Work

Harmonizing and Comparing Divergent Estimates of China’s Forest Carbon Sinks

Year: 2025

Type: article

Abstract: Various observational approaches, tools, and models have been developed to estimate forest carbon sinks to meet the global challenges of climate change under the Paris Agreement; however, significant differences persist in estimating China’s forest carbon sinks obtained using different methods. Here, remote sensing data were used to delineate managed and unmanaged forests in China, estimating the area of managed forests in 2020 at approximately 256.44 Mha, consistent with national greenhouse gas inventories (NGHGIs). Then, land-based carbon sinks were derived from methods including NGHGIs based on inventory methods, dynamic global vegetation models (DGVMs), atmospheric inversion (ATM), and the Eddy covariance method (EC), using managed and unmanaged forest ranges. The results reveal that significant gaps in forest carbon sink estimates persist across these methods. This gap is hypothesized to stem from inconsistencies in the definition of forest carbon sinks estimated using different methods. To address this, a consistent definition of NetFCsink was established and relevant data were used to bridge the gaps among different methods. The results showed that the NetFCsink of Chinese forests from 2010 to 2018 varies greatly in different methods, with a value of 239.68 TgC yr-1 for the inventory method, 174.43 ± 56.13 TgC yr-1 for DGVMs, 134.58 ± 68.19 TgC yr-1 for ATM, and 352.84 ± 113.67 TgC yr-1 for EC. This collective analysis highlights the value of consistent definitions in improving comparability across methods, demonstrating the feasibility of using multiple approaches to validate NGHGIs and providing a consistent standard for carbon negotiations at the national level.

Source: Ecological Indicators

DOI: 10.1016/j.ecolind.2025.113250

Authors: Qizhi Han, Liangyun Liu, Xinjie Liu

Institutions: Chinese Academy of Sciences, Aerospace Information Research Institute, University of Chinese Academy of Sciences, International Research Center of Big Data for Sustainable Development Goals

Language: English

Topic: Forest Management and Policy

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Life on land

Open Access status: gold

Estimated APC paid: $3,650