Design and Development of an AI-Driven Carbon Footprint Calculator for The Transport Sector in Zambia

Abstract

Zambia’s transport sector contributes significantly to greenhouse gas emissions, largely due to its reliance on fossil fuel-powered vehicles, inefficient transit systems, and underdeveloped public transportation infrastructure. This project presents the design and development of an AI-driven carbon footprint calculator tailored to Zambia’s transportation context. The tool is intended to provide policymakers, transport operators, and the general public with actionable insights by leveraging machine learning to analyze emissions data. The system integrates data on fuel consumption, vehicle classifications, traffic flow patterns, and environmental variables to build predictive models. Algorithms such as random forest regression will be applied to estimate transport-related emissions and generate scenario-based projections, such as the effects of adopting electric vehicles or improving traffic efficiency. Results will be delivered through an interactive, web-based dashboard to promote public awareness and support sustainable decision-making. The tool will undergo rigorous testing using data from both urban and rural environments to ensure contextual relevance and accuracy. Anticipated outcomes include detailed emission profiles, increased public engagement, and support for evidence-based policy interventions, including fuel efficiency strategies and low-carbon transport planning. The project also addresses common limitations, such as data scarcity and inconsistent digital infrastructure, by incorporating offline functionality to enhance accessibility and usability.