Green finance effectiveness under policy uncertainty: an integrated conceptual framework linking governance, FinTech, and artificial intelligence to corporate environmental performance
政策不確実性下におけるグリーンファイナンスの有効性：ガバナンス、フィンテック、人工知能を企業環境パフォーマンスに結び付ける統合的枠組み (AI 翻訳)
Vidura perera
🤖 gxceed AI 要約
日本語
本稿は、政策不確実性・ガバナンス・制度・技術（FinTech・AI）の条件によってグリーンファイナンスの有効性が変化することを説明する適応的グリーンファイナンス有効性理論（AGFET）を提案する。同理論は、グリーンファイナンスが企業環境パフォーマンスに結びつく条件を整理し、AIによる持続可能性評価の可能性を示す。将来的な実証研究の基盤となる。
English
This paper develops the Adaptive Green Finance Effectiveness Theory (AGFET), an integrated framework explaining how green finance effectiveness is conditional on policy uncertainty, governance, institutional quality, and technological capabilities including FinTech and artificial intelligence. It conceptualizes green finance as an adaptive process mediated by green innovation and AI-driven evaluation, providing a foundation for future empirical and AI-driven sustainability research.
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、FIT見直しや炭素価格議論などの政策不確実性が企業のグリーン投資に影響を与えており、本枠組みは日本企業のGX戦略におけるグリーンファイナンスの効果的な活用条件を理解する上で示唆に富む。また、AI・FinTechの活用は日本のデジタルGXの流れにも合致する。
In the global GX context
Globally, this paper addresses the uneven effectiveness of green finance under policy uncertainty, relevant to jurisdictions like the EU (Taxonomy) and US (SEC climate rules). By integrating AI and FinTech, it offers a scalable framework for improving environmental performance evaluation and aligning with ISSB standards.
👥 読者別の含意
🔬研究者:AGFET provides a comprehensive theoretical base for future studies on green finance, policy uncertainty, and the role of AI in sustainability.
🏢実務担当者:Firms and financial institutions can use the framework to assess how policy uncertainty and digital capabilities influence the payoff of green finance.
🏛政策担当者:The framework highlights the need for stable policy environments and support for FinTech/AI to enhance green finance effectiveness.
📄 Abstract（原文）
Green finance has emerged as a critical mechanism for supporting sustainable investment and improving corporate environmental performance (CEP); however, its effectiveness remains highly uneven across firms, industries, and institutional environments. Existing literature has largely examined green finance through fragmented and predominantly linear perspectives, providing limited understanding of how policy uncertainty, governance quality, institutional conditions, and technological capability jointly shape sustainability outcomes. To address this gap, this study develops the Adaptive Green Finance Effectiveness Theory (AGFET), an integrative and context-dependent theoretical framework explaining the conditional effectiveness of green finance under varying macroeconomic, institutional, organisational, and technological conditions. Drawing upon Institutional Theory, Stakeholder Theory, the Resource-Based View, and investment-under-uncertainty perspectives, AGFET conceptualises green finance effectiveness as an adaptive and nonlinear process mediated through green innovation, environmental investment, ESG practices, and technological transformation. The framework further incorporates the emerging roles of FinTech innovation and artificial intelligence (AI) in enhancing sustainability-related decision-making, predictive analytics, and environmental performance evaluation. The study contributes to sustainable finance literature by advancing a unified multi-level framework that integrates financial, governance, institutional, and technological dimensions into a coherent theoretical structure. In addition to clarifying the conditions under which green finance translates into improved environmental outcomes, AGFET provides a scalable foundation for future empirical, comparative, and AI-driven sustainability research. The findings offer important implications for policymakers, firms, and financial institutions seeking to improve the effectiveness of green finance in supporting climate transition and sustainable development objectives.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3389/fclim.2026.1869980first seen 2026-06-22 05:17:17
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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。