Technical Assistance for Developed Analytical Basis for ...
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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
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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
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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
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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
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• 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
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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
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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
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(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
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(2/2)
Vehicle categories and fuels (energy) considered
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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 ✓ - - - - - - - - -
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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
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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
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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
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(uniform presentation and alignment of years and
countries by source)
(for each vehicle category)
(e.g. U/R/H speeds and shares)
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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
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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
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➢ Total (for each vehicle category)
➢ Split per fuel
➢ Disaggregation into segments
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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
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(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
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(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
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(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
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(2/2)
Alternative fuels
EU28 passenger cars stock - segments
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• 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
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• 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
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Total
Percentage split of diesel energy consumption in 2016
47% PCs
16% LCVs
37% HDTs + Buses
Diesel
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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
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• 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
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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
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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
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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
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• “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
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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
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• 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
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Summary
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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
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2010 2050
GHG, E, AP 2010
GHG, E, AP
Activity
Stock
Activity
Stock
Activity
Stock
2050
SIBYL modular structure
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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
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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
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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
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Stock Equilibration module: attempts to equalize the stock change.
ScrappageStock
Equilibration
Total Stock
Implementation Matrix
Registration
Stock modification
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Registration module: distributes the new and second-hand registrations.
ScrappageStock
Equilibration
Total Stock
Implementation Matrix
Registration
Stock modification
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Total Stock module: combines all the previously calculated data, to yield the detailed stock.
ScrappageStock
Equilibration
Total Stock
Implementation Matrix
Registration
Stock modification
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Implementation matrix module: produces the Euro standard attribute for the stock.
ScrappageStock
Equilibration
Total Stock
Implementation Matrix
Registration
Stock equilibrium
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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
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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
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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
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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
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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
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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!