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arXiv · 2311.12304

Discovering Effective Policies for Land-Use Planning with Neuroevolution

Daniel Young·Olivier Francon·Elliot Meyerson·Clemens Schwingshackl·Jacob Bieker·Hugo Cunha·Babak Hodjat·Risto Miikkulainen
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Abstract

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.

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Daniel Young, Olivier Francon, Elliot Meyerson, Clemens Schwingshackl, Jacob Bieker, Hugo Cunha, Babak Hodjat, Risto Miikkulainen. 2023-11-21. Discovering Effective Policies for Land-Use Planning with Neuroevolution. https://doi.org/10.1017/eds.2025.18

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