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 a major challenge. We present a methodology for estimating fuel economy in such cities. Vehicle characteristics and activity data, for both th...
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MDPI AG
2019-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/6/1177 |
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| author | Aderiana Mutheu Mbandi Jan R. Böhnke Dietrich Schwela Harry Vallack Mike R. Ashmore Lisa Emberson |
| author_facet | Aderiana Mutheu Mbandi Jan R. Böhnke Dietrich Schwela Harry Vallack Mike R. Ashmore Lisa Emberson |
| author_sort | Aderiana Mutheu Mbandi |
| collection | DOAJ |
| description | 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 a major 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 (<i>matatus</i>), three-wheelers (<i>tuktuks</i>), goods vehicles (<i>AskforTransport</i>) and two-wheelers (<i>bodabodas</i>)—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 <i>bodabodas</i> (4.6 ± 0.4 L/100 km), <i>tuktuks</i> (8.7 ± 4.6 L/100 km), passenger cars (22.8 ± 3.0 L/100 km), and <i>matatus</i> (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. |
| first_indexed | 2024-04-12T19:55:41Z |
| format | Article |
| id | doaj.art-73031f28cd74495e92ad1bd9e2c70f11 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-12T19:55:41Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-73031f28cd74495e92ad1bd9e2c70f112022-12-22T03:18:41ZengMDPI AGEnergies1996-10732019-03-01126117710.3390/en12061177en12061177Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, KenyaAderiana Mutheu Mbandi0Jan R. Böhnke1Dietrich Schwela2Harry Vallack3Mike R. Ashmore4Lisa Emberson5Stockholm Environment Institute, Environment Department, Environment Building, Wentworth Way, University of York, York YO10 5NG, UKDundee Centre for Health and Related Research, School of Nursing and Health Sciences, University of Dundee, Dundee DD1 4HJ, UKStockholm Environment Institute, Environment Department, Environment Building, Wentworth Way, University of York, York YO10 5NG, UKStockholm Environment Institute, Environment Department, Environment Building, Wentworth Way, University of York, York YO10 5NG, UKStockholm Environment Institute, Environment Department, Environment Building, Wentworth Way, University of York, York YO10 5NG, UKStockholm Environment Institute, Environment Department, Environment Building, Wentworth Way, University of York, York YO10 5NG, UKIn 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 a major 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 (<i>matatus</i>), three-wheelers (<i>tuktuks</i>), goods vehicles (<i>AskforTransport</i>) and two-wheelers (<i>bodabodas</i>)—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 <i>bodabodas</i> (4.6 ± 0.4 L/100 km), <i>tuktuks</i> (8.7 ± 4.6 L/100 km), passenger cars (22.8 ± 3.0 L/100 km), and <i>matatus</i> (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.https://www.mdpi.com/1996-1073/12/6/1177Africa<i>matatu</i><i>bodaboda</i>GHGsair pollutionin-use vehicleinformal transportfuel economy |
| spellingShingle | Aderiana Mutheu Mbandi Jan R. Böhnke Dietrich Schwela Harry Vallack Mike R. Ashmore Lisa Emberson Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya Energies Africa <i>matatu</i> <i>bodaboda</i> GHGs air pollution in-use vehicle informal transport fuel economy |
| title | Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya |
| title_full | Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya |
| title_fullStr | Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya |
| title_full_unstemmed | Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya |
| title_short | Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya |
| title_sort | estimating on road vehicle fuel economy in africa a case study based on an urban transport survey in nairobi kenya |
| topic | Africa <i>matatu</i> <i>bodaboda</i> GHGs air pollution in-use vehicle informal transport fuel economy |
| url | https://www.mdpi.com/1996-1073/12/6/1177 |
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