Highly mechanized timber harvesting and timber logistics emit CO2. In turn, the provided timber stores CO2 from the atmosphere as biogenic carbon. This basic assumption resulted in the calculation of net carbon storage of supplied timber. For this, we first developed a formula that represents the carbon content of freshly harvested timber. Coniferous wood contains about 734 kg CO2 m-3 and deciduous wood about 1000 CO2 m-3. Contrary to this, CO2 emissions from trucks, harvesters, and forwarders were calculated using the variable parameters for actual diesel consumption and the distance to the sawmill and constant parameters for the transport of the machine to the stand, lubricants, transport of operators, loading, and fabrication, supply, and maintenance. The method was tested on an actual harvest. The principal findings are that the method is practical, the net carbon storage of the supplied timber is reduced by 1.5% to 5% by harvesting and transport activities, and timber logistics is the largest contributor to emissions. The CO2 emissions for harvesters and forwarders are about 4 kg CO2 m-3, and for downstream timber logistics across all assortments and distances is 11 kg CO2 m-3. We conclude that the emissions are low, vis-a-vis the storage capacity. Emissions and a standardized calculation model are imperative. The model developed here for mapping the net carbon storage of roundwood highlights the climate protection performance of timber and contributes to optimizing climate-friendly timber supply chains.
In commercial transaction of stacked roundwood, the estimation of the stack net volume plays a key role. One generalized method to determine the net volume is using conversion factors that relate the gross and net volumes. In this literature review the developed methods to estimate the conversion factors as well as their influencing parameters were analyzed based on 153 references from America and Europe. According to the results, 48 different methods (including their variants) for estimating the conversion factors were developed. The newest methods enabled their accurate determination inexpensively, e.g., photo-optical methods or 3D simulation models. The analyzed references revealed that 30 parameters influence the conversion factors. Based on this comprehensive review, each stakeholder involved in the roundwood supply chain can know which method is used for estimating the conversion factors in the analyzed territories and which influencing parameters should be considered when purchasing roundwood in order to accurately assess the solid wood content in the stacks.