发表机构
University of Technology Sydney（悉尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一个基于降雨条件化的绿度动态世界模型，用于归因土地响应和计算敏感性，在肯尼亚样地上显著降低预测误差并支持合同定价。
AI 中文摘要
碳信用、碳抵消和指数保险依赖于反事实：即土地在没有管理的情况下在相同天气下会如何表现，或者一个糟糕的季节会造成多大损失。当前的基线在两个方面存在不足：有些忽略降雨，另一些则按线性和年度方式对降雨进行条件化处理。但真实的响应滞后于降雨，依赖于诸如来自其他来源的水分等隐藏状态，并且不同像素的响应各不相同。以草地牧场作为概念验证，我们基于 CHIRPS 降雨数据，在肯尼亚莱基皮亚的两个 45 平方公里样地上拟合了一个 MODIS 绿度的时间世界模型：一个给定近期和当前降雨的绿度转移密度图。我们在概念上像使用 Black-Scholes 那样使用该模型。通过对观测到的绿度进行模型反演，得到隐含残差，即土地所做的、降雨无法解释的一切。由此解决方案产生两种能力：1) 归因，即土地在降雨之外所做的事情；2) 敏感性，即滚动预测对降雨和状态的导数，与希腊字母（Greeks）密切相关。在莱基皮亚测试样地上，我们学习的模型在未见年份上以比仅基于历史的基线低 23% 的误差、比最佳手工编写模型低 13% 的误差恢复了三个月绿度。在 2022 年干旱中，土地最终比正常水平低 0.17 NDVI；失败的降雨解释了其中大约一半，而在相同残差但正常降雨下，它仍将比正常水平低 0.10。Delta，即绿度对降雨的导数，在每个像素处计算，平均为每额外 100 毫米降雨 0.075 NDVI。每个像素的 Delta 是对降雨的敞口，可以进行对冲，从而实现合同的高效定价和准确的归因。
英文摘要
Carbon credits, offsets and index insurance rest on counterfactuals: what the land would have done under the same weather without management, or what a bad season cost. Current baselines fail in two ways: some ignore rain, others condition on rain linearly and annually. But the true response lags the rain, depends on hidden state such as moisture from other sources, and responds differently from pixel to pixel. Focussing on grassland pasture as a proof of concept, we fit a temporal world model of MODIS greenness conditioned on CHIRPS rain over two 45 km^2 sites in Laikipia, Kenya: a transition density map for greenness given recent and current rainfall. We use it, conceptually, as Black-Scholes is used. Inverting the model on observed greenness it yields an implied residual, everything the land did that the rain does not explain. Two capabilities drop out of this solution: 1) attribution, what the land did beyond the rain; 2) sensitivities, derivatives of the rollout with respect to rain and state, close analogues of the Greeks. On Laikipia test sites, our learned model recovers three-month greenness with 23% less error than a history-only baseline and 13% less than the best hand-written model on unseen years. In the 2022 drought, the land ended 0.17 NDVI below normal; the rain that failed explains roughly half of that, and under normal rain with the same residual it would still have ended 0.10 below. Delta, the derivative of greenness with respect to rain, is computed at every pixel and averages 0.075 NDVI per extra 100 mm of rain. A Delta at every pixel is an exposure to rain that can be hedged, allowing efficient pricing of contracts and accurate attribution.
Comments8 pages, 6 figures, 4 tables. Submitted to the FMTS workshop at NeurIPS 2026
