Remote sensing is one technique used to measure real-world NOx emissions in Europe. Remote sensing measurements conducted by the Canton of Zurich are unique in terms of how consistently they have been collected since 2000 and the steep road grade at the main remote sensing monitoring site.

While the Dieselgate scandal raised awareness of defeat devices—software calibrations that manipulate pollutant emission controls when vehicles are in the real world—third-party evaluation remains difficult because these devices are embedded in sophisticated computer code.

Remote sensing is potentially the best option for fleet emissions monitoring, the development of an emissions factor, the identification of individual high- or low-emitting vehicles, and the screening for groups of high-emitting vehicles for market surveillance.


Remote sensing of emissions has a number of important characteristics that make it particularly useful for real-world emissions surveillance.

The 2018 Toyota Camry incorporates eight technology upgrades that are specifically modelled in the U.S. Environmental Protection Agency’s Optimization Model for Reducing Emissions of Greenhouse Gases from Automobiles (OMEGA) and Lumped Parameter Model (LPM).

Finds that for cars, the cost for meeting a 2025 target value of 70 g/km (as measured in the New European Driving Cycle - NEDC) is between 250 and 500 euros higher than would be the case in a footprint-based CO2 target system.

This paper reviews the political science, regulatory, and economics literature to illuminate the international competitiveness impacts of motor vehicle emission standards.

Analyzes emerging vehicle-efficiency technologies with respect to cost and capacity to lower carbon emissions from passenger cars and light-duty trucks in the 2025–2030 time frame.

Analyzes results of emissions tests on 32 Euro 6 diesel passenger cars from 10 different manufacturers. Results show some automakers meeting diesel NOx emissions standards under more realistic driving conditions, while others lag badly.

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