About me

Hi! I am Da Huo, a researcher working on climate change and greenhouse gas (GHG) emissions. I am the lead developer of the Carbon Monitor Cities dataset (https://cities.carbonmonitor.org/), which is the first near-real-time daily CO2 emission dataset for 1500+ cities. I am also modeling and comparing life cycle GHG emissions for vehicle fleets to guide decarbonisation policies.

I am also interested in assessing climate change impacts from a natural science perspective, including glaciers and ice sheets, natural carbon sinks and global carbon cycle. For my Ph.D. I studied glaciers in the Himalayas using physics-based numerical models and satellite remote sensing.

My background

I received my Ph.D. degree in Geography from Texas A&M University in 2020. I received my M.Sc. degree in Geoscience from Rice University and B.Sc. in Geophysics from Tongji University. I am a Rising Stars in Clean Energy post-doctoral fellow at the University of Toronto, and I have worked as a post-doctoral researcher at Queen’s University and Tsinghua University.

Planetary health is the ultimate concern of my research, my recent work provide data, AI-powered tools, and quantitative analysis to support climate change science and mitigation strategies.

My research

  • Modeling and comparing life cycle GHG emissions and decarbonization pathways for light-duty vehicle fleets in US, China and UK (University of Toronto)
  • Developing OpenPit-AI: an AI-powered fleet management system for emission reduction in mining operations (Queen’s University)
  • Investigating relationship between electrification and low carbon transitions of cities (Queen’s University)
  • Leading the development of Carbon Monitor-Cities: the first near-real-time daily CO2 emission dataset for cities worldwide (Tsinghua University)
  • Leading a project on city-level carbon neutrality in collaboration with Alibaba Cloud
  • Developing a ground penetrating radar data processing software for DOE (Texas A&M University)
  • Studying climate-glacier dynamics using numerical simulations (Texas A&M University)
  • Studying Earth ambient noise using data collected from ocean-bottom seismometers (Tongji University)

News

  • 01/2024: A paper on AI application in low-carbon mining “Smart dispatching for low-carbon mining fleet: A deep reinforcement learning approach” is published on Journal of Cleaner Production
  • 11/2023: A paper for our recent study on EV and decarbnization pathway is submitted to Energy Policy “How Do We Decarbonize One Billion Vehicles by 2050? Insights from a Comparative Life Cycle Assessment of Electrifying Light-Duty Vehicle Fleets in the United States, China, and the United Kingdom”
  • 04/2023: Abstract “Smart Mining Fleet Dispatching System to Reduce Greenhouse Gas Emissions Using Deep Reinforcement Learning” has been accepted by 11th International Conference on Industrial Ecology (ISIE2023).
  • 04/2023: Abstract “Charging toward decarbonized electrification: Revisiting Beijing’s power system” has been accepted by 11th International Conference on Industrial Ecology (ISIE2023).
  • 04/2023: Abstract “Carbon Monitor Cities, Near-Real-Time Monitoring of Daily Fossil-Fuel CO2 Emissions from Cities Worldwide” has been accepted by 11th International Conference on Industrial Ecology (ISIE2023).
  • 09/2022: I am invited to give a keynote speech on “Near-Real-Time Estimates of Daily Fossil-Fuel CO2 Emissions from Cities Worldwide” at the Metrology for Climate Action Workshop 2022
  • 05/2022: I have been selected as a Rising Stars in Clean Energy Postdoctoral Fellow at the University of Toronto (Climate Positive Energy fellowships)

Links and media reports

Carbon Monitor Cities Discover Magazine Planet Earth Energy Central News Tsinghua University News

For more info

Please check out other pages (publications, talks, teaching, CV) for more information.