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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xinyu Wei & Mingwang Cheng & Kaifeng Duan & Xiangxing Kong, 2024. "Effects of Big Data on PM 2.5 : A Study Based on Double Machine Learning," Land, MDPI, vol. 13(3), pages 1-21, March.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:327-:d:1350794
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    References listed on IDEAS

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