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

Last registered on November 25, 2020

Pre-Trial

Trial Information

General Information

Title
Using Remote Sensing to Reduce Heavy-Duty Truck Emissions in California
RCT ID
AEARCTR-0006812
Initial registration date
November 24, 2020
Last updated
November 25, 2020, 10:33 AM EST

Locations

Primary Investigator

Affiliation
University of Chicago

Other Primary Investigator(s)

PI Affiliation
University of Warwick
PI Affiliation
University of Chicago

Additional Trial Information

Status
On going
Start date
2019-06-01
End date
2022-12-31
Secondary IDs
Abstract
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

Citation
Burlig, Fiona, Ludovica Gazze and Michael Greenstone. 2020. "Using Remote Sensing to Reduce Heavy-Duty Truck Emissions in California." AEA RCT Registry. November 25. https://doi.org/10.1257/rct.6812-3.0
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-02-01
Intervention End Date
2022-03-01

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?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
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)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Chicago Social & Behavioral Sciences Institutional Review Board
IRB Approval Date
2020-11-20
IRB Approval Number
IRB20-1846