{"query_echo":{"q":"","filters":{"analysis_type":["empirical modelling"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'empirical modelling' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":1,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=empirical+modelling","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"sei-000712","url":"https://climateinsightsdirectory.org/document/sei-000712","title":"Estimating on-road vehicle fuel economy in Africa","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"research paper","language":"en","date_published":"2019-03-26","source_url":"https://www.sei.org/publications/estimating-on-road-vehicle-fuel-economy-in-africa/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"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.","ai_generated":true,"unit":"sei-000712#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Estimating fuel economy in sub-Saharan African (SSA) cities is hindered by official data that is often incomplete, inaccurate, inconsistent, and outdated, as well as the prevalence of unregistered, poorly maintained, and overloaded informal transport vehicles.","ai_generated":true,"unit":"sei-000712#insight-1","date_modified":"2026-10-23"},{"text":"In Nairobi, the fuel economy for specific vehicle types—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 from which they were imported.","ai_generated":true,"unit":"sei-000712#insight-2","date_modified":"2026-10-23"},{"text":"The study compared two empirical models—general linear modelling (GLM) and artificial neural network (ANN)—to analyze the relationship between vehicle characteristics and fuel economy, concluding that GLM provided the better estimate.","ai_generated":true,"unit":"sei-000712#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","air quality","urban transport"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodology","empirical modelling"]}]}