AI-Driven Smart Haulage Technologies for Decarbonization and Sustainable Mine Transportation: A Comprehensive Review

Abstract

Haul trucks generate more than half of direct greenhouse-gas emissions in typical mining operations, making haulage decarbonization central to the industry’s net zero commitments. Artificial intelligence (AI) is increasingly applied to haulage, but existing reviews examine individual technologies rather than linking them to decarbonization and ESG outcomes. This comprehensive review synthesizes 73 peer-reviewed sources from 2017 to 2026 across route optimization, electric and hybrid trucks, predictive maintenance, fleet monitoring, carbon footprint reduction, and ESG performance, grades reported outcomes by validation basis, and develops a framework linking AI-enabled technology domains, through their operational mechanisms, to ESG outcomes. Reinforcement-learning dispatching reduced haulage emissions by more than 30% relative to fixed scheduling in simulation, trolley assist reduced fuel use by up to 91%, and fleet electrification modeling projected GHG reductions of up to 92.6% in a hypothetical case, contingent on grid carbon intensity. Most reported reductions derive from simulation, and AI’s advantage over conventional control of electrified fleets remains undemonstrated. Gaps include scarce field-verified emission baselines, limited modeling of operator behavior, immature cybersecurity and data governance practice, and weak engagement between ESG research and haulage technology. AI-enabled operational measures offer a promising near-term decarbonization pathway that complements, rather than awaits, fleet electrification.