With the rapid development of social economy, the ecological environment problems caused by the change of wetland land use have been widely concerned. This paper takes the Caohai National Nature Reserve (CNNR) of China as the research object on the basis of referring to previous research results. Firstly, the remote sensing data was employed to examine the spatio-temporal evolution process of the CNNR from three aspects: land use structure change, land use dynamic degree and land use space change. Then the change of ecological environment quality was studied from the greenness, the wetness, the dryness and the heat. Based on the spatiotemporal changes of land use types and ecological environment quality in the CNNR from 2000 to 2020, a comprehensive index, the remote sensing ecological index (RSEI), was constructed to analyze the ecological environmental effects of land use changes. The results indicate that the land use changes in the CNNR went through two major periods: first, a period of rapid decline in cultivated land, and second, a period of sharp increase in constructed land. During the period of rapid decline in cultivated land, the ecological environment quality in the study area showed an upward trend. However, during the period of increased constructed land, the ecological environment quality gradually stabilized. This study provides a basis for the coordinated development of the ecological environment and social economy in the CNNR area.

There is a close link between water resources and economic development. To understand the relationship between water resources and economic development in Qian’nan, Guizhou, the study utilized the water resources ecological footprint and decoupling model to analyze the relationship between water resource ecological footprint and economic growth in the region. Data from 2009 to 2019 were collected and analyzed to understand the trends and patterns. The results indicate that from 2009 to 2019, the ecological footprint of water resources in Qian’nan remained less than the ecological carrying capacity, indicating a surplus of water resources and low ecological pressure. However, the water resources ecological footprint gradually increased over the study period. Furthermore, the ecological footprint of water resources was found to be higher in the northern areas compared to the southern regions. Additionally, areas with higher economic levels exhibited larger ecological footprints of water resources, while areas with lower economic levels had smaller ecological footprints. Although some counties (cities) showed an increasingly severe relationship between water resource ecological footprint and economic growth, overall, most counties (cities) demonstrated a weak decoupling state, suggesting that economic development is not significantly constrained by water resources. The findings suggest that Qian’nan, Guizhou, has experienced a surplus of water resources with low ecological pressure over the past decade. However, the increasing water resources ecological footprint warrants attention to ensure sustainable management. The spatial disparities in the ecological footprint of water resources highlight the need for targeted interventions in different regions. Additionally, the weak decoupling state between water resources ecological footprint and economic growth indicates the potential for further economic development without significant constraints from water resources. However, proactive measures should be implemented to maintain this balance and promote sustainable development in the region.

Accurately estimating the contribution of afforestation/deforestation to gross primary productivity (GPP) of an ecosystem is necessary to develop future afforestation policies. However, there is currently a lack of quantitative assessments of the potential consequences of afforestation and deforestation on GPP at a global scale. In this study, we used a 30 m high-resolution forest raster map and a satellite-driven GPP product to assess GPP differences under various afforestation/deforestation scenarios, using spatial rather than temporal comparisons. Our results showed that (1) the simultaneous occurrence of high-intensity afforestation and deforestation was extremely low globally (4.64%). Under this hypothetical scenario, the potential GPP of afforestation could reach 734.13 g C m⁻² yr⁻¹, significantly higher than that in the other scenarios. While the percentage of concurrent medium- to low-intensity afforestation and deforestation was up to 41.37%, the potential value of afforestation to promote GPP increase was only 219.56 g C m⁻² yr⁻¹. (2) The potential of afforestation to boost GPP varied significantly across space and time. Proximity to equatorial forests, such as evergreen broad-leaved forests, generally facilitate GPP accumulation. However, as latitudinal zonality increased, the fixed GPP potential of high-latitude coniferous forests decreased significantly. Summer (particularly June) showed the highest potential for afforestation to enhance GPP, more than twice as much as in the other seasons, and this pattern was consistent globally. (3) Afforestation costs vary substantially depending on forest type and cover. Afforestation in rainforest areas with a better water-heat balance often requires a higher GPP to achieve the desired effect. Low-density forests dominated by temperate or cold zones yield significantly lower GPP benefits than afforestation in tropical rainforests. This study quantifies the potential impact of afforestation on GPP for the first time and provides guidelines for future afforestation planning across various regions.

Understanding the decision-making mechanisms of stakeholders in land use changes within wetland areas is critical for managing land resources effectively and mitigating conflicts. This study applies a game-theoretic approach to examine the strategic interactions among key stakeholders—managers, developers, and residents—within the Caohai National Nature Reserve (CNNR) in China. By integrating real data on land use changes, ecological quality indices, and economic incentives, this study identifies the driving forces behind stakeholder behavior and land use evolution from 2000 to 2020. The land use changes and ecological effects in the CNNR can be divided into two main stages: From 2000 to 2010, the primary direction of land use change was the conversion of grassland into construction land and farmland, resulting in the deterioration of ecological environment quality. From 2010 to 2020, the main direction of land use change shifted to the conversion of farmland into grassland and forest land, leading to an improvement in ecological environment quality. Game-theoretic analysis demonstrates that managers play a decisive role in shaping stakeholder strategies through regulatory mechanisms, such as land rent adjustments, penalties, subsidies, and ecological compensation. Stronger enforcement of penalties and incentives significantly enhances cooperative behavior among stakeholders, reducing land use conflicts and promoting ecological recovery. These findings emphasize the necessity of targeted governance strategies to align stakeholder interests and balance ecological conservation with socio-economic development. The insights from this study provide valuable guidance for policymakers and land managers in designing effective land use policies and improving conservation efforts in wetland-protected areas.

Amid accelerating forest degradation and climate warming, afforestation is increasingly regarded as a strategic intervention to reconfigure land–atmosphere energy exchange. However, most existing studies have predominantly emphasized direct biophysical effects, namely surface cooling resulting from forest replacement of non-forest cover, while largely overlooking the indirect regulation caused by environmental feedbacks during forest expansion. To address this gap, a dual-pathway analytical framework was proposed to diagnose afforestation-induced changes in land surface temperature ( LST ). Within this framework, a ‘zero-impact line’ was defined to represent the LST response solely from forest cover conversion, and the indirect effect amplification index ( τ ) was introduced to quantify the magnitude and direction of indirectly driven LST variation. Results indicated that each 1% increase in forest cover reduced LST by 0.029 °C–0.036 °C through direct effects ( DE ). In the early stages of forestation, τ reached 187%, 227%, and 242% in Yunnan, Guizhou, and Guangxi, respectively, revealing that indirect feedbacks can equal or exceed DE , thereby amplifying the overall cooling outcome in the subtropical evergreen broadleaf forests of Southwest China.

Urban vitality is a crucial indicator of a city’s sustainable development and the quality of life of its residents. Investigating the spatiotemporal patterns and influencing mechanisms of urban vitality is essential for optimizing the built-environment and improving governance. Using the central urban area of Guiyang, China, as a case study, this research integrates multi-source urban sensing data to investigate the spatiotemporal patterns of urban vitality and their driving factors. Geographically weighted regression (GWR) and machine learning combined with SHapley Additive exPlanations (SHAP) are applied to capture spatial heterogeneity, nonlinear relationships, and threshold effects among influencing variables. Results show that urban vitality exhibits a Y-shaped, single-core, multi-center, and clustered spatial configuration, with slightly higher intensity on weekdays and similar diurnal rhythms across weekdays and weekends. The effects of influencing factors display strong spatial non-stationarity, characterized by a concentric gradient radiating outward from the historic Laocheng core. Building density (BD), residential point density (RED), normalized difference vegetation index (NDVI), and road density (RD) emerge as the dominant contributors to urban vitality, while topographic conditions play a relatively minor role. The relationships between key landscape and built-environment variables and urban vitality are highly nonlinear, with distinct threshold effects. By integrating spatial econometric modeling and explainable machine learning, this study advances methodological approaches for urban vitality research and provides practical insights for landscape-oriented urban planning and human-centered spatial design.