Development of a Web-Based Tool for Estimating Lifecycle CO₂ Emissions of Electric vs. Diesel Vehicles in Germany

Abstract

Due to rapid technological development, the transport sector has emerged as one of the most difficult areas to reduce decarbonization, balancing its high global greenhouse gas emissions. In Germany, the shift from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) has become a key focus of national and EU climate policies. Nevertheless, the transition to EVs is associated with a multitude of factors that need to be considered, including emissions from manufacturing, the energy used during operations, and post-operational treatment. This thesis focuses on the design, development, and refinement of a web-based CO₂ Lifecycle Emission Calculation tool that compares cradle-to-grave emissions of EVs and diesel ICEVs based on different driving scenarios in Germany. It is based on Life Cycle Assessment (LCA) Methodology as indicated in ISO 14040/14044. With the tool, the manufacturing, operational, and end-of-life emissions are estimated based on validated datasets from peer-reviewed literature and authoritative bodies (IPCC, IEA, UBA). The tool’s modular structure, which was developed in Python with the Streamlit framework, also allows for other languages to be added, selection of the region’s grid mix, scenario comparisons, and results visualization in multi-level dashboards. Findings of the literature review show that the tool is accurate for both types of vehicles as the known lifecycle emissions ranges for both types are captured. Carbon payback periods for EVs ranged from less than three years to over eight years depending on the grid context, from renewable-rich to coal-intensive. Sensitivity analysis demonstrates that both grid CO₂ intensity and annual mileage hold the greatest influence over lifecycle results. This research contributes to sustainability as it fills the void that exists between academic LCA models and consumer-facing applications with a transparent, reproducible, and user-configurable decision-support tool. Improvements can be made by incorporating real-time grid emission data, expanding the vehicle datasets, and including additional CO₂ environmental impact categories