Governance gaps in urban decarbonization: Illuminating structural exclusion in residential energy transitions

Efforts to decarbonize cities often rely on incentivizing individual behavior, yet such strategies risk reinforcing inequality when they overlook the structural barriers that govern who can adopt clean energy technologies. Most urban energy modeling approaches rely on aggregate representations of demographic variables or treat them as descriptive factors rather than direct model constraints; this obscures their embedded effect on decision-making, creating a significant research void. In this study, we introduce a novel, socially disaggregated agent-based model of 1.38 million households in Los Angeles County that simulates adoption dynamics across seven residential energy technologies—ranging from LED lighting to HVAC systems. By embedding structural variables such as income, tenure, and infrastructure access into household decision-making, we contribute a uniquely transferable modeling framework for anticipatory governance, generate empirical insights into divergent technology adoption patterns, and identify targeted policy leverage points. The model reveals persistent adoption hierarchies: for example, homeowners adopt smart thermostats at over twelve times the rate of renters (79.5 ​% vs. 6.8 ​%), despite comparable behavioral intent. While nearly universal uptake is achieved for low-barrier technologies (e.g., 98.5 ​% adoption of LEDs), higher-impact measures remain sharply stratified, undermining the equity and effectiveness of citywide climate goals. Validated against real-world data, our model offers a scalable framework for diagnosing exclusionary dynamics and testing equity-centered interventions before deployment. By shifting the focus from isolated behavior to systemic constraint, this approach equips planners and policymakers with a tool for early-stage program design and actionable insights for inclusive and just decarbonization pathways. • Housing tenure outweighs behavioral intent in predicting adoption. • The model embeds energy justice principles as structural constraints. • Structural exclusion limits access to high-cost technologies. • Structural factors outperform behavioral variables in sensitivity tests. • The framework diagnoses equity impacts and informs policy design.