Future Trends of Secondary Raw Materials and Critical Raw Materials

TC Future Trends of Secondary Raw Materials and Critical Raw Materials Deliverable 4.1 Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 2 Project: Future Availability of Secondary Raw Materials Acronym: FutuRaM Grant Agreement: 1010585 Funding Scheme: Horizon Europe Webpage: www.futuram.eu Work Package: WP4 Work Package Leader: UNITAR Deliverable Title: Future Trends of Secondary Raw Materials and Critical Raw Materials Deliverable Leader: UNITAR Version: V4 Status: Final Author(S): G. Iattoni, T. Yamamoto, S. Martins da Cunha, R. Arvidsson, C. Böcher, S. Bottausci, D. Chen, D. Das, B. Guth, S. Henriksson, M. Ljunggren, F. Maisel, S. Marriyapillai Ravisandiran, J.M. Mogollón, D. Monfort Climent , A. Philippe, K. Remmen, J. Stegemann, M. Tippner, L. Van den Abeele, J. van Stee, R. Vingerhoets, P. Wäger, C.P. Baldé E-Mail: giulia.iatt[email protected], tales.yamamot[email protected]g, [email protected] Reviewed By: R. Arvidsson, C.P. Baldé, C. Böcher, S. Bottausci, D. Chen, D. Das, I. Dorri, B. Guth, S. Henriksson, S. Heuss-Aßbichler, J. Horne, G. Iattoni, K. Kippert, M. Ljunggren, F. Maisel, S. Marriyapillai Ravisandiran, S. Martins da Cunha, J.M. Mogollón, D. Monfort Climent, A. Philippe, K. Remmen, Stegemann, M. Tippner, L. Van den Abeele, J. van Stee, R. Vingerhoets, P. Wäger, T. Yamamoto Due Date: 31st May 2025 Date Of Submission: 7th July 2025 Dissemination Level: Public Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 3 Table 1 Version History VER. NO. DATE REASONS FOR RELEASE RESPONSIBLE V1 20/05/2025 Review by PMT & WS Leads UNITAR V2 01/07/2025 Final proofread & addition of references UNITAR V3 07/07/2025 Submission J. Horne V4 17/11/2025 Cover page update, authorship editing, and EC approval UNITAR Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 4 Notice The contents of this document are the copyright of the FutuRaM consortium and shall not be copied in whole, in part, or otherwise reproduced (whether by photographic, reprographic or any other method), and the contents thereof shall not be divulged to any other person or organisation without prior written permission. Such consent is hereby automatically given to all members who have entered the FutuRaM Consortium Agreement, dated 1st June 2022, and to the European Commission to use and disseminate this information. This information and content of this report is the sole responsibility of the FutuRaM consortium members and does not necessarily represent the views expressed by the European Commission or its services. Whilst the information contained in the documents and webpages of the project is believed to be accurate, the author(s) or any other participant in the FutuRaM consortium makes no warranty of any kind regarding this material. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 5 Content ABBREVIATIONS................................................................................................................................... 8 GLOSSARY ............................................................................................................................................ 9 EXECUTIVE SUMMARY ....................................................................................................................... 12 1 INTRODUCTION .......................................................................................................................... 14 2 METHODOLOGY .......................................................................................................................... 16 2.1 DERIVING STOCKS AND FLOWS DATASETS PRIOR TO RECOVERY TREATMENT ............................................ 17 2.1.1 Framework for waste batteries, end-of-life vehicles, waste electrical and electronic equipment, and construction and demolition waste from buildings and wind turbines ................. 18 2.1.2 Framework for mining waste and slag and ashes ........................................................... 20 2.1.3 Data collection and modelling........................................................................................ 20 2.2 CONSOLIDATING COMPOSITION DATASETS ...................................................................................... 21 2.3 PROVIDING TRANSFER COEFFICIENTS DATASETS................................................................................ 22 2.4 EXTENDING COMPOSITION, STOCK AND FLOW AND TRANSFER COEFFICIENTS UNDER THREE SCENARIOS UP TO 2050 23 2.5 OBTAINING COMPOSITION OF ALL STOCK AND FLOWS........................................................................ 27 2.6 DETERMINING THE THEORETICAL AVAILABILITY OF SECONDARY RAW MATERIALS ..................................... 28 2.6.1 Recovery model ............................................................................................................. 28 2.6.2 Uncertainty analysis ...................................................................................................... 29 3 DATASETS AND METHODS SPECIFIC TO WASTE STREAMS .......................................................... 32 3.1 BATTERY WASTE ....................................................................................................................... 32 3.1.1 Scope ............................................................................................................................. 32 3.1.2 Main data sources ......................................................................................................... 32 3.1.3 Method ......................................................................................................................... 32 3.1.4 Highlights ...................................................................................................................... 33 3.2 END-OF-LIFE VEHICLES ............................................................................................................... 33 3.2.1 Scope ............................................................................................................................. 33 3.2.2 Main data sources ......................................................................................................... 33 3.2.3 Method ......................................................................................................................... 34 3.2.4 Highlights ...................................................................................................................... 34 3.3 WASTE ELECTRICAL AND ELECTRONIC EQUIPMENT ............................................................................ 34 3.3.1 Scope ............................................................................................................................. 34 3.3.2 Main data sources ......................................................................................................... 35 3.3.3 Method ......................................................................................................................... 36 3.3.4 Highlights ...................................................................................................................... 36 3.4 CONSTRUCTION AND DEMOLITION WASTE FROM BUILDINGS ............................................................... 36 Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 6 3.4.1 Scope ............................................................................................................................. 36 3.4.2 Data sources .................................................................................................................. 37 3.4.3 Method ......................................................................................................................... 37 3.4.4 Highlights ...................................................................................................................... 37 3.5 CONSTRUCTION AND DEMOLITION WASTE FROM WIND TURBINES ........................................................ 37 3.5.1 Scope ............................................................................................................................. 37 3.5.2 Data sources .................................................................................................................. 38 3.5.3 Method ......................................................................................................................... 39 3.5.4 Highlights ...................................................................................................................... 39 3.6 SLAGS AND ASHES ..................................................................................................................... 39 3.6.1 Scope ............................................................................................................................. 39 3.6.2 Data sources .................................................................................................................. 39 3.6.3 Method ......................................................................................................................... 40 3.6.4 Highlights ...................................................................................................................... 40 3.7 MINING WASTE ........................................................................................................................ 41 3.7.1 Scope ............................................................................................................................. 41 3.7.2 Data sources .................................................................................................................. 41 3.7.3 Method for data collection ............................................................................................ 41 3.7.4 Highlights ...................................................................................................................... 41 4 CONCLUSIONS & OUTLOOK ........................................................................................................ 43 REFERENCES ....................................................................................................................................... 45 ANNEX ............................................................................................................................................... 52 A. DATASET STRUCTURE ................................................................................................................. 52 B. DATA CHECKS........................................................................................................................... 55 C. DATA QUALITY FOR MONTE CARLO ASSESSMENT ............................................................................. 57 Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 7 Tables Table 1 Stock and flow modelling approach per waste stream, and scenario for waste batteries, endof-life vehicles, waste electrical and electronic equipment, construction and demolition waste from buildings and wind turbines............................................................................................................... 20 Figures Figure 1: Stock and flow modelling framework applied in FutuRaM. The scope defined by the green box represents core components. For specific waste streams, such as batteries, this scope was expanded to include additional steps (e.g., metallurgical recovery processes) to ensure the completeness and reliability of the resulting datasets. ..................................................................... 14 Figure 2: Schematic representation of the workflow used to generate data on material stocks and flows, including secondary raw materials, their detailed compositions and theoretical availability, and uncertainty analysis, across three future scenarios. .......................................................................... 17 Figure 3: Data flows of the flow-driven (blue) and the stock-driven model (green) ........................... 19 Figure 4: Structure of composition data for a) slags and ashes and mining waste, and b) waste batteries, end-of-life vehicles, construction and demolition waste from buildings and wind turbines, and waste electrical and electronic equipment. ................................................................................ 22 Figure 5: Data used in the quantification of the three FutuRaM scenarios – BAU, REC, and CIR. ....... 26 Figure 6: Representation of how inflow composition can be combined in a stock and flow model for waste directly originating from products ........................................................................................... 28 Figure 7: Schematic of the Monte Carlo simulation process. ............................................................. 30 Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 8 Abbreviations List of abbreviations used in the report. Abbreviation Description BAU Business as Usual CIR Circularity EU27 27 Member States of the European Union EU27+4 27 Member States of the European Union plus Iceland, Norway, Switzerland and United Kingdom OBS Observed past stocks and flows REC Recovery WEEE Waste Electrical and Electronic Equipment POM Placing on the market; Placed on the market; or Put on the market Glossary Term Definition Source Waste Any substance or object which the holder discards, intends to discard or is required to discard. Directive 2008/98/EC Mining waste specific Waste resulting from prospecting, extraction, treatment and storage of mineral resources and the working of quarries covered by Directive 2006/21/EC of the European Parliament and of the Council of 15 March 2006 on the management of waste from extractive industries (OJ L 102, 11.4.2006, p. 15). Directive 2006/21/EC Placing on the market Placing on the market (also commonly referred to as ‘placed on the market’ or ‘put on the market’) means the first time a product is sold on the market within the territory of a country on a professional basis. Directive 2012/19/EU Stocks and Flows ‘Stocks’ refer to accumulated materials that remain within a system over time. ‘Flows’ refer to the movement of materials into, through, and out of a system over a specific period. For the purpose of this project, stocks and flows are measured annually in terms of mass, surface area, installed capacity or units in each year. Additionally, the default geographical boundaries are the national territories of the EU27+4. (European Commission, 2001) Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 16 2 Methodology The aim of this chapter, together with its supplementary documentation on the respective waste streams, is to provide the methodological foundation for the generation of datasets that will be used in the FutuRaM Urban Mine Platform. Figure 2 illustrates the overall workflow developed for quantifying the theoretical availability of secondary raw materials across the six waste streams. This included the creation of waste stream specific stock and flow models to quantify waste generated and associated end-of-life flows (e.g. waste collected) (see 2.1), the development of a composition dataset (see 2.2), building a dataset of transfer coefficients for waste handling and treatment operations (see 2.3) and the development and quantification of three scenario storylines: Business-as-Usual, Recovery, and Circularity (see 2.4). In the subsequent step, the composition data were integrated into the waste stream specific stocks and flow models (see 2.5). Furthermore, the stock and flow outputs with the related composition as well as the transfer coefficients were used as inputs to the FutuRaM recovery model (see 2.6). In the final step, a tailored uncertainty assessment was designed to be used for the resulting datasets allowing the evaluation of data variability and confidence levels. The final dataset, with its structure detailed in Annex A, was then prepared for integration into the FutuRaM Urban Mine Platform which is being developed within the project. To facilitate harmonization and ensure quality assurance across all steps of the modelling framework, standardized code lists (Kippert, Hu, et al., 2025) and data collection templates (Kippert, Korf, et al., 2025a, 2025b; Yamamoto et al., 2025) were developed. These code lists enable consistent classification and referencing of data throughout the model and support interoperability between datasets. To promote transparency and reuse, all code lists are publicly available (Kippert, Hu, et al., 2025). To estimate waste generation and associated end-of-life flows, FutuRaM applies stock and flow modelling for waste batteries, end-of-life vehicles, waste electrical and electronic equipment, and construction and demolition waste from buildings and wind turbines. While the primary goal is to quantify the waste generated annually, the modelling framework also captures the key stocks and flows from placing on the market (POM), through in-use stocks, to end-of-life flows prior to recovery. For slags and ashes, data on waste generation were directly taken from waste statistics and converted to the FutuRaM classification (Ibid.). The theoretical availability for recovery of secondary raw materials, with particular focus on strategic and critical raw materials, is handled in a dedicated model, which is described in 2.6. Whilst the stocks and flows prior to the recovery are defined for each of the states of the EU27+4, the theoretical recovery is only modelled as an aggregate of these countries. The approach for mining waste was developed in close collaboration with the geological surveys in the project, and the Geological Service for Europe project (GSEU), a FutuRaM sister project. A detailed description is provided in the separate document containing supplementary information on mining waste submitted as part of this report. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 17 Figure 2: Schematic representation of the workflow used to generate data on material stocks and flows, including secondary raw materials, their detailed compositions and theoretical availability, and uncertainty analysis, across three future scenarios. 2.1 Deriving stocks and flows datasets prior to recovery treatment The FutuRaM project distinguishes between two types of waste streams, reflecting different stages of the material life cycle and requiring different approaches to quantify waste generated that is theoretically available for the recovery of critical raw materials. The first type includes waste which originates from final products and goods manufactured using materials and semi-finished products and are POM, such as waste batteries, end-of-life vehicles, waste electrical and electronic equipment, and construction and demolition waste from buildings and wind turbines, These products are often complex in composition and multi-layered with components and subcomponents, and, due to their intended long lifetimes, they remain in use for extended periods, resulting in delayed waste generation. In this context, stock and flow modelling is applied to forecast Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 18 future waste arisings and support extended waste monitoring, particularly for flows not captured in official waste statistics. The approach developed for deriving stocks and flows datasets for this first waste type is particularly suitable for: • Complex products or product categories (e.g., vehicles, electronics, appliances) • Systems that can be described based on sub-product groups, components, and assemblies, including their material and element composition • Products with different lifespans, resulting in diverging end-of-life pathways The second type involves waste which stems from mining, quarrying, manufacturing, and waste management activities—specifically mining quarrying, manufacturing of metals, and combustion processes such as coal, biomass, and waste incineration. This includes wastes such as mining waste and slag and ashes. These processes generate waste continuously, depending on material and energy demand and waste generation. Owing to their typical mineral nature, such waste is commonly disposed of in landfills and mining waste sites. These streams also include historic stocks, such as old mining sites and legacy landfills, which may serve as sources of secondary raw materials. The approach developed for deriving stocks and flows datasets for this second waste type is most suitable for: • Waste generation from mining and quarrying activities, and manufacture of chemicals, chemical products, basic metals, and waste incineration, etc. • Waste streams with a relatively stable or characteristic composition, linked to process parameters and material throughput • Modelling of bulk material and residues flows and their deposits (e.g., slags, ashes, process residues) 2.1.1 Framework for waste batteries, end-of-life vehicles, waste electrical and electronic equipment, and construction and demolition waste from buildings and wind turbines To estimate waste generation and associated end-of-life flows during waste management, (e.g. waste batteries and waste electrical and electronic equipment collected, and end-of-life vehicles that are deregistered for recycling) FutuRaM applies stock and flow modelling for waste batteries, end-of-life vehicles, waste electrical and electronic equipment, and construction and demolition waste from buildings and wind turbines. While the primary goal is to quantify the waste generated annually, the modelling framework also captures the key stocks and flows from POM, through in-use stocks, to various end-of-life pathways prior to recovery. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 19 Figure 3: Data flows of the flow-driven (blue) and the stock-driven model (green) The stock and flow modelling results in a consistent dataset at national level for the EU27+4 and for a time series from 2010 to 2050. Beyond waste generation, the stock and flow modelling includes multiple waste management pathways, such as formal collection but also alternative or undocumented flows (e.g., mixing with other waste streams, exports, or informal waste management activities). It extends the scope to waste flows commonly captured in statistics in line with the Waste statistics framework suggested by (UNECE, 2022). This approach provides insights into material flows and stocks, clarifying the fate of these products within the broader resource management system. Overlaps between different waste streams were identified and assigned to a single stream to prevent double-counting. For example, waste batteries embedded in end-of-life vehicles and in waste electrical and electronic equipment are included under the waste batteries stream and displayed as such in the FutuRaM Urban Mine Platform. Depending on the type of the waste stream, either a stock-driven or a flow-driven modelling approach was applied. An overview of the modelling approach is shown in Figure 3, but can be found in more detail in the supplementary information reports on the specific waste streams submitted as part of this report. In a stock-driven model, the development of in-use stock over time serves as the primary input, with inflows and outflows calculated from product lifespans. In contrast, a flow-driven model takes inflows over time as the primary input, and calculates stocks and outflows accordingly. An overview of the waste streams that used which of the stock-driven and flow-driven models is summarized in Table 1. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 20 Table 1 Stock and flow modelling approach per waste stream, and scenario for waste batteries, end-oflife vehicles, waste electrical and electronic equipment, construction and demolition waste from buildings and wind turbines. Waste stream Scenario Model Waste batteries OBS Flow driven BAU Flow driven REC Flow driven CIR Stock driven Construction and demolition waste from buildings OBS Flow driven BAU Flow driven REC Flow driven CIR Flow driven Construction and demolition waste from wind turbines OBS Flow driven BAU Flow driven + Stock driven REC Flow driven + Stock driven CIR Flow driven + Stock driven End-of-life vehicles OBS Stock driven BAU Flow driven REC Flow driven CIR Flow driven Waste electrical and electronic equipment OBS Flow driven (apart from photovoltaic panels, which is stock driven) BAU Flow driven REC Flow driven CIR Stock driven 2.1.2 Framework for mining waste and slag and ashes For mining waste, only historical and closed mine waste stocks were considered based on a database compiled by geological services organizations in Europe and enriched during the FutuRaM project (GSEU, n.d.). Freshly generated mining waste is not included in the current database, as ongoing mining operations were not addressed within the scope of FutuRaM. In the case of slags and ashes, scattered data sources have been combined and complemented through data gap filling. Official statistics (e.g., Eurostat) and the JRC Raw Materials Information System (European Commission, n.d.) were consulted to estimate the stocks and annual flows. However, the annual flows were complemented by reports from national institutions, industrial organizations and scientific literature. Additional data gap filling was performed to estimate slag and ash volumes based on associated data (e.g., slag volumes were based on reported metal volumes). 2.1.3 Data collection and modelling The stock and flow modelling for calculating waste generation and complementary flows was developed independently for each waste stream, using a variety of data sources. This approach was necessary given the distinct characteristics and data requirements of each waste stream, which required tailored modelling strategies. Further details on the specific data sources and methodological steps, and limitations are provided in the Supplementary information for each waste stream that accompany this report. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 21 2.2 Consolidating composition datasets Composition data provides information on mass fractions and content of elements, materials and components in products and waste stocks and flows. This data is crucial to understanding how much and which secondary, strategic, and critical raw materials are present in the waste streams, where they are located, e.g. in which components or materials, and which measures enable their recovery. Building on the hierarchical approach to composition data developed in the ProSUM project (Huisman, et al., 2016b), the FutuRaM project has extended and adapted this model. The FutuRaM composition data model distinguishes between hierarchical and nested layers within a waste stock or flow. As illustrated in Figure 4, each layer in the hierarchy is composed of the entities of the previous one. This nested structure enables a systematic and scalable description of composition across diverse waste streams. To better reflect the varying characteristics of the waste streams, the composition data model employs two distinct approaches. In alignment with the two waste types described in 2.1, two different approaches were also developed for consolidating the composition datasets. One approach was developed for waste streams such as waste batteries, end-of-life vehicles, waste electrical and electronic equipment, and construction and demolition waste from buildings and wind turbines, where the waste is composed of discarded products. This approach focuses on waste arising from complex product systems, where materials and components are tied to the product composition and use phase. This modelling is typically based on classifying “cohorts”, groups of products POM during a specific time period, to trace how they enter the waste stream over time. This enables the analysis of time-dependent flows and end-of-life treatment options in line with circular economy strategies. The other approach is applied to mining waste, slag, and ashes — waste streams generated during industrial processes — which are typically more homogeneous in nature, with mineralogical composition being the key factor for subsequent processing. This approach models waste as an output of industrial or extractive processes, where the characteristics of the waste are primarily determined by the input materials and process technologies. The composition datasets generated for waste streams such as waste batteries, end-of-life vehicles, waste electrical and electronic equipment and construction and demolition waste from buildings and wind turbines, allow the mapping of respective components, materials, and elements within the product. Figure 4 b) illustrates the FutuRaM approach, which establishes a hierarchical linkage among waste streams, products, components, materials, and elements. Within the FutuRaM composition data model, each element of interest is linked to a specific material, thereby providing not only information on secondary, strategic, and critical raw material content but also a comprehensive overview of the materials used. The material is then also linked to a component, which is again assigned to a certain product. For mining waste and for slags and ashes, waste composition was determined using available sitespecific data from geological surveys complemented by information from relevant literature sources. Composition data is frequently reported on the element or mineral/alloy level. To remain consistent with the FutuRaM data hierarchy, the links between the stock/flow - compound/mineral - element level were also mapped. This data structure aligns with the approach illustrated in Figure 4 a), enabling a consistent and geochemically grounded representation of composition within the respective waste streams. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 22 While the detailed composition methodology and its conceptual framework are beyond the scope of this report and will be published separately in a subsequent phase of the FutuRaM project, key elements such as the harmonized code lists and the data collection template for composition, are publicly accessible templates (Kippert, Korf, et al., 2025a, 2025b; Yamamoto et al., 2025). This supports alignment with FAIR data principles (Wilkinson et al., 2016) by promoting findability, accessibility, interoperability, and reusability. Figure 4: Structure of composition data for a) slags and ashes and mining waste, and b) waste batteries, end-of-life vehicles, construction and demolition waste from buildings and wind turbines, and waste electrical and electronic equipment. 2.3 Providing transfer coefficients datasets Waste recovery systems are characterized by a high variability in applied technologies and are typically sensitive to variations in input composition and process technology, as well as process parameters. To address these complexities, process-based models have been developed and applied. General approaches for modelling recovery processes can be broadly categorized into mechanistic models and data-driven models. These mechanistic models can simulate detailed process behaviour and are wellsuited to capturing short-term fluctuations in input streams and operating conditions (M. A. Reuter & van Schaik, 2015, 2024; M. Reuter & van Schaik, 2012). However, when assessing material flows at the industry or economy-wide level - where changes in processes and flows are generally slower and more predictable - simplified models based on Material Flow Analysis and transfer coefficients are more commonly used. These models offer a practical approach to estimate the distribution, accumulation, and theoretical recovery of secondary raw materials across systems over time (Allesch & Brunner, 2015; Klinglmair et al., 2016; Nakamura & Kondo, 2018; Reck & Graedel, 2012). In the context of waste management, Material Flow Analysis plays a crucial role in identifying where material losses occur and which fractions can be recovered as secondary raw materials. By mapping the pathways and transformation of materials, Material Flow Analysis supports both system-level optimization and evidence-based policy design aimed at improving resource efficiency and circularity (Baars et al., 2020, 2022). In FutuRaM, the Material Flow Analysis-based recovery model is built on the concept of process and material specific transfer coefficients. Transfer coefficients describe the partitioning of a good or Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 23 substance in a process (Brunner & Rechberger, 2016) and assume a linear correlation between input and output. These coefficients enable systematic modelling of material destinations across different waste stream recovery systems. Data for waste stream specific transfer coefficients were collected according to a common framework (Yamamoto et al., 2025). The harmonized code lists are publicly available (Kippert, Hu, et al., 2025), promoting transparency, interoperability, and consistency across datasets. This open access approach aligns with FAIR data principles. After collection of the data, the transfer coefficients were screened, harmonized and consolidated to represent average recovery practices across the EU27 and associated countries (EU27+4). Given the variability and limited availability of reliable transfer coefficients data, this step was associated with a high degree of uncertainty. These transfer coefficients, reflecting current recovery systems, were projected into future timelines, accounting for anticipated technological developments and scenario-specific assumptions. This included backcasting procedures to ensure coherence with the defined scenario narratives. Generally, data for transfer coefficients is from a mixture of data sources e.g., literature, expert knowledge and official reporting systems. However, this information proved to be scattered. This fragmentation makes it hard to get a clear picture of how much material is recovered, especially at such a large scale as the EU27+4. Further detail can be found in the Supplementary information reports for the respective waste streams. 2.4 Extending composition, stock and flow and transfer coefficients under three scenarios up to 2050 The estimation of future theoretical availability of secondary raw materials up to 2050 was carried out through scenario analysis. Scenarios are not meant to be predictions but rather an analysis of what could happen based on current trends and expert assumptions. Three scenarios were developed and quantified across the six waste streams to extend the timeseries beyond the past observed (OBS) stock and flows (either measured or modelled). As illustrated in Figure 5, these scenarios are: BAU, REC, and CIR. The BAU scenario assumes that current trends continue unchanged until 2050. Economic and population growth drive increased demand for materials and consequent increased waste generation. In this scenario, there are no significant changes in policy, technology, or waste management practices, and primary extraction remains the predominant approach to obtaining raw materials. The REC scenario introduces improvements in the collection, treatment, recycling, and recovery of waste, reflecting a shift toward a recovery-oriented economy, in which more raw materials are collected from secondary sources. In this scenario, the EU achieves its recycling and recovery targets, leading to enhanced recovery rates. However, on the waste generation side, there are no substantial changes in the stocks and flows. The CIR scenario envisions a fully realized circular economy. In addition to the improved recovery seen in the REC scenario, this scenario introduces measures to reduce material demand, such as longer product lifetimes through repair and refurbishment, as well as expanded sharing models. The quantification of this scenario reflects both the enhanced recovery rates from the REC scenarios and changes in stocks, flows, and waste generation patterns. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 24 The same population projections and Gross Domestic Product, either corrected or not by Purchasing Power Parity, were used in all three scenarios, as illustrated in Figure 5. The population projections used in the background of the three scenarios are based on the most recent data from (Eurostat & European Commission, 2023) and the United Kingdom Office for National Statistics (ONS, 2022; Vanella et al., 2020). FutuRaM’s gross domestic product projections for future scenarios are derived from economic data provided by the (OECD, 2021). Detailed descriptions of the scenario storylines for each waste stream were developed in consideration of the context and targets for each stream. The quantification of each storyline by each waste stream is documented in the supplementary information reports for each waste stream. The scenarios were designed to be as consistent as possible across the waste streams. However, due to the specific characteristics of certain waste streams, not all scenarios are applicable to every stream. The quantification of the scenarios for each waste stream involved the following steps, adapted to the specific characteristics of each waste stream model: 1. Development of storylines for specific waste streams based on literature and expert knowledge. 2. Identification of modelling parameters impacted in each scenario, based on the storylines. 3. Quantification and extrapolation (e.g., backcasting and forecasting) of scenario parameters using targets, literature and expert assumptions. 4. Running the models to quantify theoretical availability of future secondary raw material. Figure 5 also illustrates the datasets used in each scenario. The future composition of the outflows depends on the composition of the products, the market shares of competing products, and the lifetimes of the different products (2.5). However, some waste streams made the assumption that there were no significant changes to product composition based on lifetime, expert knowledge, and literature. In the BAU scenario, a baseline for the theoretical future availability of secondary raw material was established by extrapolating current trends based on population, gross domestic product (expressed as purchasing power parity), stocks, product lifetimes, and waste management system. In the REC scenario, the main change from the BAU scenario is in the waste management system. The REC scenario assumes improved recovery, which could result from improved collection, sorting, recycling, etc. The changes in the waste treatment operations are represented by adjustments to transfer coefficients for recovery processes, which will then change over time or in a specific year when a particular change in the waste treatment processes is assumed to be implemented. This is illustrated in Figure 5 by the bright blue box “REC transfer coefficients”, which are also used in the CIR scenario. The CIR scenario incorporates multiple circular economy strategies, tailored to each waste stream. Some strategies, such as sharing, reduce material demand and can also shorten product lifetimes, while others, like repair and reuse, extend product lifetimes. In this scenario, because the stocks and flows associated with waste generation are affected, the composition of waste generated may also change. Hence, in the CIR scenario, the main changes from BAU scenario are in the transfer coefficients (as in the REC scenario) and in the lifetimes of the products, illustrated in Figure 5 by the “CIR lifetimes” bright green box for “Stocks and Flows” and the previously mentioned bright blue box “REC transfer coefficients”. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 25 As mentioned, the general storylines were interpreted and adapted by each waste stream according to their specific product characteristics, which could also influence how each stream quantified the scenarios. More details on how each scenario was quantified for each waste stream are available in the supplementary information reports for each waste stream and the respective datasets for waste batteries, end-of-life vehicles, construction and demolition waste from buildings and wind turbines, slag and ashes, and waste electrical and electronic equipment. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 32 3 Datasets and methods specific to waste streams 3.1 Battery waste 3.1.1 Scope The scope of the battery waste stream includes all battery types that are currently available on the market or are expected to be available up to 2050. The waste stream is divided into batteries by applications (battery types) and battery chemistries. Battery types and chemistries that are only in early stages of development are not included. The battery type classification comprises “light means of transport”, “industrial batteries”, “portable batteries”, and “electric vehicle batteries” (Kippert, Hu, et al., 2025). The chemistries considered are lithium ion primary and rechargeable batteries, sodium ion rechargeable batteries, zinc batteries, lead batteries, nickel metal hydride, and nickel cadmium, which are then further divided according to their specific chemistries (Ibid.). The component key includes the main components of the battery cell namely: casing, anode, cathode, separator, binder, and electrolyte (Ibid.). The electronic components of the battery management system are excluded in the battery models but are included in the end-of-life vehicles and not in waste electrical and electronic equipment. The stock and flow model investigates the POM batteries, the battery stock and the battery waste generated, of which the latter is divided into formally collected, exported, discarded in residual mixed waste or contained within waste electrical and electronic equipment. The battery stock and the waste generated are therefore calculated based on the battery lifetime. 3.1.2 Main data sources Raw datasets from the ProSUM project (Huisman et al., 2016b) served as the foundation and starting point for further composition research. It was supplemented with grey and peer reviewed literature. The main data sources for the stock and flow model for portable batteries were (Avicenne & Pillot, 2021) and (Eurostat, 2024a). For industrial batteries, (Avicenne & Pillot, 2021) and (World Bank, 2024). For batteries derived from light means of transport, Joint Research Centre (Bobba & Huisman, 2021). The following data sources have been used to determine recycling efficiencies (transfer coefficients) for the different recycling pathways in the recovery model. For zinc-based batteries, (Rombach et al., 2006) and (Wang et al., 2020). For Lead-acid batteries, (Tian et al., 2017). For nickel-cadmium batteries, (Zhan & Xu, 2014) and (Hämmer & Wambach, 2024). For Nickel metal hydride batteries, (Salehi et al., 2023) and (Hämmer & Wambach, 2024). For lithium-ion batteries, (Hämmer & Wambach, 2024), (Vaccari et al., 2024), (Hu et al., 2022) and (Roy et al., 2024). Additional information on data sources used can be found in the supplementary information report on waste batteries. 3.1.3 Method The supplementary information report on waste batteries provides a detailed account of the method and data used in the stock and flow model. The report includes information on the quantification approach for different battery types (portable, industrial, and light means of transport batteries), as well as the integration of the recovery model and how the quantification was extended into the future for Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 33 the three scenarios. The data quality assessment, as well as limitations and improvements, are also included. 3.1.4 Highlights Total waste battery generation is projected to stay almost constant between 2022 and 2030 before growing at an accelerated rate under both BAU and REC scenarios, with the CIR scenario showing lower values than the BAU or REC scenarios. In 2022, battery-waste generation was dominated by lead–acid chemistries, primarily employed in vehicular starting, lighting, and ignition, while lithium–ion chemistries accounted for the remainder. Per-capita waste battery generation varied widely across EU countries in 2022. Projections to 2050 show increases everywhere, driven by electric vehicles uptake, and this is especially pronounced in Norway and Luxembourg. In 2022, waste batteries contained 15 different strategic and critical raw materials, notably lithium, cobalt and nickel, and their mass is projected to rise sharply by 2050 with the shift to electric vehicles. Recovery of these key materials is also expected to scale up significantly. Such an escalation implies that end-of-life battery streams could become major secondary sources of critical metals, notably cobalt and lithium. A comprehensive breakdown by country, year, scenario, product type, component, material, and element is provided in the accompanying datasets and will be accessible via the FutuRaM Urban Mine Platform. 3.2 End-of-life vehicles 3.2.1 Scope The scope of end-of-life vehicles includes passenger cars and vans (category M1 and N1) Regulation (EU) 2018/858 weighing up to 3.5 tonnes with drivetrains currently available or expected to be POM by 2050, including petrol, diesel, battery electric, hybrid electric, plug-in hybrid electric, liquified petroleum gas, natural gas and other drivetrain types. Vehicle composition is described by several key components and materials relevant to material recovery, including catalytic converters, electric and electronic systems, power electronics, battery management systems, and traction motors (which are categorized as induction or permanent magnet types) several steel and aluminium types and magnesium. The stock and flow model tracks vehicles POM, imports of used vehicles, existing stock, and vehicles deregistered from the stock. Deregistered vehicles are further divided into formally collected end-of-life vehicles for recycling, exports of used vehicles and undocumented flows referred to as the “gap”. Although batteries are part of the end-of-life vehicles stream and analysed in the stock and flow model of vehicles, they are treated separately in the battery waste analysis to allow focused evaluation of battery-specific materials and recovery scenarios. The approach ensures that scenario projections for batteries align with those for vehicles. 3.2.2 Main data sources For the stock and flow model, the main historical vehicle statistics sources include Eurostat, International Council on Clean Transportation and European Automobile Manufacturers’ Association, which provides data on vehicles POM, stock, and documented recycling, and COMEXT for trade of used vehicles. The composition data is based on methodologies developed in the ProSUM project (Løvik et al., 2021) and the Joint Research Centre’s Raw Materials Information System (RMIS) (European Commission, n.d.). The recovery model uses transfer coefficients derived from literature (Andersson et al., 2017; European Commission, 2023; Marmy et al., 2023) to estimate the recovery efficiency of each Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 34 component and material during dismantling and further recovery. These coefficients are applied across drivetrain types and years to simulate recovery performance under different future scenarios. Additional information on data sources used can be found in the supplementary information report on end-of-life vehicles. 3.2.3 Method A detailed description of the models used to quantify the secondary, strategic, and critical raw materials in end-of-life vehicles is provided in the supplementary information report on end-of-life vehicles. The supplementary information includes a description of both the stock and flow model and the recovery model, covering scope, methodology, extension to future scenarios, data quality assessment and limitations. 3.2.4 Highlights The quantity of end-of-life vehicles varied significantly between 2010 and 2022, based on reported statistics. The real-world fluctuations in end-of-life vehicles generation are due to variations such as economic cycles. In contrast, the future projections are based on scenario assumptions of continuous economic and population growth, which in turn results in smooth deregulation growth over time. In 2022, petrol and diesel vehicles were the main contributors to end-of-life vehicle waste. However, this is expected to change by 2050, as battery-powered electric vehicles are becoming more common, and the main contributor to end-of-life vehicle waste under the FutuRaM scenarios. The mass of strategic and critical raw materials from this waste stream could also increase significantly, for example, because of increase in electric vehicles and in all vehicle types, increase in electronic equipment, and increase in metal alloys in structural components. In 2022, end-of-life vehicles waste contained 16 elements, led in mass by iron, aluminium and copper, with rare earths like neodymium and dysprosium playing key roles in electric-vehicle motors. By 2050, the total mass of these critical raw materials increases significantly if the EU vehicle fleet grows and shifts towards electric powertrains as assumed in the scenarios. A full, disaggregated breakdown by country, year, scenario, vehicle type, component, material and element is provided in the accompanying datasets and will be accessible via the FutuRaM Urban Mine Platform. 3.3 Waste electrical and electronic equipment 3.3.1 Scope The scope includes all Electrical and Electronic Equipment covered by the WEEE Directive 2012/19/EU , grouped into six categories: 1. Temperature exchange equipment 2. Screens and monitors 3. Lamps 4. Large equipment (further divided into large equipment excluding photovoltaic panels, and photovoltaic panels) 5. Small equipment Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 35 6. Small IT and telecommunication equipment Where possible, data collection followed the UNU-KEY classification system (Forti et al., 2018), which aligns with the above waste electrical and electronic equipment categories as defined in the WEEE Directive 2012/19/EU. The waste electrical and electronic equipment is assessed through projections up to 2050 using correlations of UNU-KEYs with purchasing power parity, as well as constraints such as there not being more than one washing machine per household, and considering phasing out of obsolete product technologies. A stock and flow model tracks POM volumes, in-use stocks, and waste generation. For WEEE Electrical and Electronic Equipment Generated (‘WEEE generated’) the definition of Regulation (EU) 2017/699 has been applied. This states that WEEE generated’ in a Member State means the total weight of WEEE resulting from EEE within the scope of Directive 2012/19/EU that had been placed on the market of that Member State, prior to any activity such as collection, preparation for reuse, treatment, recovery, including recycling, or export. Waste flows are categorised into formally collected waste electrical and electronic equipment, used electrical and electronic equipment exported for reuse, and non-compliant flows - including waste electrical and electronic equipment discarded in household bins, mixed with metal scrap and exported, or otherwise undocumented. A detailed composition dataset complements the model, identifying key components rich in strategic and critical raw materials, such as printed circuit boards, hard disk drives, permanent magnets, motors, various screen types, and compressors. 3.3.2 Main data sources The main data sources used in the waste electrical and electronic equipment modelling work include: (1) composition data, (2) stock and flow data, and (3) transfer coefficients data. The composition dataset includes a wide range of components found within waste electrical and electronic equipment with a focus on those containing strategic and critical raw materials, such as printed circuit boards, hard disk drives, permanent magnets, motors, various screen types, and compressors. Data were gathered from multiple sources, including outcomes from previous projects (e.g., ProSUM (Huisman et al., 2016b)), contributions from project partners (e.g., (Ecosystem, 2023), the French producer responsibility organization), and literature review (P.E.P. Association, 2023), (Babbitt et al., 2019). The raw data were consolidated to fill gaps, detect outliers, and ensure consistency with the stock and flow model. The stock and flow model begins by quantifying the amount of electrical and electronic equipment POM, using Eurostat data on imports, exports, and domestic production (Eurostat, 2025). A lifespan model, differentiated by UNU-KEY, is then applied to estimate the amount of waste electrical and electronic equipment generated over time and to derive the in-use stock of electrical and electronic equipment. These data are subsequently combined with the composition dataset at the UNU-KEY level and aggregated into the six WEEE Directive categories. Waste electrical and electronic equipment flows data were sourced from Eurostat reporting, peer-reviewed literature, grey literature from compliance schemes, and consortium partners (Baldé et al., 2020, 2023). Where necessary, data gaps were filled and outliers validated through country comparisons. The transfer coefficients data for the waste electrical and electronic equipment recovery model were compiled from both primary and literature data. Primary data were obtained through confidential industry contributions and official reporting systems (e.g., (WEEE Forum, 2025)) provided via producer responsibility organisations (PROs) involved in the project. In addition, a stakeholder consultation - carried out with the WEEE Forum and its members, including industry representatives, research centres, and producer responsibility organisations - was used to address data gaps and validate the accuracy of Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 36 the collected information. Additional information on data sources used can be found in the supplementary information report on waste electrical and electronic equipment. 3.3.3 Method The supplementary information report on waste electrical and electronic equipment provides a detailed overview of the methods and data used in the stock and flow model. The report outlines the quantification approach for the six waste electrical and electronic equipment categories, the application of the composition dataset, and the integration of the recovery model. The supplementary information report also outlines how the modelling was extended to cover future developments across three scenarios. In addition, the report includes a data quality assessment, as well as a discussion of limitations and areas for improvement. 3.3.4 Highlights Waste electrical and electronic equipment generation is expected to grow significantly by 2050, particularly under BAU and REC scenarios, while the CIR scenario could limit this growth. For most waste electrical and electronic equipment categories, waste generation is expected to increase; photovoltaic panels are expected to experience the most substantial growth. Collection under the WEEE Directive is projected to grow, especially under the REC and CIR scenarios. The recovery of raw materials from waste electrical and electronic equipment currently focuses on precious metals, copper, iron and aluminium, and this is expected to expand to include key elements such as neodymium, palladium, and tungsten, and others driven by increased dismantling of strategic and critical raw materials-rich components. A comprehensive breakdown by country, year, scenario, product type, component, material, and element is provided in the accompanying datasets and will be accessible via the FutuRaM Urban Mine Platform. 3.4 Construction and demolition waste from buildings 3.4.1 Scope Construction and demolition waste from buildings is calculated using stock-driven modelling to determine the masses. The stock-driven modelling approach considers the demolition waste stream resulting from the deconstruction and demolition of residential and non-residential buildings for the EU27 based on lifetime modelling. The datasets for Iceland, Norway, Switzerland and the United Kingdom were incomplete and therefore not included. The current dataset covers waste from demolition, but not from renovation or construction. Waste generation from outbuildings, such as sheds, garden houses, and garages, waste from infrastructure (e.g., road, railway, tunnel, and bridges), and from landscape maintenance (e.g., soil and dredging spoils) is not included. Currently, for buildings, the composition of components is not included as composition data available on this level is scarce. Waste electrical and electronic equipment in buildings covered under the WEEE Directive is included in the scope of waste electrical and electronic equipment. Regarding construction and demolition waste management, it is assumed that all waste generated is collected due to the lack of information on illegal dumping. The waste management was modelled in accordance with official reported waste statistics at Eurostat and within countries. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 37 3.4.2 Data sources The bottom-up stock and flow model is based on the data sources and hindcasting methodology developed in (Böcher et al., 2025). Building floor space is calculated using spatial datasets on building footprints from EUBUCCO (Milojevic-Dupont et al., 2023) building heights from the World Settlement Footprint 3d (WFS3D) and the German Aerospace Center (DLR)) and an assumed average floor height of 3.5 m. Buildings with footprints under 5 square meters are excluded. Construction year and use type are assigned using data from (Gevorgian et al., 2021). The composition data is mainly based on a database by (Heeren & Fishman, 2019). The composition of demolition waste is based on data for different cohorts and will change indirectly through various building configurations. The transfer coefficients in the recovery model for construction and demolition waste are based on officially reported waste statistics. The Eurostat waste statistics datasets cover the waste flows that were collected and treated from 2010 to 2020 in the EU27+4 for the following materials: mineraland metalbased materials used in buildings concrete, steel, aluminium, copper and timber. For glass, gypsum and insulation the data on waste treatment was collected from (Damgaard & Lodato, 2022). Additional information on data sources used can be found in the supplementary information report on construction and demolition waste. 3.4.3 Method The supplementary information report on construction and demolition waste presents a comprehensive description of the bottom-up, stock-driven modelling framework used to quantify demolition waste generation. It details the methodology for estimating building lifetimes, the spatial datasets employed for EU27 building footprints and heights, and the assignment of building use types. The narrative also covers the calibration of transfer coefficients for recovery pathways, and the extension of projections under the three FutuRaM scenarios. Finally, it includes a full data quality assessment and a discussion of the model’s limitations and opportunities for future improvement. 3.4.4 Highlights Construction and demolition waste volumes across Europe have been increasing since 2010 and are expected to continue increasing at a similar rate until 2050 under all of the three FutuRaM scenarios. Given the long lifetimes of buildings, a timeframe until 2050 is inadequate to capture the complete impacts of the REC and CIR scenarios. Germany is the country that contributed the most to demolition waste generation. Per capita values show that the demolition waste generation could double by 2050 in some member states. Concrete consistently dominates the waste composition, followed by masonry products, whereas metals and paper are minor contributors. A full, disaggregated breakdown by country, period, scenario, building type, material, and element is provided in the accompanying dataset and will be accessible via the FutuRaM Urban Mine Platform. 3.5 Construction and demolition waste from wind turbines 3.5.1 Scope The stocks and flows of both onshore and offshore wind turbines were estimated between 2010 and the present and were calculated at the wind park level. EU27 projections were used for the quantification of the future scenarios. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 38 Wind turbine waste covers the main types of wind turbines for onshore and offshore applications, which include two main technical types related to the type of generator: direct-drive and gearbox. These two designs can be further divided into subtypes and have significantly different compositions related to permanent magnet content (MC Govern et al., 2024). The 7 subtypes are:  Direct drive and low-speed Permanent Magnet Synchronous Generator  Direct drive and low-speed Electrically Excited Synchronous Generator  Geared and high-speed Double Fed Induction Generator  Geared and high-speed Squirrel Cage Induction Generator  Geared and high-speed Wound Rotor Induction Generator  Geared and medium/high-speed Electrically Excited Synchronous Generator  Geared and medium/high-speed Permanent Magnet Synchronous Generator Since the focus was on strategic and critical raw materials, it was decided to distinguish permanent magnets as a component. Other components are: foundations, external cables, and a ‘rest’ category integrating the tower, the rotor, and the nacelle. In waste management, it was assumed that all decommissioned wind turbines are compliantly managed based on expert judgement, as well as several Environmental Impact Assessments of new wind parks that describe expected waste management options. For technical reasons and after consultation with experts, and based on limited literature, it is considered that in some situations, certain elements are not collected, such as foundations or undersea cables. 3.5.2 Data sources The European wind parks in production and already dismantled are sourced from the Wind Power database (The Wind Power, n.d.), a paid subscription database. It also provides a wind power turbine model database, which was used to determine the type of parks based on the turbine model. Future projections of wind energy per country is published by the association Wind Europe until 2030 (Wind Europe, 2025), but without a distinction by type or technology. JRC 2020 (Carrara et al., 2020) published the current and future market shares of different technologies. Material composition used is the result of a data collection of a bill of materials reported in LCA studies published by Vestas, SURFER project dataset (Monfort & Villeneuve, 2024) as well as JRC data (MC Govern et al., 2024). For the case of wind turbines, with a limited decommissioning experience, the lifetime was assessed based on a literature review and the first available datasets. In previous studies, the mean lifetime of wind turbines was classically set between 15 and 25 years, but in the present study, the lifetime was calculated from observed data from the Wind Power database of already dismantled turbines. Data gaps were also addressed in this aspect by specific research in literature (Abrahamsen et al., 2023) and expert judgements. The recovery model considers materials such as concrete or steel scrap, which rely on transfer coefficients for other construction and demolition waste. The recovery model for permanent magnets and rare earth element recovery relies on publicly disclosed data from permanent magnet recycling projects in Europe, such as Carester and MagREEsource in France (IEA, 2024). Additional information on Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 39 data sources used can be found in the supplementary information report on construction and demolition waste. 3.5.3 Method The supplementary information report on construction and demolition waste presents a detailed account of the geographically explicit, bottom-up, stock and flow model developed to quantify wind turbine stocks and waste generation. The integrated recovery model and its extension to future scenarios are also described, along with data quality assessment, limitations, and proposed improvements. 3.5.4 Highlights As early-installed wind turbines reach their end-of-life, waste volumes are projected to rise sharply by 2050, with Sweden, Finland, Germany, and Denmark generating the most due to their pioneering windpower deployments. In this stream, copper and aluminium — mainly from cabling — dominate the mass of strategic and critical raw materials, while rare earths (neodymium, dysprosium, praseodymium, terbium) reside in permanent magnets. Except for those rare earths, all other critical elements are bound within copper, steel or aluminium alloys. The rare earth elements in permanent magnets can be reclaimed either via magnet recycling processes (functional recycling) or steel recycling processes. In steel recycling processes magnets are mixed with ferrous waste for non-functional recycling of rare earth elements, resulting in a loss of recovery. In the BAU scenario, the majority of magnet recycling is nonfunctional, resulting in a 10% loss during collection and between 70% and 100% of non-functional recycling. The CIR and REC scenarios establish a perfect collection rate, and 90% of magnet waste sent to functional recycling options. A full, disaggregated breakdown by country, year, scenario, turbine type, component, material, and element is provided in the accompanying datasets and will be accessible via the FutuRaM Urban Mine Platform. 3.6 Slags and ashes 3.6.1 Scope The scope of the slags and ashes waste stream includes slags (Fe, Al, Cu and Pb) and sludges (Al, Zn) from metal industries as well as ashes from energy generation and waste incineration processes (coal, biomass, sewage sludges and municipal solid waste). The waste stocks and flows are estimated between 2010 and 2050 and described by their appropriate List of Waste code. Some of the List of Waste codes are further differentiated according to the input material in energy generation or the type of furnace used in steel production. In waste management, it was assumed that all slags and ashes are compliantly managed. 3.6.2 Data sources A distinction was made between the flows and the historical stocks. The historical stocks of slags and ashes were only assessed to a limited extent. Only some primary data was identified for certain waste flows such as red mud or steel slags and coal fly ashes. Important quantities of slags and ashes were landfilled in the last century constituting an anthropogenic resource deposit, but a structured database does not exist and many data gaps exists within the European countries. To make an inventory of all flows (output per year by the industry) a different approach was used for the ashes and the slags. For ashes national and international databases (FAOSTAT, n.d.), (Eurostat, Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 40 2024c, 2024b), (Beyond Fossil Fuels, 2025) were consulted. The information on the yearly volume of coal, biomass and household waste sent to incinerators for each member state was combined with literature information on the resulting volume of ash per input stream. For the slags, public databases (European Commission, n.d.), (Eurometaux, n.d.), (International Aluminium Institute, n.d.) on the production volumes of the metals (Fe, Al, Cu, Zn, Pb, Ni, etc.) were used. This information was combined with literature information on waste volumes generated per ton of metal produced. For data on the associated volumes of ashes on one side and slags on the other side, a review of English language peerreviewed literature was conducted initially using protocol-driven search strategies with the electronic databases Scopus, Web of Science, and Google Scholar, also using a “snowballing” approach (Wohlin, 2014). A series of keywords were used as search strings, and these keywords were also entered as search strings into the Google search engine, to seek non-academic data about the waste of interest, e.g., industrial research reports. The consortium network was also used to identify relevant national reports and sector federation reports to supplement the sources from the protocol-driven search strategies. The recovery model data (transfer coefficients) are based on data form English language peer-reviewed literature as well as government (e.g., (Neuwahl et al., 2019), (Cusano et al., 2017)) and sector federation reports (e.g., (CEWEP, 2021)). Expert judgement was used to assess the technology readiness level, which was taken into consideration for the different scenarios, e.g., BAU scenario uses only technologies with high technology readiness level versus REC scenario also uses technologies having low technology readiness level. Additional information on data sources used can be found in the supplementary information report on slags and ashes. 3.6.3 Method The methodology used to quantify the stocks and flows for the slags and ashes waste stream can be found in the supplementary information report on slags and ashes. It covers both the stock and flow model and the secondary, strategic, and critical raw materials recovery calculation for ashes of municipal waste incineration, ashes from power stations and combustion plants, sewage sludge incineration ashes, and steel, aluminium, lead, zinc, and copper slags. 3.6.4 Highlights The slags and ashes waste stream is unique in the sense that each sub-stream has a different generation trend. It should be noted that for certain waste streams (e.g., biomass ashes) a REC or CIR scenario could not be defined due to the nature of the waste stream. The REC and CIR scenarios are set equal to the BAU scenario for these waste streams. The historic trends and future development per sub-stream show growth in waste generation for some sub-streams, such as municipal solid waste incineration ash, stagnation for other sub-streams, such as ash form biomass incineration and unprocessed steel slags, and decline for some streams such as coal ash. The expected additional “total volumes of slags and ashes” under the BAU scenario is mainly dominated by the shift in ashes from municipal solid waste (where a shift from landfill towards incineration is expected). The 2050 projected trends for REC and CIR scenarios are consistently lower than the projections for BAU scenario for all countries. Aluminium, magnesium and manganese are the three largest strategic and critical raw materials present in the slags and ashes waste stream. Currently, only technologies for recovery of copper and aluminium from municipal solid waste incineration ashes are well established (Neuwahl et al., 2019). However, some technologies to extract vanadium from steel slags are emerging and are close to maturity (e.g., Novana in Finland). A full, disaggregated breakdown by country, year, scenario, sub-stream, material Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 41 and element is provided in the accompanying datasets and will be accessible via the FutuRaM Urban Mine Platform. 3.7 Mining waste 3.7.1 Scope Mining waste within the FutuRaM project is defined as waste rock and tailings. Specifically, it refers to waste generated at closed mining sites. Active mining sites are considered operational facilities and remain the responsibility of the mining companies. Within the EU, most member states have old, closed mining waste sites, although not all have significant metal mining waste sites, such as Denmark and the Netherlands. In terms of European Waste Codes mining waste is considered in the category 01 “Wastes resulting from exploration, mining, quarrying, and physical and chemical treatment of minerals”. 3.7.2 Data sources Member states report the total amount of extractive waste generated in each country and year. These statistics deliver information at a very aggregated level, making it impossible to associate an average chemical composition to each of the 6-digit European Waste Codes. The composition data is instead taken directly from the MIN4EU database. The MIN4EU (EDGI, 2022) builds on the data structure developed in previous Horizon 2020 projects. It is a reference database for mineral occurrences and deposits in Europe, including mining waste. The dataset produced within the FutuRaM project builds upon databases maintained by national geological surveys and it is the result of collaboration with a sister project, the Geological Surveys of Europe (GSEU, n.d.) project. Additional information on data sources used can be found in the supplementary information report on mining waste. 3.7.3 Method for data collection Tools have been developed for both automatic data harvesting and manual data collection using an Access form and/or Excel templates. The Access form was specifically designed for use within the FutuRaM and the Geological Surveys of Europe project. Data is entered into the system either through the automatic harvesting mechanism or manually via the Access form and is then fed into the European Geological Data Infrastructure (EGDI) platform, which is managed by the Geological Surveys of Europe. To enable effective data acquisition and analysis, approximately ten workshops and bilateral support meetings were conducted by FutuRaM. The database structure broadly covers all aspects required by the Critical Raw Materials Act, although some of the current data still requires refinement and updates. The structure follows the INSPIRE Directive 2007/2/EC and uses INSPIRE-compatible codes. 3.7.4 Highlights Materials like copper, cobalt, zinc, gallium, rare earth elements, nickel, vanadium, fluorspar, silicon dioxide, titanium, molybdenum, magnesium, manganese and tungsten of historic mining waste show theoretical availability for future recovery, though current extraction is limited. Current and future mining produces more waste than in the past, but there are less resources of interest remaining in the waste due to increased use of these materials and improved extraction technologies. The mining waste Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 48 International Aluminium Institute. (n.d.). International Aluminium Institute. https://internationalaluminium.org/ Kippert, K., Hu, H.-J., Korf, N., Rotter, V. S., Yamamoto, T., & Remmen, K. (2025). 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Dataset structure The final datasets related to each waste stream (except for mining waste) developed within WP4 and delivered to WP6 for inclusion in the final FutuRaM Urban Mine Platform were as follows: • A dataset consisting of the output from the plug-in of the stock and flow with the composition data (as described in 2.5) and used as input in the recovery model (see 2.6). • A dataset consisting of the output from the recovery model while using as input the dataset described in the previous point and the transfer coefficients (see 2.3 and 2.6). The stock and flow and the recovery model datasets are accommodated in long table format, with each row representing one data point with the following fields: • Waste Stream • Location • Year • Scenario • additionalSpecification • Stock/Flow ID • Layer 1 • Layer 2 • Layer 3 • Layer 4 • Value • Unit A brief description for each field is provided below. Waste Stream The first column field of the data structure indicates the FutuRaM waste stream to which all data stored in the following fields are referred to. Location This column specifies the country in ISO-3 codes. The scope of the stock and flow dataset for all waste streams should cover individually the EU27+4. When one or more countries are missing from a waste stream dataset, the reason will be explained under the system boundary and scope section of the reports containing supplementary information for the respective waste streams. The recovery model is applied for the EU27+4 as a whole, and therefore in the recovery model dataset, only the entry "EU27+4" (or EU27, depending on the data availability) is being used for all waste streams. Year The scope of the stock and flow and recovery model datasets for all waste streams covers a time period going from 2010 to 2050. This includes historical (observed) data to a cut-off year that varies by waste Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 53 stream: 2019 for construction and demolition waste from buildings, 2021 for slag and ashes, 2022 for waste batteries, end-of-life vehicles, and waste electrical and electronic equipment and 2023 for construction and demolition waste from wind turbines. Future projections for the three different scenarios span from the last year of historical data (2020–2024, depending on the waste stream) through to 2050. When one or more years are missing from a waste stream dataset, the reason will be explained under the system boundary and scope section of the reports containing supplementary information for the respective waste streams. Scenario The resulting datasets will have to reflect the historically observed data (OBS) and the future projections for the three scenarios (BAU, REC, CIR) based on the storylines that have been outlined in Milestone 11 of the project and in 2.4 of the present report. additionalSpecification This field is used by some waste streams to store additional specification concerning the product or deposit flow described in Layer 1, such as in the case of waste batteries where it is including the battery application type (e.g. medical, e-mobility, cameras/games, security lighting, etc.) or in the case of construction and demolition waste from wind turbines, where it is providing the differentiation between offshore and onshore. For construction and demolition waste from buildings, end-of-life vehicles, slag and ashes, and waste electrical and electronic equipment this column is left empty. Stock/Flow ID The Stock/Flow ID field specifies which stock or flow the data in the field Value is referring to, given a specific Location, Year, and Layer. For waste batteries, end-of-life vehicles, waste electrical and electronic equipment, and construction and demolition waste, this covers the life cycle of the product from the POM, through the use phase and to the end-of-life (whether it is a building, an infrastructure, an electrical equipment, a battery or a vehicle), also including the possible fates during waste management (i.e. formal separated collection and complementary flows such as mixed with other waste, exports, undocumented, etc.). For slag and ashes, this will cover the flows arising from waste incineration, energy production, slags and sludges. The codes used have been developed to represent uniquely each stock and flow as represented in the waste streams flowcharts. A more thorough analysis of the stocks and flows was documented in the reports containing Supplementary information for the respective waste streams. The complete list of Stock/Flow ID used is available in the central FutuRaM codelists repository (Kippert, Hu, et al., 2025). Layer 1 Based on the composition data model product/flow-component-material-element (p/f-c-m-e), this field contains the information at product or flow level. The field describes specific entities within a waste stream flow, deposit or stock. It can be used to describe product types (e.g. for battery, electrical and electronic equipment, vehicles), infrastructure types, or slags, ashes and sludges according to the codes used in the European Waste Catalogue (European Commission, 2000). Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 54 Layer 2 Based on the composition data model (p/f-c-m-e), this field contains the information at component level. Layer 3 Based on the composition data model (p/f-c-m-e), this field contains the information at material level. Layer 4 Based on the composition data model (p/f-c-m-e), this field contains the information at element level. Value This field contains the actual data (for Layer 1, Layer 2, etc. in that Stock/Flow ID). Unit This field contains the unit of measurement for the data contained in Value. In the case of waste batteries, end-of-life vehicles, slag and ashes and waste electrical and electronic equipment the Value is expressed in mass unit (e.g. kg, Mg, etc.) for all Layers. For construction and demolition waste (both from buildings and wind turbines) the Value for Layer 1 is expressed in mass unit as well as in total floor area (in m2) and total wind energy capacity (in kW) respectively, while all other Layers are expressed in mass unit. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 55 B. Data checks To support the structured consolidation of the waste stream datasets, both technical and qualitative validation procedures were implemented. These were carried out in close collaboration with relevant teams across the project. For the technical validation, a centralized Python-based script was developed to check and improve the datasets through an iterative process involving the respective waste stream experts. This script was designed to execute key validation and consolidation tasks, ensuring consistency, completeness, and alignment of the datasets prior to integration. In particular, the main tasks performed were related to: • Negative values – Datasets should not contain negative entries in the Value field. • N/A or missing values – The Value field should not contain "N/A" or similar placeholders; missing data should simply be left as empty cells, following the agreed convention. • Decimal separator – A dot (.) should be used as the decimal separator, in line with the established format. • Duplicated entries – The script identifies exact duplicated rows across all fields, including the Value. • Duplicates with conflicting values – The script identifies exact duplicated rows across all fields except having a data mismatch in Value. • Missing entries in other fields – Fields other than Value should also follow the empty cell convention for missing data. • Unit consistency – Units used must be consistent with the FutuRaM codelist repository and align with the requirements of each waste stream. • Mass-based hierarchical consistency – The total mass obtained by the sum of Values for a given Layer should fall between the total mass of the adjacent Layers above and below (e.g., Layer 2 should have a total mass in Value greater than Layer 3 and smaller than Layer 1). • Code compliance – Codes used in each field must match those in the FutuRaM codelist repository to ensure interoperability across the different work packages in the FutuRaM project. • Scenario year ranges – For OBS, data should cover the years 2010 to 2019/2023 (depending on the waste stream, see55B); for BAU, REC, and CIR scenarios, the range should span from 2020/2024 to 2050. • Column structure – Column names and order must follow the agreed format. Identified issues were documented in review files, enabling multiple feedback rounds to verify, correct, and align the datasets with the agreed structure and specifications. This iterative process supported the preparation of the waste stream datasets for Deliverable D4.1 and their subsequent transfer for integration into the final FutuRaM Urban Mine Platform. The qualitative validation involved four main steps: • Mass balance analysis – Comparing secondary, strategic and critical raw materials waste generation per waste stream and per element against global mass extraction, verifying that the waste generated for one element was not too high compared to the mass extracted globally of the same element. • Analysis of largest contributors within each waste stream – Ranking waste generation per disaggregated item and per element within each waste stream. The largest fractions were then additionally validated to ensure that all expected elements for that sub-waste stream were reflected. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 56 • Stakeholder consultation – Preliminary datasets per waste stream and a short methodological report were shared with the FutuRaM stakeholder network in February 2025. Stakeholders had two weeks to submit written feedback, which was reviewed and integrated. • Visual analysis – Graphs and tables were generated to support data interpretation and to identify potential inconsistencies. These visualizations enabled iterative review and refinement of the datasets in close collaboration with the waste stream experts. The validated outcomes serve as a basis for the subsequent phases of the project. Future Trends of Secondary Raw Materials and Critical Raw Materials | www.futuram.eu | 57 C. Data quality for Monte Carlo assessment The Monte Carlo analysis is supported by a consistent framework for assessing data quality. This framework uses a four-level Data Quality Score (DQS) scale to reflect the reliability of each input value, ranging from very reliable (DQS = 1) to dubious (DQS = 4). The DQS assigned to a data point depends on:  Whether the data source is official (e.g., Eurostat) or non-official (e.g., expert judgement).  Whether the data is original or modelled/gap-filled.  Whether the data refers to historical values or future projections. The following general rules were applied:  DQS = 1–2 for official data sources deemed representative of the relevant waste stream.  DQS = 2–3 for historical data derived through estimation or modelling.  DQS = 3–4 for future projection data, depending on the availability of reliable foresight: o For projections up to 2030/2035 with clear supporting trends or policy drivers: DQS = 3 o For projections beyond 2035, or with high uncertainty or assumptions: DQS = 4 These scores were used to assign a coefficient of variation (CV) where no direct measure was available, ensuring that the uncertainty analysis remains consistent with the underlying data quality. A summary of the DQS scale is provided in Table C. 1. The application of this DQS framework, and its integration into the Monte Carlo simulation, ensures transparency and consistency across waste streams. The DQS is one of the indicators used to conduct the uncertainty analysis through the Monte Carlo simulation.