A Constraint Programming Engine for Interactive CO2 LCA and Carbon Neutrality Planning

Nowadays, the achievement of carbon neutrality has become a critical priority for modern industries in response to the escalation of climate challenges and the increasing number of stringent regulations. This thesis presents the design and implementation of an interactive decision-support tool based on Constraint Programming for strategic carbon neutrality planning, developed during an internship with Toyota. The tool integrates Life Cycle Assessment data with a powerful CP-SAT optimization engine to assess and optimize multi-year decarbonization strategies. It formalizes the corporate emissions reduction challenge as a multi-period constraint optimization problem, modeling the complex, interdependent decisions across a vehicle's entire lifecycle under technical, economic, and regulatory constraints. The studied approach supports various optimization strategies, including Goal Programming, Weighted Objectives, and Lexicographic Ordering, enabling comparative analysis of emissions and cost. Implemented using Google OR-Tools and tested on realistic scenarios provided by Toyota, the tool demonstrates strong performance in terms of optimality, scalability, and computational efficiency. Among the tested strategies, Goal Programming showed the most consistent and effective results. This work advances the state of the art in sustainability oriented decision support by integrating operational emissions data with combinatorial optimization techniques. It highlights the potential of CP to deliver explainable, flexible, and efficient solutions for industrial carbon reduction, providing a robust methodology to align business strategy with climate neutrality objectives.