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.

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.

Identifying evolving trends of agricultural nonpoint source pollution (ANSP) risks is of great practical meaning for pollution control. Land use types and nutrient application levels are key factors affecting risk conditions of ANSP. In this study, a Patch-generating Land Use Simulation (PLUS) model was used to simulate the land use types for the Chengdu–Chongqing Economic Zone (C-C E Zone) from 2030 to 2050, and an improved output risk model was constructed by introducing topographic factors and distance factors to compensate for the shortcomings of traditional models in expressing pollutant transport. Based on these 2 models, evolving trends were analyzed to assess ANSP risks in the future. The results showed that the PLUS model could accurately simulate large-scale land use. The overall risk of ANSP in this area showed a decreasing trend, with the proportion of risk grade VII–X levels decreasing from 27.68% to 23.06% during 2000–2020, and from 16.66% to 14.02% during 2030–2050. The proportion of strict control areas of risk also showed a consistent decreasing trend, with 9.75%, 9.57%, 8%, 6.73%, 5.86%, and 5.36% in 2000, 2010, 2020, 2030, 2040, and 2050, respectively. A significant positive association was observed between risks of ANSP and the adjustment of nutrient application levels, with strict control areas increasing by 9.46% and 12.05% when the output coefficients in 2030 increased by 5% and 10%, respectively. Region strategies should be applied in the future, with focus on areas with high risks/control levels, as well as areas that are sensitive to nutrient changes.

Agricultural non-point source pollution (ANPSP) presents a critical threat to water security in the upper reaches of the Yangtze River Economic Belt (UYREB). Traditional export coefficient models (ECMs) often fail to capture the spatially heterogeneous processes of pollutant transport and ecological attenuation in complex mountainous terrains. Here, we constructed an improved ANPSP risk assessment framework by integrating ecosystem regulating functions (water yield, soil retention, and habitat quality) into the ECM. By coupling the Patch-generating Land Use Simulation (PLUS) model and Geographically and Temporally Weighted Regression (GTWR), we evaluated the spatiotemporal risk evolution from 2000 to 2020, projected risk configurations for 2030 under three scenarios (Natural Development, ND; Cropland Protection, CP; Ecological Protection, EP), and identified the spatiotemporal non-stationary drivers. The key findings are: (1) Independent validation using observed river water-quality data further demonstrated that the modeled risk index was significantly positively correlated with contemporaneous TN and TP concentrations (Pearson’s r = 0.673, p < 0.05), supporting its ability to identify the spatial distribution of relative ANPSP risk while accounting for regional differences in ecosystem regulation. (2) Historically, the integrated ANPSP risk index exhibited a “rise-then-decline” pattern, peaking in 2010 with extreme risk areas covering 12.1% of the region. Under the 2030 EP scenario, high-risk areas contract by 15.3% compared to 2020, demonstrating superior mitigation compared to ND and CP scenarios. (3) GTWR reveals that cropland proportion is the dominant positive spatial associate (0.800 − 0.852), while GDP shows a negative spatial association in economically developed sub-regions, reflecting potential environmental management co-benefits rather than direct causation. This framework provides an effective relative risk assessment tool for mountainous watersheds, highlighting that land use optimization under ecological regulation may contribute to reducing potential pollutant transport risk.

Focusing on contrasting climate and land use pathways, this analysis explores the changing trajectories of water conservation function over time. An integrated framework combining the PLUS and InVEST models with Spearman’s correlation analysis and geographically weighted regression (GWR) was applied to examine the spatiotemporal heterogeneity and underlying drivers of water conservation function in the Chengdu–Chongqing Economic Zone during the period 2000–2020. Thus, it further predicted the evolution trend under two scenarios, namely SSP1-1.9 (Sustainable Development Pathway) and SSP2-4.5 (Medium Development Pathway), for the period 2030–2050. The findings reveal that: (1) Between 2000 and 2020, the spatial distribution of water conservation function shifted markedly, with low-value areas contracting and high-value zones expanding, alongside a progressive transition toward a predominantly medium-to-high functional structure. (2) In mountainous and hilly transition zones, precipitation (PRE) and forest cover proportion (FCP) exhibited notably positive effects, whereas evapotranspiration (PET) exerted a negative effect. In contrast, in plain and urbanized areas, built-up land proportion (BUP), population density (POP), and gross domestic product density (GDP) demonstrated pronounced negative effects. (3) Future simulations indicate that under the sustainable development pathway (SSP1-1.9), the combined area of high and extreme functional zones will recover by 2050, whereas under the moderate development pathway (SSP2-4.5), such extreme functional zones will be nearly eliminated. These results underscore the substantial impact of development pathways on regional water security and sustainability.