![[2602.19329] Dynamic Elasticity Between Forest Loss and Carbon Emissions: A Subnational Panel Analysis of the United States](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19329] Dynamic Elasticity Between Forest Loss and Carbon Emissions: A Subnational Panel Analysis of the United States
Summary
This article analyzes the dynamic relationship between forest loss and carbon emissions in the U.S. using a comprehensive dataset from 2001 to 2023, revealing significant short- and long-run elasticities.
Why It Matters
Understanding the link between forest loss and carbon emissions is crucial for environmental policy and monitoring. This study provides a framework for assessing how land cover changes impact carbon dynamics, informing both regional strategies and carbon accounting.
Key Takeaways
- Forest loss significantly increases carbon emissions in the short run.
- Long-run elasticity of emissions is greater than short-run effects, highlighting cumulative impacts.
- Temporal dynamics must be considered in environmental assessments.
- The study utilizes a robust panel dataset for comprehensive analysis.
- Findings can inform regional monitoring and carbon accounting frameworks.
Statistics > Applications arXiv:2602.19329 (stat) [Submitted on 22 Feb 2026] Title:Dynamic Elasticity Between Forest Loss and Carbon Emissions: A Subnational Panel Analysis of the United States Authors:Keonvin Park View a PDF of the paper titled Dynamic Elasticity Between Forest Loss and Carbon Emissions: A Subnational Panel Analysis of the United States, by Keonvin Park View PDF HTML (experimental) Abstract: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 fores...
