Mapping China's Environmental Sustainability Drivers With Wavelet‐Based Artificial Neural Network: Role of Global Supply‐Chain Management, Rule of Law and Gender Equality

ABSTRACT Against the backdrop of China's rise as the world's manufacturing nexus, this study shows how the interaction of global supply chains, socio‐economic forces such as gender equity and governance, and the pursuit of economic sustainability will shape the country's path to resilient growth. Using China as a case study, we analyse quarterly data from 1998 Q1 to 2023 Q4 and introduce two machine‐learning approaches—Artificial Neural Network–wavelet Granger causality and ANN‐wavelet regression. The findings indicate that global supply‐chain management delivers modest short‐term relief, a slight increase in medium‐term pressures, and strong long‐term sustainability gains. Economic sustainability is associated with progressively larger reductions in ecological stress. Rising income consistently worsens environmental degradation in the medium and long run. A stronger rule of law steadily improves outcomes across all horizons. Gender equality shows no measurable effect. ANN‐wavelet Granger analysis confirms strong predictive power for environmental sustainability over time. The study concludes with targeted policy recommendations derived from these results.