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The impact of increasing spruce plantation area on the carbon balance of forests in Western Norway (BalanC)

Finished Last updated: 20.10.2025
End: oct 2023
Start: mar 2016
Status Concluded
Start - end date 01.03.2016 - 01.10.2023
Project manager O. Janne Kjønaas
Total budget 9998000

Publications in the project

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Abstract

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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Abstract

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales—sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.