ETH Zurich (ETHZ)
Leading Tasks
T2.1: Generate a centralised repository of food-linked data
Lead: ETHZ (5PM).
Effort by contributors: UOY (5PM), ZHAW (4PM), SEI (2PM), COOL (2PM). Timing: M3-12. ata on the environmental impacts of food is scattered. Here, we will gather and interrogate existing food-related datasets (LCA, IO-based/footprinting, production and processing, nutritional, supply-chain management-linked, geospatial/remote-sensing and trade resources) encompassing information and datasets linked to production, processing, consumption and end-of-life/waste stages of the food system. The purpose of this review is to support standardization efforts and help build a roadmap for how to move from the current state of the art to robust PEF standards. The purpose of the review is not distil knowledge, i.e. it is not to get a robust understanding of food system impacts. The purpose is simply to support standardization efforts. We will centrally and systematically record key components of the data. We will document the scope of information being provided (e.g. supply chain stage covered, indicators specified/available, commodity coverage, specificity/resolution, time-series etc), the management of the data by data-owners (e.g. ownership, periodicity of uptake, project resourcing and marketing etc), examples of use-cases and applications (e.g. impactful case studies, known utilisation in policy or private-sector decision making) and availability to end-users (openness, licensing, availability of meta-data and methodology materials, investment in training materials etc). Contributes to:
D2.1 (M21).
T1.2: Providing new-generation quantification of resource and energy use and eutrophying emissions in crop production
Lead: ETHZ (10PM).
Effort by contributors: SEI (5PM), ULEI (3PM), UOY (3PM), NILU (0.5PM). Timing: M6-36. Based on Task 1.1, we develop a detailed crop growth model using remote sensing based information in combination with growth and yield statistics for establishing more accurate inventory flows of crop production for key processes and biosphere exchanges. This allows us to add details on land use and land use change, with a special focus on tropical deforestation associated with food imports. Based on the detailed land use data, we will enhance water consumption estimates through an irrigation model (with increased level of detail based on previous work of the consortium). We will close the gap of detailed nutrient use and emission inventories, by enhancing fertiliser application models, differentiating artificial and organic fertiliser (for organic production) and model related N and P emissions. This also allows better estimating energy use in food supply chains due to fertiliser application. We extend model greenhouse / vertical farming production models to provide detailed information on land use, water consumption, heat and electricity demand, and material use. Contributes to:
D1.2 (M36).
T1.3: Improving footprinting of feed and other resource use for animal products
Lead: ETHZ (10PM).
Effort by contributors: ULEI (4PM), UOY (4PM), DDS (2PM). Timing: M1-37. We will utilise GLEAM to assess resource use and environmental impacts of livestock products by simulating the entire livestock production system from feed production to final product output. It considers various factors such as animal genetics, management practices, and feed composition to estimate resource inputs and environmental outputs such as greenhouse gas emissions, nutrient losses, and water use. For aquatic food, we will extend feed and energy use models, and add a model on antibiotics use in aquaculture, and assess key emissions (ULEI). For wild catch, we will explore novel parameters related to capture fisheries, including locations, fishing effort magnitude, and fishing gear type, and also consider related by-catch and trawling in benthic habitat areas. The Inventory results generated in this Task and Task 1.2 will be coupled with enhanced, regionalized impact assessment methods. Detailed assessment of land use change via use of spatially explicit inventory data and geospatial/remote-sensing information will also facilitate better modelling of related greenhouse gas (GHG) emissions, biodiversity loss, and assessment of soil health impacts due to erosion and compaction as well as loss of ecosystem services. Based on the enhanced fertiliser model, we will assess freshwater eutrophication and will improve the model for marine eutrophication. Existing models will be improved based on ongoing work at ULEI, CNRS and ETHZ. Ammonia emissions from fertiliser application will be addressed with a regionalized PM model developed by ETHZ and NILU. Continuing ongoing work at CNRS, ULEI and ETHZ, water scarcity impact assessment will be extended at the global scale distinguishing soil moisture, surface water and groundwater). Impact assessment models of wild catch are so far limited in scope and functionality. We will assess impacts of fisheries on fish stock depletion and related impacts on the marine ecosystem based on ongoing work at SRC, ETHZ, ULEI and UOY. Contributes to:
D1.3 (M36).
Responsible For Deliverables
Deliverable D1.2 — LCIs of crops and animal products
| Deliverable Number | D1.2 | Lead Beneficiary | ETHZ |
| Deliverable Name | LCIs of crops and animal products | ||
| Type | DATA | Dissemination Level | PU |
| Due Date (month) | M36 | Work Package No | 1 |
| Description | |||
| Not available |
Deliverable D1.3 — Regionalized LCIA data
| Deliverable Number | D1.3 | Lead Beneficiary | ETHZ |
| Deliverable Name | Regionalized LCIA data | ||
| Type | DATA | Dissemination Level | PU |
| Due Date (month) | M36 | Work Package No | 1 |
| Description | |||
| Not available |
Responsible For Milestones
MS1.1: Preliminary results of all data for initial implementation in WP2
Means of verification: Data available on project workspace or repository.
Due M18.
Lead Partner: ETHZ
Contributions in WP1
Contributing 10PM to:
T1.1: Breaking new ground: overcoming spatial and temporal limitations in agricultural production data
Lead: SEI (10PM).
Effort by contributors: ETHZ (10PM), UOY (6PM), DDS (2PM), NILU (0.5PM). Timing: M1-19. Mapping of the subnational (municipality, district, province) agricultural production of all key global crops, including their tonnage, area and yield. The higher accuracy and spatial-explicitness of this dataset allows important improvements in all following other analyses, particularly on resource and energy use, and as well as on environmental impacts of food production. We will build upon the current research of this consortium that developed the GSAP (Global Subnational Agricultural Production) database, covering the subnational regions of the vast majority of countries. Remaining agricultural products will be modelled based on other datasets and research of consortium members, such as the HESTIA.earth database, Cropgrids, and MapSPAM. Contributes to:
D1.1 (M18).
This task is linked to the following milestones: MS1.1 (M18).
Contributing 9PM to:
T1.4: Modelling domestic and international trade and transport modes and emissions
Lead: ULEI (0PM).
Effort by contributors: ETHZ (9PM), SEI (9PM), UOY (5PM), NILU (1PM), DDS (1PM). Timing: M10-48. This task develops a comprehensive spatially explicit approach to analyse the supply chains and footprint of agricultural products. This integrated approach will involve analysing subnational commodity flows combining Trase for selected high-impact crops with FABIO/MRIO for full coverage of all international trade flows. About 85% of all traded products are transported by sea, a number that increases for the specific case of staple foods given their bulky nature. We will model global maritime transport of food trade and assess associated maritime shipping emissions. For all other transportation modes, we will develop a detailed multi-modal transport model, incorporating assessment of greenhouse gas and particulate matter emissions. Regarding IPR, the Trase data is all open, however some input data may be restricted. While the data product used to obtain results might not be shared, the final outputs that are part of the project will be freely shareable as long as we make sure they are not traceable back to the input data. Contributes to:
D1.4 (M40).
Contributing 9PM to:
T1.5: Align nutritional and environmental footprint data for easier direct comparison of environmental and dietary profile of foods
Lead: ZHAW (11PM).
Effort by contributors: ETHZ (9PM), ULEI (3PM). Timing: M6-36. This task includes the following elements: (1) Provide information about nutritional contents of food products and food categories based on existing literature and databases; (2) Literature review to include relevant nutritional aspects (e.g. protein quality, bio-availability) with a focus on new plant-based protein sources and novel foods; (3) Literature research on existing data on the influence of different processing methods on the nutritional composition (for main crops); (4) Compile nutrient requirements of population; (5) Integrate assessment approaches or methodologies to assess how well a menu meets nutritional requirements; (6) Data preparation, harmonisation and conversion of the results into a previously defined and compatible data format for the subsequent work packages. Contributes to:
D1.2 (M24), D1.3 (M32).
Contributing 9PM to:
T1.6: Including Food Waste and Processing Into Food LCA Results
Lead: ZHAW (11PM).
Effort by contributors: ETHZ (9PM). Timing: M10-46. This task includes the following elements: (1) Updated review of existing literature and databases with information about the amounts of FLW; (2) at each level of the food value chain (FVC) including the stages of agricultural production (including harvest and postharvest losses), wholesale (including transport, storage, and postharvest losses), processing (including overproduction and edible side streams), the food service industry as well as households in Europe (with a focus on Switzerland) and in developing countries and in different food categories; (3) A compilation of aspects to consider in order to make data comparable; (4) Implications of different definitions used in various contexts (e.g. edible/potentially edible/inedible, avoidable/possibly avoidable/unavoidable, food losses/waste, including/excluding use as animal feed etc.); (5) Methodologies how to consider FLW in LCA analysis depending on the scope of the assessment. Contributes to:
D1.5 (M42), D1.1 (M21).
Contributing 9PM to:
T1.7: Technical implementation and data merging for a PEF-compliant database
Lead: ZHAW (11PM).
Effort by contributors: ETHZ (9PM), INTEC (2PM), NILU (1PM). Timing: M30-42. Datasets generated in this Work package into an LCA database that meets the standards of PEF. The task will link and further develop models and datasets to enhance data sharing and validation, increase visibility and relevance, and improve data utilisation. The results of WP1 form the basis to consistently include environmental impacts of processes and FLW at the various stages of the FVC and their impact reduction potential as well as nutritional aspects in the methodologies developed in WP2, into EU-wide modelling in WP3, case studies in WP4, stakeholder involvement in WP5, and management in WP6. Contributes to:
D1.5 (M42).
Contributions in WP2
Contributing 2PM to:
T2.3: Improve the technological readiness of key datasets
Lead: UOY (5PM).
Effort by contributors: NILU (5PM), CNRS (3PM), DDS (3PM), ZHAW (3PM), ULEI (2PM), SEI (2PM), ETHZ (2PM), COOL (2PM). Timing: M22-34. To enhance uptake, we will develop recommendations for each tool/data provider based on the assessment of weaknesses and best practice examples (T2.2). We will actively disseminate findings to data providers, who will be invited to attend one-to-one discussion meetings with the project team. We will also host two workshops across multiple partners (covering the same topic, but split to allow attendance from more parties and deeper interaction) to encourage further sharing of best practice and ‘challenges’ in responding to recommendations. An academic publication will also be produced to share outcomes openly. For tools provided by project partners we will, via project funding, implement selected recommendations. This task contributes to D2.2 (M24), D2.3 (M32) and D2.4 (M34). Milestone: M2.2 (M28) Stakeholder workshops (M28); M2.3 (M28): Assessment of which internal tool/data provider recommendations will be implemented (M28). Contributes to:
D2.2 (M24), D2.3 (M32), D2.4 (M34).
This task is linked to the following milestones: MS2.2 (M28), MS2.3 (M28).
Contributing 2PM to:
T2.4: Building statistical robustness to improve PEF quality
Lead: DDS (28PM).
Effort by contributors: ZHAW (3PM), ETHZ (2PM), COOL (2PM). Timing: M6-36. So far almost no work exists estimating the reliability or confidence parameters of any food footprint data. The motivation of this task is to better understand statistical robustness within and across LCA databases, and thus to establish error budgets and prioritise improvements. Building on T2.1 and T2.2, and outputs from WP1, we will harmonise project data and other open data sources using a common glossary, and enter the resulting outputs into a high-performance data storage. We will also deduce logical relationships between inputs and outputs using current inventory datasets, supplemented with consortium expertise and AI. This logic will be used to create product system models which can be linked into flexible supply chains, and used to calculate the synthesised best-available knowledge of agricultural and other LCA data. The models and quantitative results produced can support decision making directly, either by providing direct or user-specific LCA results through an open web research portal, or through an interface to the AICA system (T2.5). Contributes to:
D2.5 (M36).
Contributing 1PM to:
T2.5: Launch an AI conversational agent (AICA) for food LCA supply chain and environmental impact info
Lead: INTEC (24PM).
Effort by contributors: ARBO (22PM), UOY (2PM), SEI (2PM), NILU (1PM), ULEI (1PM), ETHZ (1PM), CNRS (1PM), DDS (1PM). Timing: M26-46. We will develop a prototype AICA that answers user-posed questions in natural language about the impacts of the EU’s food system. It will utilise food linked LCA, footprint and other data developed within and outwith the project (incl. sources developed in WP1 and assessed in WP2), collated into a central structured database. Quantitative data will be complemented by information derived from research outputs (including outputs from WP3 and WP4) and other assessments. Training of the AICI will take place via development of common questions inspired by the EU’s policy context and the guidance received via stakeholders (WP5). We will undergo a thorough validation process, drawing on expertise from across the consortium. Iteration of the model based on feedback from the experts will take place before final production launch towards the end of the project. Contributes to:
D2.6 (M42), D2.7 (M42).
This task is linked to the following milestones: MS2.4 (M30), MS2.5 (M32), MS2.6 (M34).
Contributions in WP3
Contributing 4PM to:
T3.6: Tallying the environmental gains of novel food solutions at scale
Lead: ULEI (15PM).
Effort by contributors: UOXF (5PM), ETHZ (4PM), ZHAW (3PM), NILU (2PM), UOY (1PM). Timing: M35-48. EU-wide assessment of promising food system solutions identified in WP4 case studies (e.g. seaweed, in vitro meat, and spices), using standardised product and sector-specific effect sizes (from Task 4.8), will be undertaken to assess their impact mitigation potential for different European country when adopted at scale. Consumer and producer data (Task 3.3, 3.4, and 4.7), alongside industry-level insights on novel food solutions (Task 5.5), will inform adoption rates for consumer- and farmer-specific solutions proposed. Contributes to:
D3.3 (M44).
Contributing 2PM to:
T3.1: Integrate actor-level food consumption and production data to FABIO
Lead: ULEI (16PM).
Effort by contributors: ETHZ (2PM), NILU (1PM), UOY (1PM). Timing: M1-9. To identify consumer groups, we will use micro expenditure data from the Household Budget Survey to distinguish consumption patterns of households by income group and NUTS-2 regions. Equivalent disaggregation of macroeconomic agricultural production accounts using agricultural census data will be undertaken to distinguish the contribution of farmers to national food production by farm type, revenue, size and NUTS-2 region.
Contributing 2PM to:
T3.2: Benchmark actor-level environmental footprints in European food system
Lead: ULEI (16PM).
Effort by contributors: SEI (6PM), ETHZ (2PM), INTEC (2PM). Timing: M9-23. To harmonise impact and actor-level data, LCIs generated in WP1 and validated in WP2 will be aligned by concordance to sectors present within the chosen MRIO database(s). The contribution of food consumption and production groups to the environmental impacts of EU food systems (e.g. climate, pollution and biodiversity) will be benchmarked at regional (NUTS-2), national, EU and global scales. The nutritional quality and socio-economic impacts of diets will also be evaluated to assess synergies and trade-offs with environmental sustainability. Network analysis will complement ranking of groups’ (actors and regions) impacts to identify existing best practices and key targets for improvement actions.
This task is linked to the following milestones: MS3.1 (M23).
Contributions in WP4
Contributing 2PM to:
T4.2: Exploring Solutions
Lead: NILUAB (11PM).
Effort by contributors: ULAT (7PM), HKR (6PM), ETHZ (2PM), CHALMERS (2PM), TETRA (1PM), SANTAMAR (1PM). Timing: M1-49. This Task consists of four case studies. Case study 1, on Sustainable spices (participants: SANTAMAR,) will assess the value chain of spices and estimate the environmental impact and improvement potential. Spices have been identified as the factor that can make European consumers accept novel foods. Therefore, in a system perspective it is of highly importance that the spices are produced in a lifecycle with minimum environmental impact. Firstly, identification of suitable spices for novel foods will take place together with industry. Then the system boundary of the study will be set as well as the functional unit. Data will be collected along the life cycle with site specific quality at the industry and at least country specific for the other parts of the life cycle. Several environmental impact categories will be assessed as for example climate impact, eutrophication, acidification, biodiversity and water usage. The improvement actions and potential challenges to decrease the environmental impact will be identified and assessed. Case study 1, on the Footprint of Seaweed (participants: ULAT) will assess the European seaweed value chain and estimate the potential environmental benefits from the implementation of seaweed in the food system. A systematic analysis of scientific literature, reports, and databases will be conducted to collect data on seaweed cultivation, nutritional value, processing technologies, and environmental impacts. Information will be scrutinised to estimate the potential environmental impact reduction achievable through seaweed implementation in the food system. Consultations and interviews with industry experts, stakeholders, potential end-users, and researchers will be undertaken to obtain valuable insights into seaweed cultivation and food innovation. The feasibility of integrating seaweed into the food chain will be assessed, taking into account factors such as scalability, sustainability, and economic viability. Case study 3, on the Footprint of Insect Meal for Human and Pet Food (Lead: HKR, participants: NILU AB, TETRAPAK) aims to develop tasty and sustainable food products based on insects. In parallel also pet food will be developed. One option in the transformation to a future sustainable food system is to use insects as food. Insects contain proteins with all essential amino acids and may be compared to beef or fish. Further, insects also contain poly-unsaturated and essential fatty acids. In comparison to both beef and fish the climate impact of rearing insects is low, and the production of protein is far more effective. The fulfilment of the aim is done by following actions: Development of recipes based on insects for both human and animal (pets) consumption. There will be a minimum of four varieties of the food where meat will be replaced by different amounts of insects, from 0% to 100% replacement. Both products, intended for humans and pets, are wet foods meant for shelf stable distribution. The products will be packaged and subjected for retort processing. For human food samples sensory profiles and consumer acceptance will be performed. For pet food samples acceptance tests will be performed in a test with pets. Calculations of nutritional value and climate impact. Strategies for increasing the acceptance of foods for humans and pets based on insects will be developed. Case study 4, on the Footprint of Cellular Agriculture (participant: CHALMERS) will assess the sustainability improvement potential as well as improve the existing LCA data of cellular agriculture. This is achieved by performing an inventory of the published LCA studies on cultivated meat and seafood. Assessing the future potential volume of production of cultivated meat in the EU. How much current meat consumption can we realistically replace with cultivated meat in the EU, and what would be the gain in reduction of the EU's environmental impact? Contributes to:
D4.2 (M24).
This task is linked to the following milestones: MS4.1 (M30).
Contributions in WP5
Contributing 1PM to:
T5.2: Stakeholder insights: Horizon Scanning to Identify Gaps Toward TRL 7
Lead: GLOBE (16PM).
Effort by contributors: ULEI (1PM), UOY (1PM), ETHZ (1PM). Timing: M10-18. Primary research will be conducted with members of the Advisory Group and their wider industry networks of hundreds of food system stakeholders across Europe in the form of an online survey to explore current context around industry use of LCA data e.g. economic and technical barriers to access, consumer expectations, needs, priorities, opportunities, current use patterns, and suggestions for improvements etc. Horizon scanning is a triage-based method designed to elicit top concerns and policy priorities of different groups through workshops or surveys, and will be used to revise, refine and rank research priorities or questions for LCA/footprint tool development, like 'Top 30 food industry priorities for food footprinting tools'.
Additionally as part of this task we will undertake a research and insights dissemination and feedback session. During an in-person or online briefing and collaboration session (online session would utilise GlobeScan’s proprietary collaboration forum software which offers automatic translation into alternate languages) the Advisory Group would be presented with relevant primary data and insights (from Task 3.3 and Task 5.3), plus other proprietary research from the Governance Committee (e.g. EAT/GlobeScan’s Grains of Truth report). Advisory Group feedback and reactions to this data will help identify where there is stakeholder and end-consumer alignment or differences (synergies or trade-offs) in the needs and wants around the environmental information on food and the potential solutions. It will identify areas where solutions are needed, alongside barriers and motivators, with findings from discussions feeding into WP4. Contributes to:
D5.1 (M18).
Contributions in WP6
Contributing 1PM to:
T6.1: Management of financial legal and administrative requirements
Lead: NILU (9PM).
Effort by contributors: NILUAB (2PM), UOY (2PM), ETHZ (1PM), GLOBE (1PM), SEI (1PM), UOXF (1PM), ARBO (1PM), COOL (1PM), CLIMCO (1PM), ULAT (1PM), BUW (1PM), DDS (1PM), ZHAW (1PM), ULEI (0.5PM), INTEC (0.5PM). Timing: M1-49. In this task we will manage and coordinate all financial and administrative activities in the project, including monitoring and maintaining the overall adherence to the financial budgets. T1.1 will deliver a project toolbox to ensure a smooth communication and cooperation between the project partners. This task will forward the EU contribution according to the work plan, the Consortium Agreement and the decisions made by the consortium. The administrative and financial monitoring of the project will be done by the project coordinator in cooperation with the Project Management Board (coordinator and WP leaders).
Contributing 1PM to:
T6.2: Technical project management and coordination
Lead: NILU (9PM).
Effort by contributors: UOY (1PM), ETHZ (1PM), UOXF (1PM), INTEC (0.5PM). Timing: M1-49. In this task, we will perform technical project management and coordination of the consortium. NILU will oversee that deliverables, milestones and tasks are complemented on time and submitted in accordance with the contractual obligations. To monitor progress and quality a management system will be set up. In the first 6 months of the project, NILU will work with project partners to implement an effective and agile management system detailing different roles and responsibilities for each task and subtask. The project management board, consisting of coordinator and work package leads will meet frequently to oversee implementation, internal coherence and information flow. We will keep a continuous risk register and associated mitigation strategies, updated by task leaders along the project duration, to inform the management board of potential challenges in implementation.
Contributing 1PM to:
T6.3: Outreach communication dissemination and clustering
Lead: NILU (9PM).
Effort by contributors: UOY (3PM), GLOBE (2PM), ETHZ (1PM), UOXF (1PM), ARBO (1PM), ZHAW (1PM), ULEI (0.5PM), INTEC (0.5PM). Timing: M1-49. In this task, we will manage activities related to outreach, communication and dissemination. We will develop and regularly update a communication and dissemination plan that will identify effective communication and dissemination activities and guide partners in WP1-5 to implement these. We will develop a visual identity and project website, and manage and update the various communication channels (website and social media accounts). We will create a log of events to guide partners in communication activities. We will support WP5 with the stakeholder workshops. Further, the task will stimulate knowledge sharing activities such as training and webinars across the consortium. Lastly, the task will actively seek to link with other cluster EU HE activities.
This task is linked to the following milestones: MS6.1 (M4).