混凝土桥作为公路建设的重要结构物之一，其建设期产生的碳排放较高，因而亟需构建精度较高的碳排放测算模型以助推其建设过程低碳化。本文将公路混凝土桥建设期碳排放来源划分为材料生产、材料运输、场外加工、现场施工四个部分，使用碳排放因子法计算了某新建高速公路31座混凝土桥建设期的碳排放。通过碳排放特征及相关性分析，发现解释混凝土桥建设期碳排放的关键要素为桥梁长度、材料总重量、机械工作时间，Spearman相关系数分别为0.96、0.88、0.82，且三个要素之间存在共线性。使用上述要素为自变量，构建岭回归、Lasso回归及弹性网络回归模型以消除共线性的影响，发现Lasso回归模型碳排放测算精度最高，R2为0.9762，故将其作为混凝土桥建设期碳排放的测算模型。该模型可基于桥梁长度和材料总重量测算混凝土桥不同设计方案和建设方案的碳排放，为混凝土桥低碳方案设计及建设期减碳要素优化提供方法参考。

Concrete bridges as critical structures in highway construction, generate significant carbon emissions during their construction phase, necessitating the development of a relatively precise carbon emission estimation model to promote low-carbon construction practices. This study categorizes the sources of carbon emissions during the construction of highway concrete bridges into material production, transportation, off-site processing, and on-site construction. The carbon emission factor method is used to calculate the carbon emissions during the construction period of 31 concrete bridges on a newly built expressway. Analysis of carbon emission characteristics and their correlations reveal that factors such as bridge length, total material weight, and machinery working hours significantly influence emissions during bridge construction. The Spearman correlation coefficients for these factors are 0.96, 0.88, and 0.82, respectively, with collinearity observed among them. Employing these variables, ridge regression, Lasso regression, and elastic net regression models were developed to mitigate collinearity. The Lasso regression model demonstrates the highest accuracy in estimating carbon emissions, with an R2 of 0.9762, making it the preferred model for estimating emissions during bridge construction. This model can calculate carbon emissions for various design and construction plans of concrete bridges based on bridge length and total material weight, serving as a methodological reference for the development of low-carbon designs and the optimization of carbon reduction strategies during the construction process.

国家自然科学基金(51878062)；陕西省自然科学基础研究计划(2022JQ-527)。