Green Finance Policy Impact Analysis Based on Integrated Knowledge Graph
統合ナレッジグラフに基づくグリーンファイナンス政策影響分析 (AI 翻訳)
L. Lin, X. Yao, J. L. Liang
🤖 gxceed AI 要約
日本語
本論文は、政策・市場・ESG・炭素データを統合したナレッジグラフ（KG）とNLPを用いて、グリーンファイナンス政策の影響を定量評価する手法を提案。中国のグリーンファイナンス試験区の2016〜2023年のデータを用いたDID分析により、グリーンクレジット比率が平均2.15ポイント増加し、炭素強度の削減を確認。KG手法は89.7%の精度で、従来の回帰法より22.5ポイント向上し、政策因果の可視化と意思決定支援に有効と結論。
English
This paper proposes a method using an integrated Knowledge Graph (KG) and NLP to quantify green finance policy impacts by integrating policy, market, ESG, and carbon data. Validation with DID on China's green finance pilot zones (2016-2023) shows green credit share increases by 2.15 percentage points and carbon intensity reductions. The KG method achieves 89.7% accuracy, 22.5 points higher than traditional regression, enhancing causal inference and decision support.
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開示や有報でのサステナビリティ情報が拡充される中、政策・ESG・炭素データの統合分析は投資家対応や政策評価に有用。本手法は日本のグリーンファイナンス政策の効果検証や、データ連携基盤構築に示唆を与える。
In the global GX context
Globally, this paper contributes to the growing field of AI-driven ESG and climate policy evaluation, aligning with ISSB and CSRD disclosure trends. The KG-based approach offers a novel method for tracing policy impacts on sustainability outcomes, relevant for policymakers and financial regulators seeking robust causal evidence.
👥 読者別の含意
🔬研究者:Provides a novel KG+NLP methodology for policy impact assessment, with rigorous DID validation.
🏢実務担当者:Offers a framework for integrating ESG and carbon data to track policy effects, useful for disclosure and strategy.
🏛政策担当者:Demonstrates how KG-based analysis can enhance causal inference in green finance policy evaluation.
📄 Abstract（原文）
Due to the inherent fragmentation of policy, market, ESG and carbon data, tracking genuine sustainable impact remains challenging. This paper addresses prevailing issues in green finance policy impact assessments—specifically, unaccounted data heterogeneity, omitted-variable bias, and fragmented multi-source information—by applying an analytical approach based on an integrated Knowledge Graph (KG). The method utilizes a multidimensional KG constructed from policy, market, ESG (Environmental, Social, and Governance), and carbon data to mitigate fragmentation in green finance assessment. Natural Language Processing (NLP) is employed to structure policy knowledge, and a graph embedding algorithm is designed to quantify dynamic policy impacts on the scale of green credit, corporate emission reduction, and regional carbon intensity. Validation via a Difference-in-Differences (DID) model using data from 2016 to 2023 in China’s green finance pilot zones demonstrates a significant positive policy effect: the green credit share increases by an average of 2.15 percentage points, and reductions in carbon intensity are confirmed. The KG method achieves 89.7% accuracy, representing a 22.5-percentage-point improvement over traditional regression methods. The study concludes that integrating KG technology substantially enhances visualization and causal inference capabilities in green finance policy impact analysis, enabling policymakers to trace cause-and-effect relationships—from the implementation of a new green credit scheme to measurable sustainability improvements—thus providing robust decision support for optimizing the policy tool mix.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.7716/aem.v15i3.3571first seen 2026-08-17 05:30:15 · last seen 2026-08-19 05:22:24
🔔 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。