グリーンファイナンス意思決定支援システム：再生可能エネルギーとスマートインフラのためのMLおよびXAIベースの融資評価
Green Finance Decision Support System: ML and XAI-based Loan Assessment for Renewable Energy and Smart Infrastructure (原題)
R.Kaladevi, V.Umarani, V. N, V. Ravichandran
🤖 gxceed AI 要約
日本語
本研究は、機械学習と説明可能AIを用いてグリーンローン適格性を判定するシステムを構築。合成データで複数モデルを評価し、勾配ブースティングが高精度を示した。SHAP分析により、信用スコアやエネルギー効率などが重要特徴と判明。モデルの透明性向上が投資信頼醸成に寄与する。
English
This study builds an ML and explainable AI system to assess green loan eligibility. Using synthetic data, multiple models were evaluated, with Gradient Boosting achieving high accuracy. SHAP analysis identified credit score and energy efficiency as key features. Enhanced model transparency supports trust in loan evaluation and promotes low-carbon investment.
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、グリーンローン原則やトランジション・ファイナンスの拡大が進む中、AIによる融資判定の透明性は金融機関の審査プロセスに有用。SSBJ開示や投融資先の脱炭素評価にも応用可能。
In the global GX context
Globally, this aligns with the growing demand for transparent and reliable green finance mechanisms. The use of XAI in loan assessment can support the credibility of green bonds and sustainability-linked loans, aiding in the transition to a low-carbon economy.
👥 読者別の含意
🔬研究者:AIとESG評価の交差領域における実践的な応用事例として、モデル選択と説明可能性の手法を参考にできる。
🏢実務担当者:金融機関や企業のサステナビリティ部門が、グリーン融資の判定プロセスにAIを導入する際の参考になる。
🏛政策担当者:グリーンファイナンスの信頼性向上に向けたAI活用の可能性を示しており、規制やガイドライン策定に示唆を与える。
📄 Abstract（原文）
Industries worldwide are accountable for nearly 40 percent of the total emissions of CO2 in the world, which indicates the critical importance of concentrating more on renewable and sustainable energy alternatives. However, funds and infrastructure are the major challenges for sectors interested in changing to green energy-related projects. This research work builds a Machine Learning and explainable AI-based architecture to identify the green loan eligibility, in accordance with the objective of sustainable and smart infrastructure. To check eligibility synthetic dataset is generated based on various industries, research sources, and considers the key parameters as energy efficiency, credit score, environmental certification, sustainability score, operational costs, energy capacity, and revenue. ML models such as Support Vector Classifier (SVC), Random Forest Classifier (RFC), Gradient Boosting, and Logistic Regression Classifier (LRC), with Gradient Boosting, are used for eligibility checking. SVC, LRC, and RFC produced accuracy scores of 92 percent, F1-scores of 0.88, and Gradient Boosting had accuracy values of 89 percent and F1-scores of 0.87. Model feature predictions were evaluated with Shapley Additive exPlanations (SHAP) analysis, which identified that credit score, risk level, energy efficiency, and sustainability score are the most influential features. The visualization of the feature importance, sustainability classification, and the risk level distribution enhances model transparency and supports the decisions. This work shows that ML and explainable AI can be the key to establishing trust in loan evaluation systems, Government and corporate investment in renewable energy sources, and motivating the move towards a low-carbon economy.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/icseti67678.2026.11636736first seen 2026-09-01 05:44:58
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