gxceed
← 論文一覧に戻る

The Impact of Artificial Intelligence, Green Finance, Environmental Technologies, and Renewable Energy on Greenhouse Gas Emissions

人工知能、グリーンファイナンス、環境技術、再生可能エネルギーが温室効果ガス排出に与える影響 (AI 翻訳)

Muhammad Usman, Yongming Huang, Muhammad Sohail Amjad Makhdum

Sustainable Development📚 査読済 / ジャーナル2026-04-17#AI×ESGOrigin: CN
DOI: 10.1002/sd.71024
原典: https://doi.org/10.1002/sd.71024

🤖 gxceed AI 要約

日本語

本研究は、BRICS-T諸国を対象に、人工知能（AI）、グリーンファイナンス、環境技術、再生可能エネルギーが温室効果ガス（GHG）排出に与える影響を分析。第2世代の計量経済手法を用いた結果、AI、グリーンファイナンス、再生可能エネルギーは排出削減に寄与するが、環境技術は長期的に排出を増加させる可能性が示された。AIとグリーンファイナンスの相乗効果は環境質を向上させる一方、AIと環境技術の相互作用は排出を悪化させる。

English

This study analyzes the impact of AI, green finance, environmental technologies, and renewable energy on GHG emissions in BRICS-T countries. Using second-generation econometric methods, it finds that AI, green finance, and renewable energy reduce emissions, while environmental technologies increase emissions in the long run due to rebound effects. The synergy between AI and green finance improves environmental quality, but AI-environmental technology interaction exacerbates emissions.

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AIとグリーンファイナンスの融合がGX推進に重要とされるが、本論文は環境技術のリバウンド効果にも注意を促す。日本のGX政策において、AI活用とグリーンファイナンスの連携強化に示唆を与える。

In the global GX context

This paper provides global evidence on the interplay between AI, green finance, and renewable energy in reducing emissions, relevant for international frameworks like TCFD and ISSB. The finding that environmental technologies may increase emissions highlights the need for careful implementation in transition finance strategies.

👥 読者別の含意

🔬研究者:Useful for understanding the complex interactions between AI and green finance in emissions reduction, and the rebound effect of environmental technologies.

🏢実務担当者:Offers insights for corporate sustainability teams on the potential drawbacks of environmental technologies and the benefits of integrating AI with green finance.

🏛政策担当者:Suggests that policies should promote AI-green finance synergy while being cautious about environmental technology rebound effects.

📄 Abstract（原文）

ABSTRACT The complex interrelationships among artificial intelligence (AI), green finance, environmental technologies, renewable energy and their collective impact on environmental sustainability remain underexplored in the existing literature. To address this research gap, this study investigates the effects of AI, green finance, environmental technologies, and renewable energy on greenhouse gas (GHG) emissions. Recognizing the strong environmental interdependencies among the BRICS‐T (Brazil, Russia, India, China, South Africa, and Turkey) countries, this study employs advanced second‐generation econometric techniques that account for cross‐sectional dependence and slope heterogeneity. The findings of the method of moment quantile regression (MMQR) reveal several key insights. First, AI, green finance, and renewable energy reduce environmental degradation across all emission quantiles. Second, environmental technologies, despite their intended purpose, are found to increase emissions in the long run, potentially due to rebound effects, technological inefficiencies, or transitional adjustment challenges. Third, the synergy between AI and green finance enhances environmental quality, while the interaction between AI and environmental technologies unexpectedly exacerbates GHG emissions. Based on these findings, the study offers targeted policy recommendations to better align AI‐driven innovations with green financial mechanisms and renewable energy developments, thereby fostering sustainable environmental management.

🔗 Provenance — このレコードを発見したソース

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。