利用机器学习来探索双重的亲环境意图-行为差距中的影响机制
Zihao Dong1, Yu Zhang2, Yanying Mao3,4
1School of Economics and Management, Chongqing Jiaotong University, Chongqing, 400074, China.
Scientific reports
|March 5, 2026
概括
弥合意图-行为差距是支持环境行为 (PEB) 和低碳发展的关键. 这项研究使用机器学习来分析影响负面和积极意图-行为差距的因素.
科学领域:
- 环境心理学 环境心理学
- 行为经济学是一种行为经济学.
- 机器学习应用 机器学习应用
背景情况:
- 亲环境行为 (PEBs) 对于低碳发展至关重要.
- 意图与行为之间的差距,意图与行为不匹配,阻碍了PEB的采用.
- 这项研究探讨了负面 (意图>行为) 和正面 (行为>意图) 的意图-行为差距.
研究的目的:
- 调查影响负面和积极意图-行为差距的个人和情境因素.
- 确定最佳的机器学习模型来分析这些差距.
- 提出有针对性的干预措施,以促进PEB和低碳发展.
主要方法:
- 对2216份问卷进行了分析.
- 八个机器学习算法的比较.
- 选择和应用LightGBM用于预测意图-行为差距的预测建模.
主要成果:
- 态度和责任归属显示了对合作社-灰色-PEBs的反向U形效应.
- 环境知识有助于在低意向的环境中表现行为.
- 关键态度值 (4.5) 减少了负-灰-PEBs的负差距.
- 责任赋予和自我效能减少了负面差距.
- 基础设施的可见性矛盾地扩大了由于"责任稀释效应"造成的负差距.
结论:
- 需要量身定制的策略来解决不同的意图-行为差距类型.
- 了解心理和情境因素对于有效的环境政策至关重要.
- 机器学习,特别是LightGBM,为分析复杂的行为数据提供了强大的工具.
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