The economic drivers of AI’s impact on renewable energy

Artificial intelligence is anticipated to make fundamental changes in the operation of economic systems. Can it have a similar impact on renewable energy consumption? This study examines thisresearch questionin terms ofAI’s indirect impact on renewable energy through the mediation of key drivers of renewable energy inan economic system: firm profitability, firm value, firm competitiveness, and economic equality. The study analyzes not only the consistent (positiveor negative) but also the inconsistent (dark) indirect effects of AI on renewable energy, as AI is currently being incorporated into production and energy systems. The study employs recently developed ‘Pattern Causality’ analysis to estimate these effects. It uses temporal network, quadruple wavelet, and quantile wavelet analyses for the robustness check of these estimations. The study yields three conclusions. First, AI exerts positive indirect effects of 0.27, 0.75, 1.20, and 0.37 on renewable energy in Germany, Sweden, the United States, and Canada, respectively. The inconsistentindirect impacts of AI are 0.06, 0.07, 0.02, and 0.07 for the corresponding countries, respectively. Second, the consistent and inconsistentimpacts of AI are persistent over time, despite temporal fluctuations. Third, there are no significant disparities in the magnitudes of AI’s quartile effects (0.25, 0.50, and 0.75) on renewable energy sources. The results of the temporal network, quadruple wavelet, and quantile wavelet analyses for the robustness check validate the pattern causality estimations. The study provides pertinent policy recommendations to amplify AI’s positive indirect impacts on renewable energy and to convert its inconsistent andnegative impactsinto positive impacts with specific reference to the Sustainable Development Goals.