Decarbonizing road mobility: how to estimate pollutant emissions?

Road mobility is facing a double environmental challenge: it contributes to global warming by representing a significant share of greenhouse gas (GHG) emissions, and it has a negative impact on air quality in urban areas. In France, road traffic is responsible for more than a third of CO2 emissions, and more than half of nitrogen oxides (NOx) (1). Emissions of GHGs and pollutants linked to road mobility are now significant and meaningful dimensions in the assessment of road development and traffic management projects. Properly evaluating them is therefore critical.

Objectifying the phenomenon, or the need for an emissions model

Several methods for estimating pollutants and GHGs linked to road traffic have emerged since the 2000s and these mainly use emission models. Indeed, although the direct measurement of emissions in real conditions is possible thanks to atmospheric sensors, once the concentrations of pollutants have been measured, it is very difficult to distinguish the part due to road traffic from other possible sources in urban areas (industries, heating, etc.). The action of the dispersion due to the wind rapidly mixes the substances from the different sources, so that in order to know precisely the contribution of road traffic, it would be necessary to equip the area around roads, lanes and streets on a massive scale, moreover by taking into account all possible weather conditions in the area. Besides its prohibitive cost, this method remains unreliable and does not enable to distinguish the emissions from different types of vehicles.

In order to obtain measurements at a low cost and to better understand the origin of emissions, current methods thus separate the factors involved with models. These methods tell us that the two predominant components are the type of vehicle (encompassing its technology, its engine and tailpipe), and the distance it travels. Then comes the speed at which it is traveling, and then other second order factors such as its acceleration / deceleration, the slope of the road, the weather conditions, etc. To understand how the first two factors combine to give the total emission of a trip, we can use the formulation of Kaya's identity applied to transport (2): the latter tells us that the CO2 emission of a trip is equal to the carbon intensity of the vehicle (its technology, the first factor) multiplied by the number of kilometers traveled (the second factor). The term “carbon intensity” can be generalized to “emission factor” for a given pollutant, it is a unit emission model (1 km traveled) for a given average speed, and a type of vehicle and given engine. This is the approach mostly used to properly estimate emissions of CO2 and other pollutants.

What are the key factors for a good estimate?

The first factor is related to the notion of “rolling vehicle fleet”. The vehicle fleet describes the proportion of the different types of vehicles and their motorization in all the kilometers traveled in a territory. The following main categories are generally distinguished: light vehicles, utility vehicles, heavy goods vehicles, coaches / buses, and motorized two-wheelers.

The engines are mainly gasoline or diesel, more rarely hybrid or electric. In Europe, the Euro engine standard (Euro 1 to 6) largely determines the level of emissions. Very often an average national fleet is used, but the proportions of these types of vehicles can actually vary significantly from region to region within a country. The fleet data is then combined with an emission factor model which can also use second order input data such as speed or acceleration. Ultimately, this model produces an estimate of emissions for one kilometer traveled for each category of vehicle.

The second factor is the distance traveled. The data is coming from different sources depending on the targeted spatio-temporal scale: local / national statistics, counting data, surveys, or traffic models and simulations. In order to be consistent with that of the vehicle fleet data, it is essential that the distance traveled is distinguished according to the types of vehicles. It will be possible with level of distinction depending of the source of data available (by engine, standard Euro, large categories light vehicles / heavy goods vehicles, etc.).

Including pollutant emissions in projects assessments: it is possible!

A good assessment of GHG and pollutant emissions certainly requires a good understanding of the factors involved, but the current scientific knowledge has enabled to establish robust methods during recent decades. Several emission models coexist, but their common functioning teaches us that only certain key data are essential for the first order, namely the fleet of rolling vehicles and their distances traveled. These data are often available in national statistics enabling evaluation over large spatial scales. The analysis of a particular territory will certainly require refinement of these data.

However, this process, and this is the good news, is often carried out at marginal cost. It is therefore no longer possible, under the economic pretext, not to include a vision of GHG and pollutant emissions in the evaluation of road mobility projects. Informing the decision from this angle also means accelerating the transition to a mobility system that responds to ecological and environmental challenges.


(1) Citepa, June 2020. Inventory of atmospheric pollutant and greenhouse gas emissions in France - Secten format.

(2) ATEC ITS France, Decarbonizing mobility.

Wishing to discover how Neovya Hubsim is integrating GHG and pollutants emissions? Engage conversation with an expert ➜ Book a meeting!

Neovya Mobility by Technology

145 Cours Lafayette

69006 Lyon, France

  • LinkedIn - White Circle
  • Twitter - Cercle blanc

© 2021 Neovya Mobility by Technology SAS. All rights reserved.