Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development

TL;DR

This paper introduces Carbon-Aware Governance Gates (CAGG), an architecture integrating carbon management and sustainability validation into GenAI development governance to reduce environmental impact.

Contribution

It presents a novel architecture with components for carbon tracking, budgeting, and green validation, enhancing sustainable GenAI development practices.

Findings

CAGG effectively tracks energy and carbon footprints.

It manages carbon budgets within AI development workflows.

CAGG promotes sustainable practices in GenAI development.

Abstract

The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activities. At the same time, organizations are increasingly embedding governance mechanisms into GenAI-assisted development to support trust, transparency, and accountability. However, these governance mechanisms introduce additional computational workloads, including repeated inference, regeneration cycles, and expanded validation pipelines, increasing energy use and the carbon footprint of GenAI-assisted development. This paper proposes Carbon-Aware Governance Gates (CAGG), an architectural extension that embeds carbon budgets, energy provenance, and sustainability-aware validation orchestration into human-AI governance layers. CAGG comprises three components: (i) an Energy and Carbon Provenance Ledger, (ii) a Carbon Budget Manager, and (iii) a Green Validation Orchestrator, operationalized through governance policies and reusable design patterns.