为测算省域普通公路网车辆碳排放量, 基于断面交通流量观测数据, 采用“自下而上”法, 构建了省域普通公路网车辆碳排放量测算模型。运用车辆理论油耗模型, 结合车型、车辆总质量、汽车行驶工况等因素, 实现路段级碳排放量的精细化计算。引入最大熵OD(origin-destination)反推模型, 将反推得到的OD矩阵再分配, 使部分路段的观测流量数据转化为全路网的流量分布。将BPR(Bureau of Public Roads)函数计算出的路段平均速度划分为自由流与非自由流状态, 得到不同速度区间下普通公路车辆的行驶工况。针对模型中燃耗率参数难以获取的问题, 以MOVES开源数据库中不同工况片段下的每秒能耗值为基础, 推算对应工况下的燃耗率。结合工况曲线与行驶量, 分别计算各车型的燃耗与电耗, 最终依据碳平衡原理将燃耗与电耗转化为碳排放量。以宁夏回族自治区为实例, 计算月度省域普通公路网碳排放量, 分析碳排放分布特征, 通过MOVES、COPERT模拟并对比省级统计局发布的各类燃耗数据, 验证测算方法的可行性。结果表明：2019年11月宁夏普通公路网碳排放总量为19.59万t; 货车是碳排放的主要来源, 占比高达87.85%; 重型货车的贡献尤为显著, 占货车碳排放量的76.47%; 小型客车碳排放量仅占所有车型的12.15%, 占客车总碳排放量的82.93%。经实例验证, 本文模型可行, 具备高精度特点, 重构的工况适配复杂的中国路网。研究可为省域普通公路网车辆碳排放量精细化测算提供支撑。

Objective: This study aims to propose a vehicle carbon emission calculation model for provincial highway networks. Drawing on actual traffic volume on observation data, the model seeks to integrate vehicle type differences, gross vehicle weight, driving conditions, and a theoretical fuel consumption framework to enable refined, road-segment-specific emissions estimation. Methods: 1) A "down to up" approach was adopted. Based on a maximum entropy OD backpropagation model, the OD matrix was redistributed to transform the observed traffic data of some road sections into the entire road network traffic distribution. 2) The average speed of road sections estimated using the function proposed by Bureau of Public Roads was divided into free-flow and non-free-flow states, and the driving cycles of highway vehicles in different speed intervals were obtained. 3) CATC series driving circles were deconstructed based on vehicle type, total mass, and average speed. Typical operating condition segments were extracted, and new driving circles adapted to provincial highway networks were reconstructed using Python. 4) Using the operating condition curves and driving volume, the fuel consumption and electricity consumption of each vehicle type were calculated and then converted to carbon emissions based on the carbon balance principle. 5) Because the fuel consumption rate model parameter was difficult to obtain, fuel consumption rates were calculated based on energy consumption values per second under different operating conditions in the MOVES open-source database. 6) The results of the proposed model were compared with those of simulations based on MOVES 4.0 and COPERT 5.8, as well as various fuel consumption data released by the provincial bureau of statistics, and the model's feasibility was verified in terms of carbon emission deviation rate and vehicle energy consumption reference value. Results: Considering the Ningxia Hui Autonomous Region as a case study, the monthly total carbon emissions of the provincial highway network were calculated, and the distribution characteristics of carbon emissions on the highway sections were analyzed. The results showed that the total carbon emission of the provincial highway network in November 2019 was 195 900 tons. Trucks were the main source of carbon emissions, accounting for as high as 87.85%. Heavy-duty trucks made a particularly significant contribution, constituting 76.47% of the carbon emissions from trucks. The carbon emissions from the operation of small passenger cars accounted for only 12.15% and 82.93% of all vehicle types and passenger vehicles, respectively. Conclusions: Through the case study, the proposed model was verified to have high calculation accuracy. The reconstructed working conditions are suitable for complex road networks in China. The proposed model considered the driving circles of various vehicle types in different speed ranges, avoiding the use of inadequate international carbon emission software such as MOVES and COPERT. This research will support the precise calculation of vehicle carbon emissions in provincial highway networks.