ArticleFrontiers in public health2026
Mitigating environmental public health risks via artificial intelligence: mechanisms and boundary conditions.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Environmental pollution threatens population health through multiple and overlapping pathways, including gaseous emissions, wastewater discharge, and industrial solid waste. Artificial intelligence (AI) can improve environmental monitoring, energy management, and production optimization, but its broader relationship with multidimensional environmental public health risks remains insufficiently understood. This study examines whether and under what conditions artificial intelligence is associated with lower pollution-related environmental public health risks. Methods: Provincial panel data covering 30 regions in China from 2014 to 2023 were analyzed. A multidimensional environmental public health risk index was constructed from carbon dioxide emissions, sulfur dioxide emissions, nitrogen oxide emissions, industrial wastewater discharge, and industrial solid waste. Two-way fixed-effects models were combined with mediation analysis, heterogeneity and marginal-effect analysis, alternative measurement, additional control and winsorization tests, dynamic panel estimation, and a panel threshold model. Results: Higher levels of artificial intelligence were associated with lower environmental public health risks in the principal fixed-effects models, and the negative relationship remained consistent across alternative measurement, additional digital-infrastructure controls, winsorization, and concurrent policy specifications. Technological expenditure emerged as an implementation pathway through which digital capability can be translated into monitoring systems, cleaner equipment, and environmental management infrastructure, while green patents reflected a longer-horizon innovation process. Environmental investment strengthened the negative association by providing the financial and physical capacity required for artificial intelligence deployment. Electricity consumption identified greater potential for energy and production optimization, although the contribution of artificial intelligence varied across energy-use conditions. Artificial intelligence remained negatively associated with environmental public health risks across environmental regulation regimes, while its marginal contribution changed non-linearly with regulatory intensity. Conclusion: Artificial intelligence functions as a conditional environmental capability rather than an automatic technological solution. Its public health value is more likely to emerge when digital development is supported by technological expenditure, environmental investment, operational implementation, and coordinated regulatory design. These findings provide a multidimensional framework for understanding how artificial intelligence can contribute to pollution-related environmental public health risk mitigation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.