Scaling solar photocatalytic hydrogen production in China: Integrated geospatial-meteorological analysis

Solar photocatalytic hydrogen production is considered a promising technology owing to its sustainable nature, while facing the challenges of improving and maintaining photocatalytic efficiency under prolonged variable weather conditions. Herein, we screen out the MoTe 2 /ZrS 2 system with a theoretical solar-to‑hydrogen conversion efficiency of 10.37 %. To improve the actual efficiency among all 97,711 grid cells in China, we propose an original meteorological data-driven machine learning model to optimize photocatalytic H 2 production and results show that the annual average STH efficiency gives a mean of 3.16 % within a range of 0.44 % – 4.91 %. Incorporating geospatial data, we determine that China's photocatalytic H 2 potential is about 216.65 Mt./year, which has been improved by almost sixfold compared to that without optimization, and the country's 2060 hydrogen demand can be met by using 11.08 % of its total land area. Compared with photovoltaic electrolysis, a photocatalyst cost of 23 USD/m 2 cat /10 3 h would make photocatalysis economically competitive. Last, considering hydrogen production fluctuations, we explore optimal operation of hydrogen storage and utilization facilities to fulfil downstream demands. • An original meteorological data-driven machine learning model is proposed. • Photocatalytic H 2 potential boost almost sixfold based on geospatial data. • About 11 % of land allocated could satisfy China's H 2 demand by 2060. • Techno-economic target for photocatalyst is benchmarked against PV-electrolysis. • Photocatalytic H 2 variability is analyzed across timescales.