Paper
Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development
Authors
Mateen A. Abbasi, Tommi J. Mikkonen, Petri J. Ihantola, Muhammad Waseem, Pekka Abrahamsson, Niko K. Mäkitalo
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.19718v1</id>\n <title>Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development</title>\n <updated>2026-02-23T11:11:56Z</updated>\n <link href='https://arxiv.org/abs/2602.19718v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.19718v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.SE'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-02-23T11:11:56Z</published>\n <arxiv:comment>5 pages, 1 figure. Preprint version under review</arxiv:comment>\n <arxiv:primary_category term='cs.SE'/>\n <author>\n <name>Mateen A. Abbasi</name>\n </author>\n <author>\n <name>Tommi J. Mikkonen</name>\n </author>\n <author>\n <name>Petri J. Ihantola</name>\n </author>\n <author>\n <name>Muhammad Waseem</name>\n </author>\n <author>\n <name>Pekka Abrahamsson</name>\n </author>\n <author>\n <name>Niko K. Mäkitalo</name>\n </author>\n </entry>"
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