28,706 rated or chosen profiles from 2,393 respondents, in up to 6 tasks of 2 profiles varying on 7 attributes.

One of 2 tables from the same source; this one is the de table. The others are listed under Related tables.

About this table

Description	German adults (n = 2,393, quota sample) compared two mid-sized communities applying for flood-protection funding, varying in flood frequency, job change, use of funds (flood-control infrastructure or relocation incentives), lives lost, property damage, mayor's political position and location, 6 tasks; allocated a $10 million grant between them and rated support for each.
Reference	Freihardt, J., Buntaine, M. T., & Bernauer, T. (2024). Choosing to protect: public support for flood defense over relocation in climate change adaptation. Environmental Research Letters, 19(10), 104012.
DOI	10.1088/1748-9326/ad6781
Licence	CC BY 4.0
Source data	https://doi.org/10.5281/zenodo.13253754

Notes

Conjoint table (irw_conjoint_2). rating_allocation = millions of a $10 million grant given to this community (EUR in the German sample; 0-10, the two communities sum to 10, higher = more funding); rating_support = 1-5 support for funding it (5 = strongly support); no choice, no opt-out. Task 6 repeats task 1 with A and B swapped (trial_repeat_of = 1); attribute order randomized per respondent (attrpos_). IP and lat/long dropped; 5 respondents with US wording dropped, 13-17 tasks with missing answers dropped.

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

Design

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

Outcomes:

rating_allocation	Please distribute the $10 million grant between community A and community B. (translated) [rating, 0 ($0 million) to 10 ($10 million)]
rating_support	How much do you support or oppose funding for community A and community B? (translated) [rating, 1 (Strongly oppose) to 5 (Strongly support)]

Columns

attr_economic_vitalityattr_flood_frequencyattr_funding_useattr_lives_lostattr_locationattr_political_positionattr_property_damageattrpos_economic_vitalityattrpos_flood_frequencyattrpos_funding_useattrpos_lives_lostattrpos_locationattrpos_political_positionattrpos_property_damagecov_age_groupcov_birth_yearcov_educationcov_gendercov_languagecov_left_rightcov_party_ididprofilerating_allocationrating_supporttasktrial_repeat_of

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("freihardt_2024_flood_de", source = "conj")

# Python
pip install irw

import irw
df = irw.fetch("freihardt_2024_flood_de", source="conj")

How to cite

@article{Freihardt_2024, title={Choosing to protect: public support for flood defense over relocation in climate change adaptation}, volume={19}, ISSN={1748-9326}, url={http://dx.doi.org/10.1088/1748-9326/ad6781}, DOI={10.1088/1748-9326/ad6781}, number={10}, journal={Environmental Research Letters}, publisher={IOP Publishing}, author={Freihardt, Jan and Buntaine, Mark T and Bernauer, Thomas}, year={2024}, month=Aug, pages={104012} }

Respondents	2,393
Rows (one per respondent, task and profile)	28,706
Tasks per respondent (most)	6
Profiles per task	2
Attributes	7

IRW version	v584
Redivis dataset	irw_conjoint_2 v1.4
Redivis dataset DOI	10.57761/hk97-wv30
Manifest pin for this IRW version	v1.4
Metadata source	irw_meta v44.0

freihardt_2024_flood_de