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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training
 

Consequences of forgone production from nature-based climate projects

Carbon credits offer a much-needed mechanism to finance the conservation and creation of natural habitats, thereby slowing global warming and protecting biodiversity. Global adoption of carbon credits derived from nature-based projects is hampered in part by a lack of standardised, scalable and transparent techniques to  quantify their benefits for society. A fundamental component of such evaluation is the ability to predict and estimate the consequences of forgone food, fibre and mineral production resulting from these activities. Without commensurate income from carbon finance, forgone production will have negative consequences on local economies, while reducing the supply of essential goods by a site is expected to drive increased supply from elsewhere in the world, along with associated greenhouse gas emissions (i.e. leakage). Thus, there is a need to estimate forgone production as well as its local and global consequences, to fully appraise the value of carbon credits.
This project will use statistical counterfactual reasoning along with established and emerging geospatial datasets of actual and attainable yields and trade of goods across the world (e.g. GAEZ and SPAM) to estimate forgone production and its likely consequences. The effect on local economies will be assessed through high-level business models that estimate the net present values of the project and counterfactual (without project) scenarios. Leakage will be assessed by developing a machine-learning approach to predict where production is most likely displaced to, again informed by yield and profitability models. During the MRes year we expect the student to focus on detecting international patterns of leakage resulting from national / jurisdictional policies to reduce tropical deforestation (as per Roopsind et al., 2019, and Ferraro et al., 2020): the student would use global models of commodity production, along with historical trends in deforestation and agricultural price to predict where leakage is most likely to occur (WIlliams et al., 2020). This would be validated using observed patterns in deforestation.

Useful skills for this project include: 
-    Knowledge of machine learning and statistical analysis
-    Programming experience with R and python
-    Understanding of socio-economic concepts
-    Experience with computational model systems

The successful candidate will join the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) Centre for Doctoral Training (CDT) based at the University of Cambridge. The AI4ER CDT programme consists of a one-year Master of Research (MRes) course (two terms of formal teaching via lectures, practicals and team challenges plus a three-month research project),  followed by a 3 year PhD project. Both the masters and PhD project will be based on the above project description. 

For further information about the project please contact Prof Andrew Balmford.