Frankie Cho

Department: Economics
Discipline: Economics
Research Centre/Unit: Land, Environment, Economics & Policy Institute (LEEP)

Project Summary

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.

Supervisory Team

Professor Brett Day, Professor of Environmental Economics, Director of the Land, Environment, Economics and Policy Institute (LEEP), University of Exeter, UK

Professor Jonathan Rhodes, Professor, School of Earth and Environmental Sciences, University of Queensland, Australia

Wider Research Interests

  • 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

Authored Publications/Reports

Ran, L., Tian, M., Fang, N., Wang, S., Lu, X., Yang, X. & Cho, H. T. F. (2018) Riverine carbon export in the arid-semiarid Wuding River catchment on the Chinese Loess Plateau, Biogeosciences, 15(12), 3857-3871

Chen, W. Y., & Cho, H. T. F. (2019) Environmental information disclosure and societal preferences for urban river restoration: Latent class modelling of a discrete-choice experiment, Journal of Cleaner Production, 231, 1294-1306

Li, X., Chen, W. Y., & Cho, H. T. F. (2020) 3-D spatial hedonic modelling: environmental impacts of polluted urban river in a high-rise apartment market, Landscape & Urban Planning , (accepted for publication)

Chen, W. Y. & Cho, H. T. F. (29th December 2020) Understanding China’s transition to environmental information transparency: citizens’ protest attitudes and choice behaviours, Journal of Environmental Policy & Planning, 10.1080/1523908X.2021.1880314

Hua, J., Chen, W. Y., Liekens, I., & Cho, F. H. T. (31st January 2021) Partial attribute attendance in environmental choice experiments: A comparative case study between Guangzhou (China) and Brussels (Belgium)., Journal of Environmental Management, 10.1016/j.jenvman.2021.112107

Cho H. T. F., Chen, W. Y, Hua, J. (31st January 2021) Validating citizens’ preferences for restoring urban riverscape: discrete choice experiment versus analytical hierarchy process, Journal of Water Resources Planning and Management

Li, X., Chen, W. Y., Cho, F. H. T., & Lafortezza, R. (27th January 2021) Bringing the vertical dimension into a planar multilevel autoregressive model: A city-level hedonic analysis of homebuyers’ utilities and urban river attributes, Science of The Total Environment, 10.1016/j.scitotenv.2021.145547