机器学习可以识别关键的个人和国家层面的因素,预测气候相关的信念和行为
Boryana Todorova1, David Steyrl1, Matthew J Hornsey2
1Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.
npj climate action
|May 12, 2025
概括
这项研究对55个国家影响气候信仰和行动的因素进行了排名. 环境认同,对气候科学的信任和动机始终预测气候友好的结果.
科学领域:
- 环境心理学 环境心理学
- 关于气候变化的沟通
- 跨国研究跨国研究
背景情况:
- 许多研究探讨了气候友好信念和行为,但它们的相关性缺乏系统的跨国排名.
- 了解关键预测因素对于制定全球有效的气候变化干预和政策至关重要.
研究的目的:
- 系统地对不同国家的气候相关结果的个人和国家级预测指标进行排名.
- 量化气候变化信念,政策支持,信息共享和亲环境行为的可预测性.
- 在多个与气候相关的结果中识别预测因素的一致和分歧模式.
主要方法:
- 利用可解释的机器学习技术来分析来自55个国家的数据 (N=4635).
- 量化了19个个人和国家一级预测因素对四个气候相关结果的重要性.
- 评估每个结果的解释差异,并根据其影响对预测因素进行排名.
主要成果:
- 解释差异在各个结果中显著不同,从气候变化信仰的57%到支持环境行为的10%.
- 环保主义者身份,对气候科学的信任,内部环境动机,以及人类发展指数一致预测了所有结果.
- 大多数预测因素表现出不同的模式,影响某些结果而不是其他结果,甚至显示相反的效果.
结论:
- 复杂的因素相互作用影响气候相关的认知和行动,在不同的结果和背景下具有不同的重要性.
- 未来的研究应该纳入多层次的因素,并考虑不同的环境 (公共与私人),以更好地模拟与气候有关的现象.
- 调查结果强调了需要在气候变化沟通和政策制定方面采取细微的方法,承认跨国和个人差异.
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