City Research Online

Abstract

Navigating the transition to net-zero requires energy planners to rapidly assess and contrast the impacts of competing policy decisions. While computational models excel at large-scale optimization, they often lack the context to navigate the nuanced trade-offs and conflicting priorities of public policy. To bridge this gap, we present a visual analytics system that integrates human-in-the-loop expertise into the planning process, ensuring data-driven insights are tempered by practical judgment. Our approach introduces a modular planning paradigm designed to reduce the cognitive load of balancing multi-objective goals. Building upon previous work in human-in-the-loop decarbonization platforms, we present a visualization framework using model-driven glyphs to provide a consistent representation of multivariate data across geographic scales, ensuring an equitable assessment of interventions. Finally, we provide a tightly integrated workflow that allows planners to move fluidly between composing interventions, simulating decarbonization impacts, and performing cross-scenario comparisons. Through a detailed application scenario, we illustrate how this system accelerates the sensemaking process, allowing planners to benchmark strategies and deliver evidence-based policies with confidence.

Publication Type:	Article
Additional Information:	Crown Copyright © 2026 Published by Elsevier Ltd. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Visual analytics; Multivariate visualization; Scenario modeling
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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