Unlocking sustainability through data: the environmental effects of public data

Abstract

Public data access may improve urban environmental outcomes by expanding the information available to firms and the public. This study applies the double/debiased machine learning (DDML) framework to examine the effects of public data access platforms (PDAPs) on air pollutants and carbon emissions across Chinese cities. The results show that PDAPs significantly reduce both air pollution and carbon emissions, indicating a joint environmental benefit of public data access. Mechanism analyses suggest that these effects are associated with improvements in total factor energy efficiency, green innovation, as well as greater public environmental attention and participation. Heterogeneity analysis shows stronger pollution-reduction effects in cities with higher baseline pollution, while evidence related to market integration is concentrated in SO₂-related outcomes. Sectoral analyses further show that PDAPs reduce CO₂ emissions from industry, construction, transport, and waste. These findings highlight the role of public data infrastructure in supporting integrated air-quality and climate governance and provide policy implications for the design of public data access initiatives in urban environmental management.

Keywords

Public data / Air pollutant and carbon reduction / Double/debiased machine learning

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Funding

National Natural Science Foundation of China(72503092)

Natural Science Foundation of Shandong Province(ZR2024QG046)

Rights & permissions

The Author(s), under exclusive licence to the International Society of Energy and Environmental Science