2020
DOI: 10.1111/gcb.15228
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Evaluating the terrestrial carbon dioxide removal potential of improved forest management and accelerated forest conversion in Norway

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Cited by 13 publications
(11 citation statements)
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“…To calculate living and dead tree biomass, we used single tree biometric functions with dbh and H as predictor variables for aboveground birch (Smith et al 2014) and Norway spruce (Marklund 1988), and dbh for belowground birch (Smith et al 2016) and Norway spruce (Petersson and Ståhl 2006). To estimate C stocks in living and dead biomass, a C fraction of dry matter equal to 0.5 was applied (Mäkinen et al 2006, Bright et al 2020. The estimated C stocks in dead wood were adjusted for the proportion of remaining dry biomass for the five decay stages (Naesset 1999).…”
Section: Methodsmentioning
confidence: 99%
“…To calculate living and dead tree biomass, we used single tree biometric functions with dbh and H as predictor variables for aboveground birch (Smith et al 2014) and Norway spruce (Marklund 1988), and dbh for belowground birch (Smith et al 2016) and Norway spruce (Petersson and Ståhl 2006). To estimate C stocks in living and dead biomass, a C fraction of dry matter equal to 0.5 was applied (Mäkinen et al 2006, Bright et al 2020. The estimated C stocks in dead wood were adjusted for the proportion of remaining dry biomass for the five decay stages (Naesset 1999).…”
Section: Methodsmentioning
confidence: 99%
“…Differences in surface property and flux perturbations between geoengineering-type forcings involving non-vegetative solar radiation management (SRM) and forcings from LULCC, land management change (LMC), or forest management change (FMC). is highly heterogeneous in space, the magnitude and extent of which depends on its location (Brovkin et al, 2013;de Noblet-Ducoudré et al, 2012). This is because the response pattern of climate feedbacks has a strong spatial dependency -feedbacks are generally larger at higher latitudes due to higher energy budget sensitivity to clouds, water vapor, and surface albedo, which generally increases the effectiveness of RF in those regions (Shindell et al, 2015).…”
Section: Spatial Disparity In Climate Response Between Co 2 Emissions and α Perturbationsmentioning
confidence: 99%
“…pulses (Carrer et al, 2018), or the summing of TDEE up to TH to obtain a CO2-eq. stock perturbation measure (Bright et al, 2020;Bright et al, 2016).…”
Section: Metric Permutationsmentioning
confidence: 99%
“…On the other hand, if the objective is to weigh the effect of a scenario against cumulative CO2 emissions in the futureas would be required to evaluate the mitigation potential of land use policies in the context of emission budgets or policy targets based on cumulative emissions (e.g. Bright et al (2020))the ΣTDEE is the more suitable measure.…”
Section: Qualitative Metric Evaluationmentioning
confidence: 99%