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Unveiling the impact of public data access on collaborative reduction of pollutants and carbon emissions: Evidence from open government data policy

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  • Wu, Desheng
  • Xie, Yu

Abstract

The renewable characteristic of data resources offers a promising solution to China's dual challenges of climate change and environmental pollution, yet empirical evidence on whether and how data accessibility affects environmental performance remains limited. Leveraging the policy shock of Open Government Data (OGD) in China, we employ a staggered difference-in-differences approach to investigate the impact of public data access on firm-level pollutants and carbon emissions, as well as their synergistic reduction effects. Our findings indicate that OGD significantly reduces exhaust gas, wastewater, solid waste, and CO2 emissions by 21.7%, 28.61%, 15.32%, and 10.36%, respectively, achieving prominent synergistic green benefits. Dynamic analysis demonstrates long-term effectiveness in reducing exhaust gas, wastewater, and CO2 emissions, but highlights risks of policy failure concerning solid waste and synergistic effects. The green benefits of OGD are linked to governance capability and enterprise life cycle, with higher governance capability and growing enterprises exhibiting more pronounced benefits, while the uneven impact on regional distribution, firm ownership, and digital technology reserves exacerbates the digital divide. Based on the resource-based theory, we reveal that the productivity enhancement, digital transformation, and citizen participation are the main channels through which OGD exerts its green effects. Our research provides valuable insights for policymakers on promoting sustainability by unlocking the green value of data resources.

Suggested Citation

  • Wu, Desheng & Xie, Yu, 2024. "Unveiling the impact of public data access on collaborative reduction of pollutants and carbon emissions: Evidence from open government data policy," Energy Economics, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:eneeco:v:138:y:2024:i:c:s0140988324005309
    DOI: 10.1016/j.eneco.2024.107822
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