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Comparing the stock-change and gain–loss approaches for estimating forest carbon emissions for the aboveground biomass poolAuthor(s): Ronald E. McRoberts; Erik Næsset; Terje Gobakken
Source: Canadian Journal of Forest Research
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionTwo approaches to greenhouse gas (GHG) inventories are common, namely the stock-change approach and the gain–loss approach. With the stock-change approach, mean annual emissions are estimated as the ratio of the difference in stock estimates at two points in time and the number of intervening years. The stock-change approach is fairly easy to implement for countries with well-established forest sampling programs. However, countries without established forest sampling programs more commonly use the gain–loss approach. With this approach, emissions are estimated as the product of the areas of classes of land use change, characterized as activity data, and the responses of carbon stocks for those classes, characterized as emission factors. Regardless of the approach, the Intergovernmental Panel on Climate Change (IPCC) good practice guidelines specify that GHG inventories produce neither over- nor under-estimates and reduce uncertainties to the degree possible. For a study area in southeastern Norway, the objectives of the study were to compare the stock-change and gain–loss approaches with respect to estimates of carbon emissions for the aboveground biomass pool and to illustrate statistically rigorous methods for complying with the two IPCC good practice guidelines for both approaches. The primary conclusions were that the two approaches produced comparable estimates of mean annual emissions, but that the stock-change approach produced considerably smaller estimates of uncertainty.
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CitationMcRoberts, Ronald E.; Næsset, Erik; Gobakken, Terje. 2018. Comparing the stock-change and gain–loss approaches for estimating forest carbon emissions for the aboveground biomass pool. Canadian Journal of Forest Research. 274: 1535-1542. https://doi.org/10.1139/cjfr-2018-0295.
Keywordsstatistical estimators, remote sensing, uncertainty
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