Influencing Factors and Predictive Methods of Greenhouse Gas Emissions from Immersed Tunnel Construction in China

The rapid expansion of China’s immersed tunnel construction has resulted in substantial consumption of reinforced concrete and construction energy, thereby generating considerable greenhouse gas (GHG) emissions during the construction stage. Unlike conventional tunnels, immersed tunnels require large cross-sectional dimensions, complicated geological conditions (e.g., varying seabed burial depth and settlement grade requirements), and unique structural parameters, leading to distinct emission characteristics that are currently insufficiently understood. To address this gap, this study aims to quantify construction-stage GHG emissions of immersed-tube segments, identify key influencing factors linking construction parameters and material input with GHG emissions, and develop simplified predictive models for design-stage estimation. A total of 51 immersed-tube segments from three representative cross-sea tunnel projects in China were examined. Under a unified system boundary and functional unit (covering material production and processing, material transportation, and on-site construction energy consumption), the life-cycle assessment (LCA) framework was applied to quantify the construction-stage emissions of each immersed-tube segment. The construction-stage GHG emissions of a single segment range from 1.56 × 104 to 2.71 × 104 t CO2 eq (mean ≈ 2.40 × 104 t CO2 eq). Correlation and partial correlation analyses demonstrated that the total mass of construction materials exhibits the strongest correlation with GHG emissions, followed by the element volume, concrete cross-sectional area, settlement grade, and burial depth. The results further indicate that material intensity is the dominant determinant of GHG emissions for immersed tubes, while the effects of seabed and settlement conditions mainly operate through structural scale and material demand. Finally, two linear regression models were developed, and the model based on total material mass provides the most accurate prediction of construction-stage emissions. The immersed-tube volume can be used to estimate approximate GHG emissions at the design stage, whereas the total material mass serves as a better predictor when detailed material input data are available. This study is based on segment-level data from three Chinese projects and focuses on the construction stage; therefore, transferability requires further validation. Material intensity is the dominant determinant, and the total-material-mass model is the most accurate predictor.