Gamifying and carbon scoring urban mobility for a greener Singapore

Last registered on February 06, 2024
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Pre-Trial

Trial Information

General Information

Title
Gamifying and carbon scoring urban mobility for a greener Singapore
RCT ID
AEARCTR-0012941
Initial registration date
February 04, 2024

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
February 06, 2024, 5:24 PM EST

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

Locations

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Primary Investigator

Name
Alberto Salvo
Affiliation
National University of Singapore
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Other Primary Investigator(s)

PI Name
Leonard Lee
PI Affiliation
National University of Singapore
Contact Investigator
PI Name
Wei Lun Yuen
PI Affiliation
National University of Singapore

Additional Trial Information

Status
In development
Start date
2024-01-15
End date
2025-06-30
Keywords
Behavior, Education, Environment & Energy
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
City—and national—governments are under pressure to mitigate carbon emissions, improve air quality and public health, ease traffic gridlock, and fund public transit (Salvo and Wong, 2023). A successful energy transition implies attracting urban commuters away from private cars and into public transport. The challenge is huge because car use—whether owning or ride-hailing a car—is aspirational, convenient, and increasingly affordable relative to rising incomes, including for the 4 billion urbanizing commuters in the “rising middle classes” (Gertler et al., 2016; Oswald et al., 2020).

By conducting a Randomized Control Trial with 300 students, this project is the first step to test the hypothesis that a mix of pro-environmental messaging, carbon scoring, and gamification-based rewards encourages public transit (Salvo and Lee, 2023; see the external link below). Researchers from the National University of Singapore are collaborating with economists from Singapore's urban transport agency, the Land Transit Authority, on this research.
External Link(s)
Op-ed published in Singapore's Business Times on September 16, 2023: How open data and gamification can make Singapore’s urban travel greener

Registration Citation

Citation
Lee, Leonard, Alberto Salvo and Wei Lun Yuen. 2024. "Gamifying and carbon scoring urban mobility for a greener Singapore." AEA RCT Registry. February 06. https://doi.org/10.1257/rct.12941-1.0
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Experimental Details

Interventions

Intervention(s)
We are testing the hypothesis that... (withheld because the intervention starts next week)
Intervention Start Date
2024-02-05
Intervention End Date
2024-05-06

Primary Outcomes

Primary Outcomes (end points)
Modal share of a subject's trips across all elements of their choice set {private car, public transport, walking, bicycling}
Modal share of a subject's distance traveled across all elements of their choice set {private car, public transport, walking, bicycling}
Modal share of a subject's trips across motorized elements of their choice set {private car, public transport}
Modal share of a subject's distance traveled across motorized elements of their choice set {private car, public transport}
Subject's average carbon score (see explanation)
The above are outcomes of interest, for each phase A to E of the experiment.
The intervention starts in phase B on February 5, 2024, after this trial was registered.
Primary Outcomes (explanation)
Explanation of a subject's average carbon score, as communicated to subjects (where applicable; see Experimental Design):
We calculate your average score based on your motorized mobility choices:
• [High] 200 grams of CO2 emissions for EVERY km traveled in a private car with no other traveler except you (size of travel party = 1)
• [Medium] 100 grams of CO2 emissions for EVERY km traveled in a private car if you are car-pooling with at least one more traveler (size of travel party > 1)
• [Low] 40 grams of CO2 emissions for EVERY km traveled in public transport (bus or train)
For example, if tomorrow I ride 10 km in a car just myself and the Grab driver, 10 km in a car with a friend who is traveling with me, and 10 km on the MRT, my Average Carbon Score will show (10*200 + 10*100 + 10*40) / (10+10+10) = 113 grams of CO2 per motor km.
These numbers are approximate emissions for the different travel modes while keeping things simple. They are good rules of thumb!

Secondary Outcomes

Secondary Outcomes (end points)
Subject's total number of trips across all elements of their choice set {private car, public transport, walking, bicycling}
Subject's total distance traveled across all elements of their choice set {private car, public transport, walking, bicycling}
Subject's trips across motorized elements of their choice set {private car, public transport}
Subject's distance traveled across motorized elements of their choice set {private car, public transport}
Car-pooling share of subject's private car trips (size of traveling party > 1, as explained in the briefing)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design: Three groups (one control, two treatment groups) and five phases (including a pre-intervention phase and a post-intervention phase). See Docs & Materials for a chart illustrating the experimental design.
Experimental Design Details
Not available
Randomization Method
Randomization done in office computer.
Randomization Unit
Individual. We randomized within each of the three "clusters" (categories or types) of students. See Intervention above. Within each category, one-third of students were assigned to Control, one-third of students were assigned to treatment Environmental Messaging (EM), and one-third of students were assigned to treatment Environmental Messaging with financial Incentives (EM+I)
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
340 students were briefed and joined the experiment.
Sample size: planned number of observations
340 students were briefed and joined the experiment.
Sample size (or number of clusters) by treatment arms
114 students (who were briefed and consented to participate) were randomly assigned to Control, 112 students were randomly assigned to treatment Environmental Messaging (EM), and 114 students were randomly assigned to treatment Environmental Messaging with financial Incentives (EM-I).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Total sample size = 246; effect size f = .1; alpha error probability = .05; 1-beta error probability = .8; number of groups = 3; see page 12 of the application to the Institute for Public Understanding of Risk Seed Grant, submitted December 15, 2023 (timestamped and available on demand).
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Economics Department DERC
IRB Approval Date
2023-12-27
IRB Approval Number
N/A
Analysis Plan