The digital economy is an important engine for promoting green economic development, and the integration of the digital and real economies can accelerate the transformation of the real economy. In order to explore the multifaceted influence of digital economy on forestry green total factor productivity and its specific presentation form, based on the panel data of 277 cities in China from 2013 to 2019, this paper first used the super SBM model to measure the level of forestry green total factor productivity and adopted the entropy method to measure the level of the digital economy in each region. Secondly, the influence and mechanism of the digital economy on green total factor productivity in forestry were explored by using fixed-effect and intermediate-effect models, and the heterogeneity of the digital economy on forestry green total factor productivity was analyzed based on different regional classification methods. Finally, the spatial spillover effect of the digital economy was explored in depth by the spatial Durbin model. The results are as follows: firstly, there is a significant inverted U-shaped relationship between the digital economy and forestry green total factor productivity, which first promotes and then inhibits. Secondly, the relationship between the digital economy and the level of urban green innovation shows a positive U-shaped relationship, first inhibiting and then promoting, and can have an indirect impact on forestry green total factor productivity by promoting the level of green innovation. Third, China is still on the left side of the inverted U-shaped relationship between the digital economy and forestry green total factor productivity, i.e., it is at a stage where the digital economy can significantly contribute to forestry green total factor productivity. Fourth, the effect of the digital economy on green total factor productivity in forestry is heterogeneous in the east, central, and west and is more pronounced in regions with faster economic development or rich natural resources. Fifth, the impact of the digital economy on forestry green total factor productivity has a significant positive spatial spillover effect.

China’s state-owned forest farms are the basic sectors of forestry production, and their carbon cycle functions, such as timber processing and forest carbon sequestration, are of great significance to the national emission reduction strategy. By three-stage DEA and Tobit models, this paper measures the carbon sequestration output efficiency of 3706 state-owned forest farms involved in China’s National Forestry and Grassland Administration’s 2008–2018 survey. We figure out how the mechanism on the carbon sequestration impacts output efficiency of these forest farms and analyze the temporal trends and spatial distributions of their outputs in various regions. Our results indicate that the overall output efficiency of state-owned forest farms in China is relatively low compared with the international advanced level and show that distinctive north-south regional differences exist. Specifically, the carbon storage of the state-owned forest farms in Northeast China and Inner Mongolia occupies more than half of the carbon storage of total amounts, but their output efficiency is unsatisfactory. Conversely, the forest farms in Southwest China have a medium amount of carbon storage and the highest output efficiency. After improving the external environments of these farms, the efficiency value in each province appears as a significant increment. Moreover, the effects of afforestation, timber harvests, the under-forest economy, and other operating behaviors exhibit regional heterogeneity to some extent. Therefore, this paper advocates reforming the current forest cultivation strategy that emphasizes afforestation and neglects management, and relevant government departments are supposed to adjust operations according to local conditions to promote sustainable forest management.

State-owned forest resources occupy an important position in China, and the development of their paid use will help to improve the economic benefits of these resources. For this study, 451 journal documents involving the paid use of state-owned forest resources in the CNKI database of China from 2008 to 2021 were selected as samples. Combining qualitative reviews with quantitative analysis, statistical analysis software was used as an analytical tool. The knowledge maps can be visualized by cluster analysis, multidimensional scaling (MDS), and co-occurrence network analysis. The change laws of this research in the time dimension were obtained using developing trend analysis. The results are as follows: 1. The number of research documents on the paid use of state-owned forest resources is increasing. 2. The core authors account for 29.27%; the research impact is relatively scattered. 3. Research institutions are primarily colleges and universities. 4. The support of provincially funded projects accounts for the highest proportion. 5. There is a relatively stable number of journals in this research field. Forestry Economy, Green Science and Technology and China Forestry Economy are the top three journals in terms of citation impact. 6. The existing research topics mainly focus on the development status of paid use, forest tourism and forest health, and the under-forestry economy (under-forestry planting, breeding, and product processing). 7. The intermediary centralities of state-owned forest farms and under-forestry economy are the highest, followed by forest tourism and forest experience, etc. With time and the promulgation of policies, the research focus in this field has gradually shifted from forest assets and forestry economics to ecotourism and forest health, and research on forest carbon sequestration is a technical branch worthy of attention in the future.

This study analyzes data from 12 prefecture-level cities in the Inner Mongolia Autonomous Region from 2011 to 2022 to investigate how digital financial inclusion(DFI) affects the increase in farmers' incomes and its spatial spillover effects. The Dagum Gini coefficient indicates that disparities in DFI development are primarily due to inter-regional differences. Using the Spatial Durbin Model, based on an economic distance matrix, the study finds that DFI strongly promotes income growth for farmers. Moreover, it reveals that the spatial spillover effects of DFI have a greater impact on increasing farmers' incomes than the direct effects of inclusion itself.

An important policy cornerstone for China to reduce poverty includes China’s goal of achieving complete poverty alleviation in its state-owned forest farms by 2020. This study describes and documents the poverty reduction effect in impoverished Chinese state-owned forest farms. Based on a sample of 4855 state-owned forest farms in 31 provinces in China from 2008 to 2017, this paper uses the difference-in-difference method to study the dynamic process of poverty reduction by policies for impoverished state-owned forest farms. The results show that the implementation of the policy significantly promoted the economic development of impoverished state-owned forest farms and caused the treatment group to increase by approximately 10.6% over the control group. Moreover, the establishment of a list of impoverished state-owned forest farms had an indirect impact on the economic performance of forest farms, through channels such as infrastructure poverty alleviation, human capital investment, and per capita income of forest farm employees. Additionally, the effects of the policy on non-impoverished counties were stronger than that on impoverished counties, and impoverished state-owned forest farms may not appear in impoverished counties. The degree of financial dependence of the local government had a significant positive impact on the economic development of impoverished state-owned forest farms. The effects of the policy on different forest farms were heterogeneous. Our study provides recommendations for the future development of impoverished Chinese state-owned forest farms and for the improvement of poverty alleviation efficiency.