Limiting global warming to 2 °C by 2100 requires the removal of 100 to 1000 billion metric tons of CO2 from the atmosphere. Through photosynthesis, trees uptake carbon from the air for maintenance and growth, thus there is growing enthusiasm in restoring forests to mitigate climate change as part of a suite of strategies named Nature-based Climate Solutions (NbCS). Despite high expectations, where, when, and how NbCS strategies could be effective remains unclear.
This project aims to evaluate carbon accumulation potentials of NbCS by synthesizing ground observations, satellite data, and scientific knowledge. The team will first compile a global dataset of restoration projects with comprehensive information on the associated climatic, ecological, and management dynamics. We will use machine learning to examine the impacts of climatic, environment, and anthropogenic factors on carbon sequestration potentials of natural climate solutions. Team members will participate in tasks such as 1) web scraping/scrawling, 2) geospatial data extraction and analysis, 3) machine learning for scientific insights.
Come and join us to fight climate change with innovation and creativity!
