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Overview
We designed and delivered a research and optimization framework to evaluate how calendar structure influences logistics-related emissions in elite global motorsport.
Working in collaboration with a leading European business school and a team competing in Formula 1, the project developed a defensible baseline model to quantify and minimize freight-related CO₂ emissions associated with a full championship season. The engagement combined sustainability research design, emissions accounting, geospatial data engineering, and routing-style optimization – producing a transparent benchmark for evaluating alternative calendar structures in pursuit of the sport's net-zero commitments.
The Challenge
Formula 1 operates across a geographically dispersed calendar, with freight moving between continents under strict timing constraints. As sustainability commitments tighten across the sport, stakeholders face a structural question:
To what extent does calendar sequencing itself drive logistics emissions?
Answering this required:
Constructing a rigorous emissions-accounting framework for international freight
Modelling multi-modal logistics at event-to-event level
Translating season timing gaps into operational routing behavior
Designing an optimization algorithm capable of evaluating alternative calendar orderings
Producing a defensible analytical benchmark suitable for academic and strategic scrutiny
The project demanded a solution that was methodologically robust, computationally scalable, and aligned with real-world logistics structures, while respecting commercial confidentiality.
Our Approach
We designed and implemented a structured sustainability research and optimization program combining emissions accounting, geospatial modelling, and algorithmic optimization.
Key elements included:
Defining a transparent baseline research framework to isolate the impact of calendar sequencing on freight emissions
Constructing a node-based logistics network anchored to a team base and all championship circuits
Building multi-modal routing matrices using API-driven geospatial data pipelines
Converting transport activity into comparable emissions-per-ton cost matrices using mode-specific factors
Encoding operational decision rules (including time-gap-triggered routing requirements and mode safeguards) to reflect real-world logistics structure
Formalizing the season as a routing-style optimization problem and implementing a search algorithm to identify lower-emissions calendar orderings
The analysis produced a defensible benchmark for evaluating the structural emissions impact of season calendar design. Rather than prescribing an immediately implementable schedule, the framework establishes a transparent theoretical reference point – quantifying the emissions sensitivity of global event sequencing in elite motorsport under consistent accounting logic.
Project Outcome
The project delivered:
A fully auditable emissions-per-ton logistics model for a complete championship season
A replicable optimization framework for evaluating alternative event sequences
A quantitative benchmark for theoretical logistics emissions savings
A foundation for further constraint-aware and policy-integrated extensions
Beyond the specific modeling outputs, the engagement demonstrated our ability to:
Design academically defensible sustainability research
Translate operational logistics systems into computational models
Integrate emissions accounting with algorithmic optimization
Manage multi-stakeholder projects spanning academia and elite sport
The framework now serves as a reference architecture for season-level sustainability analysis – enabling organizations to move from high-level ambition toward analytically grounded structural decision-making.
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