The health status of ecosystems is an important prerequisite for ensuring regional ecological security. Exploring the spatiotemporal patterns, driving mechanisms, and zoning regulation pathways of ecosystem health is of great significance for achieving co-ordinated and sustainable regional ecosystems. This study uses China as a case area and applies the InVEST model to measure integrated ecosystem services and incorporates it into an evaluation framework for ecosystem health based on the “Vigor-Organization-Resilience-Ecosystem Services” (VORS) model. It reveals the spatiotemporal evolution characteristics of ecosystem health in China from 2000 to 2020 and employs the geodetector and spatiotemporal geographically weighted regression model to analyze the main influencing factors and spatial differentiation characteristics, thereby exploring ecological management zoning and optimization pathways. The study results show that (1) during the study period, the overall ecosystem health level in China showed a declining trend, dropping from 0.397 in 2000 to 0.377 in 2020. (2) Overall, China’s ecosystem health exhibits strong spatial positive correlation and spatial clustering characteristics, with a basic pattern of lower values in the northwest and higher values in the southeast. (3) Vegetation coverage, population density, density of road network, and per capita GDP are the main influencing factors of ecosystem health in China. (4) China is divided into five types of Ecological Management Zones: Ecological Conservation Zone, Ecological Enhancement Zone, Ecological Buffer Zone, Ecological Remediation Zone, and Ecological Reshaping Zone, with differentiated strategies proposed for optimizing ecosystem health in each zone.

Amidst global climate warming and increasingly severe food security challenges, the agroforestry economy, a green ecological industry that balances ecological conservation and economic development, has attracted widespread attention. This study constructs a theoretical analytical framework based on the diamond model to systematically identify key factors influencing the development of the agroforestry economy. Using 56 practical cases from the agroforestry economy in China as samples, the study applies Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to further explore the multiple driving paths of agroforestry economic development and their supporting elements. The research findings show that (1) forest resources, technological innovation, market demand, enterprise forms, related industries, and government support do not constitute necessary conditions for the development of the agroforestry economy. The path to the development of the agroforestry economy exhibits complex and concurrent multi-faceted characteristics. (2) Technological innovation has always been at the core of all configurations, and strengthening technological innovation plays a universal role in enhancing the level of agroforestry economic development. The role of government support in the process of the development of the agroforestry economy is limited. (3) The system identified four driving paths, including the endogenous type, characterized by resource technology enterprises; the collaborative type, characterized by a resource technology market with light promotion by the government; the external expansion type, characterized by market technology enterprises; and the linkage type, characterized by market technology enterprises assisted by related industries. The consistency level of the overall solution reached 0.91, and the coverage was 0.54. It reveals the different driving mechanisms with different combinations of elements for the development of the agroforestry economy. Therefore, each region should strengthen scientific and technological research, innovation, and the transformation and application of research outcomes. It should promote the coordinated development of diverse factors, establish tailored regional development models, and explore suitable pathways for developing the agroforestry economy based on its unique resource endowments.

The coordinated development of the agroforestry economy and the ecological environment is crucial for promoting the sustainable development and high-quality transformation of the agroforestry economy. Based on TOE theory and utilizing provincial-level panel data from China covering 2012–2023, this study comprehensively employs dynamic QCA and NCA methods to explore the multi-faceted driving pathways and supporting factors for the coordinated development of the agroforestry economy and ecological environment across temporal and spatial dimensions. Key findings include: (1) Coordinated development requires synergistic contributions from multiple factors—technological, organizational, and environmental—rather than isolated effects of any single element. While no single factor alone constitutes a necessary condition for coordination, the importance of technological innovation, market demand, and industrial support is progressively increasing; (2) The coordinated development of the agroforestry economy and ecological environment involves multiple pathways and complex mechanisms. Specifically, it encompasses four distinct approaches: enterprise-driven and industry-supported model, technology-innovation-led model, market-driven factor integration model, and government-led multi-stakeholder collaboration model; (3) No significant temporal effects emerged across all pathways, but pronounced spatial heterogeneity was evident. The enterprise-driven and industry-supported model suits Northeast and Central China; the technology-innovation-led model is suitable for South China and Northeast China; the market-driven factor integration model is suitable for East China, Central China, and Southwest China; the government-led multi-stakeholder collaboration model is suitable for Southwest China and Central China. Therefore, to enhance the coordinated development of the agroforestry economy and ecological environment, each region should adopt a holistic perspective, leverage its unique resource and factor endowments, strengthen the integrated matching of technological, organizational, and environmental factors, and explore development pathways tailored to local conditions.

The water–energy–food (WEF) system acts as a critical nexus of social–ecological systems. However, rapid urbanization has intensified the regional imbalance in the supply and demand of ecosystem services (ESs). Clarifying the spatiotemporal matching of ecosystem services supply and demand (ESSD) within the WEF framework and revealing the driving mechanisms behind such imbalances are essential to formulating reasonable zoning schemes and targeted optimization strategies for the coordinated development of the regional WEF system. Taking Zhejiang Province as a case study, this research uses water yield (WY), carbon sequestration (CS), and grain production (GP) to characterize the WEF nexus system. It uses the InVEST model to assess WY and CS, applies spatial allocation methods to characterize GP, and integrates socioeconomic data to quantify the demand for the above three ESs. All indicators were standardized and integrated with equal weights to further clarify the comprehensive levels of ESSD. By integrating the Geodetector and K-Means clustering methods, the study analyzes the supply–demand matching of ecosystem services and its driving mechanisms in Zhejiang Province during this period, thereby exploring ecological management zoning and optimization strategies within the WEF system. The study findings indicate that: (1) From the supply perspective, Zhejiang Province’s WY services demonstrate a trend of elevated activity in the southwest and diminished presence in the northeast; high values for CS services are predominantly found in the vegetation-rich areas of the northwest, while high values for GP services are clustered in the northern Zhejiang Plain; from the demand perspective, high values for all three ESs in Zhejiang Province are primarily located in economically active, densely populated urban areas. (2) The correlation between ESSD within Zhejiang Province’s WEF system exhibits significant spatial heterogeneity and is driven by the combined effects of natural and socioeconomic factors, with the interaction between these two factors often producing a synergistic effect. Specifically, annual average precipitation and population density are the dominant factors influencing WY services, NDVI and human footprint are the dominant factors influencing CS services, and population density and GDP are the dominant factors influencing GP services. (3) From 2000 to 2020, the supply–demand ratio for comprehensive ESs in Zhejiang Province generally followed a pattern of being lower in the east and higher in the west. The supply–demand imbalance of ESs intensified in the core areas of eastern cities, whereas the western regions maintained a relatively sound supply–demand balance. (4) The study classifies the counties in Zhejiang Province into four ecological management zones—ecological stable zones, ecological conservation zones, ecological control zones, and ecological restoration zones—and explores differentiated approaches to optimizing these zones and implementing control strategies.

Rapid urbanization has seriously interfered with the carbon sink function of forests, and has even led to an increased risk of forest carbon imbalance. It is important to explore the regional carbon compensation mechanism and zoning optimization path based on forest carbon accounting to achieve the “dual carbon” goal and sustainable forest management in Fujian Province. Based on remote sensing and GIS technologies, this study measured forest carbon emissions and carbon sequestration of each county in Fujian Province, revealed spatial and temporal evolution of forest carbon budget during the period from 2000 to 2020, and calculated carbon compensation value of each county, so as to realize scientific accounting of forest carbon compensation, and then explored zoning optimization pathways of forest carbon compensation in Fujian Province. The results show the following: (1) From 2000 to 2020, the forest carbon budget in Fujian Province as a whole showed a spatial pattern of “coastal deficit, northwest surplus”, with obvious spatial imbalance characteristics, and showed a high growth trend of net carbon sequestration. (2) From 2000 to 2020, the average carbon compensation rate in Fujian Province was 7.92, and compensation zones were mainly concentrated in the economically developed southeast coastal regins such as Fuzhou, Quanzhou, Xiamen, Zhangzhou, and Putian, while compensation-receiving zones were mainly concentrated in northwestern mountainous areas such as Nanping, Ningde, and Longyan, which had a high forest coverage rate. (3) From 2000 to 2020, there was a significant difference in growth rates of compensation amounts and compensation-receiving amounts in Fujian Province. The cumulative increase in compensation amounts was 322.82%, while the cumulative increase in compensation-receiving amounts was only 17.5%. (4) Based on priority levels, the counties in Fujian Province are classified into six types of forest carbon compensation zones—potential compensation zones, secondary compensation zones, priority compensation zones, potential compensation-receiving zones, secondary compensation-receiving zones and priority compensation-receiving zones—and optimization paths of differentiated zones are explored.