Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness

Abstract

A more comprehensive understanding of the impact of artificial intelligence (AI) on energy transition and carbon emissions could help to use AI to achieve carbon neutrality. To this end, the STIRPAT approach, the mediation effect technique and the panel threshold technique are developed using the panel data in 69 countries from 1993 to 2019. The results show that: (i) AI promote energy transition and carbon emission reduction, and trade openness (indicated by imports, exports and total trade volume) has the mediating effect. (ii) There is a single-threshold of trade openness in the impact of AI on carbon emissions. When trade openness is below the threshold, AI has an insignificant impact on carbon emissions; when trade openness crosses the threshold, AI has a significant negative impact on carbon emissions. There is a double-threshold of trade openness in the impact of AI on energy transition. When trade openness is lower than the first threshold, the impact of AI on energy transition is not significant; When trade openness is higher than the second threshold, the positive impact of AI on energy transition is increased. (iii) When considering the heterogeneity of income levels and AI levels, the trade threshold for achieving carbon emission reductions in the high-income group is lower than that of the global group, and the trade threshold for achieving carbon emission reductions in the low-AI level group is higher than that of the global group. While this study unequivocally delineates the affirmative role of artificial intelligence in carbon emission reduction and energy transformation, particularly in the context of trade openness, we concurrently acknowledge that this viewpoint is not devoid of contention. Amidst the rapid advancement of technology and the landscape of open trade, we discern the presence of counterarguments. The efficacy of artificial intelligence is susceptible to the influence of multifaceted factors. It is imperative to consider associated factors, such as the significant energy consumption required for storing and cooling data centers and servers. The study's conclusions aid policymakers in devising nuanced emission reduction policies tailored to specific needs.