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Since 1978, China's rapid urbanization and industrialization have significantly increased carbon emissions. This study employs spatial autocorrelation, kernel density estimation, and spatiotemporal geographically weighted regression (GTWR) methods to analyze the spatiotemporal evolution characteristics of carbon emissions across 336 Chinese cities from 1978 to 2020. It also explores the dominant influencing factors for different cities at various stages of development. The findings reveal that carbon emissions in Chinese cities exhibit a stepwise growth pattern: “slow growth (1978–1995) - low-level stability (1996–2000) - rapid growth (2001–2012) - high-level stability (2013–2020).'' The gap between cities has widened rapidly, and spatially, the distribution follows a “core-periphery” pattern. The increase in carbon emissions in core cities has transformed the urban hierarchy from a “generally low-carbon” structure to a “pyramid” structure. Compared to 1995, the influence of population size on carbon emissions decreased in 2020 (0.54–0.38), while the impact of infrastructure development and technological advances increased (0.02–0.25, 0.09 to 0.19). Due to the varying stages of urban development across regions, the influencing factors of carbon emissions exhibit spatial heterogeneity. Specifically, population size has a stronger positive impact on carbon emissions in the Southeast, technological advances in East and North China, and industrial structure in the Yangtze River Basin region. Infrastructure construction and investment levels show a dampening effect on carbon emissions in the Yangtze River Basin. Finally, the study proposes policy recommendations focusing on implementing regional “gradient” carbon reduction and promoting regional collaborative carbon reduction driven by core cities.

Rapid urbanization is universally acknowledged to degrade ecosystem services, posing significant threats to human well-being. However, the effects of urban shrinkage, a global phenomenon and a counterpart to urbanization, on ecosystem services (ESs) remain unclear. This study focuses on China’s Rust Belt during the period from 2000 to 2020, constructing a comprehensive analytical framework based on long-term remote sensing data to reveal the temporal and spatial patterns of ESs and their associations with cities experiencing varying degrees of shrinkage. It employs a random forest (RF) model and a Shapley additive explanation (SHAP) model to measure and visualize the significance and thresholds of socioeconomic factors influencing changes in ESs. Our findings highlight the following: (1) Since 2010, the three provinces of Northeast China (TPNC) have begun to shrink comprehensively, with the degree of shrinkage intensifying over time. Resource-based cities have all experienced contraction. (2) Regional urban shrinkage has been found to enhance the overall provision capacity of ESs, with the most significant improvements in cities undergoing continuous shrinkage. (3) The impact of the same socioeconomic drivers varies across cities with different levels of shrinkage; increasing green-space ratios and investing more in public welfare have been identified as effective measures to enhance ESs. (4) Threshold analysis indicates that the stability of the tertiary sector’s proportion is critically important for enhancing ESs in cities undergoing intermittent shrinkage. An increase of 10% to 15% in this sector can allow continuously shrinking cities to balance urban development with ecological improvements. This research highlights the positive aspects of urban shrinkage, demonstrating its ability to enhance the provision capacity of ESs. It offers new insights into the protection and management of regional ecosystems and the urban transformation of the three eastern provinces.

The Yangtze River Delta region contributes nearly 16% of the national carbon emissions and is the key area for carbon emission reduction in China. Accurately grasping the spatial evolution characteristics of carbon emissions and the interaction between counties and regions is of great practical significance for precise and collaborative carbon reduction. This study firstly explores the spatial layout and dynamic evolution characteristics of county carbon emissions in the Yangtze River Delta region from 2000 to 2018 by using spatial statistical analysis, secondly identifies the influencing factors of county carbon emissions (CAR) in the Yangtze River Delta region from dynamic and static dimensions respectively by using static and dynamic Spatial Dubin Model, and finally judges the spatial spillover effects of each factor. We find that county carbon emissions are more complex and more diverse in non-synchronous state compared to provinces and cities. The high carbon areas in the Yangtze River Delta region are concentrated in Shanghai and its neighboring cities, as well as industrial counties under the jurisdiction of other sub-core cities, which are continuously clustered towards the center. We have made some theoretical discussions on the results of the spillover effects of various factors on carbon emissions, and concluded that economic of scale (ECO) and industrial structure (IND) have a “polarization effect”, population size (POP) is consistent with the Malthusian view, technological advance (TEC) has a “cumulative effect”, and environmental quality (ENV) The “pollution paradise effect” is mitigated. Finally, we believe that the main unit of precise carbon reduction can take the form of “city-county” combination, and the government should implement differentiated and coordinated carbon reduction policies.

Abstract Background It is reported that appropriately 50% of early breast cancer patients with 1–2 positive sentinel lymph node (SLN) micro-metastases could not benefit from axillary lymph node dissection (ALND) or breast-conserving surgery with whole breast irradiation. However, whether patients with 1–2 positive SLN macro-metastases could benefit from ALND remains unknown. The aim of our study was to develop and validate nomograms for assessing axillary non-SLN metastases in patients with 1–2 positive SLN macro-metastases, using their pathological features alone or in combination with STMs. Methods We retrospectively reviewed pathological features and STMs of 1150 early breast cancer patients from two independent cohorts. Best subset regression was used for feature selection and signature building. The risk score of axillary non-SLN metastases was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients. Results The pathology-based nomogram possessed a strong discrimination ability for axillary non-SLN metastases, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.727 (95% CI: 0.682–0.771) in the primary cohort and 0.722 (95% CI: 0.653–0.792) in the validation cohort. The addition of CA 15–3 and CEA can significantly improve the performance of pathology-based nomogram in the primary cohort (AUC: 0.773 (0.732–0.815) vs. 0.727 (0.682–0.771), P