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In response to the impacts of climate change and the intensity of human activities in the alpine meadow region, there is an urgent need to determine the ecological quality and its drivers in alpine meadow areas. In this paper, Shangri-La was adopted as an example, the spatial and temporal evolution patterns of the ecological quality in Shangri-La were determined in both natural and social dimensions, and the contributions of various driving factors were analyzed. The conclusions are as follows: (1) the natural status index of Shangri-La from 2000 to 2020 generally showed a spatial distribution pattern that decreased from the central townships toward the north and south, and the social pressure index was irregularly distributed in high-value areas and continuously distributed in low-value areas. (2) From 2000 to 2020, the areas with high values of the ecological quality index were mainly distributed in central Shangri-La, with a maximum value of 0.91, while the low values were largely distributed in some townships in the north and south, with a minimum value of 0.26. (3) In the driving factors, the influences of the normalized difference vegetation index (NDVI) and net primary productivity (NPP) were greater than those of the other factors, among which the NDVI attained the largest mean value of 0.452, while the relative humidity (RHU) attained the lowest value of 0.036. (4) In terms of relative contributions, evapotranspiration (EVP) and precipitation (TEM) shifted from a positive drive to a negative drive from south to north. The contribution of the temperature to the ecological quality was the highest, at 64%. The spatial heterogeneity in the contributions of human disturbance activity factors to the ecological quality varied significantly, with the largest negative driving contribution of the NPP, at − 42.36%. The results could provide a basis for regional ecological quality protection and restoration. [ABSTRACT FROM AUTHOR]

Green development is key to promoting regional sustainable development. We construct an evaluation index system for green development levels based on the "sansheng" dimensions—production, living, and ecology. We rely on the "sansheng" (production, living, and ecology) dimensions, combined with the analytic hierarchy process (AHP) and the entropy weight method to analyze indicator weights, to construct an evaluation index system for green development levels. This system enables the identification of the evolution patterns of green development and the analysis of driving factors in the Chengdu-Chongqing economic zone from the "sansheng" perspective. The results indicate that: (1) The green development level in the Chengdu-Chongqing Economic Zone has been continuously rising, with the average index increasing from 0.197 to 0.254. Yuzhong District and Chengdu City have shown particularly high green development levels; in 2020, the green development level index for Yuzhong District reached 0.568, while Chengdu City's index reached 0.522. (2) The spatial clustering of green development levels in the Chengdu-Chongqing economic zone exhibited a trend of first strengthening and then weakening, with the highest clustering degree observed in 2015. (3) National strategies have significantly promoted the improvement of regional green development levels. The average green development index during the pre-establishment, initial development, and rapid development stages of the Chengdu-Chongqing Economic Zone increased from 0.205 to 0.229, and then to 0.254. (4) The Theil index results show an increase in the disparity of green development levels among different regions within the Chengdu-Chongqing Economic Zone.The results of the optimal scaling regression model show that the driving factors with a significant impact on the level of green development include the Number of physicians per million people, Public library book collections Per 100 people, Per capita regional GDP, and Number of secondary schools Per million people, each contributing over 15% to the impact. These findings provide valuable data support for formulating regional economic development strategies and are conducive to advancing sustainable development. [ABSTRACT FROM AUTHOR]

This study comprehensively examined the accumulation of cadmium (Cd) in soybeans grown in low- and high-Cd soils around the high geological background areas in Guizhou province. The aim was to analyze the relationship between soybean Cd and soil pH and soil Cd, alongside assessing the potential carcinogenic and non-carcinogenic risks associated with Cd in soybeans. Cd content of soybeans cultivated in the high-Cd area (0.430 mg/kg) was significantly higher than that in low-Cd areas (0.156 mg/kg) (P < 0.05). Biological concentration factors (BCFs) of soybean for Cd in low- and high-Cd areas were 0.282 and 0.314, respectively, with no significant differences (P > 0.05). Multiple linear regression results indicated that soil pH was a determining factor for Cd accumulation in soybeans in both areas. Furthermore, soil pH and soil Cd could accurately predict Cd accumulation in soybeans according to the neural network model. These findings suggest that regulating soil pH could reduce Cd accumulation in soybeans in areas with high geological background. In both areas, there was no significant non-carcinogenic risk for the adult population (HQ value < 1) through soybean consumption. However, according to the Monte Carlo model, the percentage of Cd in soybeans exceeding the acceptable range (CR value > 1.00 × 10 −04) in areas was 99.18%, indicating an unacceptable carcinogenic risk for the adult population. Our discussion revealed that reducing the soybean intake and increasing soil pH did not effectively lower the carcinogenic risk of Cd in soybeans to an acceptable range (CR value ≤ 1.00 × 10 −04). These findings necessitate further exploration of alternative remediation strategies to ensure the safe production of soybeans, such as screening for low-Cd accumulation soybean varieties and implementing the combined remediation strategies. [ABSTRACT FROM AUTHOR]

The exploration of ecological security patterns (ESPs) can help people find those areas that are in urgent need of restoration, which is an effective way to realize ecological protection. It is of utmost significance for promoting regional sustainable development to construct ESP and put forward sub-regional optimization suggestions based on the supply and demand ratio of ecosystem services (ESs). In this paper, we assessed the level of supply and demand for five ESs based on multi-source data in 2020 with the help of InVEST, ArcGIS, and IUEMS. Based on the results of supply and demand, we calculated the supply and demand ratio of ESs and extracted the ecological source areas (ESAs) on this basis. Then, we used the Linkage Mapper tool to construct the ESP based on the principle of the minimum cumulative resistance (MCR) model and circuit theory in the Chengdu–Chongqing economic circle (CCEC). Our results indicated that there were apparent spatial differences in the supply and demand of five ESs. There were 35 ESAs in the ESP network, covering an area of about 7914 km2, and most of their land use types were woodland. The CCEC was interconnected by a network of 91 ecological corridors (ECs), spanning a total length of approximately 10,701 km. From the ECs, we extracted 29 ecological pinch points (EPPs) and 16 ecological barrier points (EBPs), which each accounted for about 0.3% of the planned area of the CCEC. Finally, we divided the ecological spaces into four types and put forward the corresponding optimization suggestions. Among them, the proportion of ecological restoration area was 7.7%, which was located in Chengdu City, northwest of the study area. The findings of this paper can give some theoretical guidance and serve as a reference for making decisions in the pursuit of ecological civilization in this region. [ABSTRACT FROM AUTHOR]

Rural green development is a concrete practice of rural revitalization. Currently, research on quantitative evaluation methods for rural green development levels are not well developed. In this study, an evaluation model of the rural green development level in Chongqing City, China was developed based on the parameters of ecology, living, and production. An entropy weight method, Theil index, optimal scale regression model, and GIS were used to analyze the spatio-temporal characteristics, trends, and influencing factors of the rural green development level from 2018 to 2020 in Chongqing City. The results showed that: (1) the overall "ecology, living, and production" dimensions and the comprehensive index of the development level in the city were generally increasing, and the proportion of counties at a high-level increased from 23.68% in 2018 to 81.58% in 2020; (2) the Theil index of the city in was 0.0185, 0.0121, and 0.0114 in 2018, 2019, and 2020 respectively, indicating that the differences in development level among regions decreased as the development level increased; (3) the level of rural green development showed a clear upwards trend, and the proportion of counties with low-speed growth, medium-speed growth, and high-speed growth from 2018 to 2020 was 5.26%, 81.58%, and 13.16%, respectively; and (4) the optimal scale regression analysis showed that the factors with greater impacts on the rural green development level are social security and employment expenditure level of government finance, health expenditure level of government finance, with their contributions is 40.3% and 26%, respectively. The results from this study demonstrate the significance of exploring research methods for rural green development and ways to improve the level of rural green development. [ABSTRACT FROM AUTHOR]

Exploring the mismatch between supply and demand (SD) for carbon sequestration services (CSS) is essential for achieving the "double carbon" goal. However, more studies are needed on the traits of the spatial mismatch between SD in mountainous cities. We used the CASA model and the IPCC emission factor approach to address this issue and quantify the SD of CSS in Chongqing. Second, we established a matching relationship model for the SD of CSS in Chongqing. Finally, we applied the Structural Equation Model with the Partial Dependence Plots model to reveal the influencing factors and internal mechanisms of spatial mismatch between the SD of CSS in Chongqing. The outcomes confirmed a decrease in fashion in the total supply of CSS in Chongqing and growth in fashion in general demand from 2000 to 2020. The SD mismatch was mainly concentrated inside the central city and other built-up areas. The SD mismatch area had increased by 390%, indicating a continuous upward trend. In exploring the factors influencing the mismatch between the SD of CSS in Chongqing, supply is mainly positively influenced by NDVI, and demand and supply-demand relationships are influenced by population density and LUCC. We proposed policy suggestions to alleviate the spatial mismatch and practical significance for achieving the "double carbon" goal and promoting sustainable development. [Display omitted] • The CSS deficit was concentrated in the West and continued to worsen. • The spatial mismatch between CSS supply and demand was predominantly banded. • The mismatch of CSS was mainly affected by human activities. • LUCC pathway coefficient was −0.37, and night lights was −0.21 in 2020. [ABSTRACT FROM AUTHOR]