Accurate quantification of the relationship between forest loss and associated carbon emissions is critical for both environmental monitoring and policy evaluation. Although many studies have documented spatial patterns of forest degradation, there is limited understanding of the dynamic elasticity linking tree cover loss to carbon emissions at subnational scales. In this paper, we construct a comprehensive panel dataset of annual forest loss and carbon emission estimates for U.S. subnational administrative units from 2001 to 2023, based on the Hansen Global Forest Change dataset. We apply fixed effects and dynamic panel regression techniques to isolate within-region variation and account for temporal persistence in emissions. Our results show that forest loss has a significant positive short-run elasticity with carbon emissions, and that emissions exhibit strong persistence over time. Importantly, the estimated long-run elasticity, accounting for autoregressive dynamics, is substantially larger than the short-run effect, indicating cumulative impacts of repeated forest loss events. These findings highlight the importance of modeling temporal dynamics when assessing environmental responses to land cover change. The dynamic elasticity framework proposed here offers a robust and interpretable tool for analyzing environmental change processes, and can inform both regional monitoring systems and carbon accounting frameworks.

翻译：准确量化森林损失与相关碳排放之间的关系对于环境监测和政策评估至关重要。尽管许多研究记录了森林退化的空间格局，但对于次国家级尺度上连接树木覆盖损失与碳排放的动态弹性，目前理解有限。本文基于Hansen全球森林变化数据集，构建了2001年至2023年美国次国家级行政单元年度森林损失与碳排放估算的综合性面板数据集。我们应用固定效应和动态面板回归技术来分离区域内变异，并考虑排放的时间持续性。研究结果表明，森林损失对碳排放具有显著的正向短期弹性，且排放表现出强烈的时间持续性。重要的是，在考虑自回归动态后，估计的长期弹性显著大于短期效应，这表明重复森林损失事件具有累积影响。这些发现强调了在评估环境对土地覆盖变化的响应时，建模时间动态的重要性。本文提出的动态弹性框架为分析环境变化过程提供了一个稳健且可解释的工具，可为区域监测系统和碳核算框架提供信息。