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Artificial intelligence, clean energy, and market integration: Evidence from multi-period quantile dynamics
1Dokuz Eylül University, İzmir, Turkey.
Journal of Environmental Management
|December 13, 2025
Summary
Artificial intelligence (AI) positively impacts clean energy markets, with effects varying by market conditions and timeframes. This research offers insights for investors and policymakers navigating AI
Area of Science:
- Financial Economics
- Energy Markets
- Technology Impact Analysis
Background:
- The integration of artificial intelligence (AI) and Big Data technologies is rapidly transforming various sectors, including the rapidly growing clean energy market.
- Understanding the dynamic interplay between AI/Big Data indices and clean energy indices is crucial for investors and policymakers.
- Existing research often overlooks the nuanced, time-varying, and quantile-dependent relationships between these markets.
Purpose of the Study:
- To investigate the dynamic and asymmetric relationships between the Indxx Artificial Intelligence and Big Data Index (IAIQ) and several clean energy market indices.
- To analyze the influence of AI technologies on clean energy markets across different time horizons and market conditions.
- To identify causal interactions between AI/Big Data and clean energy markets using advanced econometric methods.
Main Methods:
- Utilized the Rolling Windows Quantile Augmented Dickey-Fuller Unit Root Test to assess time series stationarity.
- Applied Wavelet Quantile-on-Quantile Regression to capture nonlinear and asymmetric relationships.
- Employed Wavelet Quantile-on-Quantile Granger Causality Regression to determine causal linkages.
Main Results:
- AI technologies demonstrate positive, strong, and statistically significant effects on clean energy markets.
- The impact of AI on clean energy markets is not uniform, varying significantly across different time horizons and quantile distributions.
- Causal relationships were identified, indicating that AI advancements can influence clean energy market dynamics.
Conclusions:
- AI technologies exert a substantial positive influence on clean energy markets, though this relationship is complex and conditional.
- The findings provide valuable insights for strategic investment decisions and policy formulation in the clean energy sector.
- This study contributes to the theoretical understanding of financial market interdependencies in the context of technological innovation.
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