In The Science of the total environment
Substantial evidence shows that most of China's terrestrial ecosystems are important carbon sinks. However, the nonlinear trend of the carbon sinks and their nonlinear response to driving factors are unclear. Taking the net ecosystem productivity (NEP) as a proxy for the ecosystem carbon sink, the nonlinear relationships between the monotonically increasing trends and decreasing to increasing shifts in the carbon sink to climate change and ecological engineering were investigated based on ensemble empirical mode decomposition (EEMD) and machine learning algorithm (boosted regression tree model, BRT). The results suggest that 16.75 % of the carbon sinks in China experienced a monotonic increase. Additionally, 20.55 % of the carbon sinks shifted from decreasing to increasing trends, primarily after 1995, and these carbon sinks were located in the key ecological engineering areas, such as the middle reaches of the Yellow River shelterbelt program area, the Liaohe shelterbelt program area, the Grain to Green program area, and the Three-North Forest shelterbelt program area. Moreover, carbon sinks exhibited strong spatial autocorrelation with low-low clustering in the north and high-high clustering in the south. The increase in CO2 (slope of CO2 < 1.8 g/m2/s/y) and solar radiation (slope of radiation >1 w/m2/y) promoted the monotonic increase in the carbon sinks in the center of China. The increase in the areas of forest and grassland shifted the carbon sink trend from decreasing to increasing in the key ecological engineering program areas, and economic development reversed the carbon sink reduction in the Pearl River shelterbelt program area. These findings highlight the positive effect of ecological engineering on carbon sinks and provide adaptation strategies and guidance for China to achieve the "carbon neutrality" target.
Xu Xiaojuan, Liu Jing, Jiao Fusheng, Zhang Kun, Ye Xin, Gong Haibo, Lin Naifeng, Zou Changxin
2022-Dec-21
Carbon neutrality, Carbon sink/source, Ecological engineering, Nonlinear trend, Trend shifts
