The Sichuan–Xizang Railway corridor is not only a strategic transport passage but also a distinctive and widely visited tourism route. However, empirical evidence on the interactive relationships among transport, tourism, and economy (TTE) in this corridor remains scarce, even though coordinated development of these three systems is essential for achieving high-quality growth. This study develops a ternary coupling evaluation framework and applies the Entropy Weight Method, the Coupling Coordination Degree Model, and the Relative Development Degree Model to quantify the spatiotemporal evolution of six node cities (Chengdu, Ya’an, Garze, Qamdo, Nyingchi, and Lhasa) from 2012 to 2022. The results indicate differences in temporal dynamics across subsystems. The economy grows steadily, tourism rises with pronounced fluctuations, and transport shows the strongest vulnerability to the COVID-19 shock. Spatially, CCD exhibits a persistent “dumbbell-shaped” pattern, with higher coordination at the two ends (Chengdu and Lhasa) and weaker coordination in the central section. Structurally, RDD and heatmap results indicate convergence toward a transport-lagging structure (i.e., a relative lag in carrying capacity), and ternary trajectories drift away from the transport vertex, revealing structural divergence driven by an asymmetric growth rate mismatch: tourism demand expands faster than transport supply capacity. These findings provide a pre-completion baseline for the corridor and highlight priorities for correcting subsystem imbalance, including strengthening external links in the central section, improving hub-to-scenic internal connectivity, and leveraging digital outreach to support demand monitoring and destination management.

The Xizang–Sichuan region is rich in tourism resources, yet its complex geography and lagging transportation infrastructure have resulted in pronounced spatial disparities in tourism development. From a sustainability perspective, such disparities can lead to a ‘rich-get-richer’ cycle: over-tourism and ecological stress in high-accessibility cores, versus underdevelopment and resource idling in low-accessibility peripheries. To systematically examine the spatial structure of tourism in this region, this study uses counties as basic units and integrates the Analytic Hierarchy Process (AHP) with an improved gravity model for tourism network potential accessibility (TNPA) to assess regional tourism development levels and the network-based accessibility among counties. A comprehensive evaluation framework was established across three dimensions: tourism resource endowment, service capacity, and socio-economic support. Travel times between county centers were obtained from the Amap API, and a TNPA index was computed to reflect each county’s potential for tourism interaction with the rest of the region. Results indicate that resource endowment dominates the evaluation, with scenic quality as the critical factor. TNPA exhibits a pronounced core–periphery differentiation, with Chengdu and surrounding areas forming a high-value network core, while most counties in Xizang show extremely low network potential. Equity analysis reveals a significant imbalance in the distribution of network accessibility between Sichuan and Xizang. Under the baseline setting, the population-weighted Gini coefficient, coefficient of variation, and Theil index reach 0.677, 1.724, and 0.884, respectively, indicating that tourism network potential remains highly concentrated in a limited number of counties. The population-weighted mean TNPA of Sichuan is also far higher than that of Xizang, revealing a distinct interregional accessibility gap. The development–TNPA mismatch analysis further identifies counties where tourism development foundations are relatively strong but network integration remains weak. Robustness checks indicate that the high-development–low-TNPA pattern is not simply an artefact of the median-based classification, with the most evident cases mainly concentrated in Aba and Garze in western Sichuan and a few counties in Xizang. The study highlights the asymmetric relationships among resource endowment, network accessibility, and equity, providing a scientific basis for optimizing cross-regional tourism cooperation and transportation corridors in the Xizang–Sichuan region.

Achieving sustainable transport requires not only reducing aggregate emissions but also avoiding the concentration of emission burdens and transition costs in less-developed regions. Transport carbon-emission inequality therefore captures a distributional dimension of environmental and social sustainability. Using city-level transport emissions and population data for 31 mainland Chinese provincial-level regions from 2005 to 2024, this study develops an integrated framework for measuring and monitoring this inequality. Population-weighted within-province Gini coefficients and Theil T indices are combined with a modified-gravity interprovincial network, annual network analysis, CONCOR and core–periphery analysis, two-way fixed effects, and leakage-safe machine learning. The mean provincial Gini declined from 0.311 in 2005 to 0.287 in 2024, but inequality remained higher in the northwest and southwest and lower along the eastern coast. Full-network weight increased from 61.275 to 3336.040, retained directed edges increased from 174 to 217, and reciprocity rose from 0.471 to 0.571, indicating stronger interprovincial embeddedness and a greater need for coordinated governance. Population density was negatively associated with inequality across several specifications, although wild-cluster bootstrap inference did not confirm uniform significance. Under zero province-pair overlap, LightGBM achieved R² = 0.241 with distance and 0.121 without distance; residual machine learning added no stable predictive information beyond the gravity benchmark. These findings contribute to sustainability research by quantifying the distributional fairness of transport decarbonization, providing a reproducible monitoring tool for identifying regional sustainability risks, and supporting differentiated mitigation responsibilities, sustainable transport infrastructure, and cross-provincial coordination.

The Tibetan Plateau, formed by the Indian-Eurasian collision, is dissected by the north-south trending South Tibet Rift system, but the formation mechanism of these rifts within a continuing compressional setting remains debated. Using P-wave receiver functions and joint inversion with surface wave dispersion along a ~230 km broadband seismic profile crossing the Nyima-Tingri Rift (NTR) and Xianza-Dingjie Rift (XDR), we investigated the detailed crustal structure beneath the Himalayan and Lhasa terranes. Our results reveal three key findings: (1) The crustal thickness ranges from 60 to 80 km, with the XDR exhibiting a pronounced Moho uplift (~10 km) beneath the rift axis, whereas the Moho beneath the NTR remains flat and continuous, indicating contrasting evolutionary stages. (2) A mid-crustal low-velocity layer at ~30 km depth is consistently observed west of 87.2° E, suggesting the presence of partial melt or fluids that decouple upper crustal deformation from mantle flow. (3) A prominent intracrustal discontinuity at 50–70 km depth produces a “Moho doublet” pattern; we interpreted this as the subducted Indian lower crust in the Himalayan terrane but as the relict Tibetan Moho in the Lhasa terrane, reflecting progressive northward modification. Collectively, these observations demonstrate that the north-south structures in southern Tibet lack the deep structural characteristics of mature rifts and instead represent an “infant stage” of extension. Their formation is best explained by east-west crustal stretching driven by an ongoing north-south convergence and eastward flow of lower crustal and upper mantle materials rather than by classical lithosphere-scale rifting.

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities.