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.

Carbon emissions trading pilots are an essential environmental regulation tool for incentivizing companies to reduce carbon emissions and a critical initiative for achieving “dual carbon” targets. This study, based on 2366 observations of 169 high-carbon listed companies on the Shanghai and Shenzhen stock exchanges from 2009 to 2022, uses double machine learning analysis to examine the impact and mechanisms of pilot policy on the low-carbon competitiveness of high-carbon industry-listed companies. The empirical results show that, first, pilot policy significantly enhances the low-carbon competitiveness of high-carbon industry-listed companies, and this conclusion holds after considering a series of robustness checks. Second, mechanism analysis indicates that alleviating green financing constraints and enhancing total factor productivity are pathways through which pilot policy influences low-carbon competitiveness. Heterogeneity analysis shows that the policy effects are stronger for state-owned enterprises, small- and medium-sized enterprises, and companies in eastern regions. Further analysis reveals that pilot policy enhances low-carbon competitiveness and increase enterprise value. Based on the study’s conclusions, the government should ensure the incentivizing effect of pilot policy, promote expansion of the carbon emissions trading market, assist enterprises in overcoming green financing constraints, improve total factor productivity, and formulate tailored policies according to the development levels and resource endowments of regions and companies.

Investigating the spatiotemporal distribution patterns of regional ecosystem health and the methods for optimizing their zoning is essential for enhancing ecosystem management and sustainable development. This study takes Fujian Province, a pivotal forest region in southern China, as the research object to establish a county-level ecosystem health evaluation framework grounded in the vitality–organization–resilience–ecosystem (VORS) model. It further conducts a comprehensive spatial–temporal analysis of ecosystem health dynamics from 2000 to 2020 and explores ecological management zoning and optimization strategies. The results indicate that (1) from 2000 to 2020, the overall ecosystem health level in Fujian Province maintained a relatively high status and exhibited a steady upward trajectory, with the index rising from 0.4694 in 2000 to 0.4762 in 2010, and further increasing to 0.4865 in 2020. (2) The health of ecosystems in Fujian exhibits significant geographical autocorrelation and aggregation, characterized mostly by high–high and low–low clusters. Typically, it has a geographical distribution characterized by elevated values in the northwest and diminished values in the southeast. (3) In light of the present condition and temporal dynamics of ecosystem health, the study classifies Fujian’s counties into three ecological management categories—conservation, stabilization, and reshaping zones—and puts forward tailored optimization measures for each region. The methodology employed in this study provides a general framework for ecosystem health assessment, which can offer useful references and insights for forest ecosystem health evaluation and refined, zone-specific ecological management in forest regions.

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.