Forest Ecological Product Value Realization (FEPVR) is an important way for China to promote the transition and development of forestry and realize common prosperity. It is critical to assess the effectiveness of FEPVR rationally to obtain a comprehensive understanding of the progress of ecological civilization. Based on the Driver–Pressure–State–Impact–Response (DPSIR) model, in this study, we developed an evaluation index system for FEPVR effectiveness containing five subsystems and 37 indicators and assessed the effectiveness of FEPVR in China from 2011 to 2022 by using the linear weighted sum method, the coupling degree of coordination model, and Spearman’s correlation analysis. The results showed that the composite index increased from 0.1980 in 2011 to 0.6501 in 2022, with a general upward trend, but there is still great potential for improvement. The main contribution was from the Response subsystem. The year 2017 was an important turning point for FEPVR in China because its development started to speed up; the status of coupling coordination between different subsystems was gradually improving but was still at a low level. The relationship between all indicators was dominated by a non-significant correlation (52.4%), and the overall synergistic effect (27.8%) was greater than the trade-off effect (19.8%). This study provides a new perspective for evaluating the effectiveness of FEPVR and a decision-making reference for clarifying the direction of FEPVR optimization.

Ecological Product Value Realization (EPVR) is regarded as an effective tool for ecological civilization construction in China and has received widespread attention. Based on the Driver-Pressure-State-Impact-Response (DPSIR) model, this study constructs a multi-layered evaluation index system for the effectiveness of EPVR in China from a systemic perspective and determines the weights of the indicators through the entropy weight method. On this basis, an empirical analysis of the EPVR effectiveness in China from 2011 to 2021 is conducted using the TOPSIS method, the coupling coordination degree model, and the obstacle diagnosis model. The results show that the whole effectiveness of EPVR in China has been significantly improved during the study period, with the composite evaluation index increasing from 0.1481 to 0.7680, which can be categorized into a slow exploration period (2011 to 2016) and a rapid development period (2017 to 2021). The development effectiveness and the state of coupling coordination varies between subsystems, and the main obstacle factors are not fixed. The above results verify the scientific nature and applicability of the index system, which can provide a new assessment method to promote the efficient utilization of resources and sustainability. This study also makes recommendations in terms of ecological product supply, natural resource utilization, eco-industry, and coordinated development.

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm⁻² to 25.313 t·hm⁻². We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.

Forest management is the fundamental approach to continuously improve forest quality and achieve the quadruple functions of forests. The identification of forest management types is the basis of forest management and a key technical link in the formulation of forest management plans. However, due to insufficient application of forestry informatization and digitization, there are problems in the organization and application of management types, such as inaccurate identification, diversified standards, long organizational cycles, and low decision-making efficiency. Typical technical models are difficult to widely promote and apply. To address these challenges, this study proposes the Stacking Ensemble Forest Management Type Identification (SEFMTI) method based on Stacking ensemble learning. Initially, four typical forest management types from the sustainable forest management pilot of the Yichun Forestry Group were selected as research subjects, and 19 stand parameters were chosen to form the research data, training various recognition models. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) methods were used to analyze key decision-making indicators for forest management type recognition and compare the performance of different models. The results show that (1) the SEFMTI model achieved an accuracy rate of 97.14%, effectively improving the accuracy of forest management type recognition while ensuring stability; (2) average age (AG), age group (AGG), crown density (CD), and stand origin (SO) are key decision-making indicators for recognizing forest management types; and (3) after feature selection, the SEFMTI model significantly enhanced the efficiency of model training while maintaining a high accuracy rate. The results validate the feasibility of the SEFMTI identification method, providing a basis for the gradual implementation of sustainable forest management pilots and aiding in the precise improvement of forest quality.

The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R² = 0.2326) > model ENV (AUC = 0.7664, Tjur R² = 0.1454) > model SPACE (AUC = 0.7297, Tjur R² = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China.

To protect the forest ecological environment, China implemented the Comprehensive Commercial Logging Ban Policy (CCLBP), yet it has a major impact on forest residents. Therefore, this study aimed to evaluate the CCLBP from the perspective of the satisfaction of residents. In this study, we used 242 questionnaires from residents in national forest areas in Northeast China and Inner Mongolia to evaluate satisfaction and its influencing factors by factor analysis combined with the entropy method and multiple linear regression, respectively. The results show that: (1) the overall policy satisfaction of residents in national forest areas is 60.9, which is lower than the theoretical neutral value of policy satisfaction. Increasing employment opportunities, transferring surplus employees and developing forest economy are important to improve the satisfaction of residents. (2) Life satisfaction was higher in the high-income group than in the low-income group. Increasing wage income can improve the satisfaction of residents in national forest areas. (3) The satisfaction of the worker group was significantly higher than that of the forest farmer group. Improving job stability and policy equity are important to promote life satisfaction of residents in national forest areas. (4) The satisfaction of the Yichun Forest Industry Group was significantly higher than the Inner Mongolia Forest Group. Natural resource endowment and adaptability to the CCLBP are the main factors affecting the satisfaction of different forest industry groups. At the same time, focusing on the institutional supply of external support policies in order to provide them access to information, employment advice and other services is very significant. This research provides a new approach to studying the CCLBP, which is of great practical significance for raising the wellbeing index of national forest areas.