The global shift toward renewable energy and circular economy models requires industrial systems that minimize waste and recover value across entire life cycles. This review synthesizes recent advances in by-product recovery technologies supporting renewable energy and circular industrial processes. Thermal, biological, chemical/electrochemical, and biotechnological routes are analyzed across battery and e-waste recycling, bioenergy, wastewater, and agri-food sectors, with emphasis on integration through Life Cycle Assessment (LCA), techno-economic analysis (TEA), and multi-criteria decision analysis (MCDA) coupled to process simulation, digital twins, and artificial intelligence tools. Policy and economic frameworks, including the European Green Deal and the Critical Raw Materials Act, are examined in relation to technology readiness and environmental performance. Hybrid recovery systems, such as pyro-hydro-bio configurations, enable higher resource efficiency and reduced environmental impact compared with stand-alone routes. Across all technologies, major hotspots include electricity demand, reagent use, gas handling, and concentrate management, while process integration, heat recovery, and realistic substitution credits significantly improve life cycle outcomes. Harmonized LCA-TEA-MCDA frameworks and digitalized optimization emerge as essential tools for scaling sustainable, resource-efficient, and low-impact industrial ecosystems consistent with circular economy and renewable energy objectives.

The rapid commercialization of next-generation photovoltaic (PV) technologies, particularly perovskite, thin-film roll-to-roll (R2R) architectures, and tandem devices, requires robust assessment of environmental performance at the level of industrial manufacturing processes. Environmental impacts can no longer be evaluated solely at the device or module level. Although many life-cycle assessment (LCA) studies compare silicon, cadmium telluride (CdTe), copper indium gallium selenide (CIGS), and perovskite technologies, most rely on aggregated indicators and database-level inventories. Few studies systematically compile and harmonize process-level life-cycle inventories (LCIs) for the manufacturing steps that differentiate emerging industrial routes, such as solution coating, R2R processing, atomic layer deposition, low-temperature annealing, and advanced encapsulation–metallization strategies. In addition, inconsistencies in functional units, system boundaries, electricity-mix assumptions, and scale-up modeling continue to limit meaningful cross-study comparison. To address these gaps, this review (i) compiles and critically analyzes process-resolved LCIs for innovative PV manufacturing routes across laboratory, pilot, and industrial scales; (ii) quantifies sensitivity to scale-up, yield, throughput, and electricity carbon intensity; and (iii) proposes standardized methodological rules and open-access LCI templates to improve reproducibility, comparability, and integration with techno-economic and prospective LCA models. The review also synthesizes current evidence on recycling, circularity, and critical-material management. It highlights that end-of-life (EoL) benefits for emerging PV technologies are highly conditional and remain less mature than for crystalline-silicon systems. By shifting the analytical focus from technology class to manufacturing process and life-cycle configuration, this work provides a harmonized evidence base to support scalable, circular, and low-carbon industrial pathways for next-generation PV technologies.

Carbon-aware AI demands clear links between algorithmic choices and verified emission outcomes. This study measures and steers the carbon footprint of swarm-based optimization in HPC by coupling a job-level Emission Impact Metric with sub-minute power and grid-intensity telemetry. Across 480 runs covering 41 algorithms, we report grams CO₂ per successful optimisation and an efficiency index $\eta$ (objective gain per kg CO₂). Results show faster swarms achieve lower integral energy: Particle Swarm emits 24.9 g CO₂ per optimum versus 61.3 g for GridSearch on identical hardware; Whale and Cuckoo approach the best $\eta$ frontier, while L-SHADE exhibits front-loaded power spikes. Conservative scale factor schedules and moderate populations reduce emissions without degrading fitness; idle-node suppression further cuts leakage. Agreement between CodeCarbon, MLCO2, and vendor telemetry is within 1.8%, supporting reproducibility. The framework offers auditable, runtime controls (throttle/hold/release) that embed carbon objectives without violating solution quality budgets.

Wastewater treatment is evolving rapidly with the advent of advanced deep-learning AI, graph-based, and physics-informed approaches. This study integrates graph neural networks, physics-informed neural networks, and multi-agent reinforcement learning within a hybrid digital-twin framework, evaluated on multi-scale real-world datasets. The developed models achieved over 90% dissolved organic carbon removal and reduced aeration energy by up to 22% while maintaining process stability. The results demonstrate that graph–physics synergies not only boost operational efficiency but also reveal critical trade-offs between energy savings and hydraulic performance. Our findings establish a new benchmark for resilient, low-carbon wastewater treatment, highlighting the transformative role of data-driven system design.

The growing environmental impacts associated with conventional plastics and textiles have intensified interest in bio-based and circular material alternatives. This study presents a qualitative and structured literature review of the valorization of fruit and nut agricultural residues as sustainable feedstocks for biomaterials and biotextiles, with a strategic focus on Greece. Drawing on international literature, regional agricultural production data, and validated processing technologies, the review synthesizes existing evidence on residue availability, conversion routes, environmental performance, and market trends. The reviewed literature indicates that residues such as grape pomace, olive by-products, citrus peels, and nut shells have been widely reported as suitable sources of cellulose, lignin, and pectin for the development of fibers, films, and composite materials. Findings from published life cycle assessment (LCA) studies suggest potential reductions in water use, greenhouse gas emissions, and land-use intensity compared with conventional cotton and synthetic textiles, although results vary depending on system boundaries and processing conditions. The review further highlights enabling factors, technical limitations, and policy considerations relevant to the Greek context. This study provides a qualitative integrative perspective on the opportunities and constraints associated with agricultural residue valorization, identifying key research gaps and strategic directions for future development within Greece and similar Mediterranean regions.

This study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. This achieves significant reduction in data size, especially on large integer datasets. Experimental comparisons with established compression methods—such as ZIP, Brotli, and Zstandard—demonstrate LPPIE’s exceptional effectiveness, attaining compression ratios nearly 13 times superior to established methods. However, these substantial storage savings come with elevated computational overhead due to LPPIE’s complex numerical operations. The method’s robustness across diverse datasets and minimal scalability limitations underscore its potential for specialized archival scenarios where data fidelity is paramount and processing latency is tolerable. Future enhancements, such as GPU-accelerated computations and hybrid entropy encoding integration, are proposed to further optimize performance and broaden LPPIE’s applicability. Overall, LPPIE offers a compelling alternative in lossless data compression, substantially redefining efficiency boundaries in high-volume numeric data storage.