A Machine Learning Approach to Green Finance: Pathways for Enhancing Entrepreneurship, Employment, and the Energy Pillar of the Just Transition
グリーンファイナンスへの機械学習アプローチ：起業、雇用、そして公正な移行のエネルギー柱を強化する経路 (AI 翻訳)
Ilyes Abidi, Hesham Yousef Alaraby, Ghassan Rabaiah
🤖 gxceed AI 要約
日本語
本研究は、サウジアラビアの8都市を対象に、グリーンファイナンスが起業、雇用、エネルギー転換に与える因果効果をCausal Forestsで推定。エネルギー転換への効果は大きいが、雇用や起業への波及効果は都市によって異なり、地域特性に応じた政策の必要性を示した。
English
Using Causal Forests on a panel of eight Saudi cities (2000-2024), this study finds that green finance causally improves the energy transition pillar (effect ≈0.81 standardized units) but has weaker and heterogeneous effects on entrepreneurship and employment. The results support place-based policies for inclusive green transitions.
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
サウジアラビアを対象とするが、日本の地域間格差やグリーンファイナンスの効果検証にも応用可能な手法を提供。日本の自治体レベルでの政策立案に示唆を与える。
In the global GX context
This paper advances global green finance scholarship by providing causal evidence from an oil-dependent economy and demonstrating a machine-learning method to uncover heterogeneous treatment effects. It offers a typology that can inform just transition policies worldwide.
👥 読者別の含意
🔬研究者:Causal Forestsを用いたグリーンファイナンスの効果推定手法は、他の地域や政策評価に応用可能。
🏢実務担当者:都市のタイポロジーは、自社の投資戦略や地域適応策の立案に参考になる。
🏛政策担当者:グリーンファイナンスの効果が地域によって異なるため、補完的施策（職業訓練など）の重要性を認識すべき。
📄 Abstract（原文）
Green finance is increasingly promoted as a lever for sustainable development, yet policy debates still lack clear causal evidence on whether it can deliver inclusive economic gains alongside environmental progress, especially in oil-dependent economies where transition pathways may differ across cities. This study investigates whether green finance intensity causally affects three interrelated dimensions of sustainable development—entrepreneurship, employment, and the energy pillar of the just transition (energy transition/decarbonization)—and how these effects vary across local contexts. We compile a balanced city–year panel for eight major Saudi cities over 2000–2024 and estimate both average and heterogeneous impacts using a causal machine-learning approach (Causal Forests) to recover context-dependent treatment effects in an observational setting. Results show that green finance is most consistently associated with improvements in the energy-transition dimension (average effect ≈ 0.81 in standardized units), whereas spillovers to entrepreneurship are smaller (≈0.36) and employment effects are more uneven and less precisely identified across cities. This heterogeneity reveals distinct local pathways, including “integrated” profiles where environmental and economic gains align and “eco-specialization” profiles where transition progress is not matched by comparable local economic diffusion, as illustrated by Hail. We further derive a policy-relevant city typology that helps diagnose where green finance is likely to generate broad-based benefits and where complementary interventions (e.g., skills development or SME support) may be required to translate transition gains into entrepreneurship and jobs. Overall, our findings highlight that green finance effectiveness is strongly context-dependent and support place-based strategies to convert energy-transition progress into inclusive development.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18052161first seen 2026-05-06 00:39:18
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。