EMISIA SA presentation

Technical Assistance for Developed Analytical Basis for

Formulating Strategies and Actions towards

Low Carbon Development

October 2019, Ankara

Background

• Road transport electrification is considered to significantly contribute towards EU environmental targets

– Greenhouse gases

– Urban air quality

– Specific (non-binding) targets to decrease conventionally fuelled vehicles

• Large uncertainty in projecting their penetration to the market (revolutionary rather than evolutionary technology)

– Market disruption effect difficult to predict with conservative engineering tools

• Still, it is interesting to predict the impact of different penetration scenarios

2

Starting point

• A complete and consistent dataset of historical data with no gaps on a per country basis– Harmonized with official national statistical data so as to reflect real

situation to the extent possible

• Historical trends in fleet turnover dynamics– vehicle lifetime, activity drop with mileage, second hand

registrations, age distribution in reference year, etc…

• Total activity projection agreed on a political level, e.g. PRIMES/GAINS baseline

3

Synthesis of primary information

• No single source provides all data at the level of detail required

• Gaps, incomplete times series, inconsistent information, no common vehicle classification

• Values from different sources seldom agree

4

A processing methodology is required in order to create a complete and consistent dataset with no gaps, harmonized with official

statistical data, by synthesizing information from the various sources

METHODOLOGY

Baseline creation

6

Context and background

• EMISIA actively maintains reliable and up-to-date vehicle fleet and activity road transport data, ready to be used in air pollutant and GHG emission calculation tools

• In 2018, EMISIA has performed a major update, starting the time series from 1990, with projections to 2050, taking into account all recent statistical data

• Quality, completeness, and consistency ensured with:

– Significant expertise on transport data, more than 20 years of experience

– Extensive reviews and cross-checking

• The dataset will be updated on a yearly basis

Related EC funded previous projectsThe FLEETS project

European Database of Vehicle Stock for the Calculation and Forecast of Pollutant and Greenhouse Gases Emissions with

TREMOVE and COPERT

EC / DG Environment

December 2006 – April 2008

Under the coordination of LAT/AUTH

Data cover the period 2000-2005

The TRACCS project

Transport data collection supporting the quantitative analysis of measures relating

to transport and climate change

EC / DG Climate Action

January 2012 – December 2013

Under the coordination of EMISIA

Data cover the period 2005-2010

7

• First, a complete (no gaps) and consistent dataset of historicaldata has been created on a per country basis, starting from 1990– Harmonized with official national statistical data, so as to reflect real situation to the

extent possible

– Basis for future projections

– 34 countries coverage: EU28, EFTA (IS, LI, NO, CH), FYROM, TR

• Historical trends in fleet turnover dynamics obtained– Fleet structure and evolution, new registrations (sales), petrol/diesel trends and

alternative fuels penetration, vehicle lifetime and age distribution

• Total activity projection agreed on a political level, estimation of sales, fleet projections to 2050

8

Our current approach in a nutshell

….

• Official road transport emission inventory preparation software– EEA, JRC, EMISIA (https://www.emisia.com/utilities/copert/)

• Ready to go vehicle fleet and activity (COPERT) data– EMISIA (https://www.emisia.com/utilities/copert-data/)

• Vehicle stock, air pollutants, and GHG projection policy evaluation tool– EMISIA (https://www.emisia.com/utilities/sibyl/)

• Fleet projections, impact of alternative fuels penetration in the market

• Environmental and energy studies, national emission inventories

• Policy assessment studies for EU institutes and the industry

• Impact of current and future legislation on emission reduction policies

Usage of the dataset

9

Other

• Temporal coverage: 1990 – 2050

• Update of alternatively fueled vehicles (LPG, CNG, hybrids, electric) in all categories (cars, vans, trucks, buses, mopeds, motorcycles)

• Disaggregation into subcategories based on statistical data to the extent possible (depending on data availability)

– More reliable estimation of vehicle fleet split into: segments for cars, GVW categories for vans/trucks/buses, engine capacity categories for mopeds/motorcycles

• Inclusion of mini-cars and ATVs

Significant updates compared to previous versions

10

(1/2)

• Update of age distributions, so that average age is consistent with statistical data– Better estimation of fleet structure and technology/Euro standards per country

– Recent developments in legislation have been taken into account in all vehicle categories

• Consistency of fuel consumption with national submissions in UNFCCC on a per vehicle category level– Reliable split of fuel consumption per vehicle category: cars, vans, trucks and buses,

mopeds and motorcycles

• New methodology followed for data collection and processing– Easier yearly updates

Significant updates compared to previous versions

11

(2/2)

Vehicle categories and fuels (energy) considered

12

Road transport stock and activity dataset, EMISIA, January 2019

Vehicle categoryEU

classification

Petrol Diesel LPG CNG E85Hybrid-petrol

Hybrid-diesel

BEV PHEV FCEV

Passenger cars M1 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Buses M2, M3 ✓ ✓ ✓ ✓ - - - ✓ ✓ -

Light commercial vehicles (vans)

N1 ✓ ✓ ✓ ✓ - - - ✓ - -

Heavy duty trucks N2, N3 ✓ ✓ ✓ ✓ - - - - - -

Mopeds (two- and three-wheel)

L1, L2 ✓ - - - - - - ✓ - -

Motorcycles(two-wheel and tricycles)

L3, L4, L5 ✓ - - - - - - ✓ - -

Mini-cars (or micro cars) (on-

road quads)

L6 - ✓ - - - - - ✓ - -

ATVs(all-terrain vehicles and side-by-side buggies)

L7 ✓ - - - - - - - - -

13

Road transport stock and activity dataset, EMISIA, January 2019

Vehicle category Following COPERT / SIBYL segment subcategories

Passenger cars ➢ Small / Medium / Large-SUV-Executive

Buses➢ Urban Buses: Midi <=15 t / Standard 15 - 18 t / Articulated >18 t

➢ Coaches: Standard <=18 t / Articulated >18 t

Light commercial vehicles

➢ N1-I / N1-II / N1-III

Heavy duty trucks

➢ Rigid: <=7,5 t / 7,5 - 12 t / 12 - 14 t / 14 - 20 t / 20 - 26 t / 26 - 28 t / 28- 32 t / >32 t

➢ Articulated: 14 - 20 t / 20 - 28 t / 28 - 34 t / 34 - 40 t / 40 - 50 t / 50 - 60t

Mopeds ➢ 2-stroke <50 cm³ / 4-stroke <50 cm³

Motorcycles➢ 2-stroke >50 cm³

➢ 4-stroke: <250 cm³ / 250 - 750 cm³ / >750 cm³

Mini-cars / ATVs -

Disaggregation into segments

14

Road transport stock and activity dataset, EMISIA, January 2019

Technology / Euro standards

Vehicle category Following COPERT / SIBYL Euro standard subcategories

Passenger cars

➢ Conventional: PRE ECE, ECE 15/00-01, ECE 15/02, ECE 15/03, ECE 15/04, Improved Conventional, Open Loop

➢ Euro 1, 2, 3, 4, 5

➢ Euro 6: up to 2016, 2017-2019, 2020+

Light commercial vehicles

➢ Conventional

➢ Euro 1, 2, 3, 4, 5

➢ Euro 6: up to 2017, 2018-2020, 2021+

(or up to 2016, 2017-2019, 2020+ depending on the N1-subcategory)

Buses / Heavy duty trucks

➢ Conventional

➢ Euro I, II, III, IV, V, VI

Mopeds / Motorcycles / Mini-cars / ATVs

➢ Conventional

➢ Euro 1, 2, 3, 4, 5

15

Historical years:

Basic methodological components

Data collection from statistical

sources

First processing step

Total stock and new registrations

(sales)Split per fuel

Disaggregation into segments

Age distributions and technologies / Euro standards

Typical mileage and other activity

parameters

Mileage adjustment to match statistical

FC

Flowchart of dataset creation

16

(uniform presentation and alignment of years and

countries by source)

(for each vehicle category)

(e.g. U/R/H speeds and shares)

17

Road transport stock and activity dataset, EMISIA, January 2019

Source Main information provided

EurostatStock and new registrations per fuel and engine capacity / GVW

EC Statistical Pocket Book Stock and new registrations

ACEA (and ANFAC Motor Vehicle Parc)

Stock per fuel, new registrations per fuel and per segment / GVW

ACEMStock, new registrations per fuel and engine capacity (only L-vehicles)

CO2 monitoring database New registrations per fuel and segment (PCs and LCVs)

EAFO (European Alternative Fuels Observatory)Stock and new registrations of alternative fuels (LPG, CNG, electric, H2)

NGVA Europe (Natural Gas Vehicle

Association)

NGV Global (Natural Gas Vehicle Knowledge

Base)

Stock of natural gas vehicles

UNFCCC Fuel consumption per vehicle category and fuel

Other sources: literature, studies, reports, national statistics web sites

Various information

Data sources

Synthesis of primary information

18

Road transport stock and activity dataset, EMISIA, January 2019

• No single source provides all data at the level of detail required

• Gaps, incomplete times series, whole countries/years missing

• Inconsistent information, values from different sources seldom agree

• No common vehicle classification, insufficient documentation

Summary of problems with statistical sources

A processing methodology is required in order to create a complete and

consistent dataset with no gaps, harmonized with official statistical data, by

synthesizing information from the various sources

Comparison of sources; one of them is selected as the main source to start with (based on data quantity and quality)

Gap-filling from other sources; attention for inconsistencies (e.g. trend instead of absolute value in case of significant differences between sources)

If gaps still exist, then: interpolation, trend or data from other countries (e.g. percentages for split/disaggregation), estimates and expert judgement calculations

Checking rules, e.g. all fuels add up to total, all segments of a fuel add up to this specific fuel, no negative values, percentages add up to 100%, etc.

Main steps for stock and new registrations

19

➢ Total (for each vehicle category)

➢ Split per fuel

➢ Disaggregation into segments

20

Historical years:

Results

• Total sales of cars present the “rebound effect” in recent years, after the economic (and sales) crisis in the period 2008-2013 (15.1m vehicles sold in 2017)

• Sales of petrol continuous increase since 2012– From 5.1m to 7.6m vehicles (2012→2017)

• Sales of diesel first time lower than petrol in 2017 since 2010– 6.6m vehicles in 2017

EU28 passenger cars new registrations

21

(1/2)

Percentage split in 2017:

➢ 50% petrol

➢ 44% diesel

➢ 6% alternative fuels

• Sales of cars with alternative fuels in 2017: 856k vehicles (continuous increase since 2011)

• About half of them are petrol hybrid (430k vehicles)

• LPG + CNG : 206k vehicles (24% of alternative fuels)

• BEV + PHEV : 215k vehicles (25% of alternative fuels)

EU28 passenger cars new registrations

22

(2/2)

Alternative fuels

• Total stock of cars increase from 242m to 259m vehicles (2010→2016)

• Stock of petrol constant since 2014, after a continuous decrease period 2001→2014– 138m vehicles in 2016

• Stock of diesel continuous increase since 1990– 110m vehicles in 2016, still lower than petrol

EU28 passenger cars stock

23

(1/2)

Percentage split in 2016:

➢ 53% petrol

➢ 42% diesel

➢ 5% alternative fuels

• Stock of cars with alternative fuels in 2016: 11.2m vehicles (continuous increase trend)

• Most of them (71%) are LPG(many conversions from petrol, not all are actual new sales)

• CNG, petrol hybrid : 1.2m vehicles each (11% of alternative fuels each)

• BEV + PHEV : 453k vehicles (4% of alternative fuels)

EU28 passenger cars stock

24

(2/2)

Alternative fuels

EU28 passenger cars stock - segments

25

• Segmentation of petrol cars stock does not present significant differences over time

• Percentage split in 2016:– 56% small

– 38% medium

– 6% large

In diesel cars there has been a small shiftfrom large to medium and small vehicles

Percentage split in 2016:6% small

77% medium

17% large

• Passenger cars fleet is getting older year-on-year (from 10.4 years in 2013 to 11 years in 2016)

• Many older vehicles remain in the fleet and are not deregistered → impact on the age distribution of the dataset (more vehicles in the age groups 10-20 and 20-30)

• Petrol fleet older than diesel due to historical evolution of sales– Sales of diesel cars have increased significantly after 2000, compared to the decade of 90’s, while sales of petrol

cars present significant decrease from 2000 to 2012, hence, diesel fleet is younger/newer than petrol

EU28 passenger cars stock - average age

26

• Despite a decrease period 2008-2013, total energy consumption from road transport has increased again recently (12.5 x 106 TJ in 2016)

• Percentage split in 2016 (no significant differences over time)– 60% PCs

– 12% LCVs

– 27% HDTs + Buses

– 1% Motorcycles + Mopeds

EU28 road transport energy consumption

27

Total

Percentage split of diesel energy consumption in 2016

47% PCs

16% LCVs

37% HDTs + Buses

Diesel

28

Projections:

Basic methodological components

Evolution of total stock and new registrations

(sales)

Survival rates (i.e. lifetime functions and age

distribution)

Penetration of alternative fuels and, especially, electric vehicles in the

market

Stock evolution per fuel and segment

Main parameters to consider

29

• Large uncertainty in all main parameters

• A single dataset cannot cover all possible developments

• Our baseline projection has been created taking into account

– The historical trends in fleet turnover dynamics

– Relevant studies

• The impact of different scenarios can be investigated with the tool

– For example, road transport electrification is considered to significantly contribute towards EU environmental targets

– Hence, it is interesting to predict the impact of different penetration scenarios

Uncertainties

30

EU Reference Scenario REF2016/PRIMES total activity trends used to derive total stock projections per vehicle category (assumption: future vehicles will be driven as much as today’s ones)

Total new registrations (sales) per vehicle category are estimated based on

historical trends

Age distribution for total stock is derived with lifetime functions, respecting total stock change from year-to-year and new registrations (smooth transition from

historical to future data)

Penetration of alternative fuels and, especially, electric vehicles in the market is estimated based on relevant studies

Survival rates (i.e. lifetime functions) of total stock are used as a ‘guide’ to derive age distributions per fuel and segment

Stock evolution per fuel and segment is derived from corresponding survival rates

and new registrations

Our approach for the projections

31

32

Projections:

Results

• EU28 road vehicles stock projection exhibits a continuous increase from year to year (till 2050), following corresponding passenger and freight transport activityincrease (EU REF2016)

• Although this increase may be questionable, the EU Reference Scenario is one of the EC’s key analysis tools in the areas of energy, transport and climate action

EU28 road vehicles stock development

33

2050 increase over 2015

PCs 28%

LCVs 50%

HDTs 50%

Buses 21%

Motorcycles 25%

Mopeds 5%

• EU28 passenger car sales from 1990 till now are between 12m and 16m

• Usually correlated to GDP, but still difficult to make prediction to 2050

• Our approach: following the “rebound effect” of recent years (after the 2008-2013 crisis), sales are projected to reach 16m in 2020, 16.5m in 2030, and 17m in 2050

• This increase is also in accordance with the stock increase based on EU REF2016

EU28 passenger cars total sales projection

34

• “The transition to a Zero Emission Vehicles fleet for cars in the EU by 2050”, EAFO, EC DG MOVE, 2017

• “European Roadmap Electrification of Road Transport”, ERTRAC, 2017

Our approach: electric vehicles increase their market share with a fashion in-between other scenarios, considered as ‘boundary’ ones (e.g. there are more conservative scenarios compared to our approach and there are even more aggressive ones where electric vehicles replace 100% the ICE vehicles by 2035)

EU28 PCs sales penetration of electric vehicles

35

2020 2025 2030 2035 2040 2050

BEV 5% 18% 35% 45% 55% 70%

PHEV 5% 18% 35% 32% 29% 25%

FCEV 0.0% 0.3% 1% 1.5% 2% 3%

Petrol 47.2% 36.3% 17.2% 13.9% 9.8% 1.6%

Diesel 38.6% 24.2% 9.3% 5.9% 3.3% 0.4%

LPG+CNG+E85+Hybrid-petrol+Hybrid-diesel

4.2% 3.2% 2.5% 1.7% 0.9% 0.0%

Relevant studies

• Projected passenger car sales structure follows our assumptions on penetration of electric vehicles

• 11.6m BEV+PHEV sales in 2030, increasing to 16.2m in 2050

• 2.8m petrol sales in 2030, declining to <0.3m in 2050

• 1.5m diesel sales in 2030, declining to <0.1m in 2050

EU28 passenger cars sales structure projection

36

• Our approach results in a significant number of electric vehicles in the future parc

• 24% BEV+PHEV in 2030, increasing to 78% in 2050

• 76% ICE vehicles in 2030, decreasing to 20% in 2050 → ICE vehicles still remain in 2050

EU28 passenger cars fleet composition

37

Summary

38

Road transport stock and activity dataset, EMISIA, January 2019

• In 2018, EMISIA has performed a major update in the vehicle fleet and activity road transport dataset, taking into account all recent statistical data

• 34 countries (EU28, EFTA, FYROM, TR), 1990-2050 time series

• Reliable data with ensured quality, completeness, and consistency; can be used in air pollutant and GHG emission calculation tools

• Historical data harmonized with official national statistical data, so as to reflect real situation to the extent possible

• Projections based on activity evolution as agreed in high-level EU policy related studies; penetration of electric vehicles one of the most critical parameters

METHODOLOGY

Scenario Formulation

From an idea to practical results

40

2010 2050

GHG, E, AP 2010

GHG, E, AP

Activity

Stock

Activity

Stock

Activity

Stock

2050

SIBYL modular structure

41

ScrappageStock

Equilibration

Total Stock

Implementation Matrix

Mileage Distribution Modelling

Activity

Emission Modelling

Constraints

Target

reached?

Energy Consumption

Modelling

Registration

User input:· Stock

· Driving Patterns

· Efficiency

· TtW and WtT factors

· Fuel usage

Scenario requirements

42

Stock

• New regs

• Total stock

• Survival rates

• 2nd hand regs

Activity

• Annual mileage

• Activity drop with age

• Trip patterns (mobility)

Energy/emissions

• Energy/Emission factors

• Efficiency improvement

• EURO standards

• WtT factors

Additional details

• Fuel effects

• Constants

• Details and other parameters

EXAMPLE – STOCK

Possible stock scenarios

• Growth rate

• Market development

• Accelerated scrappage

• Technology replacement

• New category/technology

44

Stock modification

45

ScrappageStock

Equilibration

Total Stock

Implementation Matrix

Registration

Scrappage module: calculates the total scrapped stock based on the base year stock and the survival rates.

Stock modification

46

Stock Equilibration module: attempts to equalize the stock change.

ScrappageStock

Equilibration

Total Stock

Implementation Matrix

Registration

Stock modification

47

Registration module: distributes the new and second-hand registrations.

ScrappageStock

Equilibration

Total Stock

Implementation Matrix

Registration

Stock modification

48

Total Stock module: combines all the previously calculated data, to yield the detailed stock.

ScrappageStock

Equilibration

Total Stock

Implementation Matrix

Registration

Stock modification

49

Implementation matrix module: produces the Euro standard attribute for the stock.

ScrappageStock

Equilibration

Total Stock

Implementation Matrix

Registration

Stock equilibrium

50

Year Year+1

EXAMPLE – ACTIVITY

Possible activity scenarios

• Growth rate

• Distribution per vehicle type

• Dropping activity with age

• eMobility

• Bifuel activity per fuel

52

Activity drop with age

53

Base mileage

Age modifier

Final Annual Mileage

eMobility

54

Activity

55

Mileage Distribution Modelling

Activity

Stock - N (age)

M(age)

Mileage Distribution Modelling Module: processes the mileage-related data and produces the exact mileage per vehicle and age.

Activity

56

Activity Module: combines stock and mileage data to produce the detailed activity. The activity/stock can be set to match baseline values on a sector basis

Mileage Distribution Modelling

Activity - vkm (age)

Stock

EXAMPLE – FUELS

Possible fuels scenarios

• Blends

• Bifuel definition

• Fuel replacement

• FFVs

58

CNG/LPG utilisation

59

EXAMPLE – ENERGY & EMISSIONS

Energy consumption in SIBYL

61

TtW

WtT

WtW

Energy calculation - TtW

62

Base energy consumption

function

Efficiency development

factor

Final energy consumption

Energy consumption factors - TtW

63

Consumed Energy

Type-Approval factors

Real-world factors

Custom factors

SIBYL modules

64

Energy Consumption Modelling Module : combines activity data with consumption functions to produce the detailed energy consumption.

Stock and activity - vkm

Emission Modelling

Energy Consumption Modelling - TJ

SIBYL modules

65

Emission Modelling Module : similar to the previous module but uses the emission factors instead.

Stock and activity - vkm

Emission Modelling - t

Energy Consumption

Modelling

Possible energy/emission scenarios

• Energy consumption factor type selection

• Efficiency development

• WtT CO2 /energy factor setting

66

Case study 1

Country XNOx Emissions from Diesel PCs

Country X Base Case

Country X Scenario i

Country X Scenario v

Case study 2

Country XNO2 AQ Compliance

SHERPA➔ AERIS IAM Overview

• The SHERPA tool can, in principle, work down to a grid-to-grid level based on the results of a very limited number of simulations (typically less than 20) of the full chemical transport model

• In this project, the SHERPA tool will be set up to explore three sub country regions in each country: Urban Zones, Commuter Zones and Rural Areas

• For each of these three sub-country regions in each of the 34 countries included in SHERPA:– The emissions by pollutant and sector have been determined

– The corresponding ‘beyond CLE’ measures cost curves (consistent with GAINS) determined

• For each sub country area the ‘emission impact potency’ of PM2.5 for each contributing pollutant (SO2, NOx, PPM2.5, NH3 and VOCs) to each sub-country region was determined

• This then facilitates optimisation at both the country and ‘sub-country’ level

SHERPA Enabled Optimisation StrategyAt Sub-Country Level

PM Impacts (YOLL)On All EU Population

SO2, NOX, NH3, PPM,

VOC

Country 1

SO2, NOX, NH3, PPM,

VOC

Country N

EU Urban Impacts

EU XU Impacts

EU Rural Impacts

U

XU

R

Country N

Curent AERIS IAMS-Rs: Country-Country

Cost Curves: Country-Pollutant

SHERPA Enabled AERIS IAMS-Rs: Country Region-Country RegionCost Curves: Sub-Country-Pollutant

U

XU

R

Country 1

Country X NO2 Compliance Picture - with highest concentration station “distance to target” over plotted and yearly AQMS compliance values

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2005 2010 2015 2020 2025 2030

Station C

ompliance w

ith A

nnual Mean

Year

NO2 Station Compliance and Distance from Compliance in Country X

Country X NO2 Compliance Picture - with highest concentration station “distance to target” over plotted and yearly AQMS compliance values

-10

-5

0

5

10

15

20

25

30

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2005 2010 2015 2020 2025 2030

Highest S

tation C

oncentration A

bove

AQLV (µg/m

3)

Station C

ompliance w

ith A

nnual Mean

Year

NO2 Station Compliance and Distance from Compliance in Country X

NO2 Compliance Picture - with highest concentration station “distance to target” over plotted and yearly AQMS compliance values

-10

-5

0

5

10

15

20

25

30

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2005 2010 2015 2020 2025 2030

Highest S

tation C

oncentration A

bove

AQLV (µg/m

3)

Station C

ompliance w

ith A

nnual Mean

Year

NO2 Station Compliance and Distance from Compliance

2010 AQMSCompliant: 206Non-Compliant:23

2015 AQMSCompliant: 212Non-Compliant:17

2020 AQMSCompliant: 218Non-Compliant:11

2030 AQMSCompliant: 224Non-Compliant:5

2025 AQMSCompliant: 222Non-Compliant:7

Country X Highest Concentration Station “Distance to Target”– Base Case with New Scenarios

-10

-5

0

5

10

15

2014 2016 2018 2020 2022 2024 2026 2028 2030Concentration (µg/m

3)

Year

NO2 Station Distance from Compliance in Country X

BC

7xLLV

Scn. i

Scn. ii

Scn. iii

Scn. iv

Scn. v

PM2.5 Impact (Years of Life Statistical Life Gain) Gap Closure Between Base Case (CLE) and MTFR Versus Optimised Additional Cost Above CLE

Country to EU28

Country Region to EU28 Urban Region

Gap Closure

Cost (MillEuro/

Yr)

25% 51

50% 553

75% 2585

100% 38723

Gap Closure

Cost (MillEuro/Y

r)25% 3250% 30275% 1851100% 38723

First Optimisation Results

First Findings

• As anticipated, optimisation which focusses on reducing impacts only in urban areas of the EU (a surrogate for meeting a tougher PM2.5 AQLV) is more (significantly more) cost efficient than reducing impacts across the whole EU population

• This difference will be even more significant when the ‘compliant’ and ‘non-compliant’ urban areas are differentiated (a next stage in the project) and optimisation focussing on only ‘non-compliant’ areas is undertaken

• This is the first time that such EU-wide sub-country level optimisation has been undertaken with potentially significant implications for future AQ policy

Case study 3

Greece vs. FranceImportance of WtW

Outline • Share a few results from road transport emission forecasting

tool (developed for Concawe) to illustrate the significant country-country differences in the ‘well to wheel’ CO2

implications in moving from conventional power trains to plug in EVs

• Also show the corresponding results for NOx emissions and the impact to illustrate the importance of widening the ‘considerations envelope’ to include emissions from the systems used to generate the required electricity

• Underlying input country data derived from PRIMES runs in support of the Commission’s Climate and Energy Package

• Focus is on France and Greece

France PC Diesel <2L CO2E6a 2015 (-40% E5) E6b 2021 (-80%)

CO2 Emissions From <2L Diesel Passenger Cars: France

Base Case: Euro 6a 2015 (-40% over E4) Euro 6b 2021 (-80%)

0

10000

20000

30000

40000

50000

60000

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Diesel FAME LPG CNG Electricity

France PC Diesel <2L CO2All PCs BEV From 2020

CO2 Emissions From <2L Diesel Passenger Cars: France

(New Registrations 100% BEVs From 2020)

0

10000

20000

30000

40000

50000

60000

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Diesel FAME LPG CNG Electricity

France PC Diesel <2L NOxE6a 2015 (-40% E5) E6b 2021 (-80%)

NOx Emissions From <2L Diesel Passenger Cars: France

Base Case: Euro 6a 2015 (-40% over E4) Euro 6b 2021 (-80%)

0

20

40

60

80

100

120

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Pre-CVD 91/441 EEC 94/12 EEC >2000 >2005

France PC Diesel <2L NOxAll PCs BEV From 2020

NOx Emissions From <2L Diesel Passenger Cars: France

(New Registrations 100% BEVs From 2020)

0

20

40

60

80

100

120

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Pre-CVD 91/441 EEC 94/12 EEC >2000 >2005

France PC Diesel <2L + PP NOxAll PCs BEV From 2020

NOx Emissions From <2L Diesel Passenger Cars

Plus Associated Power Generation NOx: France

(New Registrations 100% BEVs From 2020)

0

20

40

60

80

100

120

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Pre-CVD 91/441 EEC 94/12 EEC >2000 >2005 PP NOx

Greece PC Gasoline 1.4-2L WtW CO2Base Case

CO2 Emissions From 1.4-2L Passenger Cars: Greece

Base Case

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Gasoline Ethanol LPG CNG Electricity

Greece PC Gasoline 1.4-2L CO2All BEV From 2020

CO2 Emissions From 1.4-2L Gasoline Passenger Cars: Greece

(New Registrations 100% BEVs From 2020)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Gasoline Ethanol LPG CNG Electricity

Greece PC Gasoline 1.4-2L NOxBase Case

NOx Emissions From 1.4-2L Gasoline Passenger Cars: Greece

Base Case

0

2

4

6

8

10

12

14

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Pre-CVD 91/441 EEC 94/12 EEC >2000 >2005

Greece PC Gasoline 1.4-2L NOxAll PCs BEV From 2020

NOx Emissions From 1.4-2L Gasoline Passenger Cars: Greece

(New Registrations 100% BEVs From 2020)

0

2

4

6

8

10

12

14

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Pre-CVD 91/441 EEC 94/12 EEC >2000 >2005

Greece PC Gasoline 1.4-2L NOx+PP NOxAll PCs BEV From 2020

NOx Emissions From 1.4-2L Gasoline Passenger Cars

Plus Associated Power Generation NOx: Greece

(New Registrations 100% BEVs From 2020)

0

2

4

6

8

10

12

14

1995 2000 2005 2010 2015 2020 2025 2030 2035

Em

issio

ns k

t/year

Pre-CVD 91/441 EEC 94/12 EEC >2000 >2005 PP NOx

Technical Assistance for Developed Analytical Basis for

Formulating Strategies and Actions towards

Low Carbon Development

http://www.lowcarbonturkey.org/

Thank you for your attention!