Groundwater seepage plays a critical role in the long-term safety of high-level radioactive waste (HLW) disposal, yet its characterization remains challenging due to the complexity of fractured rock media. This study introduces the Double-Layered Seismo-Electric Method (DSEM) for imaging groundwater seepage fields with enhanced sensitivity and spatial resolution. By integrating elastic wave propagation with electrokinetic coupling in a stratified framework, DSEM improves the detection of hydraulic gradients and preferential flow pathways. Application at a representative disposal site demonstrates that the method effectively delineates seepage channels and estimates hydraulic conductivity, providing reliable input parameters for groundwater flow modeling. These results highlight the potential of DSEM as a non-invasive geophysical technique to support safety assessments and long-term monitoring in deep geological disposal of high-level radioactive waste.

The southwestern region of China is highly susceptible to landslides due to steep terrain, fractured geology, and intense rainfall. This study focuses on the Xiluodu Reservoir area in Yunnan Province and applies Geographic Information System (GIS) techniques together with ten key spatial factors—such as slope, lithology, elevation, and distance to rivers—to perform a quantitative landslide risk assessment. In addition to the individual Certainty Factor (CF) and Logistic Regression (LR) models, we developed an integrated CF–LR coupled model to overcome their respective limitations: the CF model’s sensitivity to specific factor attributes but neglect of factor interactions, and the LR model’s robust weight estimation but weak representation of attribute heterogeneity. By combining these strengths, the CF–LR model achieved superior predictive performance (AUC = 0.804), successfully capturing 92.5% of historical landslide events within moderate-to-high risk zones. The results show that lithology, slope angle, and proximity to rivers and roads are dominant controls on susceptibility, with landslides concentrated on soft rock slopes of 30–40° and within 600–900 m of rivers. Compared with previous coupled approaches in similar mountainous reservoir settings, our CF–LR model provides a more balanced and interpretable framework, enhancing both classification accuracy and practical applicability. These findings demonstrate that GIS-based CF–LR integration is a novel and reliable tool for landslide susceptibility mapping, offering important technical support for disaster prevention and risk management in large reservoir regions.

The long-term safety of high-level radioactive waste (HLW) disposal depends on understanding groundwater migration in complex, low-permeability rock environments. In particular, fractured rock formations may serve as potential pathways for radionuclide transport over geological timescales. This study investigates groundwater flow at the Beishan site in Gansu Province, a candidate HLW repository, using a three-dimensional sonar vector seepage field detection system. Applied to the Xinchang and Shiyuejing fault zones, the method measured seepage velocities of approximately 1×10-10 m/s, with dominant flow directions from northwest to southeast and northeast to southwest, respectively. Results show that variations in permeability closely follow regional structural fracture patterns, and the findings align with existing hydrogeological data indicating average rock mass permeability near 1×10-8 m/s. The detection system achieved a velocity resolution of 1×10-10 m/s and directional accuracy within 2°, confirming its precision and reliability. This work demonstrates a practical and accurate approach for identifying groundwater flow in hard, nearly impermeable rock masses, offering critical support for HLW repository evaluation and broader geotechnical applications such as dam monitoring, underground oil storage, and dewatering of deep excavations.

In the context of China’s dual-carbon goals of carbon peaking and carbon neutrality, urban low-carbon planning has become central to achieving sustainable urban transformation. However, its implementation remains hindered by fragmented legal frameworks, weak regulatory coordination, limited incentive mechanisms, and insufficient public participation. This study examines how institutional safeguard mechanisms can enhance the effectiveness and enforceability of urban low-carbon planning. Using a comparative case analysis of representative Chinese cities, it investigates the current institutional arrangements, identifies key governance deficiencies, and explores feasible pathways for systemic improvement. The findings reveal that legal institutionalization, integrated regulatory frameworks, diversified incentive policies, and participatory co-governance are critical to advancing low-carbon planning and ensuring policy coherence. These insights can improve administrative efficiency, enhance stakeholder engagement, and create a resilient governance structure that supports long-term carbon reduction. The study contributes to the theoretical understanding of urban low-carbon governance and provides actionable policy guidance for cities in developing countries pursuing sustainable and inclusive low-carbon transitions under the global climate governance framework.