DrXiaowei Gao

I am a quantitative researcher in Environmental and Geospatial Data Science, working at the intersection of GeoAI, causal inference, and techno-economic modelling for sustainability. My work uses large-scale spatial, temporal, mobility, infrastructure, and environmental datasets to understand how policies, technologies, and built systems shape urban, maritime, and climate-related outcomes.

My PhD focused on AI and causal inference for urban crash risk, with particular attention to active travel, vulnerable road users, and greener cities. I developed graph-based spatiotemporal models to predict and interpret road safety risks across both citywide and micro-spatial networks, linking heterogeneous geospatial data with evidence for safer and more sustainable urban mobility.

Beyond urban transport, my research has extended to maritime decarbonisation, port infrastructure, and environmental sustainability. I have worked with vessel trajectory and port-related data to support emission monitoring, clean port logistics, and infrastructure planning. I have also developed policy briefings on the environmental impacts of port construction, including risks to marine life and coastal ecosystems, and contributed 4 policy proposals for the International Maritime Organisation. Through related work, I have engaged with infrastructure and sustainability projects involving international organisations, including project reviews and invited talks connected to WEF, AIIB, ADB, and EBRD.

More recently, at Imperial College London, my work with the London Register of CO₂ Storage has focused on understanding the global scale-up of CCUS through data-driven modelling. I am particularly interested in combining technology growth models with geological and geophysical constraints. This work uses global project-level evidence to infer plausible future growth trajectories for subsurface CO₂ storage, and to assess where physical, technical, and economic constraints may shape the role of CCUS in climate mitigation.

Across these areas, I am interested in using GeoAI, spatial panel data, causal policy evaluation, and environmental modelling to support better decisions around climate and infrastructure transitions, including policy mechanisms such as the EU Emissions Trading System (EU ETS), pollution exposure, biodiversity co-benefits, and global sustainable futures.