Estimating on-road vehicle fuel economy in Africa : A case study based on an urban transport survey in Nairobi, Kenya

In African cities like Nairobi, policies to improve vehicle fuel economy help to reduce greenhouse gas emissions and improve air quality, but lack of data is amajor challenge. We present a methodology for estimating fuel economy in such cities. Vehicle characteristics and activity data, for both the formal fleet (private cars, motorcycles, light and heavy trucks) and informal fleet-minibuses (matatus), three-wheelers (tuktuks), goods vehicles (AskforTransport) and two-wheelers (bodabodas)-were collected and used to estimate fuel economy. Using two empirical models, general linear modelling (GLM) and artificial neural network (ANN), the relationships between vehicle characteristics for this fleet and fuel economy were analyzed for the first time. Fuel economy for bodabodas (4.6 ± 0.4 L/100 km), tuktuks (8.7 ± 4.6 L/100 km), passenger cars (22.8 ± 3.0 L/100 km), and matatus (33.1 ± 2.5 L/100 km) was found to be 2-3 times worse than in the countries these vehicles are imported from. The GLM provided the better estimate of predicted fuel economy based on vehicle characteristics. The analysis of survey data covering a large informal urban fleet helps meet the challenge of a lack of availability of vehicle data for emissions inventories. This may be useful to policy makers as emissions inventories underpin policy development to reduce emissions

Use of a vehicle-modelling tool for predicting CO2 emissions in the framework of European regulations for light goods vehicles

The reduction of CO2 emissions and fuel consumption from road transportation constitutes an important pillar of the EU commitment for implementing the Kyoto Protocol. Efforts to monitor and limit CO2 emissions from vehicles can effectively be supported by the use of vehicle modelling tools. This paper presents the application of such a tool for predicting CO2 emissions of vehicles under different operating conditions and shows how the results from simulations can be used for supporting policy analysis and design aiming at further reductions of the CO2 emissions. For this purpose, the case of light duty goods (N1 category) vehicle CO2 emissions control measures adopted by the EU is analysed. In order to understand how certain design and operating aspects affect fuel consumption, a number of N1 vehicles were simulated with ADVISOR for various operating conditions and the numerical results were validated against chassis dynamometer tests. The model was then employed for analysing and evaluating the new EU legislative framework that addresses CO2 emissions from this vehicle class. The results of this analysis have shown the weaknesses of the current regulations and revealed new potential in CO2 emissions control. Finally the TREMOVE model was used for simulating a possible scenario for reducing CO2 emissions at fleet level. © 2006 Elsevier Ltd. All rights reserved. Chemicals / CAS: carbon dioxide, 124-38-9, 58561-67-

Development of a novel model for co-modal emission calculation and inventory methodology

This paper presents a novel model for co-modal emission calculation and inventory methodology combining the various modes of public transport. A number of available emission calculation models have been reviewed and the proposed methodology has been derived so as to cover all modes effectively in a homogenised manner. Due to the large number of different vehicle types and engine technologies involved, the current approach focuses on characteristics of each country in order to reflect country specific situation in the best possible way. Two case studies are presented. The first one compares two alternative co-modal routes based on their environmental performance, but also on other parameters such as time, distance and cost. The calculated emission factors are used in the second case study for the development of an emission inventory for the public transport sector in Greece. For this inventory, actual activity data from real life were collected from all transport operators in Greece, instead of using statistical data. The calculated results are compared against top-down approaches which use statistical data; this comparison shows that the proposed bottom-up methodology and final calculated data can serve as a basis and baseline scenario for future emission inventories. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature