为生成式AI开发设计低碳治理架构，平衡合规与环保。
Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development
- 在开发流程中嵌入碳预算与能源溯源机制
- 通过绿色验证编排降低重复推理能耗
- 适合关注可持续AI的工程团队与管理者
生成式AI在软件开发生命周期中的广泛应用增加了计算需求，可能提升开发活动的碳足迹。同时，组织为保障可信、透明与问责，正将治理机制融入生成式AI辅助开发中。然而，这些治理机制引入了额外计算负载，包括重复推理、再生循环和扩展的验证流程，进一步增加能耗与碳排放。本文提出碳感知治理闸门（CAGG），一种将碳预算、能源溯源与可持续性感知的验证编排集成到人机治理层的架构扩展。CAGG包含三个组件：(i) 能量与碳溯源账本，(ii) 碳预算管理器，(iii) 绿色验证编排器，通过治理策略与可复用设计模式实现。
原文摘要 · Abstract (English)
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.
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