Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development
Mateen A. Abbasi, Tommi J. Mikkonen, Petri J. Ihantola, Muhammad Waseem, Pekka Abrahamsson, Niko K. Mäkitalo
TL;DR
The paper addresses the rising carbon footprint of GenAI-enabled software development driven by governance and validation workloads. It introduces Carbon-Aware Governance Gates (CAGG), a layered architecture that adds an Energy and Carbon Provenance Ledger, a Carbon Budget Manager, and a Green Validation Orchestrator, coupled with enforceable governance policies and reusable design patterns. The main contributions are a reference architecture, four architectural design patterns, and policy mechanisms that balance validation assurance with carbon efficiency. Practically, CAGG provides a structured means to constrain and monitor the environmental impact of governance activities within the SDLC, enabling sustainable GenAI development without sacrificing necessary oversight.
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.