Big-Data-Integrated Credit Evaluation System and Financial Support Mechanism for China' s New Energy Industry
中国新エネルギー産業向けビッグデータ統合型信用評価システムと金融支援メカニズム (AI 翻訳)
Xiaoqian Liu, Gazi Md. Nurul Islam, Xuelian Bai
🤖 gxceed AI 要約
日本語
本論文は、風力・太陽光発電所の大規模運転データ（8億件超）を統合し、AI時系列モデルとゲーム理論加重TOPSIS法を組み合わせた動的信用評価システムを開発。従来の専門家スコアリングに比べリスク識別精度が約40%向上し、承認時間を約半分に短縮。さらに「データ強化信用＋多様な融資＋リスク軽減」メカニズムを設計し、金利を従来比120～150ベーシスポイント低減。肇慶市でのパイロットでは156企業のデジタルカーボン口座を構築し、データ収集から金融転換までのループを実証した。
English
This paper develops a credit evaluation system for new energy enterprises integrating big data (807 wind/solar plants, over 84 million photovoltaic and 570 million wind power records) with AI time-series modeling, game-theoretic weighting, and improved TOPSIS. It improves risk identification accuracy by 40% and halves financing approval time. A 'data-enhanced credit + diversified financing + risk mitigation' mechanism achieves 120-150 bps lower interest rates. A pilot in Zhaoqing built digital carbon accounts for 156 enterprises, demonstrating a closed loop from data collection to financial conversion.
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
日本でも再生可能エネルギー案件の融資審査は長期化・複雑化している。本論文のデータ駆動型信用評価は、日本のグリーンファイナンス実務（特にSPC向けノンリコースローン評価）にAI活用の示唆を与える。ただし、中国の電力市場制度・データ共有環境を前提としており、日本への直接適用には制度差への調整が必要。
In the global GX context
Globally, this paper demonstrates how big data and AI can address financing bottlenecks for renewable energy assets, which is a key challenge in the energy transition. The integrated approach—combining production, grid, and repayment data with AI scoring—offers a scalable model for green credit evaluation. It aligns with TCFD/ISSB's emphasis on data-driven risk assessment and could inform transition finance frameworks, especially for emerging markets.
👥 読者別の含意
🔬研究者:Provides a novel AI + game-theoretic methodology for dynamic credit scoring on renewable energy assets, with rigorous large-scale empirical validation (91% accuracy).
🏢実務担当者:Demonstrates a deployable 'data to finance' pipeline that can lower financing costs for wind/solar projects by 120-150 bps, with blockchain for carbon tracking.
🏛政策担当者:Illustrates how integrating production and carbon data into credit systems can accelerate green finance deployment, relevant for designing carbon-accounting-linked financial products.
📄 Abstract（原文）
China's energy transition is accelerating, and the scale of the new energy industry continues to grow. By the end of 2024, non-fossil fuel power generation capacity exceeded half of total installed capacity, yet the “asset-heavy, long-cycle” nature of wind and solar projects still creates financing bottlenecks. This paper develops a credit evaluation system for new energy enterprises that integrates big data and computing technology, and links it to a tailored financial support mechanism. Using operating data from 807 wind and solar plants (about 84.97 million photovoltaic records and 570 million wind power records), we build a multi-source database that combines production, grid connection, curtailment, and repayment behavior. An AI-based time-series modeling module is embedded into a game-theoretic combined weighting scheme and an improved TOPSIS model to generate dynamic credit scores. In comparison with a benchmark expert-scoring system, the proposed framework improves risk identification accuracy by about 40%, shortens average financing approval time by roughly 50%, and reaches an overall rating accuracy of 91% on a held-out sample. Based on the scoring outputs, we design a “data-enhanced credit + diversified financing + risk mitigation” mechanism, including smart risk-control rules and “power–finance” products whose interest rates are 120–150 basis points lower than traditional channels. A blockchain layer is used to secure transaction records and enhance traceability of carbon performance. A pilot in Zhaoqing built digital carbon accounts for 156 enterprises and closed a “data collection–intelligent evaluation–financial conversion” loop. The results indicate that big-data-driven credit evaluation, when coupled with targeted green financial instruments, can support high-quality development of the new energy industry.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1145/3804504.3804568first seen 2026-07-18 08:22:24
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