The study applies machine learning to address Ghana’s solid waste disposal challenges, using a Random Forest classifier to predict illness categories from waste practices and demographics, achieving a macro F1 score of 0.63. A MobileNetV2 image classifier was also developed for automated waste sorting, reaching 88.2% accuracy and a macro F1 of 0.87 on 415 images. These quantitative results confirm a previously qualitative link between waste disposal and health, while demonstrating the feasibility of low‑cost, camera‑based sorting in resource‑constrained settings.
By Hilda Adwubi Osei, Catherine Tenewaa Osei, Desdemona Yaa Asobayire
The inappropriate disposal of solid waste remains a significant public health and environmental concern worldwide, including in Ghana. Poor sanitation and improper waste management practices contribut...
arXiv:2606. 15547v1 Announce Type: cross Abstract: Waste classification models have become highly accurate at sorting waste, often exceeding 95% on benchmark datasets.
By Raghav Senthil Kumar
Sorting a huge stream of waste accurately within a short period can be done with the support of digitalization, particularly Artificial Intelligence, instead of traditional methods. The overlap of Artificial Intelligence and Circular Economy can flourish many services in the environmental technology domain, in particular smart ewaste recycling, resulting in enabling circular smart cities.
arXiv:2607. 10610v1 Announce Type: cross Abstract: Efficient waste segregation is critical for sustainable urban management and environmental governance.
By Khush Kataruka, Harshit Maurya, Anuja Vats, Murari Mandal, Kiran Raja, Praveen Kumar Chandaliya
The paper argues that large language models (LLMs) have a growing carbon footprint and that current efficiency gains are offset by rebound effects such as Jevons Paradox. It proposes applying the EU waste hierarchy—prevention, reuse, recycling, recovery, and disposal—to LLMs, suggesting that preventing waste and unnecessary use can significantly reduce environmental impact. The authors emphasize that treating LLMs as products that can become waste offers new ways to motivate more sustainable practices.
By Erik Johannes Husom, Maria Emine Nylund, Ophelia Prillard
arXiv:2608.22950v1 Announce Type: new
Abstract: Floating waste in inland waterways threatens aquatic ecosystems and requires timely monitoring under cluttered, multi-object conditions. Existing aquat...
By Md. Asaduzzaman Shuvo, Ahsan Farabi, Md. Abdul Ahad Minhaz, Mahedi Hasan, Israt Khandaker, Ibrahim Khalil Shanto, Muhammad Nomani Kabir
arXiv:2608. 07529v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as technical assistants, but their competence in solid waste management (SWM) remains difficult to assess because existing benchmarks emphasize general knowledge rather than professional decisions under engineering, environmental, and policy constraints.
By Yi Zhang, Hongyang Wang, Zheng Hao Leong, Zihao Wu, Kaijun Lin, Zhixing Pan, Qixun Huangfu, Wei Ren, Wenyan Wu, Fangyun Wang, Wenting Yu, Hengyu Lin, Muling Yang, Zongguo Wen
arXiv:2509. 05364v2 Announce Type: replace-cross Abstract: Residential buildings contribute significantly to energy use, health outcomes, and carbon emissions.
By Abdollah Baghaei Daemei
Floating waste in inland waterways threatens aquatic ecosystems and requires timely monitoring under cluttered, multi-object conditions. Existing aquatic-waste datasets provide limited geographic cove...
arXiv:2507. 17012v2 Announce Type: replace Abstract: Reducing the rapidly growing environmental impact of the computing industry requires assessing the emissions of electronics at scale.
By Zhihan Zhang, Alexander Metzger, Yuxuan Mei, Felix H\"ahnlein, Zachary Englhardt, Tingyu Cheng, Gregory D. Abowd, Shwetak Patel, Adriana Schulz, Vikram Iyer
PermitGPT is a generative‑AI framework that transforms unstructured construction permit descriptions into structured outputs for safety hazard identification, permit requirement specification, and community impact assessment. It aligns data from the NYC Department of Buildings, OSHA, and NYC 311 to create 90,000 prompt‑response pairs, fine‑tunes three open‑weight language models, and evaluates them on 2,833 test cases, reporting complementary performance across inference speed, lexical overlap, and semantic alignment. The study presents an initial AI‑assisted approach to construction governance and outlines future evaluation and validation directions.
By Mohd Ruhul Ameen, Farjana Aktar, Akif Islam, Momen Khandoker Ope, Abu Saleh Musa Miah, Jungpil Shin