Over the past years, new urbanization in China has accelerated steadily and led to a continuous increase in ecological-environmental (eco-environmental) stress. A deep understanding of the coupling relationship between new urbanization and ecological-environmental stress is essential to benefiting the urban management in making decisions. How to realize the coordinated development of urbanization and the eco-environment is not only the key issue in world economic and social development, but has also been a hot topic of research in recent years. However, the quantitative relationship and the interaction mechanism between the new urbanization and ecological-environmental stress are still unclear. To fill this gap, this study constructed comprehensive assessment indicators for evaluating new urbanization and eco-environmental stress systems to accomplish the following objectives. We aimed to identify the spatial and temporal pattern of coupling and coordinating degree between new urbanization and eco-environmental stress in China during the period of 2005–2016. The degrees of coupling and coordination of new urbanization and eco-environmental stress systems in China in 2005, 2010, and 2016 were calculated at the provincial level. The degrees of coupling and coordination have achieved stable and continuous improvement from 0.389 to 0.484. We further aimed to evaluate the regional coupling and estimate the stage of urbanization at which an optimal outcome could be achieved in order to ensure high-quality urbanization in China. According to the model of coupling and coordination degree, this paper divided the Chinese territory into four area types: well coordination, middle coordination, primary coordination, and reluctance coordination, and about 35% of the provinces belonged to the well and middle coordination types. Lastly, this paper analyzed the spatial pattern and cluster mode of the coupling coordination of new urbanization and eco-environmental stress systems by using ArcGIS and GeoDa. The analysis implied that coupling coordination existed with obvious regional disparity. Moreover, the degrees of coupling coordination of the developed east coastal and middle area were generally higher than those of the undeveloped west area. The findings indicate that for different regions, the reluctance coordination and primary coordination subclass regions should accelerate to realize green transformation, improve the industrial structure, and strengthen the environmental law-enforcing supervision. However, we could not conduct an internal structural analysis. Future research will focus on conducting an internal structural analysis and an element system metrics analysis.

Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental controls, are crucial for predicting future climate change impacts on ecosystem carbon-water interactions and agricultural production. However, these questions remain poorly understood in this typical region. By means of continuous eddy covariance (EC) measurements and time-series MODIS data, this study revealed the distinct seasonal cycles in gross primary productivity (GPP), evapotranspiration (ET), and WUE for both grassland and cropland ecosystems, and the dominant climate factors performed jointly by temperature and precipitation. The MODIS WUE estimates from GPP and ET products can capture the broad trend in WUE variability of grassland, but with large biases for maize cropland, which was mainly ascribed to large uncertainties resulting from both GPP and ET algorithms. Given the excellent biophysical performance of the MODIS-derived enhanced vegetation index (EVI), a new greenness model (GR) was proposed to track the eight-day changes in ecosystem WUE. Seasonal variations and the scatterplots between EC-based WUE and the estimates from time-series EVI data (WUE_GR) also certified its prediction accuracy with R² and RMSE of both grassland and cropland ecosystems over 0.90 and less than 0.30 g kg⁻¹, respectively. The application of the GR model to regional scales in the near future will provide accurate WUE information to support water resource management in dry regions around the world.

Climate change is closely linked to changes in soil organic carbon (SOC) content, which affects the terrestrial carbon cycle. Consequently, it is essential for carbon accounting and sustainable soil management to predict SOC content accurately. Although there has been an extensive utilization of optical remote sensing data and environmental factors to predict SOC content, few studies have explored their applicability in karst areas. Therefore, it remains unclear how SOC content can be accurately simulated in these areas. In this study, 160 soil samples, 8 environmental covariates and 14 optical remote sensing variables were used to build SOC content prediction models. Three machine learning models, i.e., support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were applied for each of three land use classes, including the entire study area, as well as farmland and forest areas. The variables with the greatest influence were the optical remote sensing bands, derived indices, as well as precipitation and temperature for forest areas, and optical remote sensing band11 and Pop-density for farmland. The results from this study suggest that RF and XGBoost are superior to SVM in prediction accuracy. Additionally, the simulation accuracy of the RF model for the forest areas (R² = 0.32, RMSE = 6.81, MAE = 5.63) and of the XGBoost model for farmland areas (R² = 0.28, RMSE = 4.03, MAE = 3.27) was the greatest. The prediction model based on different land use types could obtain a higher simulation accuracy than that based on the whole study area. These findings provide new insights for the estimation of SOC content with high precision in karst areas.

Global environmental changes have been dramatic recently, exerting substantial effects on the structures and functions of terrestrial ecosystems, especially for the ecologically-fragile karst regions. Southwest China is one of the largest karst continuum belts around the world, which also contributes about 1/3 of terrestrial carbon sequestration to China. Therefore, a deep understanding of the long-term changes of vegetation across Southwest China over the past decades is critical. Relying on the long time series of Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g) data set, this study examined the spatial and temporal patterns of vegetation conditions in Southwest China from 1982 to 2015, as well as their response to the environmental factors including temperature, precipitation and downward shortwave radiation. Multi-year mean NDVI showed that except the northwestern region, the NDVI of Southwest China was large, ranging from 0.5 to 0.8. Meanwhile, nearly 43.7% of the area experienced significant improvements in NDVI, whereas only 3.47% of the area exhibited significant decreases in NDVI. Interestingly, the NDVI in karst area increased more quickly with 1.035 × 10⁻³/a in comparison with that in the non-karst area with about 0.929 × 10⁻³/a. Further analysis revealed that temperature is the dominant environmental factor controlling the interannual changes in NDVI, accounting for 48.19% of the area, followed by radiation (3.71%) and precipitation (3.09%), respectively.

Southwest China has one of the largest karst landscapes on the Earth and an ecologically fragile environment. A better understanding of how ecosystem services function in karst areas helps ecological preservation and policy implementation. However, little effort has been made to evaluate the long-term dynamics of ecosystem services across Southwest China. This study systematically analyzed the spatio-temporal patterns and the values of three typical ecosystem service functions, including water conservation, carbon sequestration, and soil conservation, as well as the effects of precipitation and land use changes between 2000 and 2020. The results showed that water conservation exhibited an overall decrease from southeast to northwest, while soil conservation showed the opposite trend. The regions with an increasing trend in carbon sequestration were mainly distributed in Guizhou, Guangxi, and Sichuan. Compared to the year 2000, the percentage of water conservation and soil conservation decreased by 4.50% and 0.76%, respectively. However, carbon sequestration increased by 94.35%. The total value of ecosystem services in Southwest China showed a 90.00% increase in 2020 relative to 2000. Water conservation and carbon sequestration had a much closer correlation with precipitation. The impact of soil conservation was more significantly influenced by land use changes.

As an emerging financial entity, venture capital has a significant impact on regional development and local production systems. Previous research results pay more attention to the spatial distribution and agglomeration of venture capital but pay less attention to its spatial externality. This paper used the panel data of 31 provincial-level regions in China from 2003 to 2017 to measure the industrial structure upgrading level from the two dimensions of rationalization of industrial structure and upgrading of industrial structure. Based on regional heterogeneity that determines industrial structure upgrading, local effects and externalities of venture capital, and omission factors, this paper constructs spatial econometrics models of venture capital and industrial structure upgrading. Firstly, venture capital and industrial structure upgrading all have obvious spatial autocorrelation and form different agglomeration areas in local areas. Therefore, regional venture capital and industrial structure upgrading are closely related to regional location factors. China’s venture capital has unique spatial attributes. Secondly, venture capital has a significant role in promoting the industrial structure upgrading, although its simulative effect is weak. In addition, in order to further explore the influence mechanism of regional industrial structure upgrading, this paper found the local fixed asset investment level and R&D intensity promoted the upgrading process of local industrial structure at a significant level. The study of this paper has important implications for the formulation of appropriate regional venture investment policies to promote the optimization and upgrading of industrial structures and sustainable development during the transition period of economic development.