ArticlePloS one2026
Artificial intelligence-based digital transformation and environmental sustainability.
Article in PloS one, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AI's rapid growth presents significant potential for enhancing environmental sustainability and driving digital transformation across various sectors. AI applications are scattered, uneven data integration is complex, and defined metrics are lacking, limiting its ability to support sustainable practices. In search of a holistic and interoperable solution, this study suggests Green AI, an innovative framework that blends AI, blockchain, and the IoT to address sustainability issues. This work aims to provide a scalable and secure architecture that enhances energy efficiency, reduces carbon emissions, and improves environmental monitoring accuracy. Green AI utilizes blockchain to securely manage real-time data from smart grids, environmental sensors, and energy markets via IoT devices, ensuring energy transaction transparency and accountability. Advanced machine learning algorithms maximize renewable energy integration and estimate energy demand, enabling proactive decision-making. Its unified design improves energy system sustainability and provides a reproducible model for cost-effective resource management, making this study unique. The Green AI framework guides academics, policymakers, and industry stakeholders in utilizing intelligent technology for sustainable development, thereby creating a greener and more resilient future.
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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.