PhD Candidate in Economics
Hope Hall, University of Exeter, Prince of Wales Road, Exeter, EX4 4PL
Frankie is a PhD researcher jointly at the University of Exeter (UK) and the University of Queensland (Australia). His research focuses on decision-making for ecosystem services through quantitative economic and environment modelling and optimisation under uncertainty.
His PhD project, supported by the QUEX Institute, investigates how to best make natural capital decisions under uncertainty, with empirical applications in the UK and Australia. Supervised by Professor Brett Day (Exeter) and Professor Jonathan Rhodes (UQ), his work draws upon the fields of environmental economics and ecology, building upon the novel integrated environment-economy models developed at the LEEP Institute at Exeter and the cutting-edge expertise in systematic conservation planning at UQ. This project applies state-of-the-art computational methods to (1) characterise the uncertainties arising from coupled ecological and economic models and (2) identify efficient and robust spatial landscape configurations. One case study involves designing landscapes for nation-wide grassland and woodland reversion from arable crops in the UK. These research findings will inform public policy that support sustainable natural capital management in the face of climate change.
He graduated with a BSocSc (ranked first in the Geography major) and MPhil in Geography from the University of Hong Kong. He was awarded both the HKU Outstanding Research Postgraduate Student award and the Dr. Stephen S. F. Hui Prize in Geography for his MPhil thesis focusing on environmental economics. Before starting his PhD studies, he worked as a Geographic Information Systems (GIS) analyst for the Urban Renewal Authority in Hong Kong, which furthered his interest in developing data-driven decision-support tools with policymakers.
- MPhil Geography (Environmental Economics), The University of Hong Kong
- BSocSc Geography (First Class Honours), The University of Hong Kong
- Decision-making under uncertainty – applying stochastic and robust optimisation methods, drawn from the fields of financial economics and operations research, to identify optimal natural capital landscape configurations that accounts for this uncertainty, achieving the best trade-off between risk and return
- Ecosystem services – working with decision-making agencies (e.g. Defra) to understand the ecosystem services provided by natural capital landscapes and to simulate and optimise outcomes of payment for ecosystem services schemes
- Integrated environment-economy modelling – building spatially-disaggregated models that relate economic behaviour to environmental damage to ecosystem service flow change
QUEX PhD Project: Designing Natural Capital Landscapes under changing and uncertain futures
This project investigates how to make decisions for natural landscape management that optimise ecosystem services (e.g. carbon sequestration, pollination, clean water etc.) using mathematical optimisation, econometrics, and other quantitative approaches. A focus of this project is how these decisions could be best made under uncertainty – where the current and future benefits and costs of environmental conservation are not known exactly, due to statistical errors or uncertainty over future forecasts. These advancements in spatial decision-making tools support policymakers to make spatial decisions for natural landscape management that is robust to statistical uncertainty and climate change. One empirical application of this project will on working out how to optimise landscape configurations and find optimal policies for national arable reversion to woodland programmes in the UK where there is statistical uncertainty in measurement of ecosystem services, using approaches from operations research and financial economics, such as stochastic optimisation and robust optimisation. Another empirical application of this project will be on advancing systematic conservation planning in Australia to maximise biodiversity outcomes under uncertain climate change impacts.
No publications found