Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103170
Title: GEM: A short “growth-vs-environment” module for survey research
Authors: Savin, I.
Drews, S.
van den Bergh, J.
Issue Date: 2021
Publisher: Elsevier B.V.
Citation: Savin I. GEM: A short “growth-vs-environment” module for survey research / I. Savin, S. Drews, J. van den Bergh. — DOI 10.1016/j.ecolecon.2021.107092 // Ecological Economics. — 2021. — Vol. 187. — 107092.
Abstract: Segmentation of survey respondents is a common tool in environmental communication as it helps to understand opinions of people and to deliver targeted messages. Prior research has segmented people based on their opinions about the relationship between economic growth and environmental sustainability. This involved an evaluation of 16 statements, which means considerable survey time and cost, particularly if administered by a third party, as well as cognitive burden on respondents, increasing the chance of incomplete responses. In this study, we apply a machine learning algorithm to results from past surveys among citizens and scientists to identify a robust, minimal set of questions that accurately segments respondents regarding their opinion on growth versus the environment. In particular, we distinguish three groups, called Green growth, Agrowth and Degrowth. To this end, we identify five perceptions, namely regarding ‘environmental protection’, ‘public services’, ‘life satisfaction’, ‘stability’ and ‘development space’. Prediction accuracy ranges between 81% and 89% across surveys and opinion segments. We apply the proposed set of questions on growth-vs-environment to a new survey from 2020 to illustrate its use as an efficient instrument in future surveys. © 2021 The Authors
Keywords: AGROWTH
DEGROWTH
GREEN GROWTH
MACHINE LEARNING
PUBLIC OPINION
ALGORITHM
ECONOMIC GROWTH
ENVIRONMENTAL ASSESSMENT
ENVIRONMENTAL PROTECTION
LIFE SATISFACTION
MACHINE LEARNING
PERCEPTION
PUBLIC SERVICE
RESEARCH WORK
SUSTAINABILITY
SUSTAINABLE DEVELOPMENT
URI: http://hdl.handle.net/10995/103170
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85106283258
PURE ID: 22099627
e0662fe8-bade-4f63-a035-23a86575c5a3
ISSN: 9218009
DOI: 10.1016/j.ecolecon.2021.107092
metadata.dc.description.sponsorship: This work was funded by an ERC Advanced Grant from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme [grant agreement n° 741087 ]. I.S. acknowledges financial support from the Russian Science Foundation [RSF grant number 19-18-00262 ].
RSCF project card: 19-18-00262
CORDIS project card: 741087
741087
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