The role of artificial intelligence in global green energy products trade pattern evolution: Based on the international trade network perspective

Artificial intelligence (AI) technology has been extensively applied in green technological innovation and green energy system, effectively facilitating production and consumption of green energy. However, the role of AI in shaping the green energy products trade pattern remains understudied. From the perspective of the international trade networks, this study constructs the global green energy trade networks (GGETNs) and global industrial robot trade networks (GIRTNs) representing AI interaction among economies from 2010 to 2022, analyzing their evolution of network structure and node importance. Using the temporal exponential random graph model (TERGM), this study explores the influencing mechanism of AI on the evolution of the GGETNs. The research findings suggest that (1) Although most structural indicators of GGETNs exceed those of GIRTNs, their evolution trends and growth directions demonstrate remarkable similarities. Trade reciprocity and trade clique among economies play important roles in their evolution. Both GGETNs and GIRTNs gradually exhibit “China-the United States-Germany” trade pattern, emerging markets and developing economies play pivotal bridging roles in GGETNs, with Ireland and South Africa as key connectors in GIRTNs. China, Japan, and the United States wield more significant trade influence on both networks. (2) AI trade interaction among economies significantly promotes network evolution, reciprocal trade and trade clique of GGETNs. Economies with higher trade strength and stronger central influence for industrial robot products are more likely to develop green energy products trade, and economies with higher trade strength and stronger central influence are more likely to engage in reciprocal trade and trade clique. The GGETNs exhibit strong evolutionary stability when embedded in the GIRTNs. (3) Heterogeneity tests demonstrate that the promoting effect of AI on GGETNs' evolution is more pronounced in emerging markets and developing economies. These findings carry significant implications for harnessing AI to accelerate the global green energy transition.