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Abstract
Forest carbon sequestration capacity in China remains uncertain due to underrepresented tree demographic dynamics and overlooked of harvest impacts. In this study, we employ a process-based biogeochemical model to make projections by using national forest inventories, covering approximately 415,000 permanent plots, revealing an expansion in biomass carbon stock by 13.6 ± 1.5 Pg C from 2020 to 2100, with additional sink through augmentation of wood product pool (0.6-2.0 Pg C) and spatiotemporal optimization of forest management (2.3 ± 0.03 Pg C). We find that statistical model might cause large bias in long-term projection due to underrepresentation or neglect of wood harvest and forest demographic changes. Remarkably, disregarding the repercussions of harvesting on forest age can result in a premature shift in the timing of the carbon sink peak by 1–3 decades. Our findings emphasize the pressing necessity for the swift implementation of optimal forest management strategies for carbon sequestration enhancement.
The authors show China’s forests can sequester 172.3 million tons of carbon per year in biomass by 2100, with an additional 28.1 million tons from improved management practices, but neglecting wood harvest impacts will distort long-term future projections.
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1 Nanjing University of Information Science and Technology, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), School of Ecology and Applied Meteorology, Nanjing, China (GRID:grid.260478.f) (ISNI:0000 0000 9249 2313); Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment, China’s National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Beijing, China (GRID:grid.216566.0) (ISNI:0000 0001 2104 9346)
2 Chinese Academy of Forestry, Key Laboratory of Forest Ecology and Environment, China’s National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Beijing, China (GRID:grid.216566.0) (ISNI:0000 0001 2104 9346)
3 Chinese Academy of Forestry, Key Laboratory of Forest Management and Growth Modelling, China’s National Forestry and Grassland Administration, Research Institute of Forest Resource Information Techniques, Beijing, China (GRID:grid.216566.0) (ISNI:0000 0001 2104 9346)
4 Purdue University, Forest Advanced Computing and Artificial Intelligence Laboratory (FACAI), Department of Forestry and Natural Resources, West Lafayette, USA (GRID:grid.169077.e) (ISNI:0000 0004 1937 2197)
5 Northwest agriculture and Forestry University, College of Forestry, Yangling, China (GRID:grid.144022.1) (ISNI:0000 0004 1760 4150)
6 Peking University, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319)
7 Boston College, Chestnut Hill, Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Massachusetts, USA (GRID:grid.208226.c) (ISNI:0000 0004 0444 7053)
8 Nanjing University of Information Science and Technology, Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration (ECSS-CMA), School of Ecology and Applied Meteorology, Nanjing, China (GRID:grid.260478.f) (ISNI:0000 0000 9249 2313)
9 Iowa State University, Department of Ecology, Evolution, and Organismal Biology, Ames, USA (GRID:grid.34421.30) (ISNI:0000 0004 1936 7312)
10 Fujian Agriculture and Forestry University, College of Forestry, Fuzhou, China (GRID:grid.256111.0) (ISNI:0000 0004 1760 2876)
11 University of Exeter, College of Life and Environmental Sciences, Exeter, UK (GRID:grid.8391.3) (ISNI:0000 0004 1936 8024)