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NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2022

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Sammendrag

Forest management is an important tool for GHG mitigation by representing three carbon pools: living biomass, forest soil, and wood-based products. Additionally, increasing attention has been given to the potential for wood products to substitute fossil-intensive products as a climate mitigation strategy. The goal of this paper is to analyse the theoretical GHG effects of fully replacing four common non-wood products with wood-based products of ‘low’ and ‘high’ technology options that have a similar functionality: (1) Spruce particle board substituting polyurethane (PU) foam insulation board; (2) spruce cross-laminated timber beam (CLT) substituting steel beam; (3) birch energy wood substituting electric heating; and (4) birch plywood substituting plaster board. The analysis was based on forestry in Western Norway as a case study, where forests typically consist of naturally generated birch and expanding areas of planted Norway spruce. In this study we compare wood products derived from paired stands of Norway spruce and downy birch. The analysis showed that spruce gave a higher theoretical substitution effect relative to birch for the selected pairs of woody and non-woody products. CLT substituting steel beam gave the highest substitution effect, approximately 15% higher than particle board substituting PU foam board. The theoretical substitution effect in mass units of carbon per kg wood product for the two spruce wood products was approximately 17 times higher relative to substituting Norwegian hydro energy-based electric heating, whereas plywood substituting plaster board may in fact increase GHG emissions. As the gross emissions were relatively similar for the birch plywood and the spruce particle board, the major substitution effect was related to the avoided emission of the non-woody product rather than to the tree species per se. The paper concludes that the choice of product to be substituted was the key factor that determined the final substitution effects. Furthermore, the study showed that transportation was the single most important factor that affected the emissions between planting and delivery of the timber at production gate. The analysis enables informed decisions related to CO2-emissions at the various steps from tree planting to wood conversion, and underline the importance of informed decision related to the choice of substitution products.

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Wood resources have been essential for human welfare throughout history. Also nowadays, the volume of growing stock (GS) is considered one of the most important forest attributes monitored by National Forest Inventories (NFIs) to inform policy decisions and forest management planning. The origins of forest inventories closely relate to times of early wood shortage in Europe causing the need to explore and plan the utilisation of GS in the catchment areas of mines, saltworks and settlements. Over time, forest surveys became more detailed and their scope turned to larger areas, although they were still conceived as stand-wise inventories. In the 1920s, the first sample-based NFIs were introduced in the northern European countries. Since the earliest beginnings, GS monitoring approaches have considerably evolved. Current NFI methods differ due to country-specific conditions, inventory traditions, and information needs. Consequently, GS estimates were lacking international comparability and were therefore subject to recent harmonisation efforts to meet the increasing demand for consistent forest resource information at European level. As primary large-area monitoring programmes in most European countries, NFIs assess a multitude of variables, describing various aspects of sustainable forest management, including for example wood supply, carbon sequestration, and biodiversity. Many of these contemporary subject matters involve considerations about GS and its changes, at different geographic levels and time frames from past to future developments according to scenario simulations. Due to its historical, continued and currently increasing importance, we provide an up-to-date review focussing on large-area GS monitoring where we i) describe the origins and historical development of European NFIs, ii) address the terminology and present GS definitions of NFIs, iii) summarise the current methods of 23 European NFIs including sampling methods, tree measurements, volume models, estimators, uncertainty components, and the use of air- and space-borne data sources, iv) present the recent progress in NFI harmonisation in Europe, and v) provide an outlook under changing climate and forest-based bioeconomy objectives.

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Sammendrag

To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot-based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample-based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country-specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan-European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot-based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.