A Model for predicting greenhouse gas emissions from motorcycles in Kenya - Cheruiyot, L. C. (Strathmore University, 2024)
In Kenya, inefficient public transport systems coupled with rough terrains have made motorcycles the most preferred means of transport. The transport sector is a leading emitter of greenhouse gases, the main driver of global climate change. This is due to the reliance on fossil fuels which require Internal Combustion Engines to operate. The threat posed by climate change and variability has fueled the ongoing energy transition from fossil fuels to green technologies through Emobility. Motorcycles have been described as low-hanging fruit in the E-mobility transition from fuel-based engines to electric-powered motors. However, this transition has shown little progress due to fewer and inadequate models to inform E-mobility policy and investment decisions. This study sought to develop a model for calculating GHG emissions from conventional and electric motorcycles under different scenarios. The scenarios were based on traffic conditions and engine efficiency. The study also aimed to analyze existing ICE and electric two-wheeler technologies in Kenya. A descriptive and experimental research design was adopted for the study. Primary data was collected using a structured questionnaire embedded in the Kobo Toolbox and was administered to motorcycle operators in Nairobi and Machakos counties. Secondary data was also collected from the NTSA database. The R-programming tool was used for data analysis and simulation of GHG emissions under different scenarios. The model was validated using experimental results to increase confidence in the findings. The study results provided comprehensive insights into the determinants of greenhouse gas emissions from both conventional Internal Combustion Engine (ICE) and electric motorcycles. Through an analysis of rider demographics, and electric and conventional motorcycle characteristics, the study revealed the multifaceted factors that contributed to the environmental impact of motorcycles. The specifics of electric motorcycle technologies, including battery characteristics, charging habits, and daily travel distances, were explored, offering valuable insights into the state of electric mobility in the country. Additionally, the study developed and applied a General Additive Model (GAM) for predicting motorcycle emissions, yielding high predictive accuracy and significant predictors. The model underscored the influence of fuel type and temporal trends on emissions, emphasizing the importance of considering both technological and temporal factors in policy formulation. Projection of emissions to 2045 revealed an alarming exponential increase, necessitating urgent intervention. Keywords: Predictive model, GHG emissions, electric two-wheelers
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