The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I developed high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimate conservation costs, especially at the expensive tail of the distribution. This has led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges and can help improve the targeting of public ecosystem service investments.

保护政策的合理性和目标取决于对竞争性土地利用所带来的公共和私人利益的可靠衡量。地球系统观测和建模的进步使得能够以前所未有的规模和分辨率绘制公共生态系统服务图，从而引发关于土地保护政策和优先事项的新建议。无法获得类似规模和分辨率的土地利用私人效益数据，导致数据不匹配，后果未知。在这里，我表明，土地带来的私人利益可以在大尺度和高分辨率下进行量化，这样做可以对保护政策模型产生重要影响。我通过对 600 万块土地销售的基于树的集成模型进行训练，对美国本土私人土地的公平市场价值进行了高分辨率估计。由此产生的估计预测保护成本的准确度比早期代理值高出 8.5 倍。使用较粗略成本代理的研究低估了保护成本，尤其是在分布的昂贵尾部。这导致最近的工作中政策预算被低估了高达 37.5 倍。更准确的成本核算将有助于政策制定者认识到当代保护挑战的全部规模，并有助于提高公共生态系统服务投资的针对性。