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In a world facing the urgent challenge of climate change, it's clear that we need to take action. This PhD project aims to investigate land-based strategies for removing carbon dioxide (CO2) from the atmosphere. This is crucial for achieving the goals set in the Paris Agreement and for reaching net-zero emissions.
The project focuses on several land-based strategies, including afforestation, reforestation, bioenergy crops, enhanced rock weathering, and peatland restoration. These strategies have been endorsed by expert committees and organizations like the IPCC. However, there are still unintended environmental risks that must be assessed, because they may reinforce, counteract, or even offset the climate benefits from carbon sequestration.
We invite projects that will address pressing issues related with the use of land-based CO2 removal strategies to mitigate climate change and further indirect atmospheric and climate impacts. You will use high-performance Earth System models and observations, from field trials to satellite sensors, to answer a research question of interest to you (eg, afforestation/reforestation and wildfires, CH4 emissions from peatland restoration, air quality impacts from enhaced rock weathering, etc).
To succeed in this project, you should have a basic understanding of data analysis and coding (e.g., Python, R) and a strong interest in climate change, air quality, and the interactions between the atmosphere and the biosphere. Throughout your PhD program, you'll gain valuable skills in earth system modeling and analyzing atmospheric pollutants within the context of climate change.
If you're interested in making a meaningful contribution to our understanding of land-based CO2 removal strategies and their impact on atmospheric composition and climate change, we welcome your inquiries. Reach out to Dr. Maria Val-Martin at m.valmartin@sheffield.ac.uk to learn more.
This project offers an opportunity to be part of the solution to one of the world's most pressing challenges—climate change.
Research output data provided by the Research Excellence Framework (REF)
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