An assessment of uncertainty in COPERT4 & managing differences arising from model development: from...

An assessment of uncertainty in COPERT4 & managing differences arising from model development:from COPERTII to COPERT4

Leonidas Ntziachristos

ETC/ACM

Updated version of:

Road transport emission inventory uncertainties – GHGs and APs,EEA, Copenhagen, 18 November 2010

http://www.eionet.europa.eu/events/transport%20uncertainties/

TFEIP Stockholm 22011-05-03

Agenda of the Nov. 18, 2010 meetingAgenda of the Nov. 18, 2010 meeting

International requirements, good practice and examples of reporting uncertainty in emission inventories UNFCCC/UNECE

J. Goodwin

Report from IPCC expert meeting on use of models and measurements in GHG inventories

L. Ntziachristos

National examples of quantifying and reporting uncertainty

Germany, Poland, UK

W. KnoerrS. RadzimirskiT. Murrels

Research and measurement programmes for the improvement of

transport emission and fuel consumption modelling in Europe

P. Dilara

Vehicle CO2 monitoring work – policy developments C. Pastorello

Copert II to Copert 4 L. Ntziachristos

Examples of how new scientific knowledge (eg methods, models and EFs) have changed earlier road transport emission estimates

Ireland, Netherland

St. LeinartG. Geilenkirchen

Comparison of centralised models with data reported by countries L. Ntziachristos


Projected emission factorsProjected emission factors

• Emission reductions for future vehicle technologies generally follow the rule:

• Limitation:– Real-world behaviour does not (always) follow emission

standards

Current

Future

Current

Future

ES

ES

EF

EF


Example: Euro V trucks NOxExample: Euro V trucks NOx

• Emission Level

• EF over ES ratios

0,0

5,0

10,0

15,0

20,0

0 10 20 30 40 50 60 70 80 90 100

Speed (km/h)

NO

x (g

/km

)

SCREGREmission Standard

00,20,40,60,8

11,21,41,6

0 10 20 30 40 50 60 70 80 90 100

Speed (km/h)

EF E

uro

V /

EF

Euro

IV Real SCR/EGR Mix

Emission Standard


ImpactsImpacts

• Uncertainty of projection increases due to inability to predict real-world behaviour beforehand

• Difficulties to meet targets may originate from such uncertainty in setting targets

• Best example: NECD


Towards NECD: Current AssessmentTowards NECD: Current Assessment

Source: Nitrogen oxide (NOx) distance-to-target for EEA member countries, EEA, Oct. 2010.

-50 -30 -10 10 30 50

AustriaMalta

LiechtensteinIreland

LuxembourgSpain

FranceBelgiumNorwayGreece

GermanyDenmark

United KingdomPortugalSlovenia

NetherlandsItaly

SwedenSwitzerland

FinlandPoland

HungaryCzech Republic

CyprusSlovakiaBulgariaRomania

LatviaLithuania

Estonia

Percentage points below (- ) or above (+ ) linear target path to the 2010 emission ceilings


Responsible: Model or Regulation?Responsible: Model or Regulation?

• Model projects what regulations wished to achieve

• Reality proves that regulations failed to achieve– Manufacturers followed “letter” not “spirit” of law!

• Improvements required to regulations– Different driving profile?– Non-to-exceed approach?


Quantifying uncertaintiesQuantifying uncertainties

• Use new knowledge– Models– Activity data

• Compare with– Old models– Old activity data

• Objective: Explain uncertainty due to model and activity data differences


Approach to quantify uncertainty: Approach to quantify uncertainty: input datainput data

1. RAINS activity and emission factor data used to set the NECD targets

– Cost-effective Control of Acidification and Ground-Level Ozone. Part A: Methodology and Databases. Sixth Interim Report to the European Commission, IIASA 1998.

– Actual excel files received by J. Cofala, Oct. 2010.

2. FLEETS/EC4MACS data• Updated datasets used by GAINS in the framework of LIFE

EC4MACS• Based on original data by individual MSs

• Four countries used as examples:DE, FR, IE, NL


Approach to quantify uncertainty: Approach to quantify uncertainty: modelsmodels

1. COPERT II (1997)– Used to provide removal efficiencies to RAINS

2. COPERT 4 v8.0 (Nov. 2010)– Most updated version, implementing HBEFA 3.1

HDV EFs


Runs executedRuns executed

• Run 1: Original RAINS calculation

• Run 2: COPERT 2 + RAINS Input

• Run 3: COPERT 2 + EC4MACS Input

• Run 4: COPERT 4 + RAINS Input

• Run 5: COPERT 4 + EC4MACS Input


DE: ActivityDE: Activity


DE: Technology penetrationDE: Technology penetration

0%10%20%30%40%50%60%70%80%90%

100%HDT

EUR5

EUR4

EUR3

EUR2

EUR1

Conv

DPC GPC


DE: NOx EmissionsDE: NOx Emissions

34% activity75% EF


DE 2010: Technology responsibilityDE 2010: Technology responsibility

FC NOx NOx NOx NOx(kt) (kt) (% over RAINS total) (kt) (% over RAINS total)

DPC EUR4+5 4,534 83 28 37 13DHDV EUR2 2,014 81 27 10 3DHDV EUR4+5 2,168 77 26 60 20DHDV EUR3 -541 61 21 74 25GPC Conv 1,151 33 11 0 0DHDV Conv 674 27 9 0 0DPC EUR3 -1,881 18 6 41 14GPC EUR1 831 7 2 -1 0DHDV EUR1 158 5 2 0 0DPC EUR1 -259 2 1 3 1DPC Conv -87 0 0 0 0GPC EUR4+5 -1,219 -5 -2 -4 -1DPC EUR2 -2,500 -13 -5 12 4GPC EUR2 -5,668 -26 -9 -3 -1GPC EUR3 -10,217 -26 -9 -9 -3Total G -15,122 -17 -6 -17 -6Total D 4,281 341 115 239 81

Category Technology(C4 + EC4MACS) - RAINS (C4 + RAINS) - RAINS


IE: ActivityIE: Activity


IE: NOx EmissionsIE: NOx Emissions

131% activity

64% EF


IE 2010: Technology responsibilityIE 2010: Technology responsibility

FC NOx NOx NOx NOx(kt) (kt) (% over RAINS total) (kt) (% over RAINS total)

DPC EUR4+5 705 9 69 1 9DHDV EUR4+5 300 7 59 2 19DPC EUR1 154 2 20 0 0DPC Conv 122 2 20 0 0DPC EUR3 72 2 19 1 10GPC EUR1 166 1 10 0 0DPC EUR2 8 1 8 0 4GPC Conv 28 1 5 0 0GPC EUR4+5 514 1 4 0 0DHDV EUR1 0 0 0 0 0DHDV Conv -1 0 0 0 0GPC EUR2 -34 0 0 0 0DHDV EUR2 -12 0 0 0 3GPC EUR3 -16 0 -1 0 -2DHDV EUR3 -182 -2 -18 3 21

658 2 18 0 -21,166 22 177 8 66

Total GTotal D

Category Technology

(C4 + EC4MACS) - RAINS (C4 + RAINS) - RAINS


SummarySummary

• Differences between target and reality result from both emission factors and activity data– 65-75% higher emissions due to EFs– 19-131% higher emissions due to activity data

• Emission factors– Practically all Euro 3 / III and later diesel EFs– Conventional/E1 GPC!

• Activity data:– Misallocation of HD, LD diesel consumption– Relative increase of DPC consumption– Too fast scrappage of old vehicles assumed


More InfoMore Info

http://acm.eionet.europa.eu/reports/ETCACC_TP_2010_20_Copert2vsCopert4

An assessment of uncertainty in COPERT4 & managing differences arising from model development: from...

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