E-Biking in Switzerland (EBIS)

Last registered on May 19, 2023
View Trial History

Pre-Trial

Trial Information

General Information

Title
E-Biking in Switzerland (EBIS)
RCT ID
AEARCTR-0010266
Initial registration date
November 15, 2022

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 18, 2022, 12:17 PM EST

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

Last updated
May 19, 2023, 11:45 AM EDT

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

Locations

Country
Switzerland
Region
Countrywide excluding Ticino

Primary Investigator

Name
Lucas Meyer de Freitas
Affiliation
ETH Zürich
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Jakob Roth
PI Affiliation
University of Basel
Contact Investigator
PI Name
Laura Schwab
PI Affiliation
University of Basel
Contact Investigator
PI Name
Adrian Meister
PI Affiliation
ETH Zurich
Contact Investigator
PI Name
Kay Axhausen
PI Affiliation
ETH Zurich
Contact Investigator
PI Name
Beat Hintermann
PI Affiliation
University of Basel
Contact Investigator
PI Name
Thomas Götschi
PI Affiliation
University of Oregon
Contact Investigator

Additional Trial Information

Status
On going
Start date
2022-09-01
End date
2023-07-31
Keywords
Behavior, Environment & Energy
Additional Keywords
Transport pricing, E-biking
JEL code(s)
D12, D62, H23, Q58, R41, R48
Secondary IDs
SI/502348-01
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We investigate the potential for reducing carbon emissions in the
transport sector due to e-biking based on a large sample of e-bikers in
Switzerland and using a combination of GPS tracking and surveys. The
first part of the project assesses the current situation and improves on
the present state of knowledge about mode shift due to e-biking and the
transport choices of e-bikers. The second part consists in a randomized
controlled trial, in which mobility pricing based on the external costs of
transport is implemented for a subset of the participants. We measure
the resulting causal effects on carbon emissions and the other external
costs of transport (health, congestion) and investigate mode substitution,
with a special focus on e-biking. Last, the potential for carbon reductions
due to e-biking in the population as a whole is computed, including un-
der different scenarios, i.e., future mobility pricing and transport policies.
External Link(s)
Project website

Registration Citation

Citation
Axhausen, Kay et al. 2023. "E-Biking in Switzerland (EBIS)." AEA RCT Registry. May 19. https://doi.org/10.1257/rct.10266-1.2
Sponsors & Partners

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

Request Information
Experimental Details

Interventions

Intervention(s)


Intervention Start Date
2022-10-05
Intervention End Date
2023-07-31

Primary Outcomes

Primary Outcomes (end points)
Change in distances traveled, modes, and departure time in response to Pigovian transport pricing.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Reduction in CO2 emissions, health effects, accidents and congestion if this pricing were used countrywide.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
After a baseline survey to gather basic information related to travel and demographics, participants will be tracked for a purely observational baseline period of 4 weeks, followed by a retrospective survey and another period of 5 weeks of observation. We will make use of a GPS-based technology that tracks the movement of participants through an app on their phone and automatically determines travel modes based on map matching, speeds and departure time tables. The app has been extensively tested in an earlier study during which we successfully tracked over 3,600 participants. This approach will allow us to investigate mode choice and travel behaviour in much more detail than was previously possible in studies solely relying on self-reported travel data. All participants receive CHF 50 to complete the study.

Experimental Design Details
Based on prior work by the team and data from the Swiss Federal Office of Spatial Development, we compute the external costs of transport for each trip. These consist of health effects from local pollution and accidents, congestion, and climate damages.

During the observation period, all subjects receive weekly feedback about their distances by mode. After 4 weeks, the sample is randomly split into a control and a treatment group. The control group continues to receive the same feedback as before. The treatment group is informed about external costs of transport and provided a budget (this is based on the observed external costs produced by each person during the observation period). From this budget, we then subtract the external costs that each subject in the treatment group generates during the remaining 5 weeks of the trial. In this sense, the treatment consists of a Pigovian-inspired transport price.

The personal budgets are computed based on the external costs during the observation period plus an extra 20%. If a person's personal budget is exhausted despite this buffer, we will add another 20% of the expected external costs during the trial (at least CHF 10, at most CHF 50). If the budget is exhausted again, the participant will still receive the full incentive payment of CHF 50.

In order to study the substitution between E-bikes and cars in daily transport, we restrict the trial to people that (i) own an E-bike and (ii) regularly drive.

This will deliver causal estimates of the effects to be expected of a future implementation of mobility pricing or some other transport policy that increases the attractiveness of e-biking. To further explore the potential for increasing e-bike use, we will include survey questions related to constraints and barriers associated with bicycling.
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individuals. For people that live in the same household as someone else in the study, the group assignment is the same as that of the first household member.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1000 participants
Sample size: planned number of observations
1000 participants
Sample size (or number of clusters) by treatment arms
500 individuals control, 500 individuals treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
This sample size will allow us to detect a reduction of the external costs of transport by 5% with a probability of 80%.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
ETH Zurich Ethics comission
IRB Approval Date
2022-09-22
IRB Approval Number
EK 2022-N-54
Analysis Plan

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

Request Information

Post-Trial

Post Trial Information

Study Withdrawal

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

Request Information

Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials