GEM: A short "Growth-vs-Environment" Module for survey research
Savin, Ivan (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Drews, Stefan (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
van den Bergh, Jeroen C. J. M. (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)

Fecha:	2021
Resumen:	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.
Ayudas:	European Commission 741087
Nota:	Unidad de excelencia María de Maeztu CEX2019-000940-M
Nota:	Altres ajuts: acords transformatius de la UAB
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Agrowth ; Degrowth ; Green growth ; Machine learning ; Public opinion
Publicado en:	Ecological economics (Amsterdam), Vol. 187 (September 2021) , art. 107092, ISSN 1873-6106

DOI: 10.1016/j.ecolecon.2021.107092

11 p, 3.2 MB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias > Institut de Ciència i Tecnologia Ambientals (ICTA) > Environmental and Climate Economics (ECE)
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