评估欧洲城市固体废物产生的决定因素:一种机器学习方法
Una Smailbegovic1, Edin Kadric2, Simon Glöser-Chahoud1
1Chair of Corporate Sustainability and Environmental Management, Faculty of Business Administration, TU Bergakademie Freiberg, Schlossplatz 1, 09599 Freiberg, Germany.
Waste management (New York, N.Y.)
|July 27, 2025
概括
有效的废物管理对于气候目标至关重要. 这项研究使用机器学习来分析欧洲城市固体废物 (MSW) 产生的情况,发现年平均工资在不同国家有很大的影响率.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 经济学 经济学 经济学
背景情况:
- 废物管理对全球温室气体排放和气候目标产生重大影响.
- 之前关于废物产生率的研究往往在地理上受到限制,专注于特定的地区或市镇.
- 了解影响不同欧洲国家的废物产生因素对于有效的政策至关重要.
研究的目的:
- 分析欧洲废物管理实践和立法.
- 确定影响城市固体废物 (MSW) 生产率的关键社会经济,人口和环境因素.
- 开发和评估机器学习 (ML) 模型,用于在欧洲范围内预测MSW产生的情况.
主要方法:
- 收集了40个欧洲国家的14个因素 (2000-2021) 的数据.
- 开发并比较了三个ML模型:人工神经网络 (ANN),支持向量回归 (SVR) 和内核回归 (KRR).
- 利用SHAP分析来确定个别因素对MSW产生的预测的影响,包括按收入分组国家.
主要成果:
- 所有的ML模型都表现出高的预测准确度,尽管数据异质.
- 核心回归 (KRR) 在一般和收入分组模型中始终显示出最佳表现.
- 平均年薪被确定为MSW生产率最重要的预测因素.
结论:
- 机器学习模型有效地预测了不同欧洲国家的MSW生产率.
- 收入水平和年平均工资是影响废物产生的关键因素.
- 这些发现支持了协调环境政策和跨国合作的必要性,以改善废物管理和实现气候目标.
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