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

TFEIP Stockholm Agenda 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. Knoerr S. Radzimirski T. Murrels Research and measurement programmes for the improvement of transport emission and fuel consumption modelling in Europe P. Dilara Vehicle CO2 monitoring work – policy developmentsC. Pastorello Copert II to Copert 4L. Ntziachristos Examples of how new scientific knowledge (eg methods, models and EFs) have changed earlier road transport emission estimates Ireland, Netherland St. Leinart G. Geilenkirchen Comparison of centralised models with data reported by countriesL. Ntziachristos

TFEIP Stockholm Projected emission factors Emission reductions for future vehicle technologies generally follow the rule: Limitation: –Real-world behaviour does not (always) follow emission standards

TFEIP Stockholm Example: Euro V trucks NOx Emission Level EF over ES ratios

TFEIP Stockholm Impacts 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

TFEIP Stockholm Towards NECD: Current Assessment Source: Nitrogen oxide (NOx) distance-to-target for EEA member countries, EEA, Oct

TFEIP Stockholm 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?

TFEIP Stockholm Quantifying uncertainties Use new knowledge –Models –Activity data Compare with –Old models –Old activity data Objective: Explain uncertainty due to model and activity data differences

TFEIP Stockholm Approach to quantify uncertainty: input 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 –Actual excel files received by J. Cofala, Oct 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

TFEIP Stockholm Approach to quantify uncertainty: models 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

TFEIP Stockholm Runs 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

TFEIP Stockholm DE: Activity

TFEIP Stockholm DE: Technology penetration

TFEIP Stockholm DE: NOx Emissions 34% activity 75% EF

TFEIP Stockholm DE 2010: Technology responsibility

TFEIP Stockholm IE: Activity

TFEIP Stockholm IE: NOx Emissions 131% activity 64% EF

TFEIP Stockholm IE 2010: Technology responsibility

TFEIP Stockholm Summary 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

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