Quantifying uncertainty and spatial dependence in tropical forest forecasts to guide climate change mitigation

fine spatial and temporal scales. However, useful decisions require more than just accurate point forecasts: we need to quantify uncertainty and understand spatial correlation and displacement effects. Developing statistical and machine-learning methods that capture these uncertainties is essential for directing conservation finance effectively, restoring credible carbon markets, and designing portfolios of interventions robust to shifting economic and environmental conditions.

PhD project

This PhD will develop statistical and machine-learning methods to improve how forest conservation is targeted under uncertainty. Our existing work uses Random Survival Forests (RSFs) and satellite data to forecast annual probabilities of deforestation and regrowth, helping target interventions where they are most likely to deliver additional climate mitigation. However, two methodological challenges remain.

First, although RSFs produce probabilistic forecasts of forest change, they do not naturally quantify uncertainty around pixel-level predictions. Out-of-sample model error indicates overall predictive performance, but not how uncertain an individual forecast is. Second, the framework does not represent the joint spatial distribution of forest outcomes. Forecasts based on baseline characteristics avoid predicting intermediate drivers and propagating their errors forward, but treat neighbouring locations independently. This misses correlated shocks, and spatial spillovers, where protection in one location displaces deforestation pressure elsewhere.

The PhD will extend and adapt statistical and machine-learning methods to retain the flexibility and scalability of current approaches while providing calibrated, spatially explicit uncertainty quantification and addressing both forms of spatial dependence. Recent advances in BART, deep kernel learning and variational autoencoders provide promising foundations for maintaining predictive skill while characterising uncertainty. It will also develop neural-network-based methods to capture spatial dependencies and spillovers. These methodological advances will then be applied to conservation planning, culminating in a portfolio-based decision-support tool that identifies combinations of interventions that maximise expected additionality while diversifying correlated risks and limiting displacement, with applications to public conservation spending and private carbon finance.

Applicant Profile

We are particularly interested in students with a strong background in statistics, mathematics, computer science, economics, or a related quantitative discipline, who want to apply and develop these skills to address major environmental challenges. The project would suit applicants interested in statistical or machine-learning methodology, spatial data and uncertainty quantification, who have an appetite to translate methodological advances into practical tools for nature-based climate policy.

Funding Notes

This project is part of the UNRISK CDT, which offers 15-18 fully-funded NERC studentships, covering full university tuition fees; a personal stipend at standard UKRI rates; £6000 individual research and training costs; £5000 (per student) of cohort-level training; and a ‘Flexible Fund’ for special projects.

International applicants will need to cover costs related to applying for a student visa and the international health surcharge (IHS)

Applications are open to UK and international applicants. The number of awards for international applicants is limited by UKRI rules.

More information is available on the UNRISK website: View Website

References

https://dragonchair.org.uk/project/bioadd/
https://www.researchsquare.com/article/rs-7714530/v2