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.

被调查者的细分是环境交流中的常用工具，因为它有助于理解人们的意见并传递有针对性的信息。先前的研究根据人们对经济增长与环境可持续性之间关系的看法对他们进行了细分。其中包括对16条陈述的评估，这意味着要花费大量的调查时间和成本（尤其是如果由第三方管理），并且还会增加受访者的认知负担，从而增加不完全答复的可能性。在这项研究中，我们将机器学习算法应用于公民和科学家过去的调查结果，以识别出一系列健壮的问题，这些问题可以准确地将受访者对增长与环境的看法进行细分。特别是，我们将绿色增长分为三类，生长与消亡。为此，我们确定了五种看法，分别是“环境保护”，“公共服务”，“生活满意度”，“稳定”和“发展空间”。跨调查和意见细分的预测准确性范围在81％到89％之间。我们将提出的关于增长与环境的问题集应用于2020年以后的新调查，以说明其在未来调查中作为一种有效工具的用途。