Textile waste is one of the fastest-growing material streams in the global economy, and progress towards a circular textile economy depends on automating the sorting, separation and disassembly. Implementation of Artificial Intelligence (AI) has been proposed as a promising solution, but the literature on AI in textile recycling has not previously been organised around the operational decisions that an industrial recycling line must make. In this systematic review, 173 primary published studies between 1995 and 2026 were reviewed through a six-stage pipeline framework covering the detection of 1) textile type, 2) colour and pattern, 3) surface contamination, 4) functional reusability, 5) attachment removal and separability, and 6) material composition. Stage 4 was found to be saturated by manufacturing-oriented structural defect detection, which accounts for most of the corpus, while assessment of genuinely post-consumer garment condition remains almost as sparse as stage 5. Stages 1, 2, 3 and 6 are unevenly developed, and stage 5 is almost absent, with only one paper identified in the corpus reviewed here. Most technologies remain at the laboratory prototype stage, with limited industrial adoption. The highest-priority research direction is closure of the stage 5 gap, supported by post-consumer benchmarks and cost-effective near-infrared sensing.
Governments are increasingly adopting policies to reduce plastic and packaging waste, with single-use plastics (SUPs) among the main targets. Where reuse is not adopted, wood- and paper-based products are often proposed as alternative materials. This study examines the potential supply chain and environmental implications of replacing selected SUPs with wood- and paper-based alternatives across Canada and the USA, where large single-use product markets coexist with major forest-products industries. We map relevant forest-product supply chains, assess resource availability and processing capacity, and examine indicative land-use and waste implications. For the items assessed, replacing SUPs with wood- and paper-based alternatives could substantially increase the total mass of material used and was estimated to require an additional 13.68 million m3 of wood across the USA and Canada. The associated harvested area is estimated at 84,202 hectares in 2030, equivalent to approximately a 1.6% increase in harvested area in Canada and 0.38% of planted forest area in the southern USA. Paper production capacity emerges as a key supply-chain constraint: meeting the additional demand would require an estimated 18.7 Mt increase in paper and paperboard production, approximately 25% above 2022 levels, or increased reliance on imports. Scenario analysis further shows that reuse and demand reduction can substantially reduce fibre demand. These findings demonstrate that the scale and configuration of behavioural and supply-chain responses are critical when designing material-specific SUP policies.
Accurate, high-resolution precipitation forecast is vital for urban disaster mitigation and agriculture, underpinning fine-grained social management. Furthermore, as the primary input to the terrestrial hydrological cycle, its accurate quantification is essential for sustainable water resource management and efficient circulation. However, this task remains highly challenging due to the complex spatiotemporal and non-linear dynamical evolution inherent in atmospheric processes. Deep learning has been extensively applied to weather forecast through CNN-RNN or Transformer architectures, yet these methods often struggle to capture global meteorological motions while simultaneously preserving the fine-grained details of localized strong convection. Furthermore, they are prone to producing blurred predictions or exhibiting high false alarm rates when encountering extreme precipitation events characterized by long-tailed distributions. To address these issues, we propose the Spatial-Frequency and Non-Local Former Network (SFNL-Former), which accurately predicts complex precipitation evolution via explicit spatial-frequency domain feature separation and efficient long-range spatiotemporal modeling. A Spatial-Frequency Domain Attention (SFDA) encoder is designed to separately extract low-frequency global backgrounds and high-frequency local textures of precipitation fields. Furthermore, the Locality-Sensitive Hashing (LSH) based Non-Local Sparse-Aware Transformer (NLSAT) is introduced to capture long-range spatiotemporal dependencies with linear complexity. Experiments on real-world datasets demonstrate that SFNL-Former outperforms existing baseline methods across multiple metrics and spatial scales. Specifically, under challenging high-intensity precipitation thresholds, the model improves the Critical Success Index and substantially reduces the False Alarm Rate, while maintaining optimal prediction coherence. Consequently, the framework enables critical decision support for proactive disaster response, fostering more resilient social management systems under future extreme weather scenarios.
Addressing plastic pollution requires understanding of the drivers shaping material flows and waste generation. This study applies Statistical Entropy Analysis to evaluate its potential as a circular economy metric to quantify disorder within plastic systems and identify its drivers.Santa Cruz Island in the Galápagos Archipelago, was used as a bounded system to empirically test Statistical Entropy Analysis within a real-world plastic consumption system. A mixed methods approach combined maritime imports data, shop audits, litter transects, recycling characterization and human movement tracking. Statistical analyses were used to examine the relationships between system characteristic and plastic systems, followed by Statistical Entropy Analysis to assess system disorder.Results showed that packaging, particularly multi-material, is a major contributor to low circularity and increased system disorder. Litter distribution was more strongly associated with area characteristics rather than with human movement, with higher accumulation in commercial and recreational areas, while the presence of bike paths was linked to reduced littering. Statistical Entropy values were highest in leaked plastic systems, especially in urban areas, and lowest in recycling streams due to material separation. Supermarkets’ products exhibited higher statistical entropy due to packaging complexity.System disorder is shaped by spatial context, retail format, product design and consumption preferences, highlighting the need for targeted upstream and downstream interventions. Compared with conventional circular economy metrics focused on resource efficiency, statistical entropy analysis captures material dispersion and mixing, providing a complementary and comparable indicator of circularity across contexts. This study supports the design of policies and business strategies for circularity.
Building-Integrated Photovoltaics (BIPV) can decarbonize urban settlements when energy performance, material demand, and end-of-life management are assessed together. This study integrates building modeling, bottom-up dynamic material flow analysis and life cycle assessment to quantify a 50-year neighborhood-scale BIPV deployment in a Mediterranean region. BIPV is modeled across four PV materials (conventional c-Si, light-colored c-Si, CdTe, and CIGS), two electricity utilization scenarios (net-metering and in-situ consumption) and five replacement intervals from 5 to 25 years. BIPV offsets 18 to 55% of neighborhood energy demand, depending on electricity export ratios. Total cumulative waste streams vary from 166 to 434 t, indicating substantial variation in end-of-life material flows across scenarios. Life-cycle greenhouse gas emissions are reduced by 11,000 to 45,000 tons CO2eq compared to grid electricity. This neighborhood-scale, bottom-up framework captures temporal patterns of material stock and flow, linking BIPV design choices to circular economy trajectories for net-zero urban planning.
Dry cooling, reclaimed municipal wastewater, brackish water, and produced water are potential options for reducing freshwater use for thermoelectric cooling. However, there are tradeoffs in the technical, economic, environmental, and regulatory dimensions associated with their implementation at existing power plants. This study performs a multi-criteria decision analysis in conjunction with a stakeholder survey to rank these freshwater-saving alternatives under uncertainty. Stakeholders prioritize water environmental impact with a global weight of 0.46, followed by technical feasibility (0.24), economic viability (0.18), and regulatory compliance (0.12). Dry cooling is positioned as the most preferred option, with an 80% likelihood, largely due to its ability to minimize water environmental impact, which is viewed as the most significant criterion. However, deterministic decisions can be biased toward certain outcomes if uncertainties in stakeholder judgments and option performance are not considered. Sensitivity analyses across numerous key factors confirm full preservation of the ranking. However, if either economic viability or technical feasibility is viewed as the most important criterion, reclaimed municipal wastewater is considered the superior solution. Currently, large energy and cost penalties, plus water environmental impact for treating brackish water and produced water, limit their competitiveness in the water-stressed western regions. Accelerating at-scale reuse of non-traditional water sources requires increased R&D investment in advanced desalination technologies.
Use-oriented product-service systems (PSS) are a promising business model for advancing the circular economy (CE), but successfully incorporating second-hand products into such services remains challenging. Success depends on interrelated decisions about which products to procure (Procurement), to whom they are offered (Targeting), and at what price (Pricing). Existing evaluation methods, however, treat these decisions separately; they cannot capture their interactions and therefore fail to systematically evaluate effective strategies. To fill this gap, this study proposes a data-driven discrete-event and agent-based (DE–AB) hybrid simulation framework that integrates the three decisions (Procurement, Pricing, and Targeting) into a single model. The framework was applied to a case study of a business-to-business (B2B) PC rental service to verify its effectiveness. Results show that these decisions interact with one another and that integrated evaluation reveals effective strategies overlooked by separate evaluations. Specifically, a hybrid approach combining Targeting toward specific consumer segments with a flexible stock allocation (Upgrade) strategy for cost-conscious consumers simultaneously improved profitability and environmental performance by matching second-hand product conditions to consumer segments with higher acceptance of those conditions. Compared with the current baseline strategy, this strategy improved the profit margin by 6.57% while reducing Greenhouse Gas (GHG) emissions by 4.34%. This study contributes a simulation framework that enables integrated evaluation of the Procurement, Pricing, and Targeting decisions, together with their interactions, and offers practical insights for service providers designing second-hand product procurement strategies that simultaneously improve profitability and environmental performance.
Forests play a central role in the Finland’s bioeconomy strategy and are key to addressing climate challenges and achieving national climate neutrality targets. In this context, quantifying the forest sector’s total emissions is essential for supporting effective and evidence-based climate-mitigation planning. This study develops a framework to evaluate the life cycle greenhouse gas emissions of Finland’s forest sector for the year 2023 using an organizational carbon footprinting approach that combines top-down and bottom-up methodologies, and aligned with the Scope 1, Scope 2 and Scope 3 structure. The total life cycle GHG emissions from Finnish forest sector were approximately 8.4 Mt CO₂e. Organic soils are the largest contributors, followed by processing of exported products, upstream emissions from pulp and paper production and transportation of exported items. Most of the emissions originate from Scope 3 sources (approximately 68%), primarily associated with materials and suppliers over which forest-sector stakeholders have no direct control. This was followed by Scope 1 emissions (around 27%) and Scope 2 emissions (around 5%), which together represent roughly 11% of Finland’s total energy-related emissions. Land use, land-use change, and forestry (LULUCF) in Finland’s forest sector result in a net removal, with a value of -0.38 Mt CO₂ eq. The developed framework provides a comprehensive assessment by identifying both chain-specific and scope-specific emission contributions. It also supports annual updates and can be adapted to different geographic regions. Such an approach would offer policy makers a clearer understanding of the forest sector’s role in meeting national and international climate change mitigation targets.
Rare-earth substitution in electric vehicle traction motors is increasingly discussed to reduce dependence on critical raw materials and mitigate environmental burdens associated with rare-earth production. However, such substitution is not a simple material replacement. The lower magnetic energy density of ferrite magnets requires geometric and structural design compensation, increasing bulk material throughput and potentially altering operational efficiency. The environmental outcome of rare-earth substitution depends on system-level trade-offs between manufacturing-stage material demand and use-phase electricity consumption. This study evaluates rareearth substitution as a resource strategy through a cradle-to-grave life cycle assessment of two functionally equivalent EV traction motors based on different permanent magnet technologies: a mass-produced NdFeB-based motor and a rare-earth-free ferrite-based spoke-type motor designed to achieve comparable traction performance through geometric compensation. Manufacturing, use-phase, and end-of-life stages are modelled consistently, with electricity consumption derived from efficiency maps under the WLTC driving cycle. Results show that the ferrite-based motor exhibits higher manufacturing-related greenhouse gas emissions due to increased demand for electrical steel, copper, aluminum, and composite materials, while achieving lower lifetime electricity consumption. A break-even electricity carbon intensity of approximately 60 g CO2-eq/kWh is identified. Above this threshold, the ferrite-based design delivers lower total life cycle climate impacts despite higher material demand; below it, the advantage diminishes as electricity systems approach low-carbon conditions. These findings establish quantitative boundary conditions linking material substitution decisions to electricity system characteristics and underscore the importance of evaluating critical material strategies within the broader context of energy system transformation.
Water scarcity is intensifying globally, particularly in regions facing chronic stress, underscoring the need to shift from linear water management toward circular, adaptive strategies. This study develops a tightly coupled Hybrid SD-ABM (Hybrid System Dynamics Agent-Based Modelling) framework, implemented in Stella Architect, to evaluate decentralized wastewater reuse as a pathway to enhance water circularity and resource recovery. The framework integrates macro-level feedback loops with micro-level agent behaviors, linking plant-level treatment performance, dynamic effluent-quality-behavior feedback loops, and policy incentives within a single multi-scale simulation environment. Results show that reuse adoption increases substantially when treated wastewater is priced below & euro;0.15/m3 and farm subsidies exceed 30% of irrigation costs, while effluent quality emerges as a critical determinant of user trust. The model also demonstrates environmental and operational gains from precision irrigation and nutrient recovery, including biogas offsetting. An embedded optimization analysis reveals a trade-off between maximizing reuse and maintaining financial sustainability for the water authority. By capturing the advancement of infrastructure performance, user behavior, and regulatory interventions, this work advances Hybrid SD-ABM modelling and provides a decision-support tool for adaptive water governance in water-scarce contexts.
The United States (U.S.) has one of the highest production-based ecological footprints (EFP) in the world. Consequently, reducing EFP is essential for ensuring ecological balance, protecting the environment, and reducing ecological degradation. However, the comparative analysis on the long-run associations of AI innovation (AIN), high-tech trade capability (HTTC), supply chain efficiency (SCE), information and communication technology investment growth (ICTIG), and GDP growth (GDPG) with EFP regarding the U.S. remains poorly understood. Using the autoregressive distributed lag (ARDL) method, this study shows a comparative analysis of the EFP's determinants relying on the U.S. national level data from 1990 to 2023. Based on the ARDL findings, while AIN, SCE, and HTTC show statistically significant association with EFP in the long run, ICTIG and GDPG do not exhibit significant empirical association. Among three significant associations, AIN and SCE are associated with reductions in ecological footprint in the long run, indicating that the country has secured technology-driven ecological benefits and operational efficiency enhancement within the production dynamics by emphasizing AI innovation and efficient inventory management. In contrast, HTTC's positive association represents significant ecological pressure with the high tech-industries technology advancement, driven by scale and rebound effects. All the results remained stable in FMOLS, DOLS, and CCR robustness tests. Besides, Granger causality indicates mixed predictive patterns of these relationships. The comparative analysis among these determinants' long-run associations with EFP significantly contributes to the single country level production-based ecological footprint literature and depicts several valuable empirical insights for policy actions by the federal government.
The growing implementation of renewable and green energy systems has seen a significant surge in the utilization of lithium (Li) in lithium-ion rechargeable batteries. Meeting this increasing demand requires the development of effective lithium recovery techniques from different water-based sources. Conventional lithium extraction technologies have faced several difficulties, including slow reaction rates, high energy requirements, and environmental issues, particularly related to extensive freshwater consumption. However, lithium extraction via ion exchange and adsorption has been proven to be effective at scale. This review examines the efficiency of various adsorbents for lithium extraction from aqueous solutions, using key performance indicators and comparing them with current literature benchmarks. Special attention will be paid to new types of lithium adsorbents - smart adsorbents, including ion-imprinted polymers, functional MOFs and COFs, and hybrid materials. The latest advances in smart adsorbents provide the benefits of engineered active centres, selective properties, and controlled lithium adsorption mechanisms, enabling lithium trapping despite competition with other ions. Additionally, this review assesses the ability of adsorbents to facilitate large-scale manufacturing of lithium compounds, such as Li2CO3 and LiOH, highlighting their benefits and drawbacks under different operational conditions. It further highlights the importance of standardised adsorbents, whether organic or inorganic, and agreed-upon performance indicators, enabling fair comparisons and quicker improvement of lithium recovery technologies.
The rapid expansion of wind power has increased the strategic importance of upstream material supply chains. However, existing studies often focus on isolated environmental or operational issues and provide limited guidance for integrated assessment of material-related supply-chain vulnerability. This study proposes a multi-dimensional exploratory framework to assess key upstream materials used in wind power manufacturing, focusing on epoxy resin, glass fiber, and polyurethane coatings. The framework considers four dimensions: supply reliability, geographical exposure, environmental performance, and cost volatility. Through a comparative scoring-based assessment, the study identifies differentiated vulnerability profiles among the three material supply chains. Epoxy resin exhibits relatively higher vulnerability associated with upstream chemical-intermediate dependence, supply reliability, and cost-related uncertainty. Glass fiber is primarily characterized by environmental pressure associated with energy-intensive production processes and regional exposure. Polyurethane coatings show greater sensitivity to specialized chemical feedstock dependence and supply-related uncertainty. The findings indicate that no single material performs uniformly across all vulnerability dimensions, and that resilience strategies should be aligned with material-specific vulnerability characteristics rather than applied generically. This study provides an exploratory analytical framework for understanding upstream material vulnerability and supports targeted procurement and governance strategies for sustainable wind power development.
Japan’s municipal waste-treatment regionalization policy addresses treatment continuity, facility renewal, operational efficiency, and public-health protection under population decline. This study examined how this framework could incorporate industrial steam demand while retaining municipal waste-treatment responsibilities. Three configurations for Ibaraki Prefecture in 2050 were compared using a scenario-based facility-level technoeconomic accounting framework: public-sector consolidation through 11 waste-to-energy facilities, decentralized integration with four industrial steam-demand areas, and centralized integration with a petrochemical complex. The analysis evaluated annual costs and energy-related benefits, facility-level electricity balances, industrial steam supply, operational avoided CO₂, and average and incremental abatement costs. Public-sector consolidation achieved 35.6 kt-CO₂/year of net operational avoided CO₂, with a net annual cost burden of 13.31 billion JPY/year. The decentralized configuration had the lowest net annual cost burden, at 2.52 billion JPY/year. The centralized configuration achieved the highest net operational avoided CO₂, at 199.3 kt-CO₂/year, and the lowest base-case average abatement cost, at 14,038 JPY/t-CO₂. Industrial steam substitution accounted for most operational avoided CO₂ in both integration configurations. Sensitivity analysis showed that their relative cost-effectiveness depended mainly on industrial steam utilization, the capital-cost annualization period, heat-recovery efficiency, and avoided fuel expenditure. No configuration was preferable under all criteria. Incorporating industrial heat demand into regionalization planning would require coordination of waste supply, infrastructure investment, steam off-take, and cost and risk allocation. The assessment was limited to predefined configurations and operational energy-substitution effects and did not evaluate optimization, complete life-cycle emissions, or stakeholder-specific financial viability.
Across industries, firms are redesigning packaging in diverse ways to facilitate waste sorting, yet little is known about whether and how these design efforts translate into consumers’ sorting intentions at the point of disposal. This research introduces the concept of Packaging Waste-Sorting Friendliness (WSF), defined as consumers’ perception of the extent to which a product’s packaging provides clear and actionable affordances that support correct waste sorting. Across four studies, we examine how perceived WSF influences consumer sorting intention (CSI) and identify conditions under which this effect becomes more pronounced. The results demonstrate that WSF significantly enhances CSI through increased perceived ease of use (Studies 1 and 2a). This effect is stronger under low policy intensity, as evidenced by both an experimental manipulation (Study 2a) and a real-world city comparison (Study 2b). Furthermore, consumers’ awareness of environmental benefits amplifies the positive impact of WSF on CSI (Study 3). By theorizing WSF as an affordance-based consumer perception, this research contributes to sustainable consumption research by identifying an underexamined sorting-related packaging perception, offering firms actionable guidance for sorting-supportive packaging design, and suggesting to policymakers that sustainable behavior can be promoted not only through regulation but also through consumer-facing design support.
Municipal sewage sludge disposal is under increasing pressure in China due to rising sludge generation, limited disposal capacity, and the need for more efficient treatment possibilities. Municipal solid waste (MSW) incineration plants can provide a promising infrastructure for sludge co-incineration, but the feasibility is unclear. This study developed a national-scale facility-level source-sink database covering 5683 wastewater treatment plants and 1046 MSW incineration plants in China, and optimized sludge disposal under transport-radius scenarios from 20 to 100 km. Direct feeding of sludge with 80% moisture content at a 5% blending ratio was assumed. Matched sludge increased from 31,893 to 57,501 t/d as the transport radius expanded from 20 to 100 km, raising the national co-incineration rate from 18.2% to 32.8%. The largest marginal gain occurred within 20–40 km; even in the 100 km scenario, 82% of transported sludge remained within 40 km. Relative to a mono-incineration baseline, the 100 km scenario reduced greenhouse gas emissions by 4.05 Mt CO2-eq/a, but increased PM, Pb, As, and dioxin emissions. These results indicate that sludge-MSW co-incineration should be positioned as a localized pathway within a more diverse sludge-management portfolio rather than as a stand-alone national solution.
Based on an expanded, material-focused energy system model of the Netherlands, we examined the interaction between emissions reduction and circularity, material-energy tradeoffs, and feasibility of meeting both targets by 2050. We find that non-biogenic direct material input is reduced by about 70% in the optimal net zero emissions scenario, via fossil fuel reductions, circularity, and material efficiency measures, even without a constraint on material use, providing evidence that circularity and emissions reduction goals require similar energy system investments up to a certain point. However, more stringent circularity targets lead to dramatic increases in system costs as scarce biogenic and renewable resources come under pressure. The combined emissions and circularity scenario has 17% higher costs than emissions reduction alone, and 36% higher costs than circularity alone. Biomass is critical, but additional end-of-life material and material efficiency options can alleviate the pressure for circular scenarios, when resources are used strategically, as can investments in electrification. The resulting scenarios provide insights into robust investments in a future energy system that can meet multiple societal goals.
The techno-economic analysis (TEA) of producing biochar (BC) as a sustainable solution for waste valorization and carbon management from locally available biomass resources in Qatar remains insufficiently investigated. This study critically reviews the TEA of BC from local biomass resources in Qatar, where recent reports (2022-2024) indicate the generation of approximately 2.5-3.0 million t/y of municipal solid waste, 2.6 million t/y of agricultural residues, and 0.26 million t/y of sewage sludge. Pyrolysis technologies, including slow, fast, and microwave-assisted routes, are evaluated for yield, cost, and scalability, with reported BC yields of 30-60% and 12-35% for slow and fast pyrolysis, respectively. In this review, plant scale is defined by biomass amount as small- (<0.5 t/h), medium- (0.5-2 t/h), and large-scale (>2 t/h). Global and Qatar-specific assessments report minimum selling prices (MSPs) of 120-1000 USD/t globally and 160-180 USD/t for selected large-scale systems, including Qatar-relevant cases operating above 10 t/h with poultry litter and food waste. The wide MSP range primarily reflects differences in feedstock type, plant scale, pyrolysis technology, process efficiency, and regional economic conditions. CAPEX ranges from approximately 12 million USD for medium-scale systems to 290 million USD for large facilities. Sensitivity analysis identifies CAPEX as the dominant MSP driver (approximately ±9.1%), followed by plant capacity and feedstock cost, while OPEX and BC yield show smaller effects. The available evidence suggests that medium- and large-scale biochar production in Qatar may be economically feasible under favorable conditions. However, pilot-scale validation and integrated TEA-LCA are still needed to confirm commercial viability.
Remanufacturing is a value retention strategy to return used products to a state comparable to new ones, requiring less energy and fewer materials than new products. Despite its benefits, its implementation faces intrinsic barriers, occasionally fails to balance economic, environmental, and social concerns, or partially address stakeholder requirements. To address these gaps, this study introduces Remanufacturing 5.0 (R5.0), a smart sustainable remanufacturing system that integrates Industry 5.0 principles. R5.0 uses smart systems to promote sustainability, integrate a human-centered approach, and enhance system resilience. The concept is demonstrated through an illustrative case study that simulates an R5.0 system for end-of-life (EOL) lithium-ion batteries (LIBs) from electric vehicles (EV) in Quebec, Canada. R5.0 includes a smart architecture that helps to integrate stakeholders, sustainable objectives, system resilience, product life cycle information, and operations. A machine-learning-based approach helps to reduce uncertainty by forecasting battery lifespan with errors below 10%. A multi-objective optimization model balances economic, environmental, and social concerns while establishing vehicle routing and recovery processing scheduling of EoL EV LIBs. The optimal Pareto frontier is obtained by implementing weighted factors and solving the model using Gurobi solver. Sensitivity analyses demonstrate the model’s robustness, even with parameter variations greater than 20%. Finally, the advantages and limitations of R5.0 are discussed.
Electronic waste (e-waste) remains among the fastest-growing and difficult waste streams to manage. It poses serious environmental and public-health risks when improperly handled. While the European Union (EU) has established a comprehensive regulatory framework—centred on the Waste Electrical and Electronic Equipment (WEEE) and Restriction of Hazardous Substances (RoHS) Directives—implementation outcomes continue to vary widely among member states. This review examines how e-waste governance operates across the EU through a systematic literature review (SLR) of 138 peer-reviewed publications published between 2002 and 2025 and indexed in Scopus and Web of Science. The findings show that EU e-waste governance is anchored in extended producer responsibility but remains concentrated on end-of-life management (e.g., reuse, repair, remanufacturing, recycling, and safe disposal) while upstream actions such as eco-design and sustainable production are underdeveloped. The barriers often identified are regulatory, operational, and behavioural, including uneven enforcement, informal waste flows, high collection costs, weak incentives for repair and reuse, and limited public participation. The study provides a novel and practical pathway comprising four interconnected pillars—governance, economic, social, and technological—that together strengthen the coherence and sustainability of e-waste management in the EU.
