评估企业环境投资的决定因素:一种机器学习方法
Environmental science and pollution research international
|February 10, 2024
概括
企业的环境投资决策受到公司特点,董事会组成和首席执行官特征的影响. 机器学习模型,特别是XGBoost,准确地预测了这些投资,销售业绩是关键驱动因素.
科学领域:
- 环境管理环境管理
- 企业金融公司财务
- 机器学习应用 机器学习应用
背景情况:
- 学术界对影响企业环境投资的因素越来越感兴趣.
- 最近的评估方法与传统的指标系统之间存在差异.
- 需要先进的分析方法来理解投资决定因素.
研究的目的:
- 使用机器学习方法调查企业环境投资的决定因素.
- 分析公司,董事会,主席和首席执行官特征的影响.
- 为了比较机器学习模型和解释关键预测因素.
主要方法:
- 使用了机器学习方法,特别是极端梯度增强 (XGBoost) 模型.
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
- 分析了中国上市公司的大型数据集,包括严重污染的企业.
主要成果:
- XGBoost模型实现了97.63%的高精度.
- 确定了关键决定因素:销售业绩,首席执行官任期,董事会独立性,董事会性别多样性,主席学术经验和国际化.
- 对于严重污染的公司来说,销售,董事会性别多样性,首席执行官任期,主席学术经验,董事会独立性和主席-首席执行官双重性至关重要.
结论:
- 机器学习,特别是XGBoost与SHAP,有效地识别了推动企业环境投资的因素.
- 销售业绩成为主要影响因素,其次是各种公司治理和领导力属性.
- 调查结果为政策制定者和环境管理和企业战略的实践者提供了实用的见解.
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