Sustain AI: A Multi-Modal Deep Learning Framework for Carbon Footprint Reduction in Industrial Manufacturing
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Shenglin Liu & Yuqing Xiang & Huijie Zhou, 2024. "A Deep Learning-Based Approach for High-Dimensional Industrial Steam Consumption Prediction to Enhance Sustainability Management," Sustainability, MDPI, vol. 16(22), pages 1-29, November.
- Sophia Cabral & Mikita Klimenka & Fopefoluwa Bademosi & Damon Lau & Stefanie Pender & Lorenzo Villaggi & James Stoddart & James Donnelly & Peter Storey & David Benjamin, 2025. "A Contactless Multi-Modal Sensing Approach for Material Assessment and Recovery in Building Deconstruction," Sustainability, MDPI, vol. 17(2), pages 1-30, January.
- Alpamys Pentayev & Amirhossein Ahrari & Aziza Baubekova & Maksat Faizuldanov & Nurzhan Nurtayev & Alireza Sharifi & Ali Torabi Haghighi & Stefanos Xenarios & Siamac Fazli, 2025. "Towards multi-modal oil spill detection and coverage in the Caspian Sea: a comprehensive approach," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 41(1), pages 176-203, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.More about this item
Keywords
Artificial Intelligence (AI); carbon footprint reduction; industrial sustainability; energy efficiency; Industry 4.0;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4134-:d:1648523. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.