GEM
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 sustainabilit...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:243354 |
| Acceso en línea: | https://ddd.uab.cat/record/243354 https://dx.doi.org/urn:doi:10.1016/j.ecolecon.2021.107092 |
| Access Level: | acceso abierto |
| Palabra clave: | Agrowth Degrowth Green growth Machine learning Public opinion |
| Sumario: | 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. |
|---|