No abstract

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 (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.

Decoupling energy, water, and food (EWF) consumption and production from GHG emissions could be an important strategy for achieving the UN Sustainable Development Goals (SDGs), especially SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 7 (Clean and Affordable Energy) in Africa. This study applies Tapio’s decoupling method to analyze the relationship between GHG emissions and EWF resources use in 15 African countries over the period 1990–2017. The results show a remarkable relationship, which includes the contamination of EWF by GHG emissions, that mostly exhibits unsatisfactory decoupling state to satisfactory decoupling over a period of several years. The decoupling of water and energy resources from GHG emissions in most countries of Africa has not been able to reach an excellent decoupling state or a strong positive decoupling state. This requires countries in Africa to support environmentally friendly water and energy infrastructures and to promote an integrated, mutually managed, whole resource interaction system. The study also highlights the importance of tracking sources of GHG emissions, whether within individual resource sector activities or across resources to each other.

Traffic congestion significantly contributes to climate change due to the emissions of Greenhouse Gases (GHGs) such as Carbon Dioxide (CO2), Nitrous Oxide (N2O), and Ozone (O3). Rapid urbanization and poor planning coupled with increased motorization and fragmented public transport system in cities such as Nairobi have led to increased vehicular emissions especially during heavy traffic along the various roads and within the Central Business District (CBD). To reduce GHG emissions in the urban transport sector, institutional coordination and relevant policy tools must be considered. This study aimed at estimating CO2 emissions from different vehicles during traffic congestion, using Uhuru Highway as a case study. The relationship between traffic congestion and CO2 emissions was analyzed using qualitative and quantitative methods, through a bottom-up approach. Questionnaires were administered to get individual vehicle characteristics and opinions on the best actions for the reduction of CO2 emissions along Uhuru Highway in Nairobi. The Average Annual Daily Traffic (AADT) for different vehicles from 2014 to 2019 was used to estimate the CO2 emissions. Results showed that private cars predominate over other vehicle types, contributing to 73% of the total CO2 emissions in Nairobi (CBD). Private cars are the highest contributor of CO2 emissions with a total of 25.3 million Carbo dioxide equivalent (gCO2e), between 2014 and 2019. In comparison, Public Service Vehicles, commonly referred to as Matatus emitted 6.89 million gCO2e, Light Commercial Vehicles (1.82 million gCO2e), Heavy Goods Vehicles (251,683 gCO2e), and motorcycles (181,054 gCO2e). To minimize CO2 emissions, the study recommended the enforcement of strong mobility policies to control the high motorization rate. One of these policies is the prioritization of the development of the mass public transport systems to achieve the potential health, economic and environmental gains within the CBD.