Harnessing Selectivity in CO2 Hydrogenation: Mechanistic Descriptors, Data-Driven Catalyst Design, and Sustainability Constraints

Abstract

Rising CO₂ emissions continue to motivate catalytic strategies that recycle carbon into useful products. In CO₂ hydrogenation, environmental relevance depends not only on conversion but on selectivity control and operational stability. The reverse water–gas shift, methanation, methanol synthesis, and C₂⁺ pathways operate within overlapping temperature and pressure windows, where minor changes in catalyst state or reaction conditions can markedly alter product distribution and downstream separation requirements. This review frames CO₂ hydrogenation as a selectivity-driven design challenge. Key reaction intermediates governing pathway competition, including adsorbed CO, formate, methoxy species, and carbide phases, are identified, and recurring selectivity levers across catalyst families are discussed. Operando and in situ studies are highlighted for resolving working states and dynamic restructuring under reaction conditions. Beyond mechanistic insights, emerging artificial intelligence and machine learning approaches are examined for accelerating catalyst and operating window identification. Techno-economic analysis and life cycle assessment perspectives are integrated to link selectivity to separation energy, process efficiency, and net climate impact. This framework supports environmentally informed evaluation and deployment of CO₂ hydrogenation technologies.