Understanding the evolution of ecosystem services (ESs) is crucial for sustainable development in rapidly urbanizing regions. This study quantifies the spatiotemporal evolution of habitat quality (HQ), water yield (WY), and carbon storage (CS) in the Chang-Zhu-Tan City Cluster (CZTCC) of China from 2000 to 2020. Results showed that rapid urbanization—where construction land expanded from 2.78% to 7.40% of the area—was the primary driver of ES degradation. Specifically, low-quality habitat (HQ) areas expanded by 162%, and total carbon storage (CS) declined by 1.83% (11.78 Tg C), while regional WY fluctuated with precipitation. Inter-service relationships also became more conflictual: the trade-off between HQ and WY intensified (Pearson’s R strengthened from −0.696 to −0.735), a persistent trade-off between WY and CS was observed (e.g., R = −0.607 in 2020), while the HQ-CS synergy remained stable and strong (R > 0.88). These findings highlight escalating ecological conflicts and offer a robust scientific basis for optimizing land use to manage ES trade-offs and promote sustainable development.

The Dongting Lake Region is a major river-connected lacustrine ecosystem and ecological security barrier in the middle Yangtze River Basin, where land use change has important implications for ecosystem service provision. However, how land use transition pathways and spatial clustering jointly shape land-cover-based ecosystem service value (ESV) estimates remains insufficiently understood, particularly under a coefficient-based valuation framework. This study used land-use data for 2005, 2010, 2015, 2020, and 2025 (with the 2025 layer being not fully observation-based but derived from remote-sensing interpretation and model-assisted updating rather than direct field observations). The analytical framework integrated land-use transition analysis, land-use dynamic degree analysis, equivalent-factor-based ESV estimation, spatial autocorrelation analysis, one-way parametric sensitivity analysis with a ±50% variation, and stage-specific comparisons aligned with policy implementation timelines. These methods were applied to investigate the spatiotemporal dynamics of estimated ESV in the Dongting Lake Region. Cropland, forestland, and water bodies remained the dominant land use types, while built-up land expanded through the conversion of agricultural and ecological land. Estimated total ESV declined during the study period, although the annual rate of decline was lower during 2015–2025 than during 2005–2015. Under the adopted equivalent-factor framework, water-body-related transitions contributed most to estimated ESV change, reflecting both the ecological importance of water-related systems and the high coefficients assigned to water bodies, especially for hydrological regulation. Estimated ESV showed persistent positive spatial autocorrelation, with High–High clusters concentrated around East Dongting Lake, South Dongting Lake, and West Dongting Lake. These findings provide land-cover-based evidence for territorial spatial optimization and SDG-related ecosystem management. However, the identified clusters should be interpreted as candidate lake-centered high-value ecological spaces for further assessment, rather than as definitive conservation priorities or ecological zoning boundaries.

This study examines county-level correspondence between rural labor out-migration and cultivated-land adjustment across 122 units in Hunan Province, China. The rural labor–land correspondence (RLLC) indicator uses positive net cultivated-land reduction between benchmark years, not registered farmland transfer. With 2005 as the baseline, RLLC was calculated for 2010, 2015, 2020 and 2023 and analyzed using spatial autocorrelation, ordinary least squares, geographically weighted regression and geographically and temporally weighted regression. RLLC exhibited spatial unevenness and positive autocorrelation (Moran’s I = 0.3777–0.5250). Among predictors without direct overlap with RLLC components, household register urbanization had positive mean coefficients in most counties but limited local statistical support. Associations for agricultural business entities, grain-production specialization and per capita GDP were positive and geographically localized where they exceeded the reporting threshold, while per capita fiscal expenditure had negative mean coefficients throughout. In the OLS sensitivity analysis excluding three component-overlapping predictors, the five remaining predictors retained their coefficient directions and statistical significance. RLLC does not measure farmland transfer or optimal land allocation, and the observed associations do not establish causal effects. Farmland transfer is considered a possible institutional mechanism.