Artificial Intelligence as a Catalyst for Green Finance and Sustainability: Empirical Evidence from Global ESG and Green Bond Markets
グリーンファイナンスとサステナビリティの触媒としての人工知能：グローバルなESGおよびグリーンボンド市場からの実証的証拠 (AI 翻訳)
Sayyed Sadaqat Hussain Shah, Arshad Javed, Muhammad Mahboob Khan, Awais Shabbir, Imran Kamal
🤖 gxceed AI 要約
日本語
本論文は、AI導入がグリーンボンド発行額と企業ESGスコアに与える影響を、54カ国・5万件超のデータと複数の計量モデル（固定効果回帰、分位点回帰、TVP-VAR-SV）を用いて分析。AI導入の1標準偏差増加でグリーンボンド発行が平均14.3%増加、ESGスコアも統計的に有意に向上することを示す。またCOVID-19以降、AIの役割がより顕著になった構造変化も確認。政策立案者や投資家に示唆を与える。
English
This study examines how AI adoption influences green bond issuance and corporate ESG scores using panel data from 54 economies (2019-2024) and multiple models (fixed-effects, quantile regression, TVP-VAR-SV). It finds that a one-standard-deviation increase in country-level AI adoption predicts a 14.3% rise in green bond volumes and significant ESG improvements, with a structural break post-COVID-19 strengthening AI's role. The results offer insights for policymakers, investors, and regulators seeking to leverage AI for sustainable 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
日本では、グリーンボンド市場の拡大やSSBJを踏まえたESG開示の高度化が進む中、AI活用による発行効率化やESG評価向上の可能性を示す本稿の知見は、金融機関や事業会社にとって実務的な示唆に富む。
In the global GX context
Globally, this paper contributes to the intersection of AI and sustainable finance, aligning with TCFD/ISSB disclosure trends and the growing role of technology in green bond markets. It provides empirical evidence that policymakers can use to design AI-friendly regulations that accelerate capital flows to sustainability.
👥 読者別の含意
🔬研究者:Novel application of TVP-VAR-SV to green finance data; offers a framework for studying AI–ESG causality with nonlinear dynamics.
🏢実務担当者:Quantifies the business case for AI adoption in terms of improved ESG scores and green bond issuance; relevant for corporate sustainability teams and green bond issuers.
🏛政策担当者:Provides country-level evidence that AI adoption boosts green finance; supports policies promoting AI integration in financial regulation and sustainability reporting.
📄 Abstract（原文）
The interplay between AI, green finance, and the sustainability agenda has become one of the most impactful economic trends of our times. Based on the specific choices of green financial instruments (GFIs) with the potential to facilitate capital flows to sustainability, green bonds and ESG-integrated investment funds, the study investigates the question of whether and how their use enhances the effectiveness of these instruments. We use publicly available data from the Climate Bonds Initiative, World Bank Treasury, Bloomberg Intelligence and OECD Corporate Sustainability Dataset for 2019-2024 to create a panel of 54 economies and over 54,000 bond observations. Combining fixed-effects panel regression, quantile regression and a Time-Varying Parameter Vector Autoregression with Stochastic Volatility (TVP-VAR-SV) model can help us to capture both nonlinear and dynamic relationships in the data. The volumes of green bond issues are predicted to rise by 14.3%, on average, across the globe for every one standard deviation increase in country-level AI adoption, while corporate ESG scores are predicted to improve by a statistically significant amount. Green finance, on the other hand, seems to be bringing a level of uncertainty to an end in the ESG arena and while the impact of AI on the conditions in the sustainability market is lagged, it is ultimately positive. Additionally, we will see a structural break after COVID-19, where AI's role in green finance becomes much more significant. The findings collectively address the questions of policy makers, institutional investors, and regulators seeking to harness AI as a catalyst for sustainable economic change.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://journalpsa.com.pk/index.php/JPSA/article/download/705/699first seen 2026-07-20 05:34:30
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