This paper presents a novel approach to utilizing agricultural waste. It compares three different applications for cotton stalks: fabrication of wood composites, bioethanol production, and biogas cradle-to-gate Life cycle assessment production processes. Cotton cultivation generates a lot of debris, mostly cotton stalks, which are incinerated or landfilled, Sustainable resource management is critical for maintaining the ecosystem, and economic stability, and promoting social fairness since it ensures the long-term availability of resources while minimizing environmental damage. The investigation uses the Ecological Footprint, Impact 2002 +, Global Warming Damage Potential, Greenhouse Gas Protocol, Recipe Midpoint, Ecosystem Damage Potential, and CML IA Baseline—open LCA-enabled environmental sustainability assessments. The analysis showed that bioethanol has a lower carbon footprint and climate change impact than both wood composite and biogas production processes, as a result, this could cause a preference for bioethanol production as an environmentally friendly strategy for cotton stalks utilization. While human toxicity was higher in the biogas production process, it emits less fossil CO₂ than biogenic CO₂. The total climate change of wood composite, bioethanol, and biogas production processes was 0.01761, 0.011300, and 0.01083 points, respectively. This research helps accomplish wider ecological and economic aims by giving insights into sustainable waste management practices.

Growing demand for biodegradable alternatives to conventional plastics has propelled efforts in the study of polyhydroxyalkanoates (PHAs), biodegradable plastics made from renewable carbon sources. This review highlights the utilization of lignocellulosic biowaste as a superior and cost-effective feedstock material for PHA synthesis. An extensive bibliometric review in the Scopus database detected a sudden spike in global PHA publications over the past decade, mostly after 2019, which points towards increasing research and industrial interest in economical bioplastics. The review reports 461 associated studies examining the interaction between PHAs and lignocellulosic biomass, with strong contributions from the United States, India, and China, detailing rising global collaborations. Salient points include (i) a brief introduction to various lignocellulosic biomass such as sugarcane bagasse, rice husk, and wood biomass; (ii) a detailed review of pretreatment processes—mechanical, chemical, and biological—necessary for proper release of sugars and fermentation; and (iii) an evaluation of existing problems and future trends of scale-up of PHA production. This review highlights that the integration of lignocellulosic waste valorization into biorefinery strategies can reduce production costs, advance circular economy performance, and drive the sustainable development of next-generation biodegradable plastics.

Polyethylene terephthalate (PET) pollution, due to its persistence, chemical recalcitrance, and widespread usage, represents a growing hazard to marine ecosystems. Its accumulation contributes to long-term ecotoxicological risks, food chain contamination, and environmental degradation. Addressing this challenge necessitates the adoption of scalable, efficient, and intelligent strategies for monitoring, collection, and degradation. While artificial intelligence (AI) has shown promise—particularly through machine learning (ML), deep learning (DL), and computer vision (CV)—these approaches have already been extensively covered in recent reviews and are therefore beyond the scope of this research. Instead, this article focuses on underexplored and emerging AI technologies with untapped potential in marine PET management. These include reinforcement learning, generative AI, Edge AI, soft robotics, federated learning, explainable AI, decision support systems, and large multimodal models (LMMs). These technologies offer new capabilities for real-time decision-making, distributed data processing, autonomous biodegradation, and intelligent system design—critical tools in managing hazardous waste scenarios—yet remain largely absent from current PET-focused research. We synthesize existing literature where applicable—such as in reinforcement learning, Edge AI, and soft robotics—while also investigating areas where research is still sparse or speculative, such as federated learning and LMMs. This addresses the need for a comprehensive review that not only maps current applications but also identifies conceptual gaps where emerging AI technologies can drive future innovation. Through a bibliometric analysis of leading scientific databases and a thematic synthesis of technological trajectories, this article straddles the line between a traditional review and a forward-looking conceptual overview. It aims to inform researchers, inspire interdisciplinary collaboration, and advance sustainable, hazard-mitigating solutions for marine PET management in a rapidly evolving technological landscape.