Fintech,GreenBonds andHigh-QualityGreenDevelopment inRegions
フィンテック、グリーンボンドと地域における高品質なグリーン発展 (AI 翻訳)
Ruiqiao Song, Renjie Sun
🤖 gxceed AI 要約
日本語
本論文は、フィンテックがグリーンファイナンスを強化する役割（コスト削減、金融包摂、情報改善）を説明し、グリーン識別、情報開示、製品革新、リスク評価、規制改革への技術導入を紹介する。また、ブロックチェーンによるデータ保存と資金追跡、AIによるグリーンボンドの価格決定と信用評価の発展を検討する。2007年の第一号気候ボンドから中国が世界最大の発行国となるまでのグリーンボンド市場の進化を概観し、地域成長のためのフィンテック支援グリーンボンドのライフサイクルモデルを提案する。情報開示不足、デジタル施設の不均等、グリーン資産基準の不統一などの障害を指摘し、将来の研究課題としてミクロ分析、地域間比較、移行金融・生物多様性金融を挙げる。
English
This paper explains the role of fintech in enhancing green finance by reducing costs, increasing financial inclusion, and improving information. It reviews the incorporation of technology such as blockchain and AI into green finance, covering green identification, disclosure, product innovation, risk evaluation, and regulatory reform. It traces the evolution of green bonds from 2007 to China's leading position, and proposes a lifecycle model for fintech-assisted green bonds to promote regional development. Obstacles such as insufficient disclosure, uneven digital infrastructure, and inconsistent green asset criteria are identified, with future research directions including micro-level analysis, cross-regional comparisons, transition finance, and biodiversity finance.
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
本論文は、グリーンボンドとフィンテックの融合を包括的にレビューしており、日本のグリーンファイナンス推進やデジタル化政策（例：グリーンイノベーション基金）への示唆を含む。特に、情報開示やデジタル基盤の課題は、SSBJや有報での開示強化が進む日本企業にも参考となる。
In the global GX context
This paper provides a broad overview of fintech-enabled green finance, relevant to global discussions on scaling green bonds and digital finance for climate goals. It highlights obstacles like inconsistent green asset criteria, which echo challenges in aligning taxonomies under the ISSB and CSRD frameworks.
👥 読者別の含意
🔬研究者:A useful literature review mapping the intersection of fintech and green bonds, suggesting future micro-level and comparative research directions.
🏢実務担当者:Limited direct applicability; may inform strategy on integrating digital tools in green bond issuance but no empirical evidence.
🏛政策担当者:Highlights regulatory barriers and uneven digital development, offering points for policy design in green finance ecosystem.
📄 Abstract（原文）
First of all, this paper explains the role of fintech in enhancing green finance by reducing costs, increasing financial inclusion and improving information. Furthermore, it introduces the incorporation of technology into green finance, including green identification, information disclosure, product innovation, risk evaluation and regulatory reform. Secondly, the paper examines the use of blockchain in data storage and fund traceability, as well as the developments of artificial intelligence in determining prices and assessing credit for green bonds. Thirdly, it reviews the evolution and market expansion of green bonds from the first climate bond issued in 2007 to China's status as the major issuer in the world. The paper investigates how green finance allocates capital, stimulates innovation and supports industrial transformation to promote regional progress. It analyzes the regional differences in green finance and proposes a lifecycle model for fintech-assisted green bonds to promote regional growth. The findings indicate that the combination of fintech and green bonds encounters certain obstacles, such as insufficient environmental information disclosure, uneven development of digital facilities and inconsistent criteria for identifying green assets. Future research should concentrate on the analysis at the micro level, broaden regional and international comparative studies and solve issues like transition finance and biodiversity finance. This summary outlines the main literature and gives suggestions to improve the combination of green and digital finance, which is advantageous to the "dual carbon" objectives.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://aemps.ewapub.com/article/view/34796.pdffirst seen 2026-07-07 05:17:14
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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。