Year: 2025
Type: article
Abstract: Background: While forests are pivotal for climate change mitigation, robust CO2 accounting is required to quantify their climate benefits. However, varying current methodologies complicate this process for practitioners. This study addresses the need for a low-threshold, IPCC-compliant CO2 estimation method of tree biomass in forest stands. Methods: We developed CO2 yield tables by integrating segmented allometric biomass functions into fourth-generation yield tables, combining empirical data and simulations for Northwest Germany. Above- and belowground biomass was calculated, converted into CO2, and compared with estimates from traditional expansion factors. An interactive R Shiny dashboard was designed to visualise results. Results: The main results of this article are the carbon yield tables, covering beech (Fagus sylvatica), oak (Quercus spp.), spruce (Picea abies), pine (Pinus sylvestris) and Douglas fir (Pseudotsuga menziesii), each across various yield classes and starting at age 1, thereby also encompassing the juvenile phase of forest stands. Our comparison with estimates from traditional expansion factors shows that the latter can substantially overestimate carbon content in forest stands compared to our results, ranging from 20% to 35%, with higher estimates for mature stands and improved representation of early growth. The interactive dashboard also allows readers to experiment with their own figures. Conclusions: The choice of CO2 methodology profoundly affects results. Our yield tables and a calculation tool (dashboard) deliver a transparent, accessible tool for quantifying forest CO2 stock, supporting sustainable management and carbon market participation.
Source: Forests
DOI: 10.3390/f16101580
Institutions: Johann Heinrich von Thünen-Institut, University of Göttingen
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
SDGs: Climate action, Life on land
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4415212858