Artificial Intelligence–Driven Carbon Footprint Assessment: A Cross- Sector Review of Methods, Challenges and Future Directions
人工知能駆動型カーボンフットプリント評価：方法、課題、将来の方向性に関する部門横断的レビュー (AI 翻訳)
Aman Ali, Hritik Verma, Prateek Maurya, Richa Verma
🤖 gxceed AI 要約
日本語
本稿は、製造、建設、物流、運輸、デジタルインフラ等の部門における従来の炭素会計手法の限界（固定排出係数、柔軟性欠如、Scope3の不確実性）を指摘し、AI/ML技術（深層学習、アンサンブル学習、予測分析、IoT監視）による改善可能性を体系的にレビューする。さらに、AIベースの炭素会計のための概念フレームワークを提案し、境界の不整合や相互運用性、Scope3自動化、不確実性モデリング等の研究課題を明らかにする。
English
This paper reviews traditional carbon footprint methods (LCA, emission factors) and their limitations across sectors (manufacturing, construction, logistics, transport, digital infrastructure), including fixed emission factors and high uncertainty in Scope 3. It highlights AI/ML techniques (deep learning, ensemble learning, predictive analytics, IoT) as solutions and proposes a conceptual framework for AI-driven carbon footprint assessment. Research gaps identified include boundary inconsistency, interoperability, lack of Scope 3 automation, and uncertainty modeling.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本企業にとって、SSBJ開示要求に伴うScope3算定の自動化・高精度化が喫緊の課題である。本フレームワークは、製造・建設・物流など主要産業でのAI活用の道筋を示し、日本のカーボンアカウンティング実務と研究の橋渡しとなり得る。
In the global GX context
This paper directly addresses the global demand for automated and accurate GHG reporting under TCFD, ISSB, and CSRD by systematically reviewing AI/ML applications to carbon footprint assessment. Its proposed framework can guide development of disclosure infrastructure, particularly for Scope 3 emissions, where AI-driven automation promises significant improvement.
👥 読者別の含意
🔬研究者:Identifies key research gaps and proposes a conceptual framework for advancing AI-based carbon accounting across sectors.
🏢実務担当者:Offers insights on leveraging AI/ML to automate Scope 3 calculations and improve carbon data quality for disclosure.
🏛政策担当者:Provides evidence that AI can enhance carbon accounting reliability, supporting regulatory efforts to standardize emission reporting.
📄 Abstract（原文）
The growing feeling of urgency in regard to the need to reduce climate change has amplified the need to make the calculation of carbon footprint in different industrial sectors more accurate and efficient. The traditional approaches, which include the Life Cycle Assessment (LCA) approach, the Emission Factor Approach, and the Standard Approach, provide a platform upon which the calculation of greenhouse gas emissions can be done. Nevertheless, some recent investigations undertaken on behalf of different industrial sectors, including manufacturing sector, construction sector, logistics sector, transportation sector, and digital infrastructure sector, state that the traditional means of estimating greenhouse gas emissions are limited, including the application of fixed-valued emission factors, rigidity, and high levels of uncertainty in Scope 3 emissions. Meanwhile, Artificial Intelligence (AI) and Machine Learning (ML) techniques are being developed as promising solutions for improving the carbon footprint models. Data science techniques like deep learning networks, ensemble learning, predictive analysis, and IoT monitoring systems can help forecast and monitor the emissions and carbon footprint in real-time. However, the connection between AI-based prediction systems and standardized carbon accounting systems is still fragmented. The research gaps are identified in the areas of inconsistency in boundaries, interoperability, the absence of automation in the calculation of Scope 3, and the need for better modeling of uncertainties. A conceptual framework for the application of AI-based techniques in the calculation of carbon footprint is proposed based on the analysis.India.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.47392/irjaem.2026.0159first seen 2026-07-24 06:53:09
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