SLS-100 A Hybrid Governance Scoring System for Hyperscale Data Center Permitting

We present a hybrid administrative scoring system for hyperscale data center permitting, combining a linear mean-reversion ODE with Monte Carlo ensemble classification to produce empirically derived regulatory thresholds. The framework evaluates projects across four domains — Legitimacy, Transparency, Resource Burden, and Community Benefit — and assigns a Social License Score (SLS-100) from 0 to 100. Regime classification boundaries (Denied, Conditional, Strong, Exemplary) are defined by SLS score thresholds and validated through Monte Carloensemble classification. The primary operational insight is that the Grid Burden Ratio (G) and Community Satisfaction (Cₛ) jointly govern classification outcomes: projects with G < 0.2 and Cₛ > 70% have a >90% probability of achieving Strong or Exemplary classification; projects with G > 0.4 or Cₛ < 50% have a >90% probability of entering Denied classification.