Agricultural green development plays a crucial role in addressing the conflict between agricultural production and ecological environmental protection. This study aims to analyze the impact and mechanisms through which digital inclusive finance influences agricultural green development, providing empirical evidence to support the role of digital inclusive finance in promoting sustainable agricultural practices. Using panel data from 30 provinces (cities and districts) in China spanning from 2011 to 2020, this study measures regional agricultural green development levels using the entropy power method. By clarifying the theoretical mechanisms behind the relationship between digital inclusive finance and agricultural green development, this research employs panel fixed effects, mediation effects, and threshold regression models to empirically examine the impact of digital inclusive finance, specifically its coverage breadth, usage depth, and digitization level, on agricultural green development. Furthermore, this study explores the threshold effects of digital inclusive finance on agricultural green development. The findings from this study indicate that digital inclusive finance significantly contributes to agricultural green development, with a double-threshold effect observed as follows: when digital inclusive finance surpasses critical thresholds, its impact on agricultural green development intensifies. This result remains robust after conducting a series of endogeneity and robustness tests. The mechanism analysis reveals that digital inclusive finance drives agricultural green development by fostering technological innovation and increasing farmers’ income. Heterogeneity analysis further demonstrates that digital inclusive finance has a substantial impact on agricultural green development in eastern China compared to the central and western regions. Consequently, this study suggests that digital inclusive financial services should be further expanded and deepened to foster the integration of financial services with agriculture, creating a sustainable development model that promotes shared prosperity and effectively supports the green development of agriculture.

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.

Forests provide goods and services while maintaining ecological security. However, the market does not adequately reflect their economic benefits, posing a significant challenge to the Payments for Forest Ecosystem Services (PFES). The involvement of multiple stakeholders with varying responsibilities and interests complicates collaboration and hinders effective governance. This study proposes an integrated approach using stakeholder analysis, social network analysis, and evolutionary game analysis to explore the collaborative governance of stakeholders in PFES. Through field surveys, the study empirically investigates PFES in China, demonstrating the effectiveness of this integrated approach. The results indicate the following: (i) Stakeholders are classified into three categories; the key stakeholders include the central and local governments, forest managers, and paying users. (ii) Stakeholders still need to strengthen collaboration. Local governments, forest managers, their employees, and communities exert widespread influence; paying users and research institutions have high efficiency in resource sharing. (iii) Five evolutionarily stable strategies are observed at different stages. Government intervention is crucial for changing the stagnant state. Benefits and government incentives have a positive impact on stakeholder collaborative governance. The research findings offer theoretical insights to enhance stakeholder collaboration and promote the development of the PFES. Key strategies include addressing key stakeholders’ needs, diversifying incentives, and establishing an accessible information platform.