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    题名: The Non-Linear Relationship Between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach
    作者: 楊素芬
    Yang, Su-Fen;Muzayyanah, Syamsiyatul;Hong, Cheng-Yih;Adha, Rishan
    贡献者: 統計系
    关键词: particulate pollution;labor productivity;insurance;machine learning
    日期: 2023-06
    上传时间: 2023-12-13 13:55:10 (UTC+8)
    摘要: This study explores the non-linear relationship between air pollution, socio-economic factors, labor insurance, and labor productivity in the industrial sector in Taiwan. Using machine learning, specifically multivariate adaptive regression splines (MARS), provides an alternative approach to examining the impact of air pollution on labor productivity, apart from the traditional linear relationships and parametric methods employed in previous studies. Examining this topic is imperative for advancing the knowledge on the effects of air pollution on labor productivity and its association with labor insurance, employing a machine learning framework. The results reveal that air pollution, particularly PM10, has a negative impact on labor productivity. Lowering the PM10 level below 36.2 μg/m3 leads to an increase in marginal labor productivity. Additionally, the study identifies labor insurance as a significant factor in improving productivity, with a 9% increase in the total number of labor insurance holders resulting in a substantial 42.9% increase in productivity. Notably, a link between air pollution and insurance is observed, indicating that lower air pollution levels tend to be associated with higher labor insurance coverage. This research holds valuable implications for policymakers, businesses, and industries as it offers insights into improving labor productivity and promoting sustainable economic development.
    關聯: Sustainability, Vol.15, No.12, 9404
    数据类型: article
    DOI 連結: https://doi.org/10.3390/su15129404
    DOI: 10.3390/su15129404
    显示于类别:[統計學系] 期刊論文

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