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Against the backdrop of China’s dual-carbon strategy, quantifying and characterizing carbon emissions during the construction phase of large-scale waterway infrastructure is critical for supporting the development of low-carbon transportation. This study takes the Pinglu Canal Project, a major inland waterway engineering project in China, as a case study. A carbon emission accounting framework based on the emission factor approach is developed to quantify construction-phase emissions. Spatiotemporal evolution patterns, sectoral differences, and emission intensity characteristics are systematically analyzed. Furthermore, data quality assessment using the Data Quality Indicator (DQI) method and uncertainty quantification based on Monte Carlo simulation are conducted to evaluate the robustness of the results. Results indicate that construction-phase carbon emissions exhibit a distinct stage-dependent pattern closely aligned with construction progress. By the first quarter of 2026, the cumulative emissions reached 9.26 million tonnes of CO 2 equivalent (tCO 2 e). Significant spatial heterogeneity is observed between engineering types, with hub structures contributing substantially higher emissions than waterway works due to differences in construction processes and material intensity. The overall carbon intensity of investment is estimated at 3.38 tCO 2 e per 10,000 CNY, representing a 16.75% reduction compared with typical hydropower infrastructure, indicating relatively low-carbon performance. Uncertainty analysis shows that material consumption, particularly concrete and steel usage, is the dominant source of variability in emission estimates, contributing over 80% of total uncertainty. The Monte Carlo simulation results demonstrate that the model outputs are statistically robust and consistent with deterministic calculations. This study establishes a comprehensive carbon accounting and uncertainty analysis framework for ultra-long linear waterway projects, providing methodological support for low-carbon design and management of similar large-scale infrastructure.
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Wang et al. (2026) studied this question.
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