Environmental assessment, LCA and A2C circularity monitoring (Final)

159 D7.7 – Environmental assessment, LCA and A2C circularity monitoring (Final) May 2025 Authors: Essi Paronen (VTT); Eveliina Hylkilä (VTT), Vafa Järnefelt (VTT), Iines Toivanen (VTT), Katri Behm (VTT) and Silvia Forin (VTT) Ref. Ares(2025)3662643 - 06/05/2025 A2C – Deliverable D7.7v2.0 Page 2 І159 Technical references Project Acronym Agro2Circular Project Title TERRITORIAL CIRCULAR SYSTEMIC SOLUTION FOR THE UPCYCLING OF RESIDUES FROM THE AGRIFOOD SECTOR Project Coordinator Fuensanta Monzó CETEC [email protected] Project Duration October 2021 – March 2025 (42 months) Deliverable No. D7.7D54 Dissemination level* PU Work Package WP 7 - A2C systemic solution adoption, replication and scalability Task T7.3 - Environmental assessment, LCA and A2C circularity monitoring Lead beneficiary VTT Contributing beneficiary/ies TECH PARTNERS Due date of deliverable 9 May 2025 Actual submission date 6 May2025 * PU = Public PP = Restricted to other programme participants (including the Commission Services) RE = Restricted to a group specified by the consortium (including the Commission Services) CO = Confidential, only for members of the consortium (including the Commission Services) A2C – Deliverable D7.7v2.0 Page 3 І159 Document history V Date Comments v0.1 5.3.2025 First draft of document v0.2 31.3.2025 Revised version based on the comments of Fuensanta Monzo – CETEC and Maite Ferrando (KVELOCE) v1.0 31.3.2025 First final version, approved by the WP leader and the project coordinator, (will be) submitted to EC. v1.1 30.4.2025 First draft based upon first final version v2.0 06.5.2025 Second final version, approved by the WP leader and the project coordinator, (will be) submitted to EC. Document Distribution Log Version Date Distributed to v0.1 5.3.2025 Reviewers, LCA data providers and WP7 leader v1.0 28.3.2025 Coordinator, reviewers and WP7 leader v.1.1 30.4.2025 Coordinator and WP7 leader Verification and approval Name Date Verification Final Draft by WP leader Alba Matamoros 6.5.2025 Approval Final Deliverable by coordinator Fuensanta Monzó 6.5.2025 A2C – Deliverable D7.7v2.0 Page 4 І159 Disclaimer and acknowledgement This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036838 Disclaimer This document reflects only the views of the author(s) the European Research Executive Agency (REA) is not responsible for any use that may be made of the information it contains. Whilst efforts have been made to ensure the accuracy and completeness of this document, the A2C consortium shall not be liable for any errors or omissions, however caused. A2C – Deliverable D7.7v2.0 Page 5 І159 Table of Contents 1 Executive summary ................................................................... 11 2 Introduction ................................................................................ 14 3 Methodology .............................................................................. 16 3.1 Life cycle assessment (LCA) ............................................................................ 16 3.1.1 Environmental impact assessment ............................................................... 16 3.1.2 Data collection methods ............................................................................... 18 3.1.3 Data quality analysis .................................................................................... 19 3.2 Carbon handprint .............................................................................................. 20 4 Agrifood and plastic waste system LCA .................................... 21 4.1 Goal and scope .................................................................................................. 21 4.1.1 Goal .............................................................................................................. 21 4.1.2 Scope ........................................................................................................... 21 4.1.3 Studied systems ........................................................................................... 23 4.1.4 Functional unit .............................................................................................. 25 4.1.5 Benchmark linear system ............................................................................. 26 4.1.6 Assumptions ................................................................................................. 28 4.1.7 Allocation ...................................................................................................... 29 4.1.8 Impact assessment method .......................................................................... 29 4.1.9 Limitations .................................................................................................... 30 4.2 Sensitivity analysis ........................................................................................... 31 4.2.1 Renewable electricity ................................................................................... 31 4.2.2 Water scarcity ............................................................................................... 31 4.2.3 Waste treatment function .............................................................................. 32 5 Agrifood system ......................................................................... 34 5.1 System description ........................................................................................... 34 5.1.1 Demo 4 ......................................................................................................... 36 5.1.2 Demo 3 ......................................................................................................... 38 A2C – Deliverable D7.7v2.0 Page 6 І159 5.1.3 Demo 2c ....................................................................................................... 42 5.1.4 Demo 5 ......................................................................................................... 43 5.1.5 Demo 6 ......................................................................................................... 46 5.1.6 Demo 8 ......................................................................................................... 47 5.2 Results ............................................................................................................... 47 5.2.1 Sensitivity analysis ....................................................................................... 54 5.2.2 Carbon handprint .......................................................................................... 63 5.3 Discussion ......................................................................................................... 64 6 Plastic waste system ................................................................. 67 6.1 System description ........................................................................................... 67 6.1.1 Demo 1 ......................................................................................................... 68 6.1.2 Demo 2a EG to GA and 2b TPA to PCA ...................................................... 70 6.1.3 Demo 7 ......................................................................................................... 73 6.1.4 Demo 9 ......................................................................................................... 73 6.2 Results ............................................................................................................... 74 6.2.1 Whole system ............................................................................................... 74 6.2.2 Without demo 2 a and b ............................................................................... 77 6.2.3 Sensitivity analysis ....................................................................................... 82 6.2.4 Only demo 2a and 2b – carbon footprint ...................................................... 89 6.2.5 Carbon handprint .......................................................................................... 91 6.3 Discussion ......................................................................................................... 92 7 LCA conclusions ........................................................................ 96 8 Circularity monitoring ................................................................. 99 8.1 Framework ......................................................................................................... 99 8.1.1 Objective ...................................................................................................... 99 8.1.2 Methodology ................................................................................................. 99 8.1.3 CE Monitoring Framework .......................................................................... 101 8.2 Application of the framework to demos ........................................................ 122 8.2.1 Scope of the application ............................................................................. 123 8.2.2 Criteria applied in the choice of indicators .................................................. 123 8.2.3 Analysis of the A2C demo cases ................................................................ 125 A2C – Deliverable D7.7v2.0 Page 7 І159 8.3 Discussion and conclusions .......................................................................... 138 9 Bibliography ............................................................................. 143 10 Annex ....................................................................................... 148 List of Tables Table 3-1. EF impact categories with respective impact category indicators, units, characterisation models and robustness (European Commission, 2021). ______________________________________________ 16 Table 3-2. Scale and type of the data used in the LCA. _________________________________________ 19 Table 4-1. Reference flows, functional units and linear benchmarks used in the agrifood waste system LCA. ____________________________________________________________________________________ 25 Table 4-2. Reference flows, functional units and linear benchmarks of the used in the plastic waste system LCA _________________________________________________________________________________ 26 Table 4-3. Factors for water use impact category on different level. _______________________________ 31 Table 4-4. Waste treatment processes used in the waste treatment function sensitivity analysis. ________ 32 Table 5-1. Life cycle inventory of demo 4. ___________________________________________________ 38 Table 5-2. Life cycle inventory of demo 3. ___________________________________________________ 41 Table 5-3. Life cycle inventory of demo 2c. __________________________________________________ 43 Table 5-4. Life cycle inventory of demo 5. ___________________________________________________ 45 Table 5-5. Life cycle inventory of demo 6. ___________________________________________________ 46 Table 5-6. Life cycle inventory of demo 8. ___________________________________________________ 47 Table 6-1. Demo 1 aggregated life cycle inventory. The table includes all the life cycle stages of demo 1. _ 70 Table 6-2. Life cycle inventory of demo 2a. The data is aggregated from all the processing steps. _______ 72 Table 6-3. LCI data of the demo 2b. The data is aggregated from all the demo 2 b processing steps. ____ 72 Table 6-4. Life cycle inventory of demo 7. ___________________________________________________ 73 Table 6-5. Life cycle inventory of demo 9. ___________________________________________________ 74 Table 8-1. Circular economy practice list with definitions. ______________________________________ 102 Table 8-3. Environmental impact assessment metrics. ________________________________________ 110 Table 8-4. The list of relevant indicators to be monitored in Demo 3. A full table with the descriptions of the indicators and sources are presented in Annex B. ____________________________________________ 127 Table 8-5. The list of relevant indicators to be monitored in Demo 4. A full table with the descriptions of the indicators and sources are presented in Annex B. ____________________________________________ 129 Table 8-6. The list of relevant indicators to be monitored in Demo 5. A full table with the descriptions of the indicators and sources are presented in Annex B. ____________________________________________ 131 A2C – Deliverable D7.7v2.0 Page 8 І159 Table 8-7. The list of relevant indicators to be monitored in Demo 6. A full table with the descriptions of the indicators and sources are presented in Annex B. ____________________________________________ 133 Table 8-8. The list of relevant indicators to be monitored in Demo 8. A full table with the descriptions of the indicators and sources are presented in Annex B. ____________________________________________ 135 Table 8-9. The list of relevant indicators to be monitored in Demo 2c. A full table with the descriptions of the indicators and sources are presented in Annex B. ____________________________________________ 137 List of Figures Figure 4.1. System boundary of A2C LCA marked as the red line. ________________________________ 22 Figure 4.2. The studied A2C solution systems in the LCA. The demos belonging to the agrifood waste subsystem are highlighted in green, while the demos belonging to the plastic waste sub-system are highlighted in blue. ______________________________________________________________________________ 24 Figure 5.1. A2C agrifood waste system and the principal material flows. The green boxes illustrate the demo processes, and the underlined and bolded items are the A2C demo outputs.________________________ 34 Figure 5.2. Contribution analysis by different A2C agrifood system life cycle stages (processing steps) with market electricity to the different environmental impact in EF impact categories. _____________________ 48 Figure 5.3. Contribution analysis by different A2C agrifood system process types with market electricity to the different environmental impact in EF impact categories. The results are characterised. ________________ 50 Figure 5.4. Comparison of the A2C agrifood system with market electricity to the linear benchmark system using characterised results. The value of the linear system is manually set to be 1.___________________ 52 Figure 5.5. Normalised and weighted single score environmental footprint results of the A2C agrifood system with market electricity, and of linear benchmark system. ________________________________________ 53 Figure 5.6. Contribution of different A2C agrifood system life cycle stages (processing steps) with wind electricity to the environmental impact in EF impact categories. __________________________________ 55 Figure 5.7. Contribution of different A2C agrifood system process types with wind electricity to the environmental impact in EF impact categories. _______________________________________________ 56 Figure 5.8. Comparison of the A2C agrifood system with wind electricity to the linear benchmark system using characterised results. The value of the linear system is manually set to be 1.___________________ 57 Figure 5.9. Normalised and weighted single score environmental footprint results of the A2C agrifood system with wind electricity, and of linear benchmark system. __________________________________________ 58 Figure 5.10. Normalised and weighted results of agrifood system with market electricity, reported with life cycle stages, and lemon production shown as separate code. ___________________________________ 60 Figure 5.11. Agrifood system results compared to benchmark system, and to benchmark system combined with composting treatment for the biowaste. _________________________________________________ 61 Figure 5.12. Agrifood system results with market electricity and two water scarcity factors, global and Murcia area. ________________________________________________________________________________ 63 A2C – Deliverable D7.7v2.0 Page 9 І159 Figure 6.1. A2C plastic waste system and the principal material flows. The green boxes are part of the demo 1. The underlined and bolded items are the A2C demo outputs. __________________________________ 67 Figure 6.2. Contribution analysis by different A2C plastic waste system life cycle stages (processing steps) to the different environmental impact in EF impact categories. _____________________________________ 75 Figure 6.3. Comparison of the A2C plastic system with all demos to the linear benchmark system using characterised results. The value of the A2C plastic waste system is divided with the value of the linear system. The value of the linear system is manually set to be 1. __________________________________ 76 Figure 6.4. Normalised and weighted single score environmental footprint results of the A2C plastic waste system and of the linear benchmark system. _________________________________________________ 77 Figure 6.5. Contribution analysis by different A2C plastic waste system life cycle stages (processing steps) without the demos 2 a and b to the different environmental impact in EF impact categories. ____________ 78 Figure 6.6. Contribution analysis by different A2C plastic waste system process types without the demos 2 a and b with market electricity to the different environmental impact in EF impact categories. ____________ 79 Figure 6.7. Comparison of the A2C plastic system without the demos 2 a and b to the linear benchmark system using characterised results. The value of the A2C plastic waste system is divided by the value of the linear system. The value of the linear system is manually set to be 1. _____________________________ 80 Figure 6.8. Normalised and weighted single score environmental footprint results of the A2C plastic waste system without demos 2 a and b and of the linear benchmark system. _____________________________ 81 Figure 6.9. Sensitivity analysis: Contribution analysis by different A2C plastic waste system life cycle stages without the demos 2 a and b with wind electricity to the different environmental impact in EF impact categories. ___________________________________________________________________________ 82 Figure 6.10. Sensitivity analysis: Contribution analysis by different A2C plastic waste system process types without the demos 2 and b with wind electricity to the different environmental impact in EF impact categories. ____________________________________________________________________________________ 83 Figure 6.11. Sensitivity analysis: Comparison of A2C plastic system with renewable electricity to the system with market electricity. The value of the A2C plastic waste system with wind electricity is divided with the value of the same system with market electricity. The value of the system with market electricity is manually set to be 1. ___________________________________________________________________________ 84 Figure 6.12. Sensitivity analysis: Normalised and weighted single score environmental footprint results of the A2C plastic waste system without demos 2a and b with market and wind electricity. __________________ 85 Figure 6.13. Sensitivity analysis: Normalised and weighted single score environmental footprint results of the A2C plastic waste system without demos 2a and b with Murcia watershed and global water use impact factors. ______________________________________________________________________________ 87 Figure 6.14. Sensitivity analysis: Normalised and weighted single score environmental footprint results of the A2C plastic waste system with market electricity and benchmark system added with incineration and landfill waste treatment function. ________________________________________________________________ 88 Figure 6.15. Carbon footprint of the A2C laboratory-scale glycolic acid and linear benchmark product lactic acid. The results are modelled using Spanish market electricity.__________________________________ 89 Figure 6.16. Carbon footprint of the A2C laboratory-scale PCA and linear benchmark product benzoic acid. The results are modelled using Spanish market electricity. ______________________________________ 90 A2C – Deliverable D7.7v2.0 Page 16 І159 3 Methodology 3.1 Life cycle assessment (LCA) The study follows the LCA methodology described more in detail in the Agro2Circular D7.5 Evaluation framework and Methodology. LCA is a standardised method for assessing the potential environmental impacts of a product or a service taking into account the whole life cycle. The relevant standards are ISO 14040:2006 “Environmental management – Life cycle assessment – Principles and framework” (International Organization for Standardization, 2006a) and ISO 14044:2006 “Environmental management – Life cycle assessment – Requirements and guidelines” (International Organization for Standardization, 2006b). Also, the European Commission has provided a general guide describing the LCA method for Product Environmental Footprint (PEF) calculations (European Commission, 2021). This LCA applies the impact assessment method suggested in the PEF methodology (European Commission, 2021). 3.1.1 Environmental impact assessment The EF3.1 impact assessment methodology by the European Commission (2021) was used for the characterization, normalization and weighting of the LCA results. The method includes 16 impact categories which are listed in Table 3-1. Table 3-1. EF impact categories with respective impact category indicators, units, characterisation models and robustness (European Commission, 2021). EF impact category Impact category indicator Unit Characterisation model Robustness Climate change, total Global warming potential (GWP100) kg CO2 eq Bern model - Global warming potentials (GWP) over a 100-year time horizon (based on IPCC 2013) I Ozone depletion Ozone depletion potential (ODP) kg CFC-11 eq EDIP model based on the ODPs of the World Meteorological I A2C – Deliverable D7.7v2.0 Page 17 І159 Organisation (WMO) over an infinite time horizon (WMO 2014 + integrations) Human toxicity, cancer Comparative toxic unit for humans (CTUh) CTUh Based on USEtox2.1 model (Fantke et al. 2017), adapted as in Saouter et al., 2018 III Human toxicity, non-cancer Comparative toxic unit for humans (CTUh) CTUh Based on USEtox2.1 model (Fantke et al. 2017), adapted as in Saouter et al., 2018 III Particulate matter Impact on human health Disease incidence PM model (Fantke et al., 2016 in UNEP 2016) I Ionising radiation, human health Human exposure efficiency relative to U235 kBq U235 eq Human health effect model as developed by Dreicer et al. 1995 (Frischknecht et al, 2000) II Photochemical ozone formation, human health Tropospheric ozone concentration increase kg NMVOC eq LOTOS-EUROS model (Van Zelm et al, 2008) as applied in ReCiPe 2008 II Acidification Accumulated exceedance (AE) mol H+ eq Accumulated exceedance (Seppälä et al. 2006, Posch et al, 2008) II Eutrophication, terrestrial Accumulated exceedance (AE) mol N eq Accumulated exceedance (Seppälä et al. 2006, Posch et al, 2008) II Eutrophication, freshwater Fraction of nutrients reaching freshwater end compartment (P) kg P eq EUTREND model (Struijs et al, 2009) as applied in ReCiPe II Eutrophication, marine Fraction of nutrients reaching marine end compartment (N) kg N eq EUTREND model (Struijs et al, 2009) as applied in ReCiPe II Ecotoxicity, freshwater Comparative toxic unit for ecosystems (CTUe) CTUe Based on USEtox2.1 model (Fantke et al. 2017), adapted as in Saouter et al., 2018 III Land use Soil quality index Dimensionless (pt) Soil quality index based on LANCA model (De Laurentiis et al. 2019) and on the LANCA CF version 2.5 (Horn and Maier, 2018) III Water use User deprivation potential (deprivationweighted water consumption) m3 water eq of deprived water Available WAter REmaining (AWARE) model (Boulay et al., 2018; UNEP 2016) III Resource use, minerals and metals Abiotic resource depletion (ADP kg Sb eq van Oers et al., 2002 as in CML 2002 method, v.4.8 III A2C – Deliverable D7.7v2.0 Page 18 І159 ultimate reserves) Resource use, fossils Abiotic resource depletion – fossil fuels (ADP-fossil) MJ van Oers et al., 2002 as in CML 2002 method, v.4.8 III The results of this deliverable in chapters 5.2 and 6.2 are reported as environmental footprint following the impact assessment methodology of PEF (European Commission, 2021). Next, the steps of transforming the LCA results to environmental footprint is explained. First, the characterised results are shown which mean that characterisation factors are applied to convert an assigned life cycle inventory result to the common unit of the EF impact category indicator. The characterised results are normalised by dividing them with normalisation factors that represent the overall inventory of a reference unit (e.g. a whole country or an average citizen). According to the PEF method (European Commission, 2021): “Normalised life cycle impact assessment results express the relative shares of the impacts of the analysed system, in terms of the total contributions to each impact category per reference unit.”. The normalised results are dimensionless. After normalisation, the results are weighted. In the weighting, results are multiplied by a set of weighting factors, which reflect the perceived relative importance of the impact categories. The study applies the weighting proposed by PEF where more weight is given for the most robust impact categories. Finally, the single score environmental footprint is calculated based on the normalised and weighted results. In the interpretation step of the LCA, the life cycle stages which are examined in more detail are selected based on the normalized and weighted single score results. In this LCA, the term life cycle stage corresponds to a processing step within the A2C concept. 3.1.2 Data collection methods An Excel template was developed for LCA data collection. This template was utilized to gather data from project partners for each of the demonstrators (hereinafter referred to as demo). The required information includes the amounts and types of raw materials, energy consumption, and other necessary input resources, as well as the output products, emissions, and waste generated in the manufacturing process. A2C – Deliverable D7.7v2.0 Page 19 І159 3.1.3 Data quality analysis In this LCA, the primary data was directly collected from the demo leaders and other technical project partners. However, since the development of the A2C system is still ongoing, there are some uncertainties regarding the representativeness of the data for industrial use. Therefore, all results presented in this report should be considered preliminary and not final. It is recommended to update the calculations once the solutions are fully adopted on an industrial scale. Each demonstrator description features a chapter detailing the assumptions used in the calculations. Together with the primary data, secondary information for the life cycle inventories was gathered from public data sources. For certain input materials and conventional linear benchmarks, specific datasets were unavailable, and proxies were used. The used proxies are listed in the assumptions section of each of the demos. Secondary data used in the assessment is from the ecoinvent 3.10 database, using the system model “cut-off by classification”. This represents the best technological, geographical, and most up-to-date data available for LCA with high precision and completeness as requested by ISO 14040 (2006). The demos are on different technological scales which impact the data quality. Also, part of the data was estimated instead of real measured data. Table 3-2 below lists the data scale and type used in the LCA. Table 3-2. Scale and type of the data used in the LCA. Demo Life cycle stage Scale (laboratory/demonstrator/industrial) Data type (estimated/real) 1 pre-treatment pilot estimated optical sorting demonstrator estimated delamination and sorting demonstrator real and estimated pre-treatment to enzymatic attack pilot estimated A2C – Deliverable D7.7v2.0 Page 20 І159 PET/PE enzymatic degradation to MHET demonstrator estimated MHET conversion demonstrator estimated 2a EG to GA laboratory real and estimated 2c TPA to PCA laboratory real and estimated 2c lemon extract to lemon oil laboratory real and estimated 3 Lemon enzymatic extraction pilot real 4 Artichoke Microwaveassisted extraction pilot real 5 PHBV and carotenoids production pilot real and estimated PHBV extraction pilot and industrial scaled estimated 6 - industrial estimated 7 - industrial estimated 8 - industrial estimated 9 - industrial real 3.2 Carbon handprint All products have a footprint, i.e. there are inevitable negative impacts in the life cycle of products. Some products, however, can also show positive impacts by enabling others to reduce their footprints e.g. by being more energy efficient in the use stage, creating less material loss in the production stage, or by being longer-lasting in use than the competitive products with the same function. The term handprint is used to refer to these positive impacts that products enable in other actors’ value chains. In this project, the handprint methodology suggested by Pajula et al. (2021) was used to model the potential carbon handprint of the A2C solutions. The method compares the carbon footprints of a baseline (reference situation most likely to occur in the absence of the offered solution, being the linear benchmark products in this project) and the new solution that has a lower footprint. Other environmental handprints (i.e. resource, water, air quality or nutrient handprints) suggested by Pajula et al. (2021) are not considered in this project. A2C – Deliverable D7.7v2.0 Page 21 І159 4 Agrifood and plastic waste system LCA The LCA model for the A2C solution was split into two parts to simplify the analysis: one sub-system for agrifood waste and one for plastic waste. These subsystems are referred to as agrifood waste and plastic waste systems hereafter. This chapter outlines the shared components of both systems' LCAs. More detailed descriptions of the systems are presented in dedicated chapters 5.1 and 6.2. 4.1 Goal and scope 4.1.1 Goal The first goal of this LCA is to detect which processes or life cycle stages have the highest contribution to the A2C systems’ environmental impacts. This will be performed via hotspot analysis. The second goal is to benchmark the environmental performance of the A2C circular solution to a functionally equivalent linear solution. The purpose of combining the benchmarking and hotspot analysis is to identify what are the aspects of the novel A2C system which need to be possibly improved to reach the level of sustainability as the more developed benchmark products. 4.1.2 Scope The processes included in the LCA are shown in Figure 4.1. The red line defines the cradleto-gate system boundary, which includes the part of the product’s life cycle reaching from the production of raw materials to the finalization of the product at company gate. A2C – Deliverable D7.7v2.0 Page 22 І159 Figure 4.1. System boundary of A2C LCA marked as the red line. The A2C system begins with receiving the agrifood, packaging, and agriculture film waste. The selected agrifood waste are artichoke and lemon. These waste types were selected based on data availability. These materials are processed and upcycled in the demonstration units. The system ends after the production of the demo outputs which can be considered as products. It should be noted that the manufacturing of the necessary inputs, such as chemicals, is included in the Life Cycle Assessment (LCA), but these inputs are not reflected in Figure 4.1. Also, the use stage and end-of-life of final products are not included in the LCA. These stages are excluded because the goal of the LCA is to evaluate the A2C demos, and the A2C ingredients produced in them. Additionally, the transport, used packaging materials, and infrastructure are excluded. Transport is not included because the distances between the demos are assumed to be short and the system is not yet at a commercial scale with optimised logistics routes. Packaging materials are excluded because they can be recycled again. Infrastructure, such A2C – Deliverable D7.7v2.0 Page 23 І159 as emissions related to building the machines, is not included because its impact is typically negligible in an LCA study. 4.1.3 Studied systems In the following, the two systems assessed via LCA (agrifood waste and plastic waste, highlighted in green and blue respectively in Figure 4.2) are illustrated in detail. Green boxes represent the demos 3, 4, 5, 6 and 8 and the green text in the white demo 2 box stands for demo 2c. The plastic waste system, which includes demos 1, 7, 9, 2a and 2b, is marked by blue boxes and blue text in the white demo 2 box. Detailed information on each of the demos is available in the project’s technical deliverables. A2C – Deliverable D7.7v2.0 Page 24 І159 Figure 4.2. The studied A2C solution systems in the LCA. The demos belonging to the agrifood waste sub-system are highlighted in green, while the demos belonging to the plastic waste subsystem are highlighted in blue. In theory, the agrifood and plastic systems are connected to each other. The link takes place between the demos 1 and 5 where the water rejections from demo 1 are directed to demo 5. However, in this LCA the link is excluded due to lack of data and the two systems are considered as separate. A2C – Deliverable D7.7v2.0 Page 25 І159 4.1.4 Functional unit This LCA consists of a complex system with several input waste flows and outputs with different functionalities. The main reference flows in the agrifood waste system are lemon and artichoke. For the plastic waste system, the main reference flows are plastic packages and agricultural film waste. The functional units of this LCA’s agrifood system are listed in the Table 4-1 and the plastic waste systems in the Table 4-2. The functional units are amounts of the A2C demo outputs in the column “Amount – functional unit”. The functional units of the linear benchmark systems are similarly listed in Table 4-1 and Table 4-2 in the columns “Amount – functional unit” and “Linear system benchmark”. Table 4-1. Reference flows, functional units and linear benchmarks used in the agrifood waste system LCA. Reference flow and amount Demo A2C demo output Amount – functional unit Linear system benchmark LCI data source artichoke 100 kg 4 dietary fibre 1,2 kg inulin from chicory root Hingsamer et al. 2022 antioxidant extract 0,3 kg ascorbic acid ecoinvent 3.10: ascorbic acid production, RER lemon 2314 kg 3 dietary fibre 166,6 kg inulin from chicory root Hingsamer et al. 2022 phenolic extract 43,97 kg hydroquinone ecoinvent 3.10: hydroquinone production, RER 2c microbial oil 15,11 kg crude coconut oil ecoinvent 3.10: market for coconut oil, crude, GLO 6 pellet: TPS-PBAT (90%) and A2C PHBV (10%) 537,4 kg TPS-PBAT pellet Nessi et al. 2022 8 pellet: PBAT (90%) and A2C PHBV (10%) 537,4 kg PBAT pellet Nessi et al. 2022 A2C – Deliverable D7.7v2.0 Page 32 І159 4.2.3 Waste treatment function The functional unit of this LCA takes into account manufacturing new materials for different industries. However, the A2C solution can also be considered to have another function which is the treatment of waste. Namely, waste treatment emissions can be avoided when using the waste as raw materials in A2C solution instead of directing the waste to waste treatment facilities. The objective of this sensitivity analysis is to detect the impact of the avoided waste treatment which takes place at a waste treatment plant. In the waste treatment function sensitivity analysis, the avoided waste treatment emissions are assigned to the benchmark system. This approach is selected instead of subtracting the waste treatment emissions from the A2C solution results because the waste treatment is not the primary function of the A2C solution in this LCA. Table 4-4 shows the selected waste treatment processes for the agrifood and plastic waste system which are added to the benchmark system. Table 4-4. Waste treatment processes used in the waste treatment function sensitivity analysis. A2C waste system Treated waste Waste treatment ecoinvent 3.10 process agrifood 2414 kg (lemon + artichoke) industrial composting treatment of biowaste, industrial composting, RoW plastic 2189 kg PE incineration treatment of waste polyethylene, municipal incineration, GLO 75 kg PET treatment of waste polyethylene terephthalate, municipal incineration 2264 kg landfill treatment of waste plastic, mixture, sanitary landfill, RoW For the agrifood waste system, the treated waste is the sum of the lemon and artichoke waste. This waste is treated in an industrial composting treatment. Food waste is not treated as landfill, since there was no available dataset describing landfilling of biowaste, but mostly a mixture of biological and thermal disposal. A2C – Deliverable D7.7v2.0 Page 33 І159 The plastic waste system’s waste treatment function is modelled for two options: incineration and landfill. The amount of treated waste is the plastic package waste and agriculture film waste from which the soil is subtracted because it is not plastic. The treatment of the soil is not taken into account in this sensitivity analysis. A2C – Deliverable D7.7v2.0 Page 34 І159 5 Agrifood system 5.1 System description In this chapter the agrifood system is described in more detail. The agrifood system on a higher level is shown in Figure 4.2 with green boxes and text to demonstrate the life cycle stages and demos to which they are connected. Figure 5.1 below shows the agrifood system more in detail. Figure 5.1. A2C agrifood waste system and the principal material flows. The green boxes illustrate the demo processes, and the underlined and bolded items are the A2C demo outputs. The agri-food system consists of processing of two separate agri-food waste streams, artichoke and lemon. Artichoke is processed separately from others in demo 4. Lemon is processed first in demo 3, from which the output fractions are sent to demo 2c and demo 5. From demo 5 the bio-based material is sent further to demos 6 and 8. A2C – Deliverable D7.7v2.0 Page 35 І159 The demo 4 uses artichoke waste to produce dietary fibre and antioxidant extract. The first processes are washing and cutting of artichoke waste, enzymatic extraction, and filtration into solid and liquid phases. The liquid phase then undergoes centrifugation and ultrafiltration, and finally dehydration where solid enriched in dietary fibers is achieved. The solid phase processing contains microwave-assisted extraction and purification, to gain antioxidant extract. The lemon waste enters the demo 3 at the pre-treatment stage, where the lemon peel and pulp are cut to smaller pieces. Water and chemicals are added, and the lemon waste is enzymatically extracted and separated with filtration into solid and liquid phases. The solid phase is purified and dehydrated into dietary fibre. The liquid phase is concentrated and purified with resins. From the purification stage the stream is separated into permeate liquid extract rich in sugars (to be used further) and to purified liquid phase to go through freezedrying/lyophilisation into phenolic extract. The sugary fraction from lemon waste in demo 3, is then sent further to demo 2c and demo 5. In the demo 2c the sugary fraction is used for producing microbial oil using biotechnology processes. The demo 2c consists of processing steps; yeast fermentation, deepwell plate centrifugation, deepwell extraction and solvent evaporation. In the demo 5 the sugary fraction undergoes fermentation, filtration and drying and then the dried solids would go through two processes; PHBV extraction and purification, and carotenoids production. Due to lack of data, the total mass of dried solids goes through PHBV extraction, and thus carotenoids production is excluded. The demo 5 does not have any final output product, but the PHBV material sourced from lemon waste is processed further in demos 6 and 8. In demos 6 and 8, the PHBV obtained from demo 5 is used to manufacture biodegradable pellets for food packaging (demo 6) and for agriculture film (demo 8). In demo 6 the PHBV is blended with starch-TPS and PBAT and in demo 8 with PBAT. A2C – Deliverable D7.7v2.0 Page 36 І159 5.1.1 Demo 4 This part focuses on the microwave-assisted extraction of artichoke waste. This process is operated separately from all other demos, starting with 100 kg artichoke waste. The products, dietary fibre and antioxidant extract, can be derived from the separated liquid and solid phases, respectively. Demo 4 could also process other agricultural wastes, for example lemon or grape, which would have higher sugary fraction / liquid waste stream to be utilized further in demo 5. 5.1.1.1 Assumptions The following assumptions were used when modeling the demo 4: Pre-treatment • Cutting artichoke waste is done by an electrically operated cutter. • Artichoke is washed with tap water. Extraction • The washed and cut artichoke waste is mixed with tap water. • Artichoke/water mixture undergoes extraction with an electrical microwave extractor. • The unit of used enzymes is converted from ml to grams with an assumed density of 1 g/ml. Filtration • Separation of liquid and solid phases is done with manual filters and not considered to use any electrical power. • The liquid phase contains soluble fibers, as dietary fibers. • The solid phase obtained contains antioxidant compounds. Centrifugation of liquid phase • Electrically operated centrifugation is included, but in the future this stage could be omitted by using filtration with metallic sieves. A2C – Deliverable D7.7v2.0 Page 37 І159 Ultrafiltration of liquid phase • Excess water is disposed of into general drain and general wastewater treatment. • Electricity for ultrafiltration is sourced from solar panels located on the facility. Dehydration of liquid phase • Excess water from electrical drying is disposed of into general drain and general wastewater treatment. Microwave-assisted extraction (MAE) of solid phase • The MAE process is repeated 10 times to process the total mass of solid treated with enzymes, inputs are multiplied to treat all the mass. • The ethanol used in the MAE is partly circulating and assumed that 80 % of the ethanol can be reused in the process. This 80% of ethanol is therefore excluded from the calculations, and only 20% of ethanol needed is considered as input to MAE. • The ethanol used is evaporated to air. • Excess solid waste biomass is separated from the solution of the antioxidant. This biomass could in theory be used as nitrogen/carbon source for fermentation in demo 5, but due to its low sugary content it cannot be utilized efficiently and is discarded. This biomass could be used as piglet feed, substituting other feed production, but no substitution benefits or downstream treatment processes (e.g. drying) are considered in this calculation. Purification and Concentration of solid phase • The purification-concentration process is repeated 10 times to process the total mass of solid treated with enzymes, inputs are multiplied to treat all the mass. • As in MAE, the ethanol used in the process is assumed to be 80% reusable, which can be excluded from the study. 20% of the ethanol is included in the calculations. • The ethanol used is evaporated to air. • The final product, antioxidant extract, could be encapsulated, but this process is excluded here. A2C – Deliverable D7.7v2.0 Page 38 І159 5.1.1.2 Life cycle inventory Table 5-1 shows the inventory data used in modelling the demo 4. Table 5-1. Life cycle inventory of demo 4. Data Input amount Output amount Unit ecoinvent v3.10 process and geography artichoke waste 100 kg - electricity, non-solar 590,48 kWh market for electricity, high voltage, ES solar electricity 15 kWh electricity production, photovoltaic, 3kWp slanted-roof installation, multi-Si, panel, mounted, ES water 905 kg market for tap water, Europe without Switzerland enzymes 0,01 kg market for enzymes, GLO citric acid 0,1 kg market for citric acid, GLO ethanol 130 kg market for ethanol, without water, in 99.7% solution state, from ethylene, RER solid enriched in dietary fibres 1,2 kg - antioxidant extract 0,3 kg - waste treated as wastewater 0,6697 m3 treatment of wastewater, average, wastewater treatment, Europe without Switzerland solid waste 100 kg ethanol to air 130 kg - water vapour 0,02880 m3 - 5.1.2 Demo 3 This part focuses on the lemon waste enzymatic extraction process. The high-added value substances, as dietary fibre and phenolic extracts can be derived from extraction after which the solid and liquid phases are separated. The processing is carried out by CTNC. Lemon is mostly used for its juice, and the waste is generated from fruit juice processing and vegetable canning companies. The lemon peel and pulp are discarded as waste, peel from juice extraction (squeezing) and pulp from pulp decrease stage. This white and yellow solid material has a high moisture content of circa 85%. 5.1.2.1 Assumptions The LCA model consists of 2314 kg of lemon waste which is turned into products as described earlier in Table 4-1. The created extracts are dehydrated but are not 100 % dry matter, the dietary fibre has moisture content of 5,1 % and phenolic extracts of 4,1 %. A2C – Deliverable D7.7v2.0 Page 39 І159 The following assumptions were used when modelling the demo 3: Pre-treatment • The lemon production is included in the model based on the mass allocation for lemon production. The burden of lemon waste could also be allocated as zero, since the material used is a waste stream from lemon juice production and would otherwise be discarded if not used in the A2C processes. The exclusion of the lemon production is covered with sensitivity analysis in chapter 5.2.1.2. This is the only demo where a burden for the waste material used as input is considered but it is done here as an example of the maximum impact that the agrifood waste streams could have. • Lemon peel and pulp cutting is done by an electrically operated cutter. Enzymatic Extraction • The boiler feed water is included as normal tap water, instead of decalcified water as described by demo leaders, since there is no specific dataset for decalcified water in ecoinvent database. • Ultrapure water is used for the lemon waste extraction. • Natural gas used in the heating of the mixture in a tank is included with a dataset “market group for heat, central or small-scale sourced from natural gas”, to include all possible environmental impacts in addition to CO2 emissions. The amount of natural gas used in m3 is converted to MJ with a factor of 38, meaning that 1 m3 natural gas has a heating value of 38 MJ. • The enzymes used in the process are included with a global proxy, ecoinvent dataset called “market for enzymes”, which describes the average production of three commonly used enzymes. Filtration • Liquid and solid extracts are separated with decanter. • No losses are considered at this stage. Purification of solid phase • The filtrated solid phase undergoes oxidizing washing with ultrapure water. A2C – Deliverable D7.7v2.0 Page 40 І159 • Circa 16 % of the original solid matter is lost when washing of the fibre and discarded with wastewater. Dehydration of solid phase • The excess water from drying is disposed of into general drain and general wastewater treatment. • Approximately 46% of the original solid matter ends up with the produced dehydrated fibre. Concentration of liquid phase • The excess water is separated in electrical membrane filtration equipment, and the condensed water is disposed of into general drain and general wastewater treatment. • Circa 2 % of the original solid matter is lost in the pipelines, and in the following step of resin purification. Purification of liquid phase with resins • Production of adsorption and desorption resins is excluded from the calculations, since they are circulating and used multiple times. • The electricity use of resin adsorption and desorption is included. • The resins absorb the phenolic compounds, and the sugary fraction can be separated from phenolic compounds. • The relation of the sugary fraction separated is 79:1, meaning 79 kg sugary fraction out of 80 kg liquid phase is treated with resins. • Approximately 24,4% of the original solid matter ends up with the separated sugary fraction. Lyophilisation • The excess water from drying is disposed of into general drain and general wastewater treatment. A2C – Deliverable D7.7v2.0 Page 41 І159 • Approximately 12% of the original solid matter ends up with the produced freezedried polyphenols (=phenolic extract). 5.1.2.2 Life cycle inventory Table 5-2 shows the inventory data used in modelling the demo 3. Table 5-2. Life cycle inventory of demo 3. Data Input amount Output amount Unit ecoinvent v3.10 process and geography lemon waste 2314 kg - electricity 3923,36 kWh market for electricity, high voltage, ES heat 1,06E+04 MJ market group for heat, central or small-scale, natural gas, RER water, ultrapure 17752 kg market for water, ultrapure, RER water 8794 kg market group for tap water, RER enzymes 0,23 kg market for enzymes oxidising agent 2,222 kg hydrogen peroxide, without water, in 50% solution state, RER dehydrated solid extract/ fibre extract 166,6 kg - dehydrated liquid extract / phenolic extract 43,97 kg - liquid extract rich in sugars to demos 5 and 2c 3656 kg - waste treated as wastewater 16,243 m3 treatment of wastewater, average, wastewater treatment, Europe without Switzerland water to air 8,794 m3 - Benchmark product inulin is modelled based on the inventory data in the publication of Hingsamer et al. (2022), where the carbon footprint was about 1,5 kg CO2e/kg inulin. The production was assumed to take place in the Netherlands, and thus it was modeled with Netherlands specific data for electricity and fertilisers’ production. For other inputs, European or global average data from ecoinvent was used. Water used for the inulin washing is not considered, even though harvested roots are washer, chipped and cleaned, then roots are extracted. Also, the average transport of 100 km from field to factory are excluded. The values are calculated from the annual inulin production amount of 10 300 tons. The nitrogen fertilizer used in the cultivation phase emits dinitrogen monoxide, and this is included in the calculations. Also, the waste biomass from inulin processing is included and handled as ecoinvent average data “market for biowaste”, when in Hingsamer et al. (2022) it was excluded as a zero reasoned by IPCC for being the biomass burned. A2C – Deliverable D7.7v2.0 Page 48 І159 Figure 5.2. Contribution analysis by different A2C agrifood system life cycle stages (processing steps) with market electricity to the different environmental impact in EF impact categories. The contribution analysis for the whole agrifood system with Spanish market electricity shows that all demos are quite visible in the results. The biggest impact in 12 out of 16 categories is from demo 5 (ca 30 - 58%) where the sugary fraction from lemon waste is treated with fermentation. In most categories the high impact of demo 5 is caused by the high electricity use for fermentation, impact varying between 18-90 % of total demo 5 impact in different categories. The second highest contributor in demo 5 is the production of sodium chloride used for fermentation. The fermentation uses a lot of chemicals, which could be decreased with the circulating system e.g. the remaining nitrogen and phosphorous sources in the salt water brine could be re-used. In the land use category, where demo 5 impact is largest (58 %), it is mostly (73 %) created by the land occupation and transformation in the yeast production value chain. A2C – Deliverable D7.7v2.0 Page 49 І159 The demo 2c, also processing sugary fraction, has the second highest impacts in many of these 12 categories, which is mostly caused by electricity consumption. For example, in the climate change category demo 2c’s electricity contributes to 93 % of demo impacts. In categories material resources: metals/minerals, the largest shares are caused by demo 8 (42 %), and demo 6 (26 %) both impacts mostly (89-92 %) created by the use of antimony. In ozone depletion, demo 8 is responsible for 58 %, and demo 6 responsible for 35 %, in both demos caused mostly (ca 98%) by the bromomethane (Halon 1001) emissions from the production of purified terephthalic acid used as a raw material in the PBAT production process. Demo 4, where artichoke waste is treated, has the lowest impact in 15 out of 16 categories, but the largest share in photochemical oxidant formation (49 %). This is caused mostly (91 %) from the emissions of ethanol used in the microwave-assisted extraction. The generally low impacts on demo 4 is foreseeable, since the artichoke waste treated (100 kg) is much lower than lemon waste (2 314 kg) in other demos. Demo 3 with lemon enzymatic extraction has also a very low share compared to other demos, but the highest impact in water use (44 %), which is caused mostly (95 %) by the water use for lemon production that was included in the calculations. Demo 3 is the only demo with burden for the waste material, and here the burden is as large as possible in the LCA context, since the treated waste stream is included as traditional lemons which have high irrigation demand. This could be also excluded, which would decrease the impact of water use a great deal. Exclusion of lemon production is covered in the sensitivity analysis chapter 5.2.1.2. In addition, it must be noted that the water balance in the processes and in the ecoinvent data may not be perfectly balanced, so there are some uncertainties related to this impact category. The agrifood system’s results by process types are presented in Figure 5.3. A2C – Deliverable D7.7v2.0 Page 50 І159 Figure 5.3. Contribution analysis by different A2C agrifood system process types with market electricity to the different environmental impact in EF impact categories. The results are characterised. Contribution analysis for all the demos analysed by process types show that overall, the production of the used chemicals and electricity have the largest impact when market electricity is used. Chemicals have the biggest contribution in seven categories: ecotoxicity:freshwater (49 %), eutrophication:freshwater (41 %), human toxicity:carcinogenic (52 %) and non-carcinogenic (46 %), land use (58 %), material resources (94 %), and ozone depletion (94 %). High impact from the production of chemicals is explained by the high resource use and emissions that occur in their production stage. Chemicals with the most impact are caused from traditional PBAT production in demos 6 and 8, e.g. antimony, butanediols, and adipic acid. Also, sodium chloride used in demo 5 has notable impact, and its impact could be decreased with recirculation. A2C – Deliverable D7.7v2.0 Page 51 І159 Electricity production is the biggest contributor of seven categories: acidification (53 %), climate change (53 %), energy resources (72 %), eutrophication:marine (53 %), eutrophication:terrestrial (52 %), ionizing radiation (96 %), and particulate matter formation (42 %). This is explained by the emissions caused by the energy sector, for example sulfur dioxide emissions in acidification, carbon dioxide emissions in climate change, uranium and natural gas consumption in energy resources, and Radon-222 emissions in ionizing radiation. In photochemical oxidant formation, the biggest share is caused by direct emissions (57 %), which are caused mainly by ethanol emissions to air (78 %) from MAE in demo 4 and solvent evaporation (15 %) in demo 2c. In water use, lemon production has a large impact (42 %), which is caused entirely by the use of water for the irrigation of lemon trees in Demo 3. Lemon production was included to the demo as a test, as explained in Demo 3 assumptions in chapter 5.1.2.1. The other codes used for the result analysis are barely visible: fuels, waste treatment and water. Water has some impact in eutrophication:freshwater (18 %), caused by (99 %) the use of ultrapure water in demo 3, resulting from the ultrapure water process releasing phosphates to surface waters. Demo 3 uses ultrapure water for lemon extraction, while demo 4 uses tap water in the artichoke extraction. If water impact savings are wanted, the need for ultrapure water could be challenged. The A2C agrifood system compared to benchmarks is shown in Figure 5.4. Comparing the agrifood system to benchmarks shows that the A2C system has higher impacts than benchmarks in all categories. In most categories the impact is maximum five-fold, but in ionizing radiation the impact of A2C is about 30-fold. A2C – Deliverable D7.7v2.0 Page 52 І159 Figure 5.4. Comparison of the A2C agrifood system with market electricity to the linear benchmark system using characterised results. The value of the linear system is manually set to be 1. Normalized and weighted results of agrifood system and linear benchmark system are presented Figure 5.5. A2C – Deliverable D7.7v2.0 Page 53 І159 Figure 5.5. Normalised and weighted single score environmental footprint results of the A2C agrifood system with market electricity, and of linear benchmark system. The normalized and weighted results for A2C agrifood system in total are ca. three times higher compared to the benchmarks. The benchmarks are linear products with industrialized and optimized processes. The A2C processes are mainly on pilot scale, and there is room for improvements for e.g. circulating the chemicals to decrease their consumption notably. When looking at climate change impact, all benchmarks have lower impacts compared to the relevant A2C demo production. Further development is thus needed to minimize the impacts of the A2C processes. The normalized and weighted results show that the biggest impact from A2C solution are caused from energy resources (25 %), climate change (21 %), and material resources (17 %). The impact on energy resources (25 %) is mostly caused by demo 5 (39 %) and demo 2c (36 %). Demo 5 impact on energy resources is caused mostly by electricity use in A2C – Deliverable D7.7v2.0 Page 54 І159 fermentation (74 %), and demo 2c impact from the sum of electricity used in yeast fermentation and solvent evaporation (93%). The normalized and weighted impact on climate change (21 %) is also caused mostly by demo 5 (38 %) and demo 2c (27 %). Demo 5 impact is mostly caused by electricity used in fermentation (56 %) Demo 2c impact is mostly from the electricity needed for yeast fermentation and solvent evaporation (sum 91 %). The impact of material resources (17 %) is caused by demo 8 (42 %), demo 5 (27 %), and demo 6 (26 %). Demo 8 and demo 6 impacts are mostly caused by antimony (92 % and 89 % respectively) used in PBAT production, demo 5 by sodium chloride (89 %) used in fermentation. When the normalized and weighted results are analysed on a demo level, the impact is caused as follows: 34 % from demo 5, 24 % from demo 2c, 16 % from demo 8, 12 % from demo 6, 9 % from demo 3, and 5 % from demo 4. 5.2.1 Sensitivity analysis 5.2.1.1 Renewable electricity in full agrifood system The results for the whole A2C agrifood system with wind electricity are shown in Figure 5.6. A2C – Deliverable D7.7v2.0 Page 55 І159 Figure 5.6. Contribution of different A2C agrifood system life cycle stages (processing steps) with wind electricity to the environmental impact in EF impact categories. Contribution analysis of all the agrifood demos processed with wind electricity shows that the impacts are quite evenly distributed, although demo 5 shows slightly larger impact (2860 %) than others in 12 out of 16 categories. Similarly to market electricity, Demo 8 has the highest impact in material resources (42 %) caused by use of antimony, and in ozone depletion (60 %) caused by bromomethane emissions of purified terephthalic acid production. Also, similarly to market electricity, demo 4 has the biggest impact in photochemical oxidant formation (63 %) caused by ethanol (91 %) used in the MAE. Demo 3 has the biggest impact in water use (61 %), caused by the lemon production. The contribution of different agrifood system’s process types is presented in Figure 5.7. A2C – Deliverable D7.7v2.0 Page 56 І159 Figure 5.7. Contribution of different A2C agrifood system process types with wind electricity to the environmental impact in EF impact categories. Contribution analysis for the whole agrifood system with wind electricity shows that the production of chemicals becomes even more relevant, and it has clearly the largest impact in 14 out of 16 impact categories (48-98 %) when the impacts are broken down by the process types. For example, in the climate change category, there are a few chemicals (adipic acid, sodium chloride, 1,4 butanediol and purified terephthalic acid) used in demos 8, 6 and 5 that together contribute nearly 50% of the climate impact in total. In ozone depletion with 98 % impact from the production of chemicals, the most impact (97 %) is caused from the use of purified terephthalic acid in demos 8 and 6. In the photochemical oxidant formation category direct emissions are the biggest contributor (73 %), which is mostly (78 %) caused by direct air emissions from ethanol use in demo 4 Microwave-assisted extraction. The highest impact of water use is caused by lemon production (60 %), entirely from the water consumption for lemon cultivation. A2C – Deliverable D7.7v2.0 Page 57 І159 The other codes used for the result separation are barely visible: fuels, waste treatment and water. Water has some impact in eutrophication:freshwater (23 %), caused 99 % by the use of ultrapure water in demo 3, resulting from the ultrapure water process releasing phosphates to surface waters. The A2C agrifood system with wind electricity compared to benchmarks is shown in Figure 5.8. Figure 5.8. Comparison of the A2C agrifood system with wind electricity to the linear benchmark system using characterised results. The value of the linear system is manually set to be 1. Comparing the agrifood system to benchmarks shows that the A2C system has higher impacts than benchmarks in all categories. In most categories the impact of A2C is two to three-fold, but in land use about five-fold, and in photochemical oxidant formation about seven-fold. Normalized and weighted results of agrifood system and linear benchmark system are presented in Figure 5.9. A2C – Deliverable D7.7v2.0 Page 64 І159 5.3 Discussion The A2C agrifood system had higher environmental impacts than benchmark products even when the A2C processes were modeled with renewable energy (wind power). The results of the agrifood system highlight the importance of energy and material efficiency in the processes. The production of process chemicals and electricity contribute to all impact categories remarkably. Especially the different chemicals used in demo 5, demo 8, and demo 6 generally have significant impacts across most categories. The other life cycle stages related to the use of water, waste treatment, use of fuels, and direct emissions have only minor impacts to the results. When the results are studied at a demo-level, demo 5 has the single greatest impact to most impact categories. The biggest share of the normalized and weighted results is caused by demo 5 both with market electricity (34 %) and with wind electricity (27 %). This impact is mainly caused by the production stage of the chemicals, especially sodium chloride, and electricity used in the processes. Chemical recirculation could be increased for the fermentation process to gain significant impact deductions, because now the fermentation broth is calculated to be produced for each batch separately. Impacts caused by electricity consumption decrease when the source is changed from market electricity mix to renewable wind electricity. The impact caused by demo 5 has therefore great potential to decrease with recirculation of chemicals. The impact of demo 5 could be divided to demos 6 and 8, since the demo 5 does not currently have any own output products but it only processes the sugary fraction to be used in bioplastic production. If carotenoids production would have had data available for LCA calculations and were included, demo 5 would also have output products. The use of fuels for heating in PHBV extraction in demo 5 does not have significant impact to the overall results, and hence the use of heat return would not create a considerable difference. Also, demos 2c, 8 and 6 have a notable impact on normalized and weighted results with market electricity. With wind electricity, the role of demos changes slightly, and the most impact after demo 5 comes from demos 6, 8 and 3. The impacts of demos 6 and 8 are mostly caused by the production of chemicals (especially antimony, butanediols, and adipic acid) used in the traditional fossil-based PBAT production, since only a limited amount of A2C A2C – Deliverable D7.7v2.0 Page 65 І159 food waste can be used for plastic production to keep the functionality of the plastics high enough. Demo 2c impacts mostly relate to electricity needed for yeast fermentation and solvent evaporation. The high electricity demand is explained by the data being laboratory scale, and there is great potential to be decreased with the scale up. Demo 4 has minor impacts on the results, possibly caused by the smaller amount of agrifood treated, 100 kg of artichoke in demo 4 versus 2314 kg of lemon waste in demo 3. However, in the photochemical oxidant formation category, demo 4 has the most impact (49 %) which is caused mostly from the direct emissions from ethanol used in MAE. Currently 80 % of ethanol is circulating, but if all 100 % would be reused, the impact of the whole agrifood system to the photochemical oxidant formation could be halved. The role of demo 3 relates mostly to the heating used for enzymatic extraction and the production of lemons as the origin of the waste material used. The role of lemon production is mostly visible in the impact categories water use, ecotoxicity:freshwater, terrestrial eutrophication and land use. Lemon waste could also be included burden free as discussed in the sensitivity analysis in chapter 5.2.1.2, since it is considered as waste, and this would decrease the total impact by 4% with market electricity and by 7% when wind electricity is used. However, this would not be enough to change the conclusion about the current superiority of benchmarks compared to the A2C system. When results are normalized and weighted, the categories with highest impact are energy resources: non-renewable, climate change, and material resources: metals/minerals. In the categories of energy resources: non-renewable and climate change, the impacts are mainly from electricity use in demos 5 and from the fermentation in 2c when market electricity is used. With wind electricity, the impact to these categories is from demo 5 steam production for PHBV and sodium chloride for fermentation, and from demo 6 and 8 use of three PBAT chemicals. In impact category material resources: metals/minerals, with both market and wind electricity, the most impact is caused by the use of antimony for PBAT in demo 6 and 8, and sodium chloride used in demo 5 fermentation. A2C – Deliverable D7.7v2.0 Page 66 І159 There are several possibilities to improve the environmental performance of the A2C agrifood system further. Impacts from chemicals’ production could be reduced by optimization and circulation of the fermentation broth, improving the process yields, and minimizing the losses of chemicals. The fossil fuels used for heating purposes, e.g. natural gas, are visible in the results, and some renewable alternatives could be searched for to decrease the environmental impacts from the incineration of these fuels. Some heat compensation could be gained from the organic residues the PHBV processing produces, but these are excluded from the calculations, from being outside the scope of the model. The drying of extracts (fibre extract, phenolic extracts) could also be excluded in some cases, if the material would go directly without preservation demands to the next partner, to be used in final formulations. A2C – Deliverable D7.7v2.0 Page 67 І159 6 Plastic waste system 6.1 System description The plastic waste system on a higher level is illustrated in Section 4.1.3 land shown in blue boxes and text in Figure 4.2. In this chapter the plastic waste system is described in more detail. Figure 6.1 demonstrates the plastic waste system’s life cycle stages and related demos. Figure 6.1. A2C plastic waste system and the principal material flows. The green boxes are part of the demo 1. The underlined and bolded items are the A2C demo outputs. A2C – Deliverable D7.7v2.0 Page 68 І159 The first part of the plastic system is the demo 1 in which the plastic package waste enters the system at the pre-treatment stage. The plastic waste consists of the following plastic multilayers: LDPE/Al/PET, LDPE/metAl/PET, PE/metAl/PET/PE and LDPE multilayer (including PA). In the pre-treatment the plastic waste is shredded, washed, centrifuged and dried. The water treatment after the pre-treatment is excluded due to lack of data. Therefore, the small fraction which goes from demo 1 pre-treatment to demo 5 is also excluded from the LCA due to lack of data on how the water is used in demo 5. After the pre-treatment, the plastic is optically sorted to separate the LDPE from the other plastic waste types. The LDPE is directed to demo 7 and other multilayers continue to delamination and sorting where the multilayers are separated from each other. The PET and PE layers continue to pre-treatment of enzymatic attack. Then, in the PET/PE enzymatic degradation to MHET, the PET is transformed to MHET and the PE passes through the system and is finally directed to demo 9. Then, the MHET is converted to EG and TPA which are inputs for demo 2 a and b biotechnology processing life cycle stages. In the demo 2a, the EG is converted to GA with processing steps biomass and GA production and deepwell plate centrifugation. In the demo 2b, the TPA is transformed to PCA with the process step fermentation, deepwell plate centrifugation, extraction, solvent evaporation and lyophilization. More detailed inputs and outputs of the demo 1 and demo 2ab are available in Table 6-1, Table 6-2 and Table 6-3. In demo 7, non-biodegradable pellets are manufactured from the LDPE from demo 1 optical sorting and recycled filter chunk. In demo 9, the agriculture film waste is pre-treated and non-biodegradable pellets are manufactured using also PE from demo 1 and recycled aluminum. These processes include shredding, washing, centrifuge, fan spindling and shaking. The decontamination process which would take in the end is not included in the LCA due to lack of data. 6.1.1 Demo 1 6.1.1.1 Assumptions Pre-treatment (GWC) • The gas oil is modelled as unleaded petrol which has a heat value of 43,2 GJ/t. A2C – Deliverable D7.7v2.0 Page 69 І159 • This process has no material loss. • The organic residue output of the demo (6kg) is considered burden-free because it can be recycled to the demo 5. Delamination and sorting • The input chemical mix and water are make-up values which need to be added per each batch based on expert opinion. • For the separation fluid used in the process, a generic inorganic chemical was used as a proxy. Saperatec has its own chemical mix, but due to confidentiality reasons it could not be modelled, and proxy is used instead. The selected proxy is ecoinvent 3.10 dataset “market for chemical, inorganic, GLO”. • The output PE flakes can be recycled. Thus, the same amount, 115 kg, is added to the benchmark model as part of the benchmark system. • The output aluminum is part of the filter waste. Pre-treatment to enhance enzymatic attack (CETEC) • Input water is reused; thus, it does not have emissions. • The input ice is disposed of. The melted water is directed to wastewater treatment. However, this water can also be reused. • No PE or PET loss takes place in the process. • PE exits the system at this stage, although in the demo 1 the PE removal takes place in the enzymatic degradation process. This is due to applying data which was updated after the modeling took place. PET/PE enzymatic degradation to MHET • PET with high crystallinity is treated as waste although it could be re-used in other products or recirculated in the pre-treatment to enhance enzymatic attack. • The outputs which are not directed to demos 2 a and b are treated as hazardous waste. A2C – Deliverable D7.7v2.0 Page 70 І159 6.1.1.2 Life cycle inventory The LCA data used to model the demo 1 is shown in Table 6-1. The data shown in this table is aggregated from all the demo 1 life cycle stages. Table 6-1. Demo 1 aggregated life cycle inventory. The table includes all the life cycle stages of demo 1. Data Input amount Output amount Unit ecoinvent v3.10 process and geography plastic package waste 1200 kg - electricity 7053,69 kWh market for electricity, high voltage, ES heat 95,04 MJ market for petrol, unleaded, burned in machinery, GLO chemicals 62,84 kg market for chemical, inorganic, GLO - market for sodium hydroxide, without water, in 50% solution state, RER - market for enzymes, GLO - market for sodium phosphate, Na2HPO4, RER - market for hydrochloric acid, without water, in 30% solution state, RER - market for potassium hydroxide, GLO water 6081 l market for tap water, Europe without Switzerland material to demos 2 a and b 59,66 kg - material to demos 7 and 9 976 kg - recyclable material 95,13 kg - wastewater 5833 l treatment of wastewater, average, wastewater treatment, Europe without Switzerland lost water 248 l - waste to be treated 69,21 kg treatment of waste plastic, mixture, municipal incineration, GLO - treatment of waste polyethylene terephthalate, municipal incineration, GLO - treatment of hazardous waste, hazardous waste incineration, Europe without Switzerland 6.1.2 Demo 2a EG to GA and 2b TPA to PCA 6.1.2.1 Assumptions Demo 2a) EG to GA Biomass and GA production • Tryptone is modelled as soybean meal. • No emissions are assigned to the cooling water because it can be recirculated. DWP centrifugation A2C – Deliverable D7.7v2.0 Page 71 І159 • The vacuum pump electricity consumption is considered negligible and so are emission caused by it. • The biomass is assumed to be water and is treated as wastewater. However, it has potential to be used in other industries. However, in this LCA it is directed to wastewater treatment due to the laboratory scale of this study. • The output GA is in an aqueous solution. In the LCA it is assumed to be separated from the water without any additional emissions. Demo 2b) TPA to PCA Fermentation • Sodium phosphate is used as a proxy for monosodium phosphate. • Zinc sulfide is used as a proxy for zinc sulfate. • Generic inorganic chemicals are used in the LCA due to lack of chemical specific data for the following chemicals: dipotassium phosphate, magnesium dichloride, kanamysin, sodium molybdate, cobalt chloride, manganese chloride. • The input water amount is an estimation calculated based on the mass balance due to uncertainties in measuring the water consumption. • The input cooling water can be recirculated. Thus, it is considered not to have emissions. • The electricity consumption for compressing air is not included because it could not be estimated. DWP centrifugation • Electricity consumption is negligible due to the device properties and use time. Extraction • The input octanol can be re-used and the value used in this LCA is the make-up amount which is needed to be added. The value is based on expert opinion. • The input octanol is modelled as a generic solvent due to lack of octanol-specific data in the ecoinvent database. Lyophilization A2C – Deliverable D7.7v2.0 Page 72 І159 • The input ammonium hydroxide is modelled as ammonia due to lack of ammonium hydroxide data in the ecoinvent database. • The output PCA is mixed with ammonium chloride in a powder. The PCA is the demo 2 b output. However, in this LCA no emissions are assigned to the separation process and the PCA is treated as a main output and all emissions are allocated to it. • The output emissions to air are estimations compiled by the data provider. 6.1.2.2 Life cycle inventory The used LCA data for the demo 2a is shown in Table 6-2. The table combines the processes of biomass and GA production and deepwell plate centrifugation. Table 6-2. Life cycle inventory of demo 2a. The data is aggregated from all the processing steps. Data Input amount Output amount Unit ecoinvent v3.10 process and geography EG from demo 1 16,22 kg - electricity 5799,08 kWh market for electricity, high voltage, ES chemicals 95,39129 kg market for glucose, GLO - market for fodder yeast, GLO - market for soybean meal, RoW - market for propylene glycol, liquid, RoW - market for potassium hydroxide, GLO - market for hydrochloric acid, without water, in 30% solution state, RER. water 871,9 l market for water, deionised, Europe without Switzerland glycolic acid in the final solution 15,69 kg - water in the final solution 856,2 kg - biomass 54,49 l treatment of wastewater, average, wastewater, Europe without Switzerland carbon dioxide, fossil to air 27,25 kg - water vapour to air 363,1 l - Table 6-3 shows the LCA data for the demo 2 b. Table 6-3. LCI data of the demo 2b. The data is aggregated from all the demo 2b processing steps. Data Input amount Output amount Unit ecoinvent v3.10 process and geography TPA from demo 1 43,44 kg electricity 4,36E+05 kWh market for electricity, high voltage, ES chemicals 6,05E+02 kg See Annex A water in the chemical solutions 82,8 kg market for tap water, Europe without Switzerland A2C – Deliverable D7.7v2.0 Page 73 І159 water 4679 kg market for tap water, Europe without Switzerland PCA 22,94 - carbon dioxide, fossil to air 260,90 kg - treated waste 1317 kg treatment of hazardous waste, hazardous waste incineration, Europe without Switzerland treated water 3776 l treatment of wastewater, average, wastewater treatment, Europe without Switzerland octanol to air 54,97 kg - ammonium to air 70,53 kg - ammonium chloride 168,10 kg - 6.1.3 Demo 7 6.1.3.1 Assumptions • The compatibilizer is selected to be maleic anhydride based on the information from the data provider. • The output filter chunk is recycled. Thus, no emissions are assigned to it. 6.1.3.2 Life cycle inventory The used inventory data used in the LCA model of demo 7 is shown in Table 6-4. Table 6-4. Life cycle inventory of demo 7. Data Input amount Output amount Unit ecoinvent v3.10 process and geography LDPE from demo 1 836 kg - compatibilizer 16,7 kg market for maleic anhydride, GLO electricity, non-solar 573,3 kWh market for electricity, high voltage, ES solar electricity 36,93 kWh electricity production, photovoltaic, 3kWp slanted-roof installation, multi-Si, panel, mounted, ES plastic pellet 802,9 kg filter chunk 49,78 kg 6.1.4 Demo 9 6.1.4.1 Assumptions • The input water treatment chemicals are modelled as polyacrylamide. • The emissions of manufacturing the input recycled aluminium are excluded due to its small weight. A2C – Deliverable D7.7v2.0 Page 80 І159 Figure 6.7. Comparison of the A2C plastic system without the demos 2 a and b to the linear benchmark system using characterised results. The value of the A2C plastic waste system is divided by the value of the linear system. The value of the linear system is manually set to be 1. From Figure 6.7 it can be observed that the A2C system without the demo 2 a and b is more environmentally sustainable in all impact categories except for ionising radiation. In this impact category, the higher electricity consumption and other background data of the A2C plastic waste system explains the difference to benchmark. The reason why the difference between the A2C system and the linear system is more significant in the ionising radiation than in the non-material resources category, which is also impacted widely by electricity consumption, is the share of radon used in nuclear energy in the Spanish market electricity dataset. In the linear benchmark system, the production of ethylene is the main contributor in all the impact categories except for ionising radiation and water use ranging from 80-92 % of the total impact categories’ result excluding ionising radiation and water use. The Figure 6.8 shows the single score results of the A2C plastic system without the demo 2 a and b and of the linear system. A2C – Deliverable D7.7v2.0 Page 81 І159 Figure 6.8. Normalised and weighted single score environmental footprint results of the A2C plastic waste system without demos 2 a and b and of the linear benchmark system. From Figure 6.8 it can be noted that the environmental impact of the A2C plastic waste system is 49 % smaller than the linear system’s, and that the most relevant impact categories are climate change and non-renewable energy resources. These two impact categories can be analysed by observing Figure 6.5. In the climate change impact category, the life cycle stages pre-treatment at GWC and PET enzymatic degradation to MHET contribute the most to this life cycle covering 27 % and 24 %, respectively, of the total carbon footprint of the A2C plastic waste system excluding the demo 2a and b. From Figure 6.6 it can be noted that in both these life cycle stages the high electricity consumption is the main contributor to the carbon footprints of the two life cycle stages. Similarly based on Figure 6.5 in the impact category non-renewable energy resources, the life cycle stages pre-treatment at GWC and PET enzymatic degradation to MHET contribute the most to the impact category result contributing 31 % and 21% to the total impact, respectively. Also in this A2C – Deliverable D7.7v2.0 Page 82 І159 impact category, in both these life cycle stages the high electricity consumption is the main contributor to the total impact as seen in Figure 6.6. 6.2.3 Sensitivity analysis 6.2.3.1 Renewable electricity without demo 2a and b As shown earlier in Figure 6.6, the emission derived from electricity consumption impacts the results significantly. Therefore, it is worth evaluating the A2C plastic waste system when the electricity is generated from wind to present a possible future scenario for the system. Figure 6.9 shows the contribution of plastic system life cycle stages’ impact category results. From this Figure it can be noted that PET enzymatic degradation to MHET and delamination and sorting show the highest contribution in various impact categories. Figure 6.9. Sensitivity analysis: Contribution analysis by different A2C plastic waste system life cycle stages without the demos 2 a and b with wind electricity to the different environmental impact in EF impact categories. A2C – Deliverable D7.7v2.0 Page 83 І159 Figure 6.10 shows the contribution of each process type in the studied impact categories when wind electricity is applied. From this figure, it can be noted that electricity and chemical consumption contribute the most. The relative impact of the chemicals increases compared to Figure 6.6 where market electricity was applied. Figure 6.10. Sensitivity analysis: Contribution analysis by different A2C plastic waste system process types without the demos 2 and b with wind electricity to the different environmental impact in EF impact categories. Figure 6.11 compares the results for the A2C plastic waste system without demos 2 and by using Spanish market electricity to using renewable Spanish wind electricity. A2C – Deliverable D7.7v2.0 Page 84 І159 Figure 6.11. Sensitivity analysis: Comparison of A2C plastic system with renewable electricity to the system with market electricity. The value of the A2C plastic waste system with wind electricity is divided with the value of the same system with market electricity. The value of the system with market electricity is manually set to be 1. From Figure 6.11 it can be noted that in all but two impact categories the system’s environmental impact is smaller using renewable energy than market electricity. Namely, in carcinogenic human toxicity and metal/mineral resources the impact of the system with renewable electricity is slightly higher. As seen in Figure 6.9, in carcinogenic human toxicity, the upstream wind electricity emissions are responsible for 79 % of the impact generated in this category. Specifically, anthracene used for example in the ecoinvent dataset of the Spanish wind energy contributes the most in this impact category covering 77 % of the total emissions of the carcinogenic human toxicity category. In metal/mineral resources, based on Figure 6.9, the production of the chemical comprises 52 % of the total impact in this category and electricity upstream emissions 45 %. In chemicals, the manufacturing of sodium phosphate modelled to be used in production steps PET/PE enzymatic degradation to MHET and MHET conversion comprise 57 % of the chemicals’ impact and the generic inorganic chemical used as proxy for the chemical mix in the delamination and sorting cover A2C – Deliverable D7.7v2.0 Page 85 І159 32 % of the chemical emissions. In the upstream electricity emissions, the tellurium used in the ecoinvent wind energy contains 47 % of the upstream electricity’s impact in this category. Figure 6.12 demonstrates the single score environmental footprint results for the A2C plastic waste system without the demo 2 a and b and when using market and wing electricity. From Figure 6.12 it can be noted that the environmental impact of the studied system can be reduced by 73 % if wind electricity is applied. This is due to the decreased environmental impacts in the impact categories of non-renewable energy resources and climate change as fossil sources are switched to cleaner electricity production technologies. Figure 6.12. Sensitivity analysis: Normalised and weighted single score environmental footprint results of the A2C plastic waste system without demos 2a and b with market and wind electricity. Figure 6.12 also shows that if the plastic waste system uses renewable electricity, the impact category climate change contributes the most to the environmental footprint followed by metal/mineral material resources. As seen in Figure 6.9, in impact categories climate change A2C – Deliverable D7.7v2.0 Page 86 І159 and metal/mineral resources the life cycle stages PET enzymatic degradation to MHET and delamination and sorting impact most in this category. Regarding climate change, the PET enzymatic degradation to MHET life cycle stage contributes the most to this impact category, accounting for 35% of the total climate change impact. Within this stage, the treatment of hazardous waste has the highest contribution, amounting to 32% of the impact. The second highest contribution to climate change comes from the delamination and sorting life cycle stage, which covers 28% of the total climate change impact. In this life cycle stage, manufacturing of the chemical mix, modelled using a proxy, contributes 47% of the total climate change impact of this life cycle stage. In metal/mineral material resources, the modelled sodium phosphate used in the PET enzymatic degradation to MHET life cycle stage, covers 57 % of the impact in this life cycle stage. For the delamination and sorting life cycle stage, the used proxy for the chemical mix is in charge of 85 % of the life cycle stage’s emissions in this impact category. 6.2.3.2 Water scarcity factors As detailed in section 4.2.2, a sensitivity analysis was conducted applying the watershed factor for Murcia water use. This analysis specifically focused on the A2C plastic waste system with market electricity, excluding demos 2a and 2b, to identify potential changes with greater precision. Figure 6.13 shows the results for the A2C plastic waste system without the demos 2a and 2b when Murcia watershed and global water use factors are used. A2C – Deliverable D7.7v2.0 Page 87 І159 Figure 6.13. Sensitivity analysis: Normalised and weighted single score environmental footprint results of the A2C plastic waste system without demos 2a and b with Murcia watershed and global water use impact factors. The differences between the water use factors will only affect the water use impact category. However, when observing the water use in the Figure 6.13, it can be noted that applying the Murcia watershed factor has negligible impact to the results. Specifically, it increases the environmental impact of the A2C plastic waste system by 0,9 %. The minor impact is explained by the low contribution of water process type to the total results shown in Figure 6.6. A2C – Deliverable D7.7v2.0 Page 88 І159 6.2.3.3 Waste treatment function The sensitivity analysis for the waste treatment function was conducted for the entire plastic waste system with market electricity. This approach was chosen to assess the environmental performance differences between the plastic waste system and the linear benchmark system when the waste treatment function is included in the benchmark model. Figure 6.14 shows the results for the plastic waste system waste treatment function sensitivity analysis. Figure 6.14. Sensitivity analysis: Normalised and weighted single score environmental footprint results of the A2C plastic waste system with market electricity and benchmark system added with incineration and landfill waste treatment function. From Figure 6.14 it can be noted that although the benchmark model includes the waste treatment burden, the A2C plastic waste system’s environmental impact is still significantly A2C – Deliverable D7.7v2.0 Page 89 І159 higher than the linear benchmark system’s environmental impact. Specifically, 16 times greater than the environmental impact of the benchmark system added with incineration and 22 times greater than added with landfill. 6.2.4 Only demo 2a and 2b – carbon footprint In this section the carbon footprint of manufacturing glycolic acid and protocatechuic acid is presented in Figure 6.15 and Figure 6.16 together with their linear benchmarks. The aim is to demonstrate with the benchmarks the required level of carbon footprint which the A2C demo 2a and 2b should be reaching when being scaled up from laboratory-scale in the future. Figure 6.15. Carbon footprint of the A2C laboratory-scale glycolic acid and linear benchmark product lactic acid. The results are modelled using Spanish market electricity. A2C – Deliverable D7.7v2.0 Page 96 І159 7 LCA conclusions This LCA study aims to evaluate the environmental impact of the A2C agrifood, plastic package waste and agriculture film upcycling system through a Life Cycle Assessment (LCA) study. The LCA has two primary goals: identifying the most relevant process improvements for future technology development through hotspot analysis, and to benchmark the environmental performance of the A2C circular solution to a functionally equivalent linear solution. The purpose of combining benchmarking and hotspot analysis is to identify what are the aspects of the novel A2C system which need to be possibly improved to reach the level of sustainability as the more developed benchmark products, and give guidance on how to decrease the environmental impact of the whole value chain. The LCA contributes to the A2C project objectives by providing a comprehensive evaluation of the environmental impacts which can be applied in the operations of the technological partners and in building the A2C multidimensional model that enables the solution's territorial deployment, replication, and scalability for a wider audience. The study has limitations that caused uncertainty in the results. The level of the used LCA data varies between the A2C solution and the benchmarks, which results in direct comparisons against industrial-scale production being unfair. Hence, the benchmark system serves for future sustainability goal setting purposes rather than for comparing the current level of environmental sustainability. The A2C solution is currently at a lower Technology Readiness Level (TRL), as laboratory or pilot, compared to the benchmarks. Additionally, primary data was not available for all processes and some processes were excluded from the LCA due to the lack of data. The main contributors to the results in the agrifood and plastic waste system are largely impacted by their inefficiencies in electricity and chemical consumption. These inefficiencies are due the less developed technology of the A2C solution compared to the industrially optimized processes found in the linear benchmark system. For the agrifood system, the environmental impact was found to be three times higher than the linear benchmark system. This difference is primarily due to the less developed nature of the A2C solution compared to the industrially optimized benchmark processes. Even if the A2C results were higher than of the compared benchmarks, they are in quite the same scale, and with improvements the A2C – Deliverable D7.7v2.0 Page 97 І159 same level can be reached. The processes with room for improvement are for example recirculation of water and chemicals. For example, recirculation of saltwater brine in demo 5 is tested to be possible, and in further studies with better data, reuse of sodium chloride could be included in the calculations to change the results in many impact categories. Better reuse of ethanol in demo 4 could have potential to halve the impact of photochemical oxidant formation, if 100 % of ethanol would be reused instead of 80 %. Also, if demo 2c data would be scaled up instead of laboratory scale, impacts could be decreased significantly. The traditional PBAT production in demos 6 and 8 has remarkable impact to the overall results, for chemical use and with electricity, and if the share of fossils in the manufacturing of bioplastic could be decreased, impacts of the whole A2C agrifood system would be reduced. For the plastic waste system, the environmental impact was 23 times higher than the linear benchmark system. However, when excluding the life cycle stages modelled using laboratory-scale data, the environmental impact was 49% smaller than the linear benchmark system. The results of this LCA contribute partially to the A2C project objective of decoupling of economic and human activities from the consumption of finite resources and greenhouse gas emissions. Particularly, avoiding the use of primary raw materials is evident in the results of the plastic pellets, reducing the pellets’ environmental impact compared to manufacturing them from virgin materials. However, upcycling the agrifood and plastic waste instead of treating them as waste does not create a reduction in environmental impact with the applied data in this LCA. To reach the same level of sustainability as the linear benchmarks, the A2C solution needs to improve its electricity and chemical consumption. Despite the higher environmental impact presented in this LCA, the A2C solution shows significant potential to improve its environmental performance. Various promising side-streams generated by the A2C systems can be used in other applications which supports avoiding the production of virgin materials. Additionally, A2C solution promotes collaboration among circular economy actors, resulting in more efficient use of materials. Overall, when evaluating the sustainability of circular economy solutions, it is not sufficient to only use waste as input material. Simultaneously, the production must be energy and material efficient. When conducting a complex LCA with various technology partners, cooperation among partners is essential. Understanding the overall concept and material A2C – Deliverable D7.7v2.0 Page 98 І159 flows before data collection starts is vital which also requires strong collaboration. Low TRL level solution LCA, such as A2C solution, should also aim to use more industrial-level data in the LCA through for example TEA (techno-economic analysis). Also, communicating LCA results is complex for a wide-reaching system like A2C. Therefore, it is not always possible to offer straightforward and concise statements regarding the sustainability of a circular economy system. To further assess the sustainability of the A2C system, future LCA studies should prioritize several important areas. Firstly, it is essential to examine the recirculation and valorisation of by-products, as these processes have the potential to reduce waste, improve material efficiency and substitute virgin materials. Secondly, estimating and applying industrial-scale data in the LCA of the A2C solution will offer valuable insights into its true impact by taking into account improvements in energy and material efficiencies. Thirdly, studying the final products that use the A2C demo outputs will help to evaluate sustainability more comprehensively. This includes understanding the substitution ratio, which measures how much of the circular product can replace the linear one and identifying any changes in the final product composition or production process that may arise from using A2C demo outputs. A2C – Deliverable D7.7v2.0 Page 99 І159 8 Circularity monitoring In this section, the framework for circularity monitoring is introduced, followed by description of the framework application process and suggested monitoring indicators. The systems examined in the circularity monitoring are consistent with the systems described in earlier sections 4, 5, and 6 with a few exceptions that will be explained in this section. 8.1 Framework 8.1.1 Objective The objective of this study was to establish a monitoring framework for evaluating circular economy (CE) practices. This research concentrated on identifying the elements that should be considered in the monitoring of CE, rather than the methodological approaches for measuring CE. This distinction is essential, as CE monitoring aims to offer a comprehensive overview of the progress and impact achieved through CE practices. Methods, methodologies, frameworks, and tools serve as means for monitoring and assessment. 8.1.2 Methodology First, a literature review was conducted with the search of relevant literature consisting of the following word combination (“circular econom*” AND monitor* AND framework AND (measur* OR assessment* OR indicator* OR metric*), where the goal of research was on identification of metrics for monitoring and assessing circular economy. The search yielded 180 journal papers. The search was limited to Article; Review Article; Early Access; Book in the Web of Science. The grey literature was left outside the scope of this study. The search focused primarily on identifying quantifiable metrics, including indicators and assessment methods/methodologies. The first step entailed going through article titles and abstracts and identifying relevant papers. Scale of 1-3 was used for scoring the papers. Those marked with 3 were selected for further analysis based on the abstract. Those marked with 2 required a review of the full paper to ensure its relevance. Those marked 1 were excluded from the analysis. The selected studies were then reviewed. From the 180 papers, 136 were selected for further analysis. A2C – Deliverable D7.7v2.0 Page 100 І159 This literature review yielded the identification of 437 metrics. From these groups of metrics, 26 were categorised into assessment methods such as life cycle assessment (LCA), material flow analysis (MFA), etc. From the remaining metrics, 85 were categorized as indicators that were suitable for the monitoring of agriculture and food systems. This list of indicators was used as the primary list of metrics in the application of the developed CE monitoring framework in Step 3 for the case study analysis. A CE indicator is a variable or function of variables that offers insights into circularity, such as technological cycles, or its impacts through cause-and-effect modeling. Furthermore, an indicator can be derived from a combination of quantitative and qualitative data (Alivojvodic 2024). The assessment methodologies/methods were collected into a table consisting of information about the method/ology, such as the description of the metric, the research question or the question of interest the metric can answer, the scope that the metric is fit to apply, if the metric is a method or methodology, and information on whether the metric provides direct or indirect quantification. For a closer look at the direct and indirect quantifications of CE assessment metrics, see Elia et al. 2017. As a contribution to the existing literature listing metrics for measuring the CE, and to bring clarity to existing metrics, metric types were differentiated between method and methodology, which are based on the definitions obtained from Fassio and Chirilli (2023). They describe method as a collection of tools, models, and indicators (both quantitative and qualitative) that facilitate the calculation of values associated with a particular category of impact. Methodology is defined as a set of individual characterization methods that collectively address various environmental, ecological, and social issues, considering the associated effects and impacts through a systemic thinking approach (Fassio and Chirilli 2023). The 95 identified agriculture and food sector-related indicators were divided into two types, intrinsic and impact, based on definitions adapted from Luthin et al. 2023 and Gursel et al. 2022. Intrinsic indicators measure the inherent circularity or success of implementing circular economic principles. Impact indicators measure the consequences of circularity on various sustainability dimensions, including environmental protection, economic viability, and social equity, as well as the accumulation of hazardous substances. The CE monitoring framework developed is described in the following section. A2C – Deliverable D7.7v2.0 Page 101 І159 8.1.3 CE Monitoring Framework The framework incorporates impact as a fundamental component of monitoring by providing guidance on selecting an appropriate impact assessment method, thereby establishing a foundation for its application. The process begins with the identification and description of the monitored circular economy (CE) practice, followed by the selection of the assessment method. Subsequently, based on the chosen assessment method, the necessary metrics for monitoring are identified. A significant contribution of the developed framework to existing models is its approach, which emphasizes a clear distinction between intrinsic and impact metrics in monitoring. The framework elucidates, through its outlined steps, how the selection of intrinsic and impact metrics can be integrated into the monitoring of CE practices. This distinction underscores the complexity of measuring circularity, as some metrics directly quantify the degree of circularity, while others evaluate the broader impacts and contributions of circular strategies towards a more sustainable system (Luthin et al. 2023, Gursel et al. 2022, Corona et al. 2019). The approach presented in this deliverable involved a literature review and the application of existing metrics, culminating in the proposal of a novel step-by-step framework for CE monitoring. This framework is applicable across various industries and for any product or service. In this project, the focus was concentrated on monitoring the environmental pillar of CE activities. However, the rationale underlying the framework's steps allows for its application to other pillars by utilizing metrics pertinent to each pillar, thereby laying the groundwork for expanding the framework to encompass social, governance, and economic pillars. The CE Monitoring Framework consist of three steps described below. Step 1. Defining the CE practice to be monitored. Step 1 involves defining the practice to be monitored. This step requires a comprehensive description of the Circular Economy (CE) practice or activity as delineated by the responsible entity, such as a company, organization, or municipality, overseeing the practice. The list of CE practices, comprising 36 practices identified by Masi et al. (2018) (25 CE practices) and A2C – Deliverable D7.7v2.0 Page 102 І159 Garza-Reyes et al. (2018) (11 additional CE practices), can be utilized to ascertain the type of CE practice subject to monitoring and assessment. The objective of Step 1 is to delineate the scope and focus of the practice to be monitored and assessed. The CE practice description table (Table 8-1) is designed to aid in refining and establishing the boundaries of the monitoring and assessment focus. Correctly identifying the type of CE practice is crucial for clarifying the study case focus and defining assessment boundaries, which is essential information for selecting the impact assessment method (Step 2). However, it is important to acknowledge that CE is a dynamic concept, and the provided list may not encompass all existing CE practices. The critical aspect is the ability to define and describe the practice comprehensively to understand the goals for monitoring and measuring the necessary performance aspects. As previously mentioned, the list of CE practices (Table 8-1) is derived from Masi et al. (2018) and Garza-Reyes et al. (2018). However, these publications do not offer direct descriptions of these practices. As the developed framework begins with the identification and selection of relevant CE practices for assessment, the aim has been to provide descriptions for these practices. The definitions of practices have been generated using two large language models (LLMs) and validated by an expert. Additionally, comparisons were made between the generated definitions for the CE practices, and suitable descriptions were selected by the expert based on the best available knowledge from the original sources. Table 8-1. Circular economy practice list with definitions. CE Practice Definition 1. Designing products for reduced consumption of resources A key principle of the circular economy that emphasizes resource efficiency beginning at the product design phase. This approach involves minimizing the amount of materials used, prioritizing renewable and recycled materials, and designing for durability to reduce the need for replacements. The goal is to create products that have a lower environmental impact throughout their life cycle, from material extraction to end-of-life disposal. 2. Design of products for reuse and or recovery of materials and or component parts Designing products for reuse or recovery of materials and/or component parts involves considering and applying design for reuse, disassembly, refurbishment, remanufacturing, and recycling. This approach aims to ensure that products are designed in a way that facilitates the recovery and reuse of their materials and components at the end of their life cycle, contributing to reducing material consumption and promoting circularity. A2C – Deliverable D7.7v2.0 Page 103 І159 CE Practice Definition 3. Design of processes for minimisation of waste Designing processes for waste minimisation is a circular practice that is a component of eco-design and is related to internal practices for resource utility and efficiency. Manufacturing SMEs can implement this practice by considering and applying designs that minimise waste. Implementation may include design for durability and strategies that utilise easily recyclable materials. This approach can provide short-term returns for a company. 4. Reducing material consumption Reducing material consumption is achieved through the design of products for reduced resource consumption, reuse or recovery of materials and component parts, and the design of processes that minimise waste. It also involves using renewable materials and energy in the production process and reducing energy consumption. 5. Using renewable materials and energy in the production process. Using renewable materials and energy in the production process refers to a company’s efforts to track and reduce the consumption of non-renewable resources by identifying and implementing substitutes that are derived from renewable sources. This involves maintaining records of nonrenewable material consumption, analyzing ways to decrease reliance on such materials, and taking concrete actions to replace them with renewable alternatives. Similarly, the process includes keeping track of non-renewable energy usage, assessing the feasibility of integrating renewable energy into production, and actively working towards transitioning from fossil fuel-based energy sources to renewable ones. 6. Reducing energy consumption Reducing energy consumption involves systematically tracking and assessing the use of electricity, coal, gas, and other energy sources within a company’s operations. This requires maintaining a record of energy consumption, analyzing opportunities to lower energy usage, and implementing actions aimed at achieving energy efficiency. Additionally, companies may explore whether any of their processes can generate energy and assess the feasibility of utilizing self-produced or renewable energy sources. 7. Reducing pollutants emissions Reducing pollutant emissions involves keeping records of pollution levels and monitoring greenhouse gas (GHG) emissions within a company’s operations. This practice includes assessing and identifying opportunities to minimize emissions and implementing actions aimed at reducing their environmental impact. Additionally, companies may track the use of petrol, diesel, and other fossil fuels, analyze ways to decrease their consumption, and take steps to reduce reliance on these sources. In cases where fertilizers and pesticides are used, efforts may also be made to evaluate and mitigate their environmental effects. 8. Reducing wastes Reducing waste involves maintaining records of waste generation, ensuring efficient waste separation, and actively supporting landfill prevention. Companies implementing waste reduction strategies assess and optimize waste circulation by treating waste as an input for other processes. Additionally, if waste disposal is necessary, they ensure that it is conducted in an environmentally responsible manner. 9. Green packaging Green packaging refers to the use of packaging materials that are both environmentally friendly and efficient. This involves selecting materials that minimize environmental impact while ensuring that the packaging remains functional and effective in protecting and distributing products. 10. Special training for workers on environmental issues and circular economies Special training for workers on environmental issues and circular economies involves initiatives aimed at increasing environmental awareness among all members of an organization. This includes providing formal and periodic training to both new and existing employees, ensuring the sharing of information and achievements related to circular economy practices. The goal is to integrate environmental considerations into the workforce’s daily activities and decision-making processes. 11. Including environmental factors in the internal performance evaluation systems Including environmental factors in the internal performance evaluation system involves setting specific targets for reducing the consumption of water, energy, waste, and raw materials within a company. This approach integrates environmental considerations into performance assessments, ensuring that sustainability metrics are actively monitored and managed. Additionally, companies may use an indicators dashboard to visualize and track progress toward their environmental goals, facilitating continuous improvement in circularity practices. A2C – Deliverable D7.7v2.0 Page 104 І159 CE Practice Definition 12. Environmental auditing programmes Environmental auditing programs involve identifying environmental risks and systematically measuring and monitoring environmental impacts through tools such as ISO 14000, life cycle analysis, or material flow analysis. These programs help companies track their sustainability performance and ensure compliance with environmental regulations. Additionally, organizations may adopt sustainability frameworks such as the Carbon Disclosure Project (CDP), Global Reporting Initiative (GRI), or Dow Jones Sustainability Index (DJSI) to enhance their environmental governance. The implementation of an environmental policy and actions to minimize the environmental impact of activities related to energy, water, and ecosystem conservation are also integral to such auditing initiatives. 13. Eco-labelling of products Eco-labelling of products involves sharing environmental benefits with customers to encourage the purchase of products with lower environmental impacts. This practice aims to raise awareness and motivate consumers to choose more sustainable options by providing clear and transparent environmental information about the product. 14. Selecting suppliers using environmental criteria Selecting suppliers using environmental criteria involves incorporating sustainable and circular procurement practices to enhance material efficiency and secure future sustainable resources. This includes sourcing recycled or second-hand materials whenever possible and ensuring that the supply chain is actively engaged in circular economy principles. Companies may require suppliers to provide environmental information on their activities and products and use these insights to assess and develop suppliers based on circularity standards. Additionally, purchasing decisions are made based on a total cost assessment that considers transportation, use, and waste management costs. Clear communication of environmental purchasing criteria with all stakeholders further strengthens this approach. 15. Cooperating with other firms to establish ecoindustrial chains Cooperating with other firms to establish eco-industrial chains involves forming partnerships with companies from both the same and different sectors, as well as with suppliers and educational institutions, to support circular economy initiatives. This collaboration aims to optimize resource efficiency and promote industrial symbiosis by facilitating the exchange and reuse of materials, energy, and water among businesses. Additionally, companies engaged in these partnerships may assess the employment impact of such collaborations, including both direct and indirect job creation. 16. Reusing energy and/or water across the value chain Reusing energy and/or water across the value chain refers to sharing resources like energy and water with other companies within the value chain. This practice may include sharing energy generated by one company for future use in another company’s processes or utilizing water produced by another company for their operations. It represents an effort to increase efficiency and minimize the waste of valuable resources by reintroducing them into the production processes of different stakeholders. 17. Taking back products from consumers after the end of their functional life Taking back products from consumers after the end of their functional life involves the implementation of recovery programs where companies retrieve used products that can no longer perform their intended function due to wear, damage, or obsolescence. These products have reached the end of their lifecycle and are collected to ensure proper disposal, refurbishment, or recycling. This practice aims to prevent waste mismanagement, promote material circularity, and mitigate environmental impacts. Additionally, companies may work to address misconceptions or misunderstandings related to product recovery at the end of its functional life and implement strategies to ensure consistency in waste recovery processes, reducing uncertainties in collection, sorting, and material reuse. 18. Taking back products from customers at the end of their usage Taking back products from customers at the end of their usage involves the implementation of recovery programs where companies retrieve products that are no longer wanted or needed by customers, even if they are still functional. These products may be returned due to customer upgrades, changes in preferences, or shifts in business needs, rather than functional failure. Companies collect these items to refurbish, resell, repurpose, or reintroduce them into the market through circular business models such as leasing or second-hand sales. Additionally, companies may work to address misconceptions or misunderstandings related to product recovery at the end of their usage and implement strategies to make waste recovery more predictable and efficient by standardizing collection methods and improving sorting and processing consistency. 19. Refurbishing products Refurbishing products involves restoring used items to full working condition by repairing or replacing major faulty components. As a key strategy within circular economy models, refurbishing prevents premature disposal by extending product lifespans, enabling resale or reuse. Companies adopting this approach can significantly reduce waste and conserve resources. However, they must address common misconceptions, such as concerns about the quality and reliability of refurbished products compared to new ones. Maintaining a stable supply of spare parts is also crucial to ensuring the long-term viability and efficiency of refurbishing operations. A2C – Deliverable D7.7v2.0 Page 105 І159 CE Practice Definition 20. Remanufacturing products Remanufacturing products involves restoring used or worn-out products to a like-new condition through an industrial process that includes disassembly, cleaning, repairing, replacing components, and reassembling. Unlike refurbishing, which typically focuses on repairing or replacing faulty components to make a product functional again, remanufacturing ensures that the final product meets original performance specifications, often with the same or improved quality and reliability. Companies implementing remanufacturing as a business model must address misconceptions, such as the belief that remanufactured products are inferior to new ones, and ensure a consistent supply of spare parts to maintain operational feasibility. 21. Use of recycled materials The use of recycled materials involves incorporating materials that have been recovered and reprocessed into new production cycles instead of relying solely on virgin raw materials. Companies implementing this practice must ensure that the quality and performance of the recycled materials meet production standards while also addressing potential misconceptions about the reliability of recycled materials. By integrating recycled inputs, businesses can reduce resource depletion, lower environmental impacts, and enhance circular economy initiatives. 22. Adopting a leasing or service based marketing strategy Adopting a leasing or service-based marketing strategy shifts from traditional product ownership to a model where customers access products through leasing, rental, or service agreements. This approach allows companies to retain control over the product lifecycle, ensuring better maintenance, extended usability, and easier recovery for refurbishment, remanufacturing, or recycling. By implementing leasing, businesses can reduce resource consumption and waste while fostering long-term customer relationships. However, they must address concerns about ownership loss, contract limitations, and the perceived value of leased products. Ensuring a seamless customer experience, transparent terms, and strong after-service support is crucial to making this strategy viable and appealing. 23. Cascading use of components and materials The cascading use of components and materials refers to the practice of maximizing the utility of materials by repurposing them for multiple applications once they are no longer suitable for their original function. When a material or component loses its original properties and cannot be effectively recycled, it is redirected into alternative uses to extend its lifespan and delay disposal. This approach helps to reduce waste and resource depletion by ensuring that materials are used to their fullest potential before being discarded. 24. Targeting Green segments of the market Targeting green segments of the market involves identifying and addressing the needs of environmentally conscious consumers. Companies implementing this approach take strategic actions to fully meet the expectations of green customers, ensuring their products align with sustainability values. This may include having a clear expansion plan for the green market and integrating environmental aspects into marketing strategies to highlight the ecological benefits of their products. By doing so, businesses can enhance their appeal to sustainability-driven consumers and strengthen their position in the circular economy. 25. Cross-functional cooperation for environmental improvements Cross-functional cooperation for environmental improvements involves collaboration among various departments within an organization—such as purchasing, research and development, marketing, and sustainability—to enhance the company's environmental performance. This integrated approach ensures that environmental considerations are embedded across all functions, leading to more effective and cohesive sustainability initiatives. By fostering inter-departmental collaboration, companies can better align their strategies, share knowledge, and implement practices that collectively contribute to environmental goals. 26. Design for durability Design for durability focuses on extending a product's lifespan by incorporating robust materials, modular components, and timeless aesthetics to ensure continued functionality and appeal. It involves strategies that minimize the risk of obsolescence, enabling multiple life cycles through repairability, refurbishment, or technological upgrades. Products designed with durability in mind contribute to circular economy principles by reducing waste generation and resource depletion. Additionally, consumer acceptance plays a crucial role, as durable products must retain their perceived value and usability over extended periods, mitigating concerns related to aging technology or aesthetic wear. A2C – Deliverable D7.7v2.0 Page 112 І159 Metric Description Research question/Question of interest Scope Direct/Indirect quantification of impacts Embodied Energy (EE) Embodied Energy (EE) is a method that quantifies the total energy required to produce a product or service, including all direct and indirect energy flows throughout its life cycle. This includes energy for raw material extraction, manufacturing, transportation, and disposal. It is often expressed in MJ/kg and can account for both renewable and non-renewable energy sources. EE is an indexbased method widely used in assessing resource efficiency and identifying areas where energy use can be minimized. How much total energy is embedded in the production and life cycle of a product or service, and how can this energy consumption be reduced to improve resource efficiency? The EE method is primarily applied at the micro level, focusing on individual products or processes, though it can also be applied at the macro level in certain large-scale assessments. Direct EMergy Analysis (EMA) EMergy Analysis (EMA) is an energy-based method that evaluates the total energy input, both direct and indirect, required to produce a product or service, using solar emergy joules (seJ) as the unit of measurement. Unlike traditional energy assessments, EMA incorporates both the quantity and the quality of energy involved in production processes. This metric helps assess the sustainability of a product or system by considering the relative values of different types of energy and their efficiency in the context of natural ecosystems. It is particularly useful for evaluating the role of renewable and non-renewable energy sources within circular economy systems. How much energy is required to produce a product or service, and how does the quality of energy used influence the sustainability of production processes? EMA is typically applied at the micro level to evaluate the energy flows in specific systems or processes, but it can also be applied at the meso and macro levels when evaluating broader systems like industrial parks or entire economies. Direct Energy/exergy based analysis Energy/Exergy-Based Analysis (EMA) is an assessment method that combines energy and exergy principles to evaluate the efficiency and sustainability of systems. Exergy refers to the maximum usable energy of a material or system, and its application helps in evaluating the efficiency with which energy is utilized. EMA provides insights into the thermodynamic performance of processes by considering the quantity and quality of energy consumed, especially focusing on the transformation processes. The use of exergy also highlights potential inefficiencies, making it useful for improving energy performance. How efficiently is energy used within a system, and how can the quality of energy be optimized to improve sustainability and reduce inefficiencies? EMA can be applied at the micro, meso, or macro levels, depending on the system being analyzed. It can be used for evaluating individual processes or broader systems, such as industrial parks or entire economies, providing a detailed view of energy flows and their quality across different scales. Direct A2C – Deliverable D7.7v2.0 Page 113 І159 Metric Description Research question/Question of interest Scope Direct/Indirect quantification of impacts Environmental Performance Strategy Map (EPSM) The Environmental Performance Strategy Map (EPSM) is a graphical representation tool used to integrate multiple environmental impact categories, including water, carbon, energy, emissions, and work environment (e.g., number of lost workdays per product unit). The EPSM defines a target for each footprint and maps the results on a spider diagram. The cost dimension is incorporated as the second axis, and the volume of a pyramid represents the overall environmental impact, termed as the Sustainable Environmental Performance Indicator (SEPI). EPSM provides a comprehensive view of a product or service’s environmental performance by integrating different impact categories into a single indicator, making it easier to communicate results, though it may face limitations in data availability and standardization. How can the environmental impacts across multiple categories (carbon, water, energy, etc.) be integrated and communicated effectively to support strategic decisionmaking and improve sustainability? EPSM is used primarily at the meso and macro levels, often applied to broader systems like industries or entire supply chains. It provides a high-level view of environmental performance across various categories, making it suitable for strategic decision-making. Indirect Environmentally Extended MultiRegional Input-Output (EE-MRIO) Environmentally Extended Multi-Regional Input-Output (EE-MRIO) is an extension of traditional input-output analysis that integrates environmental data, offering a comprehensive approach to quantifying the environmental impacts across global supply chains. Multi-Regional Input-Output (MRIO) analysis is an economic assessment method used to study the interdependencies between industries and regions in a global economy. It focuses on understanding how the production and consumption in one region influence the economies of other regions through trade and supply chain interactions. EE-MRIO links national and regional economic systems, providing a way to model the interdependencies between industries, countries, and environmental impacts (e.g., CO₂ emissions, resource use). It enables the assessment of indirect environmental impacts, such as those from imported goods and services, and is often used to analyze material flows, waste, and recycling at a global scale. How do global economic activities and trade affect environmental impacts, and how can material and energy flows be quantified across international supply chains? EE-MRIO operates primarily at the macro level, assessing global or regional economic and environmental flows. It is particularly useful for tracking the environmental consequences of international trade and global material cycles, and it can incorporate time series data to explore changes over time . Indirect A2C – Deliverable D7.7v2.0 Page 114 І159 Metric Description Research question/Question of interest Scope Direct/Indirect quantification of impacts Framework for measuring circularity in cities The Framework for Measuring Circularity in Cities provides a structured approach to assessing the progress and performance of urban areas transitioning to a circular economy. This framework integrates indicators across environmental, economic, and social dimensions, enabling cities to measure key aspects such as material flows, waste management, renewable resource use, and environmental impact. It focuses on fostering circularity through systemic changes, such as the adoption of smart urban metabolism and the promotion of sustainable resource use and energy systems at the city level. This framework is essential for cities aiming to achieve a circular economy by offering a set of indicators for monitoring, evaluating, and improving circular practices. How can cities track their progress towards circularity, and what indicators can be used to monitor the effectiveness of circular economy strategies in urban environments? This framework is applied at the macro level, focusing on cities as whole systems, measuring various facets of urban life and the effectiveness of circular economy strategies implemented across the urban environment. Indirect Indicator analysis (Sustainable Process Index, Dissipation Area Index, Sustainable Environmental Performance Indicator) Indicator Analysis combines multiple index-based methods to assess environmental impacts in circular economy strategies. The Sustainable Process Index (SPI) evaluates the total area necessary to embed a product or process into the biosphere in a sustainable manner, considering material and energy flows. The Dissipation Area Index (DAI), derived from SPI, focuses on the area needed to absorb the output flows of a specific process, highlighting unsustainable flows. The Sustainable Environmental Performance Indicator (SEPI) is part of the Environmental Performance Strategy Map (EPSM), which combines several environmental footprints (carbon, water, energy, emissions) and integrates them into a single indicator to assess overall environmental performance. These indices help quantify the sustainability of processes and products in a comprehensive manner, while allowing for comparisons across different strategies. How can multiple environmental impacts (e.g., carbon, water, energy) be integrated into a single indicator to assess the sustainability and environmental performance of circular economy strategies? These indicators are applicable at the micro, meso, or macro levels, depending on the system being assessed. They can be used to evaluate individual products, regional systems, or larger-scale industrial processes. Indirect Life Cycle Assessment (LCA) Life Cycle Assessment (LCA) is a standardized methodology used to quantify and evaluate the environmental impacts associated with all stages of a product's life, from raw material extraction to end-of-life disposal. It encompasses a detailed accounting of material and energy flows through the entire product life cycle, allowing for the identification of environmental hotspots and tradeoffs between different impact categories, such as carbon footprint, water usage, and resource consumption. LCA is one of the most widely used methods in sustainability and circular economy assessments due to its ability to provide a comprehensive view of environmental impacts. What are the environmental impacts associated with the full life cycle of a product or service, and how can these impacts be minimized through design, production, and endof-life management? LCA can be applied at the micro, meso, or macro level. At the micro level, LCA is used to assess individual products or processes. At the macro level, it is applied to assess systems or sectors, such as industries or regions, providing insights into larger-scale sustainability challenges. Direct A2C – Deliverable D7.7v2.0 Page 115 І159 Metric Description Research question/Question of interest Scope Direct/Indirect quantification of impacts Material Flow Analysis (MFA) Material Flow Analysis (MFA) is a systematic assessment method that tracks and analyzes the flows and stocks of materials within a defined system (e.g., a city, region, or country). It focuses on understanding the material processes in space and time, such as transformation, transportation, and storage activities. MFA helps to assess resource consumption, waste management, material availability, and material disposition, thereby informing strategies for more efficient material use and circularity. It is particularly useful for identifying material inefficiencies and waste in systems, although it does not typically focus on the environmental impacts associated with material flows. How can material flows be tracked and analyzed to assess resource efficiency and sustainability in a system, and how can circular economy strategies improve material management? MFA operates at the micro, meso, or macro level, depending on the system being analyzed. It can be applied to small-scale studies (e.g., individual products) or large-scale systems (e.g., global material flows), providing flexibility across various scales. Indirect Material Inputs Per unit of Service (MIPS) Material Inputs Per unit of Service (MIPS) is an index-based method used to measure the material intensity of a product or service. It calculates the total material inputs required for the full life cycle of a product or service, from production through use and disposal, normalized by the service provided (e.g., material per unit of product or service delivered). This method follows a cradleto-cradle approach, considering all material inputs across the entire lifecycle of a product or service. MIPS is particularly useful for evaluating the material efficiency and identifying areas where resource use can be reduced. How much material is required to provide a given service or product, and how can material efficiency be improved throughout the product lifecycle? MIPS is typically applied at the micro level, focusing on individual products or services. It can also be applied at more strategic levels to assess the material intensity of business practices or supply chains. Direct Material/substance/che mical based analysis Material/Substance/Chemical-Based Analysis is an assessment approach that focuses on understanding the flow, usage, and impacts of specific materials, substances, or chemicals within a system. This method examines the life cycle of materials from extraction, production, and use to disposal or recycling. It is particularly concerned with tracking hazardous substances, the toxicity of materials, and the potential risks they pose to human health and the environment. Chemical-based analysis provides insights into how the chemical properties of materials affect their behavior during various stages of the life cycle, such as biodegradation or persistence in ecosystems. This analysis is integral in evaluating the sustainability of circular economy strategies, especially in preventing harmful substances from reentering the supply chain. How can the use and flow of specific materials, substances, or chemicals be tracked and managed to minimize environmental and health risks, and how can circular economy strategies address the reuse or recycling of hazardous substances to prevent adverse impacts? This analysis operates at the micro or meso level, often applied to individual products or material categories. It can be used in specific sectors such as construction, electronics, or textiles, and may focus on a single substance or a group of chemicals used in products. The scope can be expanded to larger-scale assessments when considering systemic impacts, such as regional or national regulations on chemical use and waste management. Direct A2C – Deliverable D7.7v2.0 Page 116 І159 Metric Description Research question/Question of interest Scope Direct/Indirect quantification of impacts MRIO, A multi-regional input output Multi-Regional Input-Output (MRIO) analysis is an economic assessment method used to study the interdependencies between industries and regions in a global economy. It focuses on understanding how the production and consumption in one region influence the economies of other regions through trade and supply chain interactions. MRIO tracks the flow of goods and services between countries or regions and analyzes how these flows impact economic output, resource use, and environmental emissions across multiple regions. This method is particularly useful for studying global supply chains, carbon footprints, and material flows, as it allows for the assessment of indirect environmental impacts such as greenhouse gas emissions or resource consumption that result from cross-border trade. How do interregional trade and supply chains contribute to global environmental impacts, and how can circular economy strategies help minimize these impacts by redesigning global production and consumption systems for more sustainable outcomes? MRIO operates at the macro level, typically involving national or global systems. It is applied to large-scale assessments that examine the environmental, economic, and social impacts of cross-border trade and the global movement of materials. MRIO can provide insights into the environmental performance of entire supply chains and trade networks, and it is often used to study the effects of economic policies or changes in global production systems. Indirect Substance flow analysis (SFA) Substance Flow Analysis (SFA) is a method used to track and quantify the flows and stocks of specific substances within a defined system, focusing particularly on substances that may pose environmental or health risks. SFA aims to identify hazardous flows and stocks of substances, providing insights into how these substances move through various processes and their potential to cause harm. Unlike Material Flow Analysis (MFA), which deals with general material flows, SFA focuses on individual substances such as chemicals or pollutants, helping to design strategies for risk reduction and more sustainable management of hazardous substances. It plays a critical role in resource conservation, waste management, and recycling, especially for substances that are toxic or pose significant environmental threats. How can the flows and stocks of hazardous substances be effectively tracked and managed to reduce environmental and health risks, and how can circular economy strategies help in minimizing the harmful impact of these substances through improved resource management and recycling practices? . SFA operates at the micro, meso, or macro levels, depending on the system and the specific substances being studied. It can be applied at the micro level for single products or at larger scales like regions or countries. The scope is typically focused on substances that are either harmful or have the potential to cause environmental or health issues if not properly managed. Direct A2C – Deliverable D7.7v2.0 Page 117 І159 Metric Description Research question/Question of interest Scope Direct/Indirect quantification of impacts Sustainable Environmental Performance Indicator (SEPI) The Sustainable Environmental Performance Indicator (SEPI) is a composite index that integrates various environmental footprints into a single indicator to assess the overall environmental performance of a product, service, or process. It combines five different environmental footprints—such as water, carbon, energy, emissions, and work environment—into a single measure of sustainability. These footprints are represented on a spider diagram, and the overall impact is visualized as a pyramid, with the height representing the impact. The SEPI provides a way to compare the environmental performance of different strategies, offering a comprehensive yet simplified understanding of their sustainability, but it requires standardization of data and methodology, which is a challenge in its application. What is the overall environmental performance of a product, service, or process across multiple sustainability dimensions, such as carbon, water, energy, and emissions? / How can the integration of multiple environmental footprints into a single indicator like SEPI help evaluate the overall sustainability of circular economy strategies, and what are the challenges in ensuring data reliability and standardization for its application? SEPI is applied at the meso or macro level, often used to assess the environmental performance of products, processes, or business strategies at a larger scale, such as industries, regions, or entire value chains. It is particularly useful for comparing different environmental strategies across various sectors and providing a holistic view of environmental impacts. Indirect Sustainable Process Index (SPI) The Sustainable Process Index (SPI) is an environmental assessment tool designed to evaluate processes based on their sustainability, specifically by considering material and energy flows. The SPI aims to assess how much area is needed to embed a product or service sustainably into the biosphere, considering the entire lifecycle of the process or product. This index aggregates the mass and energy flows into a single metric, which provides a spatial representation of sustainability. It is useful for evaluating the environmental compatibility of industrial processes and comparing various processes or products in terms of their environmental impact and resource efficiency. What is the environmental sustainability of an industrial process in terms of its material and energy flows throughout its lifecycle? The SPI is applied primarily at the meso level, focusing on processes, activities, or regions. It measures the environmental pressure exerted by these systems, making it applicable to industries, production facilities, and eco-industrial parks. This method can also be used at the macro level for regional or national assessments, but it requires high availability of regional data, which can be uncertain and time-consuming to gather. Indirect Water Footprint (WF) The Water Footprint (WF) is an environmental indicator that quantifies the total volume of freshwater consumed or polluted over the lifecycle of a product, service, or process. It considers the entire supply chain, from raw material extraction through production, use, and disposal, and includes direct and indirect water usage. WF is a life cycle-based approach that identifies water consumption in different stages and highlights the impact of water use on specific hydrological basins. The WF also accounts for water pollution, providing a holistic measure of water use efficiency. It is particularly useful in identifying critical areas of high water use and pollution within processes and can guide decisionmaking towards more sustainable water management practices. What is the total volume of freshwater consumed or polluted throughout the lifecycle of a product or service, and how does this influence the sustainability of water use in the circular economy? The WF operates at the micro, meso, or macro level depending on the product, service, or system being analyzed. It is applied at the product level to evaluate the water use associated with individual goods and services but can also be extended to sectors, regions, or entire nations to assess large-scale water consumption patterns and guide policy decisions. Direct A2C – Deliverable D7.7v2.0 Page 118 І159 Metric Description Research question/Question of interest Scope Direct/Indirect quantification of impacts Multi-Criteria Decision-Making (MCDM) and Fuzzy Logic Multi-Criteria Decision-Making (MCDM) is a set of methods used to evaluate and prioritize different alternatives based on multiple conflicting criteria. In the context of circular economy (CE), MCDM helps assess the trade-offs between various performance metrics such as environmental impact, cost, and resource efficiency, enabling decision-makers to select the most sustainable options. Fuzzy logic, often used in conjunction with MCDM, is a mathematical approach that deals with uncertainty and imprecision in decision-making by allowing for partial truths rather than just binary decisions. This is particularly useful in CE assessments, where multiple uncertain variables (e.g., resource availability or environmental impact predictions) must be considered simultaneously. How can the trade-offs between multiple sustainability criteria (e.g., environmental impact, cost, resource efficiency) be assessed and prioritized when making decisions in circular economy systems? MCDM and fuzzy logic approaches are applied primarily at the micro and meso levels, where detailed decision-making is required for evaluating individual products, processes, or business models within the circular economy. These methods are particularly relevant for assessing performance across various lifecycle stages (e.g., production, use, end-of-life) and considering environmental, economic, and social factors in decision-making. Indirect A2C – Deliverable D7.7v2.0 Page 119 І159 Metric Description Research question/Question of interest Scope Direct/Indirect quantification of impacts I-O models Input-Output (IO) models are economic assessment tools used to understand the relationships between different sectors within an economy. They represent the interdependencies of industries, showing how the output of one industry serves as an input to another. This method provides insight into the flow of goods and services between sectors, helping to assess the economic impact of changes in production, consumption, or policy. Input-Output models can be extended to include environmental data (e.g., carbon emissions, resource use) to provide a more comprehensive view of the environmental impacts of economic activities. These models are particularly useful for analyzing the indirect effects of economic activities, such as the environmental impacts of imported goods and services. How do changes in production and consumption patterns in one sector influence the economic and environmental impacts across other sectors in the economy, and how can Input-Output models be used to assess these interdependencies? Input-Output models operate primarily at the macro level, assessing the interconnections between entire industries or economies. They are used to evaluate large-scale systems, such as national or global economies, and can be applied to study the economic and environmental effects of changes in policy, production patterns, or consumption behavior across sectors. Indirect Discrete Event Simulation (DES) Discrete Event Simulation (DES) is a method used to model and analyze complex systems where events occur at discrete points in time. It simulates the operation of a system by representing it as a series of events (e.g., arrivals, departures, or processing stages) that happen at specific times. DES is often used in supply chain management, manufacturing, logistics, and circular economy scenarios to simulate and optimize the flow of materials, resources, and products. It allows for the analysis of system performance, identification of bottlenecks, and evaluation of various strategies under different scenarios, making it particularly useful for assessing the impact of changes in process design, production rates, and inventory management. How can the behavior and performance of systems with discrete events (such as manufacturing lines or supply chains) be modeled to identify bottlenecks, optimize resource allocation, and predict the impact of different operational strategies over time? DES is applicable at the micro or meso level, focusing on individual processes, systems, or facilities. It is particularly useful for modeling systems with a high degree of variability, such as production lines, waste management systems, or recycling facilities. The model can represent specific products, services, or entire processes, offering insights into how changes in one part of the system affect the overall system performance. Indirect A2C – Deliverable D7.7v2.0 Page 120 І159 Step 3. Selection of intrinsic and impact metrics for monitoring. The identification of pertinent intrinsic and impact metrics is essential for the assessment and monitoring of circular economy (CE) cases. A wide array of CE metrics exists, among which some are intrinsic, designed to measure the circular nature of an activity, while others are impact metrics, intended to reveal the activity's impact. The literature review indicates that although existing indicators provide valuable insights into specific aspects of circularity, none comprehensively capture the necessary scope for measuring the environmental sustainability of a circular economy (Helander et al. 2019). Integrating intrinsic metrics with impact metrics, and vice versa, offers a more comprehensive approach, directly linking circular economy activities to their ultimate environmental impact. Intrinsic vs. Impact Indicators: The framework distinguishes between two types of performance indicators for the CE, which is crucial for overall performance monitoring and assessment. Differentiating between these two types of indicators is vital, as the type of metric used determines what is observed and measured. As previously described, intrinsic metrics can only indicate the realization of circularity but serve as a vital source of information for impact assessment. Impact indicators directly display the achieved impact. Intrinsic indicators serve as tools to evaluate the inherent circularity and the effectiveness of circular economy principles within a product, process, or system. These indicators focus on assessing elements such as the percentage of recycled materials, the number of times a product can be reused, and the efficiency of material flow within a closed-loop system. Often referred to as "technical circularity" indicators, they provide a measure of how well circular economy practices are being implemented. Impact indicators are essential for assessing the sustainability impacts of CE activities, including metrics such as greenhouse gas emissions, water usage, and social effects. These indicators often correspond closely with the impact categories utilized in life cycle assessment (LCA). They offer a thorough evaluation of how circularity influences various sustainability dimensions, such as environmental stewardship, economic resilience, social equity, and the management of hazardous substances. A2C – Deliverable D7.7v2.0 Page 121 І159 To prevent double counting and misinterpretation of the CE practice assessment results, it is advisable to first select an impact assessment methodology, as recommended in Step 2. This selection aids in the exclusion of environmental impact metrics that may already be incorporated within the chosen environmental impact assessment methodology. Ideally, Steps 2 and 3 should be employed iteratively to screen for suitable environmental assessment methods and the relevant indicators in Step 3. In essence, these two steps function bilaterally, assisting in the determination of appropriate metric selection prior to finalizing (meaning locking down) the environmental impact method and the selection of additional necessary impact metrics. In this iterative process, the table of CE performance metrics serves as a starting point, as it provides insight into what type of indicators (such as those in the case of LCA described above) are already included in the methodology. Consequently, the focus for the remaining metric selection should be on identifying the missing metrics necessary for a comprehensive monitoring and assessment of the CE practice in question. In the implementation of this step, practitioners will observe that the categorization of metrics into intrinsic and impact types is not a widely established practice. The majority of scientific literature does not classify or differentiate metrics into intrinsic or impact categories (Luthin et al. 2023; Gursel et al. 2022). While Luthin et al. and Gursel et al. address this distinction, they do not provide a comprehensive list of metrics categorized by type (intrinsic/impact). In this deliverable, categorization for agricultural and food-related indicators was performed, which was subsequently applied in the case (demos) applications of the developed Circular Economy (CE) monitoring framework. This is exemplified in the monitoring of agricultural side stream use cases within the EU Agro2Circular project. However, the list of metrics provided is not exhaustive and is intended solely for the demonstration of the framework's application. A more comprehensive list of indicators (cross sectoral) with metric type categorization (intrinsic/impact) is currently being developed as part of extensive PhD research and is scheduled for future publication. A2C – Deliverable D7.7v2.0 Page 128 І159 Waste generated Metric tonnes Recommended metric for use Intrinsic Water consumption Cubic meters Recommended metric for use Intrinsic Water withdrawal Megaliters Recommended metric for use Intrinsic Share of waste after processing of primary product/material Percentage or Ratio (e.g., kilograms of waste per tonne of product processed) Potential relevant metric to consider Intrinsic The share of solid matter in discharded water from all process of the production Percentage or Concentration (e.g., milligrams per liter) Potential relevant metric to consider Intrinsic Total solid waste output from the process Percentage Potential relevant metric to consider Intrinsic Waste/by-product reusability potential in the next application (Energy or Product) Percentage or Ratio Potential relevant metric to consider Intrinsic Global Resource Indicator (GRI) Typically expressed as a dimensionless index or normalized score (often using energy-equivalent units as part of the calculation) Additional metric to consider Impact Material Circularity Indicator (MCI) adapted to biological cycles (products) Dimensionless Index (expressed on a normalized scale, e.g., 0 to 1 or 0 to 100) Additional metric to consider Intrinsic Value-based Resource Efficiency indicator (VRE) Ratio or Percentage (dimensionless) Additional metric to consider Intrinsic ZeroWaste index Ratio or Percentage (dimensionless) – Often expressed as a normalized score on a scale (e.g., 0 to 100) Additional metric to consider Intrinsic 159 8.2.3.2 Demo 4 The focus of demo 4 is in use of agricultural by product/waste in the production of dietary fibre and antioxidant extract to be used in new food formulations. The main activity of demo 4 falls into CE practice of Recycling of scrap or waste, where the goal is to make use of the waste/byproduct by applying any recovery operation by which waste materials are reprocessed into products, materials, or substances, whether for the original or other purposes. Table 8-4 shows the list of relevant indicators to be monitored in demo 4. Table 8-4. The list of relevant indicators to be monitored in demo 4. A full table with the descriptions of the indicators and sources are presented in Annex B. Indicator Unit of Measure Relevance for studied case Metric Type (Intrinsic/Impact) Waste diverted from disposal Metric tonnes Recommended metric for use Intrinsic Energy consumption within the production process Joules, watt-hours or multiples Recommended metric for use Intrinsic Energy intensity Joules, watt-hours or multiples Recommended metric for use Intrinsic Industrial waste reused as a source of raw materials in relation to total waste % Percentage Recommended metric for use Intrinsic Materials used by weight or volume Metric tonnes or Cubic meters Recommended metric for use Intrinsic Nutrient extraction from discharged water Percentage Recommended metric for use Intrinsic Per cent energy recovered from residual, non-renewable and non-recoverable resource outflows, % Percentage Recommended metric for use Intrinsic Per cent water discharged in accordance with quality requirements Percentage Recommended metric for use Intrinsic Ratio (on-site or internal) water reuse or recirculation, dimensionless Ratio Recommended metric for use Intrinsic A2C – Deliverable D7.7v2.0 Page 130 І159 Recycled input materials used in the production Percentage or Metric tonnes/Cubic meters Recommended metric for use Intrinsic Reuse rate of industrial water Percentage Recommended metric for use Intrinsic Share of food waste from the sector to total food waste Percentage Recommended metric for use Intrinsic The amount of rainwater used Cubic meters Recommended metric for use Intrinsic Waste directed to disposal Metric tonnes Recommended metric for use Intrinsic Waste generated Metric tonnes Recommended metric for use Intrinsic Water consumption Cubic meters Recommended metric for use Intrinsic Water withdrawal Megaliters Recommended metric for use Intrinsic Biogenic Carbon Content (%) Percentage Recommended metric for use Intrinsic Biodegradable Product Production vs. Local Biodegradation Capacity Metric tonnes Potential relevant metric to consider Intrinsic Designed biodegrability of a product Percentage, degradation rate or comparative index Potential relevant metric to consider Intrinsic Share of waste after processing of primary product/material Percentage or Ratio (e.g., kilograms of waste per tonne of product processed) Potential relevant metric to consider Intrinsic The share of solid matter in discharded water from all processes of the production Percentage or Concentration (e.g., milligrams per liter) Potential relevant metric to consider Intrinsic Total solid waste output from the process Percentage Potential relevant metric to consider Intrinsic Waste/by-product reusability potential in the next application (Energy or Product) Percentage or Ratio Potential relevant metric to consider Intrinsic Ecocosts/Value Ratio (EVR) Ratio or Percentage (dimensionless) Additional metric to consider Impact A2C – Deliverable D7.7v2.0 Page 131 І159 Global Resource Indicator (GRI) Typically expressed as a dimensionless index or normalized score (often using energy-equivalent units as part of the calculation) Additional metric to consider Impact Material Circularity Indicator (MCI) adapted to biological cycles (agricultural systems) Dimensionless Index (expressed on a normalized scale, e.g., 0 to 1 or 0 to 100) Additional metric to consider Intrinsic Value-based Resource Efficiency indicator (VRE) Ratio or Percentage (dimensionless) Additional metric to consider Intrinsic ZeroWaste index Ratio or Percentage (dimensionless) – Often expressed as a normalized score on a scale (e.g., 0 to 100) Additional metric to consider Intrinsic 8.2.3.3 Demo 5 The focus of this demo is on the use of agricultural by product/waste to produce PHBV to be used in the production of biodegradable food packaging and agricultural film. Since the production of sugar fraction in demo 5 requires demo 3 processes, this demo falls under Recycling of scrap or waste, where the goal is to make use of the waste/byproduct by applying any recovery operation by which waste materials are reprocessed into products, materials, or substances, whether for the original or other purposes. Table 8-5 shows the list of relevant indicators to be monitored in demo 5. Table 8-5. The list of relevant indicators to be monitored in demo 5. A full table with the descriptions of the indicators and sources are presented in Annex B. Indicator Unit of Measure Relevance for studied case Metric Type (Intrinsic/Impact) Energy consumption within the production process Joules, watt-hours or multiples Recommended metric for use Intrinsic Energy intensity Joules, watt-hours or multiples Recommended metric for use Intrinsic Materials used by weight or volume Metric tonnes or Cubic meters Recommended metric for use Intrinsic A2C – Deliverable D7.7v2.0 Page 132 І159 Nutrient extraction from discharged water Percentage Recommended metric for use Intrinsic Per cent water discharged in accordance with quality requirements Percentage Recommended metric for use Intrinsic Ratio (on-site or internal) water reuse or recirculation, dimensionless Ratio Recommended metric for use Intrinsic Reuse rate of industrial water Percentage Recommended metric for use Intrinsic The amount of rainwater used Cubic meters Recommended metric for use Intrinsic Waste directed to disposal Metric tonnes Recommended metric for use Intrinsic Waste generated Metric tonnes Recommended metric for use Intrinsic Water consumption Cubic meters Recommended metric for use Intrinsic Water withdrawal Megaliters Recommended metric for use Intrinsic Biogenic Carbon Content (%) Percentage Recommended metric for use Intrinsic Monitoring recirculation of beneficial substances in internal processes Metric tonnes or percentage Potential relevant metric to consider Intrinsic Reutilisation of excess heat from own or external sources in the producuction Kilowatt-hours (kWh) or megajoules (MJ). Potential relevant metric to consider Intrinsic The share of solid matter in discharded water from all processes of the production Percentage or Concentration (e.g., milligrams per liter) Potential relevant metric to consider Intrinsic Waste/by-product reusability potential in the next application (Energy or Product) Percentage or Ratio Potential relevant metric to consider Intrinsic Material Circularity Indicator (MCI) adapted to biological cycles (agricultural systems) Dimensionless Index (expressed on a normalized scale, e.g., 0 to 1 or 0 to 100) Additional metric to consider Intrinsic ZeroWaste index Ratio or Percentage (dimensionless) – Often expressed as a normalized score on a scale (e.g., 0 to 100) Additional metric to consider Intrinsic A2C – Deliverable D7.7v2.0 Page 133 І159 8.2.3.4 Demo 6 The focus of this demo is on the use of agricultural by product/waste to produce biodegradable pellets for food packaging. Since the production of biodegradable pellets in demo 6 requires demo 3 processes, this demo falls under Recycling of scrap or waste, where the goal is to make use of the waste/byproduct by applying any recovery operation by which waste materials are reprocessed into products, materials, or substances, whether for the original or other purposes. Table 8-6 shows the list of relevant indicators to be monitored in demo 6. Table 8-6. The list of relevant indicators to be monitored in demo 6. A full table with the descriptions of the indicators and sources are presented in Annex B. Indicator Unit of Measure Relevance for studied case Metric Type (Intrinsic/Impact) Energy consumption within the production process Joules, watt-hours or multiples Recommended metric for use Intrinsic Energy intensity Joules, watt-hours or multiples Recommended metric for use Intrinsic Materials used by weight or volume Metric tonnes or Cubic meters Recommended metric for use Intrinsic Nutrient extraction from discharged water Percentage Recommended metric for use Intrinsic Per cent water discharged in accordance with quality requirements Percentage Recommended metric for use Intrinsic Ratio (on-site or internal) water reuse or recirculation, dimensionless Ratio Recommended metric for use Intrinsic Reuse rate of industrial water Percentage Recommended metric for use Intrinsic The amount of rainwater used Cubic meters Recommended metric for use Intrinsic A2C – Deliverable D7.7v2.0 Page 134 І159 Waste directed to disposal Metric tonnes Recommended metric for use Intrinsic Waste generated Metric tonnes Recommended metric for use Intrinsic Water consumption Cubic meters Recommended metric for use Intrinsic Water withdrawal Megaliters Recommended metric for use Intrinsic Biogenic Carbon Content (%) Percentage Recommended metric for use Intrinsic Biodegradation Rate Percentage Recommended metric for use Intrinsic Time to 90% Biodegradation (days) Time (days) Recommended metric for use Intrinsic The share of solid matter in discharded water from all processes of the production Percentage or Concentration (e.g., milligrams per liter) Potential relevant metric to consider Intrinsic Total solid waste output from the process Percentage Potential relevant metric to consider Intrinsic Waste/by-product reusability potential in the next application (Energy or Product) Percentage or Ratio Potential relevant metric to consider Intrinsic Global Resource Indicator (GRI) Typically expressed as a dimensionless index or normalized score (often using energy-equivalent units as part of the calculation) Additional metric to consider Impact ZeroWaste index Ratio or Percentage (dimensionless) – Often expressed as a normalized score on a scale (e.g., 0 to 100) Additional metric to consider Intrinsic Material Circularity Indicator (MCI) adapted to biological cycles (products) Dimensionless Index (expressed on a normalized scale, e.g., 0 to 1 or 0 to 100) Additional metric to consider Intrinsic Recycling rate of plastic packaging (percentage) Percentage Additional metric to consider Intrinsic Disintegration Rate Percentage Additional metric to consider Intrinsic Heavy Metal Content Concentration (mg/kg) Additional metric to consider Impact A2C – Deliverable D7.7v2.0 Page 135 І159 Residual Mass After Biodegradation Mass fraction (%) Additional metric to consider Intrinsic Compost Quality Post-Degradation Composite parameters (e.g., pH; nutrients in mg/kg; moisture %) Additional metric to consider Impact 8.2.3.5 Demo 8 The focus of this demo is on the use of agricultural by product/waste to produce biodegradable pellets for agriculture film. Since the production of biodegradable pellets in demo 8 requires demo 3 processes, this demo falls under Recycling of scrap or waste, where the goal is to make use of the waste/byproduct by applying any recovery operation by which waste materials are reprocessed into products, materials, or substances, whether for the original or other purposes. Table 8-7 shows the list of relevant indicators to be monitored in demo 8. Table 8-7. The list of relevant indicators to be monitored in demo 8. A full table with the descriptions of the indicators and sources are presented in Annex B. Indicator Unit of Measure Relevance for studied case Metric Type (Intrinsic/Impact) Biodegradation Rate Percentage Recommended metric for use Intrinsic Biogenic Carbon Content (%) Percentage Recommended metric for use Intrinsic Energy consumption within the production process Joules, watt-hours or multiples Recommended metric for use Intrinsic Energy intensity Joules, watt-hours or multiples Recommended metric for use Intrinsic Materials used by weight or volume Metric tonnes or Cubic meters Recommended metric for use Intrinsic Nutrient extraction from discharged water Percentage Recommended metric for use Intrinsic A2C – Deliverable D7.7v2.0 Page 136 І159 Per cent water discharged in accordance with quality requirements Percentage Recommended metric for use Intrinsic Ratio (on-site or internal) water reuse or recirculation, dimensionless Ratio Recommended metric for use Intrinsic Reuse rate of industrial water Percentage Recommended metric for use Intrinsic The amount of rainwater used Cubic meters Recommended metric for use Intrinsic Time to 90% Biodegradation (days) Time (days) Recommended metric for use Intrinsic Waste directed to disposal Metric tonnes Recommended metric for use Intrinsic Waste generated Metric tonnes Recommended metric for use Intrinsic Water consumption Cubic meters Recommended metric for use Intrinsic Water withdrawal Megaliters Recommended metric for use Intrinsic The share of solid matter in discharded water from all processes of the production Percentage or Concentration (e.g., milligrams per liter) Potential relevant metric to consider Intrinsic Total solid waste output from the process Percentage Potential relevant metric to consider Intrinsic Waste/by-product reusability potential in the next application (Energy or Product) Percentage or Ratio Potential relevant metric to consider Intrinsic Compost Quality Post-Degradation Composite parameters (e.g., pH; nutrients in mg/kg; moisture %) Additional metric to consider Impact Disintegration Rate Percentage Additional metric to consider Intrinsic Global Resource Indicator (GRI) Typically expressed as a dimensionless index or normalized score (often using energy-equivalent units as part of the calculation) Additional metric to consider Impact Heavy Metal Content Concentration (mg/kg) Additional metric to consider Impact A2C – Deliverable D7.7v2.0 Page 137 І159 Material Circularity Indicator (MCI) adapted to biological cycles (products) Dimensionless Index (expressed on a normalized scale, e.g., 0 to 1 or 0 to 100) Additional metric to consider Intrinsic Residual Mass After Biodegradation Mass fraction (%) Additional metric to consider Intrinsic ZeroWaste index Ratio or Percentage (dimensionless) – Often expressed as a normalized score on a scale (e.g., 0 to 100) Additional metric to consider Intrinsic 8.2.3.6 Demo 2c The focus of this demo is on the use of agricultural by-product/waste to produce sugary fraction that is used in the production of microbial oil. Since the production of sugar fraction in demo 2c requires demo 3 processes, this demo falls under Recycling of scrap or waste, where the goal is to make use of the waste/byproduct by applying any recovery operation by which waste materials are reprocessed into products, materials, or substances, whether for the original or other purposes. Table 8-8 shows the list of relevant indicators to be monitored in demo 2c. Table 8-8. The list of relevant indicators to be monitored in demo 2c. A full table with the descriptions of the indicators and sources are presented in Annex B. Indicator Unit of Measure Relevance for studied case Metric Type (Intrinsic/Impact) Biogenic Carbon Content (%) Percentage Recommended metric for use Intrinsic Energy consumption within the production process Joules, watt-hours or multiples Recommended metric for use Intrinsic Energy intensity Joules, watt-hours or multiples Recommended metric for use Intrinsic Materials used by weight or volume Metric tonnes or Cubic meters Recommended metric for use Intrinsic A2C – Deliverable D7.7v2.0 Page 144 І159 Elia, V., Gnoni, M. G., & Tornese, F. (2017). Measuring circular economy strategies through index methods: A critical analysis. Journal of Cleaner Production, 142, 2741-2751. https://doi.org/10.1016/j.jclepro.2016.10.196 European Bioplastics. (n.d.). Harmonised standards for bioplastics. https://www.europeanbioplastics.org/bioplastics/standards/ Eurostat. (2020). 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(2022) Environmental and socio-economic impacts of new plant breeding technologies: A case study of root chicory for inulin production. https://www.frontiersin.org/articles/10.3389/fgeed.2022.919392/full International Organization for Standardization. (2006a). Environmental management – Life cycle assessment – Principles and framework (ISO Standard No. ISO 14040:2006) International Organization for Standardization. (2006b). Environmental management – Life cycle assessment – Requirements and guidelines (ISO Standard No. ISO 14044:2006). International Organization for Standardization. (2012). Determination of the ultimate aerobic biodegradability of plastic materials under controlled composting conditions — Method by analysis of evolved carbon dioxide (ISO Standard No. ISO 14855-1:2012). https://www.iso.org/standard/57902.html International Organization for Standardization. (2021). Plastics — Organic recycling — Specifications for compostable plastics (ISO Standard No. 17088:2021). https://www.1.org/standard/74994.html International Organization for Standardization. (2022). ISO/IEC 27002:2022 – Information security, cybersecurity and privacy protection – Information security controls. https://www.iso.org/standard/75652.html International Organization for Standardization. (2023). Plastics — Determination of the degree of disintegration of plastic materials under composting conditions in a laboratoryscale test (ISO 20200:2023). ISO. https://www.iso.org/standard/81932.html International Organization for Standardization. (2024). Circular economy — Measuring and assessing circularity performance (ISO Standard No. 59020:2024). https://www.iso.org/standard/80650.html Lokesh, K., Matharu, A. S., Kookos, I. K., Ladakis, D., Koutinas, A., Morone, P., & Clark, J. (2020). Hybridised sustainability metrics for use in life cycle assessment of bio-based products: resource efficiency and circularity. Green Chemistry, 22(3), 803-813. https://doi.org/10.1039/c9gc02992c A2C – Deliverable D7.7v2.0 Page 146 І159 Luthin, A., Traverso, M., & Crawford, R. H. (2023). Circular life cycle sustainability assessment: An integrated framework. Journal of Industrial Ecology, 28(1), 41-58. https://doi.org/10.1111/jiec.13446 Masi, D., Kumar, V., Garza-Reyes, J. A., & Godsell, J. (2018). Towards a more circular economy: exploring the awareness, practices, and barriers from a focal firm perspective. Production Planning & Control, 29(6), 539-550. https://doi.org/10.1080/09537287.2018.1449246 Nessi, S., Sinkko, T., Bulgheroni, C., Garbarino, E., Garcia-Gutierrez, P., Giuntoli, J., Konti, A., Orveillon, G., Sanye Mengual, E., Tonini, D., Pant, R., Marelli, L. and Ardente, F. (2022). Life Cycle Assessment (LCA) of alternative feedstocks for plastics production, EUR 31085 EN, Publications Office of the European Union, Luxembourg, 2022, ISBN 978-92-76-529590, doi:10.2760/234548, JRC127175. Pajula, T., Vatanen, S., Behm, K., Grönman, K., Lakanen, L., Kasurinen, H., & Soukka, R. (2021). Carbon handprint guide: V. 2.0 Applicable for environmental handprint. VTT Technical Research Centre of Finland. https://publications.vtt.fi/julkaisut/muut/2021/Carbon_handprint_guide_2021.pdf Razza, F., Briani, C., Breton, T., & Marazza, D. (2020). Metrics for quantifying the circularity of bioplastics: The case of bio-based and biodegradable mulch films. Resources, Conservation and Recycling, 159, 104753. https://doi.org/10.1016/j.resconrec.2020.104753 Rocchi, L., Paolotti, L., Cortina, C., Fagioli, F. F., & Boggia, A. (2021). Measuring circularity: An application of modified Material Circularity Indicator to agricultural systems. Agricultural and Food Economics, 9(9). https://doi.org/10.1186/s40100-021-00182-8 Sassanelli, C., Rosa, P., Rocca, R., & Terzi, S. (2019). Circular Economy performance assessment methods: A systematic literature review. Journal of Cleaner Production, 229, 440-453. https://doi.org/10.1016/j.jclepro.2019.05.019 Vogtländer, J. G., Bijma, A., & Brezet, H. C. (2002). Communicating the eco-efficiency of products and services by means of the eco-costs/value model. Journal of Cleaner Production, 10(1), 57-67. https://doi.org/10.1016/S0959-6526(01)00013-0 A2C – Deliverable D7.7v2.0 Page 147 І159 Walzberg, J., Lonca, G., Hanes, R. J., Eberle, A. L., Carpenter, A., & Heath, G. A. (2021). Do we need a new sustainability assessment method for the circular economy? A critical literature review. Frontiers in Sustainability, 1, 620047. https://doi.org/10.3389/frsus.2020.620047 Zaman, A. U., & Lehmann, S. (2013). The zero waste index: A performance measurement tool for waste management systems in a ‘zero waste city’. Journal of Cleaner Production, 31, 1-14. https://www.researchgate.net/profile/AtiqZaman/publication/261925208_The_zero_waste_index/links/0f317535f5bf0e8130000000/ The-zero-waste-index.pdf A2C – Deliverable D7.7v2.0 Page 148 І159 10 Annex ANNEX A: Used ecoinvent 3.10 chemicals in the demo 2 b LCI Table 6-3. Data ecoinvent 3.10 process Geography hydrochloric acid market for hydrochloric acid, without water, in 30% solution state RER ammonium sulfate market for ammonium sulfate RER calcium chloride market for calcium chloride RER EDTA market for EDTA, ethylenediaminetetraacetic acid GLO glucose market for glucose GLO ammonium hydroxide market for chemical, inorganic GLO cobalt(II) chloride market for chemical, inorganic GLO copper(II) sulfate market for copper sulfate GLO dipotassium phosphate market for chemical, inorganic GLO iron(II) sulfate market for iron sulfate RER kanamycin market for chemical, inorganic GLO magnesium dichloride market for chemical, inorganic GLO manganese(II) chloride market for chemical, inorganic GLO monosodium phosphate market for sodium phosphate RER sodium molybdate market for chemical, inorganic GLO yeast extract market for fodder yeast GLO zinc sulfate zinc sulfide production RER Annex B: Full list of indicators with detailed information applied in demo cases. 159 Indicator Unit of Measure Relevance for studied case Description Metric Type (Intrinsic/Imp act) Source Waste diverted from disposal Metric tonnes Recommende d metric for use This metric captures the total weight of waste diverted from disposal, measured in metric tonnes. It includes a breakdown by waste composition, hazardous versus non-hazardous status, recovery operation (e.g., preparation for reuse, recycling), and whether disposal was onsite or offsite. All relevant contextual factors, such as methodologies and assumptions, are also provided. Intrinsic GRI Standard. Biodegradation Rate Percentage Recommende d metric for use This indicator measures the percentage of material’s organic carbon that is converted into CO₂ (aerobic) or CH₄ (anaerobic) by microbial activity under controlled composting conditions. Intrinsic International Organization for Standardization. (2012). ISO 14855-1:2012 – Determination of the ultimate aerobic biodegradability of plastic materials under controlled composting conditions Biogenic Carbon Content (%) Percentage Recommende d metric for use This indicator measures the fraction of carbon in a plastic that originates from renewable (biobased) sources using radiocarbon analysis. Intrinsic ASTM International. (2022). ASTM D6866-22 – Standard Test Methods for Determining the Biobased Content of Solid, Liquid, and Gaseous Samples Using Radiocarbon Analysis Industrial waste reused as a source of raw materials in relation to total waste % Percentage Recommende d metric for use This metric measures the percentage of industrial waste that is reused as a source of raw materials relative to the total industrial waste generated, expressed as a percentage (%). It helps assess the circularity of industrial processes and the extent to which waste is repurposed into valuable raw materials instead of being discarded. Intrinsic Cader et al. (2024) Energy consumption within the production process Joules, watt-hours or multiples Recommende d metric for use This metric quantifies the total energy consumed during a specific production process, expressed in joules, whatt hours or appropriate multiples. It includes detailed measurements of all energy inputs— covering fuel consumption from both non-renewable and renewable sources (with fuel types specified) and the use of electricity, heating, cooling, and steam. Additionally, it accounts for any energy outputs sold (e.g., surplus electricity or steam) that are directly associated with the production process. Intrinsic Adapted from GRI Standard. A2C – Deliverable D7.7v2.0 Page 150 І159 Indicator Unit of Measure Relevance for studied case Description Metric Type (Intrinsic/Imp act) Source Per cent energy recovered from residual, nonrenewable and nonrecoverable resource outflows, % Percentage Recommende d metric for use This metric quantifies the proportion of energy recovered from residual resource outflows—specifically, those derived from nonrenewable and non-recoverable resources that, due to technological, economic, environmental, social, or regulatory constraints, cannot be reused as material inputs—relative to the total energy inflow into the system. In practice, this involves converting waste or low-value by-products (such as those processed via incineration with energy recovery, pyrolysis, gasification, or biogas production) into usable energy. Intrinsic International Organization for Standardization. (2024). ISO 59020:2024, Section B.4.2 Energy intensity Joules, watt-hours or multiples Recommende d metric for use This metric assesses the efficiency of a specific production process by comparing the total energy consumed (including fuel, electricity, heating, cooling, steam, etc.) against a measure that best reflects production activity—such as the number of units produced, production volume, or production hours. In this way, it shows how much energy is used per unit of output. The metric also specifies whether it includes energy used directly in production, energy used in supporting processes, or both, helping to target improvements in process efficiency. Intrinsic Adapted from GRI Standard. Recycled input materials used in the production Percentage or Metric tonnes/Cub ic meters Recommende d metric for use This metric measures the proportion of input materials sourced from recycled streams that are used in the production process. It can be expressed as a percentage of total input materials or in terms of mass/volume (e.g., kilograms or liters) relative to the overall raw material usage. The indicator provides insight into the level of integration of secondary, recycled materials in the production process, reflecting progress in reducing reliance on virgin resources. Intrinsic Adapted from GRI Standard. Share of food waste from the sector to total food waste Percentage Recommende d metric for use This metric measures the proportion of food waste generated by a specific sector (e.g., agri-food industry) relative to the total food waste generated across all sectors. It provides insight into the contribution of a given sector to overall food waste. Intrinsic Cader et al. (2024) Materials used by weight or volume Metric tonnes or Cubic meters Recommende d metric for use This metric captures the total weight or volume of materials employed in producing and packaging an organization’s primary products and services during a selected production period. It distinguishes between non-renewable and renewable materials, thereby providing insights into resource utilization and sustainability performance. Intrinsic Adapted from GRI Standard. A2C – Deliverable D7.7v2.0 Page 151 І159 Indicator Unit of Measure Relevance for studied case Description Metric Type (Intrinsic/Imp act) Source Nutrient extraction from discharged water Percentage Recommende d metric for use This metric expresses the effectiveness of nutrient removal from water that is discharged from the system in focus. It is calculated by comparing either the volume of water that has been treated (i.e., from which surplus nutrients have been extracted) to the total water discharged (Method A) or the mass of nutrients extracted relative to the mass originally present in the discharged water (Method B). In both cases, the result is expressed as a percentage, indicating the proportion of nutrients successfully removed before discharge. Typical measurement units include cubic meters (m³) for water volumes and kilograms (kg) for nutrient masses, but the final output is dimensionless. This approach ensures that excess nutrients, which could otherwise contribute to environmental issues like eutrophication, are effectively extracted and potentially recovered for further use. Intrinsic International Organization for Standardization. (2024). ISO 59020:2024, Section B.5.2 Per cent water discharged in accordance with quality requirements Percentage Recommende d metric for use This metric assesses the extent to which water discharged from a system meets specified quality standards. It is calculated by dividing the volume of water that is discharged in compliance with the quality requirements (which could include specific limits for pollutants, pH levels, turbidity, etc.) by the total volume of water discharged, and then multiplying by 100. The result, expressed as a percentage, indicates the level of compliance with water quality criteria, thereby reflecting the system’s performance in managing effluent quality. Intrinsic International Organization for Standardization. (2024). ISO 59020:2024, Section A.5.3 Ratio (on-site or internal) water reuse or recirculation, dimensionless Ratio Recommende d metric for use This metric measures the circularity of water within a facility over a reporting period. It is calculated by comparing the total volume of water reused or recirculated on-site (after undergoing the necessary containment and treatment for reuse) to the total volume of water withdrawn from external sources. The ratio indicates the extent to which water is internally managed within the facility; values exceeding 1.0 imply that the facility’s water requirements are being met not only by direct withdrawal but also by on-site recirculation and reuse. This metric is dimensionless, as it represents a pure ratio between two volumetric flows. Intrinsic International Organization for Standardization. (2024). ISO 59020:2024, Section A.5.4. A2C – Deliverable D7.7v2.0 Page 152 І159 Indicator Unit of Measure Relevance for studied case Description Metric Type (Intrinsic/Imp act) Source Reuse rate of industrial water Percentage Recommende d metric for use This metric measures the percentage of industrial water that is reused within a specific process or sector relative to the total industrial water used, expressed as a percentage (%). It helps assess the efficiency of water management in industrial processes and tracks the extent to which water resources are reused rather than consumed. Intrinsic Cader et al. (2024) The amount of rainwater used Cubic meters Recommende d metric for use This metric measures the proportion of water that is reused within a specific process or sector relative to the total water used, expressed as a percentage (%). It helps assess the efficiency of water use and the extent to which water resources are being reused rather than consumed. Intrinsic Cader et al. (2024) Time to 90% Biodegradation (days) Time (days) Recommende d metric for use This indicator measures the time required for a plastic material to achieve 90% conversion of its carbon to CO₂ (or CH₄) under standardized composting conditions. Intrinsic International Organization for Standardization. (2012). ISO 14855-1:2012 – Determination of the ultimate aerobic biodegradability of plastic materials under controlled composting conditions Waste directed to disposal Metric tonnes Recommende d metric for use This metric measures the total weight of waste sent for disposal, expressed in metric tonnes. It includes detailed breakdowns by waste composition, disposal operation (e.g., incineration, landfilling), and whether the waste is hazardous or non-hazardous. The data also distinguish between onsite and offsite disposal, and any relevant contextual factors (such as methodologies or assumptions) are documented. Intrinsic GRI Standard. Waste generated Metric tonnes Recommende d metric for use This metric captures the total weight of waste produced, measured in metric tonnes. It also provides a detailed breakdown of the waste by its composition. Intrinsic GRI Standard. Water consumption Cubic meters Recommende d metric for use This metric measures the total volume of water consumed in a specific sector or process, expressed in cubic meters (m³). It helps assess the intensity of water use within a system and provides insight into the sustainability of water consumption practices. Intrinsic Cader et al. (2024) A2C – Deliverable D7.7v2.0 Page 153 І159 Indicator Unit of Measure Relevance for studied case Description Metric Type (Intrinsic/Imp act) Source Water withdrawal Megaliters Recommende d metric for use This metric quantifies the total volume of water withdrawn (in megaliters) across all areas. It includes a detailed breakdown by water source—surface water, groundwater, seawater, produced water, and third-party water—as well as data specific to regions under water stress. Additionally, it provides subdivisions based on water quality (freshwater with ≤1,000 mg/L TDS versus other water with >1,000 mg/L TDS) and includes contextual notes regarding the methodologies, standards, and assumptions used in the data compilation. Intrinsic GRI Standard. Biodegradable Product Production vs. Local Biodegradation Capacity Metric tonnes Potential relevant metric to consider This metric compares the mass of produced biodegradable products—expressed in metric tonnes—to the available industrial biodegradation capacity in the market area, typically also expressed in metric tonnes per year. It is reported as a ratio or percentage, indicating how the production volume aligns with or exceeds the local infrastructure’s capacity to process biodegradable materials. Intrinsic Proposing development of a novel metric—absent in reviewed literature. Designed biodegrability of a product Percentage , degradatio n rate or comparativ e index Potential relevant metric to consider This metric assesses the inherent biodegradability built into a product's design. It reflects the extent to which the product is formulated to break down under defined environmental conditions (e.g., industrial composting or landfill scenarios) within a specified time frame. The evaluation can be based on standardized biodegradability tests or performance criteria, with results expressed as a percentage, degradation rate, or comparative index. Intrinsic Proposing development of a novel metric—absent in reviewed literature. Share of waste after processing of primary product/materia l Percentage or Ratio (e.g., kilograms of waste per tonne of product processed) Potential relevant metric to consider This metric quantifies the proportion of waste generated during the processing of a primary agcultural products —after the product has been processed according to the desired or required specifications at the previous stage. This metric is for informing the following producer/processor about the share of waste that is left out of the primary product/material after it has already been processed, such as peels, pulp, seeds. It is typically expressed as a percentage of the input material or as a ratio based on a chosen unit of measure (e.g., kilograms of waste per tonne of product processed). This indicator is to assist the sector and the users of the waste/byproduct to be able to assess and estimate the quantitites of the available substitute material throuh CE. Intrinsic Proposing development of a novel metric—absent in reviewed literature.