Green Transportation Financial Risk Early Warning Based on Data Dimensionality Reduction and Improved PSO Optimization of SVM under the Background of Low-Carbon Development
低炭素発展を背景としたデータ次元削減と改良PSO最適化SVMに基づくグリーン交通金融リスク早期警戒 (AI 翻訳)
W. -J. Bai
🤖 gxceed AI 要約
日本語
本研究は、PCAによる次元削減と改良PSOで最適化したSVMを用いて、グリーン交通分野の金融リスク早期警戒システムを構築。財務・環境・政策・市場指標を統合し、94.2%の分類精度を達成。高次元データの特徴抽出と適応的最適化の有効性を示し、複雑な動的システムにおける監視・異常検知への応用可能性を提示。
English
This study develops an early warning system for green transportation financial risks using PCA for dimensionality reduction and an improved PSO-optimized SVM. Integrating financial, environmental, policy, and market indicators, it achieves 94.2% classification accuracy, demonstrating effective feature extraction and adaptive optimization for monitoring and anomaly detection in complex dynamic systems.
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
日本のGX文脈では、グリーン交通（EV・水素車両等）への移行に伴う金融リスク評価は、SSBJ開示や投資家対応で重要性が増す。本手法は、企業のリスク管理体制強化や開示データの分析に応用可能で、日本企業の脱炭素戦略におけるリスク管理高度化に寄与する。
In the global GX context
In the global GX context, this paper contributes to climate-related financial risk assessment, aligning with TCFD/ISSB disclosure requirements. The ML-based early warning framework offers a practical tool for investors and companies to monitor transition risks in the transportation sector, supporting informed decision-making under evolving climate regulations.
👥 読者別の含意
🔬研究者:Provides a novel ML framework for financial risk early warning in green transportation, combining PCA and improved PSO-SVM, useful for further research in climate risk modeling.
🏢実務担当者:Offers a data-driven tool for corporate sustainability teams to monitor and manage financial risks associated with green transportation investments, aiding in disclosure and risk management.
🏛政策担当者:Highlights the potential of AI-based early warning systems for monitoring transition risks in the transportation sector, informing policy design for low-carbon development.
📄 Abstract（原文）
Reliable early-warning systems in complex information environments require efficient feature extraction, adaptive parameter optimization, and accurate classification of high-dimensional data. This study proposes a data-driven intelligent warning framework integrating principal component analysis (PCA), an improved particle swarm optimization (PSO) algorithm, and support vector machines (SVM). PCA is employed to suppress feature redundancy and construct compact representations of multidimensional information, thereby improving computational efficiency and model robustness. To enhance classification performance, an improved PSO strategy incorporating adaptive inertia weighting, chaotic initialization, and mutation mechanisms is developed for global optimization of SVM parameters. Furthermore, a multi-source indicator architecture is established by integrating financial, environmental, policysensitive, and market-related attributes into a unified evaluation framework. Experimental results demonstrate that the proposed model achieves a classification accuracy of 94.2%, outperforming conventional SVM and random-forest approaches while reducing training complexity. The proposed framework establishes an effective methodology for high -dimensional feature extraction, intelligent signal classification, and adaptive optimization, providing a practical solution for monitoring, anomaly detection, and decision-support applications in complex dynamic systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.7716/aem.v15i3.3333first seen 2026-08-15 04:53:43
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
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。