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intitle:
The document title intitle:"climate education"
ispublisher:
The publishing organization ispublisher:CAPE
insummary:
Summary and key findings insummary:"conflict of interest"
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Climate tags, frame/perspective and analysis type hasconcept:"Climate Justice"
geography:
Regions the document covers or discusses geography:Canada
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Analysis TypeEmpirical modelling
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  • The paper develops a methodology to estimate fuel economy in sub-Saharan African cities, specifically Nairobi, by collecting data on both formal and informal vehicle fleets. Using general linear modelling (GLM) and artificial neural networks (ANN), the study found that fuel economy for bodabodas, tuktuks, passenger cars, and matatus in Nairobi is 2–3 times worse than in their countries of import. The GLM was identified as the more effective model for predicting fuel economy based on vehicle characteristics.

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    This summary is written by a language model reading the source document. It is not the publisher's words and is not a substitute for the original.

    Themes: greenhouse gas emissionsair qualityurban transport
    Stockholm Environment Institute

    Document type: Research paper

    Regions: Nairobisub-Saharan Africa
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