Sustainable AI–Enhanced Direct Air Capture (DAC): Transforming Air Recycling for Future Pollution Control

Direct Air Capture (DAC) has emerged as a viable negative-emissions technology capable of extracting carbon dioxide (CO₂) directly from ambient air. However, current DAC systems remain energy-intensive, financially demanding, and difficult to scale for urban environmental applications. Sustainable artificial intelligence (AI)—defined as efficient, low-energy computational systems—offers new pathways for optimizing DAC performance while reducing environmental burdens linked to algorithmic processes. This paper presents a comprehensive and original academic framework for integrating sustainable AI with DAC to support future air-recycling capacities and pollution-control strategies. Through an enhanced literature review, refined case study analyses, and expanded ethical reasoning, this study argues that AI-optimized DAC can significantly strengthen urban air-quality management, catalytic carbon recycling, and climate mitigation efforts. The paper concludes with policy-oriented recommendations to guide the responsible deployment of AI-enabled DAC systems within climate-neutral infrastructures