24,590 rated or chosen profiles from 1,541 respondents, in up to 8 tasks of 2 profiles varying on 4 attributes.

About this table

Description	Quota-representative Dutch respondents (n = 1,541) each chose between 8 pairs of proposed green investments described by 4 randomized attributes (type, effect, place of investment, financing); outcome: forced choice.
Reference	Beaucousin, J., Kantorowicz, J. J., & Moszoro, M. W. (2025). Building support for green investments. Applied Economics Letters.
DOI	10.1080/13504851.2025.2585154
Licence	CC0 1.0
Source data	https://doi.org/10.7910/DVN/K7TEBA

Notes

Conjoint table (irw_conjoint). choice: which of two proposed green investments the respondent preferred (paraphrase; wording not deposited), forced choice, no opt-out recorded. Dutch level text as shown; attribute order randomized per respondent (attrpos_). 33 unanswered tasks dropped; no covariates deposited. 1,541 respondents, as in the deposit description.

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Respondents	1,541
Rows (one per respondent, task and profile)	24,590
Tasks per respondent (most)	8
Profiles per task	2
Attributes	4
Tasks with no profile chosen	0

Design

Topic	climate energy environment
A profile represents	policy
Country	NL
Language shown	nl
Language of stored levels	nl
Randomization restrictions	unknown
Task order	recorded in the deposit
Profile position	recorded in the deposit

Outcomes:

choice	Which of the two green investments do you prefer? (paraphrase) [choice, forced]

Columns

attr_effectattr_financingattr_placeattr_typeattrpos_effectattrpos_financingattrpos_placeattrpos_typechoiceidprofiletask

The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.

Get the data

Download CSVno account needed Browse on Redivisexplore and query Croissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("beaucousin_2025_green_investments", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("beaucousin_2025_green_investments", source="conj")

How to cite

@article{Beaucousin_2025, title={Building support for green investments}, ISSN={1466-4291}, url={http://dx.doi.org/10.1080/13504851.2025.2585154}, DOI={10.1080/13504851.2025.2585154}, journal={Applied Economics Letters}, publisher={Informa UK Limited}, author={Beaucousin, Juliette and Kantorowicz, Jarosław and Moszoro, Marian W.}, year={2025}, month=Nov, pages={1–6} }

Version and provenance

IRW version	v584
Redivis dataset	irw_conjoint v4.2
Redivis dataset DOI	10.57761/qkye-q530
Manifest pin for this IRW version	v4.2
Metadata source	irw_meta v44.0