Artificial Intelligence, Green Innovation, and the Clean-Energy Market: A Quantile-Based Analysis

Artificial intelligence is increasingly viewed as both a catalyst for clean-energy development and a potential constraint on green innovation. This study examines the quantile-specific relationships between artificial intelligence, clean-energy market performance, and green innovation using daily market data from 15 June 2018 to 29 June 2026. The analysis employs Quantile Kernel Regularized Least Squares (QKRLS), which captures nonlinear and heterogeneous associations across different market conditions. The findings reveal a positive and statistically significant association between artificial intelligence and clean-energy-market performance across all examined quantiles, with the strongest estimated marginal associations occurring in the lower quantiles. Conversely, artificial intelligence is negatively and significantly associated with green innovation throughout the distribution, with the strongest negative associations observed at the 0.10 and 0.90 quantiles. The study therefore highlights the need for policies that align artificial-intelligence growth with the sustained development of green innovation.

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Publication Details

Journal
Energies
Published
2026-09-17
DOI
https://doi.org/10.3390/en19184397
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence, Green Innovation, and the Clean-Energy Market: A Quantile-Based Analysis

Seyed Alireza Athari, Derviş Kırıkkaleli, Emmanuel Oluwatosin Adewusi, Ruth Oluyemi Bamidele et al.
Energies
Energy, Environment, Economic Growth
article

Artificial Intelligence, Green Innovation, and the Clean-Energy Market: A Quantile-Based Analysis

Seyed Alireza Athari, Derviş Kırıkkaleli, Emmanuel Oluwatosin Adewusi, Ruth Oluyemi Bamidele, Mohamed Djafar Henni, Anar Eminov
article en

Abstract

Artificial intelligence is increasingly viewed as both a catalyst for clean-energy development and a potential constraint on green innovation. This study examines the quantile-specific relationships between artificial intelligence, clean-energy market performance, and green innovation using daily market data from 15 June 2018 to 29 June 2026. The analysis employs Quantile Kernel Regularized Least Squares (QKRLS), which captures nonlinear and heterogeneous associations across different market conditions. The findings reveal a positive and statistically significant association between artificial intelligence and clean-energy-market performance across all examined quantiles, with the strongest estimated marginal associations occurring in the lower quantiles. Conversely, artificial intelligence is negatively and significantly associated with green innovation throughout the distribution, with the strongest negative associations observed at the 0.10 and 0.90 quantiles. The study therefore highlights the need for policies that align artificial-intelligence growth with the sustained development of green innovation.

EnergiesVol. 19(18)
European University of Lefke (TR), Cyprus International University (CY), Holy Spirit University of Kaslik (LB), Azerbaijan State Agricultural University (AZ), Western Caspian University (AZ), Islamic University of Madinah (SA), University of Kyrenia (CY), Lebanese American University (LB)
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Energy, Environment, Economic Growth
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Artificial Intelligence, Green Innovation, and the Clean-Energy Market: A Quantile-Based Analysis — Seyed Alireza Athari, Derviş Kırıkkaleli, et al. · Energies (2026) | TGRS Research Map | TGRS