集群能源支持信念,以使用自组织地图揭示独特的子群体
Heather Bedle1, Christopher R H Garneau2, Alexandro Vera-Arroyo1
1School of Geosciences, University of Oklahoma, USA.
Heliyon
|July 31, 2023
概括
美国人对能源有着复杂的看法,受人口统计学的影响. 机器学习确定了四个不同的能源信仰群体,这对于了解公众对能源政策的看法至关重要.
科学领域:
- 社会科学研究社会科学研究
- 能源政策分析 能源政策分析
- 机器学习应用程序 机器学习应用程序
背景情况:
- 关于能源的公众意见是复杂的,并且根据社会人口统计学因素而有所不同.
- 了解这些不同的能源信仰对于有效的能源政策制定至关重要.
- 之前的研究往往忽略了选民中微妙的能源偏好模式.
研究的目的:
- 通过机器学习,根据个人对能源的信仰对个人进行分类.
- 研究能源偏好集群与对能源政策的态度之间的关系.
- 将新的聚类技术应用于对能源意见的社会调查数据.
主要方法:
- 使用自组织地图 (SOM) 进行无监督的调查受访者聚类.
- 皮尤研究中心 (春季2021) 关于能源意见的社会调查数据.
- 应用回归模型来分析特定集群对能源政策态度的预测能力.
主要成果:
- 确定了四个不同的集群:能源传统主义者,能源更新者,能源普世主义者,以及一个异常的太阳能/风能偏好群体.
- 证明这些SOM衍生集群对美国能源政策的态度具有高度预测性.
- 突出了公众对可再生能源与化石燃料能源的不同意见.
结论:
- 机器学习,特别是SOM,有效地分类人类对能源的复杂态度.
- 识别的能源信念集群为与公众论接触的政策制定者提供了有价值的见解.
- 了解这些不同的选民群体对于制定有针对性和有效的能源战略至关重要.
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