Classification-based forest management (CFM) is generally regarded as an important political means of achieving sustainable forest development. However, in the upsurge of publicly managed forest devolution, the impact of CFM policies on forestland restoration remains uncertain and needs to be explored. This study contributes to the scant literature on this topic in China, where CFM has long been implemented based on the ecological welfare forestland (EWF) certification program. We use provincial data from China to examine the relationship between EWF-certified areas and forest restoration. Based on inter-provincial panel data from the third to the ninth consecutive forest resource inventories in China (1984–2018), we use a dynamic spatial autoregressive model to analyze the impact of forest classification management on forest restoration. The results show that, contrary to appearances, increasing EWF-certified areas promotes forest restoration. However, after controlling for other possible influencing factors, increasing EWF-certified areas plays a minimal role in promoting forest restoration and regrowth by inhibiting investment in forest management and even has a negative impact on forest restoration in the southern collective forest area.

Maximizing the carbon sequestration of forested land is important for achieving carbon neutrality. Although some studies have discussed forest carbon sequestration efficiency (FCSE) from the perspective of total factor production, it is being increasingly recognized that forestland use regulates sequestration and emissions. When viewing forestland use as input and carbon emissions as output, there is a lack of empirical evidence on FCSE and its influencing factors. Here, a superefficiency slacks-based measurement model was applied to estimate FCSE for 66 counties in Zhejiang Province, China. The influencing factors and spatial spillover effects of FCSE were also analyzed using a spatial autocorrelation model. The findings showed that over the sample observation period, county FCSE ranged from 0.199 to 1.258, with considerable gaps. The global Moran’s I index showed that county-level FCSE was markedly spatially autocorrelated. Spatially, forestland use, cutting, pests, and diseases had negative spatial spillover effects on FCSE, whereas average annual temperature and precipitation displayed positive spillover effects. These findings suggest that the overall coordination of forest resource supervision and management among counties should be strengthened. The implementation of forestry management models aimed at consolidating or increasing forest carbon sequestration should be emphasized to improve forest quality, thereby promoting FCSE enhancement.

Natural broadleaf forests (NBFs) are the most abundant zonal vegetation type in subtropical regions. Understanding the mechanisms influencing stand productivity in NBFs is important for developing “nature-based” solutions for climate change mitigation. However, minimal research has captured the effects of nonlinearities and feature interactions that often have nonlinear impacts on stand productivity and influencing factors. To address this research gap, we used continuous forest inventory data, and a machine learning model for stand productivity of NBFs was constructed. Subsequently, through leveraging the interpretable machine learning framework of the SHapley Additive explanation (SHAP) and partial dependence plot, we determined global and local explanations of the influencing factors of stand productivity. Our findings indicate the following: (1) The Autogluon model performed the strongest based on R², RMSE, and rRMSE metrics. (2) The basal area (BA), neighborhood comparison of diameter at breast height (NC), and stand age (AGE) were the key influencing factors. Stand productivity increased with increasing BA and decreased with increasing NC and AGE. BA was maintained above 15 m²ha⁻¹ and NC was maintained below 0.45, which represent favorable conditions for NBFs to maintain optimal growth. (3) SHAP interaction values were calculated to determine the effects of the five major interactions on stand productivity. Our study provides a reference for the sustainable management of NBFs, thereby highlighting the important role of forests in mitigating climate change.

Accurate estimation of forest aboveground biomass (AGB) is pivotal for assessing forest carbon sequestration and informing global change studies. Conventional LiDAR-based AGB estimation approaches primarily rely on height and density metrics, which inadequately characterize the complex three-dimensional (3D) structure of forest canopies. This study developed and evaluated a novel method utilizing voxel-based 3D canopy structural metrics derived from airborne LiDAR (ALS) to improve AGB estimation accuracy across diverse forest types. First, voxel-based metrics (Voxel Canopy Height Model (VCHM), canopy volume, and canopy surface area) were extracted from voxelized point clouds. Their distribution patterns across five forest types (Pinus massoniana, Cunninghamia lanceolata, coniferous, broadleaf, and mixed conifer–broadleaf forests) and their correlations with AGB were systematically examined. The results revealed distinct 3D canopy architectures among forest types, with all three voxel metrics showing highly significant positive correlations with AGB; VCHM demonstrated the strongest association. We then constructed two Random Forest models: a baseline model using traditional metrics only, and an enhanced model integrating both traditional and voxel-based metrics. The 10-fold cross-validation indicated that the model incorporating voxel metrics achieved markedly higher accuracy (R² in 0.490–0.684) than the traditional model (R² in 0.480–0.607), representing a relative improvement of 2.1% to 32.7%. The most substantial gain occurred in structurally complex broadleaf forests. The enhanced model was subsequently applied to generate a wall-to-wall AGB map of the study region, yielding a total estimated AGB stock of 8.36 × 10⁶ t, which exhibited a patchy spatial distribution. Pinus massoniana forests accounted for the largest proportion (57.8%) of the total stock. This study demonstrates that voxel-based 3D canopy metrics can more effectively capture forest structural heterogeneity and substantially improve the accuracy of AGB estimation models, particularly for complex forest stands. The findings provide a significant advancement toward precise, stand-scale forest biomass monitoring founded on detailed 3D structural information.