Threshold Effects of PM 2.5 on Pension Contributions: A Fuzzy Regression Discontinuity Design and Machine Learning Approach

Bingxia Wang, Zailan Siri () and Mohd Azmi Haron
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Bingxia Wang: Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumper 50603, Malaysia
Zailan Siri: Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumper 50603, Malaysia
Mohd Azmi Haron: Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumper 50603, Malaysia

Sustainability, 2025, vol. 17, issue 19, 1-27

Abstract: Air pollution risk significantly impacts social and economic systems. Given the critical role of the pension system in socioeconomic stability, it is crucial to explore the impact of air pollution on pension contributions. Utilizing panel data from eight Chinese provinces between 2014 and 2024, this study quantifies the impact of Particulate Matter (PM 2.5 ) on pension contributions and explores its nonlinear and lagged effects through a fuzzy regression discontinuity design (FRDD) coupled with double machine learning (DML) techniques. Through the application of the FRDD, we found that pension contributions are significantly reduced when the PM 2.5 concentration exceeds the standard annual threshold of 35 µ g / m 3 , and the effects differ between the Urban Employees Basic Pension Insurance (UEBPI) and the Urban and Rural Residents’ Pension Scheme (URRPS). Further, the DML approach validated these findings and suggested that a complex hysteresis response mechanism exists in relation to air pollution. Additionally, it indicated that when PM 2.5 concentrations do not exceed the threshold, this similarly has a negative effect on pension contributions. These findings emphasize the need for policymakers and pension fund managers to integrate environmental considerations into pension sustainability strategies to increase resilience to ongoing environmental risks.

Keywords: PM 2.5 concentration; air pollution risk; fuzzy regression discontinuity design; double machine learning method; pension contribution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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