ArticleScientific reports2025
A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Personalized talent cultivation and academic prediction framework for higher education based on the HA-GNN-LSTM architecture.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasing global environmental and social challenges, it is more urgent than ever to implement effective strategies for sustainable development. Environmental, Social and Governance (ESG) criteria are also necessary requirements guiding organizations toward responsible and sustainable practices. However, the multi-dimensionality of the criteria and the uncertainty associated with judgment by an expert makes evaluation and choice of the best ESG-driven strategy a very complex task. The current paper introduces an innovative approach to the assessment of ESG strategies via Fuzzy based multi-criteria decision-making tools. Those selected shall be addressed particularly through the Fuzzy TOPSIS method, considering and ranking seven key strategies driven by ESG focus areas- AI-Powered Predictive Analytics, Renewable Energy Integration, Smart Waste Management Systems, Blockchain for Transparent Governance, AI-Enhanced Workforce and Community Development, Sustainable Supply Chain Optimization and Generative AI for Eco-Friendly Innovation. The results of this assessment indicate that the most popular ESG approach is renewable energy integration, which is in line with the industry's pivotal role in advancing energy transition and climate action. AI-Powered Predictive Analytics and Sustainable Supply Chain Optimization are closely related, emphasizing the strategic value of data intelligence as well as operational efficiency in improving sustainability practices. These results offer important novel insights about how AI-powered methods might guide environmentally friendly choices in intricate industrial settings. Our method provides a strong and transparent framework for assessing ESG strategies under sustainability by incorporating fuzzy logic into decision-making. The study adds to the rapidly expanding body of research on AI-driven sustainability evaluations, from which companies, policymakers and other stakeholders could gain insight how to enhance their ESG performance as well as advance sustainable development.
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.