The carbon substitution and storage effects related to Norwegian forests and the forest sector were compared under three potential roundwood harvest scenarios: maintaining harvests at 2021 levels, increasing harvests by 20% due to policies aimed at maximizing economic benefits from the forest sector, and reducing harvests by 20% due to biodiversity concerns. For harvested wood products, both the current product structure and hypothetical alternatives were considered. The carbon stock development in forests was projected using a forestry simulation tool for Norway. Many uncertainties in carbon storage, substitution parameters, and data have been addressed using Monte Carlo simulations. Shifting a portion of pulpwood use to produce wood-based insulation materials and textile fibres was found to increase the climate benefits from the Norwegian forest sector. In contrast, the shift to bioethanol production had only a marginal effect compared to the current production structure. The analysis spanned the next two decades, which is a period relevant to the investment and operational lifespan of industrial facilities. The results suggest that during this time, smarter use of harvested roundwood for HWPs with high carbon substitution benefits can be an effective means of climate change mitigation. However, in the long term, enhancing forest carbon sinks by reducing harvests may be more beneficial for the climate, provided that global efforts to reduce emissions from energy production are successful and lead to a decrease in emissions associated with the production of various materials.
Road transport produces 90% of greenhouse gas emissions in timber transport in Finland. It is therefore necessary to understand the factors that affect driving speed, fuel consumption, and ultimately, emissions. The objective of this study was to assess the effect of road characteristics on timber truck driving speed and fuel consumption. Data from the fleet management and transport management systems of two timber trucks were collected over a year. A sample of 104 trips was drawn, and the tracking points were overlaid on the road data in a geographical information system. Thereafter, work phases were determined for the points, and they were visually classified into road and pavement classes. Subsequently, the data of 80 trips were utilised in regression analysis to further study the effects of the visually interpreted variables on driving speed and fuel consumption. Fuel consumption was explained by the proportion of forest roads and distance travelled with a loader, and the number of crossings and season when driving without a load. When driving with a load, both asphalt and gravel pavements decreased consumption, in contrast to an unpaved road. Crossings increased fuel consumption, as did the winter and spring months, and the total laden mass of the truck. In conclusion, the study showed that the functional Finnish road and pavement classes can be used to predict driving speed and fuel consumption.