  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.
