Effects of Big Data on PM 2.5: A Study Based on Double Machine Learning

Xinyu Wei, Mingwang Cheng, Kaifeng Duan () and Xiangxing Kong
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Xinyu Wei: School of Economics and Management, Tongji University, Shanghai 200092, China
Mingwang Cheng: School of Economics and Management, Tongji University, Shanghai 200092, China
Kaifeng Duan: School of Economics and Management, Fuzhou University, Fuzhou 350108, China
Xiangxing Kong: School of Economics and Management, Tongji University, Shanghai 200092, China

Land, 2024, vol. 13, issue 3, 1-21

Abstract: The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This study employs a double machine learning model to explore the impact of big data development on the PM 2.5 concentration in 277 prefecture-level cities across China. This analysis is grounded in the quasi-natural experiment named the National Big Data Comprehensive Pilot Zone. The findings reveal a significant inverse relationship between big data development and PM 2.5 levels, with a correlation coefficient of −0.0149, a result consistently supported by various robustness checks. Further mechanism analyses elucidate that big data development markedly diminishes PM 2.5 levels through the avenues of enhanced urban development and land use planning. The examination of heterogeneity underscores big data’s suppressive effect on PM 2.5 levels across central, eastern, and western regions, as well as in both resource-dependent and non-resource-dependent cities, albeit with varying degrees of significance. This study offers policy recommendations for the formulation and execution of big data policies, emphasizing the importance of acknowledging local variances and the structural nuances of urban economies.

Keywords: big data development; PM 2.5; double machine learning; land use; high-quality urban development (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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