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.

Soil is a crucial contributor to greenhouse gas (GHG) emissions from terrestrial ecosystems to the atmosphere. The reduction of GHG emissions in plantation management is crucial to combating and mitigating global climate change. A 12-month field trial was conducted to explore the effects of different fertilization treatments (control, without fertilizer (CK); biochar-based fertilizer treatment (BFT); chemical fertilizer treatment (CFT); and mixture of 50% BFT and 50% CFT (MFT)) on the soil GHG emissions of a typical bamboo (Pleioblastus amarus (Keng) Keng f.) plantation. The results demonstrated that compared with the CK, BFT reduced the annual cumulative soil N₂O emission by 16.3% (p < 0.01), while CFT and MFT significantly increased it by 31.0% and 23.3% (p < 0.01), respectively. Meanwhile, BFT and MFT increased the annual cumulative soil CH₄ uptake by 5.8% (p < 0.01) and 7.5% (p < 0.01), respectively, while there was no statistically significant difference between CFT and the control. In addition, BFT, CFT, and MFT significantly increased the annual cumulative soil CO₂ emission by 9.4% (p < 0.05), 13.0% (p < 0.01), and 26.5% (p < 0.01). The global warming potential (GWP) of BFT did not change significantly, while CFT and MFT increased the GWP by 13.7% (p < 0.05) and 28.6% (p < 0.05), respectively, compared with the control. Structural equation modeling revealed different treatments affected soil N₂O and CH₄ emission by changing soil labile carbon and labile nitrogen pools. This study suggests utilizing BFT new ideas and strategies for mitigating GHG emissions from soils in subtropical Pleioblastus amarus plantations.

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.