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| Format: | Preprint |
| Published: |
2026
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| Online Access: | https://arxiv.org/abs/2602.19329 |
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| _version_ | 1866912919643488256 |
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| author | Park, Keonvin |
| author_facet | Park, Keonvin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19329 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Elasticity Between Forest Loss and Carbon Emissions: A Subnational Panel Analysis of the United States Park, Keonvin Applications Machine Learning 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. |
| title | Dynamic Elasticity Between Forest Loss and Carbon Emissions: A Subnational Panel Analysis of the United States |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2602.19329 |