Connectedness between artificial intelligence, clean energy, and conventional energy markets: Fresh findings from CQ and WLMC techniques

Abstract

In line with achieving the objectives of COP27 and SDG7, this paper examines the interdependence of the Artificial Intelligence market, clean energy, and conventional energy markets from 19th December 2017 to 5th May 2023 by using Cross-Quantilogram (CQ) and Wavelet Locale Multiple correlations (WLMC) techniques. Heatmaps of CQ show a bidirectional relationship between the AI market and clean energy at lag one with negative cross-quantile dependence evident throughout most quantiles, especially in normal market conditions. It also indicates a positive relationship between AI return rates and the clean energy market, but only when both datasets are in the same extreme quantiles (10th and 90th). Additionally, WMLC results reveal that time, scale, and investment horizons influence the interaction between AI and clean and non-clean energy industries. A considerable positive association exists between the AI market and traditional energy markets, ranging from 0.6 to 0.8. However, during the pandemic, this dependency turned negative, and it has since been minor, with an uptick in co-movement during Russia – Ukraine conflict. Several policy implications are suggested for the clean energy and conventional energy markets in line with AI.