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：土地被分配用于不同用途（如森林、城市和农业）的方式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可用历史数据和碳排放/吸收模拟，可以学习一个代理模型，从而高效评估决策者可用的不同选项。随后，可通过进化搜索过程发现针对特定地点的有效土地利用策略。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模拟器进行了评估。它生成了针对不同地点定制的、权衡碳影响与变化幅度的帕累托前沿，从而为土地利用规划提供了潜在有用的工具。