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.

翻译：如何将土地分配给不同用途（如森林、城市和农业）对碳平衡乃至气候变化具有重大影响。基于土地利用变化的现有历史数据以及碳排放/吸收模拟，可以学习一个替代模型，使决策者能够高效评估可用选项。随后，可利用进化搜索过程为特定地点发现有效的土地利用政策。该系统基于Project Resilience平台构建，并使用土地利用协调数据集和BLUE模拟器进行评估。该系统生成了权衡碳影响与变化程度的帕累托前沿，并针对不同地点进行定制，从而为土地利用规划提供了一个具有潜在价值的工具。