Data Governance for Sustainable AI in Organizations: A Benchmarkability-First Capability Model, Evidence Map, and Marketplace Microdata Demonstration

Sustainable AI requires more than ethical principles: organizations need measurable, auditable governance that links data controls, model lifecycle evidence, and operational monitoring to sustainability outcomes. This paper advances a measurement-first approach by (i) defining a 12-capability Data Governance for Sustainable AI (DG-SA) model with implementable evidence artifacts, (ii) mapping 18 influential standards, academic artifacts, and corporate frameworks to DG-SA using a conservative evidence-based coding protocol, and (iii) quantifying an open benchmarkability ratio over 54 governance survey items. Empirically, only 13 of 54 items (24.1%) admit defensible public/open-data proxy benchmarking, revealing a practical gap between what governance frameworks prescribe and what can be benchmarked externally. To address this gap, we incorporate a marketplace microdata pathway and demonstrate—using publicly available energy microdata distributed via cloud marketplaces—how joining governed AI telemetry with region-specific electricity carbon-intensity data yields audit-ready sustainability KPIs. In an illustrative cloud-region comparison for 2024, carbon intensity varies by approximately 17× across common regions, underscoring the need for governance-grade measurement conventions. We further provide a lifecycle operating model, dashboard blueprint, and minimum evidence pack (dashboards, runbooks, and decision gates) to make DG-SA actionable in production settings. The resulting artifacts translate sustainable AI governance from principle lists into testable, reproducible constructs and operational controls.