Introduction: Traditional villages in China serve as important carriers of rural cultural heritage but are increasingly threatened by rapid urbanisation and uneven regional development. Understanding their spatial distribution and integration within cultural–tourism networks is essential for developing effective conservation and regional planning strategies. Methods: Using a national dataset of 8,157 officially designated traditional villages, combined with city-level tourism indicators and three categories of cultural heritage carriers (A-level scenic spots, intangible cultural heritage, and protected cultural relics), this study developed an integrated analytical framework based on spatial statistical analysis, spatial correlation analysis, and K-means clustering to evaluate spatial differentiation and cultural-tourism integration patterns. Results: The results reveal a pronounced “south-dense, north-sparse” spatial distribution with persistent regional inequality. Traditional villages exhibit weak correlations with overall tourism performance but strong spatial coupling with A-level scenic spots, moderate associations with protected cultural relics, and limited integration with intangible cultural heritage, indicating a selective and uneven embedding structure. Based on these relationships, Chinese cities can be classified into four development types: comprehensive leading areas, scenic–village integration areas, balanced development areas, and potential development areas, each characterised by distinct cultural–tourism integration pathways. Discussion: The findings demonstrate that traditional villages function as selectively embedded nodes within China’s cultural–tourism network rather than uniformly integrated tourism resources. This typology provides a practical basis for differentiated conservation policies, cultural heritage management, and place-based tourism planning while offering new insights into the spatial organisation of cultural heritage systems at the national scale.

Mangrove mapping increasingly integrates multisource remote sensing, yet the geographically transferable value of additional sensors remains uncertain. This study evaluated Sentinel-2 multispectral imagery, Sentinel-1 and GF-3 SAR, GF-5 AHSI hyperspectral observations, terrain variables, and sensor-independent coastal-context information across five coastal regions of southern China. Five nested feature configurations (D1–D5) were tested with Random Forest, XGBoost, DeepLabV3, and SegFormer under true leave-one-study-area-out validation on 349,898 common 10 m coordinates; deep models were replicated across three random seeds. Model-averaged F1-scores were 0.9025, 0.9031, 0.9123, 0.9134, and 0.9220 for D1–D5, respectively, but differences were not statistically supported across held-out regions (Friedman χ² = 0.48, p = 0.9754). Sentinel-1, GF-3, and their interaction showed small, geographically inconsistent contrasts. GF-5 effects were model- and preprocessing-dependent, and cross-scene normalization altered performance in contrasting directions. Gain and grouped-permutation analyses indicated model-specific predictor use and sensitivity rather than independent sensor contribution. Overall, multisource benefits depended on sensor configuration, model representation, geographic domain, and preprocessing conditions, supporting context-specific sensor selection and geographically independent validation.

Balancing economic growth with emission reductions presents a pivotal challenge under China's "dual-carbon" strategy. Hunan Province, characterized by its diverse industrial and ecological contexts, offers a quintessential setting for county-level analysis. This study investigates 122 counties within Hunan from 2000 to 2022, employing the Tapio decoupling model, spatial autocorrelation, ordinary least squares (OLS) regression, an augmented STIRPAT framework, and scenario simulations. It analyzes the spatiotemporal dynamics between carbon emissions and GDP, elucidates the principal driving mechanisms, and forecasts peaking trajectories under scenarios of low, medium, and high carbon emissions. The findings reveal a continued rise in emissions, albeit at a reduced pace post-2010, with the proportion of counties achieving strong decoupling escalating from 6% during 2006-2010 to 53% in 2016-2022. Counties dominated by eco-cultural (80%) and agricultural activities (67%) demonstrated the most significant transitions. Regression analyses, based on standardized coefficients, underscore the positive influence of carbon intensity (0.917), economic development (0.792), and population (0.447) on emissions, whereas industrial upgrading (-0.014), energy efficiency (-0.046), and urbanization (-0.010) contribute to mitigating impacts. GeoDetector analysis corroborates strong interaction effects. Scenario simulations indicate that only the low-carbon pathway might enable all four economic regions to achieve emission peaks between 2030 and 2035, while medium- and high-carbon trajectories fail to achieve effective peaking before 2040. These results highlight the imperative for tailored low-carbon strategies at the regional level, incorporating industrial restructuring, enhancement of energy efficiency, promotion of ecological-based industries, and implementation of county-level monitoring and market incentives.

Balancing economic growth with emission reductions presents a pivotal challenge under China’s “dual-carbon” strategy. Hunan Province, characterized by its diverse industrial and ecological contexts, offers a quintessential setting for county-level analysis. This study investigates 122 counties within Hunan from 2000 to 2022, employing the Tapio decoupling model, spatial autocorrelation, ordinary least squares (OLS) regression, an augmented STIRPAT framework, and scenario simulations. It analyzes the spatiotemporal dynamics between carbon emissions and GDP, elucidates the principal driving mechanisms, and forecasts peaking trajectories under scenarios of low, medium, and high carbon emissions. The findings reveal a continued rise in emissions, albeit at a reduced pace post-2010, with the proportion of counties achieving strong decoupling escalating from 6% during 2006–2010 to 53% in 2016–2022. Counties dominated by eco-cultural (80%) and agricultural activities (67%) demonstrated the most significant transitions. Regression analyses underscore the positive influence of carbon intensity (β = 0.917), economic development (0.792), and population (0.447) on emissions, whereas industrial upgrading (− 0.154), energy efficiency (− 0.051), and urbanization (− 0.023) contribute to mitigating impacts. GeoDetector analysis corroborates strong interaction effects. Scenario simulations indicate that only the low-carbon pathway might enable all four economic regions to achieve emission peaks between 2030 and 2035, while medium- and high-carbon trajectories fail to achieve effective peaking before 2040. These results highlight the imperative for tailored low-carbon strategies at the regional level, incorporating industrial restructuring, enhancement of energy efficiency, promotion of ecological-based industries, and implementation of county-level monitoring and market incentives.