Discovering Effective Policies for Land-Use Planning (2024)
Risto Miikkulainen, Olivier Francon, Daniel Young, Elliot Meyerson, Clemens Schwingshackl, Jacob Bieker, Hugo Cunha, and Babak Hodjat
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.

A shorter version appeared in the Proceedings of the NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning.

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arXiv:2311.12304, 2024. (A shorter version appeared in the Proceedings of the NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning).
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Olivier Francon Collaborator olivier francon [at] cognizant com
Babak Hodjat Collaborator babak [at] cognizant com
Elliot Meyerson Ph.D. Alumni ekm [at] cs utexas edu
Risto Miikkulainen Faculty risto [at] cs utexas edu