Bridging the gap between the estimates of forest management emissions from the national GHG inventories and integrated assessment models via model-data fusion

Current criteria to define managed forest areas are still inconsistent among countries’ reports of GHG emissions from forestry to the UNFCCC. Integrated Assessment Models (IAM) used for assessing the countries’ mitigation pathways employ a proxy for managed forests for modelling purposes that differ from the countries’ criteria. This difference in the managed forest definition is one of the reasons for a gap of about 5.5 GtCO2 yr−1 between the modelled global land-use use GHG emissions and the one reported by the countries to the UNFCCC. Such inconsistency adds uncertainty to the contribution of the forest sector to climate change mitigation efforts and undermines its monitoring. Here we combined multiple data sources and developed an harmonized map of managed forests, consistent with official GHG inventories. The map is based on a number of data sources, in particular, the global forest management layer of the Nature map , information on road density, forest productivity, travel time to major cities, and ancillary data from forest use and the forest classes layers. We applied the map developed here in the Global Forest Model (G4M) operating on a 0.5x0.5 deg. regular grid for masking the managed forest area, which is consistent with the estimates of countries GHG emissions’ reports to the UNFCCC, and estimating the GHG emissions from that area. Currently, however, general criteria are applied to all countries, regardless of local forest practices and actual criteria applied in the countries. We conclude that a further development of managed forests maps, with consistent definitions at country-level, and country-specific rules for managed/unmanaged forest classification deserves further investigation. Such efforts can support the harmonization of GHG emissions estimates from models and official statistics and improve the design of mitigation policies informed by IAMs.