Subtropical forests play an important role in the global carbon cycle and climate change mitigation. In order to understand the effects of climate factors on soil carbon in subtropical forest ecosystems, it is necessary to make full use of carbon sequestration potential. Soil organic carbon (SOC) and soil alkali-hydrolyzed nitrogen (SAN) were tested in 255 plots of subtropical forests in Zhejiang Province, and their forest reserves from 2020 in Zhejiang Province were compared with those from 2010. The results showed that SOC content significantly increased, but SAN content decreased over those ten years. Combined with random forest (RF) and correlation analysis, the contribution of different climate factors (temperature, precipitation, etc.) to soil carbon storage was analyzed, and the main driving factors were evaluated. The RF model explained that winter (December to February) and spring (March to May) were the most dominant drivers to the 0–10 cm and 10–30 cm increases in SOC. There was a significant positive correlation between precipitation and SOC accumulation (0–30 cm) during winter and spring. The minimum temperatures in summer (June to August) and autumn (September to November) were negatively correlated with SOC accumulation (0–30 cm). Increasing the precipitation or irrigation (cloud seeding) in winter could improve the carbon sequestration capacity of subtropical forest soils. This study provides a new perspective on the sensitivity and potential response of the carbon cycle to climate change in subtropical forest ecosystems.

Soil organic carbon (SOC) strongly contributes to the operation of the global carbon cycling, and topographical factors largely influence its spatial distribution. However, SOC distribution and its leading topographical impact factors in subtropical forest ecosystems (e.g., the Zhejiang Province in China) have received relatively limited attention from researchers. In this study, 255 forest soil samples were collected from the Zhejiang Province to quantify the spatial variation in SOC and impact factors in subtropical forests. The SOC contents over soil profiles were 35.95 ± 22.58 g/kg, 20.98 ± 15.26 g/kg, and 13.77 ± 11.28 g/kg at depths of 0–10 cm, 10–30 cm, and 30–60 cm, respectively. The coefficient variations at different depths were 62.81% (0–10 cm), 72.74% (10–30 cm), and 81.92% (30–60 cm), respectively. SOC content shows a moderate intensity variation in the Zhejiang Province. The nugget coefficients of the SOC content for the three depths were 0.809 (0–10 cm), 0.846 (10–30 cm), and 0.977 (30–60 cm), respectively. Structural factors mainly influence SOC content. SOC content is positively correlated with elevation and slope, and negatively correlated with slope position (p < 0.05). However, the SOC content was negatively correlated with slope in mixed coniferous and broad-leaved forest. The distribution of the SOC content was relatively balanced between different slope positions. However, the differences became obvious when forest types were distinguished. Topographical factors affected the SOC content differently: elevation > slope > slope position. Slope becomes the main influencing factor in 30–60 cm soil. Forest type significantly influenced the SOC content but with a low statistical explanation compared to topographical factors. Topography has different effects on SOC of different forest types in subtropical forests. This reminds us that in future research, we should consider the combination of topography and forest types.

Urban forest carbon sequestration represents an important component of the global forest carbon pool; however, accurate measurements are limited by the inability of existing field stand models to match the specificity of urban greening species. Herein, canopy volume, carbon stock, and the biomass expansion factor (BEF) of 30 Koelreuteria paniculate trees were measured based on terrestrial laser scanning (TLS) and compared to the results of existing wood volume and carbon stock model measurements. The findings revealed that (1) TLS point cloud data were highly reproducible and accurate (root mean square error of tree height and diameter at breast height were ±0.35 m and ±0.33 cm, respectively). (2) Owing to human interference and cluttered urban environments, the BEF of urban greening tree species fluctuated irregularly, considerably different from that of natural forest stands. (3) Leaf carbon stocks were influenced by the size of the voxel. (4) Different tree measurement factors maintained variable degrees of influence on BEF (height under branch, volume of thick branch, crown width, and projected areas of tree-crown produced correlation coefficients of −0.64, 0.54, 0.45, and 0.43, respectively). Accordingly, the carbon stock and BEF of urban greening tree species can be accurately calculated via TLS without damage.

Agroforestry management has immense potential in enhancing forest carbon sequestration and mitigating climate change. Yet the impact and response mechanism of compound fertilization rates on carbon sinks in agroforestry systems remain ambiguous. This study aims to elucidate the impact of different compound fertilizer rates on soil greenhouse gas (GHG) emissions, vegetation and soil organic carbon (SOC) sinks, and to illustrate the differences in agroforestry systems’ carbon sinks through a one-year positioning test across 12 plots, applying different compound fertilizer application rates (0 (CK), 400 (A1), 800 (A2), and 1600 (A3) kg ha⁻¹). The study demonstrated that, after fertilization, the total GHG emissions of A1 decreased by 4.41%, whereas A2 and A3 increased their total GHG emissions by 17.13% and 72.23%, respectively. The vegetation carbon sequestration of A1, A2, and A3 increased by 18.04%, 26.75%, and 28.65%, respectively, and the soil organic carbon sequestration rose by 32.57%, 42.27% and 43.29%, respectively. To sum up, in contrast with CK, the ecosystem carbon sequestration climbed by 54.41%, 51.67%, and 0.90%, respectively. Our study suggests that rational fertilization can improve the carbon sink of the ecosystem and effectively ameliorate climate change.

Moso bamboo (Phyllostachys heterocycla cv. Pubescens) is known for its high capacity to sequester atmospheric carbon (C), which has a unique role to play in the fight against global warming. However, due to rising labor costs and falling bamboo prices, many Moso bamboo forests are shifting to an extensive management model without fertilization, resulting in gradual degradation of Moso bamboo forests. However, many Moso bamboo forests are being degraded due to rising labor costs and declining bamboo timber prices. To delineate the effect of degradation on soil microbial carbon sequestration, we instituted a rigorous analysis of Moso bamboo forests subjected to different degradation durations, namely: continuous management (CK), 5 years of degradation (D-5), and 10 years of degradation (D-10). Our inquiry encompassed soil strata at 0–20 cm and 20–40 cm, scrutinizing alterations in soil organic carbon(SOC), water-soluble carbon(WSOC), microbial carbon(MBC)and microbial residues. We discerned a positive correlation between degradation and augmented levels of SOC, WSOC, and MBC across both strata. Furthermore, degradation escalated concentrations of specific soil amino sugars and microbial residues. Intriguingly, extended degradation diminished the proportional contribution of microbial residuals to SOC, implying a possible decline in microbial activity longitudinally. These findings offer a detailed insight into microbial C processes within degraded bamboo ecosystems.

Forests play a crucial role as the primary sink for greenhouse gases, and forest mortality significantly impacts the carbon sequestration capacity of forest ecosystems. A single type of forest mortality model has been developed, and its model variables are incomplete, leading to significant bias in mortality prediction. To address this limitation, this study harnessed data collected from 773 permanent plots situated in Zhejiang Province, China, spanning a period from 2009 to 2019. The primary objectives were to pinpoint the key variables influencing forest mortality and to construct forest mortality prediction models utilizing both traditional regression methods and machine learning techniques, ultimately aiming to provide a theoretical basis for forest management practices and future predictions. Four basic linear regression models were used in this study: Linear Regression (LR), Akaike Information Criterion (AIC) Stepwise Regression, Ridge Regression, and Lasso Regression. Four machine learning models, Gradient Boosting Regression (GBR), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP), were used to model stand mortality. Mortality was used as the dependent variable, and environmental factors such as topographic factors, soil composition, stand characteristics, and climatic variables were used as independent variables. The findings unveiled that soil and stand-related factors exerted significant effects on the mortality rate, whereas terrain-related and climate factors did not exhibit statistical significance. The Random Forest model established by using stand age, tree height, ADBH, crown cover, humus layer thickness, and the biodiversity index has the highest fitting statistics such as R² and Mean Squared Error, indicating that it has a good fitting and prediction effect, which effectively predicts mortality at the stand level, and is a valuable tool for predicting changes in forest ecosystems, with practical value in estimating tree mortality to enhance forest management and planning.