Artificial Intelligence Techniques for Monitoring Carbon Emissions and Supporting Green Investment Decisions
人工知能技術を用いた炭素排出モニタリングとグリーン投資判断の支援 (AI 翻訳)
Emmanuel Ohimai Ojo, Prince Michael Akwabeng, Gloria Opoku Darkoh, Adetomiwa Adesokan
🤖 gxceed AI 要約
日本語
本論文は、炭素排出モニタリングとグリーン投資判断におけるAI技術の応用に関する系統的レビューである。2021～2025年に発表された55件の研究を分析し、機械学習・深層学習・ハイブリッドアルゴリズムが排出量推定の精度向上、リアルタイムモニタリング、排出ホットスポットの特定に有効であることを示した。また、AIが環境・社会・ガバナンス（ESG）報告や気候整合的な投資判断、規制遵守を促進する方法を明らかにした。一方で、データ品質不足、モデル解釈可能性の低さ、Scope 3排出量推定の障壁、技術アクセス格差などの課題も指摘している。
English
This systematic review examines AI applications in carbon emission monitoring and green investment decisions, analyzing 55 studies from 2021-2025. It finds that machine learning, deep learning, and hybrid algorithms significantly improve emission estimation accuracy, enable near-real-time monitoring, and identify emission hotspots. AI-derived insights help inform ESG reporting, climate-aligned investments, and regulatory compliance. Challenges include data quality, model interpretability, Scope 3 estimation barriers, and technology access disparities.
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
日本では、SSBJ基準への対応や有報開示における炭素排出量の正確な計測・報告が求められており、AIによるMRV（測定・報告・検証）の高度化は実務上有用な知見を提供する。また、グリーン投資判断へのAI活用は、日本の投資家向け開示の質向上に寄与する可能性がある。
In the global GX context
Globally, AI-driven carbon monitoring aligns with ISSB and TCFD frameworks by improving MRV accuracy and enabling real-time data for disclosure. The review highlights AI's role in bridging environmental monitoring and sustainable finance, supporting regulatory compliance (e.g., CSRD, SEC climate rule) and informed investment decisions.
👥 読者別の含意
🔬研究者:This review provides a comprehensive map of AI techniques for carbon monitoring, highlighting gaps in Scope 3 estimation and model interpretability for future research.
🏢実務担当者:Corporate sustainability teams can identify AI tools for enhancing emission monitoring accuracy and integrating insights into ESG reporting and green investment strategies.
🏛政策担当者:Policymakers can consider AI-supported MRV systems to strengthen regulatory frameworks and promote green finance certification.
📄 Abstract（原文）
Artificial Intelligence (AI) has been introduced as a revolutionary tool for carbon emission monitoring, and helps in making investment decisions that are both environmentally friendly and sustainable. The systematic review summarises the existing evidence regarding AI applications in estimating carbon emissions, using Measurement, Reporting and Verification (MRV) schemes and their incorporation into Environmental, Social and Governance (ESG) ratings, as well as green finance programs. The systematic review included 55 studies that were published between 2021 and 2025 in the industrial, energy, transportation, agricultural, and multisectoral contexts. It is shown that machine-learning, deep-learning, and hybrid algorithms are significantly more effective in enhancing the accuracy of emission estimation, promoting close to real-time monitoring, and helping locate hotspots of emissions. The review also clarifies how AI-derived insights can help to both inform ESG reporting and make climate-aligned investment decisions, and facilitate regulatory compliance. However, there are ongoing issues of data quality shortages, poor model interpretability, barriers to the estimation of Scope 3 emissions, and now disparities in technological access. The paper highlights the strategic importance of AI in the context of filling the gap between environmental monitoring and sustainable finance, providing researchers, investors, and policymakers with evidence-based suggestions to increase their pace of decarbonisation in the world.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.9734/ijecc/2026/v16i25278first seen 2026-05-05 22:08:40
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。