Discovering Effective Policies for Land-Use Planning with Neuroevolution
- URL: http://arxiv.org/abs/2311.12304v5
- Date: Tue, 2 Apr 2024 03:35:02 GMT
- Title: Discovering Effective Policies for Land-Use Planning with Neuroevolution
- Authors: Risto Miikkulainen, Olivier Francon, Daniel Young, Elliot Meyerson, Clemens Schwingshackl, Jacob Bieker, Hugo Cunha, Babak Hodjat,
- Abstract summary: 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.
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.
- Score: 9.949152590444811
- License: http://creativecommons.org/licenses/by/4.0/
- 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 potentially useful tool for land-use planning.
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