Climate Mitigation Strategies With AI and Different Energy Types: Fresh Insights Through the Fourier Approach

This study examines whether environmental sustainability is feasible for China, a country that consumes fossil fuels intensively. For this purpose, it investigates the impact of economic growth, renewable and non-renewable energy, and artificial intelligence on environmental sustainability using the novel Fourier Asymmetric ARDL (FAARDL) method for the period 1985–2022. The FAARDL is critical to capturing structural breaks in China's rapidly industrializing economic space. According to the empirical analysis results, economic growth and non-renewable energy consumption contribute to environmental unsustainability. On the other hand, renewable energy consumption ensures environmental sustainability. The study's key finding is that AI's positive partial adjustment failed to mitigate climate change significantly, but its negative partial adjustments reduce environmental sustainability by approximately 3% in the long run. These results represent a novel finding in the relationship between artificial intelligence and the environment. In other words, the asymmetric relationship that ordinary methods cannot find has been revealed by the superior FAARDL method. The Chinese government should develop policies to accelerate the transition from gray resources to green ones. Moreover, considering the negative impact of artificial intelligence on environmental quality, laws that incorporate artificial intelligence technologies should be developed, thereby optimizing their contributions to environmental progress.