Technological innovations fuel carbon prices and transform environmental management across Europe.

Abstract

This study investigates the impact of recent Artificial Intelligence (AI)-driven technological innovations on carbon prices across different quantiles, assessing the influence of AI stock prices on energy prices based on European carbon allowances while controlling for other macroeconomic factors. Using robust methods such as quantile-on-quantile regression, wavelet analysis, and transfer entropy, the research quantifies the information flow between the AI market and carbon allowances. Using daily data with four alternative AI stock prices from September 14, 2016, to December 29, 2023, the findings reveal a strong effect of AI returns on carbon prices, with significant fluctuations across price quantiles and consistent long-term average growth in market returns. The quantile-on-quantile regression analysis indicates that the short-term changes in carbon prices significantly impact the AI stock returns, with the most pronounced impact occurring below the 20th and above the 80th quantiles of carbon prices, indicating larger responses to extreme events. Additionally, large positive AI price shocks lead to substantial changes in carbon prices, particularly when the carbon prices are near their long-term average. Compared to the short term, the long-term responses are about 15 times smaller. Insights from the Rényi transfer entropy confirm these findings, while the Shannon transfer entropy estimates indicate a discernible and statistically significant information flow from the AI prices to the carbon prices. These findings offer critical insights for investors and policymakers, deepening the understanding of AI's influence on carbon market dynamics.