Ecological environment in mining cities has become an important part of ecological construction. This paper takes Tongling, a mining city, as the research area, and uses Landsat series remote sensing images from 2000 to 2020 as data sources. Using the principal component analysis method and the Remote Sensing Ecological Index (RSEI) integrated with four indexes of greenness, humidity, dryness, and heat, the ecological disturbance of the mining area was evaluated and studied. Meanwhile, the land cover spatiotemporal classification of Tongling city was extracted by the maximum likelihood method. Furthermore, landscape metrics were used, based on the information on open-pit mining areas, to quantitatively analyze the ecological environment quality and its change characteristics in the study area. The results show that (1) RSEI can better characterize the ecological quality of Tongling city, greenness and humidity are positively correlated with it, dryness and heat are negatively correlated with it, and dryness and RSEI have the highest correlation coefficient, indicating that urban expansion will cause ecological environment deterioration to a certain extent. (2) The ecological environment quality of the research area showed a “decline-rising” trend, and the mean value of RSEI decreased from 0.706 to 0.644. Spatially, the areas with poor RSEI are mainly distributed in the central urban area and the open-pit mining area in the south. (3) Land cover change leads to changes in landscape metrics, and most landscape-level metrics are positively or negatively correlated with RSEI. The more concentrated the land cover type distribution is, the smaller the change is, and the more regional RSEI can be improved. (4) The mean value of RESI of the ten open-pit mining areas in Tongling city decreased significantly, with a maximum decrease of 52.73%. Among them, the RESI decline rate in the area around the no.1 open pit mine is 0.034/year. The ecological degradation in Tongling city is attributed to the rapid expansion of built-up areas and the development of the mining industry. The research results can provide a scientific basis for protecting the ecological environment of mining cities.

Analyzing the impact of agricultural industrial agglomeration (AIG) on agricultural green development (AGD) is of a great significance to realizing the sustainable and high-quality development of agriculture. Panel data of 31 provinces in China from 2009 to 2019 were analysed. For measuring efficiency, a non-parametric DEA approach in the presence of undesirable outputs, a slack-based measure (SBM) was used. From the perspective of the spatial spillover analysis and heterogeneity analysis, Moran’s I index and the Spatial Durbin Model (SDM) were used to empirically analyze the impact of AIG on AGD to alleviate conflicts between agricultural sustainable development and environmental pollution and further explore the regional heterogeneity of AIG on AGD-efficiency due to the vast territory of China. The mediation model is constructed to explore the paths of AIG affecting AGD. The results show that: (1) Chinese efficiency of AGD was raised continuously and the high efficiency was mainly located in the southeastern coastal areas. (2) AIG not only has a significant U-shaped impact on the AGD, but also has a nonlinear U-shaped spatial spillover effect in related regions, which shows that the “siphon effect” will be triggered in the early stage of AIG and the “diffusion effect” will be evoked in the later stage of AIG. (3) From the perspective of heterogeneity analysis, AIG significantly promotes the efficiency of AGD in the central region of mainland China. In the eastern region, the AIG has an inverted U-shaped effect on the efficiency of AGD from positive to negative. On the contrary, the AIG has a U-shaped impact on the efficiency of AGD from negative to positive in the western region. (4) The analysis of the mediation model plays a partial positive mediating role for AGD to persist in promoting technology innovation and increasing the speed of talent agglomeration. Accordingly, suggestions are provided to strengthen the coordination and cooperation in sustainable agricultural development among provinces, to drive the efficiency of science and technology through the scale knowledge spillover effect, and to conduct a scientific layout of agricultural industry development.

As an important part of terrestrial carbon cycle research, net primary productivity is an important parameter to evaluate the quality of terrestrial ecosystem and plays an important role in the analysis of global climate change and carbon balance. Anhui Province is in the Yangtze River Delta region in eastern China. Based on the theoretical basis of CASA model, this paper uses MODIS NDVI, vegetation type data, meteorological data, and LUCC to estimate the NPP of Anhui Province during 2001–2020 and analyzes its spatial-temporal pattern. The results showed that the average NPP in Anhui province was 508.95 gC· (m² ·a) ^-1, and the spatial heterogeneity of NPP was strong, and the high value areas were mainly distributed in the Jiangnan Mountains and Dabie Mountains. NPP increased in most areas of Anhui Province, but decreased significantly in 17.60% of the area, mainly in the central area affected by urban and rural expansion and the transformation of the Yangtze River. The dynamic change of NPP in Anhui province is the result of climate change and land use change. Meteorological data are positively correlated with NPP. Among them, the correlation between temperature and solar radiation is higher, and the correlation between NPP and precipitation is the lowest among the three. The NPP of all land cover types was more affected by temperature than precipitation, especially forest land and grassland. The decrease of cultivated land and the increase of Artificial Surfaces (AS) may have contributed to the decrease of NPP in Anhui Province. Human activities have weakened the increase in NPP caused by climate change. In conclusion, this study refined the drivers of spatial heterogeneity of NPP changes in Anhui province, which is conducive to rational planning of terrestrial ecosystems and carbon balance measures.

Carbon storage services play an important role in maintaining ecosystem stability. Land use/cover change (LUCC) is the main factor leading to changes in ecosystem carbon storage. Understanding the impact of LUCC on regional carbon storage changes is crucial for protecting regional ecosystems and promoting sustainable socio-economic development. This paper selects Shanxi province as the study area and explores the spatial and temporal evolution characteristics of carbon storage in Shanxi province based on the InVEST model and univariate spatial autocorrelation. The results show that the total carbon storage in Shanxi Province in 2000, 2010, and 2020 is 513.51 × 10⁴ t C, 513.46 × 10⁴ t C, and 509.29 × 10⁴ t C, respectively. High carbon storage areas are distributed in forest and grassland land types, while low carbon storage areas are widely distributed in building land in urban metropolitan areas. Shanxi Province is mainly dominated by farmland, which has decreased by 3448.60 km² in the past 20 years. Grassland has decreased by 1588.31 km² and the area of building land has increased by 4205.73 km². Due to the influence of carbon conversion among different land use types, the total carbon storage loss of Shanxi Province in the past 20 years was 4.21 × 10⁴ t C. The transfer of farmland resulted in an increase in carbon stock of 14.46 × 10⁴ t C. The transfer of grassland resulted in an increase of 17.15 × 10⁴ t C, while the transfer of forest resulted in a decrease of 41.44 × 10⁴ t C. The increase in land use types with low carbon density and the decrease in land use types with high carbon density led to the decrease in carbon storage in Shanxi Province. Furthermore, social factors were more likely to influence the carbon storage than natural factors, and the influence of social factors was often negative. On this basis, regional development countermeasures were proposed for the current situation of carbon storage in Shanxi Province and provide a scientific basis for Shanxi Province to achieve the carbon neutrality target.

Understanding how plants adapt to shifting water availability is imperative for predicting ecosystem vulnerability to drought. However, the spatial–temporal dynamics of the plant–water relationship remain uncertain. In this study, we employed the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI4g), an updated version succeeding GIMMS NDVI3g spanning from 1982 to 2022. We integrated this dataset with the multiple scale Standardized Precipitation Evapotranspiration Index (SPEI 1 to 24) to investigate the spatial–temporal variability of sensitivity and lag in vegetation growth in response to water variability across China. Our findings indicate that over 83% of China’s vegetation demonstrates positive sensitivity to water availability, with approximately 66% exhibiting a shorter response lag (lag < 1 month). This relationship varies across aridity gradients and diverges among plant functional types. Over 66% of China’s vegetation displays increased sensitivity to water variability and 63% manifests a short response lag to water changes over the past 41 years. These outcomes significantly contribute to understanding vegetation dynamics in response to changing water conditions, implying a heightened susceptibility of vegetation to drought in a future warming world.

Introduction: Urban agglomerations, as pivotal spatial units in global urbanization processes, are central to urban science, their land use/cover changes directly regulate regional carbon cycles and profoundly affect ecosystem carbon storage and net primary productivity (NPP). To clarify the dynamic changes, driving factors, and future trends of these two key ecological indicators in Wanjiang Urban Agglomeration, multiple models and analytical methods were integrated. Methods: The Carbon Storage module of the InVEST model was used to assess regional carbon stocks, while the improved CASA model quantified multi-period NPP using remote sensing and climate data. A random forest algorithm combined with the SHAP method analyzed the independent effects of topographic, anthropogenic, and natural factors, and the PLUS model combined with Markov Chain forecast 2040 carbon storage. Result: Results show urban area expanded by 2,230.87 km ² (3.57% annual growth), primarily from cultivated land (4.52 × 10 km ² to 4.26 × 10 km ² ). Annual carbon storage loss reached 0.25 × 10 tC and NPP loss 17.49 × 10 ⁴ tC, with both first rising then falling. Carbon storage is predominantly driven by topographic factors (with thresholds) and secondarily by natural factors, while NPP is jointly regulated by topographic and climatic drivers. Anthropogenic factors exert the weakest influence on both. The 2040 forecast indicates continued carbon storage decline. Discussion: This work clarifies key dynamic patterns and driving mechanisms, providing scientific support for regional ecological protection and advancement of “carbon peaking and carbon neutrality” goals.