Environmental and socio-economic impacts of the circular economy transition in the EU aluminium sector : identification of circular economy levers and assessment of their impact with life cycle-based and macroeconomic models.
Date: 2025-09-23
Institutions: Donati, Franco, Joint Research Centre, Piñero Mira, Pablo, Walker, Anna Maria
Subjects: EU energy policy, aluminium, circular economy, economic consequence, economic model, economic transition, energy transition, environmental impact, macroeconomics, metallurgical industry, product life, report, social impact

Joint Research Centre

Environmental and Socio-Economic 
Impacts of the Circular Economy 
Transition in the EU Aluminium Sector

Identification of Circular Economy levers and assessment of 
their impact with life cycle-based and macroeconomic models

Albizzati, P. F., Walker, A. M., Milios, L., Besler, M., Piñero Mira, P., Pedauga, L., Donati, F., Christis, M., 
Baldassarre, B., Tonini, D.

2025

This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
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Contact information  
Name: Paola Federica Albizzati 
Address: C/ Inca Garcilaso 3, Edificio Expo, 1st floor, 41092 Seville, Spain 
E-mail: paola.albizzati@ec.europa.eu

The Joint Research Centre: EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC143128 
EUR 40437 

PDF 

ISBN 978-92-68-31122-6 

ISSN 1831-9424 

doi:10.2760/5505344  KJ-01-25-438-EN-N 

Luxembourg: Publications Office of the European Union, 2025 

© European Union, 2025 

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How to cite this report: Albizzati, P. F., Walker, A. M., Milios, L., Besler, M., Piñero Mira, P. et al., Environmental and Socio-
Economic Impacts of the Circular Economy Transition in the EU Aluminium Sector, Publications Office of the European 
Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/5505344, JRC143128. 

Contents 

Abstract ...................................................................................................................................................................................................... 5 

Acknowledgements .............................................................................................................................................................................. 6 

Authors ................................................................................................................................................................................................. 6 

Executive summary .............................................................................................................................................................................. 7 

Policy context ................................................................................................................................................................................... 7 

Key conclusions ............................................................................................................................................................................... 7 

Main findings .................................................................................................................................................................................... 8 

Related and future JRC work ................................................................................................................................................... 8 

1  Introduction to the aluminium sector in the EU............................................................................................................ 9 

1.1  EU policy landscape ............................................................................................................................................................ 9 

1.2  Aluminium value chain .................................................................................................................................................. 10 

1.3  Aluminium sector challenges ..................................................................................................................................... 12 

1.4  Report structure ................................................................................................................................................................. 12 

2  Circularity economy scenarios for the future ............................................................................................................. 14 

2.1  Circular economy strategies ....................................................................................................................................... 14 

2.2  Scenarios definition ......................................................................................................................................................... 18 

2.2.1  Status Quo 2021 scenario (STQ21) ......................................................................................................... 18 

2.2.2  Baseline scenario (BSL50) ............................................................................................................................. 18 

2.2.3  Compliance with selected Targets on Circular Economy scenario (CTC50) ....................... 19 

2.2.4  Ambitious Circular Economy scenario (ACE50) .................................................................................. 20 

2.3  Toolkit used for the assessment .............................................................................................................................. 20 

3  Material Flow Analysis ............................................................................................................................................................. 24 

3.1  Methodology ........................................................................................................................................................................ 24 

3.1.1  Compliance with selected Targets on Circular Economy scenario (CTC50) ....................... 26 

3.1.2  Ambitious Circular Economy scenario (ACE50) .................................................................................. 26 

3.2  Results .................................................................................................................................................................................... 30 

4  Life cycle assessment and costing ................................................................................................................................... 36 

4.1  Methodology ........................................................................................................................................................................ 36 

4.1.1  Scope, functional unit and system boundary ..................................................................................... 36 

4.1.2  Modelling assumptions and life cycle inventory of material flows ....................................... 39 

1 

 
4.1.3  Background energy modelling ..................................................................................................................... 42 

4.1.4  Sensitivity analyses ........................................................................................................................................... 43 

4.2  Methodology LCC .............................................................................................................................................................. 44 

4.3  Additional indicators ....................................................................................................................................................... 45 

4.4  Epistemic uncertainty of alternative socio-economy futures ................................................................. 45 

4.4.1  Glocal Eco-World ................................................................................................................................................. 46 

4.4.2  Eco-states ............................................................................................................................................................... 47 

4.4.3  Greening through Crisis ................................................................................................................................... 47 

4.4.4  Green Business Boom ...................................................................................................................................... 47 

4.4.5  Participatory evaluation of alternative socio-economic futures .............................................. 47 

4.5  Results .................................................................................................................................................................................... 49 

4.5.1  Material circularity: Primary Material Demand, Circular Material Use Rate, and 
availability and quality of secondary material .................................................................................................... 49 

4.5.2  Climate Change .................................................................................................................................................... 51 

4.5.3  Remaining impact categories ...................................................................................................................... 53 

4.5.4  Life Cycle Costs ................................................................................................................................................... 53 

4.5.5  Sensitivity analysis I: testing energy mix assumptions ................................................................. 55 

4.5.6  Sensitivity analysis II: testing A factor and market suppliers assumptions ...................... 56 

4.5.7  Epistemic uncertainty in LCA and LCC .................................................................................................... 57 

5  Input-Output Analysis ............................................................................................................................................................... 59 

5.1  Methodology ........................................................................................................................................................................ 59 

5.1.1  Overcoming the limitations of EEMRIO for Circular Economy modelling ........................... 60 

5.1.2  Bottom-up MFA and LCI integration ........................................................................................................ 61 

5.1.3  Extension tables .................................................................................................................................................. 62 

5.1.4  Prospective Multi-Regional Input-Output tables ............................................................................... 63 

5.2  Results .................................................................................................................................................................................... 64 

5.2.1  Climate Change mitigation ............................................................................................................................ 64 

5.2.2  Resource dependency ....................................................................................................................................... 67 

5.2.3  Employment and value added..................................................................................................................... 71 

6  Dynamic macroeconomic modelling ................................................................................................................................ 73 

6.1  Methodology ........................................................................................................................................................................ 73 

6.1.1  FIDELIO: Model description ............................................................................................................................ 75 

2 

 
6.1.2  FIDELIO: Modelling approach ....................................................................................................................... 75 

6.2  Results .................................................................................................................................................................................... 80 

6.2.1  Gross Domestic Product and Value Added ........................................................................................... 80 

6.2.2  Trade .......................................................................................................................................................................... 82 

6.2.3  Employment ........................................................................................................................................................... 84 

6.3  Limitations ............................................................................................................................................................................ 86 

7  Synthesis and discussion ........................................................................................................................................................ 89 

7.1  Impacts of CE: Synthesising bottom-up and macro-analyses ................................................................ 89 

7.1.1  Climate change mitigation and other environmental effects ................................................... 89 

7.1.2  Resource and trade dependency ................................................................................................................ 90 

7.1.3  Socio-economic effects ................................................................................................................................... 91 

7.1.4  Key insights ............................................................................................................................................................ 93 

7.2  Potential policy instruments to foster CE in the aluminium sector ..................................................... 93 

7.2.1  Policy mixing principles ................................................................................................................................... 93 

7.2.2  Long-term policymaking considerations ............................................................................................... 95 

7.2.3  Typology of policy instruments ................................................................................................................... 95 

7.2.4  Life-cycle approach in policymaking ....................................................................................................... 96 

7.3  Policy suggestions to activate the CE levers studied ................................................................................... 96 

7.3.1  Reduce.................................................................................................................................................................... 105 

7.3.2  Reuse ...................................................................................................................................................................... 105 

7.3.3  Recover .................................................................................................................................................................. 106 

8  Conclusions .................................................................................................................................................................................. 107 

8.1  Key scientific findings ................................................................................................................................................. 107 

8.2  Key policy recommendations .................................................................................................................................. 107 

8.3  Future Research Directions ...................................................................................................................................... 108 

References .......................................................................................................................................................................................... 110 

List of abbreviations and definitions ................................................................................................................................... 120 

List of figures .................................................................................................................................................................................... 123 

List of tables ...................................................................................................................................................................................... 126 

Annexes ................................................................................................................................................................................................ 128 

Annex 1. Circularity levers ................................................................................................................................................... 128 

3 

 
Description of circularity levers and implementation in the modelling .................................................... 131 

Combination of levers ........................................................................................................................................................... 139 

Annex 2. Life cycle inventories ......................................................................................................................................... 142 

Annex 3. Additional LCA results ....................................................................................................................................... 148 

Annex 4. Values for epistemic uncertainty sensitivity analysis ..................................................................... 150 

Annex 5. Criteria for Input-Output model selection .............................................................................................. 151 

Annex 6. FIGAROe3 ................................................................................................................................................................. 155 

Annex 7. Input-Output analysis ........................................................................................................................................ 158 

Annex 8. Implementation of the MFA and LCI for the IO analysis ............................................................... 160 

Annex 9. Extension tables ................................................................................................................................................... 163 

Annex 10. Implementation of GECO in prospective tables ............................................................................... 170 

Annex 11. Description of FIDELIO 4 modules and implementation of the CE levers in FIDELIO 171 

4 

 
 
Abstract  

The implementation of Circular Economy (CE) levers in the EU aluminium sector has the potential to 
reduce greenhouse gas emissions by 12-14 Mt CO2-eq. annually by 2050, in addition to what would 
be achieved by decarbonising the energy system only. CE levers also lead to a decrease in the 
demand for bauxite as well as reductions in energy demand, leading to a EUR 3.6 billion annual 
increase in the EU trade balance with import reductions from China, Russia, USA and the UK. 
However, this may come with potential trade-offs, such as reduced employment and Gross Value 
Added, which needs to be further investigated to obtain a more accurate assessment of the 
implications of CE levers on the service and research and development sector. The study’s findings 
are policy-relevant, highlighting the need for a holistic approach and policy mix over the lifecycle of 
materials and products to materialise the CE potential, while informing EU policymakers on feasible 
strategies to support the aluminium sector’s sustainability transition. 

5 

 
Acknowledgements 

We are grateful to Marlen Beltram (International Aluminium Institute) for providing information on 
the material flow analysis for aluminium. We are thankful to Benedetta Nucci (European 
Aluminium), Francesca di Bernardo (European Aluminium), Francesca Cavezza (European 
Aluminium), and George Karkampasis (European Aluminium) who provided information on the 
European aluminium supply chain, shared valuable reports, and provided feedback on the choice 
and values for the circular economy levers. The following people contributed with database 
management and data extraction facilitating the analysis of the aluminium sector: Pieter Fonteyn 
(VITO) and Kimon Keramidas (JRC, C6). Critical inputs in the development of the epistemic 
uncertainty of alternative socio-economic futures exercise was provided by Cristian Matti (JRC, B7) 
and Laurent Bontoux (JRC, S1), who we thank. We also want to thank to all participants of the 
foresight workshop as well as the subsequent sense-making session, which helped us to test our 
assumptions. Finally, we would like to thank from Unit B.5 Peter Eder who provided overall project 
support, and Caroline Lambert for adapting the key messages to the palates of policymakers. 

Authors 

Paola Federica Albizzati (JRC, S.4) 

Anna Maria Walker (JRC, B.5) 

Leonidas Milios (JRC, B.5) 

Malte Besler (JRC, B.5; Fraunhofer ISI) 

Pablo Piñero Mira (JRC, B.7) 

Luis Pedauga (JRC, B.7) 

Franco Donati (Leiden University) 

Maarten Christis (VITO) 

Brian Baldassarre (JRC, S.4) 

Davide Tonini (JRC, B.5) 

6 

Executive summary  

Policy context 

The EU aluminium sector faces significant challenges in relation to its sustainable transition and 
dependency on third countries. Bauxite and aluminium have been included in the list of Critical Raw 
Materials (CRMs) given their high supply risk, high economic importance in the European’s 
manufacturing industry, and sourcing from countries with weak governance. The EU adopted in 
2024 the CRMs Act that ensures to the EU a secure and sustainable supply of CRMs that are 
needed in the energy transition. The EU, under the EU Green Deal, has the ambitious goal of 
becoming the first climate-neutral continent by 2050. Aluminium is a key material in the EU’s 
decarbonisation efforts, while also being identified as one of the energy-intensive sectors that are 
‘hard-to-abate’. Most recently, the EU released the Clean Industrial Deal and the EU 
Competitiveness Compass, outlining concrete actions to turn decarbonisation into a driver of growth 
for energy-intensive European industries, including the aluminium one. An essential element within 
this Deal is circularity, aiming to reduce waste and extend the life of materials by promoting 
recycling and reuse, and to maximise the use of EU’s limited resources while reducing 
overdependencies on third countries for raw materials. Therefore, the circular economy plays a key 
role, and it is fundamental to fulfilling the long-term decarbonisation and sustainability goals 
related to the EU aluminium sector.  

This study builds upon previous studies on the effects of CE, notably Material Economics (2018) 
and Zibell et al. (2022). The novelty of this study is on one side the focus on the socio-economic 
and trade effects of CE, and on the other side the combination of different methods and tools 
applied for achieving the goals of the assessment. To this aim, an interdisciplinary team composed 
of JRC-affiliated scientists and scientists affiliated to other research institutes contributed to the 
work. 

While the conclusions and main findings of this study are useful for policymakers in the field of CE, 
the tools, methods, and data applied are valuable for scientists and consultants that aim to 
quantify the environmental and socio-economic effects of CE. 

Key conclusions 

The implementation of CE in the EU aluminium sector can lead to significant environmental 
benefits compared to the sole decarbonisation of energy mixes, including additional reductions in 
greenhouse gas (GHG) emissions, energy and resources consumption, and waste generation. This 
also determines a decreased EU dependency from extra-EU imports of fossil fuels and bauxite, 
leading to a positive increase in the EU trade balance with its global trade partners. The study also 
highlights possible trade-offs in terms of reduced employment and Gross Values Added, although 
further research is needed to better understand the consequences of implementing CE in the 
service sector, which was only limitedly assessed in this study. 

A holistic approach, careful policy design, and coordination among stakeholders are required to 
unlock the potential of CE. In this sense, the study emphasizes the importance of a policy mix 
approach, combining different policy instruments to address the challenges to unlock CE potentials, 
including mandatory and voluntary instruments, such as administrative, economic, and informative 
ones. These instruments are essential to activate the CE levers investigated in this study, i.e. 
optimise production, extend lifetime of finished products, recover materials, and enhance recycling 
quality. 

7 

 
Main findings 

The study’s key scientific findings include: 

•

•

•

•

The implementation of CE levers can achieve reductions on Climate Change of 12-14 Mt
CO2-eq. annually by 2050, additional to the sole decarbonisation of energy mixes.

CE can lead to a decrease in the EU demand for bauxite (ca. 3.6 Mt annually) and energy
demand. The decrease in energy demand for the EU aluminium sector is quantified at
about 10% for coal, 6.5% for natural gas, 5.5% for crude oil, and 22% for other fossil fuel
relative to the baseline. Most of these reductions affect imported products (ca. 87%). The
reduction of electricity demand is quite substantial at 22% in the aluminium industry.

The decrease in resource and energy demand for the EU aluminium sector leads to an
increase of the EU trade balance of ca. EUR 3.6 billion annually, and it is mainly related to a
reduction in imports from China, Russia, USA and UK.

The study also notes that, under the assumptions and limitations involved, CE
implementation leads to decreases in Gross Value Added (and, hence, in Gross Domestic
Product) and employment in the main industry sectors affected. This highlights the need for
further research about the CE implications on the service sector.

Related and future JRC work 

Relevant JRC studies that have provided background information and data in this study are Carrara 
et al. (2023), Georgitzikis et al. (2021), Passarini et al. (2018), RMIS (2024), and Zore (2024). The 
novelty of this study lies on the quantitative assessment of environmental and socio-economic 
impacts as well as the suggestion of specific policy instruments to unlock Circular Economy 
potentials. Future work should focus on better understanding: i) scrap quality, ii) primary aluminium 
demand in EU, iii) have a material flow analysis specifically for EU27, and iv) understand and 
accurately quantify socio-economic implications of CE on the EU service sector. 

8 

1 

Introduction to the aluminium sector in the EU 

1.1  EU policy landscape 

The EU policy landscape for aluminium is evolving to tackle both competitiveness and sustainability 
challenges. Under the EU Green Deal, the EU has the ambitious goal of becoming the first cli-
mate-neutral continent by 2050, with an intermediate target of reducing greenhouse gases (GHGs) 
emissions to 55% compared to 1990 levels by 2030. The aluminium sector is responsible for about 
2% of the global GHG emissions, and belongs to the “hard-to-abate” sectors, due to either technol-
ogy or cost constraints (European Aluminium, 2023). The Clean Industrial Deal1, released in 
2025, sets out a roadmap to accelerate the green transition of energy-intensive (including “hard-
to-abate”) industries, thereby including aluminium. Main pillars are affordable energy, boosting de-
mand for clean (possibly made-in-EU) products, financing the clean transition, securing circularity 
and access to materials, global scale trade measures to protect EU industry from unfairness, and 
create a skilled workforce. Building on this, the European Commission (EC) has launched various ini-
tiatives to strengthen collaboration within the sector, including the Steel and Metals Action Plan, 
which compiles concrete measures to enhance the sector's competitiveness and sustainability. The 
upcoming Circular Economy Act will tackle circularity and access to materials.  

Bauxite and aluminium have recently been included in the list of Critical Raw Materials (CRMs) 
given their high supply risk, high economic importance in the European’s manufacturing industry, 
and sourcing from countries with weak governance (Georgitzikis et al., 2021). The Critical Raw 
Materials (CRM) Act, adopted in 2024, will ensure EU access to a secure and sustainable supply of 
CRMs, enabling Europe to meet its 2030 climate and digital objectives, promoting responsible 
sourcing and reducing supply chain risks. To this aim, the EC's raw materials diplomacy establishes 
strategic raw material partnerships with non-EU countries2. Meanwhile, the EU's Circular Economy 
Package and the Ecodesign for Sustainable Product Regulation (ESPR) are frameworks for 
assessing ecodesign and circularity requirements to improve sustainability across the aluminium 
value chain. Notably, the recently adopted Packaging and Packaging Waste Regulation en-
forces requirements on packaging use and recycling to foster circularity, affecting a significant 
share of the aluminium product market. Further, the forthcoming End-of-Life Vehicles Regula-
tion will also likely include recycled content targets for aluminium in vehicles, while also establish-
ing requirements for the design of vehicles to facilitate their dismantling and recycling. 

The aluminium industry has been included in the EU Emission Trading System (ETS) since its in-
ception in 2005 with some exceptions. For example, small and medium-sized enterprises (SMEs) 
and certain aluminium production processes, such as anode baking and electrolysis, were initially 
exempt from the ETS. However, as part of the EU's Effort Sharing Regulation and the ETS revision 
for 2021-2030, the aluminium industry will be subject to more stringent GHG reduction targets and 
will have to surrender allowances for its emissions. To prevent carbon leakage, Carbon Border 
Adjustment Mechanism (CBAM) will take effect in 2026, particularly in the metal sector. Rising 
protectionism and threats of global aluminium tariffs increase the risk of diverting overcapacities 
to the EU. To counter unfair trade practices, the EC applies Trade Defence Instruments, including 

1 https://ec.europa.eu/commission/presscorner/detail/en/ip_25_550 
2 https://single-market-economy.ec.europa.eu/sectors/raw-materials/areas-specific-interest/raw-materials-diplomacy_en  

9 

 
 
 
 
anti-dumping and anti-subsidy duties, and responds to aluminium tariffs with swift, proportionate 
countermeasures. 

1.2  Aluminium value chain 

A simplified graphical representation of the aluminium supply chain is presented in Figure 1. 
Aluminium is produced from bauxite. The only active mine in EU is in Greece, which supplies only 
9% of the total EU3 demand for bauxite according to 2018 data. The EU highly relies on imports of 
bauxite (approx. 91% in 2018), with Guinea, Brazil, and Sierra Leone as the main providers of this 
ore (2018 data based on Georgitzikis et al., 2021).  

Figure 1. Representation of the aluminium supply chain 

Source: Taken from European Aluminium (2024) 

Bauxite is refined via the Bayer process into alumina, of which the EU is a net exporter (Georgitzikis 
et al., 2021). The alumina is further processed via the smelting process (Hall-Héroult process) to 
obtain primary molten aluminium. This process is the most influential contributor of GHG emissions 
along the supply chain of aluminium production with 13.1 tonne CO2-eq. per tonne of casted 
aluminium or ~77% of the total carbon footprint (International Aluminium Institute, 2019). Most of 
these emissions (~80%) are due to the electricity consumption (10.5 tonne CO2-eq per tonne; 
International Aluminium Institute, 2019) as it requires about 15 MWh per tonne of primary molten 
aluminium produced (European Aluminium, 2018). The decarbonisation of existing grids is therefore 
key to lower the indirect emissions of the process in focus and of the entire value chain (Zore, 
2024).  

Further, the smelting process employs carbon anodes that can be produced via two technologies, 
the pre-bake (with a coverage of 95%) and the Söderberg one (with a coverage of 5%) (European 
Aluminium, 2018). The carbon anodes are consumed in the electrolytic process and emit CO2 in the 
reduction reaction that occurs (~2 tonne CO2-eq per tonne of aluminium; Moya et al., 2015). To 
overcome this problem, the use of inert anodes is identified as a possible solution to decarbonise 

3 ‘EU’ refers to the 27 Member States. 

10 

 
  
 
 
the smelting process; however, the low technology readiness level (4-5) and the estimated high 
capital investments required bring a large degree of uncertainty with respect to the implementation 
of this solution (Misison Possible Partnership, 2023; Zore, 2024). The EU is reliant (approx. 40% in 
2018, according to Georgitzikis et al., 2021) on non-EU imports of unwrought aluminium that have 
significantly higher GHG emissions; indeed, the global average emissions amount to 17 tonne CO2-
eq per tonne aluminium produced compared to the EU average of 6.7 tonne CO2-eq per tonne 
aluminium produced (from cradle to gate; European Aluminium, 2020). 

The ingots are processed into semi-finished products and aluminium is usually alloyed with other 
elements to achieve specific characteristics. Specifically, two main families exist, i.e. wrought 
aluminium alloys and cast aluminium alloys. Wrought aluminium alloys admit 5% of alloys and 
impurities at most, while cast aluminium alloys allow up to 20% (Jarrín Jácome et al., 2021). 
Wrought aluminium alloys are intended for the production of wrought semi-finished products (i.e., 
rolling, extruding, forging and drawing), while cast aluminium alloys are intended for the production 
of castings (European Aluminium, 2002).  

Semi-finished products are further manufactured into finished products containing aluminium. 
Aluminium is mostly used in the transport sector (40%), followed by the construction sector (24%), 
packaging sector (19%), engineering sector (11%), and consumer durables sector (6%) (European 
Aluminium, 2023). Wrought aluminium alloys are mainly employed in the construction, engineering, 
consumer durables, and packaging sectors, while cast aluminium alloys have mainly found their 
application in the transport sector. Yet, with vehicle fleets changing to electric ones, the demand for 
wrought aluminium alloys in the transport sector is expected to increase (Billy & Muller, 2023; 
Buchner et al., 2017; European Aluminium, 2020). It is expected that the aluminium use will grow, 
with an estimated increase of 40% in demand in Europe by 2050 (European Aluminium, 2020). The 
growth will be driven by replacing materials with aluminium, but also by, for example, lightweight 
requirements and increased demand for electric vehicles in the transport sector. Other factors 
affecting future aluminium demand are the employment of efficient aluminium solutions in 
building renovations, and policy changes such as in the packaging sector due to the adoption of the 
EU Single Use Plastics Directive, and the changing material requirements in the renewable, 
machinery, ICT, and aerospace and defence sectors (Carrara et al., 2023; European Aluminium, 
2020). 

Except for most products for the packaging sector, aluminium has a long lifespan and remains 
locked in the use phase for periods longer than 1 year adding to the societal stock of materials 
(e.g., in the construction sector it has a lifetime of 25-50 years, while in transport of 15 years; 
European Aluminium, 2020). When reaching the end-of-life, post-consumer scrap represents a pool 
of resources that can be reintroduced in the economy. Two technologies are employed for 
aluminium recycling depending on the aluminium alloy family that is produced, namely remelting 
and refining, the latter also providing inputs to the steel industry (where aluminium is used as a 
deoxidizing agent). Remelting produces aluminium wrought ingots from both post-consumer and 
pre-consumer scrap, which is generated at different production processes (i.e., fabrication and 
manufacturing). Refining produces aluminium cast ingots that find their main application in the 
transport sector for engine blocks in cars with combustion engines (European Aluminium, 2020; 
Georgitzikis et al., 2021). In Europe, the demand for recycled aluminium casting alloys is lower than 
the availability of unsorted scrap, i.e. scrap that is either highly alloyed or too mixed to produce 
wrought aluminium alloys and can only be employed for cast aluminium alloys production 
(Georgitzikis et al., 2021), with research forecasting a global aluminium scrap surplus of 5.4 Mt per 
year in 2030 (van den Eynde et al., 2022). Once mixed with cast aluminium alloys, it is no longer 
possible without further sorting (e.g. employing laser induced breakdown spectroscopy) to produce 

11 

 
wrought aluminium alloys from these secondary sources due to high contamination levels. Further, 
the EU is a net exporter of aluminium scrap (approx. 1 Mt in 2021) to Asia, on top of which exports 
of vehicles need to be accounted for (estimated to be about one third of all vehicles; Liesbet & van 
Acker, 2022), leading to a significant flow of aluminium leaving the EU. The scrap that is exported 
is likely to be recycled due to its high value; nevertheless, this represents a loss for the European 
economy and contributes to the dependency on third countries for a material in scarcity in the EU 
(European Aluminium, 2020). 

1.3  Aluminium sector challenges 

The focus of the current analysis is not on specific technologies to be employed to decarbonise the 
sector, which are discussed in, among others, European Aluminium (2023), Mission Possible 
Partnership (2023), and Zore (2024), but rather on the actions that can be implemented to make 
the value chain more circular. Nevertheless, feeding the sector with secondary aluminium 
ultimately reduces the emissions of it, as recycled aluminium can reduce emissions by 92% 
compared to virgin one (based on estimates in Mission Possible Partnership, 2023). Furthermore, 
retaining aluminium in the European supply chain allows decreasing the dependency of the EU on 
third countries and increases the security of its supply. However, it is key to increase the quality of 
the scrap. 

Despite being highly recycled, the sector presents several challenges. Particularly, ensuring closed 
loop recycling, especially for wrought aluminium alloys, requires better collection and more refined 
sorting technologies, such as advanced Eddy Current Separators, or inductive sensor-based sorters 
(European Aluminium, 2020). Currently, different alloys are mixed restricting their recycling into 
cast aluminium alloys only. However, as discussed in Section 1.2, cast aluminium alloys primarily 
find an application into combustion engines that are expected to see their market share decrease in 
favour of electric vehicles (European Aluminium, 2020). Therefore, supporting better collection and 
sorting can lead to high-quality recycling ensuring closed-loops and thus keeping scrap and alloy 
type separates. 

Another important aspect to address to increase recycling is designing products to support easier 
material identification, disassembly, and recycling. Indeed, some fractions are difficult to sort as 
they are attached to other materials thus hindering their recycling into closed loops. Furthermore, 
missorted aluminium (herein intended as aluminium collected with mixed waste) can undergo 
either incineration or landfill. This can be overcome by introducing, prior to final disposal, facilities 
to recover aluminium-containing products, ultimately increasing the amount of aluminium being 
recycled (European Aluminium, 2020). 

Finally, where possible, production processes should be further optimised to decrease the amount 
of pre-consumer scrap produced. Nevertheless, this scrap is easy to sort and has a high economic 
value; hence, it is already fully recycled (European Aluminium, 2020). 

1.4  Report structure 

The report is structured into eight additional sections. Section 2 lines out how the concept of the 
circular economy (CE) can support more efficient management of material flows in the aluminium 
sector, followed by a description of future scenarios to be analysed. Thereafter, the bottom up 
perspective of the assessment is divided into Section 3, sketching the material flow analysis, and 
Section 4, describing the life cycle assessment and costing. The top-down perspective is enabled 

12 

 
through an environmentally extended input output analysis laid out in Section 5, and dynamic 
modelling to capture socio-economic rebound effects in Section 6. A discussion in Section 7 
synthesises the results from the different parts and infers their implications for policy makers. 
Finally, Section 8 concludes the study with a summary and further research avenues. 

13 

 
 
2  Circularity economy scenarios for the future 

The research design of this study is composed of several components. First, the circular economy 
(CE) strategies are introduced to streamline different types of circularity for the analysis. In a 
second step, three scenarios are explored, one that simulates the absence of CE strategies, one in 
which the current CE policy targets for the aluminium sector are attained, and one that aims to 
maximise their material savings and minimise the negative environmental impact. These three 
scenarios are then assessed with the methodologies proposed in the following chapter.  

2.1  Circular economy strategies 

The concept of CE has proliferated over the last decade, especially after the introduction of the first 
CE Action Plan for Europe published by the European Commission in 2015. Although there is no 
single definition for CE, it is generally understood as a systemic approach to the sustainable 
management of natural resources aiming at ensuring that the value of materials is maximised and 
maintained for as long as possible as a way to reduce environmental impacts, waste, and 
improving socio-economic conditions (Kirchherr et al., 2023). Or as presented by ISO 59004 Circular 
economy standard (2024), CE is an ‘economic system that uses a systemic approach to maintain a 
circular flow of resources, by recovering, retaining or adding to their value, while contributing to 
sustainable development.’ 

This is to show that the concept of CE is multi-dimensional (Ghisellini et al., 2016) and relates to 
other concepts, including but not limited to, the so-called R-framework that distinguishes different 
strategies to achieve the goals of circularity, the concept of resource productivity or resource 
efficiency, and sustainable materials management. Though there are several types of classification 
of CE (Calisto Friant et al., 2020), the more techno-centric narrative used in this report has the 
objective of reducing natural resource extraction and decreasing environmental impacts, while 
limiting potential undesirable impacts on socio-economic indicators. In line with this argumentation, 
the implementation of CE strategies not only facilitates the sustainability transition, but should also 
contribute to increased competitiveness, job creation and re-industrialisation (OECD, 2024).  

CE strategies take place at different levels, including different governance levels (regional, national, 
supra-national), different firm/sector levels and different geographical areas (Ghisellini et al., 
2016). The most common approach to identifying and defining CE strategies is the ‘R Framework’ 
(Reike et al., 2018). The number of ‘R’s featured in the R Framework has evolved over time, from 
the Japanese Government’s ‘3R Initiative’ (reduce, reuse, recycle) in 2004, to the European Union’s 
waste hierarchy in its 2008 Waste Framework Directive featuring four Rs (reduce, reuse, recycle, 

14 

 
recover), to ten Rs which constitute the 2017 “Circularity Ladder” (Potting et al., 2017), illustrated in 
Figure 2. 

Figure 2. Overview of CE strategies, separated into narrowing, slowing and closing resource flows 

Source: Based on Potting et al. (2017) 

The R-Framework is useful in presenting a whole array of CE strategies aiming at a certain material 
flow outcome, specified as (i) reduction of material input, (ii) extension of material useful life, and 
(iii) recirculation of materials at the end of life of products. The specific CE strategies exemplify the 
different ways to achieve similar material flow outcomes, by diverse operations. For instance, the 
R-strategies ‘Reuse’, ‘Repair’, and ‘Refurbish’, all constitute distinct ways of extending the lifetime 
of products; yet, their outcome with regards to material use is the same. Therefore, the R-
framework can be simplified into a resource outcome-oriented R-framework that puts forward the 
broad categories of “Reduce”, “Reuse”, and “Recover” as the primary clusters. Similarly, research on 
CE strategies concurs on the usefulness and applicability of this approach, especially when taking a 
business or policy perspective (OECD, 2024). For instance, Bocken et al. (2016) have introduced the 
‘narrowing, ‘slowing’, and ‘closing’ resource loops approach, corresponding to the aforementioned 
‘Reduce’, ‘Reuse’ and ‘Recover’ clusters of R-strategies. A more detailed description of the three 
clusters is presented below:  

•  Reduce: Narrowing resource loops and flows increases resource efficiency, either by de-
creasing the total amount of resources used per unit of output or by making more eco-
nomic use of existing production capacity, and using natural resources, materials, and prod-
ucts more efficiently. This could be achieved either through the development and diffusion 
of new production technologies, the increased utilisation of existing assets, or shifts in con-
sumption behaviour away from material intensive goods and services. 

15 

 
 
•  Reuse: Slowing resource loops reduces the need for additional consumption and demand 

for primary raw materials by extending the lifetime of existing goods. This can be achieved 
by e.g. building long-lasting products that are easy to repair and the ownership of which 
can change during their lifecycle. 

•  Recover: Closing resource loops prevents waste from being generated by reprocessing ma-
terials that have reached their end of use stage and re-introducing them into new products 
as secondary raw materials, thus substituting demand for primary raw materials in produc-
tion. 

Furthermore, each of the R-strategies can be applied in isolation or in connection with another, 
according to anticipated outcomes. There is a certain dependency of the CE clusters in relation to 
their application, with the ‘Reduce’ cluster affecting also the outcomes of the remaining CE clusters, 
as illustrated in Figure 3. Since, on the one hand, in a ‘narrow’ resource loop – at the beginning of 
a product’s lifecycle – the volume of material is defined, only that amount can become available 
for the application of the remaining CE clusters (i.e. slowing or closing loops). On the other hand, 
closing resource loops and reclaiming secondary raw materials can also have an effect on reducing 
the demand of primary raw materials.  

16 

 
Figure 3. Conceptualisation of circularity clusters into Reduce, Reuse and Recover 

Source: Adapted from Konietzko et al. (2020); OECD (2024)  

17 

 
 
In conclusion, taking stock of the available knowledge on the definition of CE and respective R-
strategies commonly applied, in this study we consider the following CE clusters:  

•  Reduce resource input (corresponding to R0-R2 and the ‘narrowing’ resource loop 

strategies), referred to as ‘Reduce’ in this study. 

•  Slow resource throughput (corresponding to R3-R7 and the ‘slowing’ resource loop 

strategies), referred to as ‘Reuse’ in this study. 

•  Enhance resource recirculation of resources (corresponding to R8 and the ‘closing’ resource 

loop strategies), referred to as ‘Recover’ in this study. 

From the CE cluster framework applied in this study, the energy recovery from resources is 
explicitly excluded, since the focus is on material resource circularity. Once materials go through an 
energy recovery process, they cannot be reused in any physical operation, neither as secondary 
material inputs nor as spare parts/components and, therefore, are exiting the material economy.  

2.2 

Scenarios definition 

To quantify the environmental and socio-economic impacts related to different CE strategies, a 
status quo reflecting today’s situation, and three different future scenarios are defined as follows:  

•  Status Quo scenario (STQ21), reflecting current conditions as of 2021.  

•  Baseline scenario (BSL50), intended as the continuation of a historic trajectory for the 
material flows, but assuming energy transition occurs in line with relevant projections 
(GECO; Keramidas et al., 2023). 

•  Compliance with selected Targets on Circular Economy scenario (CTC50), where selected CE 

policies (CEAP1-2) are implemented on the BSL50, and their quantitative targets are 
achieved (see list of selected policies in Table 1). 

•  Ambitious Circular Economy scenario (ACE50), reflecting the implementation of ambitious 

CE levers on the BSL50, to maximise circularity. 

2.2.1  Status Quo 2021 scenario (STQ21) 

This scenario reflects the sector in its current conditions, which is here represented by year 2021 
because of the data available on relevant flows of the supply chain. The status quo is used as a 
reference of comparison with the future 2050 scenarios (BSL50, CTC50, and ACE50) to visualize 
the changes in production flows and associated impacts, also due to the anticipated changes 
energy transition towards a cleaner grid. 

2.2.2  Baseline scenario (BSL50) 

This scenario reflects the evolution of the sector during the time horizon investigated (2021-2050) 
based on historical trends. It implies that the EU CE policies contained in the Circular Economy 
Action Plans (CEAP 1&2) after 2015 are not included and therefore the trends between 2005 and 
2021 are extrapolated (i.e. we assume CEAP 1&2 policies have not determined an impact on the 
available waste/materials statistics up to 2021). However, this scenario accounts for all the 
remaining framework conditions prior to CEAP1-2 such as the previous EU Waste Framework 
Directive (as of 2008 or earlier) and associated targets, as well as decarbonisation efforts 

18 

 
occurring in the energy production sector. In line with the EC Better Regulation toolbox, this scenario 
thus represents a benchmark against which the benefits generated by the EU policies on CE can be 
estimated. The background conditions considered include a future energy mix assumed to evolve 
towards a cleaner energy than the current one, in line with the energy mix projected by the Global 
Energy and Climate Outlook 2023 by JRC and DG CLIMA. 

2.2.3  Compliance with selected Targets on Circular Economy scenario (CTC50) 

This scenario reflects the evolution of the sector during the time horizon investigated (2021-2050) 
assuming that the policy targets listed in Table 1 are achieved. These targets reflect only selected 
quantitative material/waste policy targets contained in the CEAP 1-2. The difference between the 
BSL50 and CTC50 can be interpreted as the reduced environmental impact resulting from achieving 
the CEAP1-2 targets, as listed in Table 1. Notice that the list provided in Table 1 is not exhaustive 
and upcoming policies, such as the Ecodesign for Sustainable Product Regulation (ESPR) or the 
forthcoming End-of-Life Vehicles Regulation, could not be included during this study. 

Table 1. CE policy targets considered in the scenario ‘CTC50’ 

Policy 
Waste Framework 
Directive  

End-of-Life 
vehicles 

Packaging and 
Packaging Waste 
Directive4 
Batteries 
Regulation 

Waste electric and 
electrical 
equipment 
Directive* 

Target 
Preparing for re-
use and recycling 
of municipal 
waste 
Reuse and 
recycling 

Year 
2035 

2015 

Recycling 

2030 

Value 
65% by weight 

Sector affected 
All sectors gener-
ating municipal 
waste 

85% by average 
weight per 
vehicle 
60% by weight 

Transport 

Packaging 

Collection rate for 
waste portable 
batteries 
Collection rate for 
waste light mean 
of transport 
batteries 
Recovery, 
category 1 or 4 
Prepared for re-
use and recycling, 
category 1 or 4 
Recovery, 
category 2 
Prepared for re-
use and recycling, 
category 2 

2030 

73% 

Electrical 

2031 

61% 

Electrical 

2018 

2018 

2018 

2018 

85% 

80% 

80% 

70% 

All sectors gener-
ating WEEE 
All sectors gener-
ating WEEE 

All sectors gener-
ating WEEE 
All sectors gener-
ating WEEE 

4 A review of existing policies was performed in 2023. At that time, the Packaging and Packaging Waste Regulation was 

not adopted; hence, the Packaging and Packaging Waste Directive was considered instead. 

19 

 
 
 
Recovery, 
category 5 or 6 
Prepared for re-
use and recycling, 
category 5 or 6 
Recycled, 
category 3 

2018 

2018 

75% 

55% 

2018 

80% 

All sectors gener-
ating WEEE 
All sectors gener-
ating WEEE 

All sectors gener-
ating WEEE 

*Notice that for WEEE it was not possible to distinguish between the different categories. Therefore, in the 
assessment, the highest target has been assumed for the entire stream of aluminium (i.e. 85%). 
Source: JRC elaboration 

2.2.4  Ambitious Circular Economy scenario (ACE50) 

This scenario reflects a normative scenario, in which an ambitious level of circularity is achieved in 
the aluminium sector based on the selection of a portfolio of R-strategies. Amid the different ways 
of increasing material circularity, the results support the identification of the most sustainable 
trajectory. This circularity scenario addresses both the issue of strategic autonomy (by ensuring the 
circulation of materials inside the EU), as well as the contribution of CE towards net-zero GHG 
emissions by 2050. It needs to be underlined that the scenario is not modelled in a way to achieve 
net-zero emissions by 2050 but simply includes R-strategies that are likely reducing GHG emissions 
in parallel to certainly decreasing mass flows. Moreover, the R-strategies are not employed at their 
technical maximum, but rather at an estimate defined by assumed future market conditions. The 
CE clusters outlined in Section 2.1 are functional to capture a wide spectrum of possible CE R-
strategies, which can bring about favourable sustainability impacts. Their implementation from a 
business and policy perspective requires introducing the notion of “circularity lever” (Bressanelli et 
al., 2021). This concept refers to specific interventions, and it has already been adopted by, for 
example, a recent study aiming to assess the economic and environmental impact of different 
circularity levers in a specific economic sector in the EU (SYSTEMIQ, 2022). In this study, we define 
the notion of a circularity lever as follows:  

A circularity lever is a specific intervention based on one or more circular economy R-strategies, 
applied in the context of a specific material and sector, to increase resource efficiency, material 
durability and recirculation.  

In line therewith, a selection of suitable circularity levers in the EU aluminium sector is identified 
from literature and discussed with the industry, and the corresponding economic and environmental 
impacts on the material system assessed. The levers are clustered according to the respective CE 
clusters previously defined (i.e. Reduce, Reuse and Recover). The CE levers assessed only include 
measures related to material flows and do not consider measures aiming at reducing emissions per 
se. For example, changes in the smelting technology targeting the reduction of direct emissions 
(e.g. via the use of inert anodes, or new smelters) are not included among the CE levers assessed 
as this would answer a different research question, i.e. how to decarbonise the sector. Further, the 
abovementioned improvements are not considered in the baseline as these technologies currently 
have low TRLs, as mentioned in Section 1.2. 

2.3 

Toolkit used for the assessment 

To quantitatively assess the impacts of the CE scenarios, we apply a toolkit composed of Material 
Flow Analysis (MFA), (bottom-up) Life Cycle Assessment and Costing (LCA and LCC), 
Environmentally Extended Input-Output Analysis (EEIOA), and dynamic macroeconomic modelling to 

20 

 
assess environmental and socioeconomic impacts of the scenarios investigated. A graphical 
representation of the research design is displayed in Figure 4. 

Figure 4. Graphical representation of the research design applied in the study 

Source: JRC elaboration 

While the individual methodologies are described in detail in Section 3.1 (MFA), Section 4.1-4.2 
(LCA and LCC, respectively), Section 5.1 (EEIOA) and Section 6.1 (dynamic economic modelling), 
we herein summarise the main strengths and weaknesses of each tool, and thus their 
complementarities in view of our broader research questions (see also Table 2).  

•  Material flow analysis provides a detailed understanding of material flows. It includes the 

status quo and its projection up to 2050. 

21 

 
 
• 

• 

• 

LCA allows for the assessment of environmental impacts across the entire life cycle of 
materials and products. LCA is a linear accounting method and, as such, it does not account for 
macroeconomic effects, such as spillover effects across countries and economic sectors, 
effects due to changes in prices, investments, and trades. Sensitivity analyses are done on the 
side to test the most critical assumptions related to methodology and other aspects. 

LCC provides insights into the changes in costs incurred by different circular economy 
strategies, given existing (gross) market prices. LCC is a linear accounting method and, as such, 
it does not account for macroeconomic effects such as spillover effects, changes in prices, 
investments, and trades. 

EEIOA, based on FIGARO tables (Cazcarro et al., 2025), captures the interdependencies 
between sectors, allowing for analysis of upstream and downstream effects, including 
spillover effects across sectors. FIGARO tables provide a detailed representation of economic 
transactions between industries, which allows for the assessment of the flow of materials and 
emissions throughout the EU and Global economy. Relative to LCA and LCC, the added value of 
this analysis is a better understanding of the spillover effects and selected macroeconomic 
indicators, such as Gross Value Added (GVA) changes, Gross Domestic Product (GDP), 
employment, and resource dependency. However, EEIOA does not consider investments, price 
change effects, and change in trade patterns. Therefore, GDP and employment are better 
captured via dynamic macroeconomic modelling. 

•  Dynamic macroeconomic modelling provides a framework for understanding the broader 

economic consequences of the CE transition. The analysis is performed employing the FIDELIO 
model (Rocchi et al., 2025), a dynamic, multi-regional Input-Output model with new-Keynesian 
features, suitable for analysing medium-term transitions. It is designed to capture spillover 
and rebound effects, and quantify impacts on jobs, growth, energy savings, resource use, and 
trade balance. Additionally to the EEIOA, it accounts for investments, price change effects, and 
change in trade patterns offering a better angle on the full macroeconomic effects.  

Table 2. Illustration of the scope, strength and limitations of the methods applied, and their 
complementarities. DMM: dynamic macroeconomic modelling. EEIOA: Environmentally Extended Input-Output 
Analysis; LCA: Life Cycle Assessment; LCC: Life Cycle Costing 

Scope 

Granularity  

Strengths  

Limitations 

Assessment 
Method 
Material Flow 
Analysis (MFA) 

European 
aluminium supply 
chain, inclusive 
of imports and 
exports to satisfy 
production and 
demand needs. 

Life Cycle 
Assessment and 
Costing (LCA & 
LCC) 

Limited to 
industry 
production and 
consumption in 
Europe, incl. 
waste 

Detailed flows for the 
aluminium material 
system under 
assessment. 

It is not always 
calculated as a 
dynamic material 
flow analysis 

Detailed flows and 
supply chain 
representation for the 
aluminium material 
system under 
assessment. 

Relative to EEIOA 
and DMM: 
Truncation error (it 
does not consider 
spillovers, 
investments, price 

The system 
boundary 
represents 
detailed flows of 
aluminium from 
production to 
waste 
management. 
Imports and 
exports are 
included. 
The system 
boundary 
represents 
detailed flows of 
aluminium from 
production to 

22 

 
management. 
Trade flows are 
simplified via net 
trade. The rest of 
the economy is 
truncated. 

Global economy 
with 
disaggregated EU 
aluminium 
industry. The 
economy is not 
truncated. 

Environmentally 
Extended Input-
Output Analysis 
(EEIOA) 

Dynamic 
macroeconomic 
modelling 

Global economy 
with aggregated 
industries. The 
economy is not 
truncated. 

Source: JRC elaboration 

waste 
management 
within EU. EU 
exported waste 
and its 
management is 
also considered. 
The EU and 
Global economy 
are represented 
by 165 sectors 
(FIGARO IO 
tables); the EU 
aluminium sector 
is further 
disaggregated 
(sector C24E and 
C24F). 
The EU and 
Global economy 
are represented 
by 64 sectors. 

Interdependencies 
between EU and 
global economic 
sectors, spillover 
effects. 

Investments, price 
change effects, and 
change in trade 
patterns. 

change effects, 
and change in 
trade patterns). 

Relative to LCA: 
Aggregation error  

Relative to DMM: 
It does not 
consider 
investments, price 
change effects, 
and change in 
trade patterns. 

Relative to LCA 
and EEIOA: 
Aggregation error. 

23 

 
 
 
 
 
3  Material Flow Analysis 

To assess the performance of the European economy in its transition to a more circular one, and to 
provide guidance on the creation of effective CE policy instruments, a range of different 
methodologies are combined. The aim is to identify the most promising CE lever(s) that, ultimately, 
can be transposed into policy options. The first methodology employed is material flow analysis 
(MFA), which then feeds into the bottom-up life cycle assessment (LCA) and life cycle costing (LCC) 
in a subsequent step. Before assessing the impact of material flows in the different scenarios, it is 
essential to understand how they are affected by the CE clusters – Reduce, Reuse and Recover – in 
the ACE50 scenario. Therefore, the CE clusters are further subdivided into CE levers based on the R-
strategies, the effects of which are described in the following subchapters. Thereafter, the resulting 
MFAs provide the basis for the LCA and LCC, as well as the subsequent top-down assessment 
approaches such as Environmentally Extended Input-Output Analysis and dynamic macroeconomic 
modelling. 

3.1 Methodology 

MFA allows for quantifying flows and stocks of a specific material (or substance) in a system and is 
needed as basis for the other methodologies employed in the assessment. Specifically, the MFA of 
aluminium is grounded on the data published by International Aluminium Institute (2021) based on 
the methodology described in Bertram et al. (2017). The geographical scope of the MFA is ‘Europe’ 
that encompasses EU27, Albania, Belarus, Bosnia-Herzegovina, Iceland, Macedonia, Moldavia, 
Montenegro, Norway, Serbia, Switzerland, Turkey, Ukraine, and United Kingdom. Despite the 
enlarged geographical scope, the MFA of International Aluminium Institute (2021) is used due to 
the detailed information it provides at semi-finished and finished product level, but also for the 
updated and complete data it provides, and for the embedded forecasts it has up to 2050 
encompassing the lifetime of products and stock formation. It is acknowledged that other studies 
targeting specifically the EU and databases that can be tailored with EU figures exist. The study by 
Passarini et al. (2018) outlines and quantifies the MFA for the aluminium sector in EU28. Yet, the 
data presented refers to 2013 and, for the purpose of the study, more recent data were needed to 
detail the Status Quo. Further, COMEXT (Eurostat, 2025a), PRODCOM (Eurostat, 2025b), and 
COMTRADE (United Nations, 2025) are databases that can be used to single out EU27 to have 
more representative data. However, it was decided to employ the data of International Aluminium 
Institute (2021) for the high level of detail it provides and that was needed for the analyses. 
Nevertheless, future efforts should be placed in scaling the MFA to EU27 only. 

The boundaries of the MFA go from bauxite extraction to final disposal and recycling of aluminium 
scrap (i.e. following a cradle to grave approach), including imports and exports at different stages 
of the European supply chain (Figure 5). After the use phase, post-consumer scrap aluminium is 
either collected or transported to sorting facilities prior to recycling. Aluminium that has been 
wrongly segregated and losses are treated via final disposal, herein assumed to be a split between 
incineration and landfilling (based on the env_wasmun statistics of Eurostat, 2024). 

In the MFA, the only distinction made on alloys is between the two principal classifications, namely 
wrought and cast alloys, as a detailed assessment of all aluminium alloys would be too complex 
and requires an analysis at product level that is beyond the scope of this study. At the fabrication 
stage, production of rolling, extrusion, foil, casting and other (assumed as an average of the four) 
are considered as semis. Further, the following sectors have been included following other studies 
(e.g. Langhorst et al., 2024; Liu et al., 2013): building, transport (automobiles and light trucks, 

24 

 
aerospace, and other), packaging (cans, foil), machinery and equipment, electrical (cable, and other), 
consumer durables, and other. 

The MFAs of STQ21 and BSL50 are entirely based on the data and projections provided by 
International Aluminium Institute (2021) for 2021 and 2050. Data related on trade is presented as 
‘net trade’ from 2022 onwards. Therefore, it was assumed that the ratio of imports and exports at 
the different stages of the supply chain was the same as for 2021, i.e. the trade pattern for 2050 
reflects the one of 2021. 

Figure 5. Visualisation of the general aluminium supply chain stages included in the material flow analysis 

I

m
p
o
r
t

I

m
p
o
r
t

I

m
p
o
r
t

t
r
o
p
x
E

I

m
p
o
r
t

t
r
o
p
x
E

t
r
o
p
x
E

Non-EU countries

Mining and 
Refining

Alumina

Aluminium 
production

Ingots

Fabrication

Semis

Manufacturing

Final products

Use

Post-consumer
scrap

Collection

Fabrication scrap

I

m
p
o
r
t

t
r
o
p
x
E

Sorting

Recycling

Disposal

Manufacturing scrap

Manufacturing and Post-consumer scrap

Source: JRC elaboration based on Bertram et al. (2017) 

25 

 
 
3.1.1  Compliance with selected Targets on Circular Economy scenario (CTC50) 

For the CTC50 scenario, the underlying MFA data is grounded on the 2050 projections provided by 
International Aluminium Institute (2021). To reflect the achievement of the targets on recycling 
rates based on the policies listed in Table 1, the corresponding collection rates were recalculated 
and adjusted, relative to the baseline, while the sorting efficiencies were kept constant. 

3.1.2  Ambitious Circular Economy scenario (ACE50) 

The material flow analysis in the ACE50 scenario is affected by the implementation of the CE 
levers. More specifically, the MFA of the BSL50 scenario is adapted to the changes related to the 
circularity levers considered, which are summarised in Table 3. It reports the relative reduction of 
material flows to the BSL50 value and the first order effect on the affected flows. For an in-depth 
description of each lever, their combination, and the modelling parameters for the environmental 
and economic assessments, consult Annex 1. As to get a better understanding of the first order 
effects, the changes are highlighted in Figure 6, depicting the material system for the ACE50 
scenario. The levers have an effect throughout the entire supply chain, but general trends can be 
observed. Levers related to the reduction of resource inputs (i.e., Reduce) are implemented 
upstream, while levers focused on slowing material cycles (i.e., Reuse) are located towards the 
middle where finished products are manufactured. Finally, levers connected to enhancing the 
recirculation of resources (i.e., Recover) are implemented downstream in the supply chain, 
corresponding to end-of-life stages.

26 

 
Table 3. Overview and categorization of circularity levers in the EU aluminium applied in the Ambitious Circular Economy scenario 

CE strategy 

CE lever 

Description 

Reduce 

1  Pre-consumer 

scrap reduction 

Reuse 

2  Design for 
reusability 

The aim of this lever is the reduction of 
fabrication and manufacturing scrap by 
improving the yields of the two processes.  A 
practical example is the aluminium scrap 
resulting from the cutting of aluminium sheets. 
Ultimately, the lever entails producing the 
same amount of final products with less input 
material. 
The aim of the lever is to extend the lifetime 
of finished products or components thereof via 
remanufacturing, reforming, relocating or 
cascading. Cumulatively, extending the lifetime 
of finished products results in reducing overall 
demand for products and materials. Ultimately, 
the finished products – despite extended 
lifespans – would become waste; however, 
their extended use has displaced the need for 
additional finished products entering the 
market for a defined period.  

3  Remanufacturing 
of automotive 
components 

The aim of the lever is to extend the lifetime 
of passenger cars components to reduce the 
demand for aluminium in the targeted 
application. Passenger cars components are 
remanufactured, which is intended as the 

27 

Quantification 

5% reduction in 
scrap generation 
at fabrication and 
manufacturing 

First order 
effect 
Reduced scap at 
fabrication and 
manufacturing 

Sectors 

Source 

All sectors 

Adapted from 
Milford et al. 
(2011) 

Extension of 
lifetime by : 
10% in Transport, 
passenger car; 
36% in Transport, 
aerospace; 
10% in Transport, 
other transport; 
5% in 
Construction; 
5% in Electrical, 
cable; 
10% in Electrical, 
other; 
10% in 
Mechanical 
equipment; 
50% in Consumer 
durables; 
10% in Other. 
Extension of 
lifetime by 10% 

Reduced 
production of 
finished products 
and related 
disposal 

All sectors, 
except for 
Packaging 

Adapted from 
Cooper & 
Allwood (2012) 

Transport, 
passenger car 

Yang et al. 
(2015) 

Reduced 
production of 
finished products 
and related 
disposal 

Recover 

4 

Improved waste 
sorting 

5 

Improved waste 
sorting and scrap 
quality 

6  Design for 
packaging 
recoverability and 
recycling 

7  Expansion of 
deposit return 
system 

8  Recovery from 
mixed waste 

restoring of end-of-life products to ‘like-new’ 
conditions through several operations, namely 
disassembly, inspection, cleaning, 
reconditioning or replacement, and reassembly. 
The lever aims at improving collection and 
sorting to increase closed-loop recycling. 
Indeed, having “cleaner” aluminium collected 
and sorted according to its application, can 
increase the share of wrought alloys remelted 
as opposed to their refining.  
The lever aims at improving collection and 
sorting to increase closed-loop recycling. 
Indeed, having “cleaner” aluminium collected 
and sorted according to its application, can 
increase the share of wrought alloys remelted 
as opposed to their refining. Further, it aims at 
closing the loop for wrought aluminium alloys 
recycling. 
The lever focuses on the changes in design of 
packaging to facilitate material recovery at the 
end-of-life to promote higher recycling 
volumes of packaging over incineration or 
landfilling.  
The lever aims at expanding the deposit return 
system (DRS) to all packaging cans within the 
EU. By collecting the packaging cans 
separately, a close-loop recycling can be 
ensured, therefore increasing the amount of 
cans undergoing remelting as opposed to 
refining. 
Advanced material recovery facilities can allow 
recovering aluminium waste that is wrongly 
segregated from household as mixed waste. 
The lever aims at expanding the coverage of 
such technologies to pre-treat all packaging 
related waste through an advanced sorting 

28 

Adapted from 
Cui & Roven 
(2010); Løvik 
et al. (2014); 
Pedneault et 
al. (2023) 
Adapted from 
Cui & Roven 
(2010); Løvik 
et al. (2014); 
Pedneault et 
al. (2023), and 
educated 
guess 
Warrings & 
Fellner (2018) 

All sectors 

All sectors 

Packaging, cans 
and foil 

Packaging, cans 

5.2% increase in 
sorted aluminium 
waste 

5.2% increase in 
sorted aluminium 
waste and 100% 
diversion of 
wrought 
aluminium scrap 
to remelting 

10% reduction in 
incinerated and 
landfilled 
packaging 
aluminium waste 
100% coverage 
of DRS on 
packaging cans 

Increase in 
collection rates 
and increase in 
remelting as 
opposed to 
refining 
Increase in 
amount of 
recycled 
aluminium and 
increase in 
secondary 
material quality 

Decrease in 
incineration and 
landfilling in 
favour of 
recycling 
Increase in 
collection rates 
and increase in 
remelting as 
opposed to 
refining 

100% coverage 
of advanced 
sorting facilities 
and increased 
efficiency to 93% 
separation 

Increase in 
recovery of 
aluminium from 
mixed waste in 
favour of 
recycling 

Packaging, cans 
and foil 

Labberton 
(2020); 
Warrings & 
Fellner (2021) 

 
 
 
 
 
 
Source: JRC elaboration 

facility prior to undergoing incineration or 
landfilling.  

A graphical representation of where in the supply chain the CE levers are implemented is provided in Figure 6. 

Figure 6. Material system in the Ambitious Circular Economy (ACE50) scenario in the aluminium sector with first order effects of changes induced by the levers 

Non-EU countries

I

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t
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I

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t
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t
r
o
p
x
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Mining and 
Refining

Alumina

Aluminium 
production

Ingots

Fabrication

Semis

Manufacturing

L1 -

L1 -

L2,3 -
Final products

Use

Post-consumer
scrap

Collection

L4,5,7 +

Fabrication scrap

I

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r
t

t
r
o
p
x
E

Sorting

L6,8 +

Recycling

L6,8 -

Disposal

Manufacturing scrap

Manufacturing and Post-consumer scrap

Circularity 
levers

L1
L2
L3
L4

Pre-consumer scrap reduction

Design for reusability

Remanufacturing passenger car

Improved waste sorting

L5
L6
L7
L8

Improved waste sorting & quality

Design for recycling

Expansion of DRS

Recovery from mixed waste

Note: Levers are labelled by numbers, with either positive (+) or negative (-) effects on material flows 

Source: JRC elaboration based on Bertram et al. (2017)

29 

 
 
3.2  Results 

The material flow analysis of 2021 and 2050 is entirely based on the data provided by 
International Aluminium Institute (2021). The full data cannot be disclosed as per an agreement of 
confidentiality; therefore, aggregated data and trends are mainly discussed in this section. Notice 
that values reported refer to million metric tonnes (Mt) of aluminium. 

The material flow analysis of the Status Quo scenario (STQ21) is displayed in Figure 7, while the 
one of the Baseline scenario (BSL50) is depicted in Figure 8. From the MFA of STQ21 and BSL50 it 
is evident that Europe is largely reliant on imports of ingots from third countries to satisfy its 
demand. The total input to the casting phase consists of approx. 30% imported semis in STQ21, 
which is expected to decrease to approx. 20% in BSL50 due to an increase in the recirculation of 
manufacturing and post-consumer scrap (approx. 40% of the input in BSL50 and approx. 33% in 
STQ21). The amount of semis demanded in Europe amounts to approx. 13 Mt in STQ21 and it is 
expected to grow by approx. 40% in BSL50, which is in line with estimations based on the 
information reported in European Aluminium (2020). At sector level, similar growth rates as in 
European Aluminium (2020) are expected in 2050 for the amount of semis used in the transport, 
packaging, and other sectors (55%, 25%, and 27%, respectively, according to European Aluminium 
(2020), and 53%, 25% and 32%, respectively, according to JRC elaborations of the projections of 
International Aluminium Institute (2021)). According to the data used in the current assessment, 
lower growth rates are expected for the building, and machinery and equipment sector (28% and 
29%, respectively, according to European Aluminium (2020) against JRC elaborations of 13% and 
18%, respectively), while higher growth rates are estimated for the electrical and consumer 
durables sectors (34% and 25%, respectively, according to European Aluminium (2020) against JRC 
elaborations of 44% and 122%, respectively). 

When focusing on the finished products demanded in Europe, they amount to approx. 12.5 Mt in 
STQ21, and they are expected to grow by approx. 31% in the BSL50. Its repartition into the 
different sectors considered in the assessment (namely, building, transport, packaging, machinery 
and equipment, electrical, consumer durables, and other) follows the same hierarchy presented in 
European Aluminium (2021), in which the authors highlight that the majority of aluminium-
containing finished products are found in transport, followed by building, packaging, engineering 
products, and consumer durables (as discussed in Section 1.2). As it can be observed in Figure 7 
and Figure 8, the output from the use phase highlights that a great amount of aluminium is locked 
in at this stage. Indeed, approx. 46% of the input is added to the stock in STQ21 and approx. 25% 
in BSL50, thus indicating that a great share of aluminium is not available for producing secondary 
materials. Further, it is also noteworthy the considerable amount of post-consumer scrap that is 
exported from Europe. This amounts to approx. 1 Mt in STQ21 and is expected to increase to 2 Mt 
in BSL50.  

The material flow analysis of the CTC50 (Figure 9) and ACE50 (Figure 10) scenarios are 
calculated departing from the data of the BSL50 scenario and applying the corresponding targets 
and CE levers, respectively. Regarding the casting phase, a decrease in the input of virgin 
aluminium and an increase in the amount of recycled aluminium is observed both in the CTC50 and 
ACE50 scenario compared to BSL50. In the CTC50 scenario, the input of virgin aluminium is 
estimated to decrease by 10%, while the amount of recycled aluminium increases by approx. 4% 
consequently to the attainment of selected targets (Table 1). In the ACE50 scenario, the input of 
virgin aluminium is estimated to decrease by 27%, while the input of secondary materials 
increases by approx. 10% consequently to the implementation of Recover levers. The differences 
between CTC50 and ACE50 suggest that current policy targets could be more ambitious. 

30 

 
Nevertheless, upcoming regulations (e.g. ESPR or the End-of-Life Vehicles Regulation) may have a 
positive effect by lowering the demand of virgin aluminium (via, e.g., recycled content targets or 
requirements) and increasing the amount of recycling (via, e.g., requirements on design for 
disassembly of vehicles). Furthermore, the demand for semis in the CTC50 scenario remains 
unchanged compared to the BSL50 scenario, while it decreases by 10% in the ACE50 scenario 
because of the implementation of Reduce and Reuse levers. 

As the CTC50 scenario only targets changes occurring at end-of-life phases, no changes are 
observed for the demand of finished products, while in the ACE50 scenario a decrease of 11% is 
estimated compared to the BSL50 scenario. With respect to the repartition of aluminium-containing 
products into the sectors considered in the assessment, the same trend as for the BSL50 scenario 
is observed for the CTC50 and ACE50 scenario, namely the sector employing the greatest amount 
of aluminium-based finished products is transport, followed by building, packaging, engineering 
products, and consumer durables. Focusing on the amount of collected waste after the use phase, 
an increase of 6% is calculated for the CTC50 scenario and of 10% in the ACE50 scenario 
compared to the BSL50 one. Finally, the amount of recycled aluminium (with respect to the total 
aluminium entering the recycling phase) increases by 1% in the CTC50 scenario compared to the 
BSL50 scenario, and by 6% in the ACE50. 

31 

 
 
Figure 7. Mass flow analysis relative to the Status Quo scenario (STQ21) 

Note: Trade is to be intended as the net trade; ‘MRF’ stands for material recovery facility 

Source: JRC elaboration based International Aluminium Institute (2021) 

32 

 
 
Figure 8. Mass flow analysis relative to the Baseline scenario (BSL50) 

Note: Trade is to be intended as the net trade; ‘MRF’ stands for material recovery facility 

Source: JRC elaboration based International Aluminium Institute (2021) 

33 

 
 
 
 
Figure 9. Mass flow analysis relative to the Compliance with selected Targets on Circular Economy scenario (CTC50) 

Note: Trade is to be intended as the net trade; ‘MRF’ stands for material recovery facility 

 Source: JRC elaboration based International Aluminium Institute (2021) 

34 

 
 
Figure 10. Mass flow analysis relative to the Ambitious Circular Economy scenario (ACE50) 

Note: Trade is to be intended as the net trade; ‘MRF’ stands for material recovery facility 

Source: JRC elaboration based International Aluminium Institute (2021) 

35 

 
 
4  Life cycle assessment and costing 

This section details the life cycle assessment (LCA) and life cycle costing (LCC) methodologies used 
to quantify the environmental and economic impacts with a bottom-up approach. Section 4.1 
details the general features of the LCA methodology, Section 4.2 describes the LCC methodology, 
Section 4.3 summarises the findings on the other environmental impact categories, while Section 
4.4 illustrates alternative socio-economic futures employed for the sensitivity analysis. Finally, in 
Section 4.5 the results obtained for the environmental and economic assessments are presented. 
In addition to the environmental and economic impacts, the secondary material used to substitute 
primary material is also quantified as an additional impact category. It is used to calculate the 
circular material use rate (CMUR), which is obtained by dividing the use of secondary material use 
(i.e. recycled material in line with the Waste Framework Directive) by the overall material use 
(Eurostat, 2018) including imports and exports. In contrast to the MFA, the use of secondary 
materials from different material cycles (e.g. by-products of other industries) is considered in 
neither the secondary material use nor the CMUR, due to its definition. 

4.1  Methodology 

This section details the Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies 
used to quantify the environmental and economic impacts with a bottom-up approach. 

4.1.1  Scope, functional unit and system boundary 

The LCA has been carried out in accordance with the guidelines of the ISO 14040/14044 standards 
(ISO, 2006a, 2006b). The scope of the LCA is the European aluminium supply chain and its global 
inter-country linkages, with the overarching goal of quantifying the global environmental impacts 
associated with a set of targets and CE levers applied in Europe under two different scenarios 
(CTC50 and ACE50). The STQ21 and BSL50 scenarios represent a benchmark of comparison to 
quantify the impact reductions incurred by the targets and CE levers in selected environmental and 
socio-economic indicators. Notice that bottom-up LCA is by nature limited in its ability to capture 
global effects due to the (well-known) truncation effect. This is why the EEIOA is performed to 
quantify environmental impacts, as it adds value by capturing inter-country interlinkages and 
spillovers (see Section 5). 

As for the LCA, when analysing the CE levers individually, we define a functional unit tailored to 
each case, which means that we include/exclude parts of the system boundary accordingly. The 
related system boundary, following ISO 14040/44, is expanded to the extent necessary to capture 
the consequences of the CE lever assessed. We do not alter final consumer demand in Europe or in 
the Rest of the World (RoW) (ceteris paribus). This corresponds to a consumption footprint 
perspective at global level. For example, increasing recycling in Europe incurs reduction of virgin 
production in Europe with beneficial environmental consequences at global level (more recycled 
content in European products), ceteris paribus; alternatively, improving European production 
influences the environmental performance of the material consumed in both Europe and at global 
level, ceteris paribus. 

It should be noticed that, while each CE lever studied individually has a tailored functional unit, 
when the entire set of CE levers are studied altogether the new functional unit is the combination of 
all the individual functional units (as all changes are applied at once, simultaneously). Since there 
may be dependencies, we ensure that synergies/diverges amid CE levers are duly considered to 

36 

 
avoid mass balance errors or similar inconsistencies. Notably, when Reduce levers are implemented, 
less waste is available for the Recover levers; similarly, when Reuse levers are applied. The order of 
implementation of the CE levers in the modelling thus affects the outcome. 

The system boundary is global, i.e. we strive to include the sectors of Europe and the global 
economy that are affected by the CE levers applied within the European aluminium economy 
(Figure 11). The system boundary includes all processes from bauxite mining to final disposal via 
incineration or landfilling, as well as import-export flows except for the use phase, which is out of 
the scope of the study5. To address multi-functionalities that may arise in the system at end-of-life, 
system expansion is performed following common practices (ISO, 2006a, 2006b; Laurent, Bakas, et 
al., 2014; Laurent, Clavreul, et al., 2014). Therefore, for example, recyclates and energy that arise 
from managing the waste are credited to the waste management system by assuming the 
displacement of the virgin (for materials) or conventional sources (for energy mixes) counterparts. 
Background life cycle impact assessment data are obtained from the ecoinvent v3.9.1 database 
taking the ‘allocation at the point of substitution’ (APOS) system model (Wernet et al., 2016). As the 
study assesses the difference in impacts due to the implementation of specific changes, the 
ecoinvent ‘consequential’ system model would have been more indicated for the purpose of the 
study as it is designed for studies following a consequential thinking. However, it was decided to 
use the APOS datasets to be able to define marginal suppliers and substitutions bearing in mind 
also that such datasets do not include impacts related to indirect land use changes.  

Given the analysis is assessing a future material system, it can be considered a prospective LCA 
(Arvidsson et al., 2023). Even though prospective LCAs have traditionally been connected to 
emerging technologies, Arvidsson et al. (2023) argue to unify future-oriented LCA under the 
definition of prospective LCA as an “LCA that models the product system at a future point in time 
relative to the time at which the study is conducted”. In contrast to traditional LCAs, critical aspects 
to be defined are the exact point in time in the future, the technological maturity and upscaling of 
systems modelled, scenario development (described in Section 2), the involvement of stakeholders 
(see Section 4.4) and the use of background data, further specified in the following sections. A 
limitation is that the life cycle impact assessment used has not been adapted to future impacts in 
terms of characterisation factors, as research on this is only available for a subset of impact 
categories with considerable uncertainties (Arvidsson et al., 2023). By taking into account the 
aforementioned issues, the prospective LCA can be considered as inspired by the more structured 
methodology proposed by Langkau et al. (2023), yet does not follow each step systematically. 

5 Excluding the use phase leads to an underestimation of the impacts (e.g. fuel consumed by a car). To be able to account 
for the impacts related to the use phase, specific finished products would need to be defined. However, this is beyond 
the scope of the analysis.  

37 

 
 
 
 
Figure 11. Simplified illustration of the system boundary of the system under assessment and main 
processes included 

System boundary

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t
r
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Non-EU countries

Mining and 
Refining

Aluminium 
production

Fabrication of 
semis

Manufacturing 
final products

Use

Collection

Fabrication scrap

Manufacturing scrap

Manufacturing and Post-consumer scrap

I

m
p
o
r
t

t
r
o
p
x
E

Sorting

Recycling

Recovery 
facility

Disposal

Note: Transport is also included in the assessment but not shown in this illustration for simplicity 

Source: JRC elaboration 

To model the electricity and heat production mixes, the official Global Energy and Climate Outlook 
(GECO) projections of the European Commission Joint Research Centre are used (further described 
in Section 4.1.3; Keramidas et al., 2023). The use of these projections has a similar function as, for 
example, the employment of the REMIND or IMAGE database of future energy scenarios (Sacchi et 
al., 2022; Stehfest et al., 2014), as is good practice in prospective LCA. It needs to be underlined, 
however, that in contrast to using these databases, which are altering the energy processes in 
ecoinvent databases, the GECO 2023 data is only applied to foreground processes and not, for 
example, to the extraction of raw materials due to limitations of the employed LCA tool. These rely 
on the unaltered ecoinvent v.3.9.1 processes, which lead to minor distortions of the results. 

The environmental impacts are quantified following the Environmental Footprint Life Cycle Impact 
Assessment method (EF, v3.1) (Andreasi Bassi et al., 2023). The 16 environmental impact 
categories are considered, namely: Climate Change; Ozone Depletion; Human Toxicity, cancer; 
Human Toxicity, non-cancer; Particulate Matter; Ionising Radiation; Photochemical Ozone Formation; 
Acidification; Eutrophication, terrestrial; Eutrophication, freshwater; Eutrophication, marine; 
Ecotoxicity, freshwater; Land Use; Water Use; Resource Use, minerals and metals; and, Resource 
Use, energy carrier. The LCA software EASETECH v.3.6.0 was used (Astrup et al., 2012; Clavreul et 
al., 2014). 

Finally, as it is common practice to use secondary aluminium together with primary aluminium in 
the production process, the Circular Footprint Formula (CFF), and, specifically, the material part of 
the formula was applied to correctly allocate burdens and credits to producers and recyclers 
between two life cycles ([Equation 1). Notice that the application of the CFF has been extended to 
the LCC, i.e. costs and revenues are also allocated to producers and recyclers between two life 
cycles. The function of the A factor is, indeed, to allocate the burdens and credits between two life 
cycles reflecting the market reality. The European Commission (2022) suggests to use in the case 
of aluminium an A factor equal to 0.2, hence representing low offer of recycled materials and high 
demand. Yet, in our assessment, we assume as base case an A factor equal to 0, both for allocating 
the impacts of the virgin production for obtaining the recycled mass, and for the burdens of 
recycling.  

38 

 
 
(1 − 𝑅𝑅1) ∗ 𝐸𝐸𝑉𝑉 + 𝑅𝑅1 ∗ �𝐴𝐴 ∗ 𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 + (1 − 𝐴𝐴) ∗ 𝐸𝐸𝑉𝑉 ∗
𝑄𝑄𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
𝑄𝑄𝑃𝑃

[Equation 1] 

𝑄𝑄𝑆𝑆𝑆𝑆𝑆𝑆
∗
𝑄𝑄𝑃𝑃 � + (1 − 𝐴𝐴) ∗ 𝑅𝑅2 ∗ (𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 − 𝐸𝐸𝑉𝑉

∗

Where  A: allocation factor of burdens and credits between supplier and user of recycled materials 

QSin: quality of the ingoing secondary material, i.e. the quality of the recycled material at the 
point of substitution 
QSout: quality of the outgoing secondary material, i.e. the quality of the recyclable material at 
the point of substitution 
QP: quality of the primary material, i.e. quality of the virgin material 
R1: it is the proportion of material in the input to the production that has been recycled from 
a previous system 
R2: it is the proportion of the material in the product that will be recycled (or reused) in a 
subsequent system. R2 shall therefore consider the inefficiencies in the collection and 
recycling (or reuse) processes. R2 shall be measured at the output of the recycling plant 
Erecycled: specific emissions and resources consumed (per functional unit) arising from the 
recycling process of the recycled (reused) material, including collection, sorting and 
transportation process 
ErecyclingEoL: specific emissions and resources consumed (per functional unit) arising from the 
recycling process at end of life (EoL), including collection, sorting and transportation process 
EV: specific emissions and resources consumed (per functional unit) arising from the 
acquisition and pre-processing of virgin material 
EV
acquisition and pre-processing of virgin material assumed to be substituted by recyclable 
materials 

*: specific emissions and resources consumed (per functional unit) arising from the 

4.1.2  Modelling assumptions and life cycle inventory of material flows 

4.1.2.1  Modelling the material supply chain  

The processes considered in the life cycle assessment are mainly based on the inventories reported 
in European Aluminium (2018, 2021) and, whenever necessary, on the inventories of ecoinvent 
v3.9.1 database (Wernet et al., 2016). Imports have been modelled considering both the production 
in non-European countries and their transport to Europe, while for exports the production of semis 
and finished products in Europe is accounted for but their transport, use and end-of-life 
management are excluded. The recycling related to exported scrap is considered in the assessment 
as the scope of the study aims to capture all the effects of CE levers throughout the material 
economy (production, consumption, though excluding the use phase6), and related waste from 
consumption of the European aluminium supply chain. Scrap that is exported to non-European 
countries is waste generated by European consumption and, hence, needs to be accounted for as a 
change in its management would incur effects either in Europe or abroad. 

6 Excluding the use phase leads to an underestimation of the impacts (e.g. fuel consumed by a car). To be able to account 
for the impacts related to the use phase, specific finsihed products would need to be defined. However, this is outside 
of the scope of the current analysis.  

39 

 
 
 
 
 
In the following paragraphs, the inventories and assumptions made for the different stages of the 
aluminium supply chain considered in the assessment are presented. 

Mining, refining and smelting 

The processes related to mining, refining, and smelting are modelled based on the processes 
“aluminium production, primary, liquid, Söderberg”7 and “aluminium production, primary, liquid, 
prebake”8 assuming a coverage of the prebake technology of 95% and for the Söderberg one of 5% 
(European Aluminium, 2018). The energy mixes of the two ecoinvent processes are isolated and 
substituted with the foreground processes based on the GECO projections, both for 2021 and for 
2050. Notice that changes in the smelting technologies (e.g. the use of inert anodes) are not 
considered as the technology readiness levels are still low and these are changes driven by the 
decarbonisation of the supply chain, which is not related to the primary goal of the study, i.e. 
investigating the potential of circular economy in the aluminium sector.  

Ingot production 

The production of ingots at the cast house is based on the inventory reported in European 
Aluminium (2018). At this stage, inputs of pre-consumer and post-consumer scrap may be mixed 
with primary aluminium, hence resulting in an output containing recycled content. Therefore, at this 
stage the CFF is implemented both on the input of primary aluminium, post-consumer scrap, and 
manufacturing scrap. Notice that fabrication scrap only carries the burdens of remelting the scrap, 
and is considered as ‘free of burden’ from mining, refining and smelting (i.e. primary production) 
following common practices (e.g. International Aluminium, 2023). If burdens related to primary 
production of fabrication scrap would be considered, one would need to implement an additional 
CFF on this input. However, what primary production processes would need to be accounted for and 
the value of the A factor to be applied are unclear. As this falls outside of the objectives of the 
current study, it was decided to take a cut-off approach. 

The production of ingots in non-European countries for import to Europe is modelled based on the 
ecoinvent process “aluminium production, primary, ingot”9 in which the energy mixes are isolated 
and substituted with the foreground processes based on the GECO projections for non-European 
countries, for both 2021 and 2050. 

Fabrication of semis 

In the study, five types of semis are considered, namely: extrusion, sheet, foil, cast, and other. The 
LCIs of extrusion, sheet, and foil are based on the inventories provided in European Aluminium 
(2018), while the one of cast is based on Liu et al. (2021). As other was not specified with any 
composition, the inventory was modelled as the average of extrusion, sheet, foil, and cast. 

7 Wu, L., aluminium production, primary, liquid, Söderberg, IAI Area, EU27 & EFTA, Allocation at the point of substitution, 

ecoinvent database version 3.9.1. 

8 Wu, L., aluminium production, primary, liquid, prebake, IAI Area, EU27 & EFTA, Allocation at the point of substitution, 

ecoinvent database version 3.9.1. 

9 Wu, L., aluminium production, primary, ingot, RoW, Allocation at the point of substitution, ecoinvent database version 

3.9.1. 

40 

 
 
 
The production of ingots in non-European countries for import to Europe is modelled based on the 
inventories of the European processes adapting the energy inputs to the ones calculated based on 
the GECO projections for non-European countries, both for 2021 and for 2050. 

Manufacturing 

An average manufacturing process was assumed for all sectors where aluminium is used. 
Specifically, the ecoinvent process “metal working, average for aluminium product manufacturing”10 
was used. In the process, the energy mixes are isolated and substituted with the foreground 
processes based on the GECO projections, for both 2021 and 2050. 

The manufacturing of non-European aluminium products is based on the ecoinvent process “metal 
working, average for aluminium product manufacturing”11. In the process, the energy mixes are 
isolated and substituted with the energy processes based on the GECO projections for non-
European countries, for both 2021 and 2050. 

Use phase 

As impacts related to the use phase would require a detailed modelling of all aluminium containing 
finished products and their usage, this specific life cycle stage is disregarded in the analysis. 
Excluding the use phase leads to an underestimation of the impacts, but it requires to define all (or 
key) aluminium-containing finished products (e.g. windows, passenger car) and the inputs of energy 
and/or auxiliary materials needed at the use phase now and especially in the future (2050). Such an 
analysis is beyond the scope of the study.   

Collection 

Collection via a collection truck was assumed only for packaging and mixed waste. The diesel 
consumption is based on the amounts reported in Albizzati, Foster, et al. (2024)12. For all the other 
sectors, it is assumed that waste is directly transported to a sorting facility prior recycling. 

Transport 

Transport is assumed throughout the supply chain, both for transport within Europe, for imports, 
and for the export of scrap. Transport distances are based on Nessi et al. (2021)13 and Albizzati, 
Foster, et al. (2024)14. Exports of ingots, semis, and final products are not associated with any 
environmental impact related to transport. 

Sorting 

10 Steiner, R., metal working, average for aluminium product manufacturing, RER, Allocation at the point of substitution, 

ecoinvent database version 3.9.1. 

11 Steiner, R., metal working, average for aluminium product manufacturing, RoW, Allocation at the point of substitution, 

ecoinvent database version 3.9.1. 

12 The diesel combustion of separately collected is 2 L per tonne, while for aluminium collected with mixed waste is 4.8 L.  
13 For imports, it is assumed that goods are transported by truck for 200km, by railway for 100 km, and by barge for 

20,000 km. 

14 Transport by truck of 150 km. 

41 

 
 
 
A sorting process prior recycling (both remelting and refining) is assumed and modelled based on 
the energy consumption reported in Rigamonti et al. (2009)15. The energy mixes are modelled based 
on the GECO projections for European countries, for both 2021 and 2050. 

Remelting and refining 

As recycling processes two technologies are considered, namely remelting and refining. The former 
is a dedicated process for only wrought aluminium, while the latter can take as input both wrought 
and cast aluminium. The LCI of remelting is based on European Aluminium (2018), while the 
inventory of refining is based on European Aluminium (2021). Notice that the split for wrought 
aluminium containing products is based on the elaboration of the figures provided in Boin & 
Bertram (2005). The same split is assumed for 2021 and 2050, i.e. no increases in the share of 
aluminium going to remelt is assumed (as also in Langhorst et al., 2024). This means that the 
baseline to 2050 assumes no improvements in sorting and recycling quality (this is instead studied 
in dedicated levers). 

Exported scrap is assumed to undergo refining, which is modelled as the European recycling process 
considering as energy mix the ones based on the GECO projections for 2021 and 2050 for non-
European countries. 

Mechanical recovery facility 

Packaging aluminium containing products that have been wrongly segregated at households, ending 
up in the so-called mixed (residual) waste stream, are assumed to be pretreated in a material 
recovery facility prior to final disposal of the mixed waste, as this is common practice in several 
Member States. The coverage of such a technology and its inventory is based on the information 
provided in Albizzati, Foster, et al. (2024). The technology allows for recovering additional 
aluminium prior to its final disposal. 

Final disposal (incineration and landfilling) 

Packaging aluminium that has been wrongly segregated at households and has not been recovered 
via MRF, losses from collection, and sorting processes are assumed to be disposed of via 
incineration and landfilling (based on the env_wasmun statistics of Eurostat, 2024, it corresponds 
to 46% landfilling and 54% incineration in 2021, and 43% landfilling and 57% incineration in 2050 
based on a linear extrapolation). The incinerator is modelled as in Albizzati, Foster, et al. (2024), 
while landfilling is based on the ecoinvent process “treatment of waste aluminium, sanitary 
landfill”16. 

4.1.3  Background energy modelling 

The projections presented in the GECO 2023 update (Keramidas et al., 2023) are used to calculate 
the composition of the average energy mix, distinguishing amongst electricity, industrial heat 
(specific to the sector analysed), district heating, and space heating, of EU and non-EU from 2020 
to 2050. Out of the three scenarios considered in the GECO 2023 (Keramidas et al., 2023), the 
Nationally Determined Contribution (NDC) Long-Term Strategies (LTS) is assumed as default energy 

15 Electricity required is 69 kWh per tonne, and heat 845 MJ per tonne. 
16 Doka, G., treatment of waste aluminium, sanitary landfill, CH, Allocation at the point of substitution, ecoinvent database 

version 3.9.1. 

42 

 
 
 
mix. In this scenario, the targets of the NDC towards CO2 reduction are considered in the medium 
and the longer term. Moreover, the scenario presupposes that the objectives in the NDCs are met in 
the target year (mostly 2030). Beyond 2030, whenever they exist, the LTS targets of the different 
countries or regions, as in the case of the EU, are pursued, which is the net-zero target for 2050 
(Keramidas et al., 2023). In the GECO 2023 projection, transport is also decarbonised. However, for 
the sake of simplicity, the decarbonisation of the transport sector is not considered in this study.  

Within the NDC-LTS scenario, the energy grid is decarbonised due to high shares of renewables in 
the mix, but also as a result of the implementation of Carbon Capture and Storage (CCS). As 
mentioned in Section 4.1.1, ecoinvent datasets are used to model the background processes, 
including the energy mixes. The life cycle inventories related to energy production from biomass, 
combined heat and power fuelled by natural gas, coal, lignite, natural gas, and oil, are modified to 
account for the energy penalty related to the usage of CCS (i.e., to produce 1kWh from coal, more 
coal is required by the process to power the CCS), which corresponds to 10% according to Bauer et 
al. (2008). The extraction emissions are not affected by the implementation of the CCS. The 
additional emissions related to the usage of CCS are slightly overestimated, because we apply the 
penalty to the entire dataset while they should only be related to the additional extraction of the 
fuel source used to produce energy. Further, we assume that the technology implemented for CCS is 
the monoethanolamide (MEA) scrubbing, having a 90% effectiveness in absorption of CO2, NOx and 
SO2 (ETC, 2022).  

Aluminium production is highly demanding in terms of electricity needs. Usually, aluminium 
production plants are located close to energy production plants to keep the costs low. Hence, the 
composition of the electricity average mix would need to be specific for aluminium. Yet, due to data 
gaps in future projections, it was decided to assume the electricity mix required by the aluminium 
industry equal to the EU one. On the other hand, the composition of the heat required by the sector 
in focus is specific to it. Yet, in the GECO model, there is not a specific sector for aluminium, but this 
is rather included in “Other industry” (Després et al., 2018). Finally, on top of accounting for the 
implementation of CCS in the energy sector (electricity, heat), the establishment of CCS in 
aluminium production itself (to reduce on-site CO2 emissions) is also considered. Yet, based on the 
projections of the GECO the coverage of CCS in the aluminium sector is low (below 10%).  

4.1.4  Sensitivity analyses 

4.1.4.1  Background energy system 

The sensitivity analysis is focused on the assumption made on the energy mixes. The GECO 2023 
(Keramidas et al., 2023) provides three different scenarios, namely a Reference scenario, the NDC-
LTS scenario, and the 1.5°C scenario. The assumption made in the default case (i.e. using the NDC-
LTS scenario for the projections of European and non-European energy system) is challenged 
against considering the energy mixes calculated under the Reference scenario conditions. The 
Reference scenario models a world where only policies legislated as of June 2023 are considered 
and stated targets or policies, whether legislated or not, that have not been supported by concrete 
actions plans are assumed not to be fulfilled (Keramidas et al., 2023). Therefore, the energy mix is 
overall less ‘green’, and the implementation of CCS is even more limited than in the NDC-LTS one. 

4.1.4.2  Methodological aspects: choice of A factor and average vs marginal suppliers 

The sensitivity analysis is conducted on key LCA methodological choices, namely the A factor 
applied in the CFF, and the datasets used to represent the suppliers of aluminium and of energy 

43 

 
(electricity and industrial heat). As for the A factor, the default value applied (A=zero) is challenged 
applying a value of A=0.2, which is the value recommended for aluminium in European Commission 
(2022). As for the market suppliers of aluminium and energy, we tested the average datasets 
(reflecting a rather attributional approach) applied as default by applying marginal datasets 
(reflecting a rather consequential approach). While both average and marginal datasets for energy 
were derived from the GECO projections, further details on the specific composition of the datasets 
may be found in Annex 2. 

4.2  Methodology LCC 

The life cycle costing (LCC) is performed following state-of-the art approaches as detailed in 
Hunkeler et al. (2008) and Martinez-Sanchez et al. (2015). The LCC shares the same goal, scope, 
system boundaries and functional unit as the LCA. Furthermore, as in the LCA the CFF is applied, the 
formula is also employed in the LCC to duly allocate costs and savings at production and end-of-life 
stages. 

In LCC, two types of costs are accounted for, namely internal and external costs (also referred to as 
externalities). Internal costs include production and management costs (i.e. costs incurred by the 
different actors in the supply chain), usually distinguishing between operating and capital costs 
(OPEX and CAPEX). They also cover transfers (i.e. taxes, subsidies, fees, and value added tax) which 
represents a re-distribution of capital amongst the different stakeholders in the system. In contrast, 
external costs are non-monetary transactions reflecting the shadow prices of the emissions. While 
there are several ways to monetise the damage costs of emissions, they are herein estimated in 
line with De Bruyn et al. (2018). They cover prices for emissions to air/soil/water, but not for other 
disamenities, such as noise, odour, accidents, or others. 

Two types of LCC are distinguished based on the definition provided in Hoogmartens et al. (2014), 
namely the environmental LCC (eLCC) and the full environmental LCC (feLCC)17. The former includes 
all internal costs, while the latter accounts for the monetised environmental emissions from the 16 
environmental impact categories assessed, which are currently not internalised in the internal costs. 
Notice that one needs to be careful with transfers in a feLCC. If the transfers already cover the 
entire external costs, then they should not be included to avoid double counting, given the 
environmental externalities are already accounted for. Finally, costs and externalities occurring in 
the future are neither corrected for inflation to ensure consistency with the LCA, and thus expressed 
as EUR2022, nor are discounted18 (that assumes future costs to be lower due to technological 
development; Gollier et al., 2009). 

17 The feLCC has originally been referred to as societal LCC by Hunkeler et al. (2008). It implies a monetisation of the LCA 

results and adds these costs on top of the internal costs. 

18 To account for future costs, social or equity discount rates should be applied. Social discount rates should be applied to 
public projects when implementing specific/targeted regulations; further, social discount rates are usually lower than 
equity discount rates and focus on society rather than revenues. Equity discount rates are usually applied both to 
CAPEX and OPEX and to non-state-owned projects (Tazi, N., Safei, F., and Hnaien, F. (2022). Assessment of the 
levelized cost of energy using a stochastic model. Energy vol. 238, Part B, 121776. 
https://doi.org/10.1016/j.energy.2021.121776).  

44 

 
 
 
4.3  Additional indicators 

On top of the 16 environmental indicators, the eLCC and feLCC, employment was quantified as an 
additional impact category. The impacts related to job creation are estimated following the 
methodology reported in Taelman et al. (2020), while cost of labour is included as an operational 
expenditure in both the eLCC and the feLCC. The underlying inventory data used to describe costs 
and employment for each process included in the system under assessment may be consulted in 
Annex 2.  

Further, the results also encompass the quantification of the Circular Material Use Rate (CMUR), 
which is an indicator that strives at measuring an economy’s circularity. The CMUR is the ratio of the 
amount of waste going to be recovered domestically corrected by the amount of imported waste to 
be recycled and the amount of exported waste destined for recovery, and of the domestic material 
consumption (inclusive of imports and exports).  

Finally, as presented in Section 1.2, two main alloy families exist, namely wrought aluminium and 
cast aluminium alloys. When reaching its end-of-life, aluminium can either undergo remelting or 
refining, with the former producing wrought aluminium ingots and the latter cast aluminium ingots. 
Once mixed with other alloys, wrought aluminium alloys cannot be recycled back into wrought 
aluminium alloys (i.e. in a ‘closed-loop’ fashion), but can only be recycled into cast aluminium alloy 
due to higher contamination levels. Herein, quality is intended as the potential of achieving closed 
loop recycling for wrought and cast aluminium. Wrought aluminium alloys are herein considered of 
high-quality (due to its low alloy and impurity levels), while cast aluminium alloys are considered of 
low-quality (due to its high alloy and impurity levels). In the bottom-up assessment and based on 
the MFA data, the share of wrought-to-wrought and wrought/cast-to-cast aluminium recycling is 
calculated as an additional indicator. 

4.4  Epistemic uncertainty of alternative socio-economy futures 

With the current design of the analysis, there is an underlying assumption that the socio-economic 
system on which the industrial system is based will not change significantly. Thus, it is expected 
that technological and socio-economic trends will continue as per the historical trajectory. Taking an 
anticipatory governance approach, it is essential to understand that the current reality might 
develop in radically different ways, subjecting the present analysis and its results to substantial 
epistemic uncertainty (Langkau et al., 2023). One way to address these is the use of alternative 
socio-economic scenarios which can be predictive, explorative or normative (Börjeson et al., 2006). A 
well-known set of scenarios used in integrated assessment models are for example the explorative 
shared socio-economic pathways scenarios (van Vuuren et al., 2017), for which global data 
regarding population, GDP growth, inequalities, material flows and related emissions corresponding 
to different socio-economic development paths are available (Narayan et al., 2023; Schandl et al., 
2020; van Beek et al., 2020). Another set of explorative scenarios was proposed by Bauwens et al. 
(2020) with a 2x2 matrix, containing the axes along high-tech and low-tech innovations and the 
type of governance, being either centralised or decentralised. With a strong focus on technology 
rather than socio-economic aspects, it needs to be underlined that these four scenarios are more 
closely related to CE than the Shared socio-economic pathway scenarios. Both scenario sets are, 
however, explorative scenarios, while for the current analysis, the goal is to identify how the CE can 
best support the net-zero transition by 2050, requiring a normative scenario approach. Therefore, 
the Strategic Foresight scenarios and transition pathways for EU 2050, developed by the Policy Lab 
of the Joint Research Centre were deemed suitable for the current purpose. They allow for the 

45 

 
imagination of different futures that all lead to a net-zero transition, though with different socio-
economic implications. The scenario of not achieving net zero by 2050 is partially already covered 
by the previously described sensitivity analysis with the more fossil-based energy mix in Section 
4.1.4.1. 

The normative scenarios by the JRC are separated into a 2x2 matrix with the axes of policy mixes 
either supportive or not supportive of sustainability and with either a collaborative or competitive 
outlook on society (Matti et al., 2023). This results in four different background scenarios, into which 
the current analysis of circularity in the aluminium sector will be embedded to address the 
epistemic uncertainty of the results. It is important to understand that it is not the intention to claim 
that the future will develop in one way or the other, but rather to show different ways how the 
future could develop and to see how the ACE50 scenario would fare in these different futures. This 
is attempted by estimating the effectiveness of the CE levers in the different scenarios, while 
considering the characterising background conditions. While it is not the goal to create exact 
predictions of how CE policy will materialise in the four different worlds, understanding their main 
drivers and actors can support reflections around the implementation of the CE levers. This will 
enable policy makers to take more informed decision, by pushing the thinking towards implications 
of circularity levers that are beyond current realities (Muiderman et al., 2020). The following 
paragraphs will describe the main characteristics of the four scenarios, depicted in Figure 12. 

Figure 12. Four scenarios leading towards a sustainable Europe 2050 

Source: taken from Matti et al. (2023, p. 101) 

4.4.1  Glocal Eco-World 

In a Glocal Eco-World, there is an overall demand reduction because of a shrinking economy with 
strong local actors. Given the limited power of national actors, fiscal resource and related state 
investment in research and development as well as public infrastructure are limited. This makes 
large-scale efforts of e.g. investing in recycling capacity more challenging. Service-based business 
models have a high uptake due to collaborative mindset of people. There is a strong sense of duty 
to act sustainably and therefore an intrinsic motivation to engage in circular behaviour such as 
reuse or recycling. There is a focus on resilience, strategic autonomy and sufficiency. The economy 

46 

 
 
is generally oriented towards local markets with short supply chain and trade with China is 
diminishing. Instead, the EU trades with likeminded partners. 

4.4.2  Eco-states 

In an Eco-states scenario, there are strong national governments with cohesive policy mixes 
towards more sustainability, leading to economic growth with limited resource use increase. Given 
the high fiscal power, there is high tax income and thus investment in green technology and 
innovation by the state. This entails a digital infrastructure, facilitating tools such as the digital 
product passport and communication through virtual platforms. As energy prices are set on EU level, 
industries are forced to save resources through stringent requirements on recycling, reuse and 
repair of goods. Given the utilitarian outlook of citizens with a focus on compliance, people conform 
to sustainable lifestyles promoted by governments and engage in individual and sharing CE 
practices. The global world order remains similar to the one of today with multilateralism 
continuing. 

4.4.3  Greening through Crisis 

In a world that is Greening through Crisis, CE is a survival strategy and serves to achieve strategic 
autonomy and to deal with resource scarcity. The overall economy is shrinking, requiring efficient 
resource use. Investment is limited to strategic independence, thus supporting the reshoring of 
manufacturing and increasing the importance of the local industry. There is ample public private 
partnership for innovation and technology made in the EU, given the low fiscal power of the EU 
itself and somewhat larger power of Member States. Policy coordination and standardisation across 
the EU is limited making the scaling of technologies and sharing business models more challenging. 
Overall, the citizens have limited trust in the EU and are focused on survival rather than the 
collective good. Trade policies have become more protectionist, increasingly imposing tariffs, and 
multilateralism has deteriorated. 

4.4.4  Green Business Boom 

In the case of a Green Business Boom, international corporations are the main actors, taking their 
decisions in a framework of carefully designed incentives towards sustainability. Both investment 
and innovation are high and led by business. There is a green fiscal framework on EU level creating 
tax income and the economy overall is growing. As resource decoupling is limited, carbon capture 
plays an important role in the energy transition in addition to CE practices. The way CE R-strategies 
are proliferated is through standards and monitoring procedures. There is a focus on improved 
resource efficiency and waste recirculation, as they are considered resources. Citizens are acting 
according to individualist incentives, with sustainable behaviour mainly steered by price, rather than 
intrinsic motivation. In an attempt to become more resilient and efficient, the EU is in competition 
for raw materials and initiates strategic trade cooperation with selected countries. 

4.4.5  Participatory evaluation of alternative socio-economic futures 

Given the inherent epistemic uncertainty of the future, the LCA and LCC are supplemented by a 
sensitivity analysis based on the “European Strategic Foresight scenarios and transition pathways 
for EU 2050”. To quantify the narratives underlying these scenarios for 2050, a participatory 
modelling approach with the inclusion of stakeholders is taken, merging the fields of foresight and 
prospective LCA (Bisinella et al., 2021; Langkau et al., 2023). This approach yielded i) quantitative 

47 

 
estimates on the effectiveness of selected CE levers under different socio-economic scenarios, and 
ii) key background variables related to future scenarios. 

The adaptation of the background system is in line with the description of the scenarios themselves, 
supported by expert consultation, while the effectiveness of the circularity levers in those scenarios 
was established through a participatory workshop as proposed by Langkau et al. (2023). This 
workshop was conducted to test the impact of the four different socio-economic futures on the 
effectiveness of the implementation of circularity levers in the aluminium sector. During the 3h 
interactive online workshop, ten experts had to rate the effectiveness of the circularity levers on a 
scale of 1 to 7, categorised into Reduce, Reuse and Recover, within the respective scenarios. 
Moreover, the participants added comments on sticky-notes onto a shared digital board, 
contextualising the scoring. The resulting effectiveness of the levers is then used in the sensitivity 
analysis, by correcting the default effectiveness value of the levers obtained from literature with 
the one established in the workshop. Indeed, we observed that the majority of previous studies 
implicitly assume a 100% effectiveness of CE levers, without considering external factors besides 
the energy transition (Material Economics, 2018, 2019; Nilsson et al., 2020; Zibell et al., 2022). 
After the workshop, a sense-making session with a subset (6) of the workshop participants helped 
to determine the values for the background variables pertaining to the scenarios, such as general 
demand increase in the economy. 

Figure 13. Impact of circular economy clusters on material flows normalised to scale 0-1 

Green Business Boom

Greening through Crisis

Eco-States

Glocal Eco-World

1

0.5

0

1

0.5

0

1

0.5

0

1

0.5

0

Reduce

Reuse

Recover

Reduce

Reuse

Recover

Reduce

Reuse

Recover

Reduce

Reuse

Recover

Note: 0 = low effectiveness, 1 = high effectiveness 

Source: JRC elaboration 

Figure 13 shows that the lever related to Reduce has the highest effectiveness in a scenario with 
more protectionist trade policies and the deployment of CE to achieve strategic autonomy due to 
resource scarcity (Greening through Crisis) or a strong government (Eco-States), whereas Reuse 
levers are expected to function the best in scenarios with strong compliance (Eco-states). The 
recovery of aluminium is most effective where centrally supported by state legislation (Eco-States). 
In accordance with the sense-making session following the workshop, the obtained effectiveness 
factors are separated into the ranges low (45-59%), medium (60-74%) and high (75-89%). As for 
the second objective (testing background conditions), the participatory approach suggested to scale 
demand for aluminium to the size of the general economy in the respective scenario, with 
numerical values inspired by the quantification of integrated assessment models such as the 
shared socio-economic pathways (Narayan et al., 2023; Schandl et al., 2020). It was also decided to 
use rather extreme values to depict these scenarios, as to assure a notable effect in a caricaturising 
manner. The final numbers for both the lever effectiveness and background adaptations for the LCA 
and the dynamic modelling are presented in Annex 4. 

48 

 
 
 
4.5  Results 

Whereas results are available for all 16 environmental categories of the EF v3.1 methodology and 
different costs categories, only a subset of them are herein presented. The rest are documented in 
Annex 3. For Primary Material Demand and the Circular Material Use Rate (CMUR), the mass of 
virgin aluminium is represented as a positive value. In the results for Climate Change and 
Environmental Life Cycle Costs, positive values represent a burden for the environment or a cost for 
the economy, while negative results reflect savings for the environment or economic revenues. As 
to account for the different impacts of the individual levers as well as their joint impact of either 
entire CE clusters or all levers at once, the results are presented in a disaggregated way. All the 
results presented are calculated considering the energy mixes based on the NDC-LTS scenario, for 
both the EU and non-European countries, and A factor equal to zero in the CFF. The results obtained 
for Climate Change relative to the sensitivity analyses performed on energy mixes and on the A 
factor and choice of suppliers are presented in Section 4.5.5 and Section 4.5.6, respectively. 
Finally, the results of the epistemic uncertainty are presented in Section 4.5.7. 

4.5.1  Material circularity: Primary Material Demand, Circular Material Use Rate, 

and availability and quality of secondary material 

The CMUR is presented in Figure 14 together with the Primary Material Demand per year. As shown 
in Figure 14a, the CMUR increases from 55% in STQ21 to 64% in BSL50. The high CMUR in the 
STQ21 scenario indicates that recycling of the material itself is already high. Despite the higher 
demand for primary material, the CMUR of the BSL50 scenario increases by 9 percentage points 
because of more waste sent to recycling (either domestically or elsewhere). In the CTC50 scenario, 
the CMUR increases to 65%, while in the ACE50 scenario it reaches 67%. The higher CMUR 
calculated in the ACE50 scenario is the consequence of implementing simultaneously Recover (that 
results in a CMUR of 67%), Reduce (that results in a CMUR 63%) and Reuse (that results in a CMUR 
64%) levers, which all contribute with lowering the demand for primary material (Figure 14b). The 
results further suggest that actions should tackle reduction in demands (Figure 14c). It is 
noteworthy that the focus of the numerator of the CMUR is to represent a country’s (or region) 
ability to collect waste for recovery, regardless of its quality and destination country for its 
treatment. Hence, the CMUR does not reward countries (or regions) that implement actions that 
retain collected waste within their borders to treat and/or enhance the quality of it. This is well 
exemplified by the results of L(4)50 and L(5)50 in which both levers look at the effects of 
improving collection and sorting, but in L(5)50 higher quality in scrap separation is achieved to 
ensure closed-loop recycling (especially for wrought aluminium). Yet, regardless of the higher 
quality of recyclates obtained, both levers result in a CMUR of 65%.  

As explained in Section 1.2, wrought aluminium alloys admit 5% of impurities at most, while cast 
aluminium alloys allow up to 20% (Jarrín Jácome et al., 2021); once wrought alloys are mixed 
together with cast alloys, it is not possible to recycle the former in a closed-loop due to the higher 
presence of impurities. Out of the total quantity of recyclates obtained, the share of wrought alloys 
recycled into a closed-loop (i.e., wrought-to-wrought) and the share of wrought and cast alloys 
recycled into cast alloys are shown in Figure 15. The results show that, under business-as-usual 
practices of sorting and recycling, there is an abundance of cast alloy recyclates both in the STQ21 
(56%), the BSL50 (61%), and the CTC50 (61%) scenarios (Figure 15a). Differences in the share of 
wrought vs cast aluminium alloys can only be observed in the ACE50 scenario (49% cast aluminium 
alloys, and 51% wrought aluminium alloys) and, especially, when looking at the results for the 

49 

 
individual levers (Figure 15c). Specifically, the share of cast aluminium alloys is the predominant 
one in all levers (ranging from 58% in L(7)50 to 62% in L(1)50) except for lever L(5)50, in which 
the potential of improving waste collection (i.e., both as an improvement in the collection rate and in 
separate collection19) is investigated. Indeed, L(5)50 shows that implementing separate collection 
would favour closed loop recycling for wrought aluminium alloys (reaching a share of cast 
aluminium alloys of 48% and of wrought aluminium alloys 52%) under the assumption that all 
wrought aluminium can be recycled back into wrought aluminium. 

Figure 14. Demand for primary material across the scenarios investigated and Circular Material Use Rate 

Note: The primary material demand is expressed as Mt (left-hand side), while the Circular Material Use Rate 
(CMUR) is expressed as % (right-hand side); a) full-fledged scenarios; b) cluster of levers scenarios; c) one-at-
a-time levers scenarios 

Source: JRC elaboration 

19 Separate collection is herein intended as separate collection by nature and type, e.g. keeping aluminium-containing 
products in the transport sector separated from the other applications and sorted according to the alloy type. 

50 

 
 
 
 
Figure 15. Share of wrought aluminium and cast aluminium alloys out of the total recyclates obtained 

Note: a) full-fledged scenarios; b) cluster of levers scenarios; c) one-at-a-time levers scenarios 

Source: JRC elaboration 

4.5.2  Climate Change 

The Climate Change impacts, quantified as Million tonnes of CO2-equivalent emissions (Mt CO2-eq.) 
for the entire system, are depicted in Figure 16. In the STQ21 scenario the impact is estimated at 
160 Mt CO2-eq., of which production of ingots20 is 22 Mt CO2-eq. (when including recycling savings, 
i.e., 22=71-49 Mt CO2-eq., Figure 16), fabrication of semis is 7 Mt CO2-eq., manufacturing of final
products 9 Mt CO2-eq., import21 (inclusive of ingot, semi-finished products, finished products, and
scrap) 117 Mt CO2-eq., collection and transport 9 Mt CO2-eq., sorting and recycling 5 Mt CO2-eq.,
material substitution (i.e. aluminium recyclates obtained from the recycling processes) -49 Mt CO2-
eq., and export -10 Mt CO2-eq. (including the burdens of recycling in non-European countries and the
savings related to material substitution). The BSL50 scenario shows a reduction in total emissions

20 When considering the impacts related to production of aluminium ingot castings, differences can be observed from the 
accounting reported in Zore (2024). Indeed, the author estimates a total impact of 100 Mt CO2-eq., of which 13.4 Mt 
CO2-eq. is related to producing virgin aluminium, 1.2 Mt CO2-eq. is related to recycling, and 85.4 Mt CO2-eq. is related 
to import. According to our calculations and considering A=0 in the CFF, the impact of producing virgin aluminium is 22 
Mt CO2-eq. (calculated as the difference between the impact of producing the virgin ingots, 71 Mt CO2-eq., and the 
impacts of material substitution, -49 Mt CO2-eq.). The higher impacts of virgin aluminium production are related to 
higher emissions of the electricity mix assumed in our assessment compared to, for example, the one assumed in the 
ecoinvent dataset for primary aluminium production (i.e., 0.29 kg CO2-eq. kWh-1 and 0.17 kg CO2-eq. kWh-1, respectively). 
The impacts related to recycling in our assessment are 4.5 Mt CO2-eq. while the ones related to imports are 85.5 Mt 
CO2-eq.  Considering  the  impacts  of  our  assessment,  the  total  impact  of  production  corresponds  to  112  Mt  CO2-eq 
(against the 100 Mt CO2-eq. estimated by Zore, 2024). 

21 An important remark needs to be made on the impact of imports and exports. Indeed, a different total impact is calculated 
when considering the totality of import and of export, and the net trade. Let us assume that there is an export of 300 
kg with an impact of 400 kg CO2-eq. kg-1 (relative to its production), and an import of 1,200 kg with an impact of 150 
kg CO2-eq. kg-1. If we consider import and export separately, we will incur a total impact of 215 t CO2-eq. (1200*150-
300*400). On the other hand, if we consider first the difference in masses between import and export, i.e. the net trade, 
we will incur a net import of 900 kg (1200-300). Hence, the total impact relative to the net (mass) trade would be 135 
t CO2-eq. (900*0.15), which is considerably different from 215 t CO2-eq. 

51 

to 101 Mt CO2-eq.22, which corresponds to a reduction of 37% compared to the STQ21 scenario. 
Despite the projected increase in demand in 2050, lower emissions are expected mainly due to the 
decarbonisation of the energy grid at both at EU and, especially, non-European level. The CTC50 
scenario results in a total emission of 98 Mt CO2-eq., which corresponds to a 3% reduction 
compared to the BSL50 scenario, suggesting that the targets considered in the study are not 
sufficient for a substantial additional reduction in GHG emissions. The greatest reduction compared 
to the BSL50 results for Climate Change mitigation is observed in the ACE50 scenario, which incurs 
a total impact of 87 Mt CO2-eq. corresponding to a 14% reduction compared to the BSL50 result 
(Figure 16a). In the ACE50 scenario, all levers are applied simultaneously. Focusing on individual 
levers, the greatest reductions can be observed for L(2)50 (i.e. design for reusability, -11%) that 
results in a demand reduction, followed by L(7)50 (i.e. expansion of deposit return systems, -4%) 
(Figure 16b). The results show that energy decarbonisation alone is not sufficient to lead the 
sector to net-zero emissions. Circular economy strategies that go beyond current legislative targets 
can contribute to significant additional Climate Change mitigation, especially if implementing levers 
focused on reducing the demand for virgin aluminium. Nevertheless, this also reduces the amount 
of secondary material availability, which results in lower savings from virgin material substitution 
(Figure 16). 

Focusing on the clusters of levers, Reduce levers only contribute with a 1% reduction in Climate 
Change impacts compared to the BSL50 ones, as the production process is already highly optimised, 
and a reduction in scrap at fabrication and manufacturing stages would not contribute significantly 
to overall reductions in GHG emissions (Figure 16b). The Reuse levers contribute with an 11% 
reduction in Climate Change impacts compared to the BSL50 ones, and the greatest reduction is 
relative to L(2)50 as this lever targets all the sectors in which aluminium-containing products have 
a lifetime longer than 1 year (Figure 16c). Finally, the combination of Recover levers incurs in a 
total 6% reduction in Climate Change impacts compared to the BSL50 ones (Figure 16b). The 
levers contributing the most are L(7)50 (i.e. expansion of deposit return systems, -4%) and L(5)50 
(i.e. improved waste sorting and scrap quality, -3%). 

22 In the BSL50, where the energy transition is achieved, the total impact of production falls to 63 Mt CO2-eq. Indeed, primary 
production contributes with 25 Mt CO2-eq. (calculated as the difference between the impact of producing the virgin 
ingots,  67  Mt  CO2-eq.,  and  the  impacts  of  material  substitution,  -42  Mt  CO2-eq.),  recycling  with  4  Mt  CO2-eq.,  and 
imports with 34 Mt CO2-eq. 

52 

 
 
 
 
 
Figure 16. Results obtained for Climate Change expressed as Mt CO2-eq per entire system per year 

Note: a) full-fledged scenarios (STQ21, BSL50, CTC50, ACE50); b) cluster of levers scenarios (RED50, REU50, 
REC50); c) one-at-a-time levers scenarios 

Source: JRC elaboration 

4.5.3  Remaining impact categories 

When comparing ACE50 to BSL50, the impacts across all impact categories are significantly 
reduced. The same is observed for the CTC50 scenario, but with less reduction occurring. Further, by 
moving from STQ21 to BSL50 some trade-offs occur due to the expected changes in the EU energy 
mix, with an increased use of renewables, e.g. for Human Toxicity, cancer and non-cancer, 
Particulate Matter, Ecotoxicity, Land Use, Water Use, and Resource Use – Minerals and metals. The 
net results in these impact categories however are significantly reduced in the ACE50 scenario. The 
remaining impact categories follow the trend observed for Climate Change. 

4.5.4  Life Cycle Costs 

The Environmental Life Cycle Costs (eLCC) results expressed in billion EUR for the entire system per 
year are displayed in Figure 17. In the CTC50 scenario, a reduction of 1% in costs (EUR 88 billion) 
relative to the BSL50 scenario is estimated because of reduced demand for primary aluminium 
thanks to increased recycling. The greatest reduction in costs is observed for the ACE50 scenario (-
13% relative to BSL50, EUR 77 billion) as a consequence of reduced demand for virgin aluminium 
production, reduced imports, and an increase in the recycled content (Figure 17a). 

The Reduce lever (i.e., L(1)50) incurs a cost reduction of 1% relative to the BSL50 scenario, 
indicating that optimisation of scrap production at fabrication and manufacturing would not result 
in high cost savings at production level. The Reuse levers incur a cost reduction of 10% relative to 
the BSL50 scenario, where the main contribution is related to the implementation of L(2)50 that, as 
opposed to L(3)50, targets all sectors of aluminium-containing finished products with a lifetime 
longer than 1 year (Figure 17b,c). The implementation of Reuse levers indicates that reducing the 
demand of virgin aluminium leads to significant cost reductions at production level. Finally, Recover 
levers incur a cost reduction of 2% relative to the BSL50 scenario, mainly related to increased 
revenues from recycling (Figure 17b,c). The levers showing the greatest cost reduction potential 

53 

out of the Recover levers are L(7)50 (i.e. expansion of deposit return systems, -1%) and L(8)50 and 
L(5)50 (i.e., recovery from mixed waste, 0.7%, and improved waste sorting and scrap quality, 0.7%, 
respectively). As for the difference in the net eLCC of the STQ21 and the BSL50, an increase of 38% 
is calculated in the Baseline scenario mainly due to an increase in costs for production of ingots and 
semis related to the expected increase in demand. 

The results obtained for the Full Environmental Life Cycle Costs (feLCC, Figure 18) follow the 
trends observed for eLCC and Climate Change. Indeed, the CTC50 scenario contributes with a 2% 
(EUR 107 billion) reduction in feLCC compared to the BSL50 scenario (EUR 109 billion), while in the 
ACE50 scenario higher reductions are achieved (13% relative to BSL50, EUR 94 billion per entire 
system per year). Specifically, external costs contribute between EUR 17 and 31 billion to the total 
feLCC across the scenarios investigated (corresponding to a share of around 18-33% of the total 
feLCC). Amongst the clusters (Figure 18b), the Reuse lever reduce the total feLCC by 10% (from 
EUR 109 billion to EUR 98 billion), the Recover levers reduce it by 3% (from EUR 109 billion to EUR 
106 billion), and the Reduce levers reduce it by 1% (from EUR 109 billion to EUR 108 billion) 
relative to BSL50. Reductions in costs are observed due to reduction in demand and related virgin 
production costs, reduction in imports, increases in recycled content, and monetised environmental 
savings from material recycling. 

Figure 17. Results obtained for Environmental Life Cycle Cost expressed as billion EUR per entire system per 
year 

Note: a) full-fledged scenarios (STQ21, BSL50, CTC50, ACE50); b) cluster of levers scenarios 
(RED50, REU50, REC50); c) one-at-a-time levers scenarios 

Source: JRC elaboration 

54 

 
Figure 18. Results obtained for Full Environmental Life Cycle Cost expressed as billion EUR per entire system 
per year 

Note: a) full-fledged scenarios (STQ21, BSL50, CTC50, ACE50); b) cluster of levers scenarios 
(RED50, REU50, REC50); c) one-at-a-time levers scenarios 

Source: JRC elaboration 

4.5.5  Sensitivity analysis I: testing energy mix assumptions 

The Climate Change results expressed as Mt CO2-eq. for the entire system per year are displayed in 
Figure 19 to analyse the impact of changing the energy mix. In this sensitivity analysis, the total 
results obtained using the energy mix based on the GECO NDC-LTS scenario are compared against 
those employing the GECO Reference scenario.  

As expected, the total impacts when assuming the evolution of the energy mixes according to the 
GECO Reference scenario are higher than the NDC-LTS ones. Clearly, no difference is observed for 
the STQ21 scenario in the NDC-LTS (henceforth, the default case) and the Reference scenario (160 
Mt CO2-eq. for the entire system per year, Figure 19a), as there are no difference in the share of 
CCS technologies implemented and decarbonisation efforts at EU and non-European level.  

The increase in emissions in the BSL50 scenario under the GECO Reference scenario energy mixes is 
mainly due to higher emissions in the production of ingots and semi-finished products, but also in 
the manufacturing of finished products, sorting and recycling, and impacts related to imports. 
Although the substitution of virgin aluminium is more emission-intensive, the savings related to it 
are not sufficient to counterbalance the rise in emissions related to the less green energy mix. This 
is especially relevant in the case of imports from non-European countries where the electricity mix 
has higher CO2 emissions per kWh under the GECO Reference scenario compared to the NDC-LTS, 
applied as default in the analysis (i.e., 0.41 kg CO2-eq kWh-1 and 0.09 kg CO2-eq kWh-1, respectively). 

The impact reduction potential of each lever and of each cluster relative to the BSL50 scenario is 
similar in relative terms when comparing the results obtained using the GECO Reference scenario 
and the GECO NDC-LTS (applied as default in the analysis). Specifically, under the GECO Reference 
scenario, the CTC50 scenario results in a decrease of 4% compared to the BSL50 one (decreasing 
from 131 to 125 Mt CO2-eq.; Figure 19a), while in the ACE50 it decreases by 14% (from 131 to 

55 

 
 
112 Mt CO2-eq.; Figure 19a). Focusing on the clusters, the cluster Reduce incurs a decrease of 1% 
compared to the BSL50 one (from 131 to 130 Mt CO2-eq.; Figure 19b), the Reuse one of 11% 
(from 131 to 117 Mt CO2-eq.; Figure 19a), and the Recover one of 6% (from 131 to 123 Mt CO2-
eq.; Figure 19a). Nevertheless, in absolute terms the impact of the BSL50 scenario is 30% higher 
under the GECO Reference scenario relative to the NDC-LTS (applied as default in the analysis), the 
CTC50 28% higher, and the ACE50 29% higher. This indicates the crucial importance of 
decarbonising the energy system. 

Figure 19. Comparison of the impacts obtained for Climate Change (expressed as Mt CO2-eq. for the entire 
system per year) when applying the GECO NDC-LTS and Reference scenarios 

Note: a) full-fledged scenarios (STQ21, BSL50, CTC50, ACE50); b) cluster of levers scenarios (RED50, REU50, 
REC50); c) one-at-a-time levers scenarios 

Source: JRC elaboration 

4.5.6  Sensitivity analysis II: testing A factor and market suppliers assumptions 

The Climate Change results expressed as Mt CO2-eq. for the entire system per year are displayed in 
Figure 20 to analyse the impact of changing i) the A factor, and ii) the market suppliers. As 
explained in Section 4.1.4.2, the function of the A factor in the CFF is to allocate burdens and 
savings of recycling between production and end-of-life stages of a product’s life. The comparison 
herein made is between the net results obtained when applying A=0 and the ones obtained under 
A=0.2, which is the value recommended for aluminium in European Commission (2022). Further, the 
effect in the net result of applying marginal market suppliers (that affects the share of European vs 
non-European ingots) and marginal energy mixes, both for electricity and heat based on the GECO 
NDC-LTS scenario, are tested. The marginal market suppliers are calculated based on the shares 
provided by the ecoinvent dataset for ‘market’ processes of virgin ingot production; specifically, 
based on the consequential (market) ecoinvent dataset 52% of the supply comes from European 
production. Notice that in the default calculation, a share of 60-73% (depending on the scenario 
and lever) from European aluminium production is assumed based on JRC elaborations of the MFA 
data.  

Total impacts under A=0 and A=0.2 are mostly consistent, with minor variations due to the different 
allocation of the impacts between production and end-of-life stages. The relative change between 

56 

 
 
the BSL50 and the CTC50 and ACE50 is preserved, as well as the relative changes calculate at the 
level of cluster and individual levers (Figure 20).  

Total impacts under A=0, and A=0 and market suppliers highlight a 4% difference with higher 
absolute emissions in the latter case. Indeed, as previously mentioned, the share of ingots supplied 
by non-European countries is higher under this configuration, and non-European countries have 
energy mixes with higher CO2-eq. intensities per kWh than the European ones. Further, the savings 
estimated for material substitution under A=0 and market suppliers are higher (due to larger 
substitution of imports); yet, these are not sufficiently high to compensate for the higher impacts 
related to imported goods. Nevertheless, when observing the relative changes between the BSL50 
and the CTC50, ACE50, clusters and one-at-a-time levers, the same percentage difference as in the 
default case (i.e. A=0) are calculated. Notwithstanding such changes in magnitude of some of the 
results, the overall conclusions and outcome remain the same. 

Figure 20. Comparison of the impacts obtained for Climate Change (expressed as Mt CO2-eq. for the entire 
system per year) when applying A=0 (default), A=0.2, and A=0 considering marginal market suppliers 

Note: a) full-fledged scenarios (STQ21, BSL50, CTC50, ACE50); b) cluster of levers scenarios (RED50, REU50, REC50); c) 

one-at-a-time levers scenarios 

Source: JRC elaboration 

4.5.7  Epistemic uncertainty in LCA and LCC 

The Climate Change impacts, expressed as Mt CO2-eq. for the entire system per year, under varying 
background conditions are displayed in Figure 21. Two new baselines are calculated, i.e. a growth-
oriented (GRO50 in Figure 21) and a shrink-oriented one (SHR50 in Figure 21). The levers L(1)50-
to-L(8)50 are implemented under these alternative baselines and different effectiveness in the 
implementation of the levers. Indeed, in the Business-as-Usual (BAU (default) in Figure 21) the 
effectiveness of the levers is assumed at 100%, meaning that the full potential of the lever is 
considered. However, in the other four scenarios (Eco-States, Glocal Eco-World, Green Business 
Boom, and Greening through Crisis) different effectiveness potentials were assumed based on the 
results of the sense-making session (Section 4.4.5). For the four scenarios, the impact of 
implementing the levers at once (i.e., the ACE50 scenario) was calculated according to the relative 
baseline and effectiveness potential of the levers. 

57 

 
 
The results show that under a growing economy (i.e., growing larger than what assumed in the 
BSL50 scenario), the impact on Climate Change is expectably higher than in BSL50 (101 Mt CO2-eq. 
in BSL50 and 131 Mt CO2-eq. in GRO50, Figure 21). Upon the implementation of the ACE50 
scenario under the Eco-States and Green Business Boom scenarios, such impacts are mitigated. 
Further, the mitigation of Climate Change impacts compared to the GRO50 is more pronounced 
under the Ecostate scenario (ECO50 in Figure 21), which reflects the perspective of the experts 
that consider the levers to be more effective under strong national governments rather than a 
business-driven society. 

Under a shrinking economy (SHR50 in Figure 21), the impact on Climate Change is expectably 
lower than in the BSL50 scenario (101 Mt CO2-eq. in BSL50 and 71 Mt CO2-eq. in SHR50). Upon the 
implementation of the ACE50 scenario under the Glocal Eco-World and the Greening through Crisis 
scenarios, such impacts are mitigated. Further, the mitigation of Climate Change impacts compared 
to the SHR50 is more pronounced under the Glocal Eco-World (GEW50 in Figure 21), which reflects 
the perspective of the experts that consider the levers to be more effective under a society with 
strong local actors rather than a resource-scarce one.  

Considering the possible variations in the background conditions until 2050 and the different 
effectiveness potential of the levers, it can be argued that the implementation of the ACE50 
scenario can incur GHG emission savings ranging between 18% and 61% relative to the STQ21 
scenario that is herein taken as benchmark for comparison. 

Figure 21. Comparison of the impact of the scenarios assessed on the Climate Change category when 
applying different background conditions than Business-as-Usual (BAU) expressed as Mt CO2-eq. for the entire 
system per year 

Note: The following acronyms are used: ACE50 ‘Ambitious Circular Economy scenario 2050’, BSL50 ‘Baseline 
2050’, ECO50 ‘Eco-States 2050’, GBB50 ‘Green Business Boom 2050’, GEW50 ‘Glocal Eco-World 2050’, 
GRO50 ‘Growth-oriented baseline 2050’, GTC50 ‘Greening through Crisis 2050’, SHR50 ‘Shrink-oriented 
baseline 2050’, STQ21 ‘Status Quo 2021’ 

Source: JRC elaboration 

58 

 
 
5 

Input-Output Analysis 

This section describes the model and assumptions used to assess the impacts of Circular Economy 
levers at the macro-level. This top-down assessment is enabled using Environmentally Extended 
Multi-Regional Input Output (EEMRIO) data. EEMRIO data is well equipped to support the 
analysis of the interaction and interdependencies between sectors within and outside regions as 
well as the relationship between intermediate consumption and final consumption of goods and 
services globally. EEMRIO completeness of sectoral and regional representation, complements the 
bottom-up assessment described in the previous chapters typically characterised by high 
granularity of processes and their respective material flows and associated impacts (Murray & 
Lenzen, 2013). 

The following subsections describes the methodology employed to analyse the scenarios using 
EEMRIO data and the model for Multi-Regional Input-Output Analysis (MRIOA). Emphasis is also 
given to the integration of the bottom-up LCI to increase the process detail of the industry of 
interest, the creation of the respective extension tables as well as the use of the GECO 2022 data 
for the prospective EEMRIO data to the year 2050. Thereafter, the results of the analysis are 
presented in Section 5.2. A more detailed description of the model used here can be found in 
Wagner et al. (2025). 

5.1  Methodology 

"MRIOA provides a useful toolbox for assessing social, environmental, and economy-wide impacts of 
goods and services” of the transition towards a more circular economy (Wiebe et al., 2019), 
avoiding a cut-off on (economic) flows (Lenzen, 2008). In this work, we refer to the MRIOA model as 
the use of the demand-driven quantity model of Leontief using MRIO data (i.e. FIGAROe3). The 
fundamental assumption of the demand-driven Leontief model is that changes in consumption of 
goods and services result in linear changes of demand of production (Leontief, 1941).  

While several IO models exist, the FIGAROe3 model was chosen for this work because of its 
comprehensive inter-country Supply and Use, and Input-Output database for 2015, featuring labour 
and environmental extensions that are in line with official statistics. The database encompasses 
data for 213 products and 176 industries across 45 geographical areas (country disaggregation is 
equal to FIGARO), as well as one aggregated Rest of the World region. The selection criteria as well 
as a comparison to other IO models are presented in Annex 5, while a more detailed description of 
FIGARO3 model’s structure, i.e. the monetary Supply and Use Tables, is presented in Annex 6. 

The MRIO model uses environmentally extended industry-by-industry tables, from which the 
Leontief inverse is calculated. This model assumes consistent input structure for products and 
industries. To each unit of output, a respective (environmental or social) impact coefficient is 
associated, enabling the assessment of sustainability impacts and resource use. The scope includes 
both indirect and direct impacts/resource use, resulting in the total (or global) impacts of the EU27 
final demand. The indirect use covers upstream production-related impacts, while the direct use 
entails impact directly generated by final demand (mostly by households, e.g. emissions at the 
chimney or at the exhaust). The detailed formulas for these calculations are available in Annex 7. 

Through this model and data, we can incorporate explicit exogenous supply (i.e., affecting 
production) and demand (i.e., affecting consumption) changes, and asses direct and indirect effects 
of what-if scenarios. Exogenous changes can be applied either in the Supply and Use Tables 
(e.g., Aguilar-Hernandez et al., 2018; Wiebe et al., 2018, 2019) or directly in the Input-Output tables 

59 

 
(e.g., Donati et al., 2020). It is preferred to model exogenous changes in the Supply and Use Tables 
as distinction between multiple production outputs of a sector are retained. An IO table is then 
generated from these counterfactual Supply and Use Tables using Model B or Model D 
transformation methods to allow for IOA later on (Eurostat, 2008). 

Modelling CE interventions in an EEMRIO must be understood as a comparison between the status 
quo (results of the baseline model) and results from modelled what-if scenarios (Aguilar-
Hernandez et al., 2018). The net effect of circularity interventions can then be quantified by 
calculating the difference between baseline results and the results from the scenarios developed in 
this study (i.e., BSL50 and the ACE50). 

5.1.1  Overcoming the limitations of EEMRIO for Circular Economy modelling 

In this subsection, we present the most important limitations of the EEMRIO and how they are 
overcome in the context of this study.  

•  To overcome the problem of high aggregation of sector and product groups in IO tables it is 
possible to disaggregate products and sectors in the Supply and Use Tables and/or IO 
tables in more detailed categories. In this study, LCI data and MFA23 is used to refine such 
disaggregation. 

•  Due to the fixed technical coefficients in IO models, caution is required when modelling 
circularity scenarios. The static and linear nature of the EEMRIO model prevents the 
inclusion of dynamic responses of an economy such as price changes or rebound effects 
(Wiebe et al., 2018). It requires that any induced or rebound effects need to be exogenously 
included in the modelling by explicitly modelling. Induced effects refer to secondary 
economic effects resulting from changes in spending or investments patterns caused by an 
initial change in economic activity24.  As such, circular economy scenarios in EEMRIO do not 
capture the volume effects on prices as well as price effect on the use of certain products 
(Aguilar-Hernandez et al., 2018). While this is not addressed in this chapter, Section 6 
concerning the application of the FIDELIO model is used to complement the limitation of 
this model.  

•  As we model scenarios in the future, it is expected that the energy system will change as 

well following the expected or desired trends. Such information is not present in the chosen 
database, therefore, the Global Energy and Climate Outlook (GECO) is used to create the 
prospective EEMRIO tables described in Section 5.1.4. This ensure that the assessment of 
prospective impacts is aligned with the previous modelling in LCA and LCC described in 
Section 4. GECO was chosen due to the comprehensiveness of the provided information 
that exceeds GDP, population, and the composition of the energy sector by providing a 

23 Notice that while the MFA data refers to ‘Europe’, the IO analysis has been performed considering EU27. The MFA data 
has been applied to the flows relative to the 27 MSs although the masses refer to an enlarged geographical scope. 
The MFA data used was considered as the best available and suitable for the types of analyses performed in this 
study. Nevertheless, future efforts should be placed in replicating the MFA considering only the 27 MSs. 

24 This includes shifts in employment levels as well as changes in firms operating surpluses. Referencing the circular flow 

of income, these effects can have a multiplier effect on the overall economy, amplifying the initial change in 
economic activity. Circular economy rebound (Zink & Geyer, 2017) refers to unintended environmental impacts that 
occur because of circular economy interventions. 

60 

 
 
 
complete prospective input-output structure for a long-term baseline scenario based on the 
computable general equilibrium (CGE) model GEM-E3.  

•  Additionally, the representation of waste flows in EEMRIO is typically in monetary terms and 
treated as waste treatment services, which implies that the very physical dimension of 
waste is not effectively captured and subject to distortions based on the price of those 
services (Aguilar-Hernandez et al., 2018). This analysis overcomes this limitation, by 
including a direct link between the MFA (Section 3) and the EEMRIOA. The mass balance of 
the industry under study is then used to understand the effects of CE across the production 
network.  

So, to overcome the aggregation error of IO tables, a disaggregation of products and sectors in 
supply and use tables and/or IO tables in more detailed categories provides a solution to overcome 
the aggregation error. Caution is required when assigning circularity in the future. Due to the fixing 
of technical coefficients of a circular economy scenario, the IO analysis does not capture the vol-
ume effects on prices as well as price effect on the use of certain products (Aguilar-Hernandez et 
al., 2018). The assessment of prospective impacts relies on exogenous scenarios, where the choice 
was aligned with the previous modelling in the LCA and LCC. Therefore, the Global Energy and Cli-
mate Outlook (GECO) was chosen due to the comprehensiveness of the provided information that 
exceeds GDP, population, and the composition of the energy sector by providing a complete pro-
spective input-output structure for a long-term baseline scenario based on the computable general 
equilibrium (CGE) model GEM-E3.  

5.1.2  Bottom-up MFA and LCI integration 

As indicated in the previous section, the GECO scenarios, LCA and MFA data is used to 1) project 
energy mix in the future, 2) increase process details of the sector, 3) and model CE levers in Supply 
and Use Tables. This workflow is visualised in Figure 22 where it is shown the direct use of the 
material flows and stocks as determined in the MFA. In addition, the details from the LCI-datasets 
of different processes are used to disaggregate the Supply and Use Tables to improve the 
granularity and quality of the Input-Output assessment. The assessment is supported by Excel-files 
and an implementation in R-studio. Further details of the methodology are provided in Annex 8. 

61 

 
Figure 22. Workflow from Material Flow Analysis, Life Cycle Inventory, to (prospective) Input-Output Analysis 

Source: JRC elaboration 

Based on the results from the MFA, a proposal for sector and product disaggregation is made, which 
in this case concerns ‘Aluminium and aluminium products’ (CPA_C24_E) and ‘Secondary aluminium 
for treatment, re-processing of secondary aluminium into new aluminium’ (CPA_C24_F). Afterwards, 
the MFA-results are incorporated into the Supply and Use Tables. Further, the sectors are disaggre-
gated according to the LCIs provided to include elementary exchanges adding resolution and quality 
to the sector disaggregated. Subsequently, the RAS balanced prospective tables based on GECO-
data are generated. At this point, the disaggregated supply and use table is generated to increase 
the granularity of the model specifically for the aluminium sector’s sectors/products. Next, the 
model is regionally disaggregated again to return to the initial geographical detail of FIGAROe3. 
Performing the IO analysis starts by creating the necessary (disaggregated) extension tables and by 
deriving the industry-by-industry IO tables.  

5.1.3  Extension tables 

Besides the monetary values, the IO analysis also provides insight into environmental and socio-
economic indicators. It does so through extension tables, which are based on existing extensions of 
FIGAROe3 and from external sources such as official emission statistics and EXIOBASE, in the case 
of environmental extensions, and other relevant extensions, such as financial costs, employment 
and issues of resource dependency. 

5.1.3.1  Environmental extensions 

As in the LCA (Section 4), the Environmental Footprint (EF) method is used, which includes 16 
impact categories. Yet, Ionising Radiation and Ozone Depletion are excluded because emissions 
related to these impact categories are missing. For the disaggregated sectors, the LCI data not only 
informs on the elementary exchanges to be included in the production recipe (Section 5.1.2), but 
also on process specific emissions and combustion emissions that are compounded with the 
upstream and downstream emissions. The emission coefficients are based on Beylot et al. (2019) 

62 

who make use of 78 elementary flows to estimate 14 out of the 16 environmental impact 
categories. Our project partners VITO created a matrix of characterisation factors in the ETC/CE 
project of the Consumption Footprint indicator commissioned by the European Environment Agency. 
This envisioned translating the 528 unique environmental extension lines from EXIOBASE v3.8.2 into 
14 out of the 16 impact categories of the EF-method requiring a conversion through 
characterisation factors. The result can also be normalised and weighted into a single factor, as 
presented in Annex 9. The FIGAROe3 extension table includes emission data by industry on 
carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases (F-gases) for 2015 
in Gg. The extension data shows the total volume of emissions (in Gg) per sector per geographical 
area and per household per geographical area. A wide coverage of environmental extensions is 
available from the EXIOBASE (currently v3.8.2) dataset. The process of matching the EXIOBASE 
extension with the EF categorisations is described in Annex 9. For impacts like toxicity, however, 
EXIOBASE includes only a very limited selection of emissions. No information, and, thus, no 
extension lines, is available in EXIOBASE to estimate the impact categories Ozone Depletion and 
Ionising Radiation. Another shortcoming of the EXIOBASE is that the end years of the real data in 
the extension tables vary and are therefore not completely up to date25. Therefore, the change in 
emissions is only based on output volumes and not actual emission data. Some of these data are 
complemented by the Eurostat Air Emissions Accounts (AEA) by NACE Rev. 2 activity, which 
include air pollutants and greenhouse gases per sector per geographical area from 1995 until 2022. 
Note that the dataset is incomplete due to missing data and unavailable data for non-EU countries. 
For this reason, only the air pollutants for which the AEA data can be supplemented with the 
EXIOBASE data are retained (total CO2, CH4, N2O, HFC, PFC, NH3, NOX, CO, PM2.5, PM10, and NMVOC).  

5.1.3.2  Socio-economic extensions 

The extension tables of FIGAROe3 show the generation of value added per sector, disaggregated 
into 9 categories, which comprise taxes, compensations of employees per skill level and different 
types of operating surpluses. Employment is based on education or occupation, both disaggregated 
into male and female as well as low-, medium- and high-skilled. Finally, the concept of EU’s 
strategic autonomy is operationalised into the domestic extraction and use of strategic metal ores, 
as per the CRM 2023 list. Thus, the focus for strategic autonomy is on the demand for 
bauxite/aluminium, copper, nickel, PGM’s and other non-ferrous metals. Nevertheless, in 
this analysis we only report results for bauxite under the different scenarios assessed. 

5.1.4  Prospective Multi-Regional Input-Output tables 

For the policies considered for the development of the scenarios in the GECO (2023-edition), the 
reader is referred to the original publication. The scenarios include energy-related policies in the 
sectors of energy, power, and transport (incl. aviation and maritime), and include GHG-related 
policies focusing as well on the ETS and non-ETS sectors for certain emission reduction targets.  

Economic Multi-Regional Input-Output tables are available, but only for the GECO Reference 
scenario. The GECO Reference scenario (Baseline GECO 2022) represents a projection of the world 

25 In the version applied in this study, the end years of the extension tables are: 2015 for energy, 2019 all greenhouse 
gases (nonfuel, non-carbon dioxide are now casted from 2018), 2013 for material use, and 2011 for most others, 
land, and water. 

63 

 
 
 
economy with corresponding energy demand and GHG emissions. The IO tables are supplemented 
by energy balances (in physical units) and GHG emissions projections. However, the database as 
well includes main economic indicators (e.g., value added), detailed energy, greenhouse gas and air 
pollutant emissions balances for several scenarios of the GECO 2023 report (Reference, NDC-LTS 
and 1.5°C) for 39 world regions and the EU27. A description of how the NDC-LTS scenario was 
included in the disaggregated Supply and Use Tables is provided in Annex 10. 

5.2  Results 

5.2.1  Climate Change mitigation 

Figure 23 presents the Climate Change impacts of four scenarios: Status Quo (STQ21), Baseline 
(BSL50), Compliance with selected Targets on Circular Economy (CTC50), and Ambitious Circular 
Economy scenario (ACE50), measured in million tonnes of CO2 equivalent (Mt CO2-eq.). The left-side 
chart (Figure 23A) shows the on-site emissions26 in the aluminium industry for each scenario. In 
the STQ21 scenario, emissions total 25.2 Mt CO2-eq., which increases by 16.7 Mt CO2-eq. to 41.9 Mt 
CO2-eq. in the BSL50 scenario due to projected increases in virgin material demand. Notice that 
technologies focused on decarbonising the aluminium production (e.g. smelters or inert anodes) are 
not modelled as the inclusion of such technologies mainly refers to actions focused on the 
decarbonisation of the sector rather than on strengthening circularity in the sector. The 
implementation of selected circular economy targets in the CTC50 scenario leads to a reduction in 
on-site emissions of 3.1 Mt CO2-eq. In contrast, the ACE50 scenario triples this reduction, resulting 
in a total reduction of 9.4 Mt CO2-eq., which represents 23% of all on-site GHG emissions in BSL50 
for the aluminium industries.  

Figure 23Error! Reference source not found.A only shows total GHG savings in on-site emissions 
for the aluminium industries. To provide insights on total GHG savings, that is, from a supply chain 
perspective (including e.g. emissions related to inputs used by EU industries versus emissions 
associated with EU imports of aluminium products, or other rebound effects), and regardless of 
where they occur from a geographic perspective (either in EU or in the Rest of the World). On the 
other hand, Figure 23  displays in panel B the difference in GHG emissions between the CTC50 and 
the BSL50 scenarios, and in panel C the difference in GHG emissions between the ACE50 and the 
BSL50 scenario, while showing the contribution in changes of GHG emissions for on-site emissions 
in EU, off-site emissions in EU, imports, and rebounds. Specifically, ‘On-site’ (Figure 23B-C) 
emissions refer to direct emissions occurring at aluminium industries in the EU to satisfy the EU and 
Rest of the World (RoW)’s final demand of goods and services; ‘Off-site’ (Figure 23B-C) refers to 
emissions from all other EU industries to satisfy the EU and RoW’s demand, specifically, it 
represents upstream GHG emissions in the aluminium supply chain occurring in industries located in 
the EU and providing inputs to the EU aluminium industries; ‘Imports’ (Figure 23B-C) refers to 
emissions occurring in the RoW to satisfy the EU’s demand; ‘Rebound’ (Figure 23B-C) GHG 
emissions refer to emissions occurring in the RoW, which are related to substitution effects of EU 
exported aluminium products that are being replaced by non-EU exporters for satisfying ROW 
demand (which remains constant across scenarios); and, ‘TOTAL’ (Figure 23B-C) refers to the sum 

26 On-site emissions include process and combustion emissions of the aluminium industries only. 

64 

 
 
 
of ‘On-site’, ‘Off-site’, ‘Imports’, and ‘Rebound’ GHG emissions. As shown in Figure 23B-C, burdens 
(values above the zero line) and savings (values below the zero line) may occur for ‘On-site’, ‘Off-
site’, ‘Imports’, and ‘Rebound’ emissions; therefore, the ‘Net change’ is calculated for each 
contributor to the final result and for the ‘TOTAL’ itself. Figure 23B  shows that the reductions in 
the CTC50 scenario compared to the BSL50 scenario are related almost entirely to ‘On-site’ 
emissions changes as a consequence to the increase in recovery of aluminium scrap. Figure 23C 
shows that the reductions in the ACE50 scenario compared to the BSL50 scenario are also primarily 
driven by the fall in ‘On-site’ emissions. This corresponds to approx. 81% of the ’TOTAL’ emission 
savings (-11.7 Mt CO2-eq.). As for the remaining 19%, ‘Off-site’ emissions contribute with an 
additional 2.0 Mt CO2-eq. saving, which accounts for up to 17% of the ’TOTAL’ due to the use of less 
carbon-intensive inputs in the supply chain. The remaining 2% reduction of the ‘TOTAL’ occurs in 
industries located in the RoW. Specifically, ‘Rebound’ emissions increase to 1.5 Mt CO2-eq (‘Net 
changes’ in Figure 23C) due to increased production in other countries to replace EU exports of 
aluminium products (as RoW demand remains constant across scenarios). This basically offsets the 
GHG savings obtained from decreased imports to the EU economy (’Imports’; -1.8 Mt CO2-eq., as 
‘Net changes’ in Figure 23CError! Reference source not found.). 

Figure 23. On-site GHG emissions in aluminium industries and changes in total GHG emissions for the CTC50 
and ACE50 scenarios relative to the BSL50 scenario 

Note: Panel A) displays the on-site GHG emissions of the EU aluminium industry for the Status Quo (STQ21), 
Baseline (BSL50), Compliance with selected Targets on Circular Economy (CTC50) and Ambitious Circular 
Economy (ACE50) scenarios; Panel B) displays the changes in GHG emissions between the CTC50 and BSL50 
scenarios (CTC50-BSL50); Panel C) displays the changes in GHG emissions between the ACE50 and BSL50 
scenarios (ACE50-BSL50). ‘On-site’ GHG emissions refer to direct emissions occurring at aluminium industries 
in the EU to satisfy the EU and Rest of the World (RoW)’s demand; ‘Off-site’ GHG emissions refer to emissions 
from all other EU industries to satisfy the EU and RoW’s demand; ‘Imports’ GHG emissions refer to emissions 
occurring in the RoW to satisfy the EU’s demand; 'Rebound’ GHG emissions refer to emissions occurring in the 
RoW, which are related to substitution effects of EU exported aluminium products that are being replaced by 
non-EU exporters for satisfying ROW demand (which remains constant across scenarios); ‘TOTAL’ GHG 
emissions refers to the sum of ‘On-site’, ‘Off-site’, ‘Imports’, and ‘Rebound’ GHG emissions. Notice that the 
contribution of ‘On-site’, ‘Off-site’, ‘Imports’, and ‘Rebound’ GHG emissions can be a burden (above the zero 

65 

 
 
line) or a saving (below the zero line) for Climate Change. The net change is displayed with a black solid 
diamond in the figure. All values are expressed as Mt CO2-eq year-1 

Source: JRC elaboration 

Figure 24 further decomposes the changes between the BSL50 and the ACE50 scenarios to 
provide insight into the regions, destinations, industries, and final goods (or services) driving the net 
reduction of 11.7 Mt CO2-eq. between the two scenarios. As expected from Figure 23, mostly all 
net GHG savings occur in the EU. The data also reveals that most of the impact (81%, ‘Domestic’ in 
Figure 24) is driven by final use of products in the EU, whereas the remaining 19% relates to EU 
exports to the RoW (‘Exports’ in Figure 24). Furthermore, when focusing on the aluminium industry, 
the main activity contributing to the savings is ‘Ingot production’, which accounts for 67% of the net 
reduction, followed by RoW production of aluminium products (approx. 10%, ‘Aluminium ROW’ in 
Figure 24), and electricity generation with an 8% share (‘Electricity’ in Figure 24). In total, the EU 
aluminium industries (herein intended as the sum of ‘Ingot production’ in the EU and ‘Other 
aluminium’ industries in the EU) explain 74% of the savings when looking at the industry 
decomposition. Finally, when focusing on the decomposition of the net GHG savings between the 
ACE50 and the BSL50 scenario, it is possible to identify what final products (or services) mainly 
contribute to it. The results displayed in Figure 24 show that the manufacture of motor vehicles, 
trailers, and semi-trailers ('Vehicles' in Figure 24) is the primary driver of the changes, accounting 
for 30% of the net savings. When considered together with the manufacture of other transport 
equipment ('Other transport' in Figure 24), they explain 38% of the net savings. The transport 
industry is followed by the manufacture of electrical machinery and apparatus n.e.c. ('Electrical' in 
Figure 24) and the construction industry (‘Construction’ in Figure 24) with 16% and 15%, 
respectively, and the manufacture of machinery and equipment n.e.c. ('Machinery' in Figure 24) 
with 8% only.  

66 

 
Figure 24. Decomposition of the changes between the ACE50 and the BSL50 scenarios relative to the net 
reductions in GHG emissions 

Note: The decomposition of the changes refers to net GHG savings as percentage of origin (EU vs. RoW, 
or ROW in the figure), destination (domestic consumption vs. exports to the RoW), industry, and final good or 
service. Only contributions above 5% are shown individually 

Source: JRC elaboration 

5.2.2  Resource dependency 

The EEIOA provides results on the amount of bauxite extracted in the different scenarios as well. As 
explained in Section 1.2, bauxite is needed to produce virgin aluminium, and this represents a 
scarce resource in EU that is mostly supplied by third countries. The impact category ‘Resource 
dependency’ can therefore provide valuable information on the changes in requirements for the 
aluminium industry (expressed as Mt), but also insights on whether the bauxite is EU- or RoW-
sourced. 

Figure 25A presents the bauxite direct inputs to aluminium industries in the EU in the STQ21, 
BSL50, CTC50, and ACE50 scenarios, measured as million tonnes (Mt). In the STQ21 scenario, 
bauxite input requirements to the EU aluminium industry total 8.2 Mt, which increase by 65% to 

67 

 
13.5 Mt in the BSL50 scenario consequently to increased demand for aluminium. The 
implementation of selected circular economy targets in the CTC50 scenario leads to a reduction in 
bauxite inputs of 1.3 Mt due to increased recycling and recirculation of resource. Further, the ACE50 
scenario increases this reduction of bauxite inputs to the EU aluminium industry to 3.6 Mt, 
corresponding to a reduction of 27% compared to the BSL50 scenario. In all scenarios, EU 
extractions are negligible (’EU’ in Figure 25A), with almost all bauxite supplied by the RoW (‘ROW’ 
in Figure 25A) . 

Similarly to the analysis for Climate Change, Figure 25B-C shows total changes in bauxite 
requirements from a supply chain perspective, including both direct inputs and upstream bauxite 
extractions needed for inputs used by EU industries (e.g. imports of ingots, semis, etc.). This analysis 
compares the CTC50 scenario with the BSL50 scenario (Figure 25B), and the ACE50 scenario with 
the BSL50 scenario (Figure 25C). The total savings in the CTC50 and ACE50 scenarios relative to 
the baseline BSL50 scenario amount to 1.0 Mt and 3.6 Mt, respectively. These figures are similar to 
the on-site savings (Figure 25A), suggesting that, for the specific case of bauxite extractions, most 
of the savings are attributed to the direct imports of the mineral by EU aluminium industries. 
However, it is worth noting that, in this case and in contrast to the example of Climate Change, 
rebound effects due to the replacement of EU (reduced) demand were not considered. The focus 
here is solely on the EU’s material dependency. 

Figure 25. Bauxite inputs to aluminium industries and changes in bauxite requirements for the CTC50 and 
the ACE50 scenarios compared to the BSL50 scenario 

68 

Note: Panel A) displays the bauxite inputs to aluminium industries in the EU in the Status Quo (STQ21), 
Baseline (BSL50), Compliance with selected Targets on Circular Economy (CTC50) and Ambitious Circular 
Economy (ACE50) scenarios; Panel B) displays the changes in bauxite requirements between the CTC50 and 
the BSL50 scenarios; Panel C) displays the changes in bauxite requirements between the ACE50 and the 
BSL50 scenarios. ‘EU’ refers to the extraction of bauxite within the EU, ‘ROW’ refers to the extraction of 
bauxite within the Rest of the World, ‘TOTAL’ refers to the sum of ‘EU’ and ‘ROW’. Notice that the ‘Net 
changes’ are calculated as the sum of burdens (values above the zero line), and savings (values below the 
zero line). All values are expressed as Mt 

 Source: JRC elaboration 

Figure 26 provides information on the decomposition of the total net change in bauxite 
requirements to the EU aluminium industry between the ACE50 and the BSL50 scenarios (i.e. 3.6 
Mt)  ‘By destination’ and ‘By final product’ (or service). The data ‘By destination’ shows that 67% of 
the total net reduction is attributed to decreased EU domestic consumption (‘Domestic’ in Figure 
26), while 33% of the reduction is due to lower EU exports of aluminium products to the world 
(‘Exports’ in Figure 26). The data ‘By final product’ shows that manufacture of motor vehicles, 
trailers, and semi-trailers ('Vehicles' in Figure 26) is the primary driver of the change, accounting 
for 26% of the total net deduction. When considered together with the manufacture of other 
transport equipment ('Other transport' in Figure 26), they explain 33% of the net reduction. 
Similarly to the decomposition of the net savings obtained for Climate Change when comparing the 
ACE50 and the BSL50 scenarios  (Figure 24), this is followed by the manufacture of electrical 
machinery and apparatus (n.e.c.) ('Electrical' in Figure 26) and the construction industry 
(‘Construction’ in Figure 26), each accounting for 16% of the bauxite resource savings. 

69 

 
 
Figure 26. Decomposition of the changes in bauxite requirements to the EU aluminium industry between the 
ACE50 and the BSL50 scenario 

Note: decomposition of the changes in bauxite requirements by destination (domestic consumption, 
‘Domestic’, vs. exports from the EU to the world, ‘Exports’); decomposition of the changes in bauxite 
requirements by final product (or service) 

Source: JRC elaboration 

Finally, although the EEIOA model implemented was not specifically designed to assess the impact 
of energy demand resulting from CE policies, Table 4 provides a rough indication of the changes in 
purchases of selected energy products when comparing the ACE50 and BSL50 scenarios for the EU 
economy, while Table 5 focuses on changes in the energy inputs to the aluminium industry. The 
results suggest that circular economy policies have an impact on reducing energy dependencies, 
with the greatest impact observed on electricity and other fossil fuels, when focusing on the 
requirements of the sector in focus. 

Table 4. Changes in selected products demand in ACE50 in comparison to BSL50 for the total EU economy 

Selected product 

EU electricity production 

EU heat production 

Coal and lignite, extraction of peat 

Crude petroleum 

Quantity 
reduced 

Unit 

-12 547 

GWh 

-7 875 

-19 720 

-8 970 

TJ 

TJ 

TJ 

% 
Relative 
to total 
EU 
demand 
-1.1% 

-0.15% 

-0.69% 

-0.08% 

70 

 
 
Natural gas 

Other fossil fuels 

-48 964 

-412 

TJ 

TJ 

-0.34% 

-0.09% 

Note: The relative reduction is calculated as the reduction of the ACE50 scenario with respect to the BSL50 
scenario over the total EU demand in the BSL50 scenario 

Source: JRC elaboration 

Table 5. Changes in selected products demand in ACE50 in comparison to BSL50 for the aluminium 
industries 

Selected product 

Quantity 
reduced 

Unit 

EU electricity production 

EU heat production 

Coal and lignite, extraction of peat 

Crude petroleum 

Natural gas 

Other fossil fuels 

-11 345 

GWh 

-7 724 

-15 916 

-380 

-4 173 

-162 

TJ 

TJ 

TJ 

TJ 

TJ 

% 
Relative 
to the 
aluminium 
industry 
demand 

-22% 

-11% 
-10% 

-6% 

-7% 

-22% 

Note: The relative reduction is calculated as the reduction of the ACE50 scenario with respect to the BSL50 
scenario over the total demand in the aluminium industry in the BSL50 scenario 

Source: JRC elaboration 

5.2.3  Employment and value added 

Figure 27 provides information on the re-allocation of employment and GDP between the 
Compliance with selected Targets on Circular Economy and the Baseline scenarios (CTC50-BSL50) 
and between the Ambitious Circular Economy and the Baseline scenarios (ACE50-BSL50) in relative 
terms compared to the total economy. The results in Figure 27A indicate that at the macro-level, 
the CTC50 scenario has a negligible impact on both total employment and GDP (‘Net’ in both 
Employment and GDP). In contrast, the ACE50 scenario shows a decrease of approximately 0.04% 
in total employment and 0.05% in GDP (‘Net’ in both Employment and GDP).  

In addition, the results suggest that there is no net impact on the aluminium industry, with the 
number of jobs remaining the same in both scenarios (‘Direct’ in Employment and GDP under the 
CTC50 and ACE50 scenarios when compared with the BSL50 scenario). It is worth noting that the 
EEIO modelling is a linear model that does not account for changes in labour productivity and 
instead assumes an equal number of jobs per unit of output for all aluminium industries. Moreover, 
the re-allocation of productive factors to, for example, service sectors is also not included. However, 
it does capture changes in the economy, as it adjusts to a new supply and demand balance, which 
has implications for supply chain configurations.  

Figure 27B provides insight into why there is no net impact on the aluminium industry when 
focusing on Employment effects. It is evident that there is an offsetting effect between different 
industry activities. Some activities, such as ingot production and casting of semi-finished products, 
support fewer jobs, resulting in decreases of 0.22% and 0.16%, respectively, relative to the industry 

71 

 
total. In contrast, others create more jobs, with the most notable cases being collection and sorting, 
manufacturing, and recycling, which see increases between 0.2% and nearly 0.4% in jobs. 

Figure 27. Changes relative to the total economy in Employment and GDP, and changes in the employment 
of the aluminium industry in the ACE50 scenario compared to the BSL50 scenario 

Note: Panel A) displays the changes in employment and gross domestic product (GDP) between the 
Compliance with selected Targets on Circular Economy and the Baseline scenarios (CTC50-BSL50) (left), and 
between the Ambitious Circular Economy and the Baseline scenarios (ACE50-BSL50) (right) for direct jobs and 
GDP (‘Direct’) contribution in the EU aluminium industry, indirect jobs and GDP (‘Indirect’) in other industries in 
the supply chain, and net changes (‘Net’) in comparison to the total economy; Panel B) displays the changes in 
of employment in the Ambitious Circular Economy relative to the Baseline scenarios (ACE50-BSL50)/BSL50 
for specific aluminium industries in comparison to the total industry in 2050. All values are expressed as 
percentages [%] 

Source: JRC elaboration 

72 

 
 
 
 
6  Dynamic macroeconomic modelling  

The transition from a linear to a circular economy interferes in complex systems of economic 
interactions. Comprehensive modelling approaches capable of capturing the dynamic complexity are 
essential for evaluating transition pathways. This study follows an integrated approach that 
combines MFA (Section 3), LCA and LCC (Section 4), EEIOA (Section 5) and dynamic 
macroeconomic modelling. The Fully Inter-country Dynamic Econometric Long-term Input-Output 
(FIDELIO) model developed by the JRC is applied to add to the insights of the other methods.  

In this chapter, before the implementation of CE scenarios in FIDELIO, an general overview over 
dynamic modelling approaches is provided (Section 6.1), followed by the results obtained (Section 
6.2), and discussing some of the limitations related to FIDELIO (Section 6.3). 

6.1  Methodology 

The circular flow of income is a foundational concept in economics that illustrates how money 
circulates through an economy, highlighting the interactions between households, firms, 
governments and financial institutions (Miller & Blair, 2022). Dynamic economic modelling captures 
the relationships between different agents in an economy. While IOA covers the relationship of the 
final demand to industries, dynamic modelling approaches such as FIDELIO extend the 
representation of economic relationships by covering flows such as from businesses to households 
in form of wages, or by linking government spending to taxation of firms. They therefore cover a 
wider range of economic interactions and, building on these dynamics, they can be applied for 
prospective studies.  

In a meta-analysis, (Aguilar-Hernandez et al., 2021) found that most prospective macroeconomic 
studies of the CE are centred on dynamic approaches such as macroeconometric analysis (ME), 
computable general equilibrium (CGE) models and integrated assessment models (IAM) as shown in 
Figure 28. While IOA is mainly based on retrospective data, several studies that calibrate IO tables 
for prospective stages of production and consumption based on exogenous scenarios have been 
developed (Wiebe et al., 2019); nevertheless, CGE remains the most common tool for prospective CE 
research (Aguilar-Hernandez et al., 2021). 

The data core of CGE models is set up through a social accounting matrix that in its properties is 
comparable to Input-Output Tables (Miller & Blair, 2022). The reactions of agents in the economy 
are modelled based on elasticities often assuming uniform agents acting on perfect rationality and 
perfect information for cost-optimising behaviours (Böhringer & Rutherford, 2015). On the supply 
side, constant elasticities of substitution (CES) are applied between the production factors materials, 
capital and labour. On the demand side, the price elasticity of goods and services allow 
substitutions depending on relative price changes (McCarthy et al. 2018). McCarthy et al. (2018) 
found that the economic sectors included in CGE models applied for CE research differed between 
15 and 40 sectors. 

73 

 
 
 
 
Figure 28. Overview over prospective macroeconomic CE studies based on Aguilar-Hernandez et al. (2021) 

No. of prospective macroeconomic CE studies based 
on a meta analysis by Aguilar-Hernandez et al. 
(2021)

20

15

10

5

0

IO

ME & CGE

IAM

Note: The following acronyms are used: ‘CGE’ Computable General Equilibrium model; ‘IAM’ Integrated 
Assessment Model; ‘IO’ Input output; ‘ME’ Macroeconomic analysis 

Source: JRC elaboration 

While CGE models build on a micro foundation rooted in neoclassical equilibrium theory, 
macroeconometric models are primarily based on empirical data relying on historical time-series to 
estimate parameters and relationships between economic variables over time. ME relies on 
statistical methods to determine the interdependencies between variables, helping in understanding 
how external shocks impact the economy. Building on behaviours observed in historical data, the 
models assume prospective pathways will follow historical patterns. Compared to ME, the 
theoretical assumptions of CGE on cost-optimizing behaviour are less time sensitive and 
macroeconometric models are therefore often applied primarily for the short and mid-term. With 43 
economic sectors in 71 regions, the macro econometric model E3ME is in line with the level of detail 
of CGE models used in CE research. The model has been applied to review employment impacts of 
CE interventions until 2030 (European Commission, 2018).  

Dynamic economic models have advantages over IOA in the representation of economic interactions 
and a wider range of indicators. They are well suited to highlight rebound effects of the CE. Zink & 
Geyer (2017) coined the term ‘circular economy rebound’ through which unintended environmental 
impacts follow CE interventions. This can be the case due to insufficient substitutability between 
primary and secondary products because of quality degradation leading to a separate market for 
secondary goods additional to the primary market without replacing it. The second mechanism is 
due to price effects, when lower quality secondary goods decrease costs compared to their primary 
counterparts. This can lead to either an increase in production or an income effect, depending on 
whether the benefit is captured by firms or passed on to final demand (Zink & Geyer, 2017). The 
quantification of rebound effects is often omitted in CE impact assessments (Donati et al., 2020; 
Wiebe et al., 2019) but can be assessed using dynamic models. 

74 

 
 
6.1.1  FIDELIO: Model description 

FIDELIO is a global demand-driven dynamic macroeconomic model that combines elements of 
Input-Output Analysis, macroeconometric analysis and computable general equilibrium modelling. It 
is based on Eurostat's official FIGARO Supply and Use Tables (SUT) covering 64 industries and 
products in 46 countries and regions, with 2015 as the base year. The model builds upon the 
disaggregated Supply and Use Tables described in Section 5.1. Based on FIGAROe3, 'Electricity, 
gas, steam and air conditioning supply' is extended into 14 industries covering 12 types of 
electricity generation, electricity transmission and distribution, and gas, steam and hot water supply. 
The 14 energy industries produce a common product, resulting in a full resolution of 77 industries 
and 64 products in FIDELIO. The development of FIDELIO started with a first expert workshop in 
2006 and is currently developed in its fourth version at the JRC. FIDELIO 4 is structured in modules 
to increase transparency and traceability of macroeconomic effects. Thereby, the model is extended 
step by step with blocks of equations. Starting with a static Supply and Use Table model using a 
Leontief approach, the second module adds equations for investment dynamics. The third module 
adds consumption dynamics, and the full model includes price dynamics. The use of the full model 
is the most realistic due to its comprehensive representation of the economy and economic 
interactions. A detailed description of FIDELIO 4, including all equations, will be available in the 
forthcoming model documentation (Rocchi et al., 2025). 

6.1.2  FIDELIO: Modelling approach 

The modelling approach is divided into two parts. First, shocks derived from the bottom-up LCA 
model are processed in a way that is suitable for their implementation in FIDELIO. Second, the 
shocks are implemented in the BSL50, CTC50 and ACE50 scenarios in FIDELIO. Using all the 
individual FIDELIO modules was beyond the scope of this study, and it is therefore limited to the full 
FIDELIO model for the most comprehensive representation of economic interactions. 

6.1.2.1  Processing shocks of the bottom-up model 

As a starting point, the EU-wide levers from the bottom-up model are assigned to their respective 
matrix, product, industry or end-use category in the FIGARO Supply and Use Tables (SUT). Due to the 
missing  representation  of  stocks  in  the  SUT  data,  only  flows  from  the  bottom-up  model  are 
implemented. An interface between the MFA/LCA bottom-up model and FIDELIO was developed, to 
effectively bridge the different spatial, sectoral and temporal granularity of the methods.  

•  Spatial: The granularity of the MFA/LCA bottom-up model perspective is aggregated spatially 
at  the  EU  level,  while  FIDELIO  uses  country-specific  data.  To  implement  the  CE  levers  in 
FIDELIO from the bottom-up models, their outputs must be distributed to the national level. 
This was done by aggregating the EU wide market in the FIGARO SUT and calculating national 
market  shares  of  the  27  Member  States.  The  shares  are  applied  as  a  weighting  factor  to 
distribute the EU-wide shock to the national level.  

•  Sectoral: While the MFA/LCA bottom-up model includes a detailed representation of stocks 
and flows in different production stages as well as recycling industry, the sectoral granularity 
of  FIDELIO is  less  comprehensive.  To  map  the  physical  flows  in the MFA  to the  monetary 
flows of FIDELIO, the data of the MFA is aggregated to a level compatible with the broader 
sectoral flows of FIDELIO. Where the flows in the MFA/LCA bottom-up model (e.g. aluminium 
products) still represent only a fraction of a broader sector in FIDELIO (e.g. ‘Basic metals’), a 
market penetration rate was calculated using the highly disaggregated IO table FIGAROe3. 

75 

 
The market penetration rate was then applied as a second weighting factor to bridge between 
the different sectoral granularities. This approach is adapted from Donati et al. (2020). 

•  Temporal:  FIDELIO  uses  yearly  data  while  data  from  the  bottom-up  MFA/LCA  model  is 
available for the status quo year 2022 and the target year 2050. For implementing bottom-
up scenarios from the MFA/LCA model, interpolation is necessary. In this study, an S-shaped 
logistic growth function is used to interpolate values from the bottom-up model. This function 
is common for modelling diffusion processes and begins with exponential growth, that slows 
until  a  saturation  point  is  reached  (Sterman,  2000).  For  this  study,  it  describes  the 
implementation  level  of  the  applied  levers  between  0%  to  100%,  accounting  for  full 
implementation between 2030 and 2050. 

By combing both weighing factors and interpolating for annual time steps, the changes are 
distributed to the national level. These steps are repeated for all relevant flows of the bottom-up 
model. The relative change in physical units of the bottom-up model is applied as a likewise relative 
change in the monetary units of FIDELIO. This simplifies the process and avoids price uncertainties. 
However, it is possible to use this approach by monetising the physical quantities first and then 
adding an additional step to adjust for differences in national price levels.   

6.1.2.2  BSL50 

In the BSL50 scenario, three main trends are included based on the NDC-LTS scenario from the 
Global Energy and Climate Outlook 2023: economic growth, energy transition, and electrification. 
Thereby, the national GDP growth rates were adjusted to follow the GECO 2023. Energy is 
represented in FIDELIO by the product ‘Electricity, gas, steam and air conditioning’ (CPA_D35), co-
produced by 14 disaggregated industries of D35, and ‘Coke and refined petroleum products’ 
(CPA_C19) that is mainly produced by the industry ‘Manufacture of coke and refined petroleum 
products’ (C19). Decarbonisation is represented in the BSL50 scenario by including two main trends: 
phase-out of fossil fuels in the production of electricity and electrification, as illustrated in Figure 
29. 

76 

 
Figure 29. Implementation of the energy transition in the BSL50 scenario 

Note: C: Capital account; F: Financial account; I: Income account; RoW: Rest of the World 

Source: JRC elaboration 

The transition away from fossil fuel-based electricity is reflected by lowering their production share 
in the supply matrix following the energy shares of the GECO NDC-LTS scenario. Electricity is co-
produced by 12 main industries in FIDELIO representing fossil and renewable energy carriers. 
Additionally, FIDELIO covers the transmissions and distribution of electricity in one industry and gas, 
steam and heat in another industry. The electrification of the energy sector is included by increasing 
the shares of electricity production and transmission while lowering that of gas, steam and heat. 
The ratio between electricity production and transmission were thereby kept static. In addition, the 
energy consumption of the energy intensive industries steel, aluminium, cement and concrete, and 
plastics were adjusted using data from GECO 2023. This was done by adjusting the intermediate 
use of the industries ‘Basic metals’ (C24), ‘Other non-metallic mineral products’ (C23) and 
‘Chemicals and chemical products’ (C20) based on the respective market penetration rates of the 
materials in these sectors, which are based on the highly disaggregated IO table FIGAROe3. 
Following the data from GECO 2023, this included a phasing-out of fossil fuels such as ‘Coke and 
refined petroleum products’ (C19) and ‘Mining and Quarrying’ (B), while increasing the consumption 
from ‘Electricity, gas, steam and air conditioning’ (D35). The energy expenditure in the intermediate 
input is thereby kept constant, and energy efficiency or an increase in energy expenditure, for 
example for green hydrogen, is not explicitly considered here and can be investigated more closely 
in the future. 

Overall, the Baseline scenario covers electrification using an energy mix primarily from renewable 
energies for import industries and final uses. The GECO NDC-LTS scenario relies on increases in 

77 

 
 
energy efficiency for agriculture, industries and services plus technologies such as carbon capture 
and storage (CCS) to reach European net zero goals in 2050. A full replication of the GECO analysis 
using FIDELIO was considered out of the scope of this study and, therefore, net zero is not achieved 
in the BSL50 scenario in FIDELIO. The BSL50 scenario in the dynamic modelling for this study 
focuses on main aspects, such as phasing out fossil fuels and electrification of key sectors. 

6.1.2.3  CTC50 

In the CTC50 scenario an increase in secondary material is added on top of the decarbonisation 
modelled in the BSL50 scenario (modelled as explained in Section 6.1.2.2). In the CTC50 scenario 
‘Mining products’ (CPA_ B) are substituted with waste services (CPA_E37-E39) in the intermediate 
use of ‘Basic metals’ (C24) following increased recycling to meet selected EU targets (based on 
bottom-up results from the MFA and LCA; see Section 3.2 and Section 4.5, respectively). The 
decrease in ‘Mining products’ (CPA_ B) is counterbalanced by a proportional increase in waste 
services (CPA_E37-E39), assuming the same price. Given the large uncertainties in forecasting 
future resource prices, this is considered a feasible approach (Wiebe et al., 2019). To consider the 
difference in energy consumption between primary and secondary aluminium production, the 
intermediate use of ‘Electricity, gas, steam and air conditioning’ (CPA_D35) is reduced according to 
the life cycle inventory. Figure 30 illustrates the implementation of changes in the production 
inputs of the ‘Basic metals’ industry column. 

Figure 30. Implementation of selected policy targets in the CTC50 scenario 

Note: Implementation of the policy targets (i.e. the shocks). C: Capital account; F: Financial account; I: Income 
account; RoW: Rest of the World 

Source: JRC elaboration 

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6.1.2.4  ACE50 

In the ACE50 scenario reductions in pre-consumer scrap production and the extension of the 
lifetime of aluminium finished products are added to the decarbonisation implemented in the 
BSL50 scenario. Furthermore, building on the CTC50 scenario implementation (see Section 
6.1.2.3), the amount of scrap entering recycling is also included based on the bottom-up results of 
the MFA and the LCA (see Section 3.2 and Section 4.5, respectively). The calculation of the 
market penetration rates and the implementation of the CE levers in FIDELIO is detailed in Annex 
11 and illustrated in Figure 31. 

Figure 31. Implementation of CE levers in the ACE50 scenario 

Note: Implementation of the CE levers (i.e. the shocks). C: Capital account; F: Financial account; I: Income 
account; RoW: Rest of the World 

Source: JRC elaboration 

The shocks cover the intermediate use ‘Basic metals’ (CPA_C24) and ‘Fabricated metals’ (CPA_C25) 
in the industries ‘Manufacture of electrical equipment’ (C27), ‘Manufacture of machinery and 
equipment n.e.c.’ (C28), ‘Manufacture of motor vehicles, trailers and semi-trailers’ (C29), 
‘Manufacture of other transport equipment’ (C30), ‘Manufacture of furniture; other manufacturing’ 
(C31_32) and ‘Construction’ (F). ‘Basic metals’ (CPA_C24) refers to the production of basic metals 
such as steel, aluminium, copper and other metals that are produced by melting, casting and other 
primary metalworking processes. These products are usually unprocessed and serve as the starting 
material for further industrial applications, while ‘Fabricated metals’ (CPA_C25) covers the 
manufacture of metal products made from these basic metals. The focus here is on the further 
processing and refinement of metals into finished products such as vehicles or consumer durables. 
The share of aluminium products in ‘Fabricated metals’ (CPA_C25) is estimated using data from 

79 

 
 
FIGAROe3 to determine the national market penetration rates of the CE levers in the ACE50 
scenario. The use of ‘Basic metals’ (CPA_C24) and ‘Fabricated metals’ (CPA_C25) is reduced for the 
respective industries based on the market penetration rates for aluminium products as weighing 
factors.  

6.2  Results 

The results for GDP, value added, trade, and employment obtained by using the dynamic macro-
economic modelling FIDELIO are presented in Sections 6.2.1- 6.2.3. The results are presented 
relative to the BSL50 scenario, which is taken as benchmark. Therefore, the difference between 
BSL50 and the CTC50 and the ACE50 scenario is to be interpreted as the additional effect incurred 
by the implementation of the targets and by the full-fledge implementation of the CE levers 
investigated, respectively. 

Notice that we report the results both as total absolute decrease (e.g. billion EUR) and relative 
decrease (%) compared with the baseline. The two offer complementary but different information: 
while the total decrease (e.g. in GDP or Value Added) is strongly correlated to the size of a region or 
industry in the baseline, the percentage should be rather interpreted as an impact intensity and, as 
such, identifies the most affected regions or sectors, regardless of the size.  

6.2.1  Gross Domestic Product and Value Added 

The results obtained for the changes in the Gross Domestic Product (GDP) as a consequence to the 
attainment of selected policy targets and the CE levers with respect to the BSL50 scenario are 
presented in Figure 32. Associated changes in Gross Value Added (GVA) for the EU industry are 
presented in Figure 33. 

Focusing on the EU, the changes in GDP are negligible for the CTC50 (ca. EUR 1 billion difference, or 
0.0036% relative change), while for the ACE50 scenario there is a decrease of ca. EUR 9 billion 
relative to the BSL50 scenario, corresponding to a 0.05% decrease. The largest contributions to the 
EU GDP loss are observed for Germany, France and Italy, with decreases in the order of EUR 1.1-1.7 
billion relative to the BSL50 scenario, corresponding to a relative reduction of 0.040-006% (see 
Figure 32a and Figure 32b). Regarding non-EU countries, negligible differences in GDP can also 
be observed, both for the CTC50 (EUR 0.2 billion difference to the BSL50 scenario, corresponding to 
a 0.00023% relative reduction) and for the ACE50 scenario (EUR 7 billion difference to the BSL50 
scenario, corresponding to a 0.0073% relative reduction). The non-EU countries most affected are, a 
part from Rest of the World (RoW), China, the USA, Russia and the UK, with decreases compared to 
the BSL50 scenario in the order of EUR 0.33-1.55 billion or 0.0032-0.028% relative reduction (see 
Figure 32c and Figure 32d). Overall, the results of the modelling show also for non-EU countries 
that the general trend of GDP decline is limited, both in the CTC50 and in the ACE50 scenario. 

With respect to changes in the Value Added at EU level, the results obtained in the CTC50 scenario 
are negligible, while in the ACE50 scenario the industries mainly affected are ‘Manufacture of 
fabricated metal products, except machinery and equipment’ (C25), ‘Manufacture of basic metals’ 
(C24), ‘Wholesale trade, except of motor vehicles and motorcycles’ (G46), ‘Production of electricity’ 
(D3511), and ‘Construction’ (F), with decreases compared to the BSL50 scenario in the order of EUR 
0.50-1.5 billion or 0.051-0.98% (see Figure 33a and Figure 33b). 

80 

 
 
Figure 32. Absolute and relative change in GDP in EU and non-EU country 

Note: Panel a) Relative change in GDP per EU country expressed as percentages for the CTC50 and the ACE50 
scenario; Panel b) Absolute change in GDP per EU country expressed as billion EUR for the CTC50 and the 
ACE50 scenario; Panel c) Relative change in GDP per non-EU country expressed as percentages for the CTC50 
and the ACE50 scenario level; Panel d) Absolute change in GDP per non-EU country expressed as bn EUR for 
the CTC50 and the ACE50 scenario 

Source: JRC elaboration 

Figure 33. Absolute and relative change in value added by industry in EU 

81 

Note: Panel a) Relative change in Value Added for the EU at industry level expressed as percentages for the 
CTC50 and the ACE50 scenario; Panel b) Absolute change in Value Added for the EU at industry level 
expressed as billion EUR for the CTC50 and the ACE50 scenario 

Source: JRC elaboration 

Furthermore, at the global scale, the European GDP corresponds to approx. 14% of the global GDP 
(approx. EUR 110 trillion in 2050). The CTC50 scenario results in a relative reduction of the global 
GDP of 0.001% (EUR 1 billion), while the ACE50 scenario of 0.01% (EUR 16 billion), losses that are 
also negligible. Overall, the results of the modelling show that the general trend of GDP decline is 
limited and the EU’s position in the global economy is not negatively affected by the attainment of 
selected policy targets nor by the implementation of the CE levers.  

6.2.2  Trade 

The results obtained for the changes in Trade as a consequence to the attainment of selected policy 
targets and CE levers with respect to the BSL50 scenario are presented in Figure 34. 

In the BSL50 scenario the total amount of EU imports amounts to EUR 3 391 billion, while for the 
CTC50 scenario to ca. EUR 3 391 billion, and for the ACE50 scenario EUR 3 387 billion. With respect 
to exports, in the BSL50 the total amount of EU exports equals EUR 4 350 billion, while for the 
CTC50 to ca. EUR 4 350 billion, and for the ACE50 scenario ca. EUR 4 350 billion. This corresponds 
for the EU in a surplus in trade balance27 of EUR 959 billion in the BSL50 scenario, EUR 960 billion 
in the CTC50 scenario, and EUR 963 billion in the ACE50 scenario. Considering the difference 
between the CTC50 and the ACE50 with the BSL50 scenario, an increase in trade balance of EUR 
0.20 billion in the CTC50 scenario and of EUR 3.9 billion in the ACE50 scenario are calculated. This 
means that the attainment of selected policy targets and the implementation of CE levers incur an 
increase in EU trade surplus (less demand for imports mainly, with exports proportionally less, or 
not, affected). 

The increased surplus for the EU in both the CTC50 and the ACE50 scenario are due to changes in 
trade patterns, overall inferring a reduced dependency from non-EU countries. Focusing on the trade 
partners, in the CTC50 scenario the main changes are observed for Russia, Norway and the USA 
with a surplus in trade balance in the order of EUR 0.040-0.018 billion or 0.0037-0.11% relative to 
the BSL50 scenario. For the ACE50 scenario, the main changes are observed for China, Russia, the 
USA, and the UK with a surplus in trade balance in the order of EUR 0.33-0.50 billion or 0.074-3.0% 
relative to the BSL50 scenario (see Figure 34a and Figure 34b). Specifically, Norway, Russia, 
China, and the USA are amongst the main countries from which the EU imports ingots, semi-
finished and finished products. The implementation of the CTC50 and the ACE50 would result in an 
increased autonomy. Indeed, the sector mainly affected in non-EU countries refers to ‘Mining and 
quarrying’ (B). 

With respect to what is imported in the EU, Figure 34c and Figure 34d show what commodities 
are mainly affected by the changes implemented in the CTC50 and ACE50 scenarios when 
compared to the BSL50 scenario. Negligible decreases are observed for the CTC50 scenario, while 
for the ACE50 scenario, the main commodities affected are ‘Basic metals’ (CPA_C24), ‘Mining and 

27 The trade balance is calculated as Trade balance = Exports – Imports. 

82 

quarrying’ (CPA_B), ‘Fabricated metal products, except machinery and equipment’ (CPA_C25), and 
‘Machinery and equipment n.e.c.’ (CPA_C28) in the order of EUR 0.11-1.5 billion or 0.077-1.3% 
relative reduction. 

Finally, Figure 34e and Figure 34f show what EU exported commodities are mainly affected by 
the implementation of the CTC50 and ACE50 scenarios as opposed to the BSL50 scenario. 
Negligible increases and decreases are observed for the CTC50 scenario, while for the ACE50 
scenario, the main goods decreasing their exports are ‘Basic metals’ (CPA_C24), ‘Chemicals and 
chemical products’ (CPA_C20), ‘Machinery and equipment n.e.c.’ (CPA_C28), ‘Computer, electronic 
and optical products’ (CPA_C26), and ‘Motor vehicles, trailers and semi-trailers’ (CPA_C29) in the 
order of EUR 0.018-0.058 billion or 0.0058-0.057% relative reduction.  

83 

Figure 34. Relative and absolute changes in trade balance by country, and imports and exports by 
commodity 

Note: Panel a) Relative change in trade balance with non-EU countries expressed as percentages for the 
CTC50 and the ACE50 scenario; Panel b) Absolute change in trade balance with non-EU countries expressed 
as billion EUR for the CTC50 and the ACE50 scenario; Panel c) Relative change in EU imports by commodity 
expressed as percentages for the CTC50 and the ACE50 scenario; Panel d) Absolute change in EU imports by 
expressed as billion EUR for the CTC50 and the ACE50 scenario; Panel e) Relative change in EU exports by 
commodity expressed as percentages for the CTC50 and the ACE50 scenario; Panel f) Absolute change in EU 
exports by commodity expressed as billion EUR for the CTC50 and the ACE50 scenario 

Source: JRC elaboration 

6.2.3  Employment 

The results obtained for the changes in Employment because of the attainment of the policy targets 
and the CE levers with respect to the BSL50 scenario are presented in Figure 35. 

84 

The total number of jobs in the 2050 scenarios is approx. 177 million jobs. The difference between 
the CTC50 and the BSL50 scenario highlights a reduction in employment of 4 000 jobs 
(corresponding to a 0.0022% relative reduction, not shown in Figure 35), while in the ACE50 
scenario of 60 000 jobs (corresponding to a 0.034% relative reduction, not shown in Figure 35). 

In the CTC50 scenario, the main differences in the reduction of number of jobs are observed for 
Slovakia, France, and Germany in the order of 345-1 666 jobs or 0.0008-0.063% relative reduction. 
In the ACE50 scenario more pronounced losses are observed for Germany, Italy, and France in the 
order of 6 300-11 000 jobs or 0.024-0.041% relative reduction (see Figure 35a and Figure 35b). 
Germany, France and Italy are amongst the main countries within the European aluminium supply 
chain. Within the EU, Slovakia is producing 9% of the vehicles and is strongly embedded through 
upstream linkages to automotive industries in other EU countries (Rueda-Cantuche et al. 2024) 
(Rueda Cantuche et al., 2024). This can explain its sensitivity for shocks in the automotive industry. 
Hence, a reduction in the sector would impact negatively from an employment perspective. 

With respect to the industries mostly affected by reductions in employment, the results for the 
CTC50 scenario show that the main losses compared to the BSL50 scenario are incurred by 
‘Electricity, gas, steam and air conditioning’ (D3511) and ‘Construction’ (F) in the order of 250-2 
200 jobs), while an increase of 200 jobs is observed for ‘Sewerage; waste collection, treatment and 
disposal activities; materials recovery; remediation activities and other waste management services’ 
(E37T39, not shown in Figure 35) due to, among the others, an increase in recycling in the CTC50 
compared to the  BSL50 scenario. In the ACE50 scenario, the main reductions in employments are 
observed for ‘Manufacture of fabricated metal products, except machinery and equipment industry’ 
(C25), ‘Manufacture of basic metals’ (C24), ‘Electricity, gas, steam and air conditioning’ (D3511), 
‘Wholesale trade, except of motor vehicles and motorcycles’ (G46), and ‘Construction’ (F) in the 
order of 3 000-11 400 jobs or 0.054-0.63% relative reduction. As opposed to the CTC50 scenario, 
decreases in ‘Sewerage; waste collection, treatment and disposal activities; materials recovery; 
remediation activities and other waste management services’ (E37T39, not shown in Figure 35) 
are observed, although very limited. 

The modelling results show overall a negative trend in job demand. It is important to note that 
amongst the levers herein studied the extension of the lifetime of products via repairing and 
remanufacturing requiring skilled workers, changes in design of products to improve recyclability 
and new sorting technologies both requiring research and development, are considered. 
Nevertheless, the creation of jobs related to the mentioned activities are not modelled. Hence, the 
results do not fully represent the consequences of attaining policy targets and implementing CE 
levers. 

85 

Figure 35. Relative and absolute changes in EU employment by country and industry 

Note: Panel a) Relative change in employment by EU country expressed as percentages for the 
CTC50 and the ACE50 scenario; Panel b) Absolute change in employment by EU country expressed 
as 1 000 jobs for the CTC50 and the ACE50 scenario; Panel c) Relative change in EU employment by 
industry expressed as percentages for the CTC50 and the ACE50 scenario; Panel c) Absolute change 
in EU employment by industry expressed as 1 000 jobs for the CTC50 and the ACE50 scenario 

Source: JRC elaboration 

6.3  Limitations 

Decarbonisation in this study is focused on the energy sector and four energy-intensive industries, 
namely steel, aluminium (the industry in focus), cement and plastics. The electrification of service 
sectors such as transport and residential energy use, as well as energy efficiency and the market 
diffusion of carbon capture technologies or green hydrogen were considered as out of scope. The 
analysis with FIDELIO in the BSL50 scenario therefore lacks the depth of the energy transition in 
the Global Energy and Climate Outlook 2023 and should be further investigated to examine the 
long-term contributions of the CE in more detail. An integrated approach combining FIDELIO with an 
energy system model at the appropriate spatial and sectoral level of detail, as recommended by 
Elberry et al. (2024), and an MFA or LCA model to track stocks and flows of the evolution towards a 
CE would be a way forward, allowing to build on the specific strengths of the different types of 
modelling. 

The dynamic modelling approach is consistent with the EEIOA, where shocks are implemented in the 
intermediate use matrix. However, this approach treats CE interventions as cost reductions. While 
the literature on CE rebound effects suggests that cost reductions through CE are likely and can 
lead to environmental rebound effects (Zink & Geyer, 2017), CE usually requires more services, 
which may partially counteract the cost reductions. This study used a combined approach of life 
cycle assessment, material flow analysis and macroeconomic modelling. While this approach is rich 

86 

in information on stocks and flows of aluminium, additional data inputs are required to consider 
repair and rental services, as well as research and development activities, which are important for 
longer product life, intensification of use and material efficiency. However, robust economic data for 
services were not available and were not specifically collected in this study. The consideration of 
this system boundary is important for the interpretation of economic indicators, such as GDP or 
employment, that do not include the increase in services required by the CE and should therefore be 
considered as a lower bound after technical optimisation of product design and processes to reduce 
material use. In addition to the costs of services, investment costs for additional recycling 
infrastructure, repair and refurbishment infrastructure and research facilities should be covered in 
more detail in the future. Future research projects can highlight these in more detail by adding data 
on life cycle costs, capital expenditure (CAPEX) and operating and maintenance expenditure (OPEX). 
This study highlights the potential to reduce industrial demand for aluminium rather than social 
innovations and impacts of behavioural change of consumers. Combining FIDELIO with a modelling 
approach that goes beyond the MFA inputs would allow scenarios of consumer behaviour change to 
be explored in more depth, broadening the scope of this study from technical optimisation to social 
innovation. This can be achieved, for example, through system dynamic or agent-based modelling 
approaches. 

The study is limited to the sectoral granularity of FIDELIO. Although the level of detail is 
comparatively high compared to other dynamic macroeconomic models used for CE analysis, there 
is a bias due to aggregation error. For example, the mining sector (NACE B) includes both energy 
extraction, such as coal, gas and oil, and non-energy extraction, such as iron ore. Therefore, 
modelling a reduction in the use of iron ore by reducing mining inputs of the commodity CPA B will 
also lead to a reduction in energy inputs. Another aggregation error is found in industry E37T39, 
which includes GHG-intensive processes such as waste incineration or processing of sewage sludge, 
in addition to the collection and processing of secondary materials. A further disaggregation of 
FIDELIO for non-energy mining and a differentiation of the waste streams for processing of 
secondary materials, incineration, landfilling or sewage services would help to better capture the 
changes due to the CE. This can be based on the highly disaggregated IO table FIGAROe3, following 
the example of the disaggregated energy sector used for this study. For the mining sectors, data 
quality checks can be carried out using the OECD's Inter-Country Input-Output tables (ICIO), which 
distinguish between energy and non-energy mining as well as mining support services. A CE sector 
specific disaggregated version of FIDELIO would allow future CE research projects to reduce the 
aggregation bias in FIDELIO. However, in dynamic economic modelling, an increased number of 
industries in models comes at the cost of reduced data reliability (Duchin & Levine, 2016; Hu et al., 
2023). The data available for parameter validation is retrospective, spatially limited, and restricted 
to a unique setting, leading to limitations on the total number of industries used in dynamic 
macroeconomic models (Duchin & Levine, 2016). The trade-off between the benefits of higher 
sectoral granularity and lower data reliability should be considered when increasing the number of 
sectors in the model.  

FIDELIO allows to use a modular approach. This study was limited to the use of the full model and 
therefore missed the traceability of macroeconomic spillovers and rebounds that is offered by using 
the different modules of FIDELIO to track intersectoral, investment, income and price effects. Using 
all modules would increase the depth of the analysis. A replication of the results using the other 
modules of FIDELIO can be considered to increase the traceability and transparency of the results. 

The trade pattern modelled in the BSL50 scenario of FIDELIO do not mirror the post Ukrainian-war 
situation. Therefore, in the changes in trade balances and patterns, it is expected to see less 

87 

 
reduction following CE levers for Russia as a consequence to the implementation of existing 
European sanctions. 

88 

 
7  Synthesis and discussion 

The results obtained via LCA, EEIOA, and dynamic macroeconomic modelling and key insights are 
synthetised in Section 7.1. Possible policy actions that would be needed to activate the CE levers 
investigated in this study are presented in Section 7.2. 

7.1  Impacts of CE: Synthesising bottom-up and macro-analyses 

7.1.1  Climate change mitigation and other environmental effects 

Our results indicate that the potential of CE to mitigate climate change, additional to what would be 
achieved by solely the decarbonisation of the energy sector, lies between 12-14 Mt CO2-eq. (year 
2050; Figure 36). The two analyses show comparable results, with the ones obtained via EEIOA in 
the lower end of the range as they include rebound effects related to increased production in third 
countries due to reduced exports. If these effects would be excluded, as they are in the boundaries 
of the LCA, then both analyses would show a yearly reduction of approx. 14 Mt CO2-eq. (year 2050). 

Figure 36. Greenhouse gas emission savings for the aluminium sector in the ACE50 scenario and breakdown 
into the Reduce, Reuse, and Recover clusters 

Note: All values are expressed in Mt CO2-eq and relative to the BSL50 scenario 

Source: JRC elaboration 

The higher granularity of the LCA allows for showing the effects of the individual CE levers. The 
Reduce lever, which is focused on decreasing pre-consumer scrap, contributes with limited 
reductions of 1 Mt CO2-eq. Moderate climate change mitigation effects were expected as production 
processes are already highly optimised, but also because pre-consumer scrap has high economic 
value and is easy to sort, hence it is already fully recycled (European Aluminium, 2020). Levers that 
entail reducing the demand for aluminium ultimately substituting it with other materials were not 
considered. Indeed, the demand for aluminium is project to increase (see, for example, European 
Aluminium, 2020) and it is expected that aluminium will substitute other materials (e.g., steel in 
cars). The Reuse cluster contributes with a reduction of 11 Mt CO2-eq., where extending the lifetime 
of aluminium-containing finished products across all sectors with a lifespan greater than 1 year 
appears as an impactful lever. The Recover cluster contributes with a reduction of 6 Mt CO2-eq. with 
levers targeting high-quality closed loop recycling as the most impactful ones. Indeed, both L(5) 
(‘Improved waste sorting and scrap quality’) and L(7) (‘Expansion of deposit return systems’) strive 
to achieve closed-loop recycling by establishing an alloy-to-alloy separation, which is rather limited 
in Europe and that appears as a key feature to avoid downcycling of wrought aluminium into cast 

89 

 
 
aluminium of which a surplus in supply is forecasted by several scholars (Bertram et al., 2017; van 
den Eynde et al., 2022). Moreover, a better use of the scrap would also contribute in decreasing the 
dependence on virgin aluminium (European Aluminium, 2020; Georgitzikis et al., 2021).  

Deriving and reporting ‘percentages of reduction’ (e.g. GHG reduction in %) from these figures may 
be misleading, as the values depend on the reference of comparison chosen, i.e. from how the 
boundaries of the system, for which the emissions are quantified, are defined. Taking the boundary 
of the LCA as an example, the results of the bottom-up assessment indicate that the CE levers 
studied achieve an additional impact reduction on the impact category Climate Change equal to 
14% relative to that of the Baseline scenario, i.e. what would be achieved only via decarbonisation 
of the energy sectors. In the scope of the LCA both on-site and off-site emissions are included, 
while the EEIOA results can provide insights on EU on-site emissions for the sector under study. As 
technologies focused on reducing on-site emissions (e.g. switching from carbon anodes to inert 
ones) were not included in this study and since an increase in the demand for aluminium is 
forecasted in 2050, the results of the EEIOA highlight an increase in on-site emissions in the 
Baseline scenario of 66% relative to the Status Quo scenario. Nevertheless, in the Ambitious 
Circular Economy scenario a reduction of 22% relative to the Baseline scenario is calculated due to 
reduced demand for aluminium and replacement of virgin aluminium with high-quality scrap. 

Our findings further indicate that the CE levers studied incur a reduction of the impacts on all the 
environmental impact categories considered other than the Climate Change one, and no trade-offs 
are observed upon implementing the Ambitious Circular Economy scenario. 

7.1.2  Resource and trade dependency 

The full implementation of all CE levers (ACE50 scenario) leads to a decrease relative to the 
Baseline scenario in EU demand for bauxite of 3.6 Mt, representing 27% of all bauxite mineral 
requirements in BSL50, of which the large majority is supplied by countries other than the EU. These 
figures are calculated via EEIOA and only account for the direct bauxite input requirements of the 
EU aluminium industry for maintaining the EU final demand and EU exporting sector. If inter-
country and inter-sector spillover effects are accounted for, then a reduction of 11% in the ACE50 
scenario relative to the BSL50 scenario is observed. 

For the European aluminium sector, the implementation of CE levers corresponds to a reduction in 
demand for energy products. Relative to the Baseline scenario, it is calculated that the electricity 
use would decrease by 22% according to EEIOA results. With respect to the use of fossil fuels, it is 
quantified via EEIOA a reduction in the demand for coal (10%), crude oil (6%), natural gas (7%), and 
other fossil fuels (22%) relative to the Baseline scenario. These demand reductions mostly affect 
imports; indeed, imports represent approx. 87% of the total reduction in fossil energy carriers 
(Figure 37).  

The reduction in resource dependency reflects decreased imports from foreign regions. The results 
of the dynamic macroeconomic modelling suggest that the implementation of all CE levers (ACE50 
scenario) by 2050 increases the trade balance of the EU by annual EUR 3.6 billion. This is related to 
reduction of imports (EUR 3.9 billion) from China, Russia, USA and the UK. The imported 
commodities most affected in absolute terms relative to the Baseline scenario (cumulatively approx. 
EUR 2.6 billion) are ‘Basic metals’, ‘Mining and quarrying’, and ‘Fabricated metal products, except 
machinery and equipment’. Exports from the EU are also reduced, although to a much lesser extent 
(EUR 0.3 billion relative to the Baseline scenario) (Figure 37). This result is to be taken with much 
caution as it is affected by the assumptions made on cost-neutrality of some of the CE levers 

90 

 
applied. On top, the increase of economic output (employment and GVA) from repair and research 
services was not thoroughly investigated and only approximately modelled, assuming that the 
reduction of economic input in manufacturing would be diverted to repair and research, ceteris 
paribus. However, this is only an assumption, and more research is needed on this aspect to 
improve accuracy of distribution within and across sectors.  

Figure 37. Effects of CE levers with respect to the BSL50 scenario on resource and trade dependency for the 
aluminium sector 

Source: JRC elaboration 

7.1.3  Socio-economic effects 

As a result of the implementation of CE levers in the aluminium sector, we calculate a decrease in 
jobs and value added. The decrease in value added is in the order of 0.054% of GDP across the 
entire EU economy relative to the Baseline scenario, while the decrease in job requirements is in the 
order of 0.034% across the entire EU economy relative to the Baseline scenario (Figure 38). 

91 

 
 
Figure 38. Effects of the CE levers relative to the BSL50 scenario on GDP and employment for the aluminium 
sector 

Source: JRC elaboration 

The most affected industries in relative terms are ‘Basic metals’ (-0.63% in employment, -0.98% in 
GVA), ‘Fabricated metal products, except machinery and equipment’ (-0.40% in employment, -
0.65% in GVA), ‘Mining and quarrying’ (-0.19% in employment, and -0.24% in GVA), and the 
‘Electricity, gas, steam and air conditioning’ (-0.22% in employment, -0.22% in GVA). All values are 
obtained via the dynamic macroeconomic modelling. 

The decrease in GVA in the EU aluminium sector corresponds to 1:1728 the decrease in direct GHG 
emissions in the same sector, suggesting a strong decoupling of economic growth and GHG 
emissions incurred by CE.  

As for costs, we only estimated non-discounted life cycle costs which offer a limited representation 
of the actual costs incurred by the economic system because of the CE levers studied. Recover 
levers, based on the static (i.e. EEIOA) and dynamic macroeconomic modelling analyses, increase 
the economic output mainly following increases in cost for collection, sorting, and recycling while 
maintaining industry production and final consumption unchanged, aligning with the life cycle 
costing results. However, no robust information was found to duly account for the technology 
investments and operational costs incurred by a shift towards high-quality recycling. For Reuse and 

28 The ratio is calculated as: the relative change in EU territorial emissions relative to the baseline, and the relative change 

in EU territorial GVA relative to the baseline. Both relative changes are calculated based on the EEIOA results. 

92 

 
 
 
 
Reduce levers, on the other hand, no robust information on investment and operational costs could 
be found. 

7.1.4  Key insights 

The aluminium sector is considered as a circular one; however, the aluminium supply chain would 
benefit from additional efforts implemented at collection, sorting and recycling for sectors other 
than packaging. Indeed, the results highlighted that maintaining alloys families separated to ensure 
closed-loop recycling and reduce scrap surplus is key, considering also the predicted oversupply of 
cast aluminium alloys. Nevertheless, this requires additional investments in collection, dismantling 
practices and sorting technologies able to separate different alloys to preserve quality. Reducing the 
amount of virgin aluminium can on the one hand decrease GHG emissions and, on the other one, 
increase the independence of this critical and strategic material for EU decarbonisation. Lifetime 
extension practices have similar effects but require additional changes in business models (e.g. 
repair services), the costs and employment of which were not duly accounted for in this study. 

7.2  Potential policy instruments to foster CE in the aluminium sector 

The transition to a circular economy (CE) is a multi-level and multi-stakeholder long-term process 
that requires a well-aligned strategic direction and a carefully designed operational framework. The 
CE could contribute to mitigating resource and environmental constrains in the EU by addressing 
aspects of the climate and resource crisis, enhancing competitiveness in a global environment, 
strengthening the strategic autonomy of the EU, without diminishing the quality of life of EU 
citizens nor shifting environmental burdens elsewhere. This diversity of CE objectives causes 
considerable complexities for policymakers, since multiple policy objectives need to be fulfilled at 
the same time. Since there is no single policy instrument that would be equally suited for all 
problem areas, goals, actors, type of resources, life-cycle stages, etc., a systems perspective would 
be required to prevent problem shifting, conflicting aims and incoherence among policy fields and 
implementation levels (Wilts & O’Brien, 2019). 

It is widely recognised that supporting sustainability transitions, such as the CE transition, requires a 
consistent combination of strategies and accompanying instruments in so-called policy mixes 
(Sovacool et al., 2025). Within a policy mix, the synergies between various policy instruments are 
intended to address the complex challenges of CE more holistically, leveraging their strengths to 
target different aspects of a problem at different levels. A coordinated policy mix approach aims at 
mitigating potential conflicts or redundancy between policy instruments, ensuring coverage of 
various dimensions of the issue and enhancing the overall effectiveness of the applied policies 
(Howlett & Rayner, 2007; Rogge & Reichardt, 2016).  

The EU aluminium sector consists of an extensive multi-actor value chain, extending both within 
and outside the EU, and involving economic and societal actors at regional, national and 
international levels. It becomes evident that a CE transition of the sector needs a systemic (holistic) 
policy approach, designing a balanced policy mix targeting all CE clusters and actors involved.   

7.2.1  Policy mixing principles  

The three main reasons for adopting a policy mix approach can be summarised as multiple market 
failures (including transaction costs and information asymmetries), governance constraints, and 
behavioural factors (Bouma et al., 2019). Therefore, an effective design of a policy mix intervention 
requires a good understanding of the market, governance and behavioural failures that need to be 

93 

 
tackled for the intended policy mix to have the desired effect. A policy mix approach should exhibit 
certain design characteristics to achieve a high level of efficacy in addressing the targeted 
problem(s). These characteristics are consistency, coherence, comprehensiveness, credibility, and 
congruence (Howlett & Rayner, 2007; Rogge & Reichardt, 2016). 

Consistency refers to ‘how well the elements of the policy mix are aligned with each over, thereby 
contributing to the achievement of policy objectives’ (Rogge & Reichardt, 2016). Single instruments 
in a policy mix can be considered as consistent when they function synergistically to support a 
policy objective. This implies the elimination of contradictions between instruments and the 
existence of synergies within and between the elements of the policy mix. While consistency 
focuses on the contents of the mix, the term coherence focuses on the design policy process 
dimension (OECD, 2016). Consistency and coherence in a policy mix can be fostered by combining 
primary with supportive instruments. Primary instruments are mainly used to achieve a defined 
policy objective. Supportive instruments are used to minimise or mitigate unintended negative side 
effects of primary measures and, therefore, to increase their acceptability and feasibility (Rogge & 
Reichardt, 2016). Credibility refers to the extent to which a policy mix is considered reliable, which 
may be affected by several factors, such as the commitment from political actors, the consistency 
of the instrument mix, and the competence of the implementing authorities (Rogge & Reichardt, 
2016). Comprehensiveness refers to the ‘…the degree to which the instrument mix addresses all 
market, system and institutional failures, including barriers and bottlenecks’ (Rogge & Reichardt, 
2016). Congruence among instruments and (socio-economic) goals means the compatibility 
between the strategic objective of a policy goal and the design of the policy mix to achieve the 
intended outcomes (Howlett & Rayner, 2007) 

In order to effectively respond to the specific context of a policy vision in a long-term perspective, 
the development of policy mixes needs to consider the following (Howlett & Rayner, 2007; Rogge & 
Reichardt, 2016): 

•  The full range of policy instruments (types of instruments, see Section 7.2.3);  

•  The full cost of policies (including implementation, transaction and compliance costs); 

•  Avoiding negative interactions between single policies (i.e. instruments already in place and 

new ones) and emphasising mutual benefits and potential with existing policies; 

•  The potentially negative side-effects of the instruments on the target groups, e.g. issues of 

competitiveness in industry or adverse effects on lower income households; 

•  The political processes during the design and implementation of the mix. 

A comprehensive policy mix needs to go beyond just combining the individual policy instruments 
statically. In a policy mix, the long-term qualitative strategic objectives and short- to mid-term 
quantitative targets should be combined in a time-dynamic approach to effectively achieve the 
overall goals. A policy mix design also requires forward-looking strategic planning (foresight), by 
relating different policy instruments in a time sequence that enables the optimisation of synergistic 
effects while minimising the unintended negative side effects (Ekvall et al., 2016). 

Moreover, the literature on policy mixes for sustainability transition processes emphasise that 
change does not materialise by fostering solely new solutions and innovations, but also by putting 
pressure on incumbents and the established socio-technical configurations, e.g. through phasing out 
existing measures that reinforce the status quo (Kivimaa & Kern, 2016; Sovacool et al., 2025).  

94 

 
7.2.2  Long-term policymaking considerations  

Socio-economic transition processes take a long time and can have variable directions and 
outcomes, so a necessary strategic vision and appropriate policy instruments must be formulated in 
advance. The transition to a CE is expected to develop gradually over the following years and the 
impact assessment modelling in this study foresees anticipated outcomes by the year 2050. In this 
context, the notion of long-term policy design is coming to prominence. Fundamental elements to 
consider in long-term policy design include: 1) achieving extended coordination between the actors 
involved; 2) taking a holistic view on socio-economic and (parallel) political developments; 3) 
preventing unpredictable outcomes; 4) sustaining a vision for the long-term goals of the policy, 
without suppressing diversity; and 5) retaining adaptability towards the complex dynamics of 
change. In order to constructively deal with all these issues in long-term policy guidance, within a 
short-term context, most approaches to strategic planning pragmatically combine top-down and 
bottom-up considerations (Voß et al., 2009).  

7.2.3  Typology of policy instruments  

Policy instruments are the actual tools governments use to implement their policies. Policymakers 
have the option to select from a wide range of instruments to address a certain policy problem and 
achieve a desired outcome. A policy instrument constitutes a steering function and provides 
incentives for achieving a certain policy. Policy instruments can be divided into three types 
(administrative, economic, and informative) in relation to their nature and into two types 
(mandatory, voluntary) concerning their implementation mode (Mont & Dalhammar, 2005; Vedung, 
1998). Other typologies in literature might include more than three categories and distinguish 
between, for example, voluntary agreements and self-regulation as separate categories 
(Gunningham et al., 1998). However, the categorisation that is most consistent among policy 
sources is presented in Table 6), including also a few indicative examples of related measures per 
category.  

Table 6. Policy instruments typology and examples of measures 

Mandatory 

Voluntary 

Administrative  e.g. bans, standards, quotas, licences  e.g. standards, agreements between government 

and industry 

Economic 

e.g. taxes, fees, tariffs, subsidies 

e.g. public procurement, loan guarantees, charges 

Informative 

e.g. reporting requirements, labelling, 
education 

e.g. certification schemes, awareness raising 
campaigns, environmental management systems 

Source: JRC elaboration 

A well-defined command and control administrative policy instrument is typically more effective 
than a mere informative instrument, which is mostly applied as a supportive instrument. Moreover, 
the potential effect of a mandatory policy instrument, under monitoring obligations by a competent 
authority, which is applied throughout a sector or a national context, can be more effective than a 
voluntary agreement between industry players and a central government authority. In Figure 39, 
the potential effectiveness of policy instruments according to their typology is illustrated.   

95 

 
 
Figure 39. Anticipated effectiveness (horizontal line) of policy instruments by type 

Source: Own illustration 

7.2.4  Life-cycle approach in policymaking 

To reduce the risk of burden shifting from one life-cycle stage to another, a wide systems 
perspective is required in the designing of a policy mix (Ekvall et al., 2016). This implies that the 
policy mix would target every stage of the life cycle of a process or product to avoid potential 
counteractive or unintended effects of the selected instruments upstream or downstream the 
supply chain, while in the meantime striving to achieve synergies between the various policy 
instruments to reinforce the anticipated outcome of the policy mix (Dalhammar, 2015). Therefore, 
in the context of CE, a carefully designed policy mix has the potential to result in a strengthened 
policy framework with a significant influence towards saving resources and increasing the mass and 
quality of circulated materials and products in the economy. The direct effects of the policy mix 
would be expected to have higher impact than what a mere combination of the individual policy 
instruments would be able to achieve, and would target proportionately all life cycle stages (Milios, 
2018). Conceptually, a CE policy mix would include a set of complimentary policy instruments 
applied across the life cycle of materials/products. 

7.3  Policy suggestions to activate the CE levers studied 

Table 7 presents a summary of the levers, their quantified effects as reduction of the primary 
material demand and of climate change mitigation, along with a list of potential policy instruments 
that can ‘activate’ the lever. The instruments suggested are also displayed in Figure 40 based on 
their positioning along the aluminium supply chain. 

In total, a mix of eleven policy instruments is suggested, on top of existing policy instruments, to 
‘activate’ the proposed levers investigated in the study. The instruments proposed cover all stages 
of the material life cycle, except for distribution (not shown for simplicity), ore extraction and 
aluminium production. It is important to note that advancements in smelting technologies and the 
use of inert anodes are not herein investigated as these interventions mainly pertain to 
decarbonisation actions rather than to CE. Nevertheless, research and investments at the production 

96 

 
 
stage are certainly needed to lower direct and indirect emissions of the sector. These policy 
measures would therefore fall into the stage of aluminium production. 

All instruments suggested are mandatory, except for three voluntary ones, namely the use of 
Horizon Europe, the implementation of financial investments to support the development of new 
and advanced sorting technologies, and the employment of subsidies for recyclers to foster high-
quality recycling. The instruments proposed below are further discussed in Sections 7.3.1-to-7.3.3; 
notice that increased burdens (either economic or administrative) for industry and/or consumers are 
not assessed herein. 

Figure 40. Summary of the policy instruments suggested to ‘activate’ the levers analysed in the study and 
their position along the material supply chain 

Note: The distribution stage is not shown for simplicity. DRS: Deposit Return System; EPR: Extended Producer 
Responsibility; GPP: Green Public Procurement; M: Mandatory; V: Voluntary 

Source: Own illustration

97 

Table 7. List of policy instruments that can trigger the effects of the CE levers 

Lever 

R-cluster 

Reduce 

Pre-consumer 
scrap reduction 

Primary 
material 
demand & GHG 
reduction 
-0.1 Mt (2%)29 

1 Mt CO2-eq (1%) 

Sector affected 

Existing policy instruments? 
What 

All sectors are 
affected, i.e. building, 
transport 

No specific instrument in 
place to optimise 
fabrication and 
manufacturing processes. 

Type of 
instrument & 
Lifecycle stage 
- 

Reuse 

Design for 
reusability 

0.4 Mt (10%) 

Building 

11 Mt CO2-eq 
(11%) 

Yes, the Green Public 
Procurement (GPP) for 
construction products 
already encompasses 
criteria on design for 
reusability. 

Type: 
Admin/Econ; 
Voluntary 
Lifecycle stage: 
Manufacture, Use 

Policy instruments suggested? 
What 

Type of instrument 
& Lifecycle stage 

Type: 
Econ; Voluntary 
Lifecycle stage: 
Fabrication, 
Manufacture 
Type: 
Admin/Econ; 
Mandatory 
Lifecycle stage: 
Manufacture, Use 

Type: Inform; 
Mandatory 
Lifecycle stage: 
Manufacture, Use 

Horizon Europe can support 
research in the 
optimisation of fabrication 
and manufacturing 
processes. 

Make GPP requirements on 
design for reusability of 
construction products 
mandatory for aluminium-
based construction 
products (e.g. windows). 

The Construction Product 
Regulation (CPR) 
(Regulation EU 2024/3110) 
is mainly targeting cement, 
steel, and insulation 
materials. Requirements 
for design for reusability 
and reparability should 
also be established for 
aluminium-based 
construction products. 

29 In this lever, the amount of primary material demand increases compared to the corresponding amount in the Baseline 2050 scenario. Despite this being counterintuitive, this is 

mathematically correct, and it is influenced by the assumptions made in the mass flow analysis to close the mass balance. Indeed, the import has been re-proportioned according to 
the shares in the Baseline 2050 scenario. 

98 

 
 
 
 
 
 
 
 
Transport 

Machinery and 
equipment 

Electrical 

Yes, the Ecodesign for 
Sustainable Products 
Regulation (ESPR) is 
expected to deliver on 
requirements for 
products’ reusability with 
respect to aluminium 
intermediate products. 
Yes, the End-of-Life 
Vehicle Regulation 
proposal (amending 
Regulations EU 2018/858 
and 2019/1020 and 
repealing Directives 
2000/53/EC and 
2005/64/EC) already 
addresses the need to 
design vehicles that are 
easy to dismantle, reuse 
and recover. 

Yes, the Ecodesign for 
Sustainable Products 
Regulation (ESPR) is 
expected to deliver on 
requirements for 
products reusability with 
respect to aluminium 
intermediate products. 
Yes, the Ecodesign for 
Sustainable Products 
Regulation (ESPR) is 
expected to deliver on 
requirements for 
products reusability with 
respect to aluminium 
intermediate products. 
Yes, Ecodesign  
for batteries addressing 
design for removability 
and replaceability. 

99 

Type: 
Admin; Mandatory 
Lifecycle stage: 
Fabrication 

. 

Type: 
Admin; Mandatory 
Lifecycle stage: 
Manufacturing, 
End-of-Life 

Type: Admin; 
Mandatory 
Lifecycle stage: 
Fabrication 

Type: Admin; 
Mandatory 
Lifecycle stage: 
Fabrication 

Type: Admin; 
Voluntary 
Lifecycle stage: 
Manufacture, End-
of-Life 

Make Ecodesign 
requirements on design for 
removability and 
replaceability mandatory. 

Type: 
Admin; Mandatory 
Lifecycle stage: 
Manufacture, Use 

 
 
 
 
 
 
 
 
 
 
 
 
 
Type: Admin; 
Mandatory 
Lifecycle stage: 
Fabrication 

Type: Admin; 
Mandatory 
Lifecycle stage: 
Manufacture, End-
of-Life 

Type: Admin/Econ; 
Voluntary 
Lifecycle stage: 
Manufacture, End-
of-Life 

Make GPP requirements on 
design for reusability of 
aluminium-based products 
mandatory. 

Type: 
Admin/Econ; 
Mandatory 
Lifecycle stage: 
Manufacture, Use 

Type: Admin; 
Mandatory 
Lifecycle stage: 
Fabrication 

Consumer durables, 
Other 

Yes, the Ecodesign for 
Sustainable Products 
Regulation (ESPR) is 
expected to deliver on 
requirements for 
products reusability with 
respect to aluminium 
intermediate products. 

Yes, the Waste from 
Electrical and Electronic 
Equipment (WEEE) 
Directive (Directive 
2012/19/EU) supports 
the need for cooperation 
among producers and 
recyclers to facilitate re-
use, dismantling and 
recovery of WEEE, its 
components and 
materials. 
Yes, Green Public 
Procurement (GPP) and 
Ecodesign are in place 
for electronic equipment 
& ICT, household 
appliances and address 
design for reusability and 
reparability. 

Yes, the Ecodesign for 
Sustainable Products 
Regulation (ESPR) is 
expected to deliver on 
requirements for 
products reusability with 
respect to aluminium 
intermediate products. 

100 

 
 
 
 
 
 
 
 
 
 
 
 
 
Remanufacturing 
of automotive 
components 

0.01 Mt (0.2%) 

1 Mt CO2-eq (2%) 

Transport, passenger 
car 

Type: Admin; 
Mandatory 
Lifecycle stage: 
Manufacture, End-
of-Life 

Yes, the End-of-Life 
Vehicle Regulation 
proposal (amending 
Regulations EU 2018/858 
and 2019/1020 and 
repealing Directives 
2000/53/EC and 
2005/64/EC) already 
addresses the need to 
design vehicles that are 
easy to dismantle, reuse 
and recover. 

No, although Green 
Public Procurement (GPP) 
on “Transport” is 
established. 

Type: Admin; 
Voluntary 
Lifecycle stage: 
Manufacturing 

Mandatory GPP 
requirements for 
remanufacturing of vehicle 
parts. 

Type: 
Admin/Econ; 
Mandatory 
Lifecycle stage: 
Manufacture, Use 

Recover 

Improved waste 
collection and 
sorting 

0.3 Mt (9%) 

2 Mt CO2-eq (2%) 

All sectors when 
reaching End-of-Life 

Type: Admin; 
Mandatory 
Lifecycle stage: 
Fabrication 

Type: Admin/Econ; 
Mandatory 
Lifecycle stage: 
End-of-Life 

Extension of EPR to further 
aluminium-containing 
products (e.g., windows). 

Type: Admin/Econ; 
Mandatory 
Lifecycle stage: 
Manufacture, End-of-
Life 

Yes, the Ecodesign for 
Sustainable Products 
Regulation (ESPR) is 
expected to deliver on 
requirements for 
products reusability with 
respect to aluminium 
intermediate products. 
Yes, partially by the 
targets on recycling 
included in the Waste 
Framework Directive 
(WFD) (Directive EU 
2018/851), Packaging 
and Packaging Waste 
Regulation (PPWR) 
(Regulation EU 2025/40) 
that also encompasses 
the establishment of 
mandatory Deposit 
Return System (DRS) and 
the use of matching 
labels for products and 
bins. Both the WFD and 

101 

 
 
 
 
 
 
 
 
 
 
Improved waste 
collection and 
sorting, and 
scrap quality 

0.3 Mt (9%) 

3 Mt CO2-eq (3%) 

All sectors when 
reaching End-of-Life 

Introduction of waste 
sorting labels into 
packaging and aluminium-
based products. 

Financial incentives for the 
deployment of new and 
advanced sorting 
technologies. 

Horizon Europe can support 
research in the 
development of new 
sorting technologies. 
Implementation of eco-
modulated fees linked to 
output quality via closed-
loop recycling for 
packaging and building 
components. 

Type: Inform; 
Mandatory 
Lifecycle stage: End-
of-Life 

Type: Econ; Voluntary 
Lifecycle stage: End-
of-Life 

Type: 
Econ; Voluntary 
Lifecycle stage: 
End-of-Life 
Type: Econ; Mandatory 
Lifecycle stage: End-
of-Life 

Type: Admin/Econ; 
Mandatory 
Lifecycle stage: 
End-of-Life 

PPWR encompass the 
establishment of 
Extended Producer 
Responsibility (EPR) 
schemes. 

Yes, partially by the 
targets on recycling 
included in the Waste 
Framework Directive 
(WFD) (Directive EU 
2018/851), Packaging 
and Packaging Waste 
Regulation (PPWR) 
(Regulation EU 2025/40) 
that also encompasses 
the establishment of 
mandatory Deposit 
Return System (DRS) and 
the use of matching 
labels for products and 
bins. Both the WFD and 
PPWR encompass the 
establishment of 
Extended Producer 
Responsibility (EPR) 
schemes. 

102 

 
 
 
 
 
 
 
 
Financial incentives for the 
deployment of new and 
advanced sorting 
technologies. 

Subsidise the recycle 
industry to foster 
preservation of quality. 

Horizon Europe can support 
research in the 
development of new 
sorting technologies. 

Implementation of eco-
modulated fees into the 
EPR schemes to reduce the 
fees for easy to recycle 
packaging. 

Mandate existing EPR 
schemes for packaging to 
implement DRS in all MS. 

Type: Admin/Econ; 
Voluntary 
Lifecycle stage: End-
of-Life 

Type: Econ; Voluntary 
Lifecycle stage: End-
of-Life 

Type: 
Econ; Voluntary 
Lifecycle stage: 
End-of-Life 
Type: Admin/Econ; 
Mandatory 
Lifecycle stage: 
Manufacture, End-of-
Life 

Type: Admin; 
Mandatory 
Lifecycle stage: End-
of-Life 

EU-wide ban of mixed 
residual waste to be 
landfilled without a 
mandatory pre-sorting 
stage to recover aluminium 
that can be recycled. 

Type: Admin; 
Mandatory 
Lifecycle stage: End-
of-Life 

Design for 
packaging 
recoverability 
and recycling 

0.01 Mt (0.3%) 

0.1 Mt CO2-eq 
(0.1%) 

Packaging when 
reaching End-of-Life 

Expansion of 
Deposit Return 
Systems 

0.2 Mt (4%) 

3 Mt CO2-eq (4%) 

Packaging when 
reaching End-of-Life 

Recovery from 
mixed waste 

1 Mt (14%) 

Packaging when 
reaching End-of-Life 

2 Mt CO2-eq (2%) 

Type: Admin; 
Mandatory 
Lifecycle stage: 
Manufacture, End-
of-Life 

Type: Admin; 
Mandatory 
Lifecycle stage: 
End-of-Life 

Type: Admin; 
Mandatory 
Lifecycle stage: 
End-of-Life 

Yes, the Packaging and 
Packaging Waste 
Regulation (PPWR) 
(Regulation EU 2025/40) 
establishes that all 
packaging on the market 
shall be recyclable. 
Yes, the Packaging and 
Packaging Waste 
Regulation (PPWR) 
(Regulation EU 2025/40) 
establishes the 
implementation of 
deposit return systems. 
Yes, partially via the 
requirement of attaining 
municipal waste recycling 
targets established 
within the Waste 
Framework Directive 
(WFD) (Directive EU 
2018/851), and attaining 
the targets on limiting 
landfilling of the Landfill 
Directive (Directive EU 
2018/850). 

103 

 
 
 
 
 
 
 
 
 
 
 
 
Note: The reduction in primary material demand and GHG reduction are expressed relative to the baseline in year 2050, taken as the reference (i.e. values should be 
interpreted as the additional savings incurred by the levers relative to the baseline in 2050) 

Source: JRC elaboration

104 

 
7.3.1  Reduce 

Background: The manufacturing of aluminium-containing products and, especially, the fabrication 
of aluminium semis are highly optimised processes. Based on discussions with the industry, limited 
improvements are expected in further optimisations of such processes to reduce losses.  

Policy instrument in place: No specific instrument exists to optimise the fabrication and 
manufacturing processes as the industry already strives to fully exploit such a valuable material. 

Policy instruments and/or research (further) suggested: Improvements in the fabrication and 
manufacture process can be achieved by supporting research and development (R&D) in the sector. 
This can be supported via, for example, the Horizon Europe programme. 

Increased burdens for industry or consumers: None are suggested as this requires further 
investigation that is not herein performed.  

7.3.2  Reuse 

Background: The potential to extend the lifetime of aluminium contained in buildings and in the 
transport sector is debated due to possible trade-offs between lifetime extension and efficiency 
loss (e.g. performance of windows to ensure energy efficient buildings) and changes in the vehicle 
fleet (i.e. transition from combustion engines to electric vehicles). Further, based on discussions with 
the industry, repairing of vehicles represents a niche market as of today and it is not expected to 
expand in the future. Nevertheless, better design of products ensuring easier remanufacturing, 
disassembly, and replacement can reduce the demand of virgin aluminium and decrease 
environmental impacts, as supported by our analysis. 

Policy instruments in place: For buildings, Green Public Procurement (GPP) already encompasses 
criteria on design for reusability for construction products. For vehicles, the End-of-Life Vehicles 
(ELV) Regulation proposal addresses the need to design vehicles that are easy to dismantle, reuse 
and recover. Further, transport and road vehicles are also addressed via GPP, although criteria 
addressing the extension of lifetime of vehicles are not included. Other transport sectors 
(aerospace, railway cars, etc.) are not specifically targeted by any legislation. Within the electrical 
sector, batteries are herein included and Ecodesign requirements addressing design for removability 
and replaceability exist. With respect to consumer durables and other, the Waste from Electrical and 
Electronic Equipment (WEEE) Directive is in place and supports the need for cooperation among 
producers and recyclers to facilitate reuse, dismantling and recovery of WEEE, its components and 
materials. Further, GPP addressing design for reusability and reparability exists for electronic 
equipment and ICT, and household appliances. Machinery and equipment are not targeted with a 
specific policy intervention. Overall, across all sectors, the Ecodesign for Sustainable Products 
Regulation (ESPR) is in place and is expected to deliver on requirements for products’ reusability 
with respect to aluminium intermediate products that are fed into all analysed sectors. 

Policy instruments and/or research (further) suggested: It is suggested to establish 
mandatory GPP requirements on design for reusability of aluminium-based products (e.g. windows). 
Further, additional requirements on design for reusability and reparability for aluminium in 
construction products could be implemented, for example, via the Construction Product Regulation 
(CPR). The CPR mainly targets cement, steel, and insulation materials while setting standards and 
introducing measure on Digital Product Passport, CE marking needs reflecting both technical 
performance and environmental impacts, and reporting on CO2 emissions and energy use; yet its 

105 

 
scope and requirements could be expanded to include aluminium based products. With respect to 
electrical, vehicles, consumer durables and others, the existing GPP and eco-design requirements 
should be made mandatory specifically addressing design for reusability and repairability.  

Increased burdens for industry or consumers: Despite not being quantitatively assessed in our 
analysis, the implementation of ESPR might lead to additional administrative and compliance 
burdens for producers. 

7.3.3  Recover 

Background: despite being highly recycled, different alloys are frequently mixed together 
restricting their recycling into cast aluminium alloys only. Ensuring closed loop recycling, especially 
for wrought aluminium alloys, represents a challenge that requires better collection and more 
refined sorting technologies. Separate collection of, for example, aluminium-containing construction 
products could be now justified by emerging consistent flows that, ultimately, could be recycled 
more closed loop. Further, design of products targeting material identification, disassembly, and 
recycling also represent another challenge that needs to be addressed to increase recycling and its 
quality. Finally, missorted aluminium could be additionally recovered from sorting facilities prior 
final treatment. 

Policy instruments in place: the Packaging and Packaging Waste Regulation (PPWR), the Waste 
Framework Directive (WFD), and the Landfill Directive are in place and set targets on recycling and 
on limits of municipal waste to be landfilled. None of these instruments targets improving quality of 
recycling. 

Policy instruments and/or research (further) suggested: the Horizon Europe program could 
support R&D of new sorting technologies able to sort waste based on, for example, alloys to 
enhance the quantity of material recycled and its quality. The quality aspect could also be 
addressed via, for example, introducing financial incentives for the deployment of new sorting 
technologies or by subsidising the recycling industry to preserve quality. Extended Producer 
Responsibility (EPR) schemes introduced in the WFD could be extended to, for example, building 
components with the introduction of eco-modulated fees linked to output quality via closed-loop 
recycling. Within EPR schemes, eco-modulated fees could also be implemented to reduce the fees 
for easy to recycle packaging supporting design for recycling. Further, EPR schemes could also be 
used for implementing Deposit Return Systems (DRS) in all Member States. Finally, within existing 
policies, such as the WFD and the Landfill Directive, an EU-wide ban of mixed residual waste to be 
landfilled without a mandatory pre-sorting stage to recover aluminium to be recycled could also be 
mandated. 

Increased burdens for industry or consumers: despite not being quantitatively assessed in our 
assessment, mandating DRS in countries where a separate collection system does exist may incur 
transition costs, especially for Producer Responsibility Organisations. Finally, centralised sorting 
plants would require financial support to become economically viable, which could have implications 
for consumers having to pay higher municipal waste management costs.  

106 

 
 
 
8  Conclusions  

8.1  Key scientific findings 

This study has investigated the potential of circular economy (CE) levers to mitigate climate change, 
reduce resource dependency, and promote socio-economic benefits in the EU aluminium sector. The 
results indicate that the implementation of CE levers can lead to significant reductions in GHG 
emissions, primary material demand, and waste generation. The study highlights the importance of 
a holistic approach to CE, considering the entire life cycle of aluminium and the interactions 
between different stakeholders and sectors. 

The analysis suggests that the CE levers studied can achieve an additional impact reduction on 
Climate Change of between 12-14 Mt CO2-eq. annually by 2050, on top of what would be achieved 
by decarbonisation of the energy system only. The results also show that CE can lead to a decrease 
in the EU demand for bauxite, which is the precursor of aluminium, as well as reduction in energy 
demand. Implementing the CE levers at once would also positively affect the EU’s trade balance, 
which is estimated to increase by approx. EUR 3.6 billion annually while reducing imports from 
outside the EU. However, these changes also come with observed decreases in Gross Values Added, 
Gross Domestic Product, and employment. While these are expected because of the reduction in 
material demanded by the industry, it emphasizes the need for further research on the CE 
implications on the service (e.g. repair) sectors. In this study, a simplified but commonly used 
approach was followed, whereby the reductions in economic input demanded by a sector were 
diverted to repair/services and innovation, but this reallocation of resources may be very different 
and deserves further investigation. 

The study emphasizes the importance of a consistent policy mix approach, combining different 
policy instruments to address the complex challenges of CE. The study suggests a range of policy 
instruments to activate the CE levers, including mandatory and voluntary instruments, such as 
administrative, economic, and informative instruments. The policy instruments suggested 
encompass measures to optimise production, extend lifetime, recover materials, and enhance 
recycling quality.  

In conclusion, this study demonstrates the potential of CE to contribute to a more sustainable and 
resource-efficient aluminium sector in the EU. The implementation of CE levers can lead to 
significant environmental benefits, but it requires a holistic approach, careful policy design, and 
coordination among stakeholders. The study's findings and recommendations can inform 
policymaking and support the transition to a more circular and sustainable economy. 

8.2  Key policy recommendations 

The following policy recommendations are designed to support the implementation of the CE levers 
in the aluminium sector, with the goal of reducing primary aluminium production and use, 
promoting sustainable use of materials, and minimising waste. 

•  Reduce (reduce production of pre-consumer scrap optimising the fabrication and 

manufacturing process): 

o  Provide financial instruments (via, e.g., Horizon Europe) to the industry to support 
research and development in the sector to optimise production processes by 
increasing the yield while minimising pre-consumer waste. 

107 

 
•  Reuse (extending lifetime of products): 

o 

o 

o 

o 

Implement mandatory Green Public Procurement criteria for (public) building to 
promote reusability of aluminium-containing construction products (e.g. windows).  

Introduce regulatory requirements on reusability and reparability for aluminium-
based construction products. 

Introduce mandatory Ecodesign requirements on design for removability and 
replaceability for aluminium-based electrical products.  

Implement Green Public Procurement criteria for transport to promote 
remanufacturing of vehicle parts.  

•  Recover (improving recycling rate and quality): 

o  Extend the scope of Extended Producer Responsibility to additional aluminium-

containing products (e.g. windows) and implement the use of ecomodulated fees to 
reduce the fees for easy to recycle packaging and building components. 

o  Provide financial and administrative instruments to support advanced sorting 
technologies and ensure high-quality recycled scrap and subsidise the recycle 
industry to foster preservation of quality. 

o 

Introduce mandatory Deposit Return Systems and foster sorting, notably by 
deployment of centralised sorting plants for mixed waste to increase material 
recovery rates.  

o  Ban of mixed residual waste to be transferred for incineration or landfilling without 

obligatory pre-sorting. 

8.3  Future Research Directions 

The following limitations and challenges of this study could be addressed by further research: 

•  Economic growth of service sector: this study takes simplified assumptions on the effects 
of CE levers on service sectors, such as repair or remanufacturing. We encourage to analyse 
the implications of CE levers on repair and similar service activities as well as research and 
innovation activities in detail. This may also be done by employing Input-Output analysis or 
dynamic  macroeconomic  models  such  as  FIDELIO  (or  similar).  However,  the  focus  should 
specifically  be  on  the  socio-economic  consequences  in  service  sectors,  in  terms  of  trade, 
growth, and employment change, as a response to selected CE lever implementation. 

•  Strengthening integration of bottom-up and top-down methos: this study strengthens 
the  link  between  bottom-up  (MFA,  LCA)  and  top-down  (EEIOA,  dynamic  macroeconomic 
modelling) tools in several ways, notably by disaggregating the Eurostat compliant FIGAROE3 
Input-Output tables in accordance with detailed sector-specific material flow analyses and 
life cycle datasets. However, while the life cycle inventories from the LCA were used as input 
for  the  EEIOA  for  steel,  aluminium,  cement/concrete  sector  (and  to  some  extent,  also  for 
plastic),  ideally,  the  assessment  would  integrate  the  four  sectors  into  one  dynamic 
macroeconomic model in FIDELO. 

108 

 
•  Prospective assessment on demand-shocks: this study is the first that the authors are 
aware  of  to  illustrate  the  trade  effects  of  CE  levers.  However,  in  view  of  the  ongoing 
geopolitical landscape characterised by tariffs and trade wars, we encourage research looking 
at extreme scenarios identified through foresight tools, e.g. in terms of drastic changes to 
trade relationships, ‘buy EU’ requirements, GDP growth, or energy scenarios (e.g. use FIDELIO 
to model the selective application of tariffs to distort current market prices, affecting trade 
partnerships and final consumer goods prices). 

• 

Implementation of the levers via policy: this study focuses on the impacts of CE levers 
and does not focus on how to concretely implement them in policy or on the transition 
pathways required to achieve a full-fledge CE implementation. The former is a typical task 
of impact assessment supporting studies, including identifying appropriate policy measures 
(and options) to enable the levers, assessing administrative costs (on top of compliance 
ones, which are partially captured in this study), and distributional effects across affected 
income groups. Studying transitional pathways requires instead dedicated foresight 
analyses of exploratory nature. 

109 

 
 
 
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List of abbreviations and definitions  

Abbreviations 

Definitions 

ACE50 

BAU 

BSL50 

CAPEX 

CBAM 

CCS 

CE 

CEAP 

CFF 

CGE 

CMUR 

CPR 

CRM 

CTC50 

DRS 

EC 

ECO50 

EEIOA 

EEMRIO 

EF 

Ambitious Circular Economy scenario (2050) 

Business as Usual 

Baseline scenario (2050) 

Capital Expenditures 

Carbon Border Adjustment Mechanism 

Carbon Capture and Storage 

Circular Economy 

Circular Economy Action Plan 

Circular Footprint Formula 

Computable General Equilibrium 

Circular Material Use Rate 

Construction Products Regulation 

Critical Raw Material 

Compliance with selected Targets on Circular Economy 
scenario (2050) 

Deposit Return System 

European Commission 

Eco-States scenario (2050) 

Environmentally Extended Input-Output Analysis 

Environmentally Extended Multi Regional Input-Output 

Environmental Footprint 

120 

 
Abbreviations 

Definitions 

eLCC 

EPR 

ESPR 

ETS 

EU 

feLCC 

GBB50 

GDP 

GECO 

GEW50 

GHG 

GPP 

GRO50 

GTC50 

GVA 

IAM 

IOA 

LCA 

LCC 

ME 

MFA 

Environmental Life Cycle Costing 

Extended Producer Responsibility 

Ecodesign for Sustainable Products Regulation 

Emissions Trading System 

European Union 

Full Environmental Life Cycle Costing 

Green Business Boom scenario (2050) 

Gross Domestic Product  

Global Energy and Climate Outlook 

Glocal Eco-World scenario (2050) 

Greenhouse Gas 

Green Public Procurement 

Growth-oriented baseline scenario (2050) 

Greening through Crisis scenario (2050) 

Gross Value Added 

Integrated Assessment Model 

Input-Output Analysis 

Life Cycle Assessment 

Life Cycle Costing 

Macroeconomic analysis 

Material Flow Analysis 

MREEIO 

Multi Regional Environmentally Extended Input Output 

121 

 
Abbreviations 

Definitions 

Mt 

NDC-LTS 

OPEX 

PJ 

PPWR 

Megatonne 

Nationally Determined Contribution - Long-Term Strategies 

Operating costs 

Petajoule 

Packaging and Packaging Waste Regulation 

ROW/RoW 

Rest of the World 

SHR50 

SME 

STQ21 

SUT 

WEEE 

WFD 

Shrink-oriented baseline scenario (2050) 

Small and medium-sized enterprises 

Status Quo scenario (2021) 

Supply and Use Tables 

Waste from Electrical and Electronic Equipment 

Waste Framework Directive 

122 

 
 
 
List of figures  

Figure 1. Representation of the aluminium supply chain .......................................................................................... 10 

Figure 2. Overview of CE strategies, separated into narrowing, slowing and closing resource flows
 ....................................................................................................................................................................................................................... 15 

Figure 3. Conceptualisation of circularity clusters into Reduce, Reuse and Recover ................................. 17 

Figure 4. Graphical representation of the research design applied in the study ......................................... 21 

Figure 5. Visualisation of the general aluminium supply chain stages included in the material flow 
analysis ..................................................................................................................................................................................................... 25 

Figure 6. Material system in the Ambitious Circular Economy (ACE50) scenario in the aluminium 
sector with first order effects of changes induced by the levers ........................................................................... 29 

Figure 7. Mass flow analysis relative to the Status Quo scenario (STQ21) .................................................... 32 

Figure 8. Mass flow analysis relative to the Baseline scenario (BSL50) .......................................................... 33 

Figure 9. Mass flow analysis relative to the Compliance with selected Targets on Circular Economy 
scenario (CTC50) ................................................................................................................................................................................. 34 

Figure 10. Mass flow analysis relative to the Ambitious Circular Economy scenario (ACE50) ............ 35 

Figure 11. Simplified illustration of the system boundary of the system under assessment and 
main processes included ................................................................................................................................................................. 38 

Figure 12. Four scenarios leading towards a sustainable Europe 2050........................................................... 46 

Figure 13. Impact of circular economy clusters on material flows normalised to scale 0-1 ............... 48 

Figure 14. Demand for primary material across the scenarios investigated and Circular Material 
Use Rate  .................................................................................................................................................................................................. 50 

Figure 15. Share of wrought aluminium and cast aluminium alloys out of the total recyclates 
obtained ................................................................................................................................................................................................... 51 

Figure 16. Results obtained for Climate Change expressed as Mt CO2-eq per entire system per year
 ....................................................................................................................................................................................................................... 53 

Figure 17. Results obtained for Environmental Life Cycle Cost expressed as billion EUR per entire 
system per year ................................................................................................................................................................................... 54 

Figure 18. Results obtained for Full Environmental Life Cycle Cost expressed as billion EUR per 
entire system per year ..................................................................................................................................................................... 55 

Figure 19. Comparison of the impacts obtained for Climate Change (expressed as Mt CO2-eq. for 
the entire system per year) when applying the GECO NDC-LTS and Reference scenarios ...................... 56 

Figure 20. Comparison of the impacts obtained for Climate Change (expressed as Mt CO2-eq. for 
the entire system per year) when applying A=0 (default), A=0.2, and A=0 considering marginal 
market suppliers .................................................................................................................................................................................. 57 

123 

 
Figure 21. Comparison of the impact of the scenarios assessed on the Climate Change category 
when applying different background conditions than Business-as-Usual (BAU) expressed as Mt CO2-
eq. for the entire system per year ............................................................................................................................................. 58 

Figure 22. Workflow from Material Flow Analysis, Life Cycle Inventory, to (prospective) Input-
Output Analysis .................................................................................................................................................................................... 62 

Figure 23. On-site GHG emissions in aluminium industries and changes in total GHG emissions for 
the CTC50 and ACE50 scenarios relative to the BSL50 scenario ........................................................................... 65 

Figure 24. Decomposition of the changes between the ACE50 and the BSL50 scenarios relative to 
the net reductions in GHG emissions ....................................................................................................................................... 67 

Figure 25. Bauxite inputs to aluminium industries and changes in bauxite requirements for the 
CTC50 and the ACE50 scenarios compared to the BSL50 scenario ...................................................................... 68 

Figure 26. Decomposition of the changes in bauxite requirements to the EU aluminium industry 
between the ACE50 and the BSL50 scenario ..................................................................................................................... 70 

Figure 27. Changes relative to the total economy in Employment and GDP, and changes in the 
employment of the aluminium industry in the ACE50 scenario compared to the BSL50 scenario..... 72 

Figure 28. Overview over prospective macroeconomic CE studies based on Aguilar-Hernandez et al. 
(2021) ........................................................................................................................................................................................................ 74 

Figure 29. Implementation of the energy transition in the BSL50 scenario .................................................. 77 

Figure 30. Implementation of selected policy targets in the CTC50 scenario ............................................... 78 

Figure 31. Implementation of CE levers in the ACE50 scenario ............................................................................ 79 

Figure 32. Absolute and relative change in GDP in EU and non-EU country .................................................. 81 

Figure 33. Absolute and relative change in value added by industry in EU .................................................... 81 

Figure 34. Relative and absolute changes in trade balance by country, and imports and exports by 
commodity .............................................................................................................................................................................................. 84 

Figure 35. Relative and absolute changes in EU employment by country and industry.......................... 86 

Figure 36. Greenhouse gas emission savings for the aluminium sector in the ACE50 scenario and 
breakdown into the Reduce, Reuse, and Recover clusters ........................................................................................... 89 

Figure 37. Effects of CE levers with respect to the BSL50 scenario on resource and trade 
dependency for the aluminium sector .................................................................................................................................... 91 

Figure 38. Effects of the CE levers relative to the BSL50 scenario on GDP and employment for the 
aluminium sector ................................................................................................................................................................................ 92 

Figure 39. Anticipated effectiveness (horizontal line) of policy instruments by type ............................... 96 

Figure 40. Summary of the policy instruments suggested to ‘activate’ the levers analysed in the 
study and their position along the material supply chain ........................................................................................... 97 

124 

 
 
Figure A 1. Schematic presentation of an inter-country supply table, detailing two geographical 
areas (EU27 and non-EU27) ...................................................................................................................................................... 155 

Figure A 2. Schematic presentation of an inter-country use table, detailing two geographical 
areas (EU27 and non-EU27) ...................................................................................................................................................... 156 

Figure A . Details on the value added extension table ............................................................................................. 168 

Figure A . Details on the employment extension table ............................................................................................ 169 

Figure A . Details on the strategic autonomy extension table ............................................................................. 169 

Figure A 6. First module of FIDELO: Static Supply-module ................................................................................... 171 

Figure A 7. Second module of FIDELIO: Investment dynamics ........................................................................... 172 

Figure A 8. Third module of FIDELIO: Consumption dynamics ............................................................................ 172 

Figure A 9. Fourth module of FIDELIO: Full model with price dynamics ........................................................ 173 

125 

 
 
List of tables  

Table 1. CE policy targets considered in the scenario ‘CTC50’................................................................................ 19 

Table 2. Illustration of the scope, strength and limitations of the methods applied, and their 
complementarities. DMM: dynamic macroeconomic modelling. EEIOA: Environmentally Extended 
Input-Output Analysis; LCA: Life Cycle Assessment; LCC: Life Cycle Costing ................................................... 22 

Table 3. Overview and categorization of circularity levers in the EU aluminium applied in the 
Ambitious Circular Economy scenario ..................................................................................................................................... 27 

Table 4. Changes in selected products demand in ACE50 in comparison to BSL50 for the total EU 
economy ................................................................................................................................................................................................... 70 

Table 5. Changes in selected products demand in ACE50 in comparison to BSL50 for the aluminium 
industries ................................................................................................................................................................................................. 71 

Table 6. Policy instruments typology and examples of measures ....................................................................... 95 

Table 7. List of policy instruments that can trigger the effects of the CE levers ........................................ 98 

Table A 1. List of literature sources for the identification of circularity levers for aluminium in the 
EU .............................................................................................................................................................................................................. 128 

Table A 2. Modelling implementation of the lever focused on pre-consumer scrap reduction ........ 132 

Table A 3. Modelling implementation of the lever focused on design for reusability ........................... 133 

Table A 4. Modelling implementation of the lever focused on remanufacturing of automotive 
components ......................................................................................................................................................................................... 134 

Table A 5. Modelling implementation of the lever focused on improved waste sorting ...................... 135 

Table A 6. Modelling implementation of the lever focused on improved waste sorting and scrap 
quality ..................................................................................................................................................................................................... 136 

Table A 7. Modelling implementation of the lever focused on design for packaging recoverability 
and recycling ....................................................................................................................................................................................... 137 

Table A 8. Modelling implementation of the levers focused on expansion of deposit return systems
 .................................................................................................................................................................................................................... 138 

Table A 9. Modelling implementation of the lever focused on recovery from mixed waste.............. 139 

Table A 10. Razor matrix with interrelationships of Circular Economy (CE) levers in the Ambitious 
Circular Economy (ACE50) scenario ...................................................................................................................................... 141 

Table A 11. Cost data used in the life cycle costing. Notice that the values reported are not 
corrected by inflation. .................................................................................................................................................................... 142 

Table A 12. Energy mixes considered in the assessment. The values reported represent the share 
of the different sources in the average mix for EU27 ............................................................................................... 145 

Table A 13. Energy mixes considered in the assessment. The values reported represent the share 
of the different sources in the marginal mix for EU27 .............................................................................................. 146 

126 

 
Table A 14. Savings relative to the Baseline scenario (BSL50) calculated via LCA ................................ 148 

Table A 15. Savings relative to the Baseline scenario (BSL50) calculated via LCA ................................ 149 

Table A 16. Values for epistemic uncertainty sensitivity analysis of prospective LCA ......................... 150 

Table A 17. FIGAROe3 data download .............................................................................................................................. 156 

Table A 18. Matching of MFA flows and FIGAROe3 categories .......................................................................... 160 

Table A 19. Disaggregated sectors with respective market prices ................................................................... 161 

Table A 20. Normalisation and weighting factors of the Environmental Footprint method version 
3.1 ............................................................................................................................................................................................................. 164 

Table A 21.  Data downloaded for environmental extensions. ........................................................................... 167 

127 

 
 
 
Annexes  

Annex 1. Circularity levers 

This section outlines the method employed to define circularity levers in the aluminium sector. The 
method consisted of four main steps: system boundaries definition, levers identification, levers 
validation, levers quantification.  

Step 1: System boundary definition 

The first step was to define the system boundaries for the analysis. These were set by considering 
all the stages of the material supply chain (from mining to waste management – see Figure 11) 
with a focus on the relevance for the European aluminium sector. To this end, the scope of the 
analysis was mainly focused to a set of key applications for EU industry, defined according to 
former scientific work in support of EU policy (Passarini et al., 2018) and a recent industry report on 
their circularity relevance (European Aluminium, 2020), in consultation with a team of experts from 
European Aluminium (association of 100+ members representing the industry in 30 countries). The 
circularity levers identified mainly focus on interventions on the transport, construction, and 
packaging sectors. Yet, whenever the information was available in the selected publications, the 
effect of the circularity lever was extended to the other sectors, namely electrical, mechanical 
equipment, consumer durables, and other.  

Step 2: Levers identification  

The second step was the identification of circularity levers relevant for the aluminium sector. To this 
end, three researchers collected data from both scientific and grey literature on the subject. 
Selected grey literature included relevant reports on aluminium and its circularity (mainly in the 
European context) developed over the past 6 years, provided by experts from the aluminium 
industry, consultancy firms, and policymaking. The inclusion of such grey literature was functional to 
capture up-to-date information and key industry and policy developments in the EU and, when 
relevant, globally. Scientific literature included peer reviewed journal articles, conference 
proceedings and academic dissertations. First, the supplementary information of the literature listed 
in the study performed by Creutzig et al. (2022) was analysed to identify any previously collected 
literature that could be reused. After this first rapid search, Google Scholar was used to search the 
combination of R9 strategies (according to the definition of Potting et al., 2017) with the keyword 
“Aluminium”. The papers were then selected based on number of citations and availability of 
quantified results that could be reused within the modelling. A list with all the literature sources 
used for the identification of circularity levers for aluminium in the EU is provided in Table A 1 

Table A 1. List of literature sources for the identification of circularity levers for aluminium in the EU 

Author 

Year 

Agora Industrie 
& SYSTEMIQ 

2023 

Bertam et al. 

2017 

Brough & 
Jouhara 

Cooper & 
Allwood 

2020 

2012 

Title 

Resilienter Klimaschutz durch eine zirjuläre Wirtschaft. 
Perspektiven und potenziale für energie-intensive 
Grundstoffindustrien 
A regionally-linked, dynamic material flow modelling tool for 
rolled, extruded and cast aluminium products 
The aluminium industry: A review on state-of-the-art 
technologies, environmental impacts and possibilities for 
waste heat recovery 
Reusing Steel and Aluminium Components at End of Product 
Life 

Source 

Industry report 

Scientific 
journal 

Scientific 
journal 

Scientific 
journal  

128 

 
Cui & Roven 

2010  Recycling of automotive aluminium 

Cullen & 
Allwood 
Dev Srivyas & 
Charoo 

Ducker 

2013 

2019 

2023 

Duflou et al. 

2015 

Mapping the Global Flow of Aluminium: From Liquid 
Aluminium to End-Use Goods 
Application of Hybrid Aluminium Matrix Composite in 
Automotive Industry 
Aluminium Content in Passenger Vehicles (Europe). 
Assessment 2022 and Outlook 2026, 2030.  
Environmental assessment of solid state recycling routes for 
aluminium alloys: Can solid state processes significantly 
reduce the environmental impact of aluminium recycling? 

Scientific 
journal 
Scientific 
journal 
Scientific 
conference  

Industry report 

Scientific 
conference  

Energy 
Transition 
Commission 
European 
Aluminium 
European  
Aluminium  
European  
Aluminium 
European 
Aluminium  
European 
Aluminium 
European 
Commission  

2023  Material and Resource Requirements for the Energy Transition  

Industry report 

2004 

2018 

2020 

Collection of Aluminium from Buildings in Europe: A study by 
Delft University of Technology 
Environmental Profile Report: Life-Cycle inventory data for 
aluminium production and transformation processes in Europe 
Circular Aluminium Action Plan: A Strategy for Achieving 
Aluminium's Full Potential for Circular Economy by 2030 

Industry report 

Industry report 

Industry report 

2022  Aluminium beverage can recycling rates 2020 

Industry report 

2023 

2020 

Fam et al. 

2006 

Farjana et al. 

2019 

Fernström et al.  2022 

Han et al. 

2017 

End-of-life of vehicles Directive revision: European aluminium 
top priorities  
Study on the EU’s list of Critical Raw Materials (2020) – Final 
report 
Repair of damaged aluminium truss joints of highway 
overhead sign structures using FRP 
Impacts of aluminium production: A cradle to gate 
investigation using life-cycle assessment 
Circularity of aluminium: Mapping difficulties in scrap 
circulation. A literature study and interviews with actors with 
focus on aluminium circulation in Sweden. 
Circular economy measures that boost the upgrade of an 
aluminium industrial park 

2023 

Market development for secondary casting alloys beyond 
motor blocks - study on casting alloy market and recycling 

2022 

The Role of Critical Minerals in Clean Energy Transitions 

2020 

2022 

The ideal aluminium packaging sorting model 
Metals for Clean Energy: Pathways to solving Europe’s raw 
materials challenge 
Long-Term Strategies for Increased Recycling of Automotive 
Aluminium and Its Alloying Elements 
A solid-state electrolysis process for upcycling aluminium 
scrap 

Institut de 
Recherche 
Technologique 
International 
Energy Agency 
Labberton 
Liesbet & van 
Acker 

Løvik et al. 

2014 

Lu et al. 

2022 

Material 
Economics 
Material 
Economics 

Milford et al. 

2011 

2018 

The circular economy—A powerful force for climate mitigation 

Industry report 

2019 

Industrial Transformation 2050: Pathways to Net-Zero 
Emissions from EU Heavy Industry 
Assessing the potential of yield improvements, through 
process scrap reduction, for energy and CO2 abatement in the 
steel and aluminium sectors 

Mission 
Possible 
Partnership 

2023 

Making net-zero aluminium possible. An industry-backed, 
1.5°C-aligned transition strategy 

Passarini et al. 

2013 

Aluminium flows in vehicles: Enhancing the recovery at end-
of-life 

Scientific 
journal 

129 

Industry report 

Policy  
report 
Scientific 
journal 
Scientific 
Journal 

Academic 
dissertation 

Scientific 
Journal 

Scientific 
report 

Policy  
report 
Industry report 

Industry report 

Scientific 
journal 
Scientific 
journal 

Industry report 

Scientific 
journal 

Industry report 

 
Passarini et al. 

2018 

Material Flow Analysis of Aluminium, Copper, and Iron in the 
EU-28  

Pavel & Tzimas  2016  Raw materials in the European defence industry 

Pedneault et al.  2023 

Rech et al. 

2014 

Sevigné-Itoiz et 
al. 

2014 

Singhvi 

2021 

Slingerland et 
al. 
Spinger & 
Hasanbeigi 
US Department. 
of Energy 
Warrings & 
Fellner 
Warrings & 
Fellner 

2015 

2016 

2018 

2021 

Widener et al. 

2018 

Wong et al. 

2018 

World Bank  

2022 

How much sorting is required for a circular low carbon 
aluminium economy? 
Different Cold Spray Deposition Strategies: Single- and Multi-
layers to Repair Aluminium Alloy Components 
Environmental consequences of recycling aluminium old scrap 
in a global market 
The incentives for and the problems faced in aluminium 
recycling: A case study of the process of singulation for 
aluminium scrap of 50-180 mm size range 
Energy efficiency and GHG emissions – Prospective scenarios 
for the aluminium industry 
Emerging energy efficiency and carbon dioxide emissions – 
Reduction technologies for industrial production of aluminium 

Current status of circularity for aluminium from household 
waste in Austria 
How to increase recycling rates. The case of aluminium 
packaging in Austria 
Structural repair using cold spray technology for enhanced 
sustainability of high value assets 
Recycling of end-of-life vehicles (ELVs) for building products: 
Concept of processing framework from automotive to 
construction industries in Malaysia 
Competitiveness of global aluminium supply chains under 
carbon pricing scenarios for solar PV 

2023  Critical materials assessment 

World 
Economic 
Forum 

2021 

The answer to the aluminium industry’s emissions issue? 
Aluminium’s infinite recyclability 

Yang et al. 

2015 

Yılmaz et al. 

2017 

Zibell et al.  

2022 

Source: JRC elaboration 

The Impact of Automotive Product Remanufacturing on 
Environmental Performance 
A Study of Lightweight Door Hinges of Commercial Vehicles 
Using Aluminium Instead of Steel for Sustainable 
Transportation 
Impacts of Circular Economy on EU climate policies - 
mitigation and adaptation 

Policy  
report 
Policy report 
Scientific 
journal 
Scientific 
journal 
Scientific 
journal 

Academic 
dissertation 

Policy report 

Policy report 

Policy  
report 
Scientific 
journal 
Scientific 
journal 
Scientific 
conference 

Scientific 
journal 

Scientific 
report 

Online article 

Scientific 
conference  

Scientific 
journal 

Policy report 

The data from selected sources was analysed using a qualitative method based on a deductive 
coding procedure (Fereday & Muir-Cochrane, 2006). This method entails performing a thematic 
clustering of insights derived from diverse written sources, guided by pre-existing conceptual 
categories relevant for the analysis. Information on aluminium circularity from selected sources was 
merged and integrated into a list of levers structured according to the classification provided in 
Section 2.1. By doing so, the list that was obtained by including all references reported in Table A 
1 was reduced to a preliminary list of 43 levers.  

Step 3: Levers validation   

The preliminary list of levers based solely on the analysis of literature was consequently validated 
with industry experts from European Aluminium as well as with academics and colleagues from the 
Joint Research Centre of the European Commission. Following a conversational interview approach 
(Patton, 2002), a video conference was organised with the abovementioned experts. In this 
consultation, various levers were discarded, enriched with new detail and/or merged in view of 

130 

 
feasibility considerations within the industry context. This resulted in an updated list of 11 levers. 
This step also enabled to collect initial information on a possible quantification of the effects of the 
levers.  

Step 4: Levers quantification 

After validation, the fourth step entailed the quantification of the effect of the levers. This was done 
by retrieving quantitative data available from the original source documents and the supplementary 
material provided by a subset of documents from the full list of selected literature presented in 
Table A 1. In the process, the list of levers was again updated by adding, eliminating, or subtracting 
elements, depending on bottom-up and top-down modelling possibilities and constraints. The list 
was further refined and scaled down to 6 levers of the preliminary 11 ones. Two additional levers 
were then added after the online consultation based on additional literature that was not initially 
considered. This resulted in the final list of 8 levers, complemented with categorisation and 
quantification, as presented in the overview provided in Table 3. In the following section, each lever 
from Table 3 is explained in greater detail.   

Description of circularity levers and implementation in the modelling 

1. Pre-consumer scrap reduction 

This lever addresses pre-consumer scrap reduction. Pre-consumer scrap includes both fabrication 
scrap from creating semi-finished products and scrap arose during manufacturing of finished 
products (Bertram et al., 2017). This is generated, for example, during aluminium bar extrusion or 
from cutting aluminium sheets into final shapes. 

According to Material Economics (2018), in the processes of casting, fabrication and manufacturing 
large amounts of pre-consumer scrap are generated; indeed, it is estimated that about 35-41% of 
the material ends up as pre-consumer scrap rather than as products. Yet, scrap generation varies 
significantly across sectors. For example, some car manufacturers produce over 40% scrap, while 
best practice is closer to 25% (Material Economics, 2018). Most of the pre-consumer scrap enters 
recycling directly from the fabricators. Hence, the quality and alloys used are known and, usually, 
the pre-consumer scrap is uncoated. Prior to remelting in reverbatory furnaces, little preparation is 
needed to produce wrought alloys. Yet, when arising at manufacturing, the pre-consumer scrap may 
be coated with paints, plastics or ink. Hence, this needs to be de-coated, which is usually the only 
significant scrap pre-treatment needed prior to its remelting (European Aluminium, 2018) 

This circularity lever focuses on reducing such losses, through process improvements and 
optimization, ultimately reducing the amount of virgin material needed. According to Milford et al. 
(2011), three possible strategies are applicable to reduce yield losses (i.e. pre-consumer scrap 
generation), namely: i)  developing new manufacturing processes with intermediate products closer 
in geometry to final components; ii) operating existing processes more effectively to improve yield; 
or, iii) modifying component designs to enhance tessellation. Yet, Milford et al. (2011) argue that: i) 
developing new manufacturing processes is limited by lack of economies of scale; ii) operating 
existing processes more effectively is often limited by the boundaries of the site; and, iii) modifying 
component designs can lead to a reduction in the demand for molten aluminium only if the scrap 
would take the place of some of the original product mass. 

According to Milford et al. (2011), the reduction potential corresponds to 20%. This was thoroughly 
discussed with experts in European Aluminium as production processes in the aluminium industry 
are known to be already well optimized and such a value was considered to be too optimistic. After 

131 

 
consultations with the industry, it was decided to have a more conservative approach and assume 
that such improvements would only result in a 5% reduction, at most.  

Further, the 5% reduction was first applied to the amount of manufacturing scrap. Having modified 
this, the amount of semi-finished products and corresponding fabrication scrap were re-calculated. 
Based on the ‘new’ amount of fabrication scrap, a further 5% reduction was applied. Table A 2 
summarises the modelling implementation of the lever discussed. 

Table A 2. Modelling implementation of the lever focused on pre-consumer scrap reduction 

Year 

Value 

Type 

2050 

-5% 

Supply side 

Circular economy strategy 

Reduce  

Modelling interpretation (bottom-
up assessment) 

Assumptions on the elaboration 
of the data taken from literature 

Limits and improvements 
(bottom-up assessment) 

Demand reduction for virgin aluminium due to the improved 
mechanical processes at fabrication and manufacturing stages. The 
lever results in a reduction in virgin molten aluminium, but also in a 
reduction of generated pre-consumer scrap that results in less recycled 
aluminium. 

Functional unit: Meeting the demand of final products in EU in 2050 

The value taken from literature (i.e. 20% reduction in 2030) is 
considered as not realistic after discussions with the industry. 
Therefore, a conservative approach is used instead and a reduction of 
5% is assumed for both manufacturing and fabrication scrap.  

-  No specific strategy among the ones listed in Milford et al. (2011) 
are indicated as most probable to occur. Hence, the technical 
implementation of the lever is not clear. 

-  According to our calculations, the mass of primary aluminium 

required is higher than in the Baseline scenario. This is 
counterintuitive as if less input is required to produce the same 
unit of product, then one would expect that the mass of primary 
aluminium would also decrease. However, from a mathematical 
point of view, this is correct – it is affected by the way the mass 
balance has been constructed.   

Source: JRC elaboration 

2. Design for reusability  

This lever addresses design for reusability and modularity of final products. It focuses on extending 
product lifetimes to avoid recycling, which is more cost-effective and environmentally beneficial, as 
it requires less energy (Cooper & Allwood, 2012). Achieving this involves various interventions, at 
the product and component level (Material Economics, 2018).  

At the product level, there are four concrete approaches (Ardente et al., 2018; Kirchherr et al., 
2017): i) direct reuse, where a product can find a second life in the same application by another 
consumer; ii) repair, which involves fixing a defective product to continue its original function; iii) 
refurbishing, which updates and restores an old product; and, iv) remanufacturing, which 
incorporates parts of a discarded product into a new one serving the same function. 

132 

 
 
At the component level, life extension is influenced by two factors: performance and demand 
(Cooper & Allwood, 2012). When performance is high, and demand is high, end-of-life components 
can be directly relocated into a new product. When performance is low, and demand is low they can 
be repurposed either by cascading “as-is” or by reshaping their geometry to fit into a different 
product with less sophisticated needs. When performance is low, but demand is high, component 
remanufacturing takes place, which entails further disassembly, redrilling, and surface recovery 
techniques to restore properties and re-use in a fully functional new product. Challenges to consider 
entail disassembly time, labour cost, logistical issues, and lack of an established supply chains and 
economies of scale.   

According to Cooper & Allwood (2012), the following reductions could be achieved in 2012 by ex-
tending the lifetime of products: 40% in Transport, passenger car; 36% in Transport, aerospace; 
10% in Transport, other; 50% in Construction, 50% in Electrical, cable; 10% in Electrical, other; 10% 
in Mechanical equipment; and, 50% in Consumer durables. Packaging is not affected by the lever as 
its products are characterised by a lifetime of 1 year. The values reported in the publication for 
Transport, passenger car, Construction, and Electrical, cable were thoroughly discussed with experts 
in European Aluminium. For passenger cars, it was discussed that the amount reported by Cooper & 
Allwood (2012) was not realistic due to the changes in the vehicle fleet expected in the next years 
(i.e. transition from combustion to electric engines), but also considering that the repairing of vehi-
cles is a niche market. With respect to the construction sector, it was discussed that the amount re-
ported by Cooper & Allwood (2012) was also not realistic. Indeed, aluminium is predominately used 
in windows. A possible repair of refurbishment of aluminium in a window would have negative im-
pacts on the overall energy efficiency of a building. Finally, for cables, it was also discussed that the 
amount reported by Cooper & Allwood (2012) was not realistic as the aluminium used in cables is 
of very high quality and would be recycled while refurbishing might lead to losses of conductivity. 
Therefore, in the three mentioned applications it was decided to have a conservative approach. 
Hence, we departed from the reductions reported in the publication and assumed a 5% reduction 
for cables and the construction sector, and a 10% reduction for passenger cars. Finally, notice that 
values calculated for 2012 were assumed for 2050. Table A 3 summarises the modelling imple-
mentation of the lever discussed. 

Table A 3. Modelling implementation of the lever focused on design for reusability 

Year 

Value 

2050 

Transport, passenger car -10%; Transport, aerospace -36%; Transport, 
other -10% 
Construction -5% 
Electrical, cable -5%; Electrical, other -10% 
Mechanical equipment -10% 
Consumer durables -50% 
Packaging, cans -0%; Packaging, foil -0% 
Other -10% 

Type 

Supply and demand side 

Circular economy strategy 

Reuse  

Modelling interpretation 
(bottom-up assessment) 

The reductions are applied to the final product demand resulting in a 
decrease in primary material needs and waste to be processed. 

Functional unit: Fulfilling the needs in terms of services in EU in 2050 

133 

 
Assumptions on the elaboration 
of the data taken from 
literature 

-  Design for reusability and modularity will affect all durable goods.  

-  Data obtained from Cooper & Allwood (2012). 

-  Intervention may affect both Business-to-Business but also 

Limits and improvements 
(bottom-up assessment) 

Business-to-Consumers. 

-  Potential as per year 2012. 

-  For “Construction” it is not yet standard practice for windows and 

other building components to extend their lifetime, but it will develop 
to some extent. It requires easier deconstruction and greater 
standardisation. 

-  The value for “Other” is assumed as the lowest potential reported in 

Cooper & Allwood (2012). 

-  As the model is not dynamic, it is assumed that the extension of 

lifetime has a pulse response in the year 2050, i.e. the reduction is 
100% of its potential already in 2050. 

-  In terms of environmental and economic impacts, remanufacturing 

is not accounted for.  

Source: JRC elaboration 

3. Remanufacturing of automotive components 

This lever addresses remanufacturing of automotive components. When a vehicle reaches the end-
of-life, dismantling specific aluminium parts for remanufacturing before shredding allows to 
increase resource efficiency. Most relevant parts in this sense include engines, heat exchangers, 
doors, bonnets, fenders, bumpers, and gearboxes (European Aluminium, 2020). This allows on the 
one hand segregating alloys to facilitate recycling and, on the other hand, it enables 
remanufacturing, which is preferable from an environmental perspective as discussed in Yang et al. 
(2015) where the authors estimated a reduction in energy inputs (between 68% to 83%) and lower 
CO2 emissions (between 73% to 87%) compared to the corresponding virgin manufacturing process.  

In automotive, remanufacturing is intended as the process restoring end-of-life components to a 
“new” condition, after disassembly, cleaning, inspection, reconditioning, replacement, and 
reassembly into a new vehicle, ultimately extending their lifetime. This process demands more 
labour but limited investment in machinery and energy compared to recycling. It is estimated that 
approximately 20% of a vehicle's weight could be affected by remanufacturing (Yang et al., 2015). 
The most promising aluminium components for remanufacturing are engine blocks, alternators, 
transformers, wheels, and transmission casing (Cooper & Allwood, 2012; Yang et al., 2015). 
Remanufacturing challenges include achieving “good as new” quality and dealing with poor-quality 
returned parts, technology constraints, and low recoverable value. To address these, challenges, 
product design is crucial, avoiding protruding structures to prevent damage during transportation, 
standardizing fasteners, and joints for easy disassembly, and adopting platform design or 
modularity to facilitate product upgrading (Yang et al., 2015). Table A 4 summarises the 
modelling implementation of the lever discussed. 

Table A 4. Modelling implementation of the lever focused on remanufacturing of automotive components 

Year 

Value 

Type 

2050 

-10% 

Supply side 

134 

 
Circular economy strategy 

Reuse 

Modelling interpretation 
(bottom-up assessment) 

The reductions are applied to the demand of the final product “Transport, 
passenger car” only resulting in a decrease in primary material needs and 
waste to be processed. 

Functional unit: Fulfilling the needs in terms of services in EU in 2050 

Reference components for intervention is international combustion 
engine vehicles  (Yang et al., 2015).  

-  As the model is not dynamic, it is assumed that the extension of 

lifetime has a pulse response in the year 2050, i.e. the reduction is 
100% of its potential already in 2050. 

-  In terms of environmental and economic impacts, remanufacturing is 

not accounted for. 

Assumptions on the 
elaboration of the data taken 
from literature 

Limits and improvements 
(bottom-up assessment) 

Source: JRC elaboration 

4. Improved waste sorting  

Products are used and often mixed when discarded at their end-of-life, scattering post-consumer 
aluminium across various waste streams like beverage cans, food packaging, vehicle components, 
and building materials, where it is frequently mixed with other materials. Further, this negatively 
affects the recycling of wrought aluminium alloys (having an alloy concentration comprised 
between 1-10%) as opposed to cast aluminium alloys (having an alloy concentration of up to 20%) 
(European Aluminium, 2018). Indeed, wrought aluminium alloys that have been mixed together with 
cast aluminium alloys cannot be recycled back to pure aluminium hence determining substantial 
amounts of wrought aluminium alloys to be recycled in lower quality applications, especially in the 
automotive industry (European Aluminium, 2020; Liesbet & van Acker, 2022). Indeed, the 
automotive industry is a sink for cast aluminium alloys that are needed for the production of engine 
blocks in combustion engines (Institut de Recherche Technologique, 2022). However, as components 
evolve and traditional parts like engine blocks disappear with the transition to electric cars, the 
market may in the future not be able to absorb this lower-quality cast aluminium (European 
Aluminium, 2020; Material Economics, 2018a). Cooperation between industries is crucial to ensure 
continued recycling of automotive aluminium in a closed loop to prevent downcycling (Løvik et al., 
2014). Further, advanced sorting equipment and pre-treatment methods like pyrolysis can 
effectively remove contaminants (European Aluminium, 2020), while newly developed techniques, 
like laser-induced breakdown spectroscopy and solid-state recycling show promise in aluminium 
refining (Cui & Roven, 2010). 

This lever addresses an improvement in waste sorting and collection. Europe has a well-developed 
recycling infrastructure, which counted 220 recycling plants in 2015, mostly small and medium-
sized enterprises (European Aluminium, 2018). However, out of 4.3 Mt of end-of-life aluminium old 
scrap generated, only about 2.9 Mt were collected for recycling, resulting in a 69% end-of-life 
recycling rate (Passarini et al., 2018). Table A 5 summarises the modelling implementation of the 
lever discussed. 

Table A 5. Modelling implementation of the lever focused on improved waste sorting 

Year 

Value 

2050 

-5.2% 

135 

 
Type 

Supply side 

Circular economy strategy 

Recover 

Modelling interpretation 
(bottom-up assessment) 

Assumptions on the 
elaboration of the data taken 
from literature 

Limits and improvements 
(bottom-up assessment) 

The collection rates are re-calculated to have additional material sorted 
and entering recycling. This is done only for post-consumer scrap. 
Further, the improvements in collection and sorting result in a higher 
share of post-consumer scrap directed to remelting as opposed to 
refining, hence ensuring a closed-loop recycling. Overall, the lever results 
in higher masses collected, sorted and recycled. 

Functional unit: Collection, sorting and treatment of the European 
aluminium post-consumer scrap in 2050. 

-  In this case, we model the consequences of improved sorting.  

-  Quantification sourced from Cui & Roven (2010), Løvik et al. (2014), 

and Pedneault et al. (2023).  

-  Due to the high amount of post-consumer scrap exported, it is not 

possible to reach the reduction reported in the literature (i.e. 30% for 
2050). Hence, it was not possible to apply the value taken from 
literature. The value used as reduction is calculated based on the 
material flow analysis considering the substantial amount of post-
consumer scrap currently exported. 

-  No specific technology is identified for achieving the improvements. 

Source: JRC elaboration 

5. Improved waste sorting and scrap quality 

As mentioned in Section 1.3, reaching high-quality of post-consumer scrap via collection and 
improved sorting is identified as a possible avenue to increase closed-loop recycling, especially for 
wrought aluminium alloys. Building on the previous lever (i.e., ‘Improved waste sorting’), we assume 
that alloy-to-alloy separation occurs and collected wrought aluminium is fully directed to remelting, 
instead of refining. No specific technology is identified for achieving the improvements. It is 
assumed that the electricity consumption of the sorting plant is doubled according to estimations 
based on Albizzati, Foster, et al. (2024) and Daehn et al. (2019). In terms of labour and 
investments, no adjustments are made on the inputs. Table A 6 summarises the modelling 
implementation of the lever discussed. 

Table A 6. Modelling implementation of the lever focused on improved waste sorting and scrap quality 

Year 

Value 

Type 

2050 

-5.2% and 100% diversion of wrought aluminium alloys to remelting 

Supply side 

Circular economy strategy 

Recover 

Modelling interpretation 
(bottom-up assessment) 

The collection rates are re-calculated to have additional material sorted 
and entering recycling. This is done only for post-consumer scrap. 
Further, the improvements in collection and sorting result in the 
diversion of wrought aluminium alloys to remelting as opposed to 
refining, hence ensuring a closed-loop recycling. Overall, the lever 
results in higher masses collected, sorted and recycled. 

Functional unit: Collection, sorting and treatment of the European 
aluminium post-consumer scrap in 2050. 

136 

 
Assumptions on the elaboration 
of the data taken from 
literature 

-  In this case, we model the consequences of improved sorting.  

-  Quantification sourced from Cui & Roven (2010), Løvik et al. (2014), 

and Pedneault et al. (2023).  

Limits and improvements 
(bottom-up assessment) 

-  Due to the high amount of post-consumer scrap exported, it is not 
possible to reach the reduction reported in the literature (i.e. 30% 
for 2050). Hence, it was not possible to apply the value taken from 
literature. The value used as reduction is calculated based on the 
material flow analysis considering the substantial amount of post-
consumer scrap currently exported. 

-  No specific technology is identified for achieving the improvements. 

-  The electricity consumption of the sorting plant is doubled according 
to estimations based on Albizzati, Foster, et al. (2024) and Daehn et 
al. (2019). 

Source: JRC elaboration 

6. Design for packaging recoverability and recycling  

This lever addresses design for packaging recoverability and recycling. It concerns enhancing the 
design of new packaging products to facilitate aluminium recovery at the end of their lifecycle, 
thereby promoting recycling, and decreasing the amount ending up in incineration or landfilling 
(European Aluminium, 2020). For example, several are the challenges related to recycling of 
aluminium foils, where a portion still remains uncollected and ends up in incineration or landfill 
(European Aluminium, 2020). Design for recoverability results in 8-10% packaging diversion from 
incineration and landfilling according to Warrings & Fellner (2018). Changes in design are not 
accounted for per se, i.e. a possible increase/decrease in mass is not considered. The only effect 
modelled is a reduction in packaging waste disposed and a proportional increase in recycled 
aluminium packaging. Table A 7 summarises the modelling implementation of the lever discussed. 

Table A 7. Modelling implementation of the lever focused on design for packaging recoverability and 
recycling 

Year 

Value 

Type 

2050 

-10% 

Supply side 

Circular economy strategy 

Recover  

Modelling interpretation 
(bottom-up assessment) 

Assumptions on the 
elaboration of the data taken 
from literature 

The changes are applied only to ´Packaging, cans’ and ‘Packaging, foil’. 
The effect of the lever is intended as a reduction in packaging currently 
treated through incineration/landfilling and a proportional increase of 
recycled packaging aluminium. Finally, having a different design for 
packaging is assumed not to affect either collection rates or sorting 
rates. 

Functional unit: Management of aluminium packaging waste collected in 
2050 in Europe. 

-  Lack of a packaging category so fabricated metal products are used 

as a reference. 

-  Data obtained from Warrings & Fellner (2018). 

Limits and improvements 
(bottom-up assessment) 

-  Changes in design are not modelled per se, i.e. a possible 

increase/decrease in mass is not taken into account. The only effect 

137 

 
modelled is a reduction in packaging waste disposed and a 
proportional increase in recycled aluminium packaging. 

Source: JRC elaboration 

7.  Expansion of deposit return systems 

Regarding aluminium cans, recycling is progressing well. Unlike other packaging materials, 
aluminium beverage cans can be recycled to a product with similar quality as the original, with an 
average collection rate of 74.5% across the EU (European Aluminium, 2020). However, there 
remains a wide range of performance among EU countries, with some reaching recycling rates 
below 40% (e.g., Romania) and others nearing 98% (e.g., Finland) (European Aluminium, 2020). 
Recycling rates are affected by a country's collection and sorting schemes. An avenue to ensure 
high collection and recycling rates is the implementation of deposit return systems for, among other 
packaging items, aluminium cans. Croatia, Denmark, Estonia, Finland, Germany, Malta, Lithuania, the 
Netherlands, and Sweden have successfully implemented deposit return systems and in Europe the 
coverage of such a collection scheme reaches approx. 11% (estimated from International 
Aluminium Institute, n.d.). Moving forward, achieving a 100% recycling rate for every aluminium can 
across Europe is essential to ensure closed loop can-to-can recycling (European Aluminium, 2020).  

This lever aims at quantifying the impacts of having a 100% coverage of deposit return systems on 
all packaging cans waste generated in the EU with a 100% collection rate to investigate the 
maximum potential of such practice. Table A 8 summarises the modelling implementation of the 
lever discussed. 

Table A 8. Modelling implementation of the levers focused on expansion of deposit return systems 

Year 

Value 

Type 

2050 

100% coverage and collection rate 

Supply 

Circular economy strategy 

Recover 

Modelling interpretation 
(bottom-up assessment) 

The lever is applied to ‘Packaging, cans’ only. A 100% coverage and 100% 
collection rate are assumed to explore the maximum potential of deposit 
return systems. 

Source: JRC elaboration 

8. Recovery from mixed waste  

Products are used and often mixed when discarded at their end-of-life, scattering aluminium house-
hold waste across various waste streams, frequently mainly being segregated together with mixed 
waste. Warrings & Fellner (2021) have identified advanced material recovery facilities for mixed 
waste as a promising avenue to reach the ambitious recycling rates targets for aluminium waste.  

Based on Albizzati, Foster, et al. (2024), as of 2020 the coverage of material recovery facilities 
across EU countries was 41%, i.e. of the mixed waste, 41% is sorted in a material recovery facility 
to recover recyclable materials prior to incineration or landfilling. The lever aims at quantifying the 
maximum potential of having advanced material recovery facilities with a coverage of 100%, i.e. all 
mixed waste is first sorted in a material recovery facility to recover recyclable materials and reduce 
the amount of waste sent to incineration or landfilling. Having an advanced material recovery facil-
ity is intended as a technology with recovery rates increasing from 54% (based on Albizzati, Tonini, 

138 

 
et al., 2024) to 93% (based on Labberton, 2020). Table A 9 summarises the modelling implemen-
tation of the lever discussed. 

Table A 9. Modelling implementation of the lever focused on recovery from mixed waste 

Year 

Value 

Type 

2050 

100% coverage 

93% recovery 

Supply 

Circular economy strategy 

Recover 

Modelling interpretation 
(bottom-up assessment) 

The lever is applied to ‘Packaging, cans’ and ‘Packaging foil’ only as these 
are the flows assumed to be found in household mixed waste. Departing 
from a coverage of 41% and 54% recovery of aluminium from mixed 
waste in the Baseline scenario, in the ACE50 scenario a 100% coverage 
and 93% recovery rate are assumed to explore the maximum potential 
of advanced material recovery facilities 

Assumptions on the 
elaboration of the data taken 
from literature 

The configuration of the advanced material recovery assumed involves 
two eddy current separators and one induction sorting system, based on 
Labberton (2020), followed by manual sorting. 

Limits and improvements 
(bottom-up assessment) 

-  It should be investigated whether other sectors might end up in 

household mixed waste.  

-  Additional energy consumption and/or the need for additional 

ancillary materials for the functioning of the advanced material 
recovery facility are disregarded. 

Source: JRC elaboration 

Combination of levers 

As anticipated, the levers are applied individually. When combining the CE levers at cluster (i.e. 
either Reduce, Reuse or Recover) or at ACE50 scenario level trade-offs between the levers might 
occur, meaning that the maximum circularity at cluster or ACE50 scenario level is not the same as 
the sum or multiplication of the different levers.  

When implementing both levers belonging to the ‘Reuse’ cluster, a multiplicative effect is 
considered for ‘Transport, passenger car’. Lever 2 (i.e. Design for reusability in Table 3) is assumed 
to be implemented first as it prolongs the lifetime of the product, while lever 3 (i.e. 
Remanufacturing of automotive components in Table 3) is subsequently implemented as it is an 
extension of lifetime of products close to their end-of-life. Further, as the two levers act on the 
same product at the same stage, a multiplicative effect is assumed. 

When implementing the levers belonging to the recover cluster, it is assumed that lever 4 (i.e. 
Improved waste sorting in Table 3) is overruled by lever 5 (i.e. Improved waste sorting and scrap 
quality in Table 3) where one needs also to take into account that lever 7 (i.e. Expansion of deposit 
return systems in Table 3) overrules on the changes related to the stream of aluminium waste 
‘Packaging, cans’. Subsequently, lever 8 is implemented (Recovery from mixed waste in Table 3) 
and, finally, lever 5 is applied (i.e. Design for packaging recoverability and recycling in Table 3) as in 
the modelling this lever translates into more recycling. 

139 

 
Finally, when implementing all levers at once in the ACE50 scenario, it is assumed that the Reuse 
levers would be implemented first having these an effect on the Reduce lever, as the Reduce lever 
is only an optimisation of the fabrication and manufacturing process, while the Reuse levers reduce 
the demand for aluminium. Finally, the Recover levers are implemented. 

A visual representation of the interrelationships between the levers is displayed in Table A 10, 
where a ‘Combine’ effect is intended as a multiplicative effect, an ‘Exclusive’ effect is intended as 
one lever overruling the other, an ‘Additive’ effect is intended as the two levers boosting their 
effect, and, finally, ‘No effect’ indicates that mass flows are not affected. 

140 

 
Table A 10. Razor matrix with interrelationships of Circular Economy (CE) levers in the Ambitious Circular Economy (ACE50) scenario 

CE lever 

Pre-consumer 
scrap reduction – 
L(1) 
Design for 
reusability – L(2) 
Remanufacturing 
of automotive 
components – L(3) 
Improved waste 
sorting – L(4) 
Improved waste 
sorting and scrap 
quality – L(5) 
Design for 
packaging 
recoverability and 
recycling – L(6) 
Expansion of DRS – 
L(7) 
Recovery from 
mixed waste – L(8) 

Pre-
consumer 
scrap 
reduction - 
L(1) 

Design for 
reusability 
– L(2) 

Remanufacturing of 
automotive 
components – L(3) 

Improved 
waste 
sorting – 
L(4) 

Improved waste 
sorting and 
scrap quality – 
L(5) 

No effect 

No effect 

No effect 

No effect 

Design for 
packaging 
recoverability 
and recycling 
– L(6) 
No effect 

Expansion of 
DRS – L(7) 

Recovery 
from mixed 
waste – L(8) 

No effect 

No effect 

No effect 

No effect 

No effect 

No effect 

Combine 

No effect 

No effect 

No effect 

No effect 

No effect 

No effect 

No effect 

Exclusive 

No effect 

No effect 

No effect 

No effect 

Additive 

No effect 

No effect 

No effect 

No effect 

No effect 

No effect 

Additive 

No effect 

No effect 

No effect 

No effect 

Combine 

No effect 

No effect 

No effect 

No effect 

Additive 

No effect 

No effect 

Exclusive 

No effect 

No effect 

No effect 

No effect 

No effect 

No effect 

No effect 

No effect 

No effect 

Additive 

No effect 

Note: For example, from line two ‘Design for reusability’ has no effect on ‘Pre-consumer scrap reduction’ but has a combined effect I.e. multiplicative) with ‘Design for 
recycling’, meaning that the two levers have a combined effect on passenger cars. ‘Improved waste sorting’ has no effect on ‘Pre-consumer scrap reduction’ but should be 
seen as additive to the effect of ‘Improved waste sorting and scrap quality’. Notice that DRS stands for deposit return systems 

Source: JRC elaboration

141 

 
 
 
 
 
 
 
 
 
Annex 2. Life cycle inventories 

With respect to the life cycle inventories of the aluminium production and recycling processes, the 
modelling was entirely based on European Aluminium (2018, 2021) and complemented (when 
needed) with ecoinvent datasets (Wernet et al., 2016). 

Table A 11 provides the cost data used in the life cycle costing. The data is not corrected by 
inflation; in the life cycle costing all costs are referred to EUR2022. 

Table A 11. Cost data used in the life cycle costing. Notice that the values reported are not corrected by 
inflation. 

Process 

Cost 

Amount 

Ingot cast production 

CAPEX 

OPEX 

47 

52 

Unit 
EUR t-1 

Year 

2021 

EUR t-1 

2021 

Labour cost 

30 

EUR t-1 

2021 

Employment 

1.9E-05 

FTE t-1 

2021 

Fabrication of rolling 

CAPEX 

260 

EUR t-1 

2022 

OPEX 

1787 

EUR t-1 

2022 

Labour cost 

30 

EUR t-1 

2022 

Employment 

1.9E-05 

FTE t-1 

2022 

Fabrication of foil 

CAPEX 

260 

EUR t-1 

2022 

OPEX 

668 

EUR t-1 

2022 

Labour cost 

30 

EUR t-1 

2022 

142 

Reference 

Elaborations 
based on Luthin 
et al. (2021) 
Elaborations 
based on Luthin 
et al. (2021) 
Elaborations 
based on Luthin 
et al. (2021) 
Elaborations 
based on Luthin 
et al. (2021) 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 

 
Employment 

1.9E-05 

FTE t-1 

2022 

Fabrication of casting 

CAPEX 

260 

EUR t-1 

2022 

OPEX 

44 

EUR t-1 

2022 

Labour cost 

30 

EUR t-1 

2022 

Employment 

1.9E-05 

FTE t-1 

2022 

Fabrication of other 
(assumed as casting) 

CAPEX 

260 

EUR t-1 

2022 

OPEX 

44 

EUR t-1 

2022 

Labour cost 

30 

EUR t-1 

2022 

Employment 

1.9E-05 

FTE t-1 

2022 

Manufacturing of 
aluminium into 
construction/transport/p
ackaging/machinery/ele
ctrical/consumer 
durables/other uses 
finished products 

Total cost 

681 

EUR t-1 

2021 

CAPEX 

179 

EUR t-1 

2021 

OPEX 

503 

EUR t-1 

2021 

Labour cost 

30 

EUR t-1 

2021 

Employment 

5.7E-04 

FTE t-1 

2021 

Sorting 

CAPEX 

18.3 

EUR t-1 

2015 

143 

JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
JRC elaboration 
based on LCI and 
cost of raw 
materials 
Elaborations 
based on Luthin 
et al. (2021) 
Elaborations 
based on Luthin 
et al. (2021) 
Elaborations 
based on Luthin 
et al. (2021) 
Elaborations 
based on Luthin 
et al. (2021) 
Elaborations 
based on Luthin 
et al. (2021) 
Albizzati, Tonini, 
et al. (2024) 

 
OPEX 

Labour cost 

50 

35 

EUR t-1 

2015 

EUR t-1 

2022 

Employment 

6.7E-04 

FTE t-1 

2022 

Remelting/Refining 
Incineration 

Total cost 
CAPEX 

OPEX 

1040 
50 

75 

EUR t-1 
EUR t-1 

2015 
2015 

EUR t-1 

2015 

Landfill 

Labour cost 

8.3 

EUR t-1 

2015 

Employment 

1.6E-04 

FTE t-1 

2015 

Tax 

CAPEX 

OPEX 

21 

12 

18 

EUR t-1 

2022 

EUR t-1 

2015 

EUR t-1 

2015 

Employment 

7.5E-05 

FTE t-1 

2022 

Wrought aluminium 
alloy 
Cast aluminium alloy 

Tax 

Price 

Price 

48 

1710 

1001 

Transport – truck 

Total cost 

0.140 

Transport – ship 

Total cost 

0.0022 

Transport - barge 

Total cost 

0.0011 

Source: JRC elaboration 

EUR t-1 

2022 

EUR t-1 

2022 

EUR t-1 

2022 

EUR t-1 
km-1 
EUR t-1 
km-1 
EUR t-1 
km-1 

2006 

2019 

2019 

Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Zore (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Tonini, 
et al. (2024) 
Albizzati, Foster, 
et al. (2024) 
Schrottpreise-
aktuell (n.d.) 
Schrottpreise-
aktuell (n.d.) 
Schade et al. 
(2006) 
Schade et al. 
(2006) 
Bernacki (2021) 

As for the energy mixes assumed in the study, the mixes elaborated based on GECO for electricity, 
industrial heat, and space heat for EU and non-EU are reported in Table A 12 and Table A 13 for 
both 2021 and 2050, and for the NDC-LTS and Reference scenarios. Notice that for the non-EU (in 
Table A 12 and Table A 13 reported as ‘RoW’), the following countries were considered with the 
respective shares: China (22%), Egypt (5%), India (5%), Rest of Persian Gulf (16%), Rest of Sub-
Saharan Africa (9%), Russian Federation (16%), and United States (8%). For all other non-EU 
countries/regions (namely, Argentina, Australia, Brazil, Canada, Chile, UK, Indonesia, Iran, Japan, 
Korea (Republic), Mediterranean Middle-East, Mexico, Malaysia, Tunisia+Morocco+Western Sahara, 
Algeria+Libya, Rest of Balkans, Rest of CIS, Rest of Pacific, Rest of South America, Saudi Arabia, 
Thailand, Turkey, Ukraine, Vietnam, South Africa), the remaining 19% of is split evenly across (i.e. 
each country contributes with 0.80%). 

The complete datasets for the energy mixes are available in the Supplementary Material, 
available upon request. 

144 

Table A 12. Energy mixes considered in the assessment. The values reported represent the share of the 
different sources in the average mix for EU27 

Electricity mix –NDC-LTS 
scenario, EU27 

Electricity mix –Reference 
scenario, EU27 

Technology 

Biomass 
Solid coal 
Solid Lignite 
Geothermal 
Hydro, lakes 
Hydro, river 
Natural gas, 
conventional 
Natural gas, 
combined 
Nuclear 
Oil 
Solar panels 
Solar plants 
Wind onshore 
Wind offshore 
CHP 

Technology 

Natural gas 
Oil 
Coal 
District heating 
Biomass 
Hydrogen 

Technology 

Biomass 
Natural gas 
Oil 
Coal 

Technology 

Natural gas 
Oil 
Coal 
Electricity 
District heating 
Biomass 
Hydrogen 

2021 [%] 
7% 
7% 
7% 
0% 
7% 
6% 
3% 

12% 

25% 
2% 
5% 
0% 
13% 
2% 
5% 

2050 [%] 
10% 
0% 
0% 
0% 
5% 
4% 
0% 

2021 [%] 
7% 
7% 
7% 
0% 
7% 
6% 
3% 

0% 

3% 
0% 
28% 
0% 
41% 
6% 
3% 

12% 

25% 
2% 
5% 
0% 
13% 
2% 
5% 

2050 [%] 
9% 
2% 
0% 
1% 
5% 
4% 
2% 

1% 

4% 
0% 
26% 
0% 
39% 
5% 
3% 

Industrial heat mix –NDC-LTS 
scenario, EU27 

Industrial heat mix – Reference 
scenario, EU27 

2021 [%] 
47% 
13% 
3% 
9% 
27% 
0% 

2050 [%] 
16% 
3% 
1% 
18% 
60% 
2% 

2021 [%] 
47% 
13% 
3% 
9% 
27% 
0% 

2050 [%] 
32% 
13% 
3% 
19% 
29% 
4% 

District heating mix –NDC-LTS 
scenario, EU27 

District heating mix – Reference 
scenario, EU27 

2021 [%] 
42% 
38% 
0% 
20% 

2050 [%] 
85% 
15% 
0% 
0% 

2021 [%] 
42% 
38% 
0% 
20% 

2050 [%] 
85% 
15% 
0% 
0% 

Space heating mix –NDC-LTS 
scenario, EU27 

Space heating mix – Reference 
scenario, EU27 

2021 [%] 
41% 
16% 
4% 
10% 
14% 
16% 
0% 

2050 [%] 
0% 
1% 
0% 
48% 
31% 
20% 
0% 

2021 [%] 
41% 
16% 
4% 
10% 
14% 
16% 
0% 

2050 [%] 
3% 
9% 
2% 
37% 
28% 
21% 
0% 

Electricity mix –NDC-LTS 
scenario, RoW 

Electricity mix –Reference 
scenario, RoW 

Technology 

2021 [%] 

2050 [%] 

2021 [%] 

2050 [%] 

145 

 
 
 
 
 
 
Biomass 
Solid coal 
Solid Lignite 
Geothermal 
Hydro, lakes 
Hydro, river 
Natural gas, 
conventional 
Natural gas, 
combined 
Nuclear 
Oil 
Solar panels 
Solar plants 
Wind onshore 
Wind offshore 
CHP 

2% 
49% 
1% 
0% 
16% 
1% 
7% 

7% 

8% 
1% 
3% 
0% 
5% 
0% 
1% 

4% 
4% 
0% 
0% 
11% 
0% 
2% 

5% 

10% 
0% 
25% 
0% 
36% 
3% 
1% 

2% 
49% 
1% 
0% 
16% 
1% 
7% 

7% 

8% 
1% 
3% 
0% 
5% 
0% 
1% 

0% 
27% 
0% 
0% 
9% 
0% 
6% 

7% 

4% 
0% 
26% 
0% 
18% 
1% 
1% 

Industrial heat mix –NDC-LTS 
scenario, RoW 

Industrial heat mix – Reference 
scenario, RoW 

Technology 

2021 [%] 

2050 [%] 

2021 [%] 

2050 [%] 

Natural gas 
Oil 
Coal 
District heating 
Biomass 
Hydrogen 

Source: JRC elaboration 

29% 
22% 
27% 
12% 
10% 
0% 

28% 
18% 
30% 
17% 
6% 
2% 

29% 
22% 
27% 
12% 
10% 
0% 

28% 
18% 
30% 
17% 
6% 
2% 

Table A 13. Energy mixes considered in the assessment. The values reported represent the share of the 
different sources in the marginal mix for EU27 

Electricity mix –NDC-LTS scenario, EU27 

Technology 

Biomass 
Solid coal 
Solid Lignite 
Geothermal 
Hydro, lakes 
Hydro, river 
Natural gas, 
conventional 
Natural gas, 
combined 
Nuclear 
Oil 
Solar panels 
Solar plants 
Wind onshore 
Wind offshore 
CHP 

2050-2021 [%] 
8% 
0% 
0% 
0% 
1% 
0% 
0% 

0% 

0% 
0% 
36% 
0% 
47% 
8% 
0% 

Industrial heat mix –NDC-LTS scenario, EU27 
Technology 

2050-2021 [%] 
0% 

Natural gas 

146 

 
 
Oil 
Coal 
District heating 
Biomass 
Hydrogen 

0% 
0% 
0% 
58% 
42% 

District heating mix –NDC-LTS scenario, EU27 
Technology 

Space heating mix –NDC-LTS scenario, EU27 
Technology 

2050-2021 [%] 
0% 
100% 
0% 
0% 

2050-2021 [%] 
0% 
0% 
0% 
97% 
3% 
0% 
0% 

Biomass 
Natural gas 
Oil 
Coal 

Natural gas 
Oil 
Coal 
Electricity 
District heating 
Biomass 
Hydrogen 

Electricity mix –NDC-LTS scenario, RoW 

Technology 

Biomass 
Solid coal 
Solid Lignite 
Geothermal 
Hydro, lakes 
Hydro, river 
Natural gas, 
conventional 
Natural gas, 
combined 
Nuclear 
Oil 
Solar panels 
Solar plants 
Wind onshore 
Wind offshore 
CHP 

2050-2021 [%] 
4% 
1% 
0% 
1% 
5% 
0% 
1% 

4% 

6% 
0% 
33% 
0% 
41% 
4% 
0% 

Industrial heat mix –NDC-LTS scenario, RoW 
Technology 

2050-2021 [%] 
46% 
11% 
35% 
0% 
1% 
8% 

Natural gas 
Oil 
Coal 
District heating 
Biomass 
Hydrogen 

Source: JRC elaboration

147 

 
Annex 3. Additional LCA results 

The relative reduction to the Baseline scenario (BSL50) calculated via LCA for the Compliance with 
selected Targets on Circular Economy scenario (CTC50), the Ambitious Circular Economy scenario 
(ACE50), and the Reduce, Reuse, Recover clusters are reported in Table A 14. 

Table A 14. Savings relative to the Baseline scenario (BSL50) calculated via LCA 

Impact category 
Climate change 
Ozone depletion 
Human toxicity, cancer effects 
Human toxicity, non-cancer effects 
Particulate matter 
Ionising radiation 
Photochemical ozone formation 
Acidification, terrestrial 
Eutrophication, terrestrial 
Eutrophication, freshwater 
Eutrophication, marine 
Ecotoxicity, freshwater 
Land use 
Water use 
Resource use, minerals and metals 
Resource use, energy 

CTC50 
-3.0% 
-2.0% 
-6.0% 
-5.0% 
-5.0% 
-1.0% 
-3.0% 
-4.0% 
-2.0% 
-2.0% 
-2.0% 
-3.0% 
1.0% 
-1.0% 
-0.14% 
-2.0% 

ACE50  Reduce 
-1.0% 
-14% 
-1.0% 
-13% 
-0.38% 
-14% 
-1.0% 
-13% 
-1.0% 
-14% 
-1.0% 
-11% 
-1.0% 
-13% 
-1.0% 
-14% 
-1.0% 
-13% 
-1.0% 
-14% 
-1.0% 
-13% 
-1.0% 
-15% 
-2.0% 
-10% 
-1.0% 
-11% 
-1.0% 
-10% 
-1.0% 
-13% 

Reuse 
-11% 
-10% 
-10% 
-10% 
-11% 
-10% 
-11% 
-11% 
-11% 
-11% 
-11% 
-11% 
-10% 
-10% 
-10% 
-10% 

Recover 
-6.0% 
-4.0% 
-9.0% 
-7.0% 
-7.0% 
-1.0% 
-5.0% 
-6.0% 
-3.0% 
-4.0% 
-4.0% 
-5.0% 
0.17% 
-2.0% 
0.01% 
-4.0% 

Note: CTC50: Compliance with selected Targets on Circular Economy Targets, ACE50: Ambitious Circular 
Economy scenario 

Source: JRC elaboration 

The relative reductions to the Baseline scenario (BSL50) for the levers considered in the study are 
reported in Table A 15. Notice that L(1) corresponds to the ‘Pre-consumer scrap reduction lever’, 
L(2) to ‘Design for reusability’, L(3) to ‘Remanufacturing of automotive components’, L(4) to 
‘Improved waste sorting’, L(5) ‘Improved waste sorting and scrap quality’, L(6) to ‘Design for 
packaging recoverability and recycling’, L(7) to “Expansion of deposit return systems’, and L(8) to 
‘Recovery of mixed waste’.

148 

 
Table A 15. Savings relative to the Baseline scenario (BSL50) calculated via LCA 

Impact category 
Climate change 
Ozone depletion 
Human toxicity, cancer effects 
Human toxicity, non-cancer effects 
Particulate matter 
Ionising radiation 
Photochemical ozone formation 
Acidification, terrestrial 
Eutrophication, terrestrial 
Eutrophication, freshwater 
Eutrophication, marine 
Ecotoxicity, freshwater 
Land use 
Water use 
Resource use, minerals and metals 
Resource use, energy 

L(1) 
-1.0% 
-1.0% 
-0.38% 
-1.0% 
-1.0% 
-1.0% 
-1.0% 
-1.0% 
-1.0% 
-1.0% 
-1.0% 
-1.0% 
-2.0% 
-1.0% 
-1.0% 
-1.0% 

L(2) 
-11% 
-10% 
-11% 
-11% 
-11% 
-10% 
-11% 
-11% 
-11% 
-11% 
-11% 
-11% 
-9.8% 
-10% 
-10% 
-10% 

L(4) 
L(3) 
-2.3% 
-1.5% 
-1.5% 
-1.1% 
-3.2% 
-2.5% 
-2.9% 
-2.0% 
-2.7% 
-2.0% 
-0.14% 
-0.40% 
-1.7% 
-1.1% 
-2.4% 
-1.8% 
-1.3% 
-0.80% 
-1.4% 
-0.95% 
-1.35% 
-0.84% 
-2.3% 
-1.2% 
0.31% 
0.24% 
-0.50%  0.40% 
-0.08%  0.40% 
-1.7% 
-0.9% 

L(5) 
-2.8% 
-2.7% 
-2.8% 
-2.9% 
-3.0% 
-2.4% 
-2.8% 
-2.9% 
-2.8% 
-2.8% 
-2.8% 
-2.9% 
-2.6% 
-2.7% 
-2.7% 
-2.7% 

L(6) 
-0.13% 
-0.09% 
-0.15% 
-0.13% 
-0.15% 
-0.05% 
-0.11% 
-0.14% 
-0.10% 
-0.11% 
-0.10% 
-0.20% 
-0.06% 
-0.07% 
-0.05% 
-0.13% 

L(7) 
-3.6% 
-2.8% 
-5.4% 
-4.2% 
-4.3% 
-1.1% 
-2.9% 
-3.8% 
-2.3% 
-2.1% 
-2.3% 
-3.1% 
-0.56% 
-1.4% 
-0.60% 
-2.7% 

L(8) 
-2.0% 
-1.5% 
-3.1% 
-2.6% 
-2.6% 
-0.51% 
-1.5% 
-2.3% 
-1.1% 
-1.3% 
-1.1% 
-1.6% 
0.40% 
-0.70% 
-0.12% 
-1.2% 

Note: CTC50: L(1) corresponds to the ‘Pre-consumer scrap reduction lever’, L(2) to ‘Design for reusability’, L(3) to ‘Remanufacturing of automotive components’, L(4) to 
‘Improved waste sorting’, L(5) ‘Improved waste sorting and scrap quality’, L(6) to ‘Design for packaging recoverability and recycling’, L(7) to “Expansion of deposit return 
systems’, and L(8) to ‘Recovery of mixed waste’ 

Source: JRC elaboration

149 

 
Annex 4. Values for epistemic uncertainty sensitivity analysis 

The workshop determined the effectiveness of the lever in the alternative future through stress-
testing the assumptions around the impact of CE clusters on material flows. For the analysis, the 
average of the low, medium and high range of effectiveness were taken, as reported Table A 16. 
Table A 16 also contains the expected change in material flow due to the macroeconomic changes, 
as proposed by the experts in the sense-making session, in line with the narrative of the scenarios. 
The magnitude of the change is in line with the one proposed by Schandl et al. (2020), where the 
reference is the difference between the scenario in line with the historical trajectory (SSP2) and the 
other four scenarios that either exhibit growing or contracting economies. 

Table A 16. Values for epistemic uncertainty sensitivity analysis of prospective LCA 

Glocal Eco-World (Collectivist / not supportive) 

Material 
flow change 
-20% 

Ecostates (Collectivist / supportive) 

20% 

Green Business Boom (Individualist / supportive) 

20% 

Greening through Crisis (Individualist / not 
supportive) 

-20% 

Source: JRC elaboration

Reduction  Reuse 

Recirculation 

Medium 
67% 
High 
82% 
Low 
52% 
Low 
52% 

High 
82% 
High 
82% 
Low 
52% 
Medium 
67% 

Medium 
67% 
High 
82% 
High 
82% 
Medium 
67% 

150 

 
 
Annex 5. Criteria for Input-Output model selection 

To fit the purpose of this study, the Input-Output (IO) model should be regularly updatable and 
verifiable by EU Commission services. The scope of the IO model should cover the global economy 
but should at least allow to separate impacts inside and outside the EU27. A higher sector 
disaggregation is preferred, especially a disaggregation of sectors linked to the selected case 
studies is considered an added value. The development of a disaggregation methodology is part of 
the methodology which allows to overcome the issue of too aggregated sector classifications. Also, 
either the use of product-by-product tables or the use of industry-by-industry tables is considered. 
Finally, it should be kept in mind that the selected model will be expanded to include dynamic 
analysis. The pre-existence of such a link (e.g. between FIGARO and FIDELIO) is a plus.  

We prefer to first decide on the selection of the most appropriate monetary IO-model without 
considering the availability of environmental and other extensions. In case the selected model does 
not include sufficient coverage of environmental and other extensions, an additional step is needed 
to add the missing data via alternative sources (e.g. using LCI-data from ecoinvent). The reason to 
focus in a first step only on the monetary tables, is that it will allow to select the most appropriate, 
updatable, and high-quality IO model available fitting the purpose of this study and future work 
within the JRC. A minimum requirement for the IOA is to use tables that are widely available and 
known to the scientific community. The options would be too limited if the availability of 
environmental and other extensions is considered already in this decision step. 

The following models are considered:  

•  FIGARO (the 2024 FIGARO edition) 30: The EU inter-country supply, use and input-output 
tables (developed by Eurostat and the JRC) are part of official EU statistics (2000-2021 
data). The FIGARO tables are benchmarked against the most recent macroeconomic 
aggregates and respect the same quality standards as official statistics and are released 
annually by Eurostat (T-2). The tables present the relationship between the EU27 and 18 
non-EU countries plus a rest of world region, covering 64 industries (NACE rev.2 
classification).  

Extension data in FIGARO: Air emissions accounts are collected under Regulation (EU) No 
691/2011 on European Environmental Economic Accounts. Air emission accounts are 
compiled according to the system of environmental economic accounting and can therefore 
be readily combined with input-output tables for further analysis (assuming the use of 
industry-by-industry tables). The data on employment for each EU Member State at the 
level of 64 industries (based on NACE Rev. 2) are expressed in numbers of persons 
employed. These data are collected via the European system of accounts (ESA 2010) 
transmission programme and are available on Eurostat’s website [nama_10_a64_e].  

Note: These data sources are restricted to EU27 data (and in addition UK-data), but do not 
cover data for non-EU countries.  

30 https://ec.europa.eu/eurostat/web/esa-supply-use-input-tables/information-data#figaro  

151 

 
 
 
•  FIGAROe331 is a comprehensive inter-country supply, use, and input-output database for 

2015, featuring labour and environmental extensions that are in line with official statistics. 
The database encompasses data for 213 products and 176 industries across 45 
geographical areas (country disaggregation is equal to FIGARO), as well as one aggregated 
rest of the world region. Labour accounts detail total employment and employment broken 
down by gender and skill level. Energy accounts include primary energy supply and net 
energy use, while air emission accounts cover four types of greenhouse gases - CO2, CH4, 
N2O, and fluorinated gases - for both combustion and non-combustion processes. 

•  EXIOBASE 32: v3.8.2/v4 includes a timeseries of supply, use and input-output tables 

covering 1995-2022. The 163 sectors and 200 products disaggregation provide highly 
detailed sector/product relations between the EU27 MS, 17 non-EU countries and 5 rest of 
world regions. The wide coverage of environmental extensions allows to (partly) cover 14 
out of the 16 environmental impact categories33 as well as employment. Hybrid versions 
and country extended versions of EXIOBASE (EXIOBASE3rx 34) exist as well.  

•  OECD harmonised national Input-Output Tables (OECD IOT tables) 35: Inter-industrial 
flows of goods and services for 76 countries (all OECD countries and several non-member 
economies (including all G20 countries)) and 45 sectors, covering the time-period 1995-
2020 are available. Extension tables only cover CO2-emissions from fuel combustion.  

Based on the above listed characteristics, the use of either FIGARO or FIGAROe3 are both 
considered as the most suitable models in this project. FIGARO has the drawback of the limited 
sector disaggregation, while FIGAROe3 is currently only available for the year 2015. The project 
team’s preference goes to the use of FIGAROe3 model, as it is best the one best in line 
with its needs. Still, the methodology will be developed in such a way that it will be applicable to 
the more common IO-tables. The following issues were considered in making this choice: 

•  Up-to-date multiregional input-output model covering at least the EU27 region 

and other major economies in the world: Both the FIGARO and the OECD IOT tables are 
regularly updated as both are institutionalised in Eurostat and OECD, respectively. The 
FIGAROe3 has currently only data for one year, namely 2015. The development of a time-
series version of this experimental dataset is being planned. EXIOBASE is the culmination of 
work in the FP7 DESIRE project and builds upon earlier work on EXIOBASE 2 in the FP7 
CREEA project and EXIOBASE 1 of the FP6 EXIOPOL project. Future updates are infrequent 
and uncertain. The latest data year for FIGARO is 2021; for FIGAROe3 is 2015; for 
EXIOBASE is 2022 (based on nowcasting); and for OECD IOT tables is 2020.  

•  The model should be verifiable by EU Commission services: The FIGARO tables are 
benchmarked against the most recent macroeconomic aggregates and respect the same 

31 https://data.jrc.ec.europa.eu/collection/id-00403  
32 Stadler, K., Wood, R., Bulavskaya, T., Södersten, C.-J., Simas, M., Schmidt, S., Usubiaga, A., Acosta-Fernández, J., Kuenen, 

J., Bruckner, M., Giljum, S., Lutter, S., Merciai, S., Schmidt, J. H., Theurl, M. C., Plutzar, C., Kastner, T., Eisenmenger, N., 
Erb, K.-H., … Tukker, A. (2021). EXIOBASE 3 (3.8.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5589597 

33 Correspondence tables are found here and here.  
34 Bjelle, Stadler, & Wood. (2019). EXIOBASE 3rx (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.2654460 
35 https://www.oecd.org/sti/ind/input-outputtables.htm  

152 

 
 
 
quality standards as official statistics and are released annually by Eurostat. Also, 
FIGAROe3 is line with official European statistics. This quality is not guaranteed in the use 
of EXIOBASE or the OECD IOT tables.  

•  The scope of the IO model should cover the global economy but should at least al-
low to separate impacts inside and outside the EU27: All models (FIGARO, FIGAROe3, 
EXIOBASE and OECD IOT tables) include the EU27 Member States and the rest of the world, 
although the country aggregation differs between the models.  

•  A higher sector disaggregation is preferred, especially a disaggregation of sectors 
linked to the selected case studies is considered an added value: The highest sector 
disaggregation is available in the FIGAROe3 model covering 176 sectors (and 213 product 
groups). Also, EXIOBASE uses a higher sector disaggregation level, detailing 163 sectors 
(and 200 product groups). Both the FIGARO and the OECD IOT tables use a more aggre-
gated sectors classification, detailing only 64 and 45 sector groups, respectively. This pro-
ject case studies focus on steel, aluminium, plastic, and cement. A first analysis focusing on 
mining, manufacturing and end-of-life treatment of each case study shows a difference in 
the relevance of sector disaggregation between the different IO models.  

o  The limited sector detail in FIGARO and the OECD IOT tables does not allow to 
directly link the model to the case studies. The mining step is aggregated into 
mining and quarrying (in FIGARO) or into non-energy producing products (in OECD 
IOT). In manufacturing, only for the case of textiles one separate manufacturing 
step is available. Waste management is aggregated into the sector group ‘Water 
supply, sewerage, waste management and remediation activities’.  

o  Both FIGAROe3 and EXIOBASE have a much higher detail in their sector 

classification allowing to make a distinction between mining sectors (e.g. the mining 
of iron ores and aluminium ores are covered by separated sectors) and in 
manufacturing already some level of detail is available per case study (e.g. a 
distinction between textile production and production of wearing apparel is made). 
Also, for most cast studies a first distinction between primary and secondary 
production is available. Multiple steps in the waste management system are 
available.  

•  The use of product-by-product tables or the use of industry-by-industry tables is 
considered: The OECD IOT tables are only available following the industry-by-industry ap-
proach. FIGARO, FIGAROe3 and EXIOBASE are all available in both the industry-by-industry 
approach and the product-by-product approach.  

•  The availability of environmental and social extension tables is a plus: None of the 
IO models already include all the necessary environmental and social extension tables. 
EXIOBASE is the model with the most expanded set of mainly environmental extensions. In 
contrast, FIGARO does not include extension data, but its structure easily allows linkages to 
existing (official) EU datasets. FIGAROe3 covers extension data on employment, energy, and 
air emissions. The extension tables of the OECD IOT tables only cover CO2-emissions from 
fuel combustion. 

153 

 
•  The selected model will be expanded to include dynamic analysis. The pre-exist-

ence of such a link is a plus: There is a link between FIGARO (and FIGAROe3) and the dy-
namic econometric IO model FIDELIO. No link with a dynamic model is available for 
EXIOBASE or the OECD IOT tables. 

154 

 
Annex 6. FIGAROe3 

The monetary supply and use tables from the FIGAROe3 model cover 45 geographical areas 
plus one rest-of-the-world (FIGW1) region. Per region the model details 213 products and 176 
industries. Labour accounts detail total employment and employment broken down by gender and 
skill level. Energy accounts include primary energy supply and net energy use, while air emission 
accounts cover four types of greenhouse gases - CO2, CH4, N2O, and fluorinated gases - for both 
combustion and non-combustion processes. 

The geographical areas covered by FIGAROe3 are the EU27 Member States and the individual 
countries Argentina, Australia, Brazil, Canada, Switzerland, China, Indonesia, India, Japan, South 
Korea, Mexico, Norway, Russia, Saudi Arabia, Turkey, United Kingdom, United States, and South 
Africa. In addition, one aggregated rest of the world region (FIGW1) is added to cover the full world 
economy.  

The products classification in the FIGAROe3 model is linked to the CPA 2.1 nomenclature. The sector 
classification is linked to the NACE 2.1 nomenclature. The CPA product categories are related to the 
economic activities of the statistical classification of economic activities (NACE).  

An inter-country supply table (matrix representation shown in Figure A 1) shows the supply of 
goods and services by type of product of an economy for a given period. Each column shows the 
output per sector (in NACE classification) disaggregated into different products (in CPA 
classification). As each sector can supply multiple products, the supply table is heterogeneous. The 
column headings show an area code (counterpartArea) and a sector classification code (colPi). The 
row headings show an area code (refArea) and a product classification code (rowPi). The column 
totals show the total output per sector; the row totals show the total output per product. Only the 
cells highlighted in blue will show observed values.  

Figure A 1. Schematic presentation of an inter-country supply table, detailing two geographical areas 
(EU27 and non-EU27) 

icsupCol →

EU27_A01

…

EU27_U

extraEU27_A01

…

extraEU27_U

counterpartArea →

colPi →

A01

EU27

…

U

A01

…

U

extra EU27

Total Output
b y Product

icsupRow ↓

refArea ↓

rowPi ↓

EU27_CPA_01

CPA_A01

…

EU27

...

EU27_CPA_U

extraEU27_CPA_A01

CPA_U

CPA_A01

…

extra EU27

...

extraEU27_CPA_U

CPA_U

Total Output b y Industry

Source: JRC elaboration based on FIGARO description tables 

An inter-country use table (matrix representation shown in Figure A 2) shows the use of goods 
and services by type of product (in CPA classification) per sector (in NACE classification) of an 
economy for a given period. The use of domestically produced goods and services is shown in the 
dark coloured areas on main diagonal, separately for intermediate use (blue) and for final use 
(green). The light areas represent, vertically, the intermediate (blue) and final use (green) use of 
goods and services produced abroad (imports). In contrast, when presented horizontally they 
represent exports broken down by user and by trading partner.  

155 

 
 
The use table is supplemented with data on tax less subsidies (AB21x31), direct purchases abroad 
by residents (OP_RES), and non-resident purchases in the territory (OP_NRES). The components of 
value added are compensation of employees (D1), other taxes less subsidies on production 
(AB29x39), and gross operating surplus and mixed income (B2A3G).  

The final demand is disaggregated into final demand categories: government consumption 
(P3_S13), consumption of households (P3_S14), consumption of non-profit institutions serving 
households (NPISH; P3_S15), gross fixed capital formation (P51G), and changes in valuables and 
inventories (P5M).  

Figure A 2. Schematic presentation of an inter-country use table, detailing two geographical areas (EU27 
and non-EU27) 

Intermediate use  b lock

Final use  b lock

icuseCol →

EU27_A01

EU27_U

extraEU27_A01

…

extraEU27_U

EU27_P3_S13

…

EU27_P5M extraEU27_P3_S13

…

extraEU27_P5M

counterpartArea →

colPi →

A01

EU27

…

U

A01

…

U

P3_S13

extraEU27

EU27

…

P5M

P3_S13

…

P5M

Total Use (TU)

extraEU27

icuseRow ↓

refArea ↓

rowPi ↓

EU27_CPA_01

CPA_A01

…

EU27

...

EU27_CPA_U

extraEU27_CPA_A01

CPA_U

CPA_A01

…

extraEU27

...

extraEU27_CPA_U

CPA_U

W2_D21X31

W2_OP_RES

W2_OP_NRES

W2_D1

W2_B2A3G

W2_D29X39

W2

W2

W2

W2

W2

W2

D21X31

OP_RES

OP_NRES

D1

B2A3G

D29X39

Total Output (P1)

Source: JRC elaboration based on FIGARO description tables 

The monetary values in the supply and use tables show basic prices (in million euros, current 
prices). A full description of the inter-country supply and use tables is given in the FIGARO manual36 
(2019-edition).  

The FIGAROe3 database is downloaded from the Joint Research Centre Data Catalogue37. This 
includes the download of the inter-country supply and use matrices (Table A 17).  

Table A 17. FIGAROe3 data download 

Name 

Data provider 

Type 

Last update 

Unit 

Inter-Country Supply 
Matrix 

Inter-Country Use 
Matrix 

FIGAROe3 by JRC 

CSV-file 

27/02/2024 

FIGAROe3 by JRC 

CSV-file 

27/02/2024 

Basic prices, in 
million euros 

Basic prices, in 
million euros 

36 European Commission, Eurostat, Rémond-Tiedrez, I., Rueda-Cantuche, J., EU inter-country supply, use and input-output 
tables – Full international and global accounts for research in input-output analysis (FIGARO) – 2019 edition, 
Rémond-Tiedrez, I.(editor), Rueda-Cantuche, J.(editor), Publications Office, 2019, 
https://data.europa.eu/doi/10.2785/385561  

37 https://data.jrc.ec.europa.eu/collection/id-00403#details  

156 

 
 
 
 
Note: please make sure the values are correctly read by the software of choice (Excel, R …). Commas are used 
to separate values; decimal points indicate decimal numbers 

Source: JRC elaboration  

The FIGAROe3 inter-country supply and use tables (in matrix format) can be directly imported in R 
using the read.csv()-function. The tables have one row with column headers and one column with 
row labels, commas are used to separate values, and decimal points are used to indicate decimal 
numbers. 

If the column headers are read in as variable names, the FIGAROe3 supply table has 9 798 rows 
(213 product categories * 46 regions) and 8 097 columns (1 row labels column + 176 industries * 
46 regions). Similarly, the FIGAROe3 use table has 9 809 rows (213 product categories * 46 regions 
+ 11 domestic rows) and 8 327 columns (1 row labels column + 176 industries * 46 regions + 5 
final use categories * 46 regions). 

The inter-country input-output tables of FIGARO are compiled using the standard assumptions 
for all countries. From inter-country supply and use tables, two inter-country input-output tables are 
created following the analytical transformation processes for deriving the input-output tables38: 
inter-country product-by-product input-output tables and inter-country industry-by-industry input-
output tables. 

•  Model B (industry technology assumption) is applied to derive the industry technology 

product-by-product input-output tables.  

•  Model D (fixed product sales structure assumption) is applied to derive the fixed product 

sales structure industry-by-industry input-output tables.  

The inter-country input-output tables are not downloaded but are directly created in the R-software. 
During the modelling of the scenarios, changes will be applied to the inter-country supply and/or 
use tables (and/or to the extension tables). After these changes are applied, a new inter-country 
input-output table is generated via the same analytical transformation process as the original inter-
country input-output table.  

A quality check is done to compare the self-created inter-country input-output table starting from 
the downloaded inter-country supply and use tables and the inter-country input-output table from 
the JRC data catalogue. Only minor deviations due to rounding differences are found (absolute 
value of deviations typically below 0.001).

38 Eurostat (2008), Eurostat Manual of Supply, Use and Input-Output Tables, Publications Office of the European Union, 

Luxembourg 

157 

 
 
 
Annex 7. Input-Output analysis 

The global distribution of pressures and effects related to final consumption of households have 
been calculated using an extended multiregional input-output models. For this purpose, 
environmentally extended industry-by-industry tables were used. The calculation started from the 
following identities:   

(Equation A1)  

 is the total output vector, 

where 
𝑥𝑥 = 𝐴𝐴 ∙ 𝑥𝑥 + 𝑦𝑦
technological coefficients), and 

𝑥𝑥

 the matrix of direct input coefficients (or matrix of 

 is the final demand vector. Solving the model for output gives:  

𝐴𝐴

𝑦𝑦

(Equation A2) 

−1

∙ 𝑦𝑦 = 𝐿𝐿 ∙ 𝑦𝑦 

, and matrix 

 the Leontief inverse also known as the multiplier matrix or 

with identity matrix 
𝑥𝑥 = (𝐼𝐼 − 𝐴𝐴)
matrix of direct and indirect output requirements per unit produced for final demand. The Leontief 
model implies the following assumptions: prices are fixed in the short term, input coefficients are 
constant regardless of output or final demand level changes, structure of the economy is taken to 
be constant, at least in the reported period.  

𝐿𝐿

𝐼𝐼

The direct environmental effects of national production are the result of the sum of the direct 
effects associated with each unit produced in each industry: 

(Equation A3)  

𝐸𝐸

𝑟𝑟
1

𝑟𝑟𝑟𝑟𝑡𝑡

𝑟𝑟𝑟𝑟𝑡𝑡

〉 ∙ 𝑥𝑥
 and 

 is the total environmental or socio-economic effect associated with the corresponding 

𝑟𝑟
𝑇𝑇
= ∑ 𝐸𝐸𝑡𝑡
1 = ∑ 𝑒𝑒𝑡𝑡
where 
= 〈𝑒𝑒
𝐸𝐸
amounts of the final output 
𝑇𝑇
vector. Each element of 
of a product group. By multiplying the environmental pressure per output unit (measured in physical 
units per euro worth of output) by the total output of each industry (measured in million euro), 
defined by equation (2), an environmentally extended input-output model is created: 

 represents the amount of the effect directly caused by the production 
𝑥𝑥

 is the environmental or socio-economic effect intensity 

𝑟𝑟𝑟𝑟𝑡𝑡

𝑟𝑟𝑟𝑟𝑡𝑡

𝑒𝑒

𝑒𝑒

(Equation A4)  

𝑇𝑇

𝑟𝑟𝑟𝑟𝑡𝑡

𝑟𝑟𝑟𝑟𝑡𝑡

−1

∙ 𝑦𝑦

〉 ∙ (𝐼𝐼 − 𝐴𝐴)

 is the vector of total environmental pressures associated with the corresponding 
〉 ∙ 𝑥𝑥 = 〈𝑒𝑒
 the environmental pressure 
𝑇𝑇

in which 
= 〈𝑒𝑒
𝐸𝐸
amounts of the products groups finally used (vector 
intensity vector. Each element of 
caused by the production of a product group. Each element of 
combination, which, for example, allows to derive the EU27 shares in the total footprint.  

represents the amount of the environmental pressure directly 

 is allocated to a sector-region 

) and 

𝑟𝑟𝑟𝑟𝑡𝑡

𝑟𝑟𝑟𝑟𝑡𝑡

𝑟𝑟𝑟𝑟𝑡𝑡

𝐸𝐸

𝑦𝑦

𝑒𝑒

𝑒𝑒

𝑒𝑒

The global distribution of pressures and effects related to the final EU27 consumption have been 
calculated using an extended multiregional input-output model based on FIGAROe3 (industry-by-
industry tables). The calculation is based on the following formula:  

with:  

𝑇𝑇
𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟

𝑇𝑇
+ 𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟

𝑟𝑟𝑟𝑟𝑡𝑡

𝑇𝑇
〉. 𝑥𝑥 + 𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟

= 〈𝑒𝑒

𝑟𝑟𝑟𝑟𝑡𝑡

−1

= 〈𝑒𝑒

〉. (𝐼𝐼 − 𝐴𝐴)

𝑇𝑇
. 𝑦𝑦 + 𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟

(Equation A5) 

• 
• 
• 

• 

 environmental footprint (indirect impacts) 
 environmental footprint (direct impacts generated by final demand) 
𝑇𝑇
𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟
:
: environmental effect intensity vector, the amount of the effect directly caused by 
𝑇𝑇
𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟
:
the production of a product group 
𝑟𝑟𝑟𝑟𝑡𝑡
〈𝑒𝑒
: sectoral (monetary) output 

〉

𝑥𝑥

158 

 
 
 
 
 
 
 
   
  
• 

• 

: Leontief inverse, representing the economic structure of the supply chain net-

work 
(𝐼𝐼 − 𝐴𝐴)

−1

: final demand, EU27 

𝑦𝑦

The scope includes both indirect and direct impacts/resource use. The indirect impacts/resource use 
covers impacts upstream the global production network. It covers impacts from all kinds of 
activities, for example, manufacturing, agriculture, and transport. The direct impacts/resource uses 
cover impacts directly generated by households. For example, the burning of fuels for heating 
houses or driving a car. 

Applying the formula gives individual results for each environmental extension available from the 
FIGAROe3-dataset.  

Applying the formula results in a single value output. It is the total impacts (direct and indirect) 
related to EU27 final demand. To increase the details of the results, one needs to diagonalise either 
the environmental coefficient vectors, or the final demand vector, both result in a column and row 
vector respectively. Diagonalising the coefficient vector results in a column vector detailing by which 
region (geographically) and by which sector the impact is generated. Diagonalising the final demand 
vector results in a row vector detailing by which consumption (per region and per sector output) the 
impact is triggered. Diagonalising both the coefficient vector and the final demand vector results in 
an output matrix detailing both the origin of impacts and their triggers. Due to the large resolution 
of this matrix, this latter output is not preferred. 

159 

 
 
Annex 8. Implementation of the MFA and LCI for the IO analysis 

As first step, a matching between the aluminium-related material flows and stocks and the product 
and sector classification of FIGAROe3 is performed. Table A 18 reports the similarities between the 
MFA and the products and sector classification from FIGAROe3. 

Table A 18. Matching of MFA flows and FIGAROe3 categories 

Production phase (based on 
MFA) 

Product/sector classification 
(based on FIGAROe3) 

Code (based on 
FIGAROe3) 

Bauxite 
Aluminium fluoride 
Anodes, prebake 
Anodes, Söderberg 
Finished products, other uses 

Finished products, electric 
cables & electric others & 
consumer durables 
Finished products, Machinery 
and equipment 
Finished products, Transport 
(cars and light trucks) 
Finished products, Transport 
(Aerospace) & Transport 
(Other) 
Finished products, Building 
Finished products, Packaging 
(Cans) & Packaging (Foil) 

CPA_B07_E 
CPA_B07_E 
CPA_C192_Q 
CPA_C192_Q 
CPA_C_25 

CPA_C_27 

CPA_C_28 

CPA_C_29 

CPA_C_30 

CPA_F_A 
CPA_A01_A 
CPA_A01_B 
CPA_A01_C 
CPA_A01_D 

CPA_A01_E 
CPA_A01_F 

CPA_A01_G 
CPA_A01_H 
CPA_A01_I 
CPA_A01_J 
CPA_A01_K 
CPA_A01_L 
CPA_A01_M 
CPA_A01_O 
CPA_A01_P 
CPA_A01_P 

CPA_A01_Q 

CPA_A03 

Aluminium ores and concentrate 
Aluminium ores and concentrate 
Petroleum coke 
Petroleum coke 
Manufacture of fabricated metal 
products, except machinery and 
equipment 
Manufacture of electrical machinery 
and apparatus n.e.c. 

Manufacture of machinery and 
equipment n.e.c. 
Manufacture of motor vehicles, 
trailers and semi-trailers 
Manufacture of other transport 
equipment 

Construction 
Cultivation of paddy rice 
Cultivation of wheat 
Cultivation of cereal grains n.e.c. 
Cultivation of vegetables, fruit, nuts 
Cultivation of oil seeds 
Cultivation of sugar cane, sugar beet 
Cultivation of plant-based fibers 
Cultivation of crops n.e.c. 
Cattle farming 
Pigs farming 
Poultry farming 
Meat animals n.e.c. 
Animal products n.e.c. 
Raw milk 
Wool, silk-worm cocoons 
Manure treatment (conventional), 
storage 
Manure treatment (biogas), storage 
and land application 
Fishing, operating of fish hatcheries 
and fish farms, service activities 
incidental to fishing 
Processing of meat cattle 
Processing of meat pigs 
Processing of meat poultry 

160 

 
 
 
 
 
 
 
 
Production of meat products n.e.c. 
Manufacture of fish products 
Processing vegetable oils and fats 
Processing of dairy products 
Processed rice 
Sugar refining 
Processing of food products n.e.c. 
Manufacture of beverages 

CPA_C10_A 
CPA_C10_B 
CPA_C10_C 
CPA_C10_D 

CPA_C10_E 
CPA_C10_F 

CPA_C10_G 
CPA_C10_H 
CPA_C10_I 
CPA_C10_J 
CPA_C11 

Source: JRC elaboration 

A disaggregation of the sector related to aluminium is required to allow for a better linkage 
between the results from the MFA and modelling of changes in the supply and use tables. The 
sector disaggregation includes, first, to make the sum of the existing sectors ‘Aluminium production 
(C24_E) and ‘Reprocessing of secondary aluminium into new aluminium’ (C24_F), and second to 
disaggregate these sectors into 9 subsectors, depicted in Table A 19. 

Table A 19. Disaggregated sectors with respective market prices 

Adapted 
NACE 
code 
C24_EF1 

C24_EF2 

C24_EF3 

C24_EF4 

C24_EF5 

Production sector 

refining, 
ingot 

Bauxite 
smelting,  and 
cast production 
Production  of  semis  – 
rolling 
Production  of  semis  – 
extrusion 
Production  of  semis  – 
casting 
Production  of  semis  – 
other 

C24_EF6  Manufacturing 

C24_EF7 

Collection and sorting 

C24_EF8 

Recycling 
and refining) 

(remelting 

C24_EF9  Others 

Source: JRC elaboration 

Estimated 
market price 
[EUR2020 kg-1] 
1.7 

2.8 

2.0 

4.1 

4.1 

4.2 

1.0 

1.3 

- 

Price reference 

Eurostat  trade  data;  2020;  CN7601; 
import; extraEU-trade by EU-27 

Eurostat  trade  data;  2020;  CN7606; 
import; extraEU-trade by EU-27 
Eurostat  trade  data;  2020;  CN7605; 
import; extraEU-trade by EU-27 
Eurostat  trade  data;  2020;  CN7604; 
import; extraEU-trade by EU-27 
Eurostat  trade  data;  2020;  CN7604-
7616; import; extraEU-trade by EU-27 
Eurostat  trade  data;  2020;  CN7610; 
import; extraEU-trade by EU-27 
Eurostat  trade  data;  2020;  CN7602; 
import; extraEU-trade by EU-27 
Average of C24_EF1 and C24_EF7 

- 

Notice that the sector ‘Others’ (C24_EF9) is added to entail the (minor) supply of other non-
aluminium products as mentioned by the original aluminium sector (C24E + C24F) sector in 
FIGAROe3. Via this sector, the original balance of the FIGAROe3 model is maintained. 

The newly added sectors have other (non-aluminium related) inputs which need to be included in 
the use table to complete the ‘production recipe’. The input structure for sectors C24_EF1 to 

161 

 
 
 
 
C24_EF8 is based on data based on life cycle inventories of the LCA. Notice that C24_EF1 includes 
bauxite refining into alumina, alumina smelting into aluminium, and ingot casting. 

First, the inputs of resources, materials, fuels, electricity, and heat is scaled to the sector size and 
converted into monetary units. Each input is linked to one or multiple CPA-codes of the FIGAROe3 
classification.  

After converting all the inputs from the LCI-data into the FIGAROe3 CPA-nomenclature, still many 
inputs (e.g. input of services) remain unspecified. For these inputs, the original values from the 
FIGAROe3 sector C24_E and C24_F are used and spread across the disaggregated sectors based on 
the monetary output shares. The CPA-codes related to aluminium flows are left untouched, as these 
are already based on data from the material flow analysis or from the life cycle inventories of the 
LCA.  

The result is transferred into the use table. To do this, the data needs to be split across domestic 
(i.e. EU27) consumption and imports. This split is based on the original data from FIGAROe3. 
Afterwards, the RAS method is applied to balance the tables. Finally, at this point, the disaggregated 
supply and use table (both product and sector level detail) is increased to increase the granularity 
of the model specifically for the case-related sectors/products. Next, the model is regionally 
disaggregated again to return to the initial geographical detail of FIGAROe3.

162 

 
Annex 9. Extension tables 

9.1 Environmental Footprint 

The Environmental Footprint (EF) method includes 16 impact categories of the environmental 
footprint method. The environmental categories included in the EF-method are: 

•  CC, Climate Change: Global impact due to changes induced to the climate, including in-
creased average global temperatures and sudden regional climatic changes, as a conse-
quence of the emissions to the atmosphere of the so-called greenhouse gases, such as CO2, 
CH4, and N2O. 

•  ODP, Ozone Depletion: Global impact related to the broken-down of stratospheric O3, in-
cluding increased skin cancer cases in humans and damage to plants, as a consequence of 
man-made emissions of halocarbons (as CFCs and HCFCs), halons, and other long-lived 
gases containing chloride and bromine. 

•  HTOX_nc, Human Toxicity, non-cancer: Local and regional impact to humans due to the 
exposure (i.e. due to inhalation of air, drinking water, etc.) to toxic substances emitted in the 
environment and responsible for diseases (e.g. respiratory disease) other than cancer. 
•  HTOX_c, Human Toxicity, cancer: Local and regional impact to humans due to the expo-
sure to toxic substances emitted in the environmental and responsible for cancer effects.  
•  PM, Particulate Matter: Impact on human health due to the increased ambient concentra-
tions of particulate matter (PM) due to the emissions of primary and secondary particulates 
(i.e. precursors, NOx, SO2).  
IR, Ionising Radiation: Impact to human health due to the exposure to ionising radiation 
(radioactivity) under normal operating conditions (i.e. excluding accidents in nuclear plants) 

• 

•  POF, Photochemical Ozone Formation: Local and regional impact to the environment 

and human health related to the formation of tropospheric ozone resulting from the oxida-
tion of solvents and other volatile organic compounds (VOCs) released to the atmosphere 
that affects organic compounds in animals and plants and can increase the frequency of 
respiratory problems.  

•  AC, Acidification: Regional impact to the environment regarding the modification of acidity 
of soils, as consequences of emission and deposition of acids (and compounds that can be 
converted to acids) into the environment. 

•  TEU, Eutrophication, terrestrial: Local and regional impact on the terrestrial ecosystems 
due to substances containing nitrogen (N) or phosphorus (P) which leads to the disappear-
ance of ecosystems that are poor in nutrients.  

•  FEU, Eutrophication, freshwater: Local and regional impact on the freshwater ecosys-

tems due to substances containing phosphorus (P) which leads to the reduced oxygen avail-
ability consequent from increased algal growth.  

•  MEU, Eutrophication, marine: Local and regional impact on the marine ecosystems due to 
substances containing nitrogen (N) which leads to reduced oxygen availability consequent 
from increased algal growth.  

•  LU, Land Use: Impacts due to the effects of occupation and transformation of land in 

terms of reduction of soil qualities (e.g. modification in the organic matter content of soil, or 
loss of the soil itself (erosion)) 

•  ECOTOX, Ecotoxicity freshwater: Local and regional impact to freshwater ecosystem due 
to the release of toxic substances that can accumulate and affect individual species as well 
as the functioning of the entire ecosystem.  

•  WU, Water Use: Impact related to the consumption of freshwater (lakes, rivers, or ground-

water) 

•  FRD, Resource Use, fossils: Global impact related to the decreased availability and the 

potential scarcity for future generations of the total reserve of fossil resources.  

163 

 
•  MRD, Resource Use, minerals and metals: Global impact related to the decreased avail-
ability and the potential scarcity for future generations of the total reserve of mineral and 
metal resources.  

A detailed description of each impact category can be found in Annex 1 of Mengual et al., 2023. 
Those impacts might ultimately lead to impairment of human health, biodiversity and natural 
resource loss, climate change, land use, water use, etc. These 16 impact categories can be 
normalised and weighted into a single weighted score39 (see Table A 20). Normalisation means 
that all impact indicators are multiplied by normalisation factors that represent the overall impact 
of a reference unit (e.g. a whole country or an average citizen). Normalised results based on the 
Environmental Footprint method express the relative shares of the impacts of EU consumption by 
citizens compared to global impacts (per person). Weighting means that all impact indicators are 
given a weight factor that expresses the ‘importance’ of the impact compared to the others. This 
allows the aggregation (summing up) of all impact indicators in one single value. 

Table A 20. Normalisation and weighting factors of the Environmental Footprint method version 3.1 

EF Impact Category 

Unit 

Normalization factor 

Weighting factor 

(EF-method 3.1) 

Climate Change 

kg CO2 eq 

7 553.0832 

Ozone Depletion 

kg CFC11 eq 

0.0523484 

Ionising Radiation 

kBq U-235 eq 

4 220.1634 

Photochemical Ozone 
Formation 

kg NMVOC eq 

40.859198 

Particulate Matter 

disease inc. 

Human Toxicity, non-cancer 

Human Toxicity, cancer 

CTUh 

CTUh 

Acidification 

mol H+ eq 

Eutrophication, freshwater 

kg P eq 

Eutrophication, marine 

kg N eq 

Eutrophication, terrestrial 

mol N eq 

Ecotoxicity, freshwater 

Land Use 

Water Use 

CTUe 

Pt 

m3 depriv. 

Resource Use, fossils 

MJ 

0.0005954 

0.0001287 

1.725E-05 

55.569541 

1.6068521 

19.545182 

176.755 

56 716.586 

819 498.18 

11 468.709 

65 004.26 

0.2106 

0.0631 

0.0501 

0.0478 

0.0896 

0.0184 

0.0213 

0.062 

0.028 

0.0296 

0.0371 

0.0192 

0.0794 

0.0851 

0.0832 

39 In this study we apply the EF 3.1 normalisation and weighting factors, see 

https://eplca.jrc.ec.europa.eu/LCDN/developerEF.html 

164 

 
 
 
Resource Use, minerals, and 
metals 

Source: JRC elaboration 

kg Sb eq 

0.0636226 

0.0755 

The elementary flows need to be converted into environmental impacts using a matrix of 
characterisation factors. Ionising Radiation and Ozone Depletion are excluded because emissions 
related to these impact categories are missing. A matrix of characterization factors reports the 
impact intensity per unit of resource extracted or substance emitted in the environment. Beylot et 
al. (2019)40 make use of 78 elementary flows to estimate 14 out of the 16 environmental impact 
categories. VITO created a matrix of characterisation factors in the ETC/CE project of the 
Consumption Footprint indicator commissioned by the EEA, translating the 528 unique 
environmental extension lines from EXIOBASE v3.8.2 into 14 out of the 16 impact categories of the 
EF-method (Mengual et al., 202341). 

The FIGAROe3 extension table includes emission data on carbon dioxide (CO2), methane (CH4), 
nitrous oxide (N2O), and fluorinated gases (F-gases). This table contains data on air emissions by 
industries and direct emissions by households for 2015 in Gg. We opted for using the data in Gg 
instead of in Gg CO2-equivalents, to allow the data to be usable as well for the non-Climate Change 
impact categories if relevant (e.g. for CH4-emissions as part of the Photochemical Ozone Formation 
impact category). The extension data shows the total volume of emissions (in Gg) per sector per 
geographical area and per household per geographical area.  

A wide coverage of environmental extensions is available from the EXIOBASE (currently v3.8.2) 
dataset. The wide coverage allows to calculate 14 out of the requested 16 impact categories in the 
EF-method. Ionising Radiation and Ozone Depletion are excluded because emissions related to 
these impact categories are missing. Whereas the EF-method also recommends assessing the 
impacts in terms of Ionising Radiation and Ozone Depletion, these two impact categories are 
excluded from this study. Indeed, in EXIOBASE coefficients relative to ionising radiations are missing 
while ozone-depleting substances very often lack a value as well (Beylot et al., 2019). 

A remark here is that the EF-method defines characterization factors for more emissions and 
resources extracted than available in EXIOBASE. For some environmental impacts, like Climate 
Change, the coverage of EXIOBASE is quite complete. For other impacts however, like toxicity, 
EXIOBASE includes only a very limited selection of emissions. No information, and, thus, no 
extension lines are available in EXIOBASE to estimate the impact categories Ozone Depletion and 
Ionising Radiation.  

Characterization factors for an Environmental Footprint estimate via Input-Output 
Analyses (based on Beylot et al., 201940):  
Firstly, when it was not possible to match one EXIOBASE extension to a flow in EF2017, a proxy 
characterisation factor (CF) was introduced. The selection of the proxy was based on the same 
pollutants group (e.g. Persistent Organic Pollutants and Persistent Bioaccumulative and Toxic) or 

40 Beylot, A., Secchi, M., Cerutti, A., Merciai, S., Schmidt, J., Sala, S., 2019. Assessing the environmental impacts of EU 
consumption at macro-scale. J. Clean. Prod. 216, 382–393. https://doi.org/10.1016/j.jclepro.2019.01.134  

41 Sanye Mengual, E. and Sala, S., Consumption Footprint and Domestic Footprint: Assessing the environmental impacts of 
EU consumption and production, EUR 31390 EN, Publications Office of the European Union, Luxembourg, 2023, ISBN 
978-92-76-61754-9, doi:10.2760/218540, JRC128571  

165 

 
 
 
on the same chemical group (e.g. dioxins). Secondly, the oxidation state of chromium and arsenic 
emissions to air is missing in EXIOBASE, whereas it very often influences the fate and the effect 
of the chemical and, subsequently, the impact quantification (regarding human toxicity and 
ecotoxicity). The “unspecified” CF was therefore assigned to chromium, as available in EF2017, 
whereas the highest CF was selected regarding arsenic (no “unspecified” CF in that case, but CFs 
relative to arsenic III and arsenic V). Finally, all the aggregated flows from EXIOBASE needed the 
calculation of a CF encompassing all the substances included in the aggregation. Each of these 
CFs was estimated either i) as a weighted average considering the number of emissions or 
resource produced at EU27 or at global level, as the preferred solution when feasible, or ii) as an 
arithmetic average of the CFs available in EF2017 for the flows included in the group. 

Overall, considering each impact category, a limited number of elementary flows is considered 
for the impact assessment step compared to the total number of flows for which a CF is 
available in the EF-method. In particular, toxicity and ecotoxicity impacts are calculated 
considering 11 to 15 substances, compared to 1 321 to 7 566 substances characterized in the 
EF-method. Substances in EXIOBASE assigned with a CF considering Human Toxicity (cancer and 
non-cancer) and Ecotoxicity freshwater are almost entirely emissions to air, whereas emissions 
to soil and water represent a share comparable to emissions to air in EF-method. Similarly, non-
toxic impacts are calculated with considering from 2 to 5 elementary flows, compared to 7 to 
212 substances differentiated in the EF-method. Finally, similar observations can be made 
regarding resource use (e.g. 13 resource flows assigned with a CF in EXIOBASE with respect to 
minerals and metals resource use, compared to 48 for which a CF is available in EF-method). 
This lower number of substances characterized (and therefore contributing to impacts in the 
assessment) using EXIOBASE compared to the full set of substances available in EF-method is 
essentially due to the absence of the corresponding substances in the EXIOBASE environmental 
extensions. The aggregation of some elementary flows in EXIOBASE (e.g. “other industrial 
minerals”) also additionally contributes to this discrepancy in the number of substances 
characterized. On the contrary, six elementary flows calculated from EXIOBASE have been 
unmapped (that is, these substances have been excluded from the impact characterization) in 
the absence of the corresponding flows (or proxy flows) in the EF-method. 

A shortcoming of EXIOBASE is that the end years of the real data in the extension tables vary and 
are therefore not completely up to date. It means that the extension tables are based on real data 
till a certain year and then the extension coefficients (i.e. the environmental impact per monetary 
unit of sectoral output) are kept constant. This means that, after the data series based on real data 
end, the footprint calculations only capture changes in environmental impacts due to changes in 
output volumes. Changes in environmental efficiency per unit of output are not captured. The end 
years of the extension tables are: 2015 for energy, 2019 all greenhouse gases (nonfuel, non-carbon 
dioxide are nowcasted from 2018), 2013 for material use, and 2011 for most others, land, and 
water. 

The Eurostat Air Emissions Accounts (AEA) by NACE Rev. 2 activity include air pollutants and 
greenhouse gases per sector per geographical area from 1995 till, currently (last update 
20/12/2023), 2022. The database contains carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), 
hydrofluorocarbons (HFC), perfluorocarbons (PFC), nitrogen trifluoride and sulphur hexafluoride (NF3 
and SF6), carbon dioxide from biomass used as fuel, sulphur oxides (SOX), ammonia (NH3), nitrogen 
oxides (NOX), carbon monoxide (CO), particulates (both PM2.5 and PM10), and non-methane volatile 

166 

 
 
organic compounds (NMVOC). In addition, this database provides aggregated air emissions like 
greenhouse gases42, acidifying gases43, and ozone precursors44.  

The data is disaggregated into the NACE Rev. 2 sector classification using 64 branches of activities 
(A64) and some aggregated values plus direct impacts by activities of households (disaggregated 
into heating/cooling activities, transport activities, and other activities by households). The 
disaggregation in geographical areas is available for the individual 27 EU Member States and 
Iceland, Norway, Switzerland, Serbia, and Turkey. Note, that the dataset is incomplete due to 
missing data and unavailable data for non-EU countries. For this reason, only the air pollutants for 
which the AEA data can be supplemented with the EXIOBASE data are retained (total CO2, CH4, N2O, 
HFC, PFC, NH3, NOX, CO, PM2.5, PM10, and NMVOC). This results in the addition of new extension 
lines for these air pollutants, where EXIOBASE data is used to fill in missing values in the AEA data 
(AEA-adjusted extension lines). 

As a result of the not-updated data in EXIOBASE and the incomplete data (missing values and no 
complete coverage of all geographical areas) in the Eurostat Air Emissions Accounts, we apply 
checks and corrections based on external data.  

For Climate Change related air emissions, the corrections are based on the PRIMAP-hist national 
historical emission time series (1750-2022) v2.5.1 dataset45. It is a comprehensive set of 
greenhouse gas emission pathways for every country and Kyoto gas covering the years from 1750 
to 2022, and all UNFCCC (United Nations Framework Convention on Climate Change) member 
states, as well as most non-UNFCCC territories.  

The material extraction data can be overwritten to match, at country level, with the UNEP Global 
Material Flows Database46 (including 13 material categories). This domestic extraction used in 
EXIOBASE can be overwritten to match with the total domestic extraction per material category, per 
year and per country from the UNEP-database. Per material category the sectoral distribution at 
country level available from EXIOBASE remains unchanged. This means that per material category 
and per country the totals are overwritten, but the allocation to the sectors of extraction remains 
unchanged, i.e. based on EXIOBASE.  

Table A 21.  Data downloaded for environmental extensions. 

Name 

Data provider 

Type 

Last update 

Unit 

Air Emissions 
Accounts Matrix 
(Gg) 

FIGAROe3 by JRC 

CSV-file 

27/02/2024 

Gg 

42 Greenhouse gas emissions include CO2, N2O, CH4, HFC, PFDC, SF6 and NF3, all in CO2-equivalents.  
43 Acidifying gases include SOX, NOX and NH3, all in SO2-equivalents.  
44 Ozone precursors include NMVOC, NOX, CO and CH4, all in NMVOC-equivalents.  
45 Gütschow, Johannes ; Pflüger, Mika ; Busch, Daniel (2024): The PRIMAP-hist national historical emissions time series 

(1750-2022) v2.5.1. Zenodo. https://doi.org/10.5281/zenodo.10705513  

46 Global Material Flows Database | Resource Panel (https://www.resourcepanel.org/global-material-flows-database) 

167 

 
 
 
F and Fhh from 
the file 
IOT_2015_ixi.zip\ 
satellite 

Air emissions 
accounts by NACE 
Rev. 2 activity 

EXIOBASE v3.8.2 
by Stadler et al. 
(2021)47 

txt-file 

21/10/2021 

Eurostat 
[env_ac_ainah_r2],  

CSV-file 

20/12/2023 

See 
IOT_2015_ixi.zip\ 
satellite\unit.txt 

tonne (note that 
some indicators 
are provided in 
equivalents) 

PRIMAP-hist 
database v2.5.1  

Guetschow et al. 
(2024) 

CSV-file 

27/02/2024 

Gg 

no rounding 

Source: JRC elaboration 

9.2 Financial costs 

The extension tables of FIGAROe3 show the generation of value added per sector, disaggregated 
into 9 categories as reported in Figure A 3. 

Figure A 3. Details on the value added extension table 

M.EUR
Taxes less subsidies on products purchased: Total
M.EUR
Other net taxes on production
Compensation of employees; wages, salaries, & employers' social contributions: Low-skilled
M.EUR
Compensation of employees; wages, salaries, & employers' social contributions: Medium-skilled M.EUR
M.EUR
Compensation of employees; wages, salaries, & employers' social contributions: High-skilled
M.EUR
Operating surplus: Consumption of fixed capital
M.EUR
Operating surplus: Rents on land
M.EUR
Operating surplus: Royalties on resources
M.EUR
Operating surplus: Remaining net operating surplus

Source: FIGAROe3 

9.3 Employment 

The extension tables of FIGAROe3 show two methods for skills, namely i) based on education, and 
ii) based on occupation. For each method, the data is disaggregated across male and female and 
across low-medium-high skilled. The unit is in 1 000 persons (Figure A 4).  

47 Stadler, K., Wood, R., Bulavskaya, T., Södersten, C.-J., Simas, M., Schmidt, S., Usubiaga, A., Acosta-Fernández, J., Kuenen, 

J., Bruckner, M., Giljum, S., Lutter, S., Merciai, S., Schmidt, J. H., Theurl, M. C., Plutzar, C., Kastner, T., Eisenmenger, N., 
Erb, K.-H., … Tukker, A. (2021). EXIOBASE 3 (3.8.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5589597  

168 

 
 
 
 
Figure A 4. Details on the employment extension table 

FIGe3 - EMPLS_M_EDU
FIGe3 - EMPLS_F_EDU
FIGe3 - EMPMS_M_EDU
FIGe3 - EMPMS_F_EDU
FIGe3 - EMPHS_M_EDU
FIGe3 - EMPHS_F_EDU
FIGe3 - EMPLS_M_OCC
FIGe3 - EMPLS_F_OCC
FIGe3 - EMPMS_M_OCC
FIGe3 - EMPMS_F_OCC
FIGe3 - EMPHS_M_OCC
FIGe3 - EMPHS_F_OCC

Source: FIGAROe3 

1000 p
1000 p
1000 p
1000 p
1000 p
1000 p
1000 p
1000 p
1000 p
1000 p
1000 p
1000 p

9.4 EU strategic autonomy 

Global demand triggers production chains that rely on access to resources. Extension data on the 
use of metal ores is disaggregated into 12 categories, as shown in Figure A 5. These extension 
data is available from the EXIOBASE extension tables.  

Figure A 5. Details on the strategic autonomy extension table 

Domestic Extraction Used - Metal Ores - Bauxite and aluminium ores
Domestic Extraction Used - Metal Ores - Copper ores
Domestic Extraction Used - Metal Ores - Gold ores
Domestic Extraction Used - Metal Ores - Iron ores
Domestic Extraction Used - Metal Ores - Lead ores
Domestic Extraction Used - Metal Ores - Nickel ores
Domestic Extraction Used - Metal Ores - Other non-ferrous metal ores
Domestic Extraction Used - Metal Ores - PGM ores
Domestic Extraction Used - Metal Ores - Silver ores
Domestic Extraction Used - Metal Ores - Tin ores
Domestic Extraction Used - Metal Ores - Uranium and thorium ores
Domestic Extraction Used - Metal Ores - Zinc ores

Source: EXIOBASE 

kt
kt
kt
kt
kt
kt
kt
kt
kt
kt
kt
kt

The extension lines show the amount of (global) metal ores extraction. All the lines available from 
the EXIOBASE model are included, but indeed not all of them are labelled as strategic. Gold, iron, 
lead, silver, tin, uranium, and zinc are identified as non-critical and non-strategic in the 2023 CRM-
list. So, the focus for strategic autonomy should be on the demand for bauxite/aluminium, 
copper, nickel, PGM’s and other non-ferrous. Unfortunately, no data is available for all 
individual strategic materials. 

169 

 
 
 
 
 
 
Annex 10. Implementation of GECO in prospective tables 

Including the NDC-LTS scenario in the disaggregated supply and use tables 

From the GECO-database only the Multi-Regional Input-Output tables for the GECO Reference 
scenario are available. To align with the NDC-LTS scenario multiple datapoint from GECO are used 
to adapt the FIGe3 supply and use tables to reflect future changes following the GECO NDC-LTS 
scenario.  

First, the IO tables from GECO (using the reference scenario) are used to project the change 
(compared to 2015) in growth of value added and final demand across the 31 sectors and final 
demand categories in GECO. Next, the value added for each sector is aligned with specific data on 
the value added for the NDC-LTS scenario. This data is available from the main macroeconomic 
indicators reported in the original publication.  

In addition, the changing energy production mix and energy use of the GECO NDC-LTS scenario is 
implemented into the disaggregated supply and use tables of FIGe3. The production and use of 
energy in the disaggregated supply and use tables are aligned with the projections in the GECO 
NDC-LTS scenario. The integration is based upon an intermediate step creating balanced tables (in 
ktoe) on the supply and use of energy. This balance is building upon the GECO Reference scenario 
and including the specific data on the GECO NDC-LTS scenario on the production and demand for 
energy by sector. A similar procedure is followed for the CO2 combustion and non-combustion 
emissions and the other air pollutants (CH4, NH3, NOX, PM2.5, SO2, and F-gases).  

This alignment is limited to the aggregated EU27 and RoW changes derived from the GECO-results. 
No country specific results were used.  

From these GECO-projections at 31 sector level and final demand, relative changes are derived 
between the base year (2015) and a future year (e.g., 2050). Relative changes are derived for value 
added generation, energy use, electricity production, final demand consumption, and emission 
intensities. All relative changes are derived separately for the EU-27 and the RoW. A cross-table 
shows the links between the sectors from GECO and the 176 sectors from FIGe3. Both a prospective 
use and supply table are estimated using these sectoral level relative changes. For example, a 
growth in the sector of ‘crops’ from GECO between 2015 and 2050 of 24% in EU-27 and 107% in 
the RoW is used for the FIGe3 cultivation sectors A01_A-A01_H. The input structure is maintained 
(so growing a similar pace), but the input energy mix is changing according to the relative changes 
in the energy mix. For example, for the same crop sector the input of coal and oil is reduced by 73% 
and 43% and substituted by gas in the EU-27.  

To balance the prospective supply table with the updated use table, a RAS-method is applied to the 
supply table.  

The change in the extension tables is based on the relative change in the emission intensity. The 
emission intensity is calculated by dividing the emissions by the value added generated by the 
sector. The change ratio, often a reduction between 2015 and 2050, is applied to the original 
emission intensities (for CO2, CH4, NH3, NOX, PM2.5, SO2, and F-gases). All indicators keep the original 
intensities in the prospective tables. 

170 

 
 
Annex 11. Description of FIDELIO 4 modules and implementation of the CE levers 
in FIDELIO 

Static supply-use model: The first module is a static supply-use model. It allows an assessment 
of intersectoral spillovers along global supply chains, following a standard Leontief approach that 
links the demand side of the model with the production side. The outline of the SUT structure is 
shown in Figure A 6. 

Figure A 6. First module of FIDELO: Static Supply-module 

Source: JRC elaboration 

Investment dynamics: The second module captures investment dynamics by expanding the gross 
fixed capital formation column (GFCF) into an investment matrix by industry and product. 
Investment dynamics are added, linking, for example, total output and gross operating surplus in 
the value-added section of the SUT to the investment decisions of an industry. Capital accumulation 
is implemented by adding investment and subtracting depreciation over time. Comparing the results 
of the investment module with those of the supply-use module makes it possible to assess the 
spill-over effects of investment in other sectors, such as R&D. The extension of the investment 
matrix and the dynamics are shown in the stylized version of an investment extended SUT structure 
in Figure A 7. 

171 

 
 
Figure A 7. Second module of FIDELIO: Investment dynamics 

Note: CF: Capital account; Gov: Government; Hh: Household 
Source: JRC elaboration 

Consumption dynamics: The third module adds consumption dynamics. For this purpose, the table 
is restructured into a national accounting matrix (NAM), a format of IO data mainly used for 
computable general equilibrium models for a representation of the circular flow of income. 
Household consumption is divided into quintiles, allowing the impact on different parts of the 
income spectrum to be assessed. Changes in household disposable income led to changes in 
household expenditure based on an econometrically derived Almost Ideal Demand System (AIDS). 
Using the NAM structure, dynamics such as the link between wages and household disposable 
income or taxes and government spending are added. The module allows for an assessment of 
income effects highlighting the income distribution as well as CE rebounds due to re-spending 
(Figure A 8). 

Figure A 8. Third module of FIDELIO: Consumption dynamics 

Note: CF: Capital account; F: Financial account; I: Income account; RoW: Rest of the World 
Source: JRC elaboration 

172 

 
 
 
Full model: In the full model, consumer and producer prices, wages and interest rates are 
activated. A nested constant elasticity of substitution function is now active on the supply side of 
the model using a standard CGE approach. While in the other modules the production recipes were 
kept static, in the fourth module changes in production inputs are possible when their prices change 
relative to each other. The demand for imports is decided based on an Armington elasticity, which 
considers price differences between countries. In the model, exports mirror changes in the demand 
for imports. Changes in consumer prices can lead to changes in consumption and vice versa. 
Thereby, a fall in demand can lead to a fall in prices, resulting in a partial increase in demand. This 
is a price rebound and can be investigated using the full model as opposed to the third module. To 
enhance realism, FIDELIO incorporates price rigidities, acknowledging that industries and consumers 
require time to adjust to optimal production and consumption levels. Economic equilibrium is 
achieved only in the long run, reflecting a neo-Keynesian approach that is more realistic than 
assuming an immediate adjustment to optimal decisions. The price dynamics will influence all 
matrices of the model as displayed in Figure A 9. 

Figure A 9. Fourth module of FIDELIO: Full model with price dynamics 

Note: CF: Capital account; F: Financial account; I: Income account; RoW: Rest of the World 
Source: JRC elaboration 

Herein the detailed implementation of the CE levers (i.e. the shocks) other than the ‘Recover’ ones is 
presented. The shocks are first implemented in Excel and then transposed into FIDELIO. 

1. Implementation of lever 1 “Pre-consumer scrap reduction” 

The information provided by the MFA is considered for the STQ21, BSL50, and ACE50 scenarios to 
calculate the relative reduction. The lever looks at both pre-consumer scrap produced at the 
fabrication and the manufacturing stage. 

As for the changes to be applied for a reduction of the pre-consumer scrap arising at 
manufacturing, the shocks are implemented to the C24 industry (more specifically, C24_E and 
C24_F in FIGAROe3), while the commodity to which changes are applied is CPA_E37T39. 

As for the changes to be applied for a reduction of the pre-consumer scrap arising at fabrication, 
the shocks are implemented to the C24 industry (more specifically, C24_E and C24_F), while the 
commodity to which changes are applied is CPA_B and, more specifically, CPA_B07_E in FIGAROe3. 

173 

 
 
2. Implementation of lever 2 & 3 “Design for reusability” and “Remanufacturing of
automotive components”

The information provided by the MFA is considered for the STQ21, BSL50, and ACE50 scenarios to 
calculate the relative reduction. The lever looks at both pre-consumer scrap produced at the 
fabrication and the manufacturing stage. 

As for the changes to be applied for the lifetime extension of “Transport, passenger car”, the 
industry C29 and commodity CPA_C25 are targeted. Further, to represent the changes in aluminium 
production due to the lifetime extension, the industry C29 and commodities CPA_C24_E and 
CPA_C24_F are targeted. 

As for the changes to be applied for the lifetime extension of “Transport, aerospace” and “Transport, 
other”, the industry C30 and commodity CPA_C30 are targeted. Further, to represent the changes in 
aluminium production due to the lifetime extension, the industry C29 and commodities CPA_C24_E 
and CPA_C24_F are targeted. 

As for the changes to be applied for the lifetime extension of “Buildings”, the industry F_A and the 
commodity CPA_F are targeted. Further, to represent the changes in aluminium production due to 
the lifetime extension, the industry C29 and commodities CPA_C24_E and CPA_C24_F are targeted. 

As for the changes to be applied for the lifetime extension of “Electrical, cables” and “Electrical, 
other”, the industry C27 and the commodity CPA_C27 are targeted. Further, to represent the 
changes in aluminium production due to the lifetime extension, the industry C29 and commodities 
CPA_C24_E and CPA_C24_F are targeted. 

As for the changes to be applied for the lifetime extension of “Machinery and Equipment”, the 
industry C28_A and C28_B, and the commodity CPA_C28_A and CPA_C28_B are targeted. Further, 
to represent the changes in aluminium production due to the lifetime extension, the industry C29 
and commodities CPA_C24_E and CPA_C24_F are targeted. 

As for the change to be applied for the lifetime extension of “Other”, the industry C32 and the 
commodity CPA_C31_32 are targeted. Further, to represent the changes in aluminium production 
due to the lifetime extension, the industry C29 and commodities CPA_C24_E and CPA_C24_F are 
targeted. 

174 

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