关键词: 生物量-蓄积量转换模型, 生物量碳储量, 生物量碳密度, 固碳潜力, 灰色预测模型

Abstract: As a dominant component of terrestrial ecosystems, forests play a crucial role in carbon storage and sequestration. It is of important guiding significance to accurately estimate forest carbon storage and sequestration potential for better achieving China’s “dual carbon” goals. Using 923 forest plots data across Liaoning Province, we established stand-level databases of the biomass and volume for main forest types, and constructed the biomass-volume conversion equations of 12 forest types in the region. These equations were further combined with information from nine consecutive national forest resource inventories (1973-2018) and a grey prediction model to estimate forest biomass carbon storage and carbon sequestration potential in Liaoning Province. Results showed that compared with the period of 1973-1976, forest biomass carbon storage and carbon density in 2014-2018 increased by 165.53% and 57.86%, being 152.92 Tg C and 35.93 Mg C·hm^-2, respectively. Across age classes, biomass carbon storage of middle-aged and young forests contributed the largest proportion, accounting for more than 53.87% of the total. Among forest types, the mixed broad-leaved forests held the highest biomass carbon storage, comprising more than 40.96% to the total. Regionally, the eastern part of Liaoning contributed the most proportion, accounting 72.33% of the total forest biomass carbon storage. By 2035 and 2060, forest biomass carbon storage is projected to reach 262.06-275.67 and 586.34-592.23 Tg C, respectively, highlighting the region’s considerable biomass carbon sequestration potential. Our results realized the conversion of biomass carbon storage from the individual tree to stand and regional scales, laying a foundation for the management of forest ecosystems and the achievement of the “dual carbon” goals in Liaoning Province, and providing a reference for the accurate estimation of forest biomass carbon storage in other regions of China.

Key words: biomass-volume conversion model, biomass carbon storage, biomass carbon density, carbon sequestration potential, grey prediction model