Laksono, DanyJianu, RaduSlingsby, AidanSheng, YunSlingsby, Aidan2025-09-092025-09-092025978-3-03868-293-6https://doi.org/10.2312/cgvc.20251218https://diglib.eg.org/handle/10.2312/cgvc20251218Developing equitable and effective decarbonisation plans is a critical challenge for UK local authorities, who must balance complex technical, social, and economic factors. While computational models can propose optimal solutions based on a single objective, they often fail to account for the nuanced trade-offs and competing priorities inherent in public policy. We address this with a visual analytics system designed to support a human-in-the-loop planning process. Our primary contributions are threefold: (i) a modular, component-based planning paradigm that makes the construction of complex, multi-objective strategies cognitively manageable; (ii) a multi-scale visualisation framework that uses a model-driven glyph design to represent multivariate and temporal data uniformly across geographic scales, enabling fair and just assessment; and (iii) a tightlyintegrated workflow that allows planners to iteratively explore data, compose interventions, simulate outcomes, and refine their strategies in real-time. We demonstrate through an application scenario how our system empowers planners to move beyond monolithic optimisation and engage in a transparent, evidence-based dialogue with their data, ultimately supporting the creation of more robust and equitable decarbonisation plans.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Geographic visualization; Visual analyticsHuman centered computing → Geographic visualizationVisual analytics10.2312/cgvc.202512187 pages