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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/148701


    Title: The Non-Linear Relationship Between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach
    Authors: 楊素芬
    Yang, Su-Fen;Muzayyanah, Syamsiyatul;Hong, Cheng-Yih;Adha, Rishan
    Contributors: 統計系
    Keywords: particulate pollution;labor productivity;insurance;machine learning
    Date: 2023-06
    Issue Date: 2023-12-13 13:55:10 (UTC+8)
    Abstract: 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.
    Relation: Sustainability, Vol.15, No.12, 9404
    Data Type: article
    DOI link: https://doi.org/10.3390/su15129404
    DOI: 10.3390/su15129404
    Appears in Collections:[Department of Statistics] Periodical Articles

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