Improvement in the estimation and back-extrapolation of CO2 emissions from the Irish road transport sector using a bottom-up data modelling approach

• Published in: Transportation research. Part D, Transport and environment Vol. 56; pp. 18 - 32
• Main Authors: Alam, Md. Saniul, Duffy, Paul, Hyde, Bernard, McNabola, Aonghus
• Format: Journal Article
• Language: English
• Published: Elsevier India Pvt Ltd 01.10.2017
• Subjects: Bottom-up; Climate change; CO2; Emissions modelling; Regression; Transport; Regression; Climate change; Transport; Emissions modelling; CO2; Bottom-up
• DOI: 10.1016/j.trd.2017.07.011
• ISSN: 1361-9209

•Recalculation of historic CO2 emission concerning fleet age and type was conducted.•Missing vehicle mileages at a disaggregated fleet level were back extrapolated.•Mileage back extrapolation was conducted using a regression approach.•Vehicles registered before 2006 were the largest contributor of CO2 in 2013.•Underestimated diesel PC’s CO2 provided an incorrect reflection of dieselisation. Road transport is a major source of CO2 emissions in Ireland and accounts for almost 96% of the total CO2 emissions from the transport sector. Following the recent adopted UNFCCC reporting guidelines on annual inventories [24/CP.19], this study applied the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2006 IPCC GLs) tier 3 approach to estimate CO2 emissions from road transport at the vehicle category level, for the first time in Ireland. For this, disaggregated datasets were prepared based on year of vehicle registration and mileage since registration of the vehicle. Such an approach provided a more realistic national scenario in comparison to the use of average mileage degradation in emission calculations. This investigation comprised a recalculation of previous emissions estimates (1990–2012) and an estimation of CO2 emissions in 2013 using a previously unavailable level of data disaggregation for vehicle mileage as well as using vehicle class specific data and an improved bottom-up estimation methodology in COPERT. Historic vehicle fleet data were restructured, annual mileage data were estimated in relation to the fleet data and back extrapolated using a regression approach. The results showed that the mileage degradation was not only subject to fuel technology, engine size, and age but also the emissions class and vehicle category. It was also observed that the disaggregated level of data provided a different CO2 emissions split among the vehicle categories than that of previous estimations which were based on an aggregated level of data. Previous emissions inventories (1990–2012) were shown to have underestimated the share from diesel fuelled passenger cars by more than 56% in 2012. Diesel fuelled passenger cars were also found to account for the majority of CO2 emissions from road transport activities in Ireland in 2013. The level and trend assessment showed that emissions from Euro-II and Euro-III classed vehicles especially for passenger cars, which have a significant contribution to the total emission in 2013 have caused an increase in fleet level emissions in Ireland. In addition, the results also showed that the emissions share from Light Duty Vehicles and Heavy Duty Vehicles were overestimated by previous investigations. This paper highlights the importance of the resolution of data used in emissions inventory preparation which may impact upon future projections and policy formulation. The findings of this investigation are also discussed in relation their implications for road transport policy, including carbon taxation and future policy options aimed at achieving EU emissions target in 2020.