JOURNAL ARTICLE
Logging from forests and environmental protection - a case study of German forests.
Published In: Sylwan, 2025, v. 169, n. 11. P. 857 1 of 3
Database: Central & Eastern European Academic Source 2 of 3
Authored By: Pacura, Wiktor; Domagała, Julia; Mirowski, Tomasz; Wąsik, Radosław; Irslinger, Roland 3 of 3
Abstract
This article critically examines the German government's 2020 policy to suspend timber harvesting in 170,000 hectares of old public beech forests (Fagus sylvatica) and its implications for carbon balance and climate mitigation. It highlights risks that unmanaged old beech stands may become net carbon sources due to drought-induced mortality, pest outbreaks, and deadwood decomposition, potentially releasing significant CO₂ over coming decades. In contrast, Poland's integrative forest management model, involving active harvesting with regulated rotation ages (100–120 years) and sustainable thinning, maintains younger, more productive stands that sequester carbon effectively while supplying wood products that substitute high-emission materials. The study suggests that Climate Smart Forestry (CSF), combining sustainable management with wood utilization and biodiversity conservation, offers a more reliable approach to long-term carbon storage and climate goals than passive protection. It concludes that halting timber harvesting in aging German beech forests may inadvertently increase CO₂ emissions and global carbon footprints through leakage, whereas active management supports both ecological and climate objectives.
Additional Information
- Source:Sylwan. 2025/11, Vol. 169, Issue 11, p857
- Document Type:Article
- Subject Area:Forestry
- Publication Date:2025
- ISSN:0039-7660
- DOI:10.26202/sylwan.2025061
- Accession Number:191188258
- Copyright Statement:Copyright of Sylwan is the property of Polskie Towarzystwo Lesne and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.