors  for  which  typically  no  hard  data  is  available  and 
extrapolation of trends is used, will carry larger uncertainty. GAINS energy, industry, agriculture data is based 
on  import  of  statistics  but  has  similar  issues  and  uncertainties  are  larger  for  2020.  The  analysis  of  the 
informative reports (IIRs) provided along the national submissions has been very helpful to identify reasons 
for differences allowing for better alignment.  

Figure 8-6: Difference between GAINS SO2 emissions for 2005 and 2020 and the 2023 Member States 

submissions   

Figure 8-7: Difference between GAINS NOx emissions for 2005 and 2020 and the 2023 Member States 

submissions   

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While for most pollutants the larger differences occur for few countries and more likely for 2020, the picture 
for PM2.5 is different as here estimates vary much more and for both 2005 as well as for 2020. The key sources 
of these differences include residential combustion sector, open burning of agricultural residues, and in some 
cases also fugitive emissions from construction, quarrying, and road paving. Considering often lack of robust 
data for consumption of fuelwood and on structure of combustion installations as well as uncertainties in 
emission factors, differences in order of ±10% or more have to be considered acceptable. However, there are 
countries where the discrepancy is much larger and here key reason is often consideration of condensable PM 
in GAINS but  not in the  national inventory (Austria, Estonia, Germany, Lithuania).  In few other cases (e.g., 
Bulgaria, Romania), different  methods or lack  of estimates  for open burning of agricultural residues  is the 
leading cause of discrepancy. 

Figure 8-8: Difference between GAINS PM2.5 emissions for 2005 and 2020 and the 2023 Member States 

submissions   

Least differences are seen for ammonia, which is also a result of extensive documentation and typically use of 
higher tier methods in national inventories allowing for good calibration of GAINS. There are few exceptions 
where available information did not allow to arrive at a better match. 

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Figure 8-9: Difference between GAINS NH3 emissions for 2005 and 2020 and the 2023 Member States 

submissions   

For NMVOC, one of the key areas of analysis included increase in use of sanitizers during COVID period, leading 
to rather large increases in NMVOC which then decline by 2025. In some countries, there was an emission 
spike only in 2020, after which emissions returned to normal historical levels. However, a few countries, which 
had not previously accounted for the 'use of sanitizers' as a source in their national inventories, re-evaluated 
and added this source-sector across all years while updating their 2020 data. All this information has been 
continuously exchanged during and after the consultation meetings and has been implemented into the GAINS 
model. 

Figure 8-10: Difference between GAINS NMVOC emissions for 2005 and 2020 and the 2023 Member States 

submissions   

Comparison of country level totals and sectoral emissions at the GNFR level is reported in Section A3.4. 

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A3. Consultation of the Baseline with Member States  

A3.1  Objectives 

The purpose of this task is to present, discuss, and validate the assumptions and key results of the Baseline 
scenario  developed  (see  Section  3  in  the  main  report)  with  the  Member  States.  Where  necessary,  the 
discussion  includes  also  review  of  the  data  and  assumptions  used  in  the  development  of  the  historical 
emissions. As a result of consultations with the Member States, the assumptions used in the Baseline have 
been complemented or modified accordingly, where necessary. 

The  GAINS  model  update during  development of  the  Baseline  and Member  States  consultations  is  also of 
relevance  for  the  recently  started  project  on  the  EU  GHG  modelling.  This  project  is  led  by  the  Pollution 
Management research group at IIASA that also leads the CAO4 work and focuses on emissions of non-CO2 GHG 
in the Commission service request (Contract number — 090203/2023/SER/ 905239/CLIMA.A.2) on the “EU 
GHG  modelling  for  beyond  2030”;  internally  referred  to  as  EUCLIMIT-7.  IIASA  will  be  using  the 
data/assumptions/information updated prior and during the Member States consultations for estimation of 
air pollutant emissions in the GAINS model since most of the same data/assumptions are of relevance for the 
non-CO2  GHG  calculation.  This  concerns  specifically  activity  data  (energy  use,  livestock  numbers,  etc.)  for 
historical years as well as included air pollution policies and key structural assumptions (e.g., Member State 
and livestock category-specific farm size structure) for the baseline scenario. Consequently, calculation and 
reporting of air pollutant emissions in the EUCLIMIT-7 project for the past years will be entirely consistent with 
CAO4. While the  EUCLIMIT-7  project  extends  into  2025  and will  use  a  newly developed  baseline  scenario, 
consistency of key assumptions will enable robust comparisons and allow for disentangling of the impact of 
the new baseline on air pollutant emissions, if such analysis will be desired in the future.  

A3.2  Approach 

The consultations were organized as half day online meetings with experts representing each Member State 
to share the GAINS model Baseline scenario developed for this assignment and to discuss specific national 
features  included  in  the  Baseline  and  the  modelling  framework.  The  following  elements  are  of  particular 
interest in the context of this service request:  

•  Policies and measures with an air pollution impact (positive or negative) already in place or for which 
a  decision  about  implementation  in  the  near  future  has  already  been  taken,  at  different  levels  of 
governance (national, regional, local), having an impact on national emission levels and which can be 
represented in the modelling framework, 

•  Methodological features having an impact on emission inventories, in particular, how the condensable 
part of particulate matter is reflected in national inventories for various emitting sectors and in the 
modelling framework, 

•  Additionally, any unresolved emission inventory issues that remain after completion of work on the 
development of the Baseline were also discussed and solutions identified during and shortly after the 
Member States consultations, 

The  results  of  the  discussion  during  individual  Member  States  consultations  were  summarized  in  minutes 
shared with the teams, including specification of next steps. Where necessary, the GAINS model databases 
and assumptions were revised and reflected in the updated Baseline. The summary of introduced changes, in 

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responses to inputs from Member States and discussions at the consultation meetings, as well as the final 
revised Baseline were presented at the online meeting with all Member States and the Commission on the 
17th of June, 2024. 

A3.3  Preparation and organization of the consultation meetings 

On the basis of a list of issues for discussion at the consultations that IIASA prepared, DG-ENV has sent an 
invitation letter early February to the members of the Ambient Air Quality Expert Group working on NECD 
implementation.  National  Contact  Points  nominated  Member  States  experts  that  would  take  part  in  the 
consultation meetings within the fourth Clean Air Outlook and IIASA has followed up searching for suitable 
dates for the meetings.  Consultations meetings with all Member States have been scheduled and IIASA has 
carried out 27 meetings that started early March and concluded on the June 4th.  

The individual Member States consultations were organized as half day virtual meetings and were preceded 
with  analysis  of  the  national  NEC  Directive  inventory  and  projection  submission  in  2023  available  at  the 
ReportekEngine (europa.eu) and analysis of the NAPCPs and PaMs. Remaining discrepancies between GAINS 
and  the  national  emission  inventories  that  could  not  be  satisfactory  resolved  were  summarized  in  the 
presentation opening the consultation meetings along with the assumptions behind the Baseline scenario and 
its  emission  trajectory  (all  presentations  and  minutes  of  the  meetings  are  available  at  request).  Several 
countries asked for a list of questions or even datasets prior to the consultation meeting enabling the most 
efficient resolution of identified issues.  

At the beginning of every consultation meeting, the IIASA team gave an overview of the CAO4 objectives and 
purpose/goals of the consultation meeting. Additionally, information about the team involved in the CAO4 
project and an overview of the timeline was given. This information was followed by a presentation of the 
work on the development of the GAINS model baseline scenario and the discussion of differences between 
national emission estimates (NFR2023) and GAINS for historical years as well as future outlook comparing the 
WM/WaM national projection with GAINS Baseline and reviewing compliance with ERCs. This presentation 
included a comparison and discussion of key activity assumptions and emissions of all NEC Directive pollutants 
as reported by the Member States and calculated in GAINS. While national data/information has been taken 
from the inventories submitted in 2023, if substantive improvements were made (or errors identified) in the 
2024 submission, these were discussed and considered to the possible extent.  

The presentation and minutes of the meeting were shared within days after the meeting. The minutes included 
actions following the meeting and each Member States was asked to provide comments to the minutes as 
soon as possible. 

The Baseline was updated for use in the policy analysis and assessment in this work based on the results of 
these Member States consultations. In case the national elements in the GAINS Baseline cannot be confirmed 
by the Member States and an agreement between the Member States experts and the modelling team cannot 
be  reached  (due  for  instance  to  insufficient  provision  of  information  from  the  Member  States),  the 
discrepancies of views were documented, and the modelling team justified its approach. 

Following the individual consultations and bilateral exchanges, leading to the establishment of an updated 
Baseline, a meeting was convened with all Member States and the Commission on the 17th of June 2024 where 
the summary of the changes made in response to input from Member States were presented.  

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A3.4  Documentation and reporting 

The results of the discussion during individual Member States consultations have been summarized in minutes, 
including  specification  of  the  next  steps,  shared  with  the  national  teams  and  can  be  provided  to  the 
Commission upon request along with the presentations given at the meetings. Comparison of Member States 
reporting of national totals and GAINS was shown Section 2 while the next section (3.4.1) provides further 
details including sectoral comparisons and brief discussions of remaining differences. Section 3.4.2 provides 
further  material  comparing  the  GAINS  Baseline  with  the  WM  and  WaM  projections  for  each  country  and 
illustrates compliance with the ERCs calculated against both the GAINS and the national estimates of 2005 
emissions. The results presented here have been updated based on the discussions during the consultations 
and new comments or data provided through the meetings, and these have been implemented into the GAINS 
model.  

The summary includes Member States specific comparisons of historical emissions for the period from 2005 
as  well  as  remaining  issues,  i.e.,  cases  where  the  national  elements  in  the  consultant  baseline  cannot  be 
confirmed by a Member State and an agreement between the Member State and the contractor cannot be 
reached (due for instance to insufficient provision of information from the Member State).  

A3.4.1  Comparison of GAINS estimates with Member States submissions  

The  following  sections  provide  comparison  of  country  level  total  and  sectoral  (GNFR  sectors)  emissions 
reported by Member States in 2023 with current GAINS calculations. Brief discussion or highlight of some key 
remaining  differences  is  given,  while  more  details  are  available  in  the  presentations  and  minutes  of  the 
discussions with the Member States during consultations. The tables presenting comparison of national totals 
refer to the NEC Directive totals, while the figures include all sources and so also emissions of NOx and NMVOC 
from agriculture are shown explicitly for both national and GAINS estimates. The orange-coloured cells in the 
tables below highlight differences larger than 5%. 

AUSTRIA 

Table 8-1: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

Overall,  good  agreement  at  the  national  and  sectoral  level  for  SO2,  NOx,  NH3,  and  NMVOC.  The  large 
difference for PM2.5 is due the fact that GAINS includes condensable PM fraction and the national inventory 

115 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 2625-1.8%1414-0.1%1010-1.8%NOx237232-2.0%172167-3.0%1141184.2%PM2.5233343.6%162873.4%132375.9%NH363664.8%6665-2.5%6661-7.5%VOC 1191200.7%77793.2%75772.2%200520152020 
      
      
 
 
 
 
 
reports only filterable PM. This has been discussed during consultations and acknowledged by the national 
experts. This has of course an impact on the projection comparison as well. 

Figure 8-11: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

For ammonia (NH3), overall, acceptable agreement at the national level. There are small differences in 2005 
and  2020  due  to  the  transition  from  tied  to  lose  dairy  cattle  housing  systems.  Emissions  from  other  sub-
categories such as mineral fertilizers and other livestock match well. 

Figure 8-12: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

For  PM2.5,  the  reason  for  discrepancy  is  inclusion  of  condensable  PM  in  GAINS,  while  national  reporting 
includes filterable PM. This has been discussed further during the consultations. Other sectors align well. 

116 

 
      
      
 
 
 
 
 
Figure 8-13: Comparison of the 2023 MS inventory submission PM2.5 (includes all anthropogenic emission 

sources) with the GAINS estimates for 2005, 2015, and 2020.   

BELGIUM 

Table 8-2: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

Very good agreement for SO2. The issues related to sulphur content for fuel oil discussed resulting in improved 
alignment of estimate after updates. Acceptable agreement at the national and sectoral level for NOx and 
PM2.5.  

117 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 1401410.5%4141-0.6%24240.2%NOx314308-1.9%187182-2.7%126120-4.4%PM2.53433-2.6%2222-2.3%17186.6%NH38079-1.6%72765.4%687611.3%VOC 1551560.6%89901.4%8782-5.3%200520152020 
      
      
 
 
 
 
 
 
Figure 8-14: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

For ammonia (NH3), differences for 2015/20 above 5% mostly due to higher emissions from dairy cattle in 
national reporting.  

For  NMVOC,  overall,  very  good  agreement  at  the  national  and  sectoral  level  in  2005  and  2015.  Small 
differences  are  remaining  in  2020  due  to  using  different  emission  factor  for  livestock  and  crops  in  the 
agriculture sector.  The comparison is also good for PM2.5 with some differences for livestock but not having 
impact on the overall agreement. 

Figure 8-15: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.  

118 

 
      
      
 
 
 
 
 
 
 
Figure 8-16: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

BULGARIA 

Table 8-3: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

Larger  differences  remain  for  NH3,  PM2.5,  and  NMVOC.  Power  and  industry  sector  compares  well  for  all 
pollutants. Large discrepancies for PM2.5, and NMVOC in 2005 are due to agricultural residue burning (3F); see 
further text for more details. 

For SO2, overall acceptable agreement. Issues related to sulphur content for fuel oil in 2005 were resolved. 
Remaining discrepancies in NOx are due to transport where large share of fleet is old and its characteristics 
and emissions more uncertain. 

119 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 955923-3.4%135128-5.6%6967-2.5%NOx177171-3.2%1001032.8%72777.0%PM2.5395128.6%34352.9%31349.6%NH3435219.2%414816.2%424813.3%VOC 9210514.4%78813.4%71767.1%200520152020 
      
      
 
 
 
 
 
 
Figure 8-17: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

For ammonia (NH3), larger differences due to discrepancies in cattle emissions and emissions from synthetic 
fertilizer application. Major revisions were done for the Member State submission in 2024 that lead to better 
alignment with GAINS. 

For NMVOC, differences at the sectoral level other than agricultural sectors are very small. Differences in 2020 
and larger differences in 2005 are  from  agricultural waste burning.  The  PM2.5 description below addresses 
more details of agriculture burning-related issues.  

Figure 8-18: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

approach  using 

For  PM2.5,  the  reason  for  discrepancy  is  open  burning  of  agricultural  residues  (included  in  the  sector 
L_AgriOther),  which  is  consistently  higher  in  GAINS  than  in  the  national  estimates.  GAINS  builds  on  a 
estimates 
systematic 
(https://www2.acom.ucar.edu/modeling/finn-fire-inventory-ncar), a product developed at NCAR. This is also 
consistent with the use of FINN by the EMEP model for quantification of forest fires. Bulgaria shared their 
assumptions, which are drawing on bottom-up data and do not assume any trend or changes over time. GAINS 
(FINN)  shows  a  decline  and  emission  become  less  important  in  the  future.  Other  sectors  align  well;  the 

sensing  data  making  use  of 

remote 

FINN 

the 

120 

 
      
      
 
 
 
 
 
difference  for industry is due  to different  allocation of some  sources  in GAINS  –  these are  included in the 
M_Other category, therefore industry and M_Other have to be compared together and then match well. 

Figure 8-19: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

CROATIA 

Table 8-4: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

Overall, acceptable agreement, although in relative terms the difference for SO2 in 2020 is quite large and is 
due to emissions from residential combustion in GAINS, specifically fuelwood for which EFs in GAINS are larger; 
however, the difference is small in absolute terms and will become less important over time. Some sectoral 
structure differences are related to different classification of sources in industry and power sector but the 
total  agrees.  Some  differences  remain  for  NOx  where  transport  sector,  specifically  non-road,  i.e.  coastal 
shipping, is key. Data on assumptions for penetration of Euro stages and age distribution of vehicles have been 
discussed and aligned to the possible extent. Acceptable agreement for PM2.5. 

121 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 5958-1.2%16160.0%6719.1%NOx8280-1.5%51545.8%4240-5.8%PM2.54443-0.3%32346.3%2827-3.7%NH34341-5.1%33341.7%34366.6%VOC 1041127.5%61610.4%6159-3.3%200520152020 
      
      
 
 
 
 
 
 
Figure 8-20: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

For ammonia (NH3), some remaining discrepancies in 2020 emissions are driven by differences in emissions 
from dairy cattle. All other sectors match well.  

For NMVOC, discrepancies in 2005 emissions are driven by differences in estimates from passenger cars in the 
transport sector. All other sectors match well, including strongly declining emissions from transport.  

Figure 8-21: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the preliminary GAINS estimates for 2005, 2015, and 2020.   

122 

 
      
      
 
 
 
 
 
Figure 8-22: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

CYPRUS 

Table 8-5: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

Emissions  from  power  and  industry  sectors  align  well  for  all  pollutants.  Very  good  match  for  NH3.  Key 
remaining differences for PM2.5, NOx and NMVOC are due to transport sector estimates. Data on assumptions 
for  penetration  of  Euro  stages  and  age  distribution  of  vehicles  have  been  exchanged  and  aligned  to  the 
possible extent.  

For PM2.5, differences for residential combustion, where GAINS/PRIMES data for 2015 and 2020 seemed rather 
high  compared  to  the  national  statistics,  were  resolved  following  the  exchange  of  information  with  the 
national experts. 

123 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 3837-3.2%1313-1.2%12126.2%NOx21239.2%131412.8%1010-0.5%PM2.5229.9%1122.0%1111.9%NH3991.8%773.7%882.4%VOC 14157.9%7712.5%771.5%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-23: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

Figure 8-24: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

124 

 
      
      
 
 
 
 
 
 
 
Figure 8-25: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

CZECH REPUBLIC 

Table 8-6: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

Very good match for SO2 and NH3 as well as for power and industry sector for all pollutants.  

Some differences remain in estimates of PM2.5, NMVOC and NOx from transport sector. Data on assumptions 
for penetration of Euro stages and age distribution of vehicles have been exchanged and used to harmonize 
estimates  to  the  possible  extent.  Additionally,  for  PM2.5  additional  detailed  data  were  exchanged  for 
residential sector leading to good overall agreement apart from the year 2015 where larger differences remain 
(also  for  NMVOC)  due  to differences  in  handling  structure  and  assumptions about  old  inefficient  boilers  – 
however, this causes temporary problems for consistency of intermediate past estimates while the current 
and future assumptions and emission estimates (including condensables) are consistent.  

125 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 2082111.3%129129-0.3%6766-0.7%NOx283269-4.9%1821925.4%1351360.7%PM2.57473-0.7%8060-24.1%6059-0.6%NH37471-4.2%79790.0%67692.6%VOC 3433522.7%316292-7.7%263249-5.2%200520152020 
      
      
 
 
 
 
 
 
Most  of  the  differences  for  NMVOC  are  similar  like  PM  and  link  to  residential  sector.  For  NH3:  Slight 
discrepancies due to differences in emissions from other poultry and rabbits but overall, very good agreement. 
Rabbits are included in GAINS but not in the national inventory anymore.  

Figure 8-26: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

Figure 8-27: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

126 

 
      
      
 
 
 
 
 
 
 
 
Figure 8-28: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

DENMARK 

Table 8-7: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

A good agreement at national and also sectoral level for SO2, and some differences slightly over 5% for other 
pollutants. 

A larger difference for NOx for 2015 linked to road transport in GAINS, even though the data on assumptions 
for penetration of Euro stages  has been used from the  IIR. Discussion with national experts indicated that 
assumptions on emission factors for vehicles affected by ‘diesel gate’ differ but this disappears over time as 
their share declines. For PM2.5 some differences remain for off-road transport sources, especially for 2005, 
and emissions from livestock and harvesting. For NH3, the estimates for emissions from crops vary slightly and 
for NMVOC, GAINS has slightly more optimistic assumptions about emission factors for fuelwood stoves; these 
become less and less relevant over time with newer installations increasing their share.   

127 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 2626-2.9%10103.0%99-2.6%NOx1811873.7%9110616.3%70723.3%PM2.5212310.7%17184.8%12137.5%NH387913.7%73797.6%72775.6%VOC 1111142.8%7065-7.3%6056-7.2%200520152020 
      
      
 
 
 
 
 
 
Note that some categories are differently allocated and so some elements of the ‘J_Waste’ in the national 
inventory are allocated in the ‘M_Other’ in GAINS. 

Figure 8-29: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

Figure 8-30: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

128 

 
      
      
 
 
 
 
 
 
 
 
Figure 8-31: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

ESTONIA 

Table 8-8: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

Good  agreement  for  SO2,  apart  from  2020  where  cement  emission  factors  differ  slightly;  Estonia  is  not 
producing clinker, all imported from Sweden, which is also reflected in low EFs in GAINS. Consistently lower 
GAINS for NOx and this is linked mostly to residential sector emission factors. The national emission factors 
for wood boilers are very high, in fact comparable to emission factors for internal combustion engines. Since 
we have not found comparably high emission factors used in other countries or reported in peer-reviewed 
literature, GAINS factors continue to be applied. Additionally, GAINS process emissions appear higher owing 
to glass production estimates; after discussion with national experts the conclusion was reached that national 
estimate is too low and will be reviewed in the 2024 submission.  

129 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 7775-2.3%3635-4.0%1110-11.9%NOx4137-8.2%2826-7.3%2117-15.7%PM2.581242.0%7931.0%5730.6%NH31010-1.2%1110-10.2%1010-0.3%VOC 27296.4%17171.0%1918-6.9%200520152020 
      
      
 
 
 
 
 
 
For PM2.5 key differences are for residential sector where GAINS applies emission factors with condensable 
PM. GAINS uses national information about the structure of the installations.  

Figure 8-32: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

Overall, acceptable match for NMVOC, some small differences remain for transport sector but these decline 
over time and matter less. For NH3, difference slightly higher in 2015 due to higher discrepancies in dairy cattle 
emissions in spite of harmonization, to the  possible extent, of the data on N excretion and measures.  But 
overall, ammonia compares well. 

Figure 8-33: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

130 

 
      
      
 
 
 
 
 
 
 
Figure 8-34: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

FINLAND 

Table 8-9: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with the 

GAINS estimates for 2005, 2015, and 2020. 

Overall, very good consistency with the national submission for all pollutants. Slightly larger differences in 
2020 for NOx mostly in road transport but it is declining and small in absolute terms.  

131 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 7068-1.5%4141-0.1%23230.9%NOx198195-1.7%1291322.4%961037.8%PM2.526261.7%1717-3.4%1414-1.4%NH34039-1.9%3635-4.2%32321.8%VOC 130126-3.6%73741.1%6966-3.7%200520152020 
      
      
 
 
 
 
 
Figure 8-35: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

Figure 8-36: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

132 

 
      
      
 
 
 
 
 
 
Figure 8-37: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

FRANCE 

Table 8-10: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, very good consistency with the national submission. For NOx, small differences in emissions from road 
transport for 2015 remain.   

133 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 458451-1.4%150150-0.1%89912.3%NOx14201417-0.3%87596810.6%5885890.2%PM2.5335320-4.4%220210-4.5%172167-2.9%NH3627615-1.8%6036263.8%5605803.6%VOC 137213931.5%788783-0.6%7087080.0%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-38: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

For NMVOC, the agreement is good, except differences for emissions from crops where national inventory 
includes  natural  sources  in  L_AgriOther,  but  this  source  is  anyway  not  included  in  the  NEC  Directive  and 
therefore the agreement in Table 3-10 is good. 

Figure 8-39: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

France reports now condensable PM2.5 from residential sector. These emissions are larger than previous GAINS 
estimates  (with  condensables,  as  reported  in  the  dedicated  sensitivity  scenario  in  CAO3)  and  rely  on  own 
emission  factor  measurements.  These  are  now  considered  in  GAINS  and  this  result  in  good  consistency 
between GAINS and the current national inventory. 

134 

 
      
      
 
 
 
 
 
Figure 8-40: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

GERMANY 

Table 8-11: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, acceptable match with only few estimates different slightly by more than 5%, apart from PM2.5 where 
GAINS includes condensable and Germany keeps reporting filterable PM  – discussed and confirmed during 
consultations. For other pollutants, specifically SO2, national totals close but there are some smaller sectoral 
differences, e.g., for iron and steel and cement production. For NH3, slightly higher discrepancies in 2005 due 
to slightly higher discrepancies in manure nitrogen application. 

135 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 473467-1.3%3343443.0%241225-6.9%NOx14971413-5.6%12401205-2.8%866853-1.5%PM2.513515413.7%10312219.0%8110224.8%NH3612577-5.8%644630-2.2%530527-0.6%VOC 11841160-2.1%8328522.3%732730-0.3%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-41: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

Figure 8-42: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

136 

 
      
      
 
 
 
 
Figure 8-43: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

GREECE 

Table 8-12: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, very good match for SO2,  NH3, and  NMVOC. Somewhat larger differences  for NOx  in 2020 due to 
slightly lower GAINS implied emission factors for coal power plants and assumptions for road transport; both 
were discussed and harmonized to the possible extant with national experts. For PM2.5 GAINS estimates higher 
for all years and this a result of higher estimates for the open burning of agricultural residues. Open burning 
plays an important role  in Greece emissions, also because  disposal/burning of orchard trimming results in 
large emissions and might be partially also included in the remote sensing data that GAINS relies on.  

137 

National inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifferenceNational inventory 2023GAINS estimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2 585573-2.2%102100-1.7%49514.6%NOx462435-6.0%246234-5.1%204174-14.3%PM2.568726.0%42468.9%34377.7%NH375761.7%64651.3%64664.2%VOC 3203200.0%1531551.3%125120-3.8%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-44: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

Figure 8-45: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 
anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

138 

 
      
      
 
 
 
 
 
 
Figure 8-46: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

HUNGARY 

Table 8-13: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, acceptable match for SO2, NOx, and NMVOCs for 2005, but differences are increasing for 2015 and 
2020.  Larger discrepancies for PM2.5 in 2005 are due to GAINS higher estimates for agricultural burning (3F) 
but  it  is  importance  declines  over  time  owing  to  successful  policies  reducing  its  extent.  For  NMVOC, 
discrepancy in 2015 and 2020 emissions mostly due to slightly higher GAINS estimates for residential sector.   

139 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO243430.0%2423-2.5%1615-7.8%NOx163162-0.6%1071168.9%84885.2%PM2.5404716.8%51545.2%37396.3%NH380810.8%76770.8%7776-1.0%VOC1431504.7%9711215.8%84918.3%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-47: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-48: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

140 

 
      
      
 
 
 
 
 
 
Figure 8-49: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

IRELAND 

Table 8-14: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall,  very  good  agreement  for  NH3,  PM2.5,  and  NMVOC  as  well  as  for  other  pollutants  for  most  years. 
Slightly  larger  differences  remain  for  NOx  in  2015  due  to  road-transport  sector  where  emission  factor 
assumptions are slightly different and for SO2 in 2020 where higher GAINS are due some differences in coal 
use in residential sector as well as SO2 from fuelwood, but these are small in absolute terms and are expected 
to decline in the future.   

141 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO27372-1.2%16173.9%111212.3%NOx1421452.1%82898.6%6259-3.8%PM2.519203.2%14152.5%13130.2%NH31201254.2%1201232.8%1241283.8%VOC81821.4%6967-3.2%6867-2.7%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-50: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-51: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

142 

 
      
      
 
 
 
 
 
 
 
 
Figure 8-52: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

ITALY 

Table 8-15: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, very good match for 2005 for all pollutants. Differences are slightly increasing in 2020. Differences for 
SO2, NOx and NMVOC in 2020 is linked to lower emissions in GAINS for shipping. Small differences for PM2.5 
in 2015 and 2020 are linked to lower estimation in the residential sector and shipping sector.  

143 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO24114120.2%128125-1.6%8580-6.1%NOx12311230-0.1%6797327.9%542494-8.9%PM2.5186186-0.4%169162-4.2%144135-6.1%NH3421416-1.3%3573683.0%3623630.4%VOC120212020.0%778758-2.6%717675-6.0%200520152020 
      
      
 
 
 
 
 
 
 
 
Figure 8-53: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-54: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

144 

 
      
      
 
 
 
 
 
 
 
 
Figure 8-55: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

LATVIA 

Table 8-16: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, very good match for PM2.5 and NH3. For NOx and NMVOC the differences are also acceptable with 
some larger discrepancies for road transport estimates for 2005 (NOx) and residential sector in 2015/2020 for 
NMVOC. For SO2 the key difference after 2005 originates from discrepancy for non-road sector and is linked 
to assumptions about S content of diesel fuel, which in GAINS follows EU legislation and is a low-S fuel; national 
inventory has higher estimate indicating that the assumption about sulphur content was not adjusted.  

145 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2996.9%43-12.4%43-14.8%NOx4339-9.2%3332-3.0%28293.1%PM2.527282.9%16173.8%1716-4.5%NH315163.9%16173.9%16174.6%VOC42454.8%28307.3%28305.9%200520152020 
      
      
 
 
 
 
 
 
 
 
Figure 8-56: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-57: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

146 

 
      
      
 
 
 
 
 
 
 
 
Figure 8-58: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

LITHUANIA 

Table 8-17: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 
the GAINS estimates for 2005, 2015, and 2020. 

Overall, good match for SO2,NOx and NMVOC where only for 2005 a slightly elevated difference occurs due 
to road transport which becomes much less relevant over time. For NH3 several updates were made using the 
IIR and CRF (UNFCCC) information and the overall match is reasonable, but some differences remain showing 
faster decline of emissions in GAINS owing to implementation of IED; it has been challenging to validate the 
assumptions as the respective information about measure implementation is scarce or not available. 

The largest differences however are for PM2.5 (up to a factor of two) where residential sector is dominating. 
Key  reason  is  inclusion  of  condensables  in  GAINS  and  use  of  Tier  II  method  rather  than  Tier  I  without 
condensables as in the national inventory. Furthermore, GAINS includes higher emissions from open burning 
of agricultural waste,  

147 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO228280.8%1515-1.2%1111-3.6%NOx5655-2.6%48491.1%4242-1.8%PM2.5919102.0%91559.5%71263.1%NH339401.8%4037-6.0%4037-7.2%VOC46497.3%36360.1%3433-2.5%200520152020 
      
      
 
 
 
 
 
Figure 8-59: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-60: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

148 

 
      
      
 
 
 
 
 
 
Figure 8-61: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

LUXEMBOURG 

Table 8-18: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the preliminary GAINS estimates for 2005, 2015, and 2020. 

Differences tend to increase towards 2020, apart from NMVOC where overall emissions compare well. In spite 
of the discussions following consultations some differences remain, and the reasons vary across pollutants. 
Emission from industrial sources are generally low and challenging to estimate as often linked to very few 
sources for which specific data is difficult to obtain. Even small differences in assumptions about mitigation 
measures  actual efficiency lead to observed discrepancies  for industrial emissions of  SO2,  NOx,  and  PM2.5. 
While for NOx, industrial sources contribute little, for SO2 they represent currently nearly all emissions and so 
the difference is linked to one source. For PM2.5, the difference is also linked to apparent error in the estimates 
or power sector in the national report and these emissions will be updated in the 2024 submission and then 
showing a good agreement with GAINS.   

Larger differences  for  NH3  remain throughout the whole period although the livestock  numbers, excretion 
rates  and  implementation  of  polices  were  discussed  and  updated  in  GAINS  drawing  on  the  information 
available in the IIR as well as CRF (reporting to UNFCCC) in discussion with national experts. GAINS estimates 

149 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO233-0.2%11-8.1%11-18.4%NOx5656-0.2%2826-8.0%1412-12.2%PM2.53313.0%1216.7%11-11.4%NH365-14.5%65-17.1%75-28.1%VOC1212-0.1%786.3%77-0.2%200520152020 
      
      
 
 
 
 
 
a  more  optimistic  outcome  for  implemented  policies  showing  lower  emissions  than  in  national  inventory.  
There is also rather large set of emissions from industry and power plants in national reporting in 2020 which 
appears to be erroneous and will be corrected in 2024 submission leading to a better alignment with GAINS.  

Figure 8-62: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-63: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

150 

 
      
      
 
 
 
 
 
Figure 8-64: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

MALTA 

Table 8-19: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Acceptable match for SO2 and NH3. For NOx, large differences in emissions from non-road, including shipping 
and aviation although the latter two in absolute terms are smaller. Uncertainties in fuel amounts and type for 
different engines in industry and agriculture; there seem to be differences in balances in PRIMES/GAINS vs 
national dataset. Harmonization efforts was undertaken during consultations and the national team followed 
also the PRIMES modelling team  - unfortunately, differences remain. Overall emissions of PM2.5 are rather 
small with  remaining  discrepancies  for offroad  sector  (as  for  NOx)  and  to  some  extent  the  road  transport 
sector which is slightly larger in GAINS relaying on PRIMES data on vehicle km driven and fuel use for which 
some differences were identified with national data and not all discrepancies were resolved.  

151 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO212121.7%220.8%005.7%NOx109-0.6%65-8.5%43-17.0%PM2.51113.2%0011.1%0012.5%NH3223.2%111.5%11-4.4%VOC43-2.8%332.9%22-9.8%200520152020 
      
      
 
 
 
 
 
 
Figure 8-65: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-66: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

152 

 
      
      
 
 
 
 
 
 
 
Figure 8-67: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

NETHERLANDS 

Table 8-20: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, acceptable match for SO2, NH3, and NMVOC. For NOx, GAINS is lower throughout due to remaining 
uncertainties  for  offroad  sector,  which  include  fuel  allocation  and  actual  emission  factors.  Most  of  the 
remaining differences for PM2.5 are also linked to the offroad sector uncertainties.  

153 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO26867-2.2%31311.4%2019-3.6%NOx405383-5.5%248224-9.6%182162-11.1%PM2.529302.6%18195.4%151610.2%NH3154147-4.9%1291354.7%123123-0.2%VOC208198-4.7%163153-5.9%183175-3.9%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-68: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-69: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

154 

 
      
      
 
 
 
 
 
 
Figure 8-70: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

POLAND 

Table 8-21: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, good agreement at national and also sectoral level. Small differences are remaining mostly for PM2.5 
and NMVOC and are for off-road transport sources. Poland has revised PM2.5 from residential sector based on 
the  new  emission  factor  measurements  and  data  about  structure  of  installations.  These  apply  both  for 
fuelwood and coal. After discussion with the national team, GAINS and the national inventory compare well. 

155 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO211291121-0.7%639593-7.1%385377-2.1%NOx7937930.0%6536560.5%533524-1.8%PM2.53223374.7%2973176.5%307299-2.3%NH3323315-2.4%289285-1.4%310296-4.5%VOC6867489.0%6306523.5%6296523.6%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-71: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-72: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

156 

 
      
      
 
 
 
 
 
 
 
 
Figure 8-73: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

PORTUGAL 

Table 8-22: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, very good match for PM2.5 and NMVOC.  For SO2 the key difference are due to the Power sector where 
we assume the level control remains as in 2015; it seems that implied emission factor actually worsens in the 
national  estimates,  i.e.,  older  plans  have  larger  fuel  use  but  such  detailed  information  was  not  directly 
available  to  validate  such  possibility  (importance  will  be  declining  due  to  decarbonization  in  the  Baseline 
scenario). For NOx, larger differences for 2005 and 2015 are driven by higher estimation in the road transport 
sector, i.e., the share and emission factors for older vehicles and the split between light and heavy duty; the 
assumptions in GAINS were updated with information from IIR and consultations but remain slightly different, 
however  the  differences  decline  by  2020.  GAINS  estimates  slightly  lower  NH3  but  there  was  not  enough 
information about penetration of mitigation measures to justify further revisions. 

157 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO21891910.8%45486.3%3831-18.2%NOx2742885.4%16017710.9%125120-4.3%PM2.55858-0.4%4747-0.9%4444-0.9%NH36258-6.4%5855-4.5%6157-7.7%VOC1701721.4%1281301.8%1341362.0%200520152020 
      
      
 
 
 
 
 
 
Figure 8-74: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-75: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

158 

 
      
      
 
 
 
 
 
 
 
 
Figure 8-76: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

ROMANIA 

Table 8-23: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, good match for SO2 and NH3. For NOx, still differences are remaining in the offroad sector. The key 
difference for PM2.5 and NMVOC are due to the agricultural burning. As highlighted earlier for some other 
countries, GAINS relies on estimates and trends from the remote sensing product (FINN), unless there is well 
documented  bottom-up  data  available.  National  inventory  has  much  lower  estimates  of  open  agricultural 
burning. 

159 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO2603599-0.8%149146-1.6%6159-2.5%NOx306300-1.8%19421811.9%175173-1.0%PM2.512014419.9%10912616.5%11012614.7%NH3194188-3.1%170163-3.7%156152-2.4%VOC25028815.4%18121317.9%17520618.1%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-77: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-78: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

160 

 
      
      
 
 
 
 
 
 
Figure 8-79: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

SLOVAKIA 

Table 8-24: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, acceptable match for all pollutants. Small differences for NOx are from lower estimation in the off-
road sector.  

161 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO28685-1.2%6765-2.8%13141.9%NOx10095-4.6%61634.4%4946-6.5%PM2.536362.0%21224.7%1717-2.1%NH33230-6.6%2827-4.2%2727-0.6%VOC131128-2.2%9695-1.6%8181-0.4%200520152020 
      
      
 
 
 
 
 
 
Figure 8-80: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-81: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

162 

 
      
      
 
 
 
 
 
 
Figure 8-82: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 
emission sources) with the preliminary GAINS estimates for 2005, 2015, and 2020.   

SLOVENIA 

Table 8-25: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, acceptable match for all pollutants with few exceptions. Small remaining differences for NOx for 2005 
are due to uncertainties in data for off-road sector and residential combustion. For SO2 in 2020, differences 
are possibly due to different emission factor for coal power with FGD; the numbers are small and the national 
emission factor seem to be even beyond the BAT technology in GAINS .    

163 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO240401.2%661.1%4411.5%NOx5349-7.1%33344.2%23231.0%PM2.51616-1.3%13130.6%1010-3.8%NH321210.0%19190.6%18192.2%VOC42443.1%27271.4%2524-3.6%200520152020 
      
      
 
 
 
 
 
 
 
Figure 8-83: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-84: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

164 

 
      
      
 
 
 
 
 
 
 
Figure 8-85: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

SPAIN 

Table 8-26: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

NH3 and NMVOC align well with nation estimation. For NOx and PM2.5, overall, emissions very comparable for 
2005 with somewhat increasing discrepancy towards 2015 and decreased differences in 2020. Main causes 
are uncertainties in estimating emissions from non-road sector and residential combustion sector structure. 
The  chart  showing  comparison  for  PM2.5  indicates  also  a  large  difference  between  waste  and  agricultural 
burning, but this is the same thing, just allocated in the inventory and GAINS to different categories, i.e., the 
emissions from orchard trimming (burning of residues) are classified in the national inventory as waste while 
in GAINS are included in the open agricultural burning.  

165 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO212071205-0.2%260247-5.2%128104-18.5%NOx124413236.4%7318029.7%516494-4.3%PM2.5167164-1.9%153137-10.4%133126-5.5%NH3509508-0.2%4714944.9%4915154.9%VOC6216352.3%4424512.0%4654670.5%200520152020 
      
      
 
 
 
 
 
Figure 8-86: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-87: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

166 

 
      
      
 
 
 
 
 
 
Figure 8-88: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

SWEDEN 

Table 8-27: Comparison of the 2023 MS inventory submission (NEC relevant emission sources only) with 

the GAINS estimates for 2005, 2015, and 2020. 

Overall, acceptable match for all pollutants. Remaining differences for PM2.5 are due to the offroad sector for 
which data are scarce.  

167 

Nationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifferenceNationalinventory2023GAINSestimateDifference[kt][kt][%][kt][kt][%][kt][kt][%]SO234364.1%17187.5%15153.3%NOx1811894.3%1341361.4%103102-0.8%PM2.531347.9%192110.9%17185.5%NH35757-0.2%5452-3.0%5251-1.8%VOC172169-2.1%127125-1.4%109104-4.4%200520152020 
      
      
 
 
 
 
 
 
 
 
Figure 8-89: Comparison of the 2023 MS inventory submission for SO2 and NOx (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020. 

Figure 8-90: Comparison of the 2023 MS inventory submission for NH3 and NMVOC (includes all 

anthropogenic emission sources) with the GAINS estimates for 2005, 2015, and 2020 

168 

 
      
      
 
 
 
 
 
 
 
 
Figure 8-91: Comparison of the 2023 MS inventory submission for PM2.5 (includes all anthropogenic 

emission sources) with the GAINS estimates for 2005, 2015, and 2020.   

169 

 
      
      
 
 
 
 
 
 
A3.4.2  Comparison of GAINS Baseline with WM and WaM projections  

A comparison between the GAINS CAO4 baseline and the WM and WaM projections as submitted in 2023 by 
Member States is provided below. Based on the analysis of those, it appears that GAINS Baseline is often more 
ambitious  in  that  emissions  continue  past  reduction  trends  or  even  decline  faster.  National  baselines, 
especially  WM,  is  often  more  conservative  slowing  down  the  rate  of  change  after  2020  or  even  showing 
increasing emissions. WaM, whenever provided, is more ambitious and clearly targets attainment of  ERCs. 
During consultations IIASA has discussed what policies and measures are actually planned to attain projected 
reductions; such information has been considered useful to validate some of the GAINS assumptions about 
the remaining mitigation potential.  

Among reasons causing GAINS baseline emissions declining faster is faster decarbonization of the economy, 
consistent with the objectives of the European Green Deal, and often much quicker reduction of fuelwood use 
in  residential  sector  –  many  countries  have  similar,  albeit  less  optimistic,  assumptions  about  future 
development of biomass for residential uses. However, others have been pointing out that the actual change 
in fuelwood use is very slow and most of the rural users will likely remain using solid fuel stoves and boilers 
for quite some time. Whenever discussing assumptions about projections of installation stock exchange, the 
MS data shows that lifetimes for stoves and boilers are in order of 20-25 years and about 30-40 years for 
fireplaces; this is consistent with GAINS model assumptions.  

ERCs are not achieved in most cases for ammonia, and for some MS also for NOx and PM2.5, while there were 
hardly projections of any violation of ERCs for SO2.  

For some countries one can see an ‘offset’ (between GAINS and the Member States reporting) that is especially 
large for initial years (2005-2015) and is less visible in the projections. This is typically due to condensable 
particulate matter that are not always included and matter most when there is a lot of fuelwood used and 
especially in poor efficiency installations; consequently, becomes less pronounced in the future. 

Discussion of reasons for remaining differences between GAINS and Member States estimates presented in 
the previous section highlights some of the reasons, which also propagate in the future and are visible in the 
overview presented below for each Member State. Additionally, the presentations prepared for the Member 
States consultations include some comments about reasons for potential or expected non-compliance. 

The following figures compare for each country the GAINS Baseline with the WM and WaM projections as well 
as illustration of compliance with the ERCs calculated against the GAINS [NEC-GAINS – shown as a red dot] and 
the national estimate [NEC-National – shown as the blue diamond] of 2005 emissions, respectively. 

170 

 
      
      
 
 
 
 
 
AUSTRIA 

171 

 
      
      
 
 
 
 
 
 
 
BELGIUM  

172 

 
      
      
 
 
 
 
 
 
 
BULGARIA 

173 

 
      
      
 
 
 
 
 
 
CROATIA 

174 

 
      
      
 
 
 
 
 
CYPRUS 

175 

 
      
      
 
 
 
CZECH REPUBLIC 

176 

 
      
      
 
 
 
DENMARK 

177 

 
      
      
 
 
 
ESTONIA 

178 

 
      
      
 
 
 
FINLAND 

179 

 
      
      
 
 
 
 
FRANCE 

180 

 
      
      
 
 
 
 
GERMANY 

181 

 
      
      
 
 
 
 
GREECE 

182 

 
      
      
 
 
 
 
HUNGARY 

183 

 
      
      
 
 
 
 
IRELAND 

184 

 
      
      
 
 
 
ITALY 

185 

 
      
      
 
 
 
 
LATVIA 

186 

 
      
      
 
 
LITHUANIA 

187 

 
      
      
 
 
 
LUXEMBOURG 

188 

 
      
      
 
 
MALTA 

189 

 
      
      
 
 
 
NETHERLANDS 

190 

 
      
      
 
 
 
POLAND 

191 

 
      
      
 
 
 
PORTUGAL 

192 

 
      
      
 
 
 
ROMANIA 

193 

 
      
      
 
 
 
SLOVAKIA 

194 

 
      
      
 
 
 
SLOVENIA 

195 

 
      
      
 
 
 
SPAIN 

196 

 
      
      
 
 
SWEDEN 

197 

 
      
      
 
 
 
 
 
 
A4. Additional results for policy and sensitivity scenarios  

A4.1  Health impact indicators  

This section provides results for further indicators for exposure to PM2.5 and overall results for PM2.5, ozone, 
and NO2 for scenarios analysed in the project. 

Section A4.1.1 presents other indicators related to mortality from PM2.5 which were presented in earlier CAOs 
but not included in the main text of CAO4, like loss of life expectancy and years of life lost. Section  A4.1.2 
presents results for premature mortality from PM2.5 concentrations above the WHO Air Quality Guideline of 
5 µgm-3 (WHO 2021). This indicator has been used in the Impact Assessment to the AAQD, and also in CAO3 
as a sensitivity case. Section A4.1.3 presents results for mortality related to NO2 exposure above the WHO 
AQG of 10 µgm-3. 

A4.1.1  Other indicators for PM2.5 related impacts 

The main report provides mean population exposure and premature mortality. Further indicators of exposure 
to PM2.5 shown here include loss of life expectancy, YOLLs and YLLs. 

Years of Life Lost (YOLLs) attributable to the exposure to a given level of PM2.5, in terms of the cumulative 
number of life years lost that occur over the remaining lifetime of the population living in 2010, assuming 
constant PM2.5 concentrations – see Amann et al. (2011). This excludes population dynamics and is notably 
different  from  the  annual  YLL52  estimates  (in  a  particular  year),  which  are  most  relevant  for  an  economic 
assessment of the (annual) benefits of pollution control measures. YLLs are calculated as the sum of remaining 
life expectancy at age of death for all PM2.5 attributable deaths in a given scenario year. Estimates of the annual 
YLLs that take into account the population dynamics in the various Member States are provided in the benefits 
assessment sections of the report.  

Figure 8-92: Years of life lost (YOLLs) attributable to the exposure to anthropogenic PM2.5 in the EU.   

52 Years of life lost (YLL) due to premature deaths in one particular year 

198 

 
      
      
 
 
 
 
Table 8-28: Years of life lost (YOLLs) attributable to the total exposure to PM2.5 in the EU; million years lost 

using constant 2010 population data, for all analysed scenarios. 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2005 
478.6 

2010 

2015 
339.4 

2020 
271.0 
266.6 

2025 
234.2 
230.0 
233.4 

2030 
181.5 
175.8 

174.5 
172.0 
150.5 

2035 

2040 
134.2 

2045 

2050 
121.0 
119.4 

110.2 

102.9 

Figure 8-93: Years of life lost (YLL), attributable to the exposure to total PM2.5, in a specific year in the EU.   

Table 8-29: Years of life lost (YLLs), attributable to the total exposure to PM2.5, in a specific year in the EU 

for all analysed scenarios; [thousand years/year]. 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2005 
8117 

2010 

2015 
6160 

2020 
5055 
4972 

2025 
4486 
4406 
4471 

2030 
3568 
3452 

3429 
3377 
2957 

2035 

2045 

2040 
2759 

2050 
2593 
2560 

2264 

2204 

199 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 8-94: Loss in statistical life expectancy, attributable to the exposure to total PM2.5 in the EU.   

Table 8-30: Loss in statistical life expectancy, attributable to the exposure to total PM2.5 in the EU 

[months]. 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2005 
13.0 

2010 

2015 
9.2 

2020 
7.4 
7.3 

2025 
6.4 
6.3 
6.4 

2030 
4.9 
4.8 

4.8 
4.7 
4.1 

2035 

2045 

2040 
3.7 

2050 
3.3 
3.3 

3.0 

2.8 

200 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A4.1.2  Health impacts from PM2.5 above WHO Air Quality Guidelines 

The WHO Air Quality Guideline value for exposure to PM2.5 is 5 µgm-3 (WHO 2021). Figure 8-95 and Table 8-31 
summarize the estimates of premature mortality from PM2.5 concentrations (including natural sources) above 
the WHO Guideline value, using static population assumptions (constant population in 2010). The comparison 
shows a decline in premature deaths of about 82 and 94%, related to 2005, across the scenarios (excluding 
MTFR) in 2030 and 2050. Not surprisingly, these reductions are larger than in case when no cut-off at PM2.5 
concentrations of 5 µgm-3 is considered (as shown in Section 4.4.3 in the main report). 

Figure 8-95: Comparison of the cases of premature deaths attributable to the exposure to total PM2.5 
concentrations above 5 µg/m3 in the EU, for the analysed scenarios.   

Table 8-31: Cases of premature death attributable to the exposure to total PM2.5 above 5 µg/m3 in the EU; 

thousand cases per year, using constant 2010 population data. 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2005 
457 

2010 

2015 
282 

2020 
197 
191 

2025 
152 
146 
150 

2030 
88 
81 

80 
77 
52 

2035 

2045 

2040 
37 
35 

2050 
26 
25 

19 

16 

Taking into account all sources of PM2.5 (including natural sources), population-weighted exposure in the EU, 
considering a cut-off of 5 µgm-3, is expected to decline in the Baseline and policy scenarios to levels below 
1 µgm-3 in the long term, i.e., by 2050 (Table 8-32). For several EU Member States (nearly half of them) such 
exposure declines to levels below 0.2 µgm-3 or is at 0 µgm-3. 

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Table 8-32: Population-weighted PM2.5 concentrations in the EU; µg/m3, including all sources but 

considering a cut-off of 5 µg/m3. 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2005 
13.63 

2010 

2015 
8.32 

2020 
5.71 
5.55 

2025 
4.38 
4.22 
4.33 

2030 
2.61 
2.32 

2.28 
2.20 
1.50 

2035 

2045 

2040 
1.07 
1.01 

2050 
0.78 
0.74 

0.56 

0.48 

A4.1.3  Health impacts from NO2 above WHO Air Quality Guidelines 

The WHO Guideline value for long-term exposure to NO2 is 10 µgm-3. This is the lower limit at which current 
evidence  exists  for  health  impacts  of  NO2.  As  discussed  in  the  main  report,  the  expected  trends  in  NOx 
emissions  will  strongly  decrease  ambient  NO2  concentrations  and  bring  large  shares  of  the  EU  population 
below this threshold. In the main report, calculated impacts consider the full range  of NO2  concentrations 
without a cut-off. As an alternative approach and consistent with calculations in CAO3, this section presents 
results for premature mortality considering a cut-off at 10 µgm-3.  

Population-weighted mean concentrations above 10 µgm-3 are shown in Table 8-33. In 2040 and 2050, these 
drop  to  very  low  values  already  in  the  Baseline  and  even  more  in  the  MTFR.  Correspondingly,  premature 
deaths attributable to NO2 above this cut-off decrease to 1500 cases in 2050 in the Baseline, and 1100 cases 
in the MTFR (Table 8-34, Figure 8-96). 

Table 8-33: Population-weighted NO2 concentrations in the EU; µg/m3, including all sources but 

considering a cut-off of 10 µg/m3. 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2005 
15.6 

2010 

2015 
10.6 

2020 
6.3 
6.1 

2025 
4.5 
4.4 
4.5 

2030 
2.2 
2.1 

1.8 
1.8 
1.4 

2035 

2045 

2040 
0.3 

2050 
0.2 
0.2 

0.2 

0.1 

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Figure 8-96: Comparison of the cases of premature deaths attributable to the exposure to NO2 
concentrations above 10 µg/m3 (including all sources) in the EU, for the analysed scenarios.   

Table 8-34: Cases of premature death attributable to the exposure to NO2 (all sources) above 10 µg/m3 in 

the EU; thousand cases per year, using constant 2010 population data. 

Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

2005 
122.5 

2010 

2015 
83.3 

2020 
49.5 
48.5 

2025 
35.7 
34.7 
35.6 

2030 
17.2 
16.0 

14.2 
14.1 
10.8 

2035 

2045 

2040 
2.7 

2050 
1.5 
1.5 

1.4 

1.1 

203 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A4.2  Ecosystem impact indicators  

Further indicators, not shown in the main report, are included in this section.  

Figure 8-97: Nature2000 nature protection area in the EU with nitrogen exceeding critical loads for 

eutrophication. The marked 25% reduction of 2005 area with N deposition exceeding CLs refers to 
the ZPAP target for all ecosystems – for comparison only.   

Table 8-35: Nature2000 nature protection areas with nitrogen deposition exceeding CLs for 

eutrophication. 

2005 

2010 

2015 

2020 

2025 

2030 

2035 

2040 

2045 

2050 

468 

502 

357 

428 
425 

386 
369 

411 
405 
411 

Nature2000 nature protection area with nitrogen deposition exceeding critical loads for eutrophication (1000 km2) 
Baseline 
ERC  
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 
Nature2000 nature protection area with nitrogen deposition exceeding CLs for eutrophication (% of total Nature2000 areas) 
66.4 
Baseline 
ERC  
65.9 
ERC 2020 
ZPAP 
ERC+ZPAP 
MTFR 

63.8 
62.8 
63.7 

54.8 
54.7 
49.3 

353 
352 
318 

59.9 
57.3 

78.0 

72.6 

55.4 

43.1 

278 

269 

53.7 
52.0 

41.7 

346 
335 

204 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A4.3  Analysis of measures to achieve ERCs 

While the estimated non-compliance with ERCs appears most severe for ammonia, several Member States are 
estimated to be  in non-compliance  for a number of other NECD air pollutant species in the  2030 Baseline 
scenario. That was illustrated in the analysis of compliance in the main report.  

The analysis and discussion of measures to achieve the ZPAP objectives for ecosystems, necessitating mostly 
further reduction of emissions of ammonia, was shown in Section 5 of the main report. Here we illustrate the 
measures, shown as key sector-measure categories, for other pollutants.  

Since the health ZPAP target is met, there is no need for additional mitigation of PM precursors in the ZPAP 
case. However, we observe emission reduction in the ZPAP also for PM2.5, NMVOC, NOx; these are co-benefits 
of mitigation needed for NH3 or NOx to achieve the ZPAP objectives for ecosystems. However, in case of the 
ERC scenario, there are several countries where additional mitigation would be required to achieve the ERCs 
by 2030. This is mostly the case for PM2.5, NOx, and in few cases also NMVOC, not for SO2. The results of the 
analysis, where the compliance with the ERCs for all pollutants is required simultaneously, shows that even 
for SO2 there is some (very small) additional mitigation, however, this is a co benefit of measures needed to 
reduce PM2.5, i.e., curbing emissions from open burning of agricultural residue – in fact forbidden by law, but 
according to remote sensing data it still takes place in many Member States.   

A dedicated set  of measures  to reduce emissions from residential combustion (driven by need of bringing 
down PM2.5 emissions) causes also reductions of NMVOC, which is helping to achieve the needed reductions 
for that pollutant and taking pressure off more typical NMVOC source sectors like solvent use. 

205 

 
      
      
 
 
 
Figure 8-98: Distribution of further SO2 reductions in the ERCs compliant and ZPAP scenario, compared to 

Baseline, in 2030.   

206 

 
      
      
 
 
 
Figure 8-99: Distribution of further NOx reductions in the ERCs compliant and ZPAP scenario, compared to 

Baseline, in 2030.   

207 

 
      
      
 
 
 
Figure 8-100: Distribution of further PM2.5 reductions in the ERCs compliant and ZPAP scenario, compared 

to Baseline, in 2030.   

208 

 
      
      
 
 
 
Figure 8-101: Distribution of further NMVOC reductions in the ERCs compliant and ZPAP scenario, 

compared to Baseline, in 2030.   

209 

 
      
      
 
 
 
A4.4  Sensitivity to condensable PM  

This section presents more detailed information about the model setup and analysis performed to analyse the 
sensitivity of calculated PM concentrations on various assumptions about emission factors and volatility of 
condensable PM. 

The physics and chemistry of organic aerosols are complex and cannot be described in detail here. Briefly, 
primary organic aerosol (POA) emissions are classified in broad classes as either non-volatile (NV – these are 
simply treated as inert compounds in the model), semi-volatile (SV, in which a fraction of the emissions are 
allowed to evaporate to form VOC, though these may be oxidised back to the aerosol in downwind chemical 
reactions),  and  with  both  semi-  and  intermediate-volatility  compounds  (SIV,  where  as  well  as  allowing 
evaporation  of  organic  PM  emissions,  we  also  assume  an  additional  source  of  organic  aerosol  caused  by 
associated intermediate-volatility VOC compounds. For further explanation, see Denier van der Gon (2015) 
and Simpson et al. (2020; 2022). 

While the GAINS model with its linear approximation of the EMEP model can analyse the effect of different 
total  amounts  of  emissions,  analysis  of  different  assumptions  on  volatility  can  only  be  done  using  a  full 
chemistry-transport model with a detailed formulation of condensation and re-evaporation processes. The 
impact of the different assumptions on ambient concentrations was therefore assessed with the EMEP model 
at a 0.3° × 0.2° resolution using the techniques and datasets discussed in Simpson et al. (2022). 

A4.4.1  Model setup 

The  model  setup,  emission  scenarios,  and  some  acronyms  are  summarized  in  Table  8-36:  Model  setup, 
acronyms and emission scenarios. Table 8-36. Briefly, the setup used here follows closely that use in the NMR 
report  of  Simpson  et  al.,  2022.  In  general,  the  model  treatment  of  emissions  follows  the  standard  EMEP 
procedures  for  all  sources,  except  for  primary  organic  aerosol  (POA)  from  residential  combustion  sector. 
Following Simpson et al. (2022), POA emissions are assumed to consist of two fractions: FPOA is the so-called 
filterable fraction, and CPOA is the condensable fraction. 

POA = FPOA + CPOA 

Emissions of both FPOA and CPOA were provided by IIASA/TNO. FPOA emissions are always treated as non-
volatile and inert. CPOA emissions (here only from residential combustion sector) are treated as either non-
volatile (NV), semi-volatile (SV) or in the most complex ‘SIV’ case as semi-volatile but with additional emissions 
of intermediate volatility VOC (IVOC). IIASA/TNO also provided emission factors for both a central (C) emission 
factor estimate and a high (H) emission factor estimate. Thus, the emission scenarios presented in Table 8-36 
combine these different CPOA modelling approaches with the central or high emission factor estimates. For 
example,  scenario  SV-C  uses  the  SV  semi-volatile  assumption  for  CPOA,  and  the  central  emission  factor 
estimates. 

All runs use a 0.3° x 0.2° degree longitude/latitude resolution, and simulations are for the year 2019, using 
(unless  otherwise  stated)  emissions  from  the  year  2020.  In  comparison  with  measurements,  we  use  the 
standard OC emissions from the EMEP network, using sites with altitudes less than 500 m. 

210 

 
      
      
 
 
 
 
 
 
Table 8-36: Model setup, acronyms and emission scenarios. 

EMEP model  
Emissions 

Evaluation software  
POA 
FPOA 
CPOA 
Emission scenarios 
NV-F 
NV-C 
NC-H 
SV-C 

SIV-C 

rv5.4, with MARS thermodynamics 
Provided by IIASA, Sep 2024, for inorganics and PM components. Fine fraction OC and 
OM provided for filterable, central estimate, and high  estimate emission factors (EFs). 
Last updated for Swiss OC emissions 13/9/2024. 
AeroTools (MET Norway) 
Primary organic aerosol 
Primary filterable (non-volatile) organic aerosol 
condensable fraction of POA 

Only includes filterable POA (FPOA) 
All CPOA emissions as nonvolatile, C=Central (typical) emissions. 
As NVC, but with high CPOA emissions 
As NVC, but with semivolatile fine-mode OA from GNFR C, which are allowed to evaporate 
(using 1.5D VBS). 
As SVC, but with added intermediate volatility compounds from GNFR C 

A4.4.2  Time-series comparison 

We first present time-series comparisons as it is easier to visualize the overall impact of the various scenarios. 
Figure 4-22 shows the model agreement with OC (in PM2.5) measurements, averaged across all sites, for the 
three non-volatile runs, NV-F, NV-C and NV-H. Of these three runs, only the central estimate, NV-C, shows a 
satisfactory agreement with the observations. This is expected since the NV-F cases omits the known CPOA 
contributions, and the NV-H case assumes both high emission factors and that CPOA are involatile.  

Note that in the main report the NV-F is referred to as ‘Ref2_Filter’, NV-C as ‘GAINS_EF’, and NV-H as ‘High_C’ 
to keep the consistency with the definitions of emission factors sets and sensitivity scenarios analyzed (see 
Table 4.1 in section 4.3 in the main report). 

Figure 8-102: All-site comparison of modelled OC (in PM2.5) versus observations, 2019, non-volatile tests 
(see above for model scenarios; the ‘32’ refers to the model resolution used, 0.3×0.2 degrees).   

211 

 
      
      
 
 
 
 
Figure 8-103 presents results from most of the model runs compared with observed OC (in PM2.5). Here we 
see that both the SV-C and SV-H runs, which assume semi-volatile CPOA, still underestimate the observed OC. 
The central SIV-C case is also underpredicting OC in all months. However, the SIV-H case matches the observed 
OC at least as well as the NV-C case. 

Figure 8-103: All-site comparison of modelled OC (in PM2.5) versus observations, 2019, NV, SV and SIV 

tests.   

Figure 8-104 to Figure 8-106 present some statistics from these comparisons (note that although PM2.5 and 
PM10 ae shown, we only discuss OC here). In terms of normalised mean bias (Figure 8-104) the SIV-H run has 
lowest NMB (-11%) for OC. The NV-C case shows next best agreement for OC, with -26%. In terms of spatial 
correlation (Figure 8-105), the SIV-H case is again best (Rspace=0.88), though the NV-H and SV-H cases also 
show high correlations (0.84, 0.86, respestively). The NV-C case has a lower Rspace, 0.75, though this is still 
rather good for such a complex pollutant. Finally, Figure 8-106 shows the median temporal correlations. Again, 
SIV-H provides the best result for OC (Rtemp=0.78), but NV-C has very similar value, 0.75.  

For comparison we can mention that NO2 shows an NMB of -16%, Rspace = 0.80, and Rtemp=0.63. Thus, even 
though  OC modelling  is  far  more  uncertain  in terms  of  emissions estimation  and  chemical  processing,  the 
results shown here for OC are comparable in many respects. 

212 

 
      
      
 
 
 
Figure 8-104: Normalized mean bias (NMB, %), all sites, 2019 (from monthly data).   

Figure 8-105: Mean spatial correlation (Rspace), all sites, 2019 (from monthly data). (Note: R-values from 

0.5 to 1.0.)   

213 

 
      
      
 
 
 
Figure 8-106: Median temporal correlation (Rtemp), all sites, 2019 (from daily data). (Note: R-values from 

0.5 to 1.0.) 

Figure 8-107: All-site comparison of NV-C modelled OC (in PM2.5) versus observations, 2019, 2015 

emissions vs 2020 emissions 

Finally with regard to these  time-series,  we  can mention that the  year 2019 was chosen as a  focus of the 
modelling since the number of OC measurements was satisfactory, and we wished to avoid the COVID year 
2020. The emissions provided were for either 2015 or 2020, and in general we have used the 2020 emissions 
(we assume COVID impact on residential sector was minor) as the best estimate for the 2019 case. However, 
the model runs have proven rather insensitive to this choice. As illustrated in Figure 8-107 the modelled OC is 
rather similar whether we use the 2015 or 2019 emissions. Further model runs were also made for 2018 and 

214 

 
      
      
 
 
 
 
2020, but again these did not change the basic conclusions from this work, so we have restricted this note to 
the 2019 simulations. 

A4.4.3  Comparison across sites 

The time-series plots and statistics shown above offer a concise summary of the model results, but it is also 
important  to  be  aware  of  the  large  site-to-site  differences.  Figure  8-108  shows  the  normalised  mean  bias 
(NMB) for OC in PM2.5 at the available OC sites using the NV-C, NV-H and SIV-H runs. NO2 is also shown for 
comparison.  By  default,  the  EMEP  model  uses  the  NV-C  treatment  of  POA  emissions,  and  Figure  8-108(a) 
shows that this leads to some underestimation of OC in continental Europe, but substantial overestimates at 
two sites in Norway. The NV-H scenario generates too much OC at all sites (Figure 8-108(b)), and the SIV-H 
scenario gives results which look rather similar to those of NV-C. The NO2 (Figure 8-108(d)) is provided to show 
that there can be issues with particular regions (e.g. Norway) even for other pollutants whose emissions and 
chemistry are far better known than that of POA, so we should not try to over-interpret differences in POA 
emission factors and/or POA chemical schemes. 

Figure 8-108: EMEP results for OC in PM2.5 with NV-C, NV-H and SIV-H runs, and NO2, meteorological data 

for 2019 was used in simulations. 

215 

 
      
      
 
 
 
 
 
A4.4.4  Discussion 

The  above  results  show  that  OC  modelling  is  very  sensitive  to  the  emission  factors  used,  and  to  the 
assumptions concerning volatility used in the modelling. Of the schemes tested, the SIV-H combination (semi- 
and intermediate volatility scheme) seemed to give the best results, but this scheme has some highly uncertain 
assumptions concerning the volatility of SVOC, the amount and character of the IVOC, and the atmospheric 
processing (e.g. aging) assumed for the CPOA and IVOC compounds. (We can also note that the CPOA schemes 
used were designed for residential wood burning. In areas where GNFR C is dominated by, e.g. coal burning, 
it is very unclear which CPOA scheme should be used.) 

Although the default NV-C scheme was not quite as good as the SIV-H setup, the results were rather similar to 
those  of  SIV-H  and  to  observations  in  any  case  (sometimes  matching  observed  OC  better  than  for  NO2). 
Although we know that the assumption that CPOA is non-volatile is incorrect, it is also the simplest assumption 
we can make, and both the model results presented here, and other studies (see Simpson et al, 2022, for refs) 
confirm that such an approach generates OC levels consistent with much more complex approaches. 

Thus, for EMEP regional scale modelling the non-volatile approach with central emission factors still seems to 
represent  the  best  approach.  For  finer-scale  modelling  (e.g.  uEMEP)  the  impacts  of  volatility  (initial 
evaporation) would be stronger, so these issues would need further consideration. 

216 

 
      
      
 
 
 
A5. Additional results for the costs of air pollution and 

the benefits of air pollution reduction  

Results  presented  below  include  additional  information,  including  results  by  Member  States,  that  are  not 
included in the CAO4 main report.  

A5.1  Health damage 

The value of health impacts under the CAO4 Baseline for each country from 2005 to 2050 are shown in Table 
8-37 to, Table 8-40 accounting for sensitivities in mortality valuation (VLY and VSL) and the inclusion of NO2 
effects  where  there  is  potential  for  double  counting.  Tier  3  morbidity  effects  (mostly  dementia,  but  also 
diabetes) are included only in the last row of the table. Table 8-41 and Table 8-42 then provide a breakdown 
of impact numbers and monetised value respectively for each effect at the EU27 level for the CAO4 Baseline. 

Table 8-37: Health impacts (mortality and morbidity) under the CAO4 Baseline by Member State, 2005-

2050, €million (2015) per year. Mortality valued using the VOLY and all NO2 functions included. 
Tier 3 morbidity only included in last row. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 
Total with Tier 3 
morbidity 

2005 
17,182 
28,640 
25,099 
12,225 
998 
25,647 
8,095 
1,812 
4,275 
122,396 
162,922 
37,673 
27,155 
3,991 
158,535 
5,139 
5,706 
965 
692 
41,196 
99,228 
16,547 
64,380 
12,684 
5,343 
81,363 
7,974 
977,862 

1,268,005 

2020 
10,453 
14,859 
13,893 
6,860 
875 
17,155 
4,361 
1,053 
2,495 
67,396 
92,695 
18,735 
17,292 
2,490 
91,150 
2,313 
3,104 
546 
622 
21,975 
73,528 
9,574 
38,550 
7,275 
3,176 
51,617 
5,282 
579,322 

790,303 

2030 
5,814 
9,656 
7,411 
3,232 
791 
9,339 
3,197 
671 
1,814 
45,568 
58,155 
10,786 
9,954 
1,916 
54,588 
1,134 
1,724 
407 
622 
15,355 
33,921 
6,758 
22,331 
3,957 
2,167 
33,287 
3,729 
348,284 

531,220 

2040 
4,171 
7,028 
4,517 
2,012 
762 
5,791 
2,447 
454 
1,475 
32,536 
41,417 
7,797 
6,124 
1,583 
40,362 
702 
1,083 
320 
695 
11,685 
18,655 
5,360 
13,769 
2,619 
1,385 
27,706 
3,289 
245,746 

410,631 

2050 
3,628 
6,293 
3,478 
1,566 
745 
4,538 
2,154 
387 
1,314 
29,288 
36,022 
6,888 
4,739 
1,542 
34,982 
550 
872 
310 
736 
10,355 
15,219 
4,825 
9,916 
2,092 
978 
26,844 
3,214 
213,476 

373,530 

2025 
7,891 
12,077 
10,317 
4,846 
830 
12,954 
3,724 
861 
2,111 
56,800 
74,745 
13,665 
12,926 
2,260 
73,357 
1,551 
2,307 
485 
596 
18,604 
50,796 
8,297 
28,766 
5,478 
2,712 
41,390 
4,308 
454,652 

656,072 

217 

 
      
      
 
  
 
Table 8-38: Health impacts (mortality and morbidity) under the CAO4 Baseline by Member State, 2005-

2050, €million (2015) per year. Mortality valued using the VSL and all NO2 functions included. Tier 
3 morbidity only included in last row. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 
Total with Tier 3 
morbidity 

2005 
46,587 
81,433 
76,946 
34,729 
2,184 
69,021 
23,937 
5,956 
12,121 
329,223 
477,713 
100,604 
82,714 
8,709 
468,247 
15,912 
16,320 
2,290 
1,672 
104,097 
230,889 
50,171 
165,792 
29,970 
13,895 
232,924 
25,690 
2,709,746 

2020 
33,223 
50,581 
53,939 
24,570 
2,001 
55,205 
13,218 
3,715 
8,274 
221,817 
333,716 
70,298 
60,423 
5,686 
351,815 
8,555 
11,706 
1,334 
1,607 
68,185 
228,394 
35,414 
132,890 
20,317 
10,828 
176,795 
16,527 
2,001,029 

2025 
23,973 
37,061 
38,442 
17,413 
1,854 
39,441 
11,458 
3,204 
7,249 
180,582 
265,159 
50,985 
45,644 
5,351 
257,911 
6,171 
8,694 
1,094 
1,504 
54,351 
156,823 
29,444 
97,735 
15,772 
8,744 
129,460 
12,638 
1,508,157 

2030 
18,325 
30,231 
28,488 
12,196 
1,896 
30,506 
10,500 
2,547 
6,583 
150,296 
208,428 
41,615 
35,819 
4,864 
202,494 
4,639 
6,651 
943 
1,654 
47,823 
112,583 
25,080 
79,149 
12,359 
7,430 
108,172 
11,366 
1,202,636 

2040 
14,084 
23,767 
18,140 
8,392 
2,120 
20,834 
8,755 
1,758 
5,986 
117,933 
149,651 
32,357 
23,006 
4,597 
162,554 
3,024 
4,518 
817 
2,041 
41,214 
71,536 
22,055 
53,150 
9,444 
5,310 
99,514 
10,570 
917,127 

2050 
13,710 
23,060 
14,214 
6,916 
2,308 
16,633 
8,055 
1,570 
5,469 
114,933 
142,590 
31,980 
18,073 
5,103 
157,369 
2,542 
4,075 
917 
2,248 
39,449 
63,030 
21,998 
40,195 
8,202 
4,099 
111,094 
10,397 
870,226 

2,999,889 

2,212,010 

1,709,577 

1,385,572 

1,082,012 

1,030,280 

218 

 
      
      
 
  
 
 
 
Table 8-39: Health impacts (mortality and morbidity) under the CAO4 Baseline by country, 2005-2050, 
€million (2015) per year. Mortality valued using the VOLY with NO2 functions excluded where 
there is potential for double counting. Tier 3 morbidity only included in last row. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 
Total with Tier 3 
morbidity 

2005 
13,254 
22,064 
22,397 
11,191 
947 
21,960 
6,396 
1,273 
3,095 
93,653 
127,464 
29,720 
23,287 
3,033 
126,717 
4,540 
4,909 
771 
594 
30,687 
88,572 
12,553 
58,510 
11,211 
4,435 
58,616 
6,373 
788,221 

2020 
8,296 
11,314 
12,085 
6,296 
838 
14,984 
3,570 
746 
1,869 
50,398 
71,725 
13,740 
15,015 
1,928 
74,755 
1,981 
2,592 
456 
547 
16,255 
65,698 
7,647 
34,514 
6,511 
2,551 
38,158 
4,305 
468,774 

2025 
6,461 
9,418 
8,908 
4,455 
798 
11,360 
3,108 
588 
1,650 
42,710 
60,217 
9,931 
11,289 
1,753 
61,015 
1,330 
1,951 
413 
519 
14,307 
45,331 
6,700 
25,452 
4,860 
2,208 
31,665 
3,584 
371,981 

2030 
4,921 
7,769 
6,387 
2,954 
760 
8,215 
2,683 
479 
1,483 
35,331 
48,360 
8,195 
8,737 
1,520 
46,219 
973 
1,488 
354 
540 
12,219 
29,963 
5,665 
19,859 
3,531 
1,811 
26,703 
3,204 
290,323 

2040 
3,690 
6,009 
4,085 
1,851 
733 
5,203 
2,137 
388 
1,284 
28,320 
36,428 
6,694 
5,512 
1,337 
35,439 
629 
979 
289 
594 
9,800 
16,783 
4,752 
12,520 
2,397 
1,248 
23,876 
2,961 
215,939 

2050 
3,281 
5,506 
3,275 
1,447 
715 
4,113 
1,918 
347 
1,173 
26,124 
32,146 
6,113 
4,296 
1,312 
31,249 
501 
801 
282 
630 
8,902 
13,847 
4,308 
9,131 
1,927 
896 
23,571 
2,941 
190,751 

1,078,364 

679,755 

573,401 

473,259 

380,824 

350,805 

219 

 
      
      
 
 
  
 
 
 
Table 8-40: Health impacts (mortality and morbidity) under the CAO4 Baseline by country, 2005-2050, 

€million (2015) per year. Mortality valued using the VSL with NO2 functions excluded where there 
is potential for double counting. Tier 3 morbidity only included in last row. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 
Total with Tier 3 
morbidity 

2005 
33,688 
58,753 
66,084 
30,844 
2,040 
56,438 
17,965 
3,917 
8,231 
236,994 
349,406 
74,082 
67,944 
6,320 
349,818 
13,607 
13,558 
1,739 
1,381 
72,654 
199,539 
35,708 
146,332 
25,639 
10,929 
155,833 
19,398 
2,058,841 

2020 
24,814 
36,142 
45,012 
21,930 
1,893 
46,322 
10,342 
2,451 
5,798 
155,397 
240,133 
47,514 
50,465 
4,209 
271,346 
7,050 
9,354 
1,071 
1,372 
47,249 
197,606 
26,656 
115,249 
17,669 
8,193 
121,445 
12,793 
1,529,474 

2025 
18,544 
27,146 
31,621 
15,545 
1,762 
33,201 
9,148 
2,001 
5,311 
126,636 
199,658 
33,892 
38,257 
3,955 
201,968 
5,062 
6,998 
901 
1,265 
39,328 
135,083 
22,364 
83,176 
13,528 
6,701 
92,135 
10,007 
1,165,193 

2030 
14,753 
22,964 
23,351 
10,806 
1,799 
25,758 
8,419 
1,673 
5,077 
109,066 
163,145 
29,070 
30,200 
3,677 
161,984 
3,804 
5,483 
794 
1,385 
35,942 
95,722 
19,911 
67,762 
10,665 
5,872 
81,335 
9,346 
949,764 

2,348,984 

1,740,455 

1,366,613 

1,132,699 

2040 
11,958 
19,389 
15,787 
7,485 
2,011 
18,036 
7,352 
1,431 
4,974 
98,241 
125,706 
26,227 
19,992 
3,722 
135,913 
2,613 
3,940 
718 
1,670 
32,862 
62,093 
18,740 
46,728 
8,388 
4,609 
81,492 
9,202 
771,278 

936,163 

2050 
11,941 
19,310 
13,040 
6,188 
2,180 
14,536 
6,916 
1,354 
4,687 
98,544 
121,668 
26,989 
15,818 
4,148 
134,162 
2,233 
3,622 
811 
1,838 
32,337 
55,385 
18,807 
35,844 
7,334 
3,630 
92,813 
9,230 
745,366 

905,421 

220 

 
      
      
 
  
 
 
 
Table 8-41: Aggregated health impacts for the EU27 per year under the CAO4 Baseline Scenario, 2005-

2050. Units: Thousand life years lost, cases, etc. 

IMPACTS 
Note: results are in thousands 
Chronic Mortality (30yr +) deaths 
Chronic Mortality (All ages) LYL 
Chronic Mortality (30yr +) deaths 
COPD (>30) cases 
Stroke (30+) cases 
IHD events (>30) cases 
Lung cancer (>30) cases 
Asthma (new incidence, 0-18) cases 
Chronic Mortality (All ages) LYL 
Chronic Mortality (30yr +) deaths 
Asthma in adults (19+) cases 
ALRI in children (0-12) cases 
Asthma in children (0-18) cases 
Respiratory hospital admissions (>64) 
cases 
Cardiovascular hospital admissions (>64) 
cases 
Infant Mortality (0-1yr) deaths 
Bronchitis in children aged 6 to 12 cases 
Respiratory Hospital Admissions (All ages) 
cases 
Cardiac Hospital Admissions All ages) 
cases 
Restricted Activity Days (all ages) days 
Lost working days (15-64 years) days 
Bronchitis in children aged 5 to 14 cases 
Respiratory Hospital Admissions (All ages) 
cases 
Type 2 diabetes (>30) cases 
Dementia (60+) cases 

Poll. 

Tier 

O3 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
NO2 
NO2 
NO2 
NO2 
NO2 
O3 
O3 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
NO2 
NO2 
PM2.5 
PM2.5 

1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
3 
3 

Baseline 
2005 
74 
6,328 
519 
386 
189 
374 
69 
378 
1,990 
180 
136 
44 
165 
17 
89 
1 
1,148 
174 
191 
657,738 
179,075 
655 
206 
199 
584 

Baseline 
2020 
83 
3,680 
391 
254 
123 
241 
45 
254 
1,160 
131 
92 
28 
103 
16 
80 
0 
691 
105 
114 
401,742 
105,432 
438 
126 
128 
425 

Baseline 
2025 
69 
2,801 
294 
230 
110 
215 
40 
232 
866 
95 
81 
24 
91 
16 
81 
0 
615 
93 
100 
357,242 
91,944 
377 
107 
115 
406 

Baseline 
2030 
68 
2,149 
240 
191 
90 
175 
33 
181 
607 
70 
61 
17 
65 
17 
84 
0 
468 
73 
78 
285,553 
71,627 
272 
78 
93 
369 

Baseline 
2040 
69 
1,555 
195 
155 
71 
136 
26 
131 
312 
40 
34 
9 
34 
19 
91 
0 
349 
56 
58 
221,098 
52,598 
151 
42 
73 
333 

Baseline 
2050 
72 
1,345 
189 
144 
65 
124 
24 
119 
238 
35 
28 
7 
27 
20 
95 
0 
324 
50 
52 
201,693 
46,290 
125 
34 
68 
324 

221 

 
      
      
 
  
  
 
 
 
Table 8-42: Value of health impacts for the EU27 per year under the CAO4 Baseline Scenario, 2005-2050. 

Figures in italics represent sensitivity cases. Units: €M per year. 

IMPACTS 

Poll. 

Tier 

Chronic Mortality (30yr +) VOLY 
Chronic Mortality (All ages) VOLY 
Chronic Mortality (30yr +) VSL 
COPD (>30)  
Stroke (30+)  
IHD events (>30)  
Lung cancer (>30)  
Asthma (new incidence, 0-18)  
Chronic Mortality (All ages) VOLY 
Chronic Mortality (30yr +) VSL 
Asthma in adults (19+)  
ALRI in children (0-12)  
Asthma in children (0-18)  
Respiratory hospital admissions (>64)  
Cardiovascular hospital admissions (>64)  
Infant Mortality (0-1yr) VSL 
Bronchitis in children aged 6 to 12  
Respiratory Hospital Admissions (All ages)  
Cardiac Hospital Admissions All ages)  
Restricted Activity Days (all ages)  
Lost working days (15-64 years)  
Bronchitis in children aged 5 to 14  
Respiratory Hospital Admissions (All ages)  
Type 2 diabetes (>30)  
Dementia (60+)  

O3 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
NO2 
NO2 
NO2 
NO2 
NO2 
O3 
O3 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
NO2 
NO2 
PM2.5 
PM2.5 

1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
1 
2 
2 
2 
2 
2 
2 
2 
2 
2 
2 
3 
3 

Baseline 
2005 
7,044 
598,976 

Baseline 
Baseline 
2025 
2020 
6,489 
7,838 
265,182 
348,374 
1,869,596  1,409,074  1,058,394 
14,687 
10,823 
7,230 
1,202 
1,609 
82,021 
342,314 
562 
12 
628 
78 
478 
1,327 
220 
444 
589 
46,799 
14,251 
135 
515 
2,429 
198,991 

16,212 
12,074 
8,081 
1,334 
1,758 
109,788 
470,796 
639 
14 
714 
76 
471 
1,511 
247 
504 
672 
52,628 
16,342 
157 
603 
2,719 
208,262 

24,649 
18,547 
12,556 
2,045 
2,620 
188,419 
649,683 
940 
22 
1,144 
82 
524 
3,925 
411 
834 
1,127 
86,164 
27,757 
234 
988 
4,221 
285,922 

Baseline 
2030 
6,415 
203,438 
862,879 
12,158 
8,847 
5,877 
987 
1,250 
57,492 
252,403 
424 
8 
448 
82 
495 
849 
168 
352 
463 
37,407 
11,102 
97 
372 
1,962 
180,973 

Baseline 
2040 
6,496 
147,194 
702,533 
9,862 
6,953 
4,579 
788 
905 
29,552 
145,594 
238 
4 
232 
92 
534 
442 
125 
267 
344 
28,964 
8,153 
54 
201 
1,548 
163,336 

Baseline 
2050 
6,827 
127,280 
681,896 
9,179 
6,360 
4,173 
729 
823 
22,520 
124,654 
191 
4 
186 
98 
562 
263 
116 
242 
307 
26,422 
7,175 
45 
161 
1,432 
158,622 

Note: mortality values are not additive where VOLY and VSL values are provided for the same age group for any pollutant. 

222 

 
      
      
 
 
  
  
 
 
 
Table 8-43 shows the effects of changing response functions for morbidity between CAO3 and CAO4. Without 
adding in the Tier 3 effects, dementia and to a lesser extent, PM2.5 diabetes, the changes largely cancel each 
other out with costs for the effects listed in the region of €30 billion/year. However, adding dementia increases 
impacts  by  a  factor  6.  Further  research  will  be  needed  to  assess  the  reliability  of  these  functions.  Results 
underline the value of further research. 

Table 8-43: Value of morbidity health impacts for the EU27 per year under the CAO3 and CAO4 Baseline 

Scenario, 2030, where response functions have changed between CAO3 and CAO4 reflecting new 
evidence. Units: €M per year. 

Damage, MEuro/year 
Minor Restricted Activity Days (MRADs all ages) 
COPD (>30) 
Stroke (30+) 
IHD events (>30) 
Lung cancer (>30) 
Asthma (new incidence, 0-18) 
Asthma symptom days (children 5-19yr) 
Asthma in adults (19+) 
ALRI in children (0-12) 
Asthma in children (0-18) 
Type 2 diabetes (>30) 
Stroke (40-89) 
Total for the effects above 
Dementia (60+) 
Type 2 diabetes (>30) 
Total with Dementia and diabetes 

O3 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
PM2.5 
NO2 
NO2 
NO2 
NO2 
NO2 

PM2.5 
PM2.5 

CAO3 
3,834 
9,537 
10,528 
2,095 
676 
26 
284 
1,054 
- 
- 
1,119 
4,022 
33,176 
- 
585 
33,761 

CAO4 
- 
12,158 
8,847 
5,877 
987 
1,250 
- 
424 
8 
448 
- 

30,001 
180,973 
1,962 
212,936 

Note: “-“ identifies cases where a pollutant-effect pair was not quantified. 

223 

 
      
      
 
 
 
  
 
  
  
 
 
A5.2  Economic value of non-health impacts  

Damage to materials, crops, forests and ecosystems under the  Baseline scenario is shown in Table 8-44 to 
Table 8-48. For forests there is a step change in damage from 2030 to 2040. This is not an indication of change 
in harm to forests per se but is linked to a step change in the valuation of carbon sequestration according to 
the DG MOVE Handbook on the External Costs of Transport (CE Delft et al. 2020). 

Table 8-44: Value of the damage to materials under the CAO4 Baseline by country, 2005-2050, €million 

(2015) per year. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 

2005 
60.63 
128.31 
286.85 
29.10 
15.26 
194.31 
28.89 
11.39 
22.84 
306.29 
484.18 
100.01 
72.18 
15.06 
176.77 
4.36 
13.53 
9.59 
5.33 
87.77 
916.78 
14.18 
370.00 
77.42 
27.28 
108.23 
9.11 
3,576 

2020 
30.85 
31.56 
31.61 
7.12 
5.44 
73.00 
12.49 
2.17 
12.29 
102.91 
261.90 
19.02 
38.27 
5.25 
57.99 
2.76 
8.34 
2.29 
0.37 
33.06 
364.58 
3.76 
71.54 
20.95 
6.57 
20.27 
4.41 
1,231 

2030 
15.93 
25.49 
21.68 
4.73 
1.10 
38.30 
9.44 
0.78 
7.40 
72.62 
162.01 
9.22 
25.16 
3.54 
38.05 
1.98 
5.29 
1.42 
0.29 
20.95 
183.61 
2.27 
56.40 
15.22 
4.29 
12.21 
3.02 
742 

2040 
11.37 
18.98 
11.34 
3.57 
0.85 
19.46 
7.19 
0.49 
5.12 
47.93 
99.92 
6.37 
17.57 
2.57 
26.38 
1.55 
3.38 
1.02 
0.21 
17.09 
82.34 
1.53 
33.82 
11.67 
2.05 
9.01 
2.48 
445 

2050 
9.11 
17.43 
9.62 
3.12 
0.79 
16.09 
6.16 
0.43 
4.26 
41.26 
86.59 
5.41 
14.69 
2.33 
21.53 
1.35 
2.74 
0.89 
0.11 
15.14 
72.81 
1.26 
28.66 
10.37 
1.62 
8.23 
2.06 
384 

2025 
22.36 
29.44 
31.99 
5.93 
1.27 
57.03 
10.45 
1.19 
9.58 
91.25 
229.57 
11.36 
30.47 
4.54 
50.56 
2.28 
6.63 
1.86 
0.39 
27.26 
275.26 
2.87 
62.53 
20.78 
5.13 
15.62 
3.30 
1,011 

224 

 
      
      
 
 
 
  
 
 
 
Table 8-45: Value of the damage to crops under the CAO4 Baseline by country, 2005-2050, €million (2015) 

per year. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 

2005 
199 
202 
476 
184 
7 
317 
191 
34 
35 
2,699 
1,575 
521 
665 
19 
2,563 
97 
230 
4 
5 
305 
1,422 
247 
1,405 
154 
27 
1,692 
89 
15,362 

2020 
178 
205 
422 
160 
6 
287 
181 
31 
32 
2,407 
1,465 
454 
590 
17 
2,238 
89 
212 
4 
4 
321 
1,305 
221 
1,270 
136 
23 
1,499 
82 
13,840 

2030 
161 
204 
401 
146 
6 
263 
176 
29 
30 
2,239 
1,372 
424 
544 
16 
2,076 
84 
200 
4 
4 
328 
1,212 
206 
1,211 
124 
21 
1,391 
77 
12,948 

2040 
149 
198 
376 
135 
6 
243 
170 
28 
29 
2,101 
1,285 
401 
502 
16 
1,967 
80 
189 
3 
4 
324 
1,123 
198 
1,143 
113 
19 
1,341 
74 
12,220 

2050 
146 
197 
369 
132 
6 
238 
169 
27 
29 
2,083 
1,265 
397 
491 
16 
1,946 
79 
187 
3 
4 
325 
1,104 
199 
1,125 
111 
19 
1,347 
73 
12,087 

2025 
170 
205 
416 
154 
6 
276 
179 
30 
31 
2,336 
1,428 
440 
570 
17 
2,177 
87 
208 
4 
4 
326 
1,267 
214 
1,253 
131 
22 
1,448 
80 
13,481 

225 

 
      
      
 
  
 
 
 
Table 8-46: Value of the damage to forest production and carbon sequestration under the CAO4 Baseline 

by country, 2005-2050, €million (2015) per year. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 

2005 
1,026 
133 
529 
- 
- 
687 
136 
252 
1,529 
3,043 
3,515 
100 
250 
69 
1,103 
535 
302 
18 
- 
53 
1,867 
- 
1,103 
375 
271 
1,150 
2,064 
20,109 

2020 
945 
131 
490 
- 
- 
644 
133 
236 
1,422 
2,781 
3,385 
89 
230 
67 
977 
502 
285 
19 
- 
58 
1,759 
- 
1,026 
343 
241 
1,051 
1,954 
18,768 

2030 
880 
128 
475 
- 
- 
608 
132 
227 
1,364 
2,642 
3,258 
85 
217 
65 
918 
480 
273 
19 
- 
60 
1,678 
- 
990 
323 
224 
994 
1,888 
17,927 

2040 
1,897 
332 
1,154 
- 
- 
1,344 
347 
585 
3,120 
6,059 
7,525 
204 
505 
172 
2,223 
1,054 
677 
42 
- 
148 
3,965 
- 
2,371 
728 
495 
2,424 
4,134 
41,504 

2050 
1,864 
330 
1,141 
- 
- 
1,322 
346 
579 
3,110 
6,031 
7,444 
202 
496 
174 
2,201 
1,044 
670 
42 
- 
149 
3,921 
- 
2,342 
716 
485 
2,434 
4,112 
41,156 

2025 
916 
130 
486 
- 
- 
629 
133 
233 
1,398 
2,723 
3,340 
87 
224 
66 
955 
494 
281 
19 
- 
59 
1,727 
- 
1,015 
335 
234 
1,024 
1,927 
18,435 

226 

 
      
      
 
  
 
 
 
Table 8-47: Lower bound estimate of the value of the damage to ecosystems under the CAO4 Baseline by 

country, 2005-2050, €million (2015) per year. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 

2005 
118 
42 
254 
146 
4 
85 
18 
39 
51 
955 
221 
237 
124 
- 
324 
52 
57 
4 
0 
27 
438 
80 
350 
110 
54 
911 
84 
4,784 

2020 
82 
39 
221 
121 
4 
68 
18 
27 
31 
758 
188 
237 
101 
- 
223 
46 
56 
4 
0 
23 
366 
68 
327 
98 
40 
852 
74 
4,072 

2030 
52 
37 
211 
114 
4 
46 
18 
25 
11 
648 
167 
237 
89 
- 
185 
41 
55 
4 
0 
22 
313 
64 
319 
92 
32 
817 
70 
3,675 

2040 
37 
33 
193 
111 
4 
29 
18 
24 
4 
571 
150 
237 
86 
- 
172 
36 
53 
4 
0 
21 
259 
64 
305 
87 
28 
807 
65 
3,396 

2050 
32 
29 
188 
109 
4 
22 
18 
23 
2 
548 
143 
237 
85 
- 
164 
33 
52 
4 
0 
21 
249 
64 
296 
84 
27 
799 
61 
3,293 

2025 
71 
38 
219 
119 
4 
59 
18 
26 
21 
719 
180 
237 
95 
- 
208 
45 
56 
4 
0 
23 
342 
66 
325 
96 
36 
835 
71 
3,914 

227 

 
      
      
 
  
 
 
 
 
Table 8-48: Upper bound estimate of the value of the damage to ecosystems under the CAO4 Baseline by 

country, 2005-2050, €million (2015) per year. 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Totals 

2005 
353 
126 
762 
439 
12 
254 
53 
116 
154 
2,865 
663 
711 
371 
- 
972 
155 
170 
13 
0 
80 
1,315 
241 
1,049 
329 
162 
2,734 
253 
14,351 

2020 
245 
117 
664 
363 
12 
204 
53 
82 
92 
2,275 
563 
711 
302 
- 
669 
139 
168 
13 
0 
69 
1,099 
203 
982 
295 
119 
2,556 
222 
12,217 

2030 
157 
111 
634 
343 
12 
139 
53 
75 
33 
1,944 
500 
711 
266 
- 
554 
124 
165 
13 
0 
66 
939 
193 
958 
276 
96 
2,451 
210 
11,024 

2040 
110 
98 
580 
332 
12 
88 
53 
72 
12 
1,713 
449 
710 
258 
- 
515 
107 
158 
12 
0 
64 
778 
192 
915 
260 
84 
2,422 
195 
10,188 

2050 
97 
88 
563 
327 
12 
67 
53 
70 
7 
1,643 
430 
710 
255 
- 
492 
100 
156 
12 
0 
62 
746 
192 
888 
251 
80 
2,396 
183 
9,880 

2025 
214 
115 
656 
358 
12 
177 
53 
79 
64 
2,156 
539 
711 
286 
- 
624 
135 
167 
13 
0 
68 
1,026 
199 
975 
288 
108 
2,504 
213 
11,741 

228 

 
      
      
 
  
 
 
 
 
A5.3 

 Health impacts of PM2.5 and NO2 above WHO Air Quality Guidelines 

The  WHO  Air  Quality  Guidelines  (WHO,  2021)  represent  concentrations  above  which  there  is  greater 
confidence in impact quantification than below. Health impact quantification provided in the main report has 
considered the  full  range  of  concentrations.  An  alternative  to this  assumption  is  examined  in  this  section. 
Health impacts for Tiers 1 and 2 are shown in Table 5-3. Results for materials, crops, forests and ecosystems 
remain as given above in Table 8-44 to Table 8-48. Health damage up to 2025 under the Baseline scenario is 
roughly  half  the  estimates  shown  in  the  main  text  with  no  cut-off  applied.  Thereafter,  the  gap  widens  as 
increasing areas come under the cut-off point and contribute nothing to damage estimates. 

Table 8-49: Economic value of health impacts linked to PM2.5, NO2 and O3 by scenario accounting for 
sensitivity to the approach used for mortality valuation (VOLY, VSL), to the inclusion of NO2 
impacts and to use of the WHO Air Quality Guidelines as cut-off points for analysis. Units 
€ million/year, 2015 prices. Excludes Tier 3 impacts. 

2020 

2025 

2030 

2040 

2050 

712,113 

213,584 

310,143 

1,083,157 

Including all NO2 functions 
Mortality valued using VOLY  
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR  
Including NO2 functions only for adult asthma and child ALRI morbidity 
Mortality valued using VOLY  
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR  
Mortality valued using VSL  
Baseline 
ERC 
ZPAP 
ERC+ZPAP 
MTFR  

869,654 

261,106 

580,095 

182,229 

120,105 
111,757 
108,419 
105,134 
75,526 

411,357 
382,161 
369,272 
357,944 
255,531 

105,476 
98,031 
96,291 
93,026 
66,135 

346,516 
321,324 
315,567 
304,339 
214,102 

48,694 

35,943 

29,335 

25,529 

169,053 

130,319 

95,698 

87,818 

46,539 

34,799 

28,153 

24,668 

158,026 

123,585 

89,678 

82,754 

Overall  benefits  covering  health,  crops,  forest,  materials  and  ecosystems  for  each  scenario  relative  to  the 
CAO4 Baseline in the appropriate year (2030 or 2050), when applying the cut-off concentrations, are shown 
in Table 8-50. VOLY- and VSL-based estimates for health benefit are combined with the Low and High estimates 
for ecosystems, respectively, so show the range of overall estimated benefits.  

229 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8-50: Benefits from reduced PM2.5, NO2 and O3 damage relative to the CAO4 Baseline for 

the EU27. Units: € million/year, 2015 prices. Tier 3 health effects excluded. WHO Air 
Quality Guidelines used as cut-off points for analysis. 

2030 

2040 

2050 

8,685 
12,289 
15,584 
46,214 

Including all NO2 functions 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Including NO2 functions only for adult asthma and child ALRI morbidity 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

29,870 
43,319 
54,670 
158,763 

25,866 
32,182 
43,433 
135,351 

7,782 
9,787 
13,062 
40,976 

21,459 

12,373 

76,963 

45,927 

20,486 

12,090 

71,957 

44,258 

Results show that benefits by scenario increase in order from ERC_2030 to ZPAP, to ERC+ZPAP to MTFR. Going 
forward  in  time,  the  annual  benefits  of  MTFR  are  reduced,  largely  reflecting  action  accounted  for  in  the 
Baseline. Net benefits (benefits – costs) are shown in Table 5-10 and benefit-cost ratios (net benefits divided 
by net costs, taken from Table 5-2 in the main text) in Table 5-11. In all cases except MTFR in 2040 and 2050 
with mortality valued with the VOLY, results demonstrate a strong excess of benefit over further control costs. 
Impacts of the use of the cut-off points are modest in 2030, indicating that the most populated parts of the 
EU will exceed the cut-off points at that time. Proportionally, impacts on the benefit:cost ratios through the 
application  of  the  cut-off  points  increase  over  time,  as  the  area  subject  to  their  exceedance  shrinks  and 
estimated benefits fall. 

It is again stressed that care is needed in interpretation of the results for the MTFR scenarios as they could be 
interpreted  as  demonstrating  a  net  benefit  for  all  measures  included  for  MTFR.  In  reality,  some  of  the 
measures included in the cost curve will be cost-efficient whilst some others are extremely expensive per unit 
emission abated, and on their own may not generate a net benefit either across the EU27 or in all Member 
States. This is suggested by the  decline in benefit-cost  ratios when moving down through scenarios to the 
MTFR. More detailed analysis, involving a series of additional scenarios would be needed to test the costs and 
benefits of the increasingly less cost-effective measures towards the upper end of the cost curve. 

230 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8-51: Net benefits from reduced PM2.5, NO2 and O3 damage relative to the CAO4 Baseline for the 
EU27. Units: €M/year, 2015 prices. Tier 3 health effects excluded. WHO Air Quality Guidelines 
used as cut-off points for analysis. 

2030 

2040 

2050 

8,129 
11,890 
14,786 
23,201 

Including all NO2 functions 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Including NO2 functions only for adult asthma and child ALRI morbidity 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

25,310 
31,783 
42,635 
112,338 

29,314 
42,920 
53,872 
135,751 

7,225 
9,388 
12,264 
17,963 

847 

-7,522 

56,352 

26,033 

-125 

-7,805 

51,345 

24,363 

Table 8-52: Benefit-cost ratios by scenario for the EU27. Tier 3 health effects excluded. 

2030 

2040 

2050 

16 
31 
20 
2.0 

Including all NO2 functions 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL 
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Including NO2 functions only for adult asthma and child ALRI morbidity 
Mortality valued using VOLY  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 
Mortality valued using VSL  
ERC 
ZPAP 
ERC+ZPAP 
MTFR 

54 
109 
69 
6.9 

46 
81 
54 
5.9 

14 
25 
16 
1.8 

231 

1.0 

3.7 

1.0 

3.5 

0.6 

2.3 

0.6 

2.2 

 
      
      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A6. Ad-hoc support to inform the review of the NEC 

Directive  

Within  this  task,  the  team  has  undertaken  further  analysis  and  comparison  of  the  results  of  this  Service 
Request  with  the  previous  Clean  Air  Outlooks  and  as  well  as  of  Member  States’  reported  air  pollutant 
emissions  for  recent  years.  The  aim  is  to  provide  additional  insight  to  the  role  of  policies  and  measures 
resulting from the implementation of the NEC Directive versus other (national or EU level) control policies.  

This analysis provides an additional view on the impact of policies collectively that impact on emission factors 
(including NEC Directive, but also others such as source-specific legislation). It cannot fully isolate the effect of 
the NEC Directive alone.   

The work addresses evaluation, and to the extent possible, disentangling of the (i) effectiveness, (ii) efficiency, 
and (iii) coherence of various EU and national policies introduced and analysed since the implementation of 
the NEC Directive. 

Analysis  of  effectiveness  addresses  the  question  to  what  extent  EU  policies  or  external  factors  affected 
emissions of NECD pollutants. Addressing efficiency answers questions about costs and respective measures 
to implement NEC Directive, what are the costs and respective measures of implementing it, and whether 
other policies or factors have affected the costs of compliance. Finally, discussion of coherence addresses the 
question to what extent has the non-inclusion of CH4 in the NEC Directive hampered reduction of methane 
emissions (from agriculture, waste, energy) at the EU and international level. 

The  initial  analysis  has  been  performed  in  the  context  of  this  service  request  and  was  shared  with  the 
Commission and the contractors of another service request supporting the evaluation of the NEC Directive53. 
The work will continue  including further discussion and additional assessments to support the NEC review 
process under the evaluation-specific service request. 

53 Service request 090202/2024/917478/SFRA/ENV.C.3 on supporting the evaluation of Directive (EU) 2016/2284 on the 
reduction of national emissions of certain atmospheric pollutants (NEC Directive) 

232 

 
      
      
 
 
 
 
 
 
 
A7. References 

Amann M, Bertok I, Borken-Kleefeld J, et al (2011) Cost-effective control of air quality and greenhouse gases 

in Europe: Modeling and policy applications. Environ Model Softw 26:1489–1501. 
https://doi.org/10.1016/j.envsoft.2011.07.012 

CE Delft, Directorate-General for Mobility and Transport (European Commission), Essen H van, et al (2020) 

Handbook on the external costs of transport: version 2019 – 1.1. Publications Office of the European 
Union 

Denby B, Gauss M, Wind P, et al (2020) Description of the uEMEP\_v5 downscaling approach for the EMEP 

MSC-W chemistry transport model. Geosci Model Dev 13:6303–6323 

Simpson,  et al. (2020) How should condensables be included in PM emission inventories reported to 
EMEP/CLRTAP? EMEP Meteorological Synthesizing Centre - West, Norwegian Meteorological 
Institute, Oslo, Norway 

Simpson D, Kuenen J, Fagerli H, et al (2022) Revising PM2.5 emissions from residential combustion, 2005–
2019; Implications for air quality concentrations and trends. Nordic Council of Ministers, 
Copenhagen, Denmark 

WHO (2021) WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen 

dioxide, sulfur dioxide and carbon monoxide. World Health Organization, Geneva 

233 

 
      
      
 
 
 
 
 
234 

 
      
      
 
 
Getting in touch with the EU 

In person 

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You can find the address of the centre nearest you online (european-
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free, for both commercial and non-commercial purposes. The portal also 
provides access to a wealth of datasets from European countries. 

 
 
 
 
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Written by IIASA, EMRC, MET Norway, 
TNO, e-misia, RIVM, Logika Group 
February - 2025 

National InstituteforPublic Health and the EnvironmentMinistryof Health, Welfare and SportNational InstituteforPublic Health and the EnvironmentMinistryof Health, Welfare and Sport