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
arXiv:2608. 17055v1 Announce Type: cross Abstract: Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use.
By Erik Johannes Husom, Maria Emine Nylund, Ophelia Prillard
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