Thermal energy storage based on phase change materials (PCMs) can improve the efficiency of energy utilization by eliminating the mismatch between energy supply and demand. It has become a hot research topic in recent years, especially for cold thermal energy storage (CTES), such as free cooling of buildings, food transportation, electronic cooling, telecommunications cooling, etc. This paper summarizes the latest research progress of the PCMs-based CTES. Firstly, the classification of PCMs for low temperature storage is introduced; the thermal physical properties (e.g., phase change temperature (PCT) and latent heat) of suitable PCM candidates (−97 to 30 °C) for CTES are summarized as well. Secondly, the techniques proposed to enhance the thermal properties of PCMs are presented, including the addition of nanomaterials, the microencapsulation and the shape stabilization. Finally, several representative applications (−97 to 65 °C) of PCMs in different CTES systems are discussed. The present review provides a comprehensive summary, systematical analysis, and comparison for the PCMs-based CTES systems, which can be helpful for the future development in this field.

To promote green and low-carbon transformation in the transportation sector and achieve the national “dual-carbon” targets, this study examines rooftop photovoltaic (PV) deployment at 12 representative railway stations located on the Qinghai–Tibet Plateau. Using high-resolution solar radiation data, building spatial information, and regional electricity pricing, we develop an integrated analysis framework that combines a PV power-generation simulation, life-cycle cost assessment, and carbon emission reduction evaluation. The model systematically evaluates the power output, economic performance, and emission reduction potential of rooftop PV systems installed on railway station buildings. Two PV array configurations—horizontal angle and optimum tilt angle—together with three business models (T1: all-consumption; T2: all-feed-into-grid; T3: self-consumption with surplus feed-in) are compared. The results indicate that the Qinghai–Tibet Plateau possesses substantial solar energy advantages. Rooftop arrays installed at a horizontal angle significantly increase both installed capacity and lifetime electricity generation, with stations XN and LS producing 523.12 GWh and 300.87 GWh, respectively, values that exceed the corresponding optimum tilt scenarios. In terms of economic performance, the T1 model yields the highest returns, with several stations achieving a lifetime return on investment exceeding 300% over a 25-year period. The T3 model demonstrates strong profit potential at stations such as RKZ and ZN, whereas the T2 model shows the weakest economic viability due to feed-in tariff constraints. Regarding carbon reduction, horizontal systems perform the best, with cumulative CO₂ emission reductions at station XN exceeding 300,000 tonnes of CO₂-equivalent. Overall, the findings highlight the substantial PV development potential of railway station rooftops on the Qinghai–Tibet Plateau. By selecting appropriate installation angles and business models, significant economic benefits and carbon emission reduction outcomes can be achieved, providing practical guidance for renewable-energy utilization in high-altitude transportation infrastructure.

Controlling operation-stage carbon emissions (CE) from transport buildings is crucial for China’s dual-carbon goals and the ecological security of the Qinghai–Tibet Plateau (QTP), and the sustainable development of plateau transport infrastructure. For plateau railway passenger stations (RPS), limited monitoring and distinctive high-altitude, cold-climate operations make daily CE prediction difficult with conventional measurement- or simulation-based methods. This study develops a machine-learning approach based on a Monte Carlo synthetic database and derives engineering-standard formulas for direct use. Building scale, meteorology and passenger flow volume (PFV) were compiled for 12 representative RPS, and a large synthetic database of daily carbon emission was generated under multiple distribution constraints. With daily mean temperature, heating degree days, altitude, station floor area and PFV as inputs, four models were trained and assessed using mean absolute error, root mean square error, mean absolute percentage error (MAPE) and R². The results show that random forest (RF) performed best, achieving ~6% MAPE and R² > 0.99 on the test set, and markedly lower errors than multivariable linear regression. Interpretation of RF via feature importance and partial dependence shows that floor area, altitude and PFV dominate emissions and exhibit nonlinear response patterns. To improve transparency and transferability, ridge regression was used to fit a linear surrogate to RF predictions, producing engineering-standard formulas for daily and annual operation-stage CE. The formulas retain most predictive accuracy while requiring only readily obtainable variables, enabling rapid estimation and scenario analysis for cold, high-altitude RPS. The proposed workflow provides a replicable pathway for operational CE assessment in data-scarce regions and supports low-carbon planning, design and operation of RPS on the QTP, thereby contributing to more sustainable infrastructure development in high-altitude regions.