Advancing urban–rural integration (URI) is pivotal to addressing the current urban–rural development imbalance in China. The urban agglomeration, as a crucial engine propelling China’s modernization, holds significant importance in accelerating this integration process. Comprehensive quantitative analysis of URI development within the Beijing–Tianjin–Hebei (BTH) urban agglomeration is often lacking in existing research. This study constructs an indicator system for evaluating the level of integration using data from 14 cities in the region from 2010 to 2022, focusing on economic, social, and ecological perspectives. Utilizing the Coupling Coordination Model and the Obstacle Degree Model, this study analyzes the level and evolutionary trends of URI development within the BTH urban agglomeration. The main conclusions are as follows: (1) The level of URI in the BTH urban agglomeration exhibits an overall upward trend, increasing from 0.377 in 2010 to 0.543 in 2022. The economic, social, and ecological subsystems all demonstrate positive integration trends. (2) The spatial evolution of the integration level reveals a distinct core–periphery structure. Beijing and Tianjin, as the core areas, continuously foster the collaborative development of surrounding cities through radiation and spillover effects. The core of URI has shifted gradually from the central–east to the central–north, indicating an upward movement of the core area, as revealed by trend surface analysis. Although Shijiazhuang, a central city in the South BTH urban agglomeration, has seen rapid improvement in integration levels, its role in driving development is less significant than that of Beijing and Tianjin. (3) The URI subsystems in the 14 cities of the BTH urban agglomeration show improved coordination. The coordination development between Beijing and Tianjin has yielded significant results, emerging as a key driver in promoting the coordinated development of urban agglomerations. Most regions in the urban agglomeration exhibit mild imbalance or coordination, with the central and northern areas scoring higher in coordination. (4) The obstacles analysis indicates that the economic integration and social integration systems are the primary obstacles to enhancing the ecological integration of urban agglomerations. Urban–rural transportation, investment levels, and economic development are key obstacles for the BTH urban agglomeration integrated development. This study offers key insights for strategic planning in the BTH urban agglomeration region.

Enhancing the green production efficiency of cotton contributes significantly to achieving carbon neutrality and agricultural sustainability. Based on county-level panel data from 2002 to 2020 and daily average temperature and precipitation data from 53 meteorological stations in Xinjiang, this study employs a non-desirable output super-efficiency slack-based measure model, the Malmquist index, and Moran’s index to analyze the temporal and spatial changes in the green production efficiency of cotton across counties in Xinjiang. From a dynamic evolution perspective, without considering non-desirable outputs, the overall cotton production efficiency at the county level exhibits an upward trend; however, when non-desirable outputs are taken into account, the green production efficiency of cotton shows a declining trend, with a general convergence trend among counties (cities). From 2002 to 2020, the overall green production efficiency of cotton in Xinjiang counties decreased from 0.531 to 0.442, with an annual average decline rate of −0.882%. Spatially, as temperatures rise in northern Xinjiang and rainfall increases in the west, high-value areas for cotton green production efficiency have shifted northward and westward, transforming the spatial distribution pattern from “high in the south and low in the north” to “high in the north and low in the south”. The spatial clustering effect among counties is significant, exhibiting a “clustered distribution” pattern. To improve the green production efficiency of cotton, it is recommended to promote ecological protection efforts, disseminate advanced agricultural technologies, implement differentiated strategies, leverage spatial clustering effects, and strengthen theoretical and technological research.

Ecological resilience (ER) describes an ecosystem’s capacity to resist, adapt to, and recover from external shocks. Enhancing ER has become a crucial issue of high-quality development in urban agglomerations. Based on the perspective of human–land relationship, this study takes the Chengdu–Chongqing urban agglomeration (CCUA) as its research subject and constructs a three-dimensional evaluation framework of “Resistance-Adaptation-Recovery” (Res-Ada-Rec), evaluates the spatial and temporal evolution characteristics of ER from 2003 to 2022, and uses a partial least squares structural equation model (PLS-SEM) to reveal the interaction mechanism of human and natural factors on ER. Results indicate that: (1) Temporally, ER in the CCUA showed a significant upward trend, with resistance, adaptation, and recovery demonstrating fluctuating evolutionary processes. (2) Spatially, ER presented a pattern of “small agglomeration and large dispersion”, with clear spatial heterogeneity observed across the three dimensions. (3) PLS-SEM analysis revealed that green innovation, institutional policies, and the natural environment had significant positive direct effects on ER, with path coefficients of 0.54, 0.53, and 0.12, respectively. Urbanization exerted a significant indirect negative effect on ER through its impact on the natural environment. These findings deepen our understanding of how green innovation, institutional policies, and urbanization influence ER, providing scientific references for urban agglomeration to achieve modernization characterized by harmonious coexistence between humans and nature.

Enhancing the green production efficiency of cotton contributes significantly to achieving carbon neutrality and agricultural sustainability. Based on county-level panel data from 2002 to 2020 and daily average temperature and precipitation data from 53 meteorological stations in Xinjiang, this study employs a non-desirable output super-efficiency slacks-based measure model, Malmquist index, and Moran’s Index to analyze the temporal and spatial changes in the green production efficiency of cotton across counties in Xinjiang. From a dynamic evolution perspective, without considering non-desirable outputs, the overall cotton production efficiency at the county level exhibits an upward trend; however, when non-desirable outputs are taken into account, the green production efficiency of cotton shows a declining trend, with a general convergence trend among counties (cities). From 2002 to 2020, the overall green production efficiency of cotton in Xinjiang counties decreased from 0.531 to 0.442, with an annual average decline rate of -0.882%. Spatially, as temperatures rise in northern Xinjiang and rainfall increases in the west, high-value areas for cotton green production efficiency have shifted northward and westward, transforming the spatial distribution pattern from “high in the south and low in the north” to “high in the north and low in the south.” The spatial clustering effect among counties is significant, exhibiting a “clustered distribution” pattern. To improve the green production efficiency of cotton, it is recommended to promote ecological protection efforts, implement differentiated strategies, leverage spatial clustering effects, disseminate advanced agricultural technologies, and optimize planting structures.

This study is based on time-series land use data of Xinjiang from 2000 to 2022. Using grid tools, bivariate autocorrelation models and other methods, we systematically analyzed the spatiotemporal variation characteristics of land use and ecosystem service value. The results show the following: Firstly, from 2000 to 2022, Xinjiang’s LUCC exhibits differentiated evolution characteristics: cropland, forestland, and built-up land expanded continuously, while the areas of grassland and unused land showed a steady reduction trend, and the area of water bodies showed a fluctuating growth pattern. Secondly, according to the calculation of carbon emissions from LUCC in Xinjiang from 2000 to 2022, the carbon emissions from LUCC have increased significantly, from 27.79 million tons in 2000 to 226.43 million tons in 2022, with built-up land being the main source of carbon emissions, but the continuous reduction in grassland area has led to the weakening of carbon sequestration capacity. Thirdly, from 2000 to 2022, Xinjiang’s ESV shows a fluctuating upward trend, increasing from 1880.528 billion yuan in 2000 to 1894.198 billion yuan in 2022, with grassland and water area being the core contributors to ESV, accounting for over 80% of the total contribution. Fourthly, in terms of spatial distribution, there is an overall negative correlation between the intensity of carbon emissions from LUCC and the intensity of ESV, mainly aggregated as “low–low” and “low–high”, with “high–low” aggregation primarily distributed in the desert areas of the Tarim Basin and Junggar Basin and “low–high” aggregation concentrated in the marginal mountainous areas and oasis regions of Xinjiang. The findings provide a solid scientific basis for the optimization of land use structure, the achievement of carbon emission reduction targets, and the protection of ecosystems in Xinjiang and similar arid regions worldwide.

Against China’s tightening cultivated land ecological constraints, clarifying how the New Rural Collective Economy (NRCE) affects Cultivated Land Use Ecological Efficiency (CLUEE) is critical for advancing agricultural sustainability in the Yangtze River Economic Belt (YREB). Using 714 panel data observations from 42 prefecture-level cities in the YREB from 2009 to 2025, this study measures CLUEE efficiency via SBM-DEA and quantifies the NRCE via the entropy weight method. Two-way fixed effects models estimate the impact, while two-step mediation analysis explores underlying mechanisms. Results show NRCE significantly boosts CLUEE, with each 1-unit increase in NRCE level driving a 0.1633-unit improvement in CLUEE, and findings remain robust after endogeneity correction. Mediation analysis reveals indirect effects via land-scale management, agricultural socialized services, and green technology innovation. This study innovatively clarifies the endogenous institutional driving effect of rural collective economic reform on CLUEE, filling the research gap of ignoring rural organizational institutional factors in existing CLUEE research. The findings provide empirical evidence and theoretical support for optimizing the construction of rural collective economic organizations and formulating differentiated cultivated land ecological governance policies in the YREB, so as to effectively unlock the ecological dividend of rural institutional innovation and facilitate coordinated ecological and economic development of cultivated land.