Artificial intelligence and decarbonization in China: Asymmetric roles of green electricity, fossil subsidies, and trade policy uncertainty

Abstract China faces a critical challenge in balancing industrial growth with carbon reduction commitments. This study investigates the asymmetric environmental effects of artificial intelligence (AI) on decarbonization in China using monthly data from 2000 to 2020. Employing nonlinear autoregressive distributed lag (NARDL), wavelet coherence analysis, and quantile‐on‐quantile kernel‐based regularized least squares (QQKRLS), the study examines how AI interacts with green electricity, fossil fuel subsidies, and trade policy uncertainty to shape greenhouse gas emissions. The findings indicate that positive AI developments contribute to emissions reduction, while negative AI shocks linked to energy‐intensive infrastructures temporarily raise emissions before stabilizing. Green electricity emerges as a key enabler of decarbonization, whereas fossil subsidies and trade policy uncertainty hinder progress. These results highlight the dual‐edged nature of AI's environmental impact and stress the importance of supportive policy frameworks. The study recommends integrating AI deployment with renewable energy expansion, redirecting subsidies toward low‐carbon infrastructure, and stabilizing trade policy to minimize market disruptions. By uncovering these asymmetric effects, the research contributes to the discourse on digital decarbonization and supports progress toward Sustainable Development Goals 7 (Clean Energy), 9 (Industry and Innovation), and 13 (Climate Action).