d2.1 report and data on emission inventory at eu- wide

Ref. Ares(2016)1512245 - 30/03/2016

Horizon 2020

Societal Challenge: Improving the air quality and reducing the carbon footprint of European cities

Project: 690105 – ICARUS

Full project title:

Integrated Climate forcing and Air Pollution Reduction in Urban Systems

D2.1 Report and data on emission inventory at EU-wide level for the considered pollutants and GHGs

for the years 2015, 2020 and 2030

WP 2 Integrated emission modelling at the regional and urban scales

Lead beneficiary: USTUTT

Date: January 2018

Nature: Report

Dissemination level: Public

D2.1 – Report and data on emission inventory at EU-wide level for the considered pollutants and GHGs for the years 2015, 2020, 2030

WP2: Integrated emission modelling at the regional and urban scales

Security: PU

Author(s): USTUTT Version: Final revised 2/49

TABLE OF CONTENTS

1 INTRODUCTION ................................................................................................. 4

2 BASELINE INVENTORY FOR THE YEARS 2015, 2020, 2030 .......................... 5

2.1 General methodology ............................................................................................................ 5

2.2 Scenario assumptions and data sources ........................................................................... 6

2.2.1 ECLIPSE V5a emission inventory – IIASA GAINS model ................................................... 7

2.2.2 TRANSPHORM emissions inventory for railway and road transport .................................. 8

2.3 Final data structure and results on national level............................................................ 10

3 SPATIAL DISTRIBUTION OF EMISSIONS ...................................................... 15

3.1 Methodology ........................................................................................................................ 15

3.2 Spatially distributed emissions ......................................................................................... 16

4 TEMPORAL DISAGGREGATION ..................................................................... 18

5 UNCERTAINTY ASSESSMENT ....................................................................... 20

6 CONCLUSION AND OUTLOOK ....................................................................... 21

7 REFERENCES .................................................................................................. 22

8 APPENDIX ........................................................................................................ 24



Security: PU


Document Information

Grant Agreement Number

690105 Acronym ICARUS

Full title Integrated Climate forcing and Air pollution Reduction in Urban Systems

Project URL www.icarus2020.eu

Project Officer Mirjam Witschke - [email protected]

Delivery date Contractual April 2017 Actual January 2018

Status Draft Final revised ✓

Nature Demonstrator Report ✓ Prototype Other

Dissemination level Confidential Public ✓

Responsible Author (Partners)

USTUTT

Responsible Author

Name Dorothea Schmid Email [email protected]

Partner USTUTT Phone

Other partners (Institution)

AUTH

Document History

Name (Institution) Date Version

USTUTT May 2017 First draft

AUTH June 2017 Second draft

USTUTT June 2017 Final

USTUTT January 2018 Final revised

mailto:[email protected]

mailto:[email protected]



Security: PU


1 Introduction

Air pollution causes severe health impacts. Mitigation of air pollution also has effects on climate forcing. Vice versa, climate mitigation also affects air pollution. Energy efficiency measures, for example, have a direct effect on climate forcing as well as on air pollution by changing the underlying activity and thus reducing greenhouse gases as well as all kinds of other related emissions. To be able to assess the effects of policies aiming at reducing air pollution in Europe as well as climate change mitigation in an integrated way, proper knowledge of emission sources of greenhouse gases and air pollutants is necessary. For this purpose, a complete emission inventory comprising all major anthropogenic emission sources for Europe (EU28 + Switzerland + Norway) was built up as described in this deliverable. The emission inventory is based on an activity-emission-factor matrix, which contains different kinds of activities for each emission source category (e.g. fuel input to combustion processes) and corresponding emission factors for various air pollutants and greenhouse gases. Emissions are then estimated by multiplying activity levels with the related emission factors. This approach allows to simulate both technical measures affecting emission factors and non-technical measures changing human behaviour and thus underlying activity levels.

Using these emissions in regional air quality models allows to predict air quality and health impacts. Such models need emission input data with high spatially and temporal resolution. Therefore, emissions are spatially distributed across Europe. Starting from the national level of emissions, gridded emissions are derived on two different resolutions: 5km by 5km to be used on European level and 1km by 1km for city-level analysis, i.e. to consider local effects of city measures. For the temporal resolution typical release profiles for different source categories are provided. These can be easily applied to both emission grids.

The resulting dataset contains European-wide gridded emissions to be used as a baseline for policy simulation reflecting the current level of air pollution and greenhouse gas emissions as reflected in the emission inventory for the year 2015. To be able to consider long-term effects and future policies, the dataset also comprises two emission inventories for future years (2020 and 2030), representing a business-as-usual case, i.e. only currently implemented and already decided future policies to reduce air pollution are taken into account.

The aim of this deliverable is to provide a description of the resulting datasets. This includes the overall methodologies and data sources used to build the activity-emission-factor matrices (milestone 5) and thus the final emission inventories. Additionally, further possible improvements and updates are briefly presented.



Security: PU


2 Baseline Inventory for the years 2015, 2020, 2030

2.1 General methodology

The developed emission inventories are based on an activity-emission-factor-database (milestone 5) comprising all major anthropogenic emission sources for EU28 plus Norway and Switzerland on national level. The general approach follows the EMEP/EEA Air pollution emission inventory guidebook (EMEP/EEA 2016). Basically, emissions of different species are estimated by multiplying respective activity levels with appropriate activity-specific emission factors:

(1)

The final emission source structure follows the Nomenclature for Reporting 2009 Standard (NFR09), as described in (EMEP/EEA 09). The simplest approach would contain only one activity per source category on national level. However, whenever possible, each source category comprises multiple activities to improve results implementing highly disaggregated, i.e. source specific, activity data e.g. per fuel type, relevant technologies, street networks or driving patterns. Overall, eighteen pollutants are considered in the baseline emission inventory:

Three greenhouse gases:

o Carbon dioxide (CO2)

o Methane (CH4)

o Nitrous oxide (N2O)

Nine “classical” air pollutants:

o Particulate matter: PM2.5, PM10, Black Carbon (BC), Organic Carbon (OC)

o Carbon monoxide (CO)

o Sulphur dioxide (SO2)

o Nitrogen oxides (NOx)

o Ammonium (NH3)

o Non methane volatile organic compounds (NMVOC)

Four heavy metals:

o Arsenic (As)

o Cadmium (Cd)

o Mercury (Hg)

o Lead (Pb)

PCDD/F as indicator for dioxins and furans

Benzo(a)pyrene as indicator for polycyclic aromatic hydrocarbons1

In the following, the underlying models and datasets comprising emission factors and activities are briefly described.

1 For iron and steel industry, Total 4 PAHs as given by the EMEP/EEA Guidebook (2016) are used instead.



Security: PU


2.2 Scenario assumptions and data sources

The constructed emission inventory is based on different models including projections of future air pollutants and greenhouse gas emissions. The chosen scenario represents a business-as-usual case reflecting current legislation and agreed-on future policies as of 2013. With respect to climate mitigation on European level this means that all policies in context of the 2020 Climate & Energy Package2 from the European Commission, which sets a 20% cut in greenhouse gas emissions compared to 1990 levels for 2020, are included while the 2030 climate and energy framework3 which was set into place in 2014 is not considered explicitly. This policy package strives for a 40% reduction of greenhouse gas emissions in 2030. In principal, the constructed baseline scenario is rather conservative including no further attempts on climate mitigation. Thus, the 2020 targets are met, while the 2030 targets are slightly missed, which is also in line with the latest EU Reference Scenario 2016 (Capros 2016). Accordingly, the general aim of not exceeding an increase in mean temperature of more than 2° C at the end of the century is not part of the baseline scenario, but will be analysed in respective policy scenarios. Similarly, the market penetration of electric vehicles is assumed to stagnate on today’s level, i.e. the share of vehicle kilometres for electric vehicles in 2020 and 2030 is the same as 2015. This provides the necessary flexibility to model and analyse the impact of different measures promoting electric transportation modes. With regard to air pollution mitigation policies, national emission control legislations as well as EU-wide legislations are taken into account. Hence, considered policies comprise the Clean Air Policy Package 20134, including the new National Emission Ceilings Directive, on European level as well as national legislation and practices, e.g. the ban on landfill of biodegradable waste or special subsidy schemes for renewables in some European countries (see also TSAP Report #11, Amann et al. 2014).

As mentioned before, the developed emission inventory is based on different models and datasets. The next two chapters describe briefly the used datasets, namely the Eclipse V5a Scenario5 as implemented in the GAINS model (IIASA)6 and the baseline dataset from the TRANSPHORM project7 for road transport and railway.

2 https://ec.europa.eu/clima/policies/strategies/2020_en 3 https://ec.europa.eu/clima/policies/strategies/2030_en 4 http://ec.europa.eu/environment/air/clean_air_policy.htm 5 http://www.iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5a.html 6 http://gains.iiasa.ac.at/gains/EUN/index.login 7 http://transphorm.eu/

https://ec.europa.eu/clima/policies/strategies/2020_en

https://ec.europa.eu/clima/policies/strategies/2030_en

http://ec.europa.eu/environment/air/clean_air_policy.htm

http://www.iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5a.html

http://gains.iiasa.ac.at/gains/EUN/index.login

http://transphorm.eu/



Security: PU


2.2.1 ECLIPSE V5a emission inventory – IIASA GAINS model

For all source categories except railway and road transport, activity data and emission factors are based on the ECLIPSE V5a Scenario8 as implemented in the GAINS model developed by IIASA. The dataset was developed within the EU FP7 project ECLPISE9, with the aim to provide a global baseline emission inventory to assess the effect of short-lived pollutants with a focus on climate change mitigation and air quality. Similar to its predecessor, global CO2 emissions follow the RCP6.010 scenario. For the European Union, policy assumptions and data reflect the latest NEC revision scenarios as described in TSAP Report #11 (Amann et al. 2014) and TSPA Report #16 (Amann et al. 2015). Activity data and projections at national level are based on the PRIMES Reference Scenario 2013 (Capros 2013) for the energy sector and the corresponding CAPRI activity data for agriculture as well as on national data as provided by Member States in bilateral consultations. In general, all adopted EU-wide and national policies as of 2013 are thus included. Details on considered policies and measures regarding energy, climate and agricultural policies are provided in Capros (2013). TSAP Report #14 (Amann et al. 2014) contains an additional list of considered emission control legislation for non-CO2 greenhouse gas emissions and air pollutants. Overall the scenario comprises policies addressing energy efficiency, power generation and energy markets, cross-sectoral climate policies such as the EU ETS directive(s), transport related policies, policies related to infrastructure and innovation and environmental policies on EU-level as well as national measures, such as national subsidy schemes for renewable energy, ban of nuclear power plants in some countries or the ban on landfill of biodegradable waste in Austria, Germany, Denmark, Netherlands and Sweden.

This dataset was chosen as a basis since activity data and corresponding emission factors can be accessed separately (via the GAINS model interface). Additionally, the underlying activity data contains a relatively high level of disaggregation with respect to technology and emission factors consider all relevant technical mitigation measures such as filters etc., reflecting the best available techniques (BAT) requirements for industrial processes as proposed by the Industrial Emissions directive and the directive on Industrial Emissions from large combustion plants. Finally, it is important to note that biofuels are treated as being CO2-neutral.

Since the IIASA Gains model does not include heavy metals, emission factors for the considered heavy metals, benzo(a)pyrene and PCDD/F are based on the EMEP/EEA Guidebook (2016). Activities related to product and solvent use do not emit any heavy metals, benzo(a)pyrene or PCDD/F. In agriculture, only agricultural waste burning is causing any of these emissions. For aviation, possible heavy metal emissions are seen as negligible and thus are not taken into account. The EMEP/EEA Guidebook does not include any emission factors for navigation that fit to the activity data, instead emission factors for heavy metals, benzo(a)pyrene and PCDD/F are taken from the German Inventory Information Report 201711 as well as Cooper (2004). For processed based emissions in iron and steel industry, no benzo(a)pyrene emission factors were available. Instead, emission factors for Total-4-PAHs as given by the EMEP/EEA Guidebook (2016) are used.

8 http://www.iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5a.html and http://www.iiasa.ac.at/web/home/research/researchPrograms/air/Global_emissions.html 9 http://eclipse.nilu.no/language/en-GB/ProjectOverview.aspx 10 Representative concentration pathways (van Vuuren et al. 2010). 11 http://iir-de.wikidot.com/1-a-3-b-vi-emissions-from-tyre-and-brake-wear (last accessed: 13.12.2017)

http://www.iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5a.html

http://www.iiasa.ac.at/web/home/research/researchPrograms/air/Global_emissions.html

http://eclipse.nilu.no/language/en-GB/ProjectOverview.aspx

http://iir-de.wikidot.com/1-a-3-b-vi-emissions-from-tyre-and-brake-wear



Security: PU


2.2.2 TRANSPHORM emissions inventory for railway and road transport

For road transport and railway, a more detailed activity disaggregation than provided by the GAINS model is necessary, especially to model activity-changing measures. Therefore, activity-emission-factor matrices developed within the EU FP7 project TRANSPHORM12 are used (van der Gon 2012). Within this project detailed bottom-up emission inventories for the transport sector in 2005, 2020 and 2030 have been developed. Both, railway and road transport activity data are based on the TREMOVE13 model. The exact data structure of the activity data and model version used in TRANSPHORM is described in more detail in De Ceuster (2011). Table 1 gives an overview of the levels of disaggregation and their respective possible values. All activity values are given in vehicle kilometres.

Table 1: Disaggregation of activity data in TREMOVE.

level of disaggregation

country AT, BE, BG, CH, CZ, CY, DE, DK, EE, ES, FI, FR, GR, HU, HR, IT, IE, LT, LU, LV, MT, NL, NO, PL, PT, RO, SE, SK, SI, UK

region metropolitan, other urban, non-urban

network urban road, rural road, motorway, rail

period peak, off peak

vehicle category and vehicle type

passenger train (railway and locomotive), freight train (railway and locomotive), metro/tram, bus, heavy duty truck (4), light duty truck, van, motorcycle, moped, car by engine size

fuel type diesel, gasoline, LPG, CNG, electric, train diesel

vehicle technology e.g. EURO5, EURO4 etc.

vkm vehicle-km / year

Since the dataset does not comprise the year 2015, activity data for this year was built based on statistical information. For railway, person-kilometres for 2014 from the statistical pocket book 2016 (EU 2016) were extrapolated to 2015 based on their yearly average growth rate from 2005 to 2014. In a second step, these person-kilometres were then transformed to vehicle kilometres by applying occupancy and load factors taken from TREMOVE 3.3.2 since this is the latest publicly available version of the model. The modal split to fuel type, region, period and vehicle technology is assumed to be the same as in the TRANSPHORM 2020 data.

For road transport a similar approach was undertaken. Since in this case, electric vehicles needed to be included as well, the number of vehicles instead of person-kilometres were taken from statistics. For all non-electric vehicles, 2014 data from the statistical pocketbook 2016 (EU 2016) was again extrapolated based on the yearly average growth rate between 2005 and 2014. Electric vehicle

12 http://transphorm.eu/language/en-US/Abouttheproject.aspx 13 http://www.tmleuven.be/methode/tremove/home.htm

http://transphorm.eu/language/en-US/Abouttheproject.aspx

http://www.tmleuven.be/methode/tremove/home.htm



Security: PU


numbers, i.e. numbers for battery electric vehicles and plug-in hybrids, were taken from the European Alternative Fuels Observatory (EAFO)14. Electric vehicles only replace diesel and gasoline cars (though all engine sizes and technologies), mopeds, all kinds of light duty vehicles and only urban buses. Hybrids are not modelled explicitly and can be assumed to be part of EURO 5 and EURO 6 (incl. EURO 6d-TEMP and EURO 6d) vehicles. To derive vehicle-kilometres, the annual mileage per type of vehicle taken from TREMOVE 3.3.2 is applied. For electric vehicles the same average annual mileage is assumed as for conventional engines. When necessary, a split to different vehicle types was performed based on the corresponding factors from TREMOVE 3.3.2. The differentiation between different engine sizes, fuel types and technologies is based on an average factor from TREMOVE 3.3.2 2015 data and TRANSPHORM 2020 data to also account for EURO6 heavy duty vehicles which are not part of TREMOVE 3.3.2. Additionally, EURO 3 and EURO 4 motorcycles as well as EURO 6 gasoline cars are taken into account based on the age distribution of vehicles in TREMOVE 3.3.2 (relative share in vehicle numbers). With the same approach EURO 6d-TEMP and EURO 6d diesel and gasoline vehicles are added to also account for the new real-driving emission limits for NOx. Since the data set reflects a rather conservative scenario it is assumed that EURO 6d-TEMP only applies for cars sold in 2019 when the standard has to be met for all newly sold cars. Following the same principle, EURO 6d vehicles are only included for the year 2030, entering the market in 2021. For the split on region, network and period, the same driving patterns as in TRANSPHORM 2020 are assumed. With respect to the market penetration of electric vehicles until 2030, the scenario is assumed to be extremely conservative, i.e. electric vehicles have exactly the same share in vehicle-kilometres in 2020 and 2030 as in 2015. This reflects a scenario in which no further innovation in electric vehicles takes place. Although this seems to be not realistic, policies promoting electric vehicles are either only implemented recently or still under discussion and the development of the future market is highly uncertain. Hence, the pessimistic starting point allows to model any kind of these policies as initial mitigation strategies without any interference by uncertain assumptions about the future.

Besides the activity data, emissions factors partly needed to be updated as well. Emission factors for railway are based on the EMEP/EEA Guidebook (2016). For heavy metals, benzo(a)pyrene and PCDD/F, the corresponding emission factors are taken from UK NAEI15. Emission factors for road transport are mainly based on the COPERT 516 model, including updated NOx emission factors for diesel cars and differentiated factors for EURO 6, EURO 6d-TEMP and EURO 6d. Whenever the structure of emission factors and activity data does not match, average emission factors are applied or activity data is aggregated accordingly, resulting in the final emission data structure as described below. Besides exhaust emissions, non-exhaust emissions, i.e. NMVOC emissions from evaporation and Particulate matter as well as heavy metal and benzo(a)pyrene from tyre wear and abrasion, are also considered. Non-exhaust organic and black carbon emission are taken from the original TRANSPHORM database as provided by the Laboratory of Applied Thermodynamics (LAT, Aristotle University of Thessaloniki)17, since COPERT does not include a non-exhaust component for these pollutants. A description of this database is provided in Samaras (2013). For particulate matter two-

14 http://www.eafo.eu/eu Fleet, last updated 2016. 15 http://naei.beis.gov.uk/data/ef-all-results?q=103997 (last accessed: 13.12.2017) 16 http://emisia.com/products/copert/copert-5 (last accessed: 13.12.2017) 17 Can be access via ftp://transphorm:[email protected]

http://www.eafo.eu/eu

http://naei.beis.gov.uk/data/ef-all-results?q=103997

http://emisia.com/products/copert/copert-5

ftp://transphorm:[email protected]/



Security: PU


thirds of non-exhaust emissions are assumed to come from tyre and break wear and one-third from road-abrasion. The split for heavy metals is done according to the ratio as reported in the German Inventory Information Report 201711. This is also the source for benzo(a)pyren non-exhaust emission factors. With respect to biofuels, 2009 fuel-mix standards are applied since there is no appropriate differentiation in activity data.

2.3 Final data structure and results on national level

The final datasets contain activities as well as corresponding emission factors for all major anthropogenic emission sources. As already mentioned the emission source structure follows the NFR 2009 standard. Table 9 in the appendix list all NFR 2009 sectors that are directly included as well as source categories which are already part of other categories and thus not included separately to avoid double counting. Since resuspension in road traffic is not part of the NFR 2009 sector structure, it is not included in the emission inventory. If necessary it can, however, be added separately.

All countries are abbreviated by their ISO-ALPHA2 code. Basically, activity data for all sectors18 follows the same structure, always containing one or several attributes which uniquely describe a certain activity for a certain country, sector and year. Table 2 contains a more detailed description of the individual attributes for each source category. The data containing emission factors are structured accordingly containing all relevant activity attributes (including activity unit, country and year) or a dedicated column to match activity data (Key_EF).

Table 2: Structure and considered levels of disaggregation.

source category attribute description

all source categories except transport

Sector_code NFR 2009 code

Country ISO-ALPHA 2 standard country code

Year year to be considered

Activity attribute 1 (abbreviation) First level of differentiating different activities for the same NFR sector. Varies from sector to sector and depends on kind of activity, e.g. fuel type for combustion activities.

Activity attribute 1 description Long name or short description of first activity attribute.

Activity attribute 2 (abbreviation) Second level of differentiating different activities for the same NFR sector. Varies from sector to sector and depends on kind of activity, e.g. technology for combustion activities.

Activity attribute 2 description Long name or short description of second activity attribute.

Activity_level amount of/value of activity, e.g. consumed fuel.

unit Unit of activity, e.g. PJ for energy related activities.

Key_EF Consisting of both activity attributes. Unique key to match corresponding emission factors.

18 Though non-exhaust emissions from road transport (road abrasion and tyre wear) are seen and handled as separate sectors, they are based on the same activity data as exhaust emissions. The sector code is adapted accordingly when calculating emissions.



Security: PU


source category attribute description

Road transport Sector_code18 NFR 2009 code



Vehicle_category First level of differentiation (personal cars, heavy duty vehicles, buses, light duty vehicles, motorcycles). Mainly corresponds to NFR sectors.

Vehicle_type Second level of differentiation. Mainly corresponds to engine size and vehicle weight.

Fuel_type Third level of differentiation. Type of fuel used (diesel, gasoline, CNG, LPG and electricity).

Vehicle_technology Forth level of differentiation. Emission standard (EURO1-6) or engine type (battery electric vehicle, plugin hybrid).

Driving_mode Fifth level of differentiation considering driving conditions (urban peak, urban non-peak, rural and highway).

Activity_level amount of/value of activity, in this case vehicle kilometre per year

unit Unit of activity (vkm)

Railway Sector_code NFR 2009 code



Vehicle_category First level of differentiation (passenger train, freight train, metro and tram)

Vehicle_type Second level of differentiation. Mainly corresponds type of train (railcar, locomotive, metro and tram)

Fuel_type Third level of differentiation. Type of fuel used (diesel, electricity).

region Forth level of differentiation (metropolitan, other urban and non-urban)

period Fifth level of differentiation considering off-peak (OP) and on-Peak (PK) activities.

Activity_level amount of/value of activity, in this case vehicle kilometre per year

unit Unit of activity (vkm)

Aviation and Navigation Sector_code NFR 2009 code



network First level of differentiation (domestic aviation – LTO, international aviation – LTO, Inland waterways, Coastal shipping – national)

Vehicle_type Second level of differentiation (Airplane, Ship, Medium vessel, large vessel)

Fuel_type Third level of differentiation. Type of fuel used (diesel, heavy fuel oil, gasoline/kerosene). Only abbreviation.

fuel_description Type of fuel used.

Activity_level amount of/value of activity, in this case fuel consumption

unit Unit of activity (PJ)



Security: PU


Figure 1 to Figure 4 present total emissions for all considered species and years. If emission reduction is analysed on country level, reduction levels vary significantly between different countries with some countries even increasing less regulated emissions such as NH3. This is also true for total greenhouse gases as depicted in Figure 1. Huge emitters in 2015 such as Germany and the United Kingdom are reducing their emissions constantly over time, while others such as Poland are increasing them in 2020 before finally decreasing emissions in 2030. Other countries like Spain are even increasing their greenhouse gases in absolute terms. This can be partly explained by the applied burden sharing within the EU28 to reach their overall reductions targets, allowing individual countries to still increase greenhouse gases to enable better economic growth while others have to decrease even more.

Figure 1: Total GHG emissions in CO2-equivalents19

per country for 2015, 2020 and 2030.

19 Applying global warming potential according to the IPCC 5th Assessment Report (28 for CH4, 265 for N2O). See also GHG protocol (2016).

0

100

200

300

400

500

600

700

800

900

AT

B

E

BG

C

H

CY

C

Z

DE

D

K

EE

E

S

FI

FR

G

R

HR

H

U

IE

IT

LT

L

U

LV

M

T

NL

N

O

PL

P

T

RO

S

E

SI

SK

U

K

Mt Total GHG - emissions (CO2-eq.)

2015 2020 2030



Security: PU


Figure 2: Total emissions of “classical” air pollutants for all countries for 2015, 2020, 2030.

Figure 3: Total heavy metal and PAH emissions for all countries for 2015, 2020, 2030. Total 4-PAH emissions only represent process emissions from the iron and steel industry (instead of benzo(a)pyrene).

0

5

10

15

20

SO2 CO NH3 NMVOC NOX PM10 PM2.5 OC BC

Mt Total Emissions EU28+2

2015 2020 2030



Security: PU


Figure 4: Total PCDD/F emission for all countries for 2015, 2020 and 2030, expressed in “International Toxic Equivalents” (I-TEQ) as defined by the NATO.

For EU28+2 all species except heavy metals are reduced over time. The reductions from 2015 to 2030 range between 34 % and 39 % and occur for BC, NOx and SO2. On the other hand, lead is increased by roughly 14%, reaching the highest emissions levels for heavy metals. Despite new estimates for NOx emission factors in road transport, the high reduction levels need to be taken with care, since it is still uncertain to which degree the new real driving emission limits will be met and how big their effect will be.

0

1000

2000

3000

4000

5000

2015 2020 2030

g I-TEQ

Total PCDD/F Emissions - EU28+2



Security: PU


3 Spatial distribution of emissions

3.1 Methodology

Air quality models calculate concentration changes due to emissions on a fine grid. Hence, emission scenarios with a high spatial resolution need to be available as input data. To obtain the necessary input data, the baseline emission inventories on national level are spatially distributed using different sets of proxy data. Figure 5 depicts the general methodology.

Figure 5: General methodology of spatially distributing emissions.

Starting form national total emissions per NFR09 sector, emission sources are first distributed to administrative units (NUTS3 regions20, data attribution) by applying geo-referenced statistics. In a second step (spatial distribution), the emissions on NUTS3-level are then distributed to a 5km by 5km as well as 1km by 1km grid to allow even finer resolution on city level. For both steps different kind of proxy data sets are used. Additionally, the proxy data depends on the kind of sources. For (large) point sources such as power plants and industrial sources, spatial distribution is relatively accurate relying only on actual production figures and geographic coordinates. Line sources, such as transport, are usually distributed by using respective traffic and transportation statistics and transport networks (e.g. railways, roads, rivers). For area sources and diffuse emissions population data, such as industry and service specific employee numbers and population density, as well as different statistics, such as animal numbers or product use data, are used to attribute emission data to NUTS3-regions. The spatial allocation to each grid cell is then achieved by using respective land use data. A detailed description of the used model and kind of proxy data for each NFR09 sector can be found in Thiruchittampalam (2014) and Theloke et al. (2011).

20 Nomenclature des unités territoriales statistiques as used in European statistics.



Security: PU


3.2 Spatially distributed emissions

The spatially distributed emissions contain two different resolutions: 5 km by 5 km for regional analysis and 1 km by 1km for urban analysis on city level. Both data sets have the same data structure as depicted in the following table.

Table 3: Data structure of spatially distributed emissions file.

column header description

GRID_ID_5km / GRID_ID_1km Unique identifier for each grid cell

Country_code ISO-ALPHA2 standard country code

NFR NFR Sector code level 2

Pollutant_name Chemical symbol/abbreviation of pollutant

Year_ year of emission

Emi_5km / Emi_1km value of emission per grid cell

Unit unit of emission

The files contain all considered pollutants except black carbon and organic carbon. Since these two are part of PM2.5 and thus are supposed to originate from the same geographic source, their percentage of PM2.5 per country, year and NFR sector is provided instead (Appendix Table 10). The value per grid cell can then easily be calculated by multiplying the respective percentage with PM2.5 emissions per grid cell.

The following figure depicts – exemplary for results from the gridding procedure – gridded NOx emissions (5 km by 5km) from residential combustion activities (1 A 4 b i and 1 A 4 b ii) for the years 2015 and 2030. Cities such as Berlin, Paris or London can easily be identified as hot-spots. Additionally, the emission reduction from 2015 to 2030 as well as its distribution becomes apparent when comparing the two grids. Overall, NOx emission levels in 2030 from residential combustion are clearly reduced with the 2030 grid appearing to be overall greener. Nevertheless, cities are still hot-spots, becoming even more visible in 2030.



Security: PU


Figure 6: Gridded NOx emissions from residential combustion (incl. mobile) for 2015 and 2030.



Security: PU


4 Temporal disaggregation

Emissions and their environmental effects do not only rely on the geographic location of their source, but also vary with time since the underlying activities are time-dependent. Using heating systems in households, for example, is highly influenced by ambient air temperature. Hence, emissions from heating are generally higher in winter than in summer. To achieve a high temporal resolution of up to hourly emissions, different kind of indicators are used for different source categories. The following table list typical indicators for different source categories.

Table 4: Typical indicators used for temporal disaggregation of emission data. According to Lenhart and Friedrich (1995).

Sector Indicator Data for Monthly

Resolution

Indicator Data for Daily

Resolution

Indicator Data for Hourly

Resolution

Power plants Fuel use

Temperature

Load curves Load curves

Industrial combustion

plants

Production rate

Fuel use

Temperature

Working times

Holidays

Working times

Small combustion plants Fuel use

Temperature

User behaviour User behaviour

Refineries Fuel use

Oil throughput

Working times

Holidays

Working times

Shift times

Industrial processes Production rate Working times

Holidays

Working times

Shift times

Road transport Traffic counts Traffic counts Hourly traffic counts

According to Thiruchittampalam (2014), it is possible to describe the temporal variation of emissions over a year by using three different kind of profiles: monthly, weekly and hourly release profiles. Monthly profiles depict seasonal changes. For energy related sectors such as electricity and heat supply or combustion in households, these variations can be described by their temperature-dependency. For other sectors only sector specific data is used, for example traffic census data for road transport. Weekly profiles reflect the daily variation during a typical week. These profiles are partly influenced by culture. In general, two different groups of activities are present. The first group comprises activities which have a lower activity level on weekends and public holidays, while the second group of activities shows a rather constant level over the week. The last type of profiles describes the typical hourly variation over a day. Usually, there is also a difference between hourly profiles for weekdays, Saturdays and Sundays as well as for holidays. In general, the temporal resolution of emissions is described by the following formula (Thiruchittampalam 2014):



Security: PU


(2)

With: E = Emission; x = country and sector specific yearly and monthly activity; y = country and sector specific daily activity; z = country and sector specific hourly activity; n = number of days per month and year; I = grid cell; s = sector; h = hour; c = country

The provided monthly, weekly and daily profiles are based on representative profiles developed within GENEMIS (Friedrich 2004) and describe a typical temporal variation of emissions. Thus, the profiles can be applied generically for all years. While this means, that the temporally disaggregated emissions do not describe the actual course of emissions over a year, this method still provides a good enough estimate to improve air quality modelling. The files contain quotas for each month of the year, each day of the week and each hour of the day. Each quota is derived as the respective month, day, hour in relation to the average monthly, daily, hourly contribution. The emissions for a certain hour within a year can then be calculated by simply multiplying yearly emissions by the respective monthly, daily and hourly quotas and finally dividing the result by 8760.

The profiles are made available as .csv-files using a semicolon as value separator with all fields being enclosed by double quote characters. They contain quotas per NFR level 1 sector for EU28+ countries and can be access via ftp://ftp.ier.uni-stuttgart.de/ICARUS/ for project-internal use only. The following table describes the data structure and contained information of each file.

Table 5: Data structure of temporal profiles.

File name column header description

monthly_profiles_NFR.txt Country_id ISO-ALPHA 2 standard country code

Country English country name

NFR Sector code (NFR level 1)

NFR_Description Long name of NFR sector

“1” … “12” monthly quotas with column “1” standing for January and “12” for December

weekly_profiles_NFR.txt Country_id ISO-ALPHA 2 standard country code



SNAP Sector code (Selected Nomenclature for Air Pollution)

“1_” … “7_” daily quotas with column “1_” standing for Monday and “7_” for Sunday

hourly_profiles_NFR.txt Country_id ISO-ALPHA 2 standard country code



NFR_Description Long name of NFR sector

“1” … “24” hourly quotas with column “1” standing for 0:00 – 1:00 and “24” for 23:00 – 24:00

ftp://ftp.ier.uni-stuttgart.de/ICARUS/



Security: PU


5 Uncertainty assessment

Both activity data and emission factors are subject to uncertainties. The provided emission inventory combines detailed bottom-up emissions with more general emission data, depending on the relevance of activity in a specific sector and overall available data. Future activity data and emission factors are derived by employing multiple, well established and highly complex models which are supposed to reflect reality well. Nevertheless, they still depend on many assumptions about the future development of political situations and economy as well as technical innovations and thus only represent one possible future out of many. Hence, all future projections, especially in the case of long-term emission reduction, come with a high degree of uncertainty which highly depends on the accuracy of assumptions. Due to the number of models involved and the different approaches in emission calculation for different source categories, a detailed uncertainty analysis is complicated and out of scope of this task. According to the EMEP/EEA Guidebook (EMEP/EEA 2016), the default uncertainty for activity data ranges between zero and 100%, depending on the kind of data sources. Uncertainty ranges for emission factors are more difficult to obtain and depend on the source category as well as the used estimation method and variability of underlying experimental data. Depending on the estimation method, uncertainty can range between 10% and the respective order of magnitude (EMEP/EEA 2016). More detailed uncertainty analysis may be found in corresponding publications about the respective individual models and assumed scenarios (e.g. Kouridis et al. 2009). The spatial and temporal resolution of the national, annual emissions adds another uncertainty. Depending on the kind of emission source and used proxy data, uncertainty varies between different sectors and source categories. While the spatial distribution of point sources, using only site-specific data, can be seen as fairly accurate, other proxy data such as population density for combustion in households, are less accurate and thus have a higher uncertainty. Thereby, the finer the grid, the higher the associated uncertainty. An exemplary assessment of the uncertainty of the used method for spatial and temporal disaggregation is described in Thiruchittampalam (2014).



Security: PU


6 Conclusion and outlook

This report describes a common baseline emission data set on European level for the years 2015, 2020 and 2030 with a high spatial resolution of up to 1km by 1km covering all EU28 countries as well as Norway and Switzerland. Temporal disaggregation to hourly emission values is achieved by providing monthly, weekly and daily profiles on NFR-sector levels. Overall, this baseline emission inventory reflects a business-as-usual scenario and allows to evaluate the effects of future European wide policies and measures on air quality by modelling relative emission changes. The underlying activity-emission-factor-database provides a rich tool to easily model technical measures, only affecting emission factors, as well as non-technical measures, changing activity levels. Different measures can thus easily be applied individually or combined as measure bundles. Currently, the emission inventory comprises fifteen air pollutants and three greenhouse gases:

- Particulate matter: PM2.5, PM10, Black Carbon (BC), Organic Carbon (OC)

- Carbon monoxide (CO)

- Sulphur dioxide (SO2)

- Nitrogen oxides (NOx)

- Ammonium (NH3)

- Non methane volatile organic compounds (NMVOC)

- Heavy metals: Arsenic (As), Cadmium (Cd), Mercury (Hg), Lead (Pb)

- PCDD/F as indicator for dioxins and furans

- Benzo(a)pyrene as indicator for PAHs

- Carbon dioxide (CO2)

- Methane (CH4)

- Nitrous oxide (N2O).

By following the NFR sector structure, additional pollutants can easily be added by applying a tier one approach as described in the EMEP/EEA Guidelines (2016). With its high spatial resolution, it is also possible to extract top-down emission inventories for city-areas from the European dataset. Hence, it is possible to analyse the effects of urban measures, which only affect areas with high population densities, separately. Nevertheless, a top-down emission inventory is still not suitable to analyse urban air quality management in detail as has been shown by several previous EU funded projects (e.g. the FAIRMODE21, López-Aparicio et al. 2017). Hence, bottom-up emission inventories for cities are built in WP2.2 within ICARUS with a focus on the key emission sources in cities such as transport and residential combustion. The top-down databases can, however, provide the necessary structure and default emission factors of good quality as well as an initial basis of comparison for city emission inventories. To best reflect most recent policies and their effects, additional adaptions to the European emission inventory can be necessary. These changes can either be directly included in the baseline scenario or modelled as initial measures. With respect to climate mitigation strategies, this mainly concerns all policies related to the 2030 Climate and Energy Framework. This may especially be important when it comes to developing visions for climate and environmentally friendly cities with a time horizon expanding after 2030.

21 FAIRMODE: Forum for air quality modelling in Europe (http://fairmode.jrc.ec.europa.eu/)

http://fairmode.jrc.ec.europa.eu/



Security: PU


7 References

Amann, M.; Bertok, I.; Borken-Kleefeld, J.; Cofala, J.; Heyes, C.; Hoglund-Isaksson, L.; Kiesewetter, G.; Klimont, Z.; Schöpp, W.; Vellinga, N.; Winiwater, W. (2015): Adjusted historic emission data, projections, and optimized emission reduction targets for 2030 – A comparison with COM data 2013. Part A: Results for EU-28. TSAP Report #16A, version 1.1. IIASA, Laxenburg, Austria.

Amann, M.; Borken-Kleefeld, J.; Cofala, J.; Hettelingh, J.; Heyes, C.; Höglund-Isaksson, L.; Holland, M.; Kiesewetter, G.; Klimont, Z.; Rafaj, P.; Posch, M.; Sander, R.; Schöpp, W.; Wagner, F.; Winiwarter, W. (2014): The Final Policy Scenarios of the EU Clean Air Policy Package. TSAP Report # 11, Version 1.1a. IIASA, Laxenburg, Austria.

Capros, P. (2013): EU Energy, Transport and GHG Emissions Trends to 2050 – Reference Scenario 2013. European Commission, Directorate-General for Energy, Directorate-General for Mobility and Transport, Brussels, Belgium.

Capros, P. (2016): EU Energy, Transport and GHG Emissions Trends to 2050 – Reference Scenario 2016. European Commission, Directorate-General for Energy, Directorate-General for Mobility and Transport, Brussels, Belgium.

Cooper, D. (2004): HCB, PCB, PCDD and PCDF emissions from ships. IVL rapport. IVL Svenska Miljöinstitutet AB.

De Ceuster, G. (2011): TRANSPHORM Europe 2005-2020-2030 activity data, SP1 Work package 1.2 Transport emission baseline Task 1.3.2 Transport activity inventory global and EU-scale, Transport and mobility Leuven.

EMEP/EEA (2009): EMEP/EEA air pollutant emission inventory guidebook 2009, Technical Report No 9/2009.

EMEP/EEA (2016): EEA Report No 21/2016. EMEP/EEA air pollutant emission inventory guidebook 2016, Report No 9/2009.

EU (2016): EU Transport in figures. Statistical pocketbook 2016. https://ec.europa.eu/transport/facts-fundings/statistics/pocketbook-2016_en (accessed: 19.06.2017).

Friedrich, R. (2004): Generation and Evaluation of Emission Data. GENEMIS - a EURO-STRAC-2 Subproject. http://genemis.ier.uni-stuttgart.de/ (accessed: 16.06.2017)

GHG protocol (2016): Global Warming Potential Values. Greenhouse Gas Protocol. http://www.ghgprotocol.org/sites/default/files/ghgp/Global-Warming-Potential-Values%20%28Feb%2016%202016%29_1.pdf (accessed: 15.12.2017).

Kouridis, C; Gkatzoflias, D; Kioutsioukis, I; Ntziachristos, L. (2009): Uncertainty estimates and guidance for road transport emission calculations. EMISIA SA Report No. 09.RE.014.V2.

https://ec.europa.eu/transport/facts-fundings/statistics/pocketbook-2016_en

http://genemis.ier.uni-stuttgart.de/

http://www.ghgprotocol.org/sites/default/files/ghgp/Global-Warming-Potential-Values%20%28Feb%2016%202016%29_1.pdf

http://www.ghgprotocol.org/sites/default/files/ghgp/Global-Warming-Potential-Values%20%28Feb%2016%202016%29_1.pdf



Security: PU


Lenhart, L. and Friedrich, R. (1995): European emission data with high temporal and spatial resolution. Water, Air, & Soil Pollution 85, 1897-1902.

López-Aparicio, S.; Guevara, M.; Thunis, P.; Cuvelier, K.; Tarrasón, L. (2017): Assessment of discrepancies between bottom-up and regional emission inventories in Norwegian urban areas. Atmospheric Environment 154, 285-296.

Samaras (2013): Methodology for the quantification of road transport PM-emissions, using emission factors or profiles. TRANSPHORM deliverable D1.1.2.

Theloke, J.; Thiruchittampalam, B.; Orlikova, S.; Uzbasich, M.; Gauger, T. (2011): Methodology development for the spatial distribution of the diffuse emissions in Europe. Diffuse Air emissions in PRTR. European Commission (070307/2009/548773/SER/C4). Institute of Energy Economics and Rational Energy Use, University of Stuttgart, Germany.

Thiruchittampalam, B. (2014): Entwicklung und Anwendung von Methoden und Modellen zur Berechnung von räumlich und zeitlich hochaufgelösten Emissionen in Europa. Forschungsbericht Band 118. Institute of Energy Economics and Rational Energy Use, University of Stuttgart, Germany.

van der Gon, D. (2012): European Emission baseline (final dataset) incl. specific transport emission grids and projection to 2020/2030 dataset. TRANSPHORM deliverable D1.3.5.

van Vuuren, D. P.; Edmonds, J.; Kainuma, M.; Riahi, K.; Thomsom, A.; Hibbard, K.; Hurtt, G. C.; Kram, T.; Krey, V.; Lamarque, J.; Masui, T.; Meinshausen, M.; Nakicenovic, N.; Smith, S.; Rose, S. K. (2011): The representative concentration pathways: an overview. Climate Change 109:5. Doi:10.1007/s10584-011-0148-z



Security: PU


8 Appendix

Table 6: Considered source categories as NFR sectors.

NFR09 Code NFR09 Longname Included Included elsewhere

1 A 1 a Public electricity and heat production ✓

1 A 1 b Petroleum refining ✓

1 A 1 c Manufacture of solid fuels and other energy industries ✓

1 A 2 a Stationary combustion in manufacturing industries and construction: Iron and steel

✓

1 A 2 b Stationary Combustion in manufacturing industries and construction: Non-ferrous metals

✓

1 A 2 c Stationary combustion in manufacturing industries and construction: Chemicals

✓

1 A 2 d Stationary combustion in manufacturing industries and construction: Pulp, Paper and Print

✓

1 A 2 e Stationary combustion in manufacturing industries and construction: Food processing, beverages and tobacco

in 1 A 2 f i

1 A 2 f i Stationary combustion in manufacturing industries and construction: Other (Please specify in your IIR)

✓

1 A 2 f ii Mobile Combustion in manufacturing industries and construction

✓

1 A 3 a ii (i) Civil aviation (Domestic, LTO) ✓

1 A 3 a i (i) International aviation (LTO) ✓

1 A 3 b i Road transport: Passenger cars ✓

1 A 3 b ii Road transport: Light duty vehicles ✓

1 A 3 b iii Road transport: Heavy duty vehicles ✓

1 A 3 b iv Road transport: Mopeds & motorcycles ✓

1 A 3 b v Road transport: Gasoline evaporation ✓

1 A 3 b vi Road transport: Automobile tyre and brake wear ✓

1 A 3 b vii Road transport: Automobile road abrasion ✓

1 A 3 c Railways ✓

1 A 3 d ii National navigation (Shipping) ✓

1 A 4 a i Commercial / institutional: Stationary ✓

1 A 4 a ii Commercial / institutional: Mobile in 1 A 5 b

1 A 4 b i Residential: Stationary plants ✓

1 A 4 b ii Residential: Household and gardening (mobile) ✓

1 A 4 c i Agriculture/Forestry/Fishing: Stationary ✓

1 A 4 c ii Agriculture/Forestry/Fishing: Off-road vehicles and other machinery

✓

1 A 5 a Other stationary (including military) in 1 A 4 c i

1 A 5 b Other, Mobile (including military, land based and recreational boats)

✓

1 B 1 a Fugitive emission from solid fuels: Coal mining and handling ✓

1 B 1 b Fugitive emission from solid fuels: Solid fuel transformation ✓

1 B 2 a i Exploration, production, transport ✓

1 B 2 a iv Refining / storage ✓

1 B 2 a v Distribution of oil products

1 B 2 b Natural gas ✓

1 B 2 c Venting and flaring ✓

2 A 1 Cement production in 1 A 2 f i

2 A 2 Lime production in 1 A 2 f i

2 A 7 a Quarrying and mining of minerals other than coal ✓

2 A 7 b Construction and demolition ✓



Security: PU



2 A 7 c Storage, handling and transport of mineral products ✓

2 B 1 Ammonia production in 2 B 5 a

2 B 2 Nitric acid production ✓

2 B 3 Adipic acid production ✓

2 B 4 Carbide production in 2 B 5 a

2 B 5 a Other chemical industry ✓

2 B 5 b Storage, handling and transport of chemical products ✓

2 C 1 Iron and steel production ✓

2 C 2 Ferroalloys production in 2 C 5 e

2 C 3 Aluminium production ✓

2 C 5 a Copper production in 2 C 5 e

2 C 5 b Lead production in 2 C 5 e

2 C 5 c Nickel production in 2 C 5 e

2 C 5 d Zinc production in 2 C 5 e

2 C 5 e Other metal production ✓

2 D 1 Pulp and paper ✓

2 D 2 Food and drink ✓

2 D 3 Wood processing ✓

3 A 1 Decorative coating application ✓

3 A 2 Industrial coating application ✓

3 B 1 Degreasing ✓

3 B 2 Dry cleaning ✓

3 C Chemical products ✓

3 D 1 Printing ✓

3 D 2 Domestic solvent use including fungicides ✓

3 D 3 Other product use ✓

4 A 1 Cattle ✓

4 B 1 a Cattle dairy ✓

4 B 1 b Cattle non-dairy ✓

4 B 2 Buffalo ✓

4 B 3 Sheep ✓

4 B 4 Goats in 4 B 3

4 B 5 Camels an Lamas ✓

4 B 6 Horses ✓

4 B 8 Swine ✓

4 B 9 a Laying hens ✓

4 B 9 b Broilers in 4 B 9 d

4 B 9 c Turkeys in 4 B 9 d

4 B 9 d Other poultry ✓

4 B 12 Liquid System ✓

4 B 13 Other (Solid storage and dry lot) ✓

4 C 4 Other ✓

4 D 1 Direct soil emissions ✓

4 D 1 a Synthetic N-fertilizers ✓

4 D 2 a Farm-level agricultural operations including storage, handling and transport of agricultural products

✓

4 e Prescribed Burning of Savannahs ✓

4 F Field burning of agricultural wastes ✓

6 A Solid waste disposal on land ✓

6 B Waste-water handling ✓

6 B 1 Industrial Wastewater ✓



Security: PU



6 B 2 Domestic and Commercial Wastewater ✓

6 C a Clinical waste incineration (d) in 1 A 1 a and 6 C e

6 C b Industrial waste incineration (d) in 1 A 1 a and 6 C e

6 C c Municipal waste incineration (d) in 1 A 1 a and 6 C e

6 C e Small scale waste burning ✓

6 D Other waste (e) ✓ (NH3 from waste treatment)

7 A Other (included in national total for entire territory) ✓ (forest burning, non-energy use etc.)

Table 7: Shares of BC and OC of PM2.5 for all sectors, countries and years where emissions are not zero.

Country NFR09

2015 2020 2030

BC OC BC OC BC OC

AT 1 A 1 a 2.18% 2.02% 2.20% 1.93% 1.70% 1.74%

AT 1 A 1 b 9.49% 6.07% 9.51% 5.62% 9.54% 5.37%

AT 1 A 1 c 1.31% 0.25% 1.31% 0.25% 1.31% 0.25%

AT 1 A 2 a 5.93% 3.33% 5.83% 3.25% 5.60% 3.04%

AT 1 A 2 b 8.79% 22.03% 8.71% 23.93% 8.61% 26.49%

AT 1 A 2 c 18.97% 15.10% 19.24% 15.16% 19.49% 15.05%

AT 1 A 2 d 18.85% 14.48% 19.27% 14.87% 19.06% 14.68%

AT 1 A 2 f i 3.91% 3.38% 3.76% 3.24% 3.22% 2.97%

AT 1 A 2 f ii 44.65% 20.96% 33.62% 16.56% 17.35% 10.07%

AT 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

AT 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

AT 1 A 3 b i 77.50% 16.28% 55.10% 25.84% 19.53% 40.78%

AT 1 A 3 b ii 74.70% 21.25% 72.49% 21.84% 54.42% 27.89%

AT 1 A 3 b iii 68.31% 21.20% 52.36% 27.57% 20.82% 38.45%

AT 1 A 3 b iv 15.91% 83.81% 16.70% 82.78% 17.95% 81.06%

AT 1 A 3 b vi 0.06% 0.13% 0.04% 0.09% 0.04% 0.09%

AT 1 A 3 b vii 0.06% 0.13% 0.04% 0.09% 0.04% 0.09%

AT 1 A 3 d ii 28.78% 45.78% 28.31% 46.42% 26.49% 48.91%

AT 1 A 4 a i 22.40% 11.77% 21.22% 12.06% 18.57% 12.45%

AT 1 A 4 b i 37.38% 27.28% 37.56% 26.54% 37.46% 25.42%

AT 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

AT 1 A 4 c i 29.11% 21.63% 28.03% 19.21% 18.81% 13.03%

AT 1 A 4 c ii 39.66% 29.61% 37.24% 28.95% 31.64% 26.78%

AT 1 A 5 b 18.53% 60.16% 19.03% 59.88% 20.16% 59.26%

AT 1 B 1 a 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

AT 1 B 1 b 7.18% 11.81% 7.18% 11.81% 7.18% 11.81%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

AT 1 B 2 a iv 0.22% 0.00% 0.22% 0.00% 0.22% 0.00%

AT 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

AT 2 C 1 2.14% 0.25% 2.16% 0.25% 2.15% 0.25%

AT 2 C 3 0.01% 0.13% 0.01% 0.13% 0.01% 0.13%

AT 2 C 5 e 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

AT 2 D 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

AT 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

AT 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

AT 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

AT 7 A 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

BE 1 A 1 a 3.84% 6.93% 4.09% 7.23% 3.20% 5.44%

BE 1 A 1 b 10.90% 8.89% 11.00% 12.70% 11.20% 21.08%

BE 1 A 1 c 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

BE 1 A 2 a 1.23% 4.96% 1.24% 5.17% 1.32% 6.11%

BE 1 A 2 b 6.24% 59.71% 6.14% 58.34% 6.11% 56.42%

BE 1 A 2 c 6.97% 63.27% 6.97% 63.16% 6.99% 61.50%

BE 1 A 2 d 7.57% 4.00% 7.61% 4.06% 7.63% 4.08%

BE 1 A 2 f i 1.57% 2.76% 1.55% 2.73% 1.41% 2.68%

BE 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

BE 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

BE 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

BE 1 A 3 b i 76.77% 17.50% 67.79% 20.93% 30.93% 35.67%

BE 1 A 3 b ii 76.26% 20.04% 74.52% 20.64% 63.37% 24.93%

BE 1 A 3 b iii 68.64% 21.70% 65.53% 21.73% 19.32% 35.69%

BE 1 A 3 b iv 21.44% 76.04% 22.15% 74.14% 22.11% 72.61%

BE 1 A 3 b vi 0.02% 0.08% 0.03% 0.09% 0.02% 0.09%

BE 1 A 3 b vii 0.02% 0.08% 0.03% 0.09% 0.02% 0.09%

BE 1 A 3 d ii 42.04% 28.89% 41.11% 28.89% 41.11% 28.89%

BE 1 A 4 a i 22.33% 23.15% 20.00% 23.15% 19.87% 23.65%

BE 1 A 4 b i 15.19% 43.94% 15.56% 43.71% 16.28% 43.19%

BE 1 A 4 b ii 15.03% 72.34% 16.41% 70.98% 18.02% 69.38%

BE 1 A 4 c i 71.63% 24.04% 82.69% 27.50% 82.54% 28.25%

BE 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

BE 1 A 5 b 23.26% 57.56% 25.62% 56.26% 30.13% 53.78%

BE 1 B 1 a 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

BE 1 B 1 b 18.97% 14.69% 18.97% 14.69% 18.97% 14.69%

BE 1 B 2 a iv 0.08% 0.00% 0.08% 0.00% 0.08% 0.00%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

BE 2 C 1 3.13% 0.34% 3.16% 0.34% 3.17% 0.34%

BE 2 C 3 0.03% 0.28% 0.03% 0.28% 0.03% 0.28%

BE 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

BE 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

BE 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

BG 1 A 1 a 0.08% 0.34% 0.08% 0.37% 0.09% 0.46%

BG 1 A 1 b 8.73% 3.75% 8.87% 3.91% 8.26% 4.12%

BG 1 A 1 c 0.52% 0.01% 0.52% 0.01% 0.50% 0.21%

BG 1 A 2 a 0.64% 2.01% 0.70% 2.08% 0.98% 2.53%

BG 1 A 2 b 6.81% 3.71% 6.75% 3.77% 6.66% 3.90%

BG 1 A 2 c 2.53% 2.48% 2.41% 2.85% 2.41% 2.60%

BG 1 A 2 d 7.07% 4.33% 7.06% 4.36% 7.04% 4.32%

BG 1 A 2 f i 0.59% 1.51% 0.72% 1.72% 0.70% 1.70%

BG 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

BG 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

BG 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

BG 1 A 3 b i 69.77% 22.41% 43.66% 33.21% 22.16% 40.53%

BG 1 A 3 b ii 69.30% 25.38% 63.66% 27.02% 39.87% 34.58%

BG 1 A 3 b iii 64.85% 24.73% 64.28% 23.53% 46.47% 26.16%

BG 1 A 3 b iv 18.31% 80.92% 19.62% 79.19% 20.65% 77.54%

BG 1 A 3 b vi 0.01% 0.03% 0.01% 0.03% 0.01% 0.03%

BG 1 A 3 b vii 0.01% 0.03% 0.01% 0.03% 0.01% 0.03%

BG 1 A 3 d ii 41.10% 27.72% 40.96% 27.13% 40.66% 25.63%

BG 1 A 4 a i 21.02% 27.55% 21.54% 28.03% 21.73% 28.60%

BG 1 A 4 b i 23.08% 38.47% 23.15% 38.67% 24.50% 37.39%

BG 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

BG 1 A 4 c i 16.18% 27.68% 16.87% 28.73% 20.43% 31.75%

BG 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

BG 1 B 2 a iv 0.19% 0.00% 0.19% 0.00% 0.19% 0.00%

BG 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

BG 2 B 5 a 0.18% 0.00% 0.10% 0.00% 0.11% 0.00%

BG 2 B 5 b 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

BG 2 C 1 0.00% 2.40% 0.00% 2.40% 0.00% 2.40%

BG 2 C 3 0.01% 0.13% 0.01% 0.13% 0.01% 0.13%

BG 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

BG 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

BG 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

CH 1 A 1 a 1.02% 0.00% 0.63% 0.00% 0.37% 0.00%

CH 1 A 1 b 12.50% 0.00% 12.50% 0.00% 12.50% 0.00%

CH 1 A 2 a 5.77% 0.00% 4.09% 0.00% 0.00% 0.00%

CH 1 A 2 c 14.29% 0.00% 14.29% 0.00% 0.00% 0.00%

CH 1 A 2 d 14.29% 0.00% 14.29% 0.00% 0.00% 0.00%

CH 1 A 2 f i 0.83% 0.07% 0.82% 0.07% 0.90% 0.07%

CH 1 A 2 f ii 24.99% 28.49% 16.25% 23.03% 13.47% 21.22%

CH 1 A 3 b i 70.60% 19.89% 44.36% 31.30% 17.95% 42.35%

CH 1 A 3 b ii 77.60% 16.39% 41.09% 32.55% 16.14% 43.88%

CH 1 A 3 b iii 67.64% 21.98% 60.97% 26.29% 34.45% 37.45%

CH 1 A 3 b iv 18.40% 80.87% 19.70% 78.52% 20.66% 77.13%

CH 1 A 3 b vi 0.03% 0.08% 0.03% 0.07% 0.03% 0.06%

CH 1 A 3 b vii 0.03% 0.08% 0.03% 0.07% 0.03% 0.06%

CH 1 A 3 d ii 35.87% 35.46% 33.52% 38.48% 24.61% 49.97%

CH 1 A 4 a i 10.85% 15.94% 10.34% 16.14% 10.03% 16.34%

CH 1 A 4 b i 30.99% 26.03% 29.45% 24.11% 24.99% 24.42%

CH 1 A 4 b ii 12.43% 75.53% 19.69% 68.96% 24.03% 64.94%

CH 1 A 4 c i 16.88% 21.07% 17.30% 21.72% 15.61% 21.75%

CH 1 A 4 c ii 38.26% 30.21% 33.35% 30.03% 15.47% 29.73%

CH 1 A 5 b 19.53% 58.53% 19.11% 59.12% 18.28% 60.25%

CH 2 C 1 0.00% 1.39% 0.00% 1.39% 0.00% 1.39%

CH 3 D 3 7.14% 50.00% 7.14% 50.00% 7.14% 50.00%

CH 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

CH 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

CY 1 A 1 a 9.66% 4.27% 4.70% 19.39% 3.38% 17.05%

CY 1 A 2 a 8.22% 3.54% 7.64% 16.83% 7.86% 10.07%

CY 1 A 2 c 8.61% 3.68% 8.29% 3.60% 8.19% 3.79%

CY 1 A 2 d 8.61% 3.68% 8.31% 3.59% 7.01% 4.25%

CY 1 A 2 f i 0.40% 0.62% 0.36% 0.51% 0.23% 0.14%

CY 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

CY 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

CY 1 A 3 b i 63.95% 28.13% 64.74% 26.89% 51.46% 31.11%

CY 1 A 3 b ii 63.62% 31.33% 63.74% 31.13% 62.61% 31.78%

CY 1 A 3 b iii 58.95% 31.24% 58.62% 31.64% 57.31% 31.95%

CY 1 A 3 b iv 19.10% 80.06% 20.10% 78.32% 20.70% 77.44%

CY 1 A 4 a i 15.08% 12.86% 15.05% 12.89% 15.30% 12.93%

CY 1 A 4 b i 23.73% 35.92% 24.18% 35.52% 24.52% 34.86%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

CY 1 A 4 b ii 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

CY 1 A 4 c i 26.49% 29.09% 26.61% 29.10% 26.83% 28.90%

CY 2 C 1 0.00% 1.22% 0.00% 1.22% 0.00% 1.22%

CY 2 D 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

CY 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

CY 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

CZ 1 A 1 a 0.41% 0.46% 0.52% 0.55% 0.74% 0.72%

CZ 1 A 1 b 8.42% 3.72% 8.40% 3.76% 8.36% 3.69%

CZ 1 A 1 c 0.89% 0.03% 0.89% 0.03% 0.89% 0.03%

CZ 1 A 2 a 1.04% 1.64% 1.02% 1.61% 1.19% 2.35%

CZ 1 A 2 b 4.60% 38.80% 4.97% 36.62% 5.07% 36.01%

CZ 1 A 2 c 0.13% 0.89% 0.21% 0.98% 1.45% 1.64%

CZ 1 A 2 d 3.03% 2.40% 3.65% 2.76% 4.52% 3.35%

CZ 1 A 2 f i 2.35% 1.90% 2.82% 2.11% 2.94% 2.30%

CZ 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

CZ 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

CZ 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

CZ 1 A 3 b i 74.49% 19.75% 62.76% 23.19% 29.33% 36.87%

CZ 1 A 3 b ii 77.60% 15.86% 44.73% 30.08% 18.64% 41.44%

CZ 1 A 3 b iii 64.68% 25.33% 66.05% 23.39% 47.58% 29.22%

CZ 1 A 3 b iv 16.95% 82.38% 19.58% 79.07% 20.74% 77.39%

CZ 1 A 3 b vi 0.00% 0.01% 0.00% 0.01% 0.00% 0.01%

CZ 1 A 3 b vii 0.00% 0.01% 0.00% 0.01% 0.00% 0.01%

CZ 1 A 3 d ii 41.11% 28.89% 41.11% 28.89% 41.11% 28.89%

CZ 1 A 4 a i 10.74% 12.95% 13.50% 15.85% 13.72% 15.88%

CZ 1 A 4 b i 28.96% 37.54% 30.27% 33.30% 31.72% 29.09%

CZ 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

CZ 1 A 4 c i 25.01% 29.19% 25.57% 30.30% 25.55% 30.22%

CZ 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

CZ 1 A 5 b 23.26% 57.56% 25.62% 56.26% 30.13% 53.78%

CZ 1 B 1 b 16.91% 13.63% 16.92% 13.64% 16.93% 13.65%

CZ 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

CZ 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

CZ 2 B 5 a 2.47% 0.00% 1.27% 0.00% 1.27% 0.00%

CZ 2 C 1 0.08% 0.35% 0.08% 0.37% 0.08% 0.37%

CZ 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

CZ 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

CZ 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

CZ 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

DE 1 A 1 a 1.60% 2.24% 1.76% 2.49% 2.59% 3.91%

DE 1 A 1 b 10.04% 4.79% 9.94% 4.64% 9.58% 4.94%

DE 1 A 1 c 0.23% 0.71% 0.20% 0.64% 0.23% 0.60%

DE 1 A 2 a 2.50% 4.53% 2.34% 4.36% 1.93% 4.13%

DE 1 A 2 b 8.31% 12.42% 8.03% 11.76% 6.48% 3.52%

DE 1 A 2 c 3.47% 13.23% 4.99% 8.85% 5.44% 9.47%

DE 1 A 2 d 3.21% 7.43% 5.31% 5.55% 4.58% 4.94%

DE 1 A 2 f i 2.22% 3.40% 2.32% 3.32% 2.25% 3.22%

DE 1 A 2 f ii 45.37% 23.06% 38.31% 22.42% 14.92% 19.24%

DE 1 A 3 a i (i) 17.78% 59.72% 17.78% 59.72% 17.78% 59.72%

DE 1 A 3 a ii (i) 17.78% 59.72% 17.78% 59.72% 17.78% 59.72%

DE 1 A 3 b i 73.42% 18.35% 47.29% 29.38% 18.97% 41.18%

DE 1 A 3 b ii 81.79% 14.22% 62.34% 22.11% 19.45% 40.55%

DE 1 A 3 b iii 68.54% 22.23% 47.19% 29.45% 17.91% 38.95%

DE 1 A 3 b iv 18.35% 79.28% 18.70% 78.02% 20.53% 74.47%

DE 1 A 3 b vi 0.02% 0.04% 0.02% 0.04% 0.02% 0.04%

DE 1 A 3 b vii 0.02% 0.04% 0.02% 0.04% 0.02% 0.04%

DE 1 A 3 d ii 40.94% 27.19% 40.90% 26.74% 40.79% 25.70%

DE 1 A 4 a i 29.59% 10.24% 36.42% 13.08% 43.37% 16.99%

DE 1 A 4 b i 30.70% 29.50% 31.43% 28.75% 31.60% 28.44%

DE 1 A 4 c i 57.02% 18.51% 47.83% 17.61% 27.46% 17.07%

DE 1 A 4 c ii 39.68% 28.78% 37.40% 27.83% 34.18% 26.09%

DE 1 A 5 b 31.85% 47.88% 31.78% 49.20% 31.13% 52.23%

DE 1 B 1 a 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

DE 1 B 1 b 16.70% 13.55% 16.70% 13.55% 16.70% 13.55%

DE 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

DE 1 B 2 c 78.11% 15.62% 78.11% 15.62% 78.11% 15.62%

DE 2 C 1 0.60% 0.08% 0.58% 0.09% 0.57% 0.10%

DE 2 C 3 0.00% 0.03% 0.00% 0.04% 0.00% 0.05%

DE 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

DE 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

DE 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

DE 7 A 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

DK 1 A 1 a 1.75% 4.37% 1.88% 4.89% 4.72% 12.59%

DK 1 A 1 b 6.91% 46.63% 8.38% 22.90% 6.77% 33.01%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

DK 1 A 1 c 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

DK 1 A 2 a 7.04% 62.16% 7.11% 56.48% 1.52% 6.29%

DK 1 A 2 b 7.08% 66.49% 7.15% 59.87% 7.20% 54.60%

DK 1 A 2 c 6.74% 53.52% 6.62% 47.91% 6.28% 10.10%

DK 1 A 2 d 6.12% 6.97% 5.84% 7.16% 6.25% 6.56%

DK 1 A 2 f i 0.92% 1.00% 0.94% 1.00% 0.69% 0.83%

DK 1 A 2 f ii 44.97% 24.43% 38.64% 24.63% 20.19% 24.60%

DK 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

DK 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

DK 1 A 3 b i 73.37% 19.72% 63.01% 23.32% 32.51% 35.69%

DK 1 A 3 b ii 82.67% 13.78% 37.96% 33.76% 16.50% 43.50%

DK 1 A 3 b iii 67.11% 21.42% 61.83% 24.39% 29.03% 38.12%

DK 1 A 3 b iv 16.87% 82.48% 17.75% 81.23% 20.35% 77.43%

DK 1 A 3 b vi 0.05% 0.11% 0.04% 0.10% 0.04% 0.10%

DK 1 A 3 b vii 0.05% 0.11% 0.04% 0.10% 0.04% 0.10%

DK 1 A 3 d ii 41.01% 27.82% 40.76% 27.18% 40.60% 25.65%

DK 1 A 4 a i 14.09% 15.98% 12.70% 13.82% 12.56% 13.82%

DK 1 A 4 b i 12.83% 42.24% 13.62% 39.70% 15.32% 37.48%

DK 1 A 4 b ii 13.82% 73.59% 15.82% 71.58% 18.03% 69.38%

DK 1 A 4 c i 12.58% 32.10% 12.75% 28.69% 13.66% 27.99%

DK 1 A 4 c ii 39.88% 28.51% 36.99% 26.88% 26.10% 20.63%

DK 1 A 5 b 30.20% 53.75% 30.20% 53.75% 30.20% 53.75%

DK 1 B 2 a iv 0.21% 0.00% 0.21% 0.00% 0.21% 0.00%

DK 1 B 2 c 78.32% 15.38% 78.32% 15.38% 78.32% 15.38%

DK 2 C 1 0.00% 2.13% 0.00% 2.13% 0.00% 2.13%

DK 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

DK 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

DK 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

DK 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

EE 1 A 1 a 0.17% 0.38% 0.51% 0.30% 0.70% 0.41%

EE 1 A 1 b 23.57% 7.98% 24.65% 8.47% 22.34% 7.52%

EE 1 A 1 c 0.13% 0.43% 0.13% 0.43% 0.13% 0.43%

EE 1 A 2 a 6.19% 45.81% 6.32% 38.98% 1.14% 8.26%

EE 1 A 2 b 0.76% 1.00% 0.77% 1.05% 0.78% 1.25%

EE 1 A 2 c 8.76% 4.23% 7.10% 12.22% 7.14% 4.26%

EE 1 A 2 d 7.12% 4.47% 7.15% 4.45% 7.15% 4.38%

EE 1 A 2 f i 1.71% 1.05% 1.64% 1.01% 1.28% 0.83%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

EE 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

EE 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

EE 1 A 3 b i 69.27% 21.79% 51.13% 30.41% 21.58% 42.13%

EE 1 A 3 b ii 74.65% 20.88% 75.06% 19.25% 45.73% 30.14%

EE 1 A 3 b iii 69.44% 21.40% 66.74% 21.80% 27.22% 34.85%

EE 1 A 3 b iv 20.08% 78.20% 20.03% 79.06% 20.46% 78.17%

EE 1 A 3 b vi 0.02% 0.04% 0.02% 0.04% 0.01% 0.03%

EE 1 A 3 b vii 0.02% 0.04% 0.02% 0.04% 0.01% 0.03%

EE 1 A 3 d ii 41.05% 28.29% 41.05% 28.27% 40.99% 27.68%

EE 1 A 4 a i 11.09% 11.63% 11.94% 12.73% 12.35% 13.16%

EE 1 A 4 b i 15.54% 43.18% 16.08% 42.75% 17.46% 41.63%

EE 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

EE 1 A 4 c i 15.73% 41.92% 15.72% 41.67% 16.49% 40.66%

EE 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

EE 1 B 1 b 15.83% 12.77% 15.85% 12.78% 15.99% 12.89%

EE 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

EE 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

EE 2 C 1 0.00% 1.22% 0.00% 1.22% 0.00% 1.22%

EE 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

EE 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

EE 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

EE 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

ES 1 A 1 a 3.66% 4.10% 3.15% 4.21% 1.83% 4.61%

ES 1 A 1 b 10.01% 8.04% 9.70% 6.29% 9.41% 6.23%

ES 1 A 1 c 0.99% 0.00% 0.99% 0.00% 0.99% 0.00%

ES 1 A 2 a 2.92% 1.70% 2.86% 1.68% 2.76% 1.67%

ES 1 A 2 b 7.72% 4.10% 6.41% 3.16% 4.64% 2.32%

ES 1 A 2 c 3.99% 3.94% 5.19% 3.90% 5.18% 3.83%

ES 1 A 2 d 4.48% 2.66% 3.64% 2.26% 3.55% 2.23%

ES 1 A 2 f i 1.92% 1.66% 1.87% 1.62% 1.66% 1.49%

ES 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

ES 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

ES 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

ES 1 A 3 b i 74.91% 20.04% 68.10% 22.66% 54.22% 27.92%

ES 1 A 3 b ii 71.65% 24.23% 69.91% 24.82% 60.69% 27.90%

ES 1 A 3 b iii 68.11% 22.51% 67.47% 22.33% 28.97% 38.70%

ES 1 A 3 b iv 14.89% 85.00% 16.81% 82.98% 18.22% 81.48%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

ES 1 A 3 b vi 0.00% 0.01% 0.00% 0.01% 0.00% 0.01%

ES 1 A 3 b vii 0.00% 0.01% 0.00% 0.01% 0.00% 0.01%

ES 1 A 3 d ii 41.20% 27.14% 40.88% 26.06% 40.50% 24.31%

ES 1 A 4 a i 4.13% 1.97% 3.49% 1.66% 3.68% 1.83%

ES 1 A 4 b i 30.93% 33.66% 33.92% 31.31% 33.84% 31.22%

ES 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

ES 1 A 4 c i 31.02% 32.30% 34.19% 29.98% 34.14% 30.01%

ES 1 A 4 c ii 40.06% 28.22% 37.28% 26.31% 26.40% 18.56%

ES 1 B 1 b 17.88% 14.83% 17.88% 14.83% 17.88% 14.83%

ES 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

ES 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

ES 2 C 1 0.25% 0.45% 0.25% 0.44% 0.26% 0.44%

ES 2 C 3 0.00% 0.05% 0.01% 0.06% 0.01% 0.07%

ES 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

ES 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

ES 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

FI 1 A 1 a 1.18% 1.25% 1.28% 1.15% 1.48% 1.27%

FI 1 A 1 b 9.50% 4.31% 9.50% 4.35% 9.50% 4.37%

FI 1 A 1 c 4.40% 0.98% 4.40% 0.98% 4.40% 0.98%

FI 1 A 2 a 7.09% 4.28% 6.89% 4.08% 6.68% 3.63%

FI 1 A 2 b 8.02% 4.06% 7.96% 4.15% 6.51% 3.79%

FI 1 A 2 c 7.79% 1.66% 7.79% 1.66% 7.66% 2.44%

FI 1 A 2 d 0.36% 0.38% 0.35% 0.38% 0.35% 0.39%

FI 1 A 2 f i 1.22% 1.07% 1.14% 1.00% 1.06% 0.93%

FI 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

FI 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

FI 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

FI 1 A 3 b i 73.02% 20.79% 62.81% 23.95% 34.63% 35.21%

FI 1 A 3 b ii 67.32% 27.99% 67.99% 27.14% 67.14% 25.83%

FI 1 A 3 b iii 66.18% 24.16% 63.75% 24.67% 46.34% 28.63%

FI 1 A 3 b iv 18.00% 81.53% 18.64% 80.80% 20.14% 78.84%

FI 1 A 3 b vi 0.01% 0.02% 0.01% 0.02% 0.01% 0.01%

FI 1 A 3 b vii 0.01% 0.02% 0.01% 0.02% 0.01% 0.01%

FI 1 A 3 d ii 40.93% 31.15% 37.72% 33.53% 40.71% 29.44%

FI 1 A 4 a i 20.73% 5.69% 19.82% 5.66% 21.46% 6.17%

FI 1 A 4 b i 31.34% 27.06% 31.36% 26.58% 30.41% 26.81%

FI 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

FI 1 A 4 c i 19.20% 9.51% 17.79% 8.70% 18.80% 8.89%

FI 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

FI 1 A 5 b 37.19% 29.97% 35.44% 28.96% 27.63% 30.96%

FI 1 B 1 a 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

FI 1 B 1 b 0.28% 0.46% 0.28% 0.46% 0.28% 0.46%

FI 1 B 2 a iv 0.19% 0.00% 0.19% 0.00% 0.19% 0.00%

FI 2 C 1 0.48% 0.65% 0.46% 0.67% 0.44% 0.69%

FI 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

FI 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

FI 4 F 20.75% 41.60% 20.75% 41.60% 20.75% 41.60%

FI 6 C e 7.44% 60.30% 7.44% 60.30% 7.44% 60.30%

FI 7 A 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

FR 1 A 1 a 2.99% 4.29% 2.25% 4.79% 2.14% 3.20%

FR 1 A 1 b 9.75% 4.69% 9.81% 4.83% 9.77% 4.92%

FR 1 A 1 c 1.35% 0.34% 1.37% 0.34% 1.39% 0.34%

FR 1 A 2 a 0.73% 0.78% 0.75% 0.79% 0.75% 0.76%

FR 1 A 2 b 9.34% 6.50% 9.05% 7.24% 8.76% 8.02%

FR 1 A 2 c 6.30% 4.17% 6.78% 4.24% 6.72% 4.18%

FR 1 A 2 d 7.01% 8.51% 7.01% 4.83% 7.01% 5.02%

FR 1 A 2 f i 4.69% 5.68% 5.01% 5.22% 5.14% 5.09%

FR 1 A 2 f ii 46.03% 22.16% 39.25% 20.65% 14.57% 14.78%

FR 1 A 3 a i (i) 18.11% 60.38% 18.11% 60.38% 18.11% 60.38%

FR 1 A 3 a ii (i) 18.11% 60.38% 18.11% 60.38% 18.11% 60.38%

FR 1 A 3 b i 77.30% 17.33% 67.71% 20.71% 27.43% 37.06%

FR 1 A 3 b ii 81.42% 14.68% 66.18% 20.66% 19.47% 40.56%

FR 1 A 3 b iii 65.63% 23.89% 54.14% 28.52% 30.66% 35.79%

FR 1 A 3 b iv 18.61% 78.73% 20.45% 75.98% 21.73% 73.71%

FR 1 A 3 b vi 0.02% 0.04% 0.02% 0.05% 0.02% 0.05%

FR 1 A 3 b vii 0.02% 0.04% 0.02% 0.05% 0.02% 0.05%

FR 1 A 3 d ii 40.50% 26.76% 40.43% 25.92% 40.36% 25.61%

FR 1 A 4 a i 19.46% 8.85% 19.95% 9.16% 20.34% 9.77%

FR 1 A 4 b i 30.12% 33.60% 34.16% 30.42% 35.22% 29.25%

FR 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.02% 69.38%

FR 1 A 4 c i 31.82% 33.54% 35.31% 30.92% 36.93% 30.44%

FR 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

FR 1 A 5 b 23.26% 57.56% 25.61% 56.26% 30.13% 53.78%

FR 1 B 1 a 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

FR 1 B 1 b 14.89% 20.78% 14.89% 20.78% 14.89% 20.78%

FR 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

FR 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

FR 2 C 1 2.27% 2.55% 2.24% 2.53% 2.22% 2.51%

FR 2 C 3 0.00% 0.05% 0.01% 0.06% 0.01% 0.07%

FR 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

FR 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

FR 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

GR 1 A 1 a 4.82% 3.44% 7.42% 5.35% 12.41% 9.84%

GR 1 A 1 b 9.60% 3.90% 9.60% 3.90% 9.53% 3.81%

GR 1 A 2 a 0.38% 1.77% 0.42% 1.96% 0.60% 2.49%

GR 1 A 2 b 3.07% 1.51% 3.17% 1.48% 3.59% 1.72%

GR 1 A 2 c 8.90% 4.05% 8.73% 3.95% 8.42% 3.94%

GR 1 A 2 d 8.79% 4.28% 8.84% 4.38% 9.63% 4.32%

GR 1 A 2 f i 0.31% 0.18% 0.27% 0.16% 0.26% 0.15%

GR 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

GR 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

GR 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

GR 1 A 3 b i 49.13% 32.31% 24.39% 41.96% 15.49% 44.55%

GR 1 A 3 b ii 72.05% 23.77% 73.04% 22.43% 50.43% 33.52%

GR 1 A 3 b iii 67.16% 23.79% 63.14% 26.52% 48.81% 34.47%

GR 1 A 3 b iv 17.08% 82.00% 17.90% 80.58% 20.09% 76.95%

GR 1 A 3 d ii 41.63% 26.84% 40.95% 25.57% 39.82% 23.36%

GR 1 A 4 a i 76.66% 28.16% 70.68% 32.18% 54.25% 37.54%

GR 1 A 4 b i 19.79% 40.86% 20.43% 40.33% 20.62% 39.93%

GR 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

GR 1 A 4 c i 19.17% 39.95% 19.77% 39.12% 20.06% 38.62%

GR 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

GR 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

GR 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

GR 2 C 1 0.00% 1.22% 0.00% 1.22% 0.00% 1.22%

GR 2 C 3 0.00% 0.00% 0.00% 0.01% 0.00% 0.04%

GR 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

GR 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

GR 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

HR 1 A 1 a 0.85% 2.13% 0.93% 2.33% 1.54% 3.20%

HR 1 A 1 b 8.26% 3.75% 8.24% 3.62% 8.27% 3.61%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

HR 1 A 2 a 6.59% 18.92% 6.58% 18.63% 6.59% 18.86%

HR 1 A 2 b 7.92% 4.00% 7.83% 4.12% 7.79% 4.13%

HR 1 A 2 c 5.40% 13.18% 5.42% 13.84% 4.84% 18.53%

HR 1 A 2 d 6.95% 67.42% 7.01% 4.54% 7.01% 4.50%

HR 1 A 2 f i 0.66% 1.35% 0.83% 1.53% 0.83% 1.54%

HR 1 A 2 f ii 48.89% 22.22% 47.54% 21.61% 40.33% 18.33%

HR 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

HR 1 A 3 b i 65.14% 27.74% 52.37% 30.76% 28.07% 38.34%

HR 1 A 3 b ii 78.50% 17.33% 73.32% 17.78% 18.68% 41.60%

HR 1 A 3 b iii 66.56% 24.32% 66.37% 23.66% 47.52% 31.91%

HR 1 A 3 b iv 14.77% 84.64% 16.12% 83.42% 17.21% 81.97%

HR 1 A 3 b vi 0.00% 0.03% 0.02% 0.16% 0.02% 0.17%

HR 1 A 3 b vii 0.00% 0.03% 0.02% 0.16% 0.02% 0.17%

HR 1 A 3 d ii 41.11% 28.89% 41.11% 28.89% 41.11% 28.89%

HR 1 A 4 a i 21.33% 8.41% 29.96% 12.00% 39.12% 16.98%

HR 1 A 4 b i 28.79% 34.51% 29.63% 33.64% 29.03% 33.78%

HR 1 A 4 b ii 7.67% 79.86% 12.23% 75.22% 18.12% 69.29%

HR 1 A 4 c i 62.22% 16.29% 61.66% 16.24% 60.27% 16.07%

HR 1 A 4 c ii 41.11% 28.89% 40.84% 28.70% 39.84% 27.99%

HR 1 A 5 b 42.40% 27.82% 43.69% 26.78% 54.12% 18.55%

HR 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

HR 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

HR 2 B 5 a 0.69% 0.00% 0.35% 0.00% 0.36% 0.00%

HR 2 C 1 0.00% 2.95% 0.00% 2.95% 0.00% 2.95%

HR 2 C 3 0.03% 0.31% 0.03% 0.31% 0.03% 0.31%

HR 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

HR 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

HR 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

HR 7 A 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

HU 1 A 1 a 1.29% 1.95% 2.11% 3.41% 2.38% 3.37%

HU 1 A 1 b 8.55% 6.80% 8.49% 6.95% 8.68% 7.19%

HU 1 A 2 a 1.49% 7.02% 1.44% 6.15% 1.28% 4.86%

HU 1 A 2 b 7.00% 64.87% 7.00% 58.74% 7.01% 6.10%

HU 1 A 2 c 3.75% 21.63% 4.17% 20.78% 4.79% 15.41%

HU 1 A 2 d 7.00% 60.00% 7.00% 50.29% 2.56% 2.74%

HU 1 A 2 f i 0.18% 0.56% 0.27% 0.57% 0.62% 0.70%

HU 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

HU 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

HU 1 A 3 b i 67.84% 25.98% 55.53% 30.01% 25.45% 40.02%

HU 1 A 3 b ii 69.34% 26.18% 69.10% 25.67% 62.81% 26.38%

HU 1 A 3 b iii 67.92% 22.00% 64.72% 21.88% 24.70% 31.85%

HU 1 A 3 b iv 14.75% 84.91% 19.52% 79.48% 20.25% 78.24%

HU 1 A 3 b vi 0.01% 0.01% 0.01% 0.01% 0.00% 0.01%

HU 1 A 3 b vii 0.01% 0.01% 0.01% 0.01% 0.00% 0.01%

HU 1 A 4 a i 11.35% 13.63% 11.40% 13.82% 11.41% 14.31%

HU 1 A 4 b i 16.70% 39.91% 15.93% 40.18% 17.96% 40.91%

HU 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

HU 1 A 4 c i 16.13% 41.34% 15.76% 40.88% 16.28% 40.55%

HU 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

HU 1 B 1 b 16.96% 13.67% 16.96% 13.67% 16.96% 13.67%

HU 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

HU 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

HU 2 B 5 a 0.08% 0.00% 0.04% 0.00% 0.05% 0.00%

HU 2 C 1 0.70% 0.52% 0.67% 0.61% 0.65% 0.67%

HU 2 C 3 0.00% 0.04% 0.00% 0.05% 0.01% 0.06%

HU 2 D 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

HU 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

HU 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

HU 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

IE 1 A 1 a 0.13% 2.51% 0.13% 2.22% 0.51% 3.62%

IE 1 A 1 b 13.84% 1.26% 19.13% 2.17% 18.69% 2.09%

IE 1 A 1 c 0.10% 1.55% 0.10% 1.56% 0.07% 1.98%

IE 1 A 2 a 6.48% 0.06% 6.48% 0.05% 6.50% 0.05%

IE 1 A 2 b 9.65% 5.07% 9.60% 5.15% 9.52% 5.60%

IE 1 A 2 c 8.14% 10.67% 7.87% 5.84% 7.86% 5.82%

IE 1 A 2 d 1.72% 0.72% 0.52% 0.03% 1.12% 0.32%

IE 1 A 2 f i 2.61% 1.70% 2.52% 1.69% 2.30% 1.63%

IE 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

IE 1 A 3 a i (i) 18.18% 60.45% 18.18% 60.45% 18.18% 60.45%

IE 1 A 3 a ii (i) 18.18% 60.45% 18.18% 60.45% 18.18% 60.45%

IE 1 A 3 b i 67.83% 21.77% 44.73% 31.09% 26.21% 38.04%

IE 1 A 3 b ii 71.07% 24.61% 69.52% 25.53% 65.27% 26.79%

IE 1 A 3 b iii 66.65% 22.13% 31.02% 37.09% 14.67% 43.97%

IE 1 A 3 b iv 17.47% 80.67% 16.72% 81.86% 20.11% 76.21%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

IE 1 A 3 b vi 0.01% 0.01% 0.01% 0.01% 0.01% 0.02%

IE 1 A 3 b vii 0.01% 0.01% 0.01% 0.01% 0.01% 0.02%

IE 1 A 3 d ii 40.96% 27.34% 40.86% 26.35% 40.69% 24.63%

IE 1 A 4 a i 69.32% 23.92% 69.60% 24.57% 68.26% 26.00%

IE 1 A 4 b i 42.31% 40.03% 42.86% 39.78% 39.85% 40.74%

IE 1 A 4 c i 31.12% 33.34% 33.00% 31.12% 32.32% 31.33%

IE 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

IE 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

IE 2 C 1 0.00% 1.46% 0.00% 1.46% 0.00% 1.46%

IE 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

IE 4 F 20.75% 41.60% 20.75% 41.60% 20.75% 41.60%

IT 1 A 1 a 1.91% 3.66% 1.76% 2.96% 1.90% 3.09%

IT 1 A 1 b 7.92% 3.62% 7.85% 3.63% 8.61% 3.91%

IT 1 A 1 c 1.57% 0.45% 1.35% 0.37% 1.98% 0.74%

IT 1 A 2 a 1.19% 2.61% 1.33% 2.76% 1.47% 3.03%

IT 1 A 2 b 5.63% 43.47% 5.39% 53.17% 6.35% 36.64%

IT 1 A 2 c 7.05% 6.28% 7.10% 4.75% 7.19% 9.50%

IT 1 A 2 d 7.17% 3.77% 7.22% 3.93% 7.48% 8.30%

IT 1 A 2 f i 6.29% 4.65% 5.93% 4.41% 5.57% 4.15%

IT 1 A 2 f ii 48.89% 22.22% 47.09% 21.41% 13.92% 6.33%

IT 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

IT 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

IT 1 A 3 b i 76.70% 17.16% 65.25% 21.75% 32.24% 35.21%

IT 1 A 3 b ii 64.35% 30.72% 64.14% 30.83% 63.86% 30.74%

IT 1 A 3 b iii 68.87% 22.19% 64.57% 22.30% 20.53% 31.83%

IT 1 A 3 b iv 16.62% 82.89% 16.45% 83.15% 16.97% 81.94%

IT 1 A 3 b vi 0.00% 0.01% 0.00% 0.01% 0.00% 0.01%

IT 1 A 3 b vii 0.00% 0.01% 0.00% 0.01% 0.00% 0.01%

IT 1 A 3 d ii 38.63% 23.61% 37.55% 21.37% 36.24% 18.39%

IT 1 A 4 a i 14.04% 58.28% 13.67% 56.25% 14.47% 57.73%

IT 1 A 4 b i 19.15% 40.65% 20.15% 39.65% 21.39% 38.43%

IT 1 A 4 b ii 12.23% 75.22% 18.12% 69.29% 18.12% 69.29%

IT 1 A 4 c i 27.29% 34.77% 27.94% 34.23% 28.47% 33.78%

IT 1 A 4 c ii 41.03% 29.01% 38.64% 27.48% 26.22% 19.48%

IT 1 A 5 b 27.94% 54.99% 28.92% 54.45% 30.18% 53.75%

IT 1 B 1 b 20.14% 11.90% 20.36% 11.78% 20.36% 11.78%

IT 1 B 2 a iv 0.22% 0.00% 0.22% 0.00% 0.23% 0.00%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

IT 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

IT 2 B 5 a 7.53% 0.00% 7.82% 0.00% 7.76% 0.00%

IT 2 C 1 0.24% 1.34% 0.08% 1.39% 0.10% 0.75%

IT 2 C 3 0.01% 0.10% 0.01% 0.11% 0.01% 0.12%

IT 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

IT 4 F 13.33% 48.89% 13.33% 48.89% 13.33% 48.89%

IT 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

LT 1 A 1 a 2.46% 8.07% 2.39% 6.88% 2.21% 3.77%

LT 1 A 1 b 8.67% 3.69% 8.76% 3.72% 8.69% 3.69%

LT 1 A 1 c 7.01% 4.25% 7.01% 4.25% 7.01% 4.25%

LT 1 A 2 a 1.56% 2.01% 4.50% 5.73% 1.56% 1.96%

LT 1 A 2 b 8.22% 3.54% 8.22% 3.54% 8.21% 3.56%

LT 1 A 2 c 7.25% 16.43% 5.31% 18.36% 7.01% 7.16%

LT 1 A 2 d 7.01% 4.64% 7.00% 74.84% 7.01% 16.58%

LT 1 A 2 f i 0.63% 0.44% 1.61% 0.77% 0.60% 0.37%

LT 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

LT 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

LT 1 A 3 b i 72.70% 20.95% 66.57% 22.54% 50.13% 28.31%

LT 1 A 3 b ii 71.95% 23.55% 70.70% 23.02% 43.92% 32.52%

LT 1 A 3 b iii 65.11% 25.77% 64.08% 25.77% 52.57% 31.20%

LT 1 A 3 b iv 19.95% 77.32% 20.47% 78.18% 20.76% 77.49%

LT 1 A 3 b vi 0.01% 0.04% 0.00% 0.02% 0.00% 0.02%

LT 1 A 3 b vii 0.01% 0.04% 0.00% 0.02% 0.00% 0.02%

LT 1 A 3 d ii 41.11% 28.89% 41.11% 28.89% 41.11% 28.89%

LT 1 A 4 a i 3.58% 3.77% 7.65% 8.68% 11.25% 12.24%

LT 1 A 4 b i 19.67% 40.55% 19.90% 40.16% 21.35% 38.88%

LT 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

LT 1 A 4 c i 19.32% 34.94% 19.48% 34.54% 19.99% 33.57%

LT 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

LT 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

LT 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

LT 2 C 1 0.00% 1.22% 0.00% 1.22% 0.00% 1.22%

LT 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

LT 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

LT 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

LT 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

LU 1 A 1 a 2.36% 10.35% 2.13% 7.24% 2.08% 6.49%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

LU 1 A 2 a 1.87% 9.33% 3.43% 7.55% 3.06% 8.82%

LU 1 A 2 b 7.00% 74.81% 7.13% 61.62% 7.14% 60.50%

LU 1 A 2 c 7.72% 3.90% 7.72% 3.97% 7.72% 4.02%

LU 1 A 2 d 7.72% 3.95% 7.40% 35.98% 7.38% 37.38%

LU 1 A 2 f i 0.25% 0.17% 0.24% 0.16% 0.24% 0.15%

LU 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

LU 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

LU 1 A 3 b i 77.22% 16.58% 69.13% 19.95% 42.01% 31.07%

LU 1 A 3 b ii 81.79% 15.13% 77.71% 16.74% 42.55% 31.45%

LU 1 A 3 b iii 67.28% 22.83% 62.22% 24.93% 27.23% 39.71%

LU 1 A 3 b iv 18.49% 80.03% 20.65% 75.77% 21.84% 73.55%

LU 1 A 3 b vi 0.01% 0.02% 0.01% 0.02% 0.01% 0.02%

LU 1 A 3 b vii 0.01% 0.02% 0.01% 0.02% 0.01% 0.02%

LU 1 A 3 d ii 41.11% 28.89% 41.11% 28.89% 41.11% 28.89%

LU 1 A 4 a i 33.67% 23.90% 31.36% 23.73% 27.05% 29.40%

LU 1 A 4 b i 26.21% 36.60% 25.94% 36.70% 25.69% 36.74%

LU 1 A 4 c i 24.75% 35.23% 24.32% 34.80% 24.48% 34.77%

LU 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

LU 2 C 1 0.00% 1.46% 0.00% 1.46% 0.00% 1.46%

LU 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

LU 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

LV 1 A 1 a 2.41% 3.09% 2.29% 2.35% 2.25% 2.44%

LV 1 A 2 a 5.88% 5.95% 5.64% 5.50% 6.02% 5.99%

LV 1 A 2 b 6.99% 73.74% 6.99% 70.56% 6.99% 68.71%

LV 1 A 2 c 7.70% 4.62% 7.04% 5.68% 7.06% 5.74%

LV 1 A 2 d 7.01% 4.43% 7.01% 4.34% 7.01% 4.36%

LV 1 A 2 f i 1.29% 0.85% 1.66% 1.07% 1.56% 1.00%

LV 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

LV 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

LV 1 A 3 b i 70.12% 20.04% 43.06% 32.42% 19.02% 41.66%

LV 1 A 3 b ii 76.96% 19.11% 75.86% 19.18% 59.95% 24.90%

LV 1 A 3 b iii 69.21% 21.47% 61.83% 23.69% 22.05% 38.56%

LV 1 A 3 b iv 16.42% 82.80% 16.20% 83.51% 17.73% 81.49%

LV 1 A 3 b vi 0.01% 0.05% 0.00% 0.03% 0.00% 0.03%

LV 1 A 3 b vii 0.01% 0.05% 0.00% 0.03% 0.00% 0.03%

LV 1 A 3 d ii 41.32% 27.05% 40.85% 26.29% 40.68% 24.55%

LV 1 A 4 a i 3.68% 31.45% 4.27% 29.18% 5.72% 5.98%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

LV 1 A 4 b i 14.20% 44.33% 14.27% 44.24% 14.72% 43.76%

LV 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

LV 1 A 4 c i 12.14% 42.52% 12.44% 41.94% 15.10% 39.12%

LV 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

LV 2 C 1 0.00% 1.22% 0.00% 1.22% 0.00% 1.22%

LV 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

LV 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

LV 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

LV 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

MT 1 A 1 a 10.45% 4.07% 8.34% 8.28% 5.48% 11.01%

MT 1 A 2 a 8.14% 3.59% 8.15% 3.58% 8.15% 3.59%

MT 1 A 2 b 8.22% 3.54% 8.22% 3.54% 8.22% 3.54%

MT 1 A 2 c 8.22% 3.54% 8.22% 3.54% 8.22% 3.54%

MT 1 A 2 d 8.22% 3.54% 8.22% 3.54% 8.22% 3.54%

MT 1 A 2 f i 6.09% 3.09% 6.08% 3.08% 6.11% 3.07%

MT 1 A 2 f ii 45.37% 23.06% 38.35% 22.34% 14.91% 19.07%

MT 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

MT 1 A 3 b i 74.92% 17.79% 51.46% 27.54% 19.90% 40.53%

MT 1 A 3 b ii 74.19% 18.38% 30.12% 39.71% 15.54% 45.39%

MT 1 A 3 b iii 64.84% 23.92% 65.60% 23.44% 32.81% 37.18%

MT 1 A 3 b iv 21.44% 75.55% 21.38% 75.18% 21.38% 75.17%

MT 1 A 3 b vi 0.01% 0.01% 0.01% 0.02% 0.01% 0.02%

MT 1 A 3 b vii 0.01% 0.01% 0.01% 0.02% 0.01% 0.02%

MT 1 A 3 d ii 40.95% 27.30% 40.85% 26.29% 40.68% 24.55%

MT 1 A 4 a i 11.92% 14.00% 11.86% 15.44% 11.37% 21.82%

MT 1 A 4 b i 13.51% 13.65% 16.49% 13.47% 19.45% 14.40%

MT 1 A 4 c i 43.54% 15.37% 47.20% 15.39% 54.67% 16.01%

MT 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

MT 2 C 1 0.00% 1.22% 0.00% 1.22% 0.00% 1.22%

MT 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

MT 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

MT 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

NL 1 A 1 a 1.52% 6.05% 1.56% 4.29% 1.64% 4.72%

NL 1 A 1 b 9.13% 8.56% 9.13% 8.49% 9.21% 7.60%

NL 1 A 2 a 0.80% 2.08% 0.79% 1.96% 0.77% 1.76%

NL 1 A 2 b 6.88% 63.58% 6.81% 53.94% 6.56% 26.95%

NL 1 A 2 c 8.75% 21.46% 8.70% 20.89% 8.38% 18.15%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

NL 1 A 2 d 6.91% 67.07% 6.85% 61.52% 9.12% 7.41%

NL 1 A 2 f i 0.71% 2.53% 0.92% 2.49% 1.10% 2.73%

NL 1 A 2 f ii 38.55% 31.50% 30.98% 35.38% 16.72% 42.10%

NL 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

NL 1 A 3 b i 73.88% 19.01% 52.68% 27.69% 22.23% 39.93%

NL 1 A 3 b ii 15.88% 48.35% 15.04% 45.06% 15.00% 45.01%

NL 1 A 3 b iii 68.03% 23.02% 62.49% 24.59% 22.49% 40.57%

NL 1 A 3 b iv 18.15% 80.46% 18.92% 78.97% 20.35% 76.87%

NL 1 A 3 b vi 0.10% 0.22% 0.13% 0.31% 0.13% 0.30%

NL 1 A 3 b vii 0.10% 0.22% 0.13% 0.31% 0.13% 0.30%

NL 1 A 3 d ii 40.45% 28.88% 40.42% 28.99% 40.27% 29.28%

NL 1 A 4 a i 26.50% 34.98% 25.39% 34.31% 20.81% 35.40%

NL 1 A 4 b i 26.96% 33.03% 28.95% 30.95% 28.91% 30.95%

NL 1 A 4 c i 26.77% 33.07% 28.84% 30.90% 28.86% 30.88%

NL 1 A 4 c ii 38.70% 27.20% 33.52% 23.56% 11.75% 8.26%

NL 1 B 1 b 2.48% 4.08% 2.43% 3.99% 2.34% 3.85%

NL 1 B 2 a iv 0.07% 0.00% 0.07% 0.00% 0.07% 0.00%

NL 1 B 2 c 78.13% 15.62% 78.13% 15.62% 78.13% 15.62%

NL 2 B 5 a 0.01% 0.00% 0.01% 0.00% 0.01% 0.00%

NL 2 C 1 0.43% 0.30% 0.42% 0.34% 0.40% 0.37%

NL 2 C 3 0.00% 0.00% 0.00% 0.00% 0.00% 0.01%

NL 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

NL 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

NL 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

NO 1 A 1 a 1.73% 1.50% 1.75% 1.60% 1.80% 1.82%

NO 1 A 1 b 12.40% 39.59% 11.28% 45.90% 11.19% 46.50%

NO 1 A 2 a 0.83% 0.38% 0.84% 0.39% 0.82% 0.38%

NO 1 A 2 b 10.93% 47.13% 10.51% 50.13% 9.72% 55.72%

NO 1 A 2 c 2.02% 1.79% 1.92% 1.85% 1.97% 1.90%

NO 1 A 2 d 9.11% 4.06% 8.72% 4.02% 8.36% 3.99%

NO 1 A 2 f i 0.15% 0.13% 0.27% 0.20% 0.43% 0.30%

NO 1 A 2 f ii 42.72% 26.33% 35.19% 27.42% 16.03% 27.83%

NO 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

NO 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

NO 1 A 3 b i 73.89% 20.25% 67.98% 22.12% 52.92% 27.36%

NO 1 A 3 b ii 80.07% 15.92% 61.59% 23.50% 15.32% 44.68%

NO 1 A 3 b iii 68.30% 22.03% 64.99% 23.59% 29.54% 38.70%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

NO 1 A 3 b iv 17.30% 81.80% 17.20% 81.22% 18.25% 79.49%

NO 1 A 3 b vi 0.83% 1.97% 0.63% 1.49% 0.59% 1.39%

NO 1 A 3 b vii 0.83% 1.97% 0.63% 1.49% 0.59% 1.39%

NO 1 A 3 d ii 38.76% 25.08% 38.79% 25.65% 38.45% 26.23%

NO 1 A 4 a i 42.62% 17.01% 50.13% 18.00% 50.36% 18.04%

NO 1 A 4 b i 6.75% 45.90% 7.09% 45.12% 7.27% 44.04%

NO 1 A 4 c i 12.35% 43.90% 12.21% 43.40% 9.66% 43.19%

NO 1 A 4 c ii 39.07% 27.93% 33.29% 24.38% 12.67% 11.69%

NO 1 A 5 b 18.12% 60.39% 18.12% 60.39% 18.12% 60.39%

NO 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

NO 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

NO 2 C 1 0.01% 1.03% 0.00% 1.03% 0.00% 1.03%

NO 2 C 3 0.01% 0.14% 0.01% 0.12% 0.01% 0.12%

NO 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

NO 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

NO 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

PL 1 A 1 a 0.43% 0.31% 0.45% 0.37% 0.61% 0.49%

PL 1 A 1 b 8.29% 7.42% 8.30% 8.26% 8.32% 8.22%

PL 1 A 1 c 0.75% 0.72% 0.90% 0.70% 0.70% 0.73%

PL 1 A 2 a 3.59% 7.46% 4.49% 8.00% 4.60% 7.86%

PL 1 A 2 b 6.50% 9.96% 6.55% 9.94% 6.56% 9.95%

PL 1 A 2 c 4.01% 3.76% 5.10% 4.59% 5.76% 6.49%

PL 1 A 2 d 4.84% 2.64% 4.73% 2.69% 5.53% 5.07%

PL 1 A 2 f i 0.90% 0.70% 1.54% 1.31% 1.45% 1.58%

PL 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

PL 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

PL 1 A 3 b i 61.12% 24.38% 23.05% 41.14% 20.02% 40.66%

PL 1 A 3 b ii 75.59% 19.15% 66.37% 21.85% 24.35% 39.70%

PL 1 A 3 b iii 67.12% 22.82% 67.11% 22.79% 53.77% 28.98%

PL 1 A 3 b iv 14.04% 85.28% 19.25% 79.38% 20.16% 78.12%

PL 1 A 3 b vii 0.00% 0.01% 0.00% 0.00% 0.00% 0.00%

PL 1 A 3 d ii 41.80% 26.96% 40.95% 27.32% 40.82% 25.96%

PL 1 A 4 a i 2.65% 2.31% 2.37% 1.96% 2.39% 2.00%

PL 1 A 4 b i 25.34% 37.26% 25.46% 37.35% 25.35% 37.16%

PL 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

PL 1 A 4 c i 22.87% 36.31% 22.95% 35.95% 23.35% 35.08%

PL 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

PL 1 B 1 b 16.96% 13.67% 16.96% 13.67% 16.96% 13.67%

PL 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

PL 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

PL 2 B 5 a 0.38% 0.00% 0.22% 0.00% 0.24% 0.00%

PL 2 C 1 0.93% 0.40% 0.89% 0.43% 0.85% 0.43%

PL 2 C 3 0.00% 0.04% 0.01% 0.08% 0.01% 0.10%

PL 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

PL 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

PL 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

PT 1 A 1 a 3.46% 3.38% 2.57% 2.69% 1.99% 1.75%

PT 1 A 1 b 9.76% 4.17% 9.76% 4.18% 9.73% 4.17%

PT 1 A 2 a 2.82% 10.12% 2.92% 10.01% 2.81% 10.43%

PT 1 A 2 b 7.71% 4.71% 7.72% 4.49% 7.71% 4.74%

PT 1 A 2 c 7.47% 4.60% 7.46% 4.38% 7.40% 4.32%

PT 1 A 2 d 7.37% 4.07% 7.39% 4.06% 7.36% 4.08%

PT 1 A 2 f i 0.35% 0.32% 0.30% 0.29% 0.29% 0.30%

PT 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

PT 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

PT 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

PT 1 A 3 b i 75.15% 18.00% 60.68% 23.75% 28.66% 36.62%

PT 1 A 3 b ii 81.25% 15.66% 76.17% 16.52% 20.13% 39.94%

PT 1 A 3 b iii 68.27% 22.69% 68.21% 21.94% 29.42% 38.64%

PT 1 A 3 b iv 16.00% 83.83% 16.54% 83.29% 18.17% 81.51%

PT 1 A 3 b vi 0.01% 0.02% 0.01% 0.01% 0.01% 0.01%

PT 1 A 3 b vii 0.01% 0.02% 0.01% 0.01% 0.01% 0.01%

PT 1 A 3 d ii 41.74% 26.97% 41.00% 25.82% 39.69% 23.57%

PT 1 A 4 a i 53.14% 12.59% 50.63% 13.32% 52.75% 14.09%

PT 1 A 4 b i 20.38% 40.12% 21.11% 39.23% 22.60% 37.26%

PT 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

PT 1 A 4 c i 30.85% 34.35% 33.02% 32.92% 37.40% 31.05%

PT 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

PT 1 A 5 b 23.26% 57.56% 25.62% 56.26% 30.13% 53.78%

PT 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

PT 2 C 1 0.00% 0.89% 0.00% 0.90% 0.00% 0.90%

PT 2 C 3 0.02% 0.22% 0.02% 0.22% 0.02% 0.22%

PT 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

PT 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

PT 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

RO 1 A 1 a 0.40% 0.46% 0.44% 0.56% 0.53% 0.72%

RO 1 A 1 b 8.69% 4.47% 8.69% 4.49% 8.61% 4.60%

RO 1 A 1 c 0.15% 0.11% 0.15% 0.11% 0.15% 0.11%

RO 1 A 2 a 0.79% 1.04% 0.95% 1.18% 0.91% 1.20%

RO 1 A 2 b 2.74% 13.11% 3.56% 13.73% 4.45% 14.96%

RO 1 A 2 c 1.72% 4.19% 3.24% 4.38% 3.72% 4.39%

RO 1 A 2 d 6.99% 54.84% 7.01% 6.62% 7.01% 6.18%

RO 1 A 2 f i 0.41% 1.00% 0.50% 1.15% 0.60% 1.43%

RO 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

RO 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

RO 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

RO 1 A 3 b i 64.88% 23.82% 33.53% 36.70% 17.74% 42.62%

RO 1 A 3 b ii 74.00% 17.84% 28.97% 37.92% 19.01% 41.49%

RO 1 A 3 b iii 66.22% 24.32% 67.53% 21.82% 47.81% 28.30%

RO 1 A 3 b iv 19.96% 78.88% 20.20% 78.41% 20.69% 77.51%

RO 1 A 3 d ii 41.15% 28.68% 41.09% 28.55% 40.93% 28.13%

RO 1 A 4 a i 24.03% 32.24% 21.82% 28.80% 21.87% 28.81%

RO 1 A 4 b i 21.97% 39.25% 22.22% 38.97% 23.97% 37.39%

RO 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

RO 1 A 4 c i 15.91% 25.16% 15.83% 24.65% 17.52% 25.76%

RO 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

RO 1 B 2 a iv 0.19% 0.00% 0.19% 0.00% 0.19% 0.00%

RO 1 B 2 c 78.13% 15.62% 78.13% 15.62% 78.13% 15.62%

RO 2 A 7 a 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

RO 2 B 5 a 0.14% 0.00% 0.08% 0.00% 0.08% 0.00%

RO 2 C 1 0.13% 1.19% 0.12% 1.23% 0.12% 1.25%

RO 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

RO 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

RO 6 C e 7.44% 60.30% 7.44% 60.30% 7.44% 60.30%

SE 1 A 1 a 1.80% 0.91% 1.69% 0.86% 1.67% 0.83%

SE 1 A 1 b 9.68% 3.77% 9.67% 4.04% 9.54% 4.05%

SE 1 A 2 a 3.45% 2.13% 3.79% 2.37% 3.17% 2.14%

SE 1 A 2 b 5.28% 3.07% 4.01% 2.26% 0.30% 0.67%

SE 1 A 2 c 7.33% 3.76% 8.27% 3.63% 6.49% 4.39%

SE 1 A 2 d 6.61% 4.45% 6.61% 4.45% 6.60% 4.45%

SE 1 A 2 f i 4.02% 2.80% 3.34% 2.30% 4.37% 3.07%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

SE 1 A 2 f ii 45.99% 21.78% 41.35% 20.13% 32.31% 18.35%

SE 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

SE 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

SE 1 A 3 b i 71.59% 20.64% 52.03% 28.67% 26.44% 39.00%

SE 1 A 3 b ii 71.03% 18.45% 19.61% 40.66% 19.40% 40.63%

SE 1 A 3 b iii 67.77% 20.78% 58.49% 25.65% 15.58% 42.84%

SE 1 A 3 b iv 18.95% 80.08% 19.85% 77.67% 20.83% 75.42%

SE 1 A 3 b vi 0.05% 0.12% 0.05% 0.11% 0.05% 0.11%

SE 1 A 3 b vii 0.05% 0.12% 0.05% 0.11% 0.05% 0.11%

SE 1 A 3 d ii 38.84% 21.91% 40.55% 23.28% 40.38% 21.54%

SE 1 A 4 a i 17.51% 14.16% 14.91% 13.00% 13.27% 13.04%

SE 1 A 4 b i 31.61% 29.01% 31.25% 28.79% 30.40% 28.34%

SE 1 A 4 b ii 8.66% 78.83% 11.72% 75.69% 14.64% 72.78%

SE 1 A 4 c i 31.04% 28.79% 30.53% 28.58% 29.54% 28.07%

SE 1 A 4 c ii 39.32% 28.68% 36.87% 27.37% 33.44% 26.78%

SE 1 B 1 b 5.01% 3.91% 4.95% 3.86% 4.81% 3.75%

SE 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

SE 2 C 1 0.32% 0.24% 0.31% 0.26% 0.31% 0.27%

SE 2 C 3 0.00% 0.03% 0.00% 0.04% 0.00% 0.04%

SE 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

SE 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

SI 1 A 1 a 0.36% 0.65% 0.47% 0.56% 0.48% 0.72%

SI 1 A 2 a 5.52% 52.84% 5.53% 51.90% 2.41% 7.06%

SI 1 A 2 b 6.72% 4.62% 6.76% 4.91% 6.76% 5.84%

SI 1 A 2 c 7.64% 6.16% 7.25% 4.88% 8.31% 5.26%

SI 1 A 2 d 6.39% 4.44% 6.40% 5.50% 7.14% 6.79%

SI 1 A 2 f i 5.92% 5.54% 6.03% 5.20% 5.42% 4.95%

SI 1 A 2 f ii 46.56% 21.43% 40.22% 18.82% 14.03% 7.77%

SI 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

SI 1 A 3 b i 74.90% 17.57% 48.49% 29.28% 18.77% 41.85%

SI 1 A 3 b ii 73.25% 21.13% 57.29% 27.52% 25.15% 40.53%

SI 1 A 3 b iii 67.53% 23.11% 62.74% 23.95% 30.85% 36.10%

SI 1 A 3 b iv 20.68% 77.96% 20.76% 77.80% 20.75% 77.38%

SI 1 A 3 b vi 0.00% 0.01% 0.00% 0.01% 0.00% 0.01%

SI 1 A 3 b vii 0.00% 0.01% 0.00% 0.01% 0.00% 0.01%

SI 1 A 4 a i 52.10% 16.49% 52.62% 16.56% 52.25% 16.72%

SI 1 A 4 b i 14.39% 41.99% 14.36% 41.94% 14.29% 41.63%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

SI 1 A 4 b ii 14.14% 73.26% 15.82% 71.58% 18.03% 69.38%

SI 1 A 4 c i 23.69% 44.13% 37.79% 36.59% 37.60% 37.38%

SI 1 A 4 c ii 40.97% 28.85% 40.56% 28.58% 38.29% 27.07%

SI 2 C 1 0.00% 1.22% 0.00% 1.22% 0.00% 1.22%

SI 2 C 3 0.00% 0.04% 0.00% 0.05% 0.01% 0.06%

SI 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

SI 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

SI 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

SK 1 A 1 a 0.62% 0.86% 0.82% 0.92% 1.04% 0.93%

SK 1 A 1 b 8.47% 5.70% 8.44% 5.88% 8.52% 5.43%

SK 1 A 1 c 0.00% 0.01% 0.02% 0.11% 0.00% 0.01%

SK 1 A 2 a 0.80% 1.01% 0.84% 0.97% 0.74% 0.81%

SK 1 A 2 b 0.06% 0.68% 5.17% 23.34% 6.09% 21.60%

SK 1 A 2 c 7.18% 7.94% 7.16% 6.61% 7.26% 12.43%

SK 1 A 2 d 7.10% 4.55% 7.10% 4.43% 7.22% 4.56%

SK 1 A 2 f i 0.36% 0.36% 0.46% 0.43% 0.50% 0.44%

SK 1 A 2 f ii 46.71% 21.23% 40.45% 18.39% 13.92% 6.33%

SK 1 A 3 a i (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

SK 1 A 3 a ii (i) 17.98% 60.39% 17.98% 60.39% 17.98% 60.39%

SK 1 A 3 b i 66.13% 26.44% 48.33% 33.95% 28.89% 39.87%

SK 1 A 3 b ii 65.33% 29.37% 68.12% 26.16% 70.56% 19.91%

SK 1 A 3 b iii 62.51% 26.19% 63.27% 26.49% 53.56% 32.19%

SK 1 A 3 b iv 13.46% 86.44% 15.34% 83.33% 18.46% 78.45%

SK 1 A 3 b vi 0.00% 0.01% 0.00% 0.00% 0.00% 0.00%

SK 1 A 3 b vii 0.00% 0.01% 0.00% 0.00% 0.00% 0.00%

SK 1 A 3 d ii 41.11% 28.89% 41.11% 28.89% 41.11% 28.89%

SK 1 A 4 a i 1.05% 0.80% 2.32% 2.39% 6.23% 7.58%

SK 1 A 4 b i 15.83% 43.57% 15.60% 43.65% 16.04% 43.33%

SK 1 A 4 b ii 9.54% 77.97% 12.13% 75.35% 18.03% 69.38%

SK 1 A 4 c i 14.94% 39.34% 14.90% 39.15% 16.29% 40.16%

SK 1 A 4 c ii 40.09% 28.17% 37.32% 26.23% 26.40% 18.56%

SK 1 B 1 b 15.87% 12.79% 15.80% 12.74% 15.73% 12.68%

SK 1 B 2 a iv 0.20% 0.00% 0.20% 0.00% 0.20% 0.00%

SK 1 B 2 c 78.13% 15.63% 78.13% 15.63% 78.13% 15.63%

SK 2 B 5 a 0.38% 0.00% 0.20% 0.00% 0.22% 0.00%

SK 2 C 1 0.85% 0.40% 0.83% 0.42% 0.81% 0.44%

SK 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%



Security: PU


Country NFR09

2015 2020 2030

BC OC BC OC BC OC

SK 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

SK 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

UK 1 A 1 a 1.94% 11.40% 1.81% 8.95% 1.83% 8.19%

UK 1 A 1 b 8.46% 13.99% 8.32% 15.35% 8.25% 12.16%

UK 1 A 1 c 0.65% 0.52% 0.67% 0.47% 0.66% 0.39%

UK 1 A 2 a 1.17% 3.24% 1.14% 3.08% 1.10% 3.05%

UK 1 A 2 b 5.96% 24.80% 6.12% 26.41% 5.49% 54.34%

UK 1 A 2 c 6.22% 23.14% 5.41% 11.72% 5.31% 9.67%

UK 1 A 2 d 3.89% 20.96% 2.49% 21.82% 3.17% 29.28%

UK 1 A 2 f i 4.15% 8.11% 4.09% 7.88% 3.70% 8.14%

UK 1 A 2 f ii 44.52% 24.94% 42.42% 25.58% 43.11% 24.42%

UK 1 A 3 a i (i) 18.18% 60.61% 18.18% 60.61% 18.18% 60.61%

UK 1 A 3 a ii (i) 18.18% 60.61% 18.18% 60.61% 18.18% 60.61%

UK 1 A 3 b i 72.17% 18.19% 36.06% 33.77% 19.24% 40.76%

UK 1 A 3 b ii 81.43% 14.36% 57.85% 23.97% 19.98% 40.02%

UK 1 A 3 b iii 70.40% 20.74% 41.89% 24.99% 10.68% 31.88%

UK 1 A 3 b iv 21.41% 75.06% 22.14% 71.78% 22.50% 71.26%

UK 1 A 3 b vi 0.02% 0.06% 0.02% 0.06% 0.02% 0.06%

UK 1 A 3 b vii 0.02% 0.06% 0.02% 0.06% 0.02% 0.06%

UK 1 A 3 d ii 40.44% 29.08% 39.65% 29.18% 38.86% 30.53%

UK 1 A 4 a i 20.67% 26.77% 15.82% 20.06% 12.47% 21.19%

UK 1 A 4 b i 24.50% 38.35% 22.02% 39.18% 20.82% 39.47%

UK 1 A 4 b ii 16.42% 70.98% 17.69% 69.72% 17.73% 69.69%

UK 1 A 4 c i 12.02% 13.12% 11.96% 13.13% 11.86% 13.07%

UK 1 A 4 c ii 37.30% 26.21% 28.08% 19.74% 24.42% 17.17%

UK 1 A 5 b 52.35% 22.86% 50.68% 25.40% 48.97% 28.47%

UK 1 B 1 b 0.31% 0.48% 0.28% 0.46% 0.28% 0.46%

UK 1 B 2 a iv 0.10% 0.00% 0.10% 0.00% 0.11% 0.00%

UK 1 B 2 c 78.13% 15.62% 78.13% 15.62% 78.13% 15.62%

UK 2 B 5 a 0.02% 0.00% 0.02% 0.00% 0.02% 0.00%

UK 2 C 1 0.36% 1.21% 0.34% 1.20% 0.34% 1.19%

UK 2 C 3 0.00% 0.04% 0.00% 0.04% 0.00% 0.04%

UK 3 D 3 7.97% 35.49% 7.97% 35.49% 7.97% 35.49%

UK 4 F 13.17% 41.59% 13.17% 41.59% 13.17% 41.59%

UK 6 C e 9.29% 45.57% 9.29% 45.57% 9.29% 45.57%

d2.1 report and data on emission inventory at eu- wide

Documents