Information interventions and household carbon footprints in China

Last registered on November 17, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Information interventions and household carbon footprints in China

RCT ID

AEARCTR-0012325

Initial registration date

November 09, 2023

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 17, 2023, 7:59 AM EST

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

Locations

Country

China

Region

Shanghai

Primary Investigator

Name

Yuanwei Xu

Affiliation

Ruhr University Bochum

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Christoph Feldhaus

PI Affiliation

PI Name

Andreas Loeschel

PI Affiliation

Additional Trial Information

Status

On going

Start date

2023-05-01

End date

2024-06-30

Keywords

Behavior, Environment & Energy

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial is based on or builds upon one or more prior RCTs.

Abstract

We run a RCT to test the impact of information interventions on reducing household carbon footprints. A link to access the personalized report on household carbon footprints is sent via a text message. The impact of the following three elements in reducing carbon footprints are tested: (i) the detailed structure of household carbon footprints, (ii) peer comparison, and (iii) personalized feedback. individuals who participated in the baseline survey (N=3,000) are randomized into four groups, with the control group receiving only the carbon footprint amount, treatment group 1 receiving the amount and structure, treatment group 2 the amount and structure plus peer comparison, and treatment group 3 personalized feedback in addition. We calculate carbon footprints based on itemized expenditures on different activites, which are self-reported in online surveys. We would exploit variations within individuals (before and after the information intervention) and between treatment arms to analyze the impact of different information interventions on reducing household carbon footprints.

External Link(s)

Registration Citation

Citation

Feldhaus, Christoph, Andreas Loeschel and Yuanwei Xu. 2023. "Information interventions and household carbon footprints in China." AEA RCT Registry. November 17. https://doi.org/10.1257/rct.12325-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2023-05-01

Intervention End Date

2023-05-31

Primary Outcomes

Primary Outcomes (end points)

Real household per capita carbon footprints (calculated based on itemized expenditures); intended changes in carbon footprints.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We randomly assign the 3,000 people who participated in the baseline survey, where we collected socio-economic characteristics as well as household carbon footprints, into one of the following four groups. They will receive a personalized report of their household carbon footprints via a webpage link sent using text messages.
Group 1 (control group, 750 people) would receive a link with the total per capita carbon footprint.
Group 2 (treatment 1, 750 people) would receive a link with the total per capita carbon footprint, and the structure of the carbon footprint (a pie figure indicating the relative share of carbon footprints as well as the numeric amount due to (i) food consumption, (ii) private transportation, (iii) public transportation, (iv) household purchases, (v) household energy use, and (vi) household finances).
Group 3 (treatment 2, 750 people) would receive a link with the total per capita carbon footprint, the structure of the carbon footprint (a pie figure indicating the relative share of carbon footprints as well as the numeric amount due to (i) food consumption, (ii) private transportation, (iii) public transportation, (iv) household purchases, (v) household energy use, and (vi) household finances), peer comparison (we compare them with the mean values of all the households with the same household size in the sample, and we show them a pie figure indicating the relative share of carbon footprints as well as the numeric amount), and the estimated monetary saving if they could reduce carbon footprints by 10%.
Group 4 (treatment 3, 750 people) would receive a link with the total per capita carbon footprint, the structure of the carbon footprint (a pie figure indicating the relative share of carbon footprints as well as the numeric amount due to (i) food consumption, (ii) private transportation, (iii) public transportation, (iv) household purchases, (v) household energy use, and (vi) household finances), peer comparison (we compare them with the mean values of all the households with the same household size in the sample, and we show them a pie figure indicating the relative share of carbon footprints as well as the numeric amount), the estimated monetary saving if they could reduce carbon footprints by 10%, and personalized feedback.

Experimental Design Details

Randomization Method

Randomization done in office by a computer using Stata.

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

3,000 individuals

Sample size: planned number of observations

All the 3,000 individuals who participated in the baseline survey, but the tracking rate in the follow-up survey is hard to be estimated.

Sample size (or number of clusters) by treatment arms

750 individuals receive per capita carbon footprints only (control), 750 per capita footprints + footprint structure, 750 per capita footprints + footprint structure + peer comparison + estimated monetary benefit, 750 per capita footprints + footprint structure + peer comparison + estimated monetary benefit + personalized feedback.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB