Driven by next-generation digital technologies, environmental big data supervision has profoundly disrupted traditional environmental governance models and emerged as a major engine for modernizing ecological and environmental governance capabilities. Guided by the “dual carbon” goals, clarifying the internal logic of environmental big data supervision in the ecological and environmental domain, and scientifically evaluating its policy effect in the collaborative governance of pollution and carbon reduction, are of significant theoretical and practical importance for cultivating and developing green productivity.

Based on panel data from 120 prefecture-level cities in China between 2011 and 2022, this paper employs an intensity DID model. Using the establishment of municipal big data bureaus as an exogenous shock and the smart environmental protection index as the treatment intensity, it empirically examines the green and low-carbon governance effect of environmental big data supervision. The results show that environmental big data supervision significantly reduces both sulfur dioxide emission intensity and carbon emission intensity at the city level. Mechanism testing indicates that digitally-driven environmental big data supervision disrupts conventional environmental regulation models and promotes regional green and low-carbon governance through enhanced government environmental governance capacity and the stimulation of green technology innovation. Further analysis shows that environmental big data supervision helps advance pollution control in border areas and facilitates regional coordination in pollution governance; however, its effect in non-city clusters remains relatively limited at this stage.

This paper makes the following marginal contributions: First, it identifies the green and low-carbon governance effect of environmental big data supervision as a digital governance tool deployed by the government. Second, it integrates environmental big data supervision into the collaborative governance framework of “institution-market-society” in the ecological and environmental domain, illustrating how the effectiveness of this new governance tool is moderated by and dependent upon environmental legal systems, carbon market transactions, and public engagement. Third, it explores the boundary conditions that constrain the policy effect of environmental big data supervision, revealing their heterogeneous impacts on pollution and carbon reduction across border versus non-border areas, and city versus non-city clusters.