Discovering effective policies for land-use planning with neuroevolution
Abstrak
How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance and, therefore, climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and removals, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset LUH2 and the bookkeeping model BLUE. It generates Pareto fronts that trade off carbon impact and amount of land-use change customized to different locations, thus providing a proof-of-concept tool that is potentially useful for land-use planning.
Topik & Kata Kunci
Penulis (8)
Daniel Young
Olivier Francon
Elliot Meyerson
Clemens Schwingshackl
Jacob Bieker
Hugo Cunha
Babak Hodjat
Risto Miikkulainen
Akses Cepat
- Tahun Terbit
- 2025
- Sumber Database
- DOAJ
- DOI
- 10.1017/eds.2025.18
- Akses
- Open Access ✓