Facility connectivity plays a pioneering role in the Belt and Road Initiative proposed by General Secretary Xi Jinping in 2013. Xinjiang, as the core area of the Silk Road Economic Belt bordering eight Eurasian countries, plays a crucial role in cross-border transportation and humanistic exchanges and is the focus of the national connectivity initiative. While the current analysis on regional accessibility has become more diversified, analyses on long-distance cross-border corridors are still relatively rare. Therefore, this paper takes the Xinjiang Uygur Autonomous Region (XUAR) of China as the main study area extending westward to the five Central Asian countries. Modified accessibility accounting methods and gravity models are used to analyze the current status of accessibility and the strength of economic ties between Xinjiang and the five Central Asian countries. The results showed that the distance decay effect of transportation accessibility between Xinjiang and the five Central Asian countries is obvious; the constraints of “natural geography + transportation economy” affect the accessibility level from each state in Xinjiang to the five Central Asian countries and shows a trend of strength in the north and weakness in the south. From the optimization of the regional planning road network in a reverse projection, G3033 and other highways and the construction of the Yi-A railroad will improve the status quo of “east-west access but not north-south access” in Xinjiang. The “corridor effect” and spatial polarization characteristics of economic connection intensity from Xinjiang to the five Central Asian countries are significant. This study has important theoretical and practical significance for the construction of cross-border corridors.

This study develops a dynamic risk modeling approach incorporating stock-and-flow structures to comprehensively evaluate the investment risks of highway PPP projects in Western China, aiming to promote sustainable infrastructure development. Through establishing a risk accumulation model with scenario simulation and sensitivity analysis, this study systematically examines the risk factors and their propagation pathways in Western China’s highway PPP projects. The research demonstrates the dynamic complexity of project risks, particularly highlighting the prominence of operation, maintenance, and government risks. Sensitivity analysis identifies five critical risk factors: financial support adequacy, public health contingencies, legal/regulatory robustness, inflation volatility, and project uniqueness. The study proposes targeted risk management strategies emphasizing the establishment of early-warning systems and emergency response plans for critical risks. Methodologically, this research advances infrastructure risk assessment by adapting stock-and-flow modeling conventions for threshold-driven risk screening, particularly suited to underdeveloped regions like Western China. The developed rapid risk determination tool provides practical decision support for stakeholders in similar socio-economic contexts.

Against the backdrop of China’s “dual-carbon” initiative, this study innovatively applies a process-based life cycle assessment (PLCA) methodology, meticulously tracking energy and carbon flows across material production, transportation, and maintenance processes. By comparing six asphalt pavement maintenance technologies in Xinjiang, the research reveals that milling and resurfacing (MR) exhibits the highest energy consumption 250,809 MJ/10³ m²) and carbon emissions (15,095.67 kg CO₂/10³ m²), while preventive techniques like hot asphalt grouting reduce emissions by up to 87%. The PLCA approach uncovers a critical insight: 40–60% of total emissions originate from the raw material production phase, with cement and asphalt identified as primary contributors. This granular analysis, unique in regional road maintenance research, challenges traditional assumptions and emphasizes the necessity of upstream intervention. By contrasting reactive and preventive strategies, the study validates that early-stage maintenance aligns seamlessly with circular economy principles. Tailored to a local arid climate and vast transportation network, the study concludes that prioritizing preventive maintenance, adopting low-carbon materials, and optimizing logistics can significantly decarbonize road infrastructure. These region-specific strategies, underpinned by the novel application of PLCA, not only provide actionable guidance for local policymakers but also offer a replicable framework for sustainable road development worldwide, bridging the gap between scientific research and practical decarbonization efforts.

Assessing highway pavement condition is crucial for ensuring transportation safety and optimizing infrastructure maintenance. In Xinjiang, China, extreme climatic and traffic conditions pose significant challenges to pavement performance. This study introduces a machine-learning-based framework to predict asphalt pavement performance in Xinjiang. We integrate various factors (design, materials, environment, traffic, and maintenance) into regression models, creating a region-specific pavement performance decay model. Our data preprocessing methodology effectively addresses outliers and missing data, ensuring the model’s robustness. The findings offer insights into asphalt pavement behavior in Xinjiang and provide guidance for maintenance strategies. The proposed model enhances highway infrastructure safety and cost-effectiveness. Future research will focus on refining the model with more data and exploring complex variable interactions.

Within the economic and cultural context of Xinjiang, the tourism industry has rapidly developed as a strategic pillar of the national economy, with the self-driving tour market emerging prominently. However, the uneven spatial layout and insufficient service facilities of self-driving camps limit their development potential. This study aims to enhance the attractiveness of tourism in Xinjiang and improve the visitor service experience by constructing an evaluation system for the layout of self-driving camps based on online travel blog data, utilizing methods such as literature review, surveys, ArcGIS spatial analysis, and web text analysis. The Delphi method and entropy weight method were applied to determine the weights of the influencing factors. The findings reveal spatial imbalances in the layout of Xinjiang’s self-driving camps and propose eight preferred scenic areas for camp location. This study also suggests sustainable development strategies. These insights and recommendations aim to optimize the layout of self-driving camps, enhance the tourism experience, and promote the sustainable development of Xinjiang’s tourism industry.