Discovering Effective Policies for Land-Use Planning with Neuroevolution
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 42 references that are in the map. First posted on arXiv 21 Nov 2023.
Builds on
7 of 42 references.
References · 7
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Citing papers per year: 2026: 1.
1 paper.