ARTIFICIAL INTELLIGENCE AND GREEN ECONOMIC EFFICIENCY: MECHANISM ANALYSIS IN THE GUANGDONG-HONG KONG-MACAO GREATER BAY AREA

Abstract

Faced with problems such as low energy efficiency, serious environmental pollution, and ecological degradation caused by rapid economic growth, it has become crucial to seek a balance between economic vitality and ecological management. Improving green economic efficiency, that is, the ability of an economy to minimise ecological pollution while achieving sustainable growth, can play a vital role in solving these issues. Opting for the Guangdong-Hong Kong-Macao Greater Bay Area as the case study, this research illustrates the key contribution of artificial intelligence (AI) to improving the efficiency of the green economy. Through the economic dependence theory, the level of regional economic dependence is quantified, and then a multiple regression model is constructed to empirically analyse the relationship between AI, economic dependence, industrial structure, and green economic efficiency. Research results demonstrate that AI has a significant positive effect on the improvement of green economic efficiency. This desirable positive effect can be further strengthened through the mediating variable of industrial structure rationalisation. In addition, economic dependence moderates the relationship between AI and green economic efficiency, indicating that AI has a positive contribution to optimising resource allocation and reducing ecological impact. The significance of this study is far-reaching, showing that AI not only supports sustainable economic growth but also promotes the balanced development of ecology and economy. By integrating AI, regions can achieve higher efficiency and sustainability. Finally, this study provides reference suggestions for the layout of smart industries in the Guangdong-Hong Kong-Macau Greater Bay Area and the improvement of other regional economic development and green efficiency.