A comprehensive understanding of the spatiotemporal changes and influencing factors of ecosystem service levels is crucial for regional sustainable development and coordination. The Yellow River Basin faces challenges such as ecological degradation due to uneven regional burdens. This study constructed an ecosystem service evaluation framework based on five dimensions. A combined weighting model was used to assess the ecosystem service levels, spatiotemporal characteristics, and influencing factors of nine provincial capital cities in the Yellow River Basin from 2010 to 2020. The results indicated that: (1) There were notable differences in ecosystem service levels among cities in the Yellow River Basin. The first category of cities, rich in tourism resources, exhibited high ecosystem service levels. The second category of cities, currently undergoing industrial green transformation, urgently needed to achieve a balance between economic development and ecological protection. The third category of cities faced challenges due to poor socioeconomic conditions and limited resources. (2) Resource and energy use, Ecological environmental protection, and Socioeconomic development were the most significant dimensions influencing ecosystem service levels. (3) Indicators such as per capita disposable income, per capita road area, urban green coverage, electricity consumption per unit of GDP, and the number of higher education institutions had a considerable impact on ecosystem service levels. This study suggests optimizing the energy structure, promoting clean energy development, supporting industrial green transformation, and strengthening infrastructure to enhance and protect ecosystem services in the Yellow River Basin.

Buildings account for 39% of global carbon emissions, making the construction sector a pivotal contributor to climate change. In ecologically fragile plateau regions, the tension between urban development and environmental sustainability poses a significant challenge. This study examines the spatiotemporal characteristics and influencing mechanisms of building carbon emissions (BCEs) in plateau cities using an empirical analysis of 13-year panel data (2010–2022) from two municipalities and six prefectures in Qinghai Province, China. By employing the eXtreme Gradient Boosting (XGBoost) model, we comprehensively assess drivers across four dimensions: socioeconomic structure, demographic and urban environmental factors, urban expansion patterns, and climatic topographic attributes. Key findings include: (1) The XGBoost model exhibits robust predictive performance (R² > 0.9, MSE < 0.1, RMSE < 0.3), validating its effectiveness for plateau urban systems. (2) Socioeconomic structure and urban expansion characteristics significantly positively influence building carbon emissions, with GDP, per capita GDP, and built-up areas being particularly influential. (3) The interaction between climate and terrain increases carbon emissions in urban buildings. (4) While socioeconomic structure is a common factor affecting BCEs across different types of plateau urban buildings, other factors, such as urban population density, the housing construction area, and the urban shape index, exhibit variability. These insights inform policy recommendations for cross-regional carbon flow balancing and adaptive low-carbon planning strategies tailored to plateau ecosystems.

Road transport is an important source of carbon emissions worldwide, yet the factors driving these emissions may differ under varying geographical conditions. Plateau regions are characterized by high altitude and strong spatial constraints, but their transport carbon emission mechanism remains insufficiently understood. Taking Qinghai Province on the Qinghai–Tibetan Plateau as a case, this study estimates road transport CO₂ emissions from 2003 to 2022 using annual statistical data. It constructs a multidimensional indicator system covering economic development, industrial structure, energy use, and road transport activity, and applies correlation analysis, PCA, and LASSO regression to diagnose variable relationships and identify key drivers. The results show that GDPI, VATSP, CVO, and TPT have stable positive effects on road transport CO₂ emissions, indicating that economic expansion, transport services, vehicle ownership, and passenger mobility are the dominant drivers. Industrial and energy-related variables have more indirect and stage-dependent effects: NMI and NFMO are negatively associated with emissions, whereas NMEC has a weak positive effect. These findings suggest that, under the dispersed spatial development and long-distance transport dependence of plateau regions, emissions are more directly shaped by economic and transport activity than by short-term changes in energy structure. Low-carbon transport policy in Qinghai should therefore combine transport-demand management, more efficient transport organization, public-transport improvement, and gradual transport electrification. The results provide evidence for emission-reduction strategies in high-altitude and ecologically fragile regions.

Carbon and water systems exhibit complex synergies and trade-offs and are closely interconnected through the social water cycle. This study examines eight prefecture-level regions in Qinghai Province from 2010 to 2024. A comprehensive carbon–water evaluation system and coupling coordination degree (CCD) model were used to characterize the spatiotemporal evolution of carbon–water coupling coordination, while CatBoost-SHAP was applied to identify the main factors associated with coordination across water withdrawal, use, and discharge processes. The results show that: (1) the carbon–water CCD exhibited an overall fluctuating upward trend, with the comprehensive evaluation index of the carbon subsystem generally higher than that of the water resources subsystem; (2) coordination showed pronounced spatial heterogeneity, characterized by relative stability in high-level regions and gradual improvement in low-level regions; and (3) water resource use within the social water cycle was closely associated with carbon–water coupling coordination, with indicators related to agriculture, the forestry–animal husbandry–fishery sector, and water supply showing relatively high model importance. Accordingly, carbon–water synergistic management should improve water use efficiency in production sectors, strengthen matching between water supply and end-use demand, and implement differentiated management according to regional resource endowments, ecological functions, and socioeconomic development levels.

Climate change, resource scarcity, and ecological degradation have become critical bottlenecks constraining socio-economic development. Basin cities serve as key nodes in China’s ecological security pattern, playing indispensable roles in ecological civilization construction. This study established an evaluation index system spanning five dimensions to assess the effectiveness of ecological civilization construction. This study employs the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Back-Propagation (BP) neural network methods to evaluate the level of ecological civilization construction in the Yellow River Basin from 2010 to 2022, to analyze its indicator weights, and to explore the spatio-temporal evolution characteristics of each city. The results demonstrate the following: (1) Although the ecological civilization construction level of cities in the Yellow River Basin shows a steady improvement, significant regional development disparities persist. (2) The upper reaches are primarily constrained by ecological fragility and economic underdevelopment. The middle reaches exhibit significant internal divergence, with provincial capitals leading yet demonstrating limited spillover effects on neighboring areas. The lower reaches face intense anthropogenic pressures, necessitating greater economic–ecological coordination. (3) Among the dimensions considered, Territorial Space and Eco-environmental Protection emerged as the two most influential dimensions contributing to performance differences. According to the ecological civilization construction performance and changing characteristics of the 48 cities, this study proposes differentiated optimization measures and coordinated development pathways to advance the implementation of the national strategy for ecological protection and high-quality development in the Yellow River Basin.

Under the pressing imperative of achieving “dual carbon” goals and advancing urban low-carbon transitions, understanding how neighborhood spatial environments influence carbon emissions has become a critical challenge for enabling refined governance and precise planning in urban carbon reduction. Taking the central urban area of Xining as a case study, this research establishes a high-precision estimation framework by integrating Semantic Segmentation of Street View Images and Point of Interest data. This study employs a Geographically Weighted XGBoost model to capture the spatial non-stationarity of emission drivers, achieving a median R² of 0.819. The results indicate the following: (1) Socioeconomic functional attributes, specifically POI Density and POI Mixture, exert a more dominant influence on carbon emissions than purely visual features. (2) Lane Marking General shows a strong positive correlation by reflecting traffic pressure, Sidewalks exhibit a clear negative correlation by promoting active travel, and Building features display a distinct asymmetric impact, where the driving effect of high density is notably less pronounced than the negative association observed in low-density areas. (3) The development of low-carbon neighborhoods should prioritize optimizing functional mixing and enhancing pedestrian systems to construct resilient and low-carbon urban spaces. This study reveals the non-linear relationship between street visual features and neighborhood carbon emissions, providing an empirical basis and strategic references for neighborhood planning and design oriented toward low-carbon goals, with valuable guidance for practices in urban planning, design, and management.