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.
@article{arxiv.2311.12304,
title = {Discovering Effective Policies for Land-Use Planning with Neuroevolution},
author = {Daniel Young and Olivier Francon and Elliot Meyerson and Clemens Schwingshackl and Jacob Bieker and Hugo Cunha and Babak Hodjat and Risto Miikkulainen},
journal= {arXiv preprint arXiv:2311.12304},
year = {2025}
}