Decoding the DNA of Green Finance: A Kernel‐Based Learning Odyssey Into the US Economy
グリーンファイナンスのDNAを解読する：米国経済におけるカーネルベース学習の探求 (AI 翻訳)
Gizem Bediroğlu, Feyyaz Zeren
🤖 gxceed AI 要約
日本語
本論文は、1961年から2024年までの米国のグリーンファイナンス指数（GFI）の決定要因を、高度な機械学習手法（QQKRLS、WKRLS、RWKRLS）を用いて分析。経済成長とESGパフォーマンスが主要な推進要因である一方、地政学的リスクと環境悪化が脅威となることを発見。時間変動性と中長期的な影響が明らかになり、政策への示唆を提供。
English
This paper uses a novel machine learning framework (QQKRLS, WKRLS, RWKRLS) to analyze the determinants of the US Green Finance Index from 1961 to 2024. It finds that economic growth and ESG performance are key drivers, while geopolitical risk and ecological footprint pose threats. Dynamics are time-varying and manifest in medium-to-long-term horizons. Offers a policy blueprint for resilient green 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
本論文は米国を対象としているが、グリーンファイナンスの非線形分析手法（QQKRLS等）は日本市場への応用可能性がある。日本のグリーン投資やESG評価において、時間変動する要因の理解に貢献する。
In the global GX context
This paper contributes to the global GX context by demonstrating advanced ML for green finance analysis, highlighting ESG, economic growth, and geopolitical risk as intertwined factors. Its framework can be adapted for other countries, offering insights for transition finance and policy design.
👥 読者別の含意
🔬研究者:The ML methods (QQKRLS, WKRLS, RWKRLS) and their application to non-linear green finance dynamics offer a novel analytical approach for similar studies.
🏢実務担当者:The findings on ESG and economic growth as key drivers can inform green product development and risk assessment in sustainable finance.
🏛政策担当者:The paper emphasizes integrating macroeconomic stability, geopolitical risk mitigation, and ESG standards for a resilient green finance ecosystem.
📄 Abstract（原文）
The transition to a sustainable economy requires a profound understanding of the underlying drivers of green finance. This paper decodes the ‘DNA’ of the United States (US) green finance landscape by investigating its multifaceted determinants from 1961 to 2024. Beyond traditional linear assessments, we introduce a sophisticated machine learning framework—comprising Quantile‐on‐Quantile Kernel‐based Regularized Least Squares (QQKRLS), Wavelet KRLS (WKRLS), and Rolling‐Window KRLS (RWKRLS)—to capture the intricate, non‐linear interactions between a novel Green Finance Index (GFI) and its catalysts: Foreign Direct Investment (FDI), Economic growth (GDP), and Environmental, Social and Governance (ESG) performance, as well as its inhibitors: geopolitical risk (GPR) and ecological footprint (ECF). Our findings reveal that while economic expansion and ESG quality act as primary drivers, geopolitical volatility and environmental degradation pose systemic threats to green financial stability. Crucially, the analysis uncovers that these dynamics are time‐varying and predominantly manifest in medium‐to‐long‐term horizons. This paper offers a blueprint for policymakers, emphasizing that a resilient green finance ecosystem in the US necessitates a holistic integration of macroeconomic stability, geopolitical risk mitigation, and stringent ESG standards.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/sd.71262first seen 2026-06-06 05:21:44
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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。