Financial de-risking products to value biodiversity's role in reducing impacts of natural disasters

Project overview

This project will investigate how biodiversity buffers human populations against the impacts of natural disasters. The research will begin by compiling and analysing spatial, remote-sensed datasets on natural disasters to map their economic costs and recovery timelines, such as when commuting patterns return to normal.

Directionality models will then be used to trace how disaster impacts propagate through regions, identifying when and where effects peak and subside.

The project will develop predictive models to quantify the economic savings provided by biodiversity, focusing on its role in mitigating disaster impacts, such as reducing infrastructure damage or speeding up recovery. By comparing outcomes in biodiverse versus less biodiverse areas, the research will demonstrate how ecosystems act as natural disaster buffers.

Finally, the project will collaborate with economists to create financial de-risking products, such as risk assessment tools or insurance frameworks, which quantify the financial benefits of biodiversity conservation. 

This interdisciplinary approach bridges ecology and economics, offering a framework to integrate biodiversity’s protective value into disaster preparedness and planning.

The project’s findings will inform conservation strategies and enable policymakers and investors to incorporate biodiversity as a cost-effective solution for enhancing regional resilience to natural disasters.

Project Specific Training

The student will receive comprehensive training in spatial data analysis and ecological modelling, tailored to the project’s focus on biodiversity and disaster resilience. Training in handling remote-sensed and large-scale geospatial datasets will be delivered through one-to-one instruction by the supervisory team, with additional workshops or courses as needed to develop advanced skills in tools such as GIS, Python, or R. This will include methods for analysing spatial patterns of disaster impacts, recovery timelines, and biodiversity metrics.

The student will also gain expertise in predictive modelling, including the use of Bayesian regressions and machine learning techniques to quantify biodiversity’s economic benefits in mitigating disaster impacts. This will involve close guidance from the supervisory team during model development, alongside opportunities to engage with external experts in ecological and economic modelling.

In the later stages, the student will collaborate with economists to create financial de-risking tools, learning how to translate ecological data into frameworks usable by policymakers and investors. This interdisciplinary training will include exposure to risk analysis and economic evaluation methods through external partners or workshops, ensuring the student is equipped to communicate findings effectively across disciplines. This tailored training will prepare the student for a range of career pathways in ecological research, policy, and environmental management.