Using Remote Sensing to Reduce Heavy-Duty Truck Emissions in California

Last registered on December 21, 2022


Trial Information

General Information

Using Remote Sensing to Reduce Heavy-Duty Truck Emissions in California
Initial registration date
November 24, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 25, 2020, 10:33 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
December 21, 2022, 3:08 PM EST

Last updated is the most recent time when changes to the trial's registration were published.


Primary Investigator

University of Chicago

Other Primary Investigator(s)

PI Affiliation
University of Chicago
PI Affiliation
University of Warwick

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Exposure to particulate matter (PM) presents a great threat to human health and transportation is a major contributor to PM globally, in the U.S., and in California. In an attempt to reduce the PM burden that heavy-duty (HD) trucks impose on human health, California has implemented new, more stringent emissions standards. However, these standards remain challenging to enforce. This is partially due to the high direct costs of monitoring mobile polluters’ emissions, which have proven to be a substantial impediment to achieving compliance.

The purpose of this study is to examine the effectiveness of two new enforcement methods of increasing compliance of high-emitting HD trucks with California’s emissions regulations. In partnership with the California Air Resources Board (CARB), our study will use new remote sensing technology, the Portable Emissions Acquisition System (PEAQS), to detect black carbon emissions of HD trucks as they drive on the road. We will use PEAQS readings to identify high-emitting trucks most likely to be in violation of California emissions regulations and will conduct our randomized controlled trials (RCTs) with this set of trucks as the sampling frame. Each truck will be randomly assigned either to the control group, or to receive one of two enforcement strategies that differ in cost and severity: 1) a low-cost, low-severity letter intervention targeted at a single high-emitting truck and 2) a high-cost, high-severity audit intervention covering a truck’s entire fleet.

Registration Citation

Burlig, Fiona, Ludovica Gazze and Michael Greenstone. 2022. "Using Remote Sensing to Reduce Heavy-Duty Truck Emissions in California." AEA RCT Registry. December 21.
Sponsors & Partners

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Truck-level black carbon emissions, as measured by PEAQS
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
High-emitting heavy-duty trucks identified by PEAQS will comprise our study sample frame. Each truck eligible for the study will be randomly assigned into one of two enforcement experiments to be conducted in waves every two months: 1) a low-cost letter intervention to be implemented at the truck level, and 2) a high-cost audit intervention to be implemented at the fleet level. Interventions will be carried out by CARB.
Experimental Design Details
Not available
Randomization Method
Stratified randomization done on CARB’s servers through a Python script
Randomization Unit
Fleet (for audit treatment), truck (for letter treatment)
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
6,198 trucks and 612 fleets
Sample size (or number of clusters) by treatment arms
2,600 trucks in letter treatment, 3,598 in letter control. 100 fleets in audit treatment, 512 in audit control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
University of Chicago Social & Behavioral Sciences Institutional Review Board
IRB Approval Date
IRB Approval Number
Analysis Plan

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information