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.
Jia et al. (Tue,) studied this question.