Certification and monitoring of heavy vehicle CO₂ emissions in several countries are based on individual vehicle simulation. Smaller fleet subsets can be used for accurate fleet-level results while preserving the characteristics of the underlying fleet-emissions distributions. The paper focuses on three approaches to capture fleet CO₂ emissions: a) sampling directly from the fleet-data, b) sampling from data of individual vehicle components and c) using key statistics regarding the fleet composition that are available. The first and second approach deliver marginal divergences of the mean, between 1.1 and 2.1% and below 2.7 respectively, preserving the characteristics of the distribution. The third deviated by up to 5%, but lacked the detailed characteristics of the underlying statistical distribution. All three are useful when setting up fleet-wide monitoring schemes where detailed data are not available and to investigate the potential CO₂ savings of various future fleet compositions, and scenarios regarding the diffusion of different types of technologies.

在多个国家/地区，重型汽车的CO ₂排放量的认证和监控均基于单个汽车的模拟。较小的车队子集可用于获得准确的车队级别结果，同时保留基本车队排放分布的特征。本文着重介绍了捕获车队CO _2的三种方法排放：a）直接从车队数据中采样； b）从单个车辆零部件的数据中采样； c）使用有关可用车队组成的关键统计数据。第一种方法和第二种方法分别提供平均值的边际差异，分别在1.1％和2.1％之间以及低于2.7％，从而保留了分布的特征。第三位偏差最多达5％，但缺少基础统计分布的详细特征。当建立无法获得详细数据的机队范围的监测计划时，这三点都很有用，它可以用来调查未来各种机队组成的潜在CO ₂节省量，以及有关不同类型技术扩散的情景。