GEM: A short “growth-vs-environment” module for survey research

Ivan Savin*, Stefan Drews, Jeroen van den Bergh

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Article number107092
Pages (from-to)1-11
Number of pages11
JournalEcological Economics
Volume187
Early online date17 May 2021
DOIs
Publication statusPublished - Sep 2021

Bibliographical note

Funding Information:
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 ].

Publisher Copyright:
© 2021 The Authors

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Agrowth
  • Degrowth
  • Green growth
  • Machine learning
  • Public opinion

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