Decarbonisation Pathway Optimization

Decarbonisation Pathway Optimization uses AI to perform optimization of abatement levers, emissions baselines, energy demand, capital constraints, and target dates, enabling feasible net-zero planning, by solving portfolio-level pathway scenarios under cost, timing, operational, and emissions constraints, across sustainability strategy and capital planning workflows.

Decarbonisation plans require tradeoffs across energy efficiency, fuel switching, procurement, product mix, supplier action, and capital allocation. Spreadsheet scenarios become hard to maintain as assumptions change, making it difficult to compare feasible pathways against targets.

The AI performs optimization on emissions baselines, abatement levers, cost curves, operational constraints, and target trajectories and produces ranked decarbonisation pathways. The output supports reviewed planning by sustainability, finance, and operations teams.

The primary metric is emissions reduction gap to target; the target direction is a lower gap at an acceptable marginal abatement cost.

• •Current emissions baseline, target trajectory, abatement levers, cost assumptions, operational constraints, and capital limits are available.
• •Planning models can connect to carbon accounting, finance, procurement, and operations data at the required level of granularity.
• •Scenario assumptions and decision rights are defined for sustainability, finance, and operations stakeholders.