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題名 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
貢獻者 統計系
關鍵詞 particulate pollution; labor productivity; insurance; machine learning
日期 2023-06
上傳時間 13-Dec-2023 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
資料類型 article
DOI https://doi.org/10.3390/su15129404
dc.contributor 統計系
dc.creator (作者) 楊素芬
dc.creator (作者) Yang, Su-Fen;Muzayyanah, Syamsiyatul;Hong, Cheng-Yih;Adha, Rishan
dc.date (日期) 2023-06
dc.date.accessioned 13-Dec-2023 13:55:10 (UTC+8)-
dc.date.available 13-Dec-2023 13:55:10 (UTC+8)-
dc.date.issued (上傳時間) 13-Dec-2023 13:55:10 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/148701-
dc.description.abstract (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.
dc.format.extent 98 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Sustainability, Vol.15, No.12, 9404
dc.subject (關鍵詞) particulate pollution; labor productivity; insurance; machine learning
dc.title (題名) The Non-Linear Relationship Between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.3390/su15129404
dc.doi.uri (DOI) https://doi.org/10.3390/su15129404