Discovering Effective Policies for Land-Use Planning (Papers Track) Best Pathway to Impact
Risto Miikkulainen (UT Austin; Cognizant Technology Solutions); Olivier Francon (Cognizant AI Labs); Daniel Young (Cognizant AI Labs); Babak Hodjat (Cognizant AI Labs); Hugo Cunha (Cognizant AI Labs); Jacob Bieker (Open Climate Fix)
Abstract
How areas of land are allocated for different uses, such as forests, urban, and agriculture, has a large effect on carbon balance, and therefore climate change. Based on available historical data on changes in land use and a simulation of carbon emissions/absorption, 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 and the BLUE simulator. It generates Pareto fronts that trade off carbon impact and amount of change customized to different locations, thus providing a potentially useful tool for land-use planning.