TY - JOUR
T1 - Annual carbon emissions from land-use change in China from 1000 to 2019
AU - Yang, Fan
AU - Dong, Guanpeng
AU - Meng, Xiaoyu
AU - Houghton, Richard A.
AU - Gao, Yang
AU - He, Fanneng
AU - Li, Meijiao
AU - Li, Wenjin
AU - Li, Bing
AU - Liu, Zhihao
AU - Mao, Qinqin
AU - Wu, Pengfei
AU - Yao, Yuanzhi
AU - Zhai, Xudong
AU - Zhang, Hongjuan
AU - Yue, Chao
N1 - Publisher Copyright:
© 2026 Fan Yang et al.
PY - 2026/2/3
Y1 - 2026/2/3
N2 - Long-term land-use changes have a profound impact on terrestrial ecosystems and the associated carbon balance. Current estimates of China's historical carbon emissions induced by land-use change vary widely. Here, current mainland China was taken as the study area, and the 32 provincial units (excluding Macao and Hong Kong) were merged into 25 regions. We utilized a bookkeeping method to quantify China's annual carbon budget resulting from land-use change between 1000 and 2019, driven by a millennial dataset of land-use change in China at provincial level, assisted by comprehensive soil and vegetation carbon density datasets. This approach, which was supported by high-confidence land-use change data, a comprehensive carbon density database compiled from over 10 000 existing field samples, and the latest published disturbance-response curves, enhanced the accuracy of carbon budget estimates. The results revealed that cumulative carbon emissions from land-use change in China reached 19.61 Pg C over the past millennium. Moreover, critical turning points occurred in the early 18th century and early 1980s, with emissions accelerating in the 18th century and transitioning from carbon source to carbon sink in the early 1980s. Our findings revealed that the values were 68 %–328 % higher than the previous 300-year estimates, suggesting that historical carbon emissions from land-use change in China may have been significantly underestimated. This study provides a robust historical baseline for assessing both present and future terrestrial ecosystem carbon budgets at national and provincial scales. The dataset is available at https://doi.org/10.5281/zenodo.14557386 (Yang et al., 2025).
AB - Long-term land-use changes have a profound impact on terrestrial ecosystems and the associated carbon balance. Current estimates of China's historical carbon emissions induced by land-use change vary widely. Here, current mainland China was taken as the study area, and the 32 provincial units (excluding Macao and Hong Kong) were merged into 25 regions. We utilized a bookkeeping method to quantify China's annual carbon budget resulting from land-use change between 1000 and 2019, driven by a millennial dataset of land-use change in China at provincial level, assisted by comprehensive soil and vegetation carbon density datasets. This approach, which was supported by high-confidence land-use change data, a comprehensive carbon density database compiled from over 10 000 existing field samples, and the latest published disturbance-response curves, enhanced the accuracy of carbon budget estimates. The results revealed that cumulative carbon emissions from land-use change in China reached 19.61 Pg C over the past millennium. Moreover, critical turning points occurred in the early 18th century and early 1980s, with emissions accelerating in the 18th century and transitioning from carbon source to carbon sink in the early 1980s. Our findings revealed that the values were 68 %–328 % higher than the previous 300-year estimates, suggesting that historical carbon emissions from land-use change in China may have been significantly underestimated. This study provides a robust historical baseline for assessing both present and future terrestrial ecosystem carbon budgets at national and provincial scales. The dataset is available at https://doi.org/10.5281/zenodo.14557386 (Yang et al., 2025).
UR - https://www.scopus.com/pages/publications/105029485898
U2 - 10.5194/essd-18-875-2026
DO - 10.5194/essd-18-875-2026
M3 - 文章
AN - SCOPUS:105029485898
SN - 1866-3508
VL - 18
SP - 875
EP - 902
JO - Earth System Science Data
JF - Earth System Science Data
IS - 2
ER -