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.

Lithofacies characterization of organic-rich shales constitutes the essential foundation for sweet spot evaluation in lacustrine shale oil systems. This study targets the first member of the Upper Cretaceous Qingshankou Formation (K₂qn¹) in the southern Songliao Basin. Based on systematic core description of 908 m of core from eight cored wells, combined with 123 total organic carbon (TOC) measurements, 47 whole-rock X-ray diffraction (XRD) analyses, 29 major- and trace-element analyses, and six maceral identification datasets (≥500 organic particles counted per sample), together with conventional well log data from 75 wells (measured vitrinite reflectance $R_o$ = 0.34%–1.38%, mean = 0.94%), we establish an integrated lithofacies classification scheme incorporating the TOC as a classification parameter and develop a log-based lithofacies identification workflow. Eight lithofacies are recognized within K₂qn¹ across the study area, of which three are organic-rich. The high-TOC clay-rich mudstone-grade laminated shale deposited in a deep lake setting (LF-A; mean TOC = 3.18%, clay minerals ≥50%, formed under saline and strongly anoxic-euxinic conditions; mean paleosalinity = 8.06‰, V/(V + Ni) = 0.75–0.97) and the high-to-moderate-TOC felsic mudstone-grade laminated shale deposited in a semi-deep lake setting (LF-B; mean TOC = 2.18%, felsic minerals ≥50%, formed under brackish-to-saline anoxic conditions; mean paleosalinity = 5.10‰, V/(V + Ni) = 0.70–0.84) constitute the dominant organic-rich lithofacies. From Y1 to Y3, the cumulative thickness of organic-rich lithofacies expands from approximately 10 m to approximately 25 m. Areally, the mean TOC increases systematically from 1.65% in the southern delta-front zone to 2.74% in the northern deep lake center, reflecting an enrichment pattern governed primarily by paleoproductivity and modulated jointly by preservation conditions and terrigenous dilution. The log-based identification workflow, established by integrating a modified ΔlogR method with multiple linear regression, achieves a TOC prediction coefficient of determination of $R^2=0.86$ in the calibration well and lithofacies identification accuracies ranging from 64.6% to 94.0% in validation wells, with the highest performance observed in the delta-front facies zone. These results provide quantitative constraints for the genetic interpretation and log-based identification of organic-rich lacustrine shales.