University of York (UOY)
Leading Tasks
T2.2: Targeted investments for FAIRness and PEF compliance
Lead: UOY (6PM).
Effort by contributors: ULEI (3PM), DDS (3PM), SEI (2PM), COOL (2PM). Timing: M12-21. Data uptake grossly lags behind data availability. We will assess datasets (including those resources developed/owned by the project team, but also external parties) for technological ‘readiness’. Initial triage (based on Task 2.1 datasets) will identify those most important in providing (or having the potential to provide) actionable information to private sector and policy decision maker needs. Key criteria will include the development status of datasets (i.e. maturity as a product and potential longevity etc), uniqueness (i.e. ability to fill gaps in knowledge), levels of impact (i.e. existing uptake in practice, demonstrable use-cases), and scientific quality, including reported uncertainties. Triage will identify datasets which span the breadth of food sustainability issues (environmental domains, supply chain stages, end-users targeted etc) with the most ‘valuable’ use cases. For this subset of ‘most valuable products’ we will undertake assessment of adherence to FAIR principles and Product Environmental Footprint standards; systematically identifying weaknesses. For the latter, we will benchmark data provision and documentation against PEF guidance from the perspective of dataset readiness to provide information to the sixteen core environmental footprints, plus the optional ‘further environmental information’ aspects of PEF implementation. Best practice examples will be collated across the tools and datasets. Contributes to:
D2.1 (M21), D2.5 (M42).
This task is linked to the following milestones: MS2.1 (M15).
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).
Responsible For Deliverables
Deliverable D2.1 — Data assessment report
| Deliverable Number | D2.1 | Lead Beneficiary | UOY |
| Deliverable Name | Data assessment report | ||
| Type | R | Dissemination Level | PU |
| Due Date (month) | M21 | Work Package No | 2 |
| Description | |||
| Not available |
Deliverable D2.2 — Recommendations sheets
| Deliverable Number | D2.2 | Lead Beneficiary | UOY |
| Deliverable Name | Recommendations sheets | ||
| Type | OTHER | Dissemination Level | PU |
| Due Date (month) | M24 | Work Package No | 2 |
| Description | |||
| Not available |
Deliverable D2.3 — FAIR data recommendations
| Deliverable Number | D2.3 | Lead Beneficiary | UOY |
| Deliverable Name | FAIR data recommendations | ||
| Type | R | Dissemination Level | PU |
| Due Date (month) | M32 | Work Package No | 2 |
| Description | |||
| Not available |
Deliverable D2.4 — Summary of implemented recommendations
| Deliverable Number | D2.4 | Lead Beneficiary | UOY |
| Deliverable Name | Summary of implemented recommendations | ||
| Type | R | Dissemination Level | PU |
| Due Date (month) | M34 | Work Package No | 2 |
| Description | |||
| Not available |
Responsible For Milestones
MS2.1: Triage of most valuable products from initial collated datasets
Means of verification: Initial list of most valuable datasets available to the project consortium.
Due M15.
Lead Partner: UOY
MS2.2: Stakeholder workshops
Means of verification: Two workshops with data providers held.
Due M28.
Lead Partner: UOY
MS2.3: Data collection protocol developed and circulated to allow data from partners to be provided via common standards (e.g. CSV/JSON/SQL)
Means of verification: Protocol available to all project partners for the purposes of providing data.
Due M28.
Lead Partner: UOY
MS2.4: Current project data mapped and entered into synthesis database
Means of verification: Project partners confirm their data is present via online review process.
Due M30.
Lead Partner: UOY
MS2.5: Data mapping to determine sources to be used in the AI database
Means of verification: AICA tool developers are provided with a clear list of datasets to be included in the model.
Due M32.
Lead Partner: UOY
MS2.6: Customised Generative Language Model (GLM)
Means of verification: GLM developed based on an open-source LLM.
Due M34.
Lead Partner: UOY
MS2.7: Validation completed via expert engagement with prototype AICA model
Means of verification: AICI model is ‘fit for purpose’ following validation of the model’s outputs by the research team..
Due M42.
Lead Partner: UOY
Contributions in WP1
Contributing 6PM 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 5PM 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 4PM to:
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).
Contributing 3PM to:
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).
Contributions in WP2
Contributing 5PM to:
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).
Contributing 2PM 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 1PM 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 1PM 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).
Contributions in WP4
Contributing 2PM to:
T4.5: Quantify the opportunity for gains from novel food solutions
Lead: NILUAB (11PM).
Effort by contributors: NILU (4PM), UOY (2PM), ULAT (2PM), BUW (2PM), ZHAW (2PM), ULEI (1PM), UOXF (1PM), HKR (1PM), TETRA (1PM). Timing: M1-49. This task will make concluding quantified assessment of credible pathways to reduce pollution from food production based on the case studies. Information of the food system interventions, their impacts and reduction potential across multiple environmental impacts will be described based on the result of task 4.2-3. The barriers and enabling factors will be identified and related to interventions which weaken and strengthen the impact reduction potential of good practices. The result will be used as an input to Task 3.6 where the consumption data for each European country will be added in order to expand the system result from individual food items to country level. Contributes to:
D4.5 (M44).
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).
Contributing 1PM to:
T5.4: Standard uptake through engagement with the European Financial Reporting Advisory Group
Lead: CLIMCO (23PM).
Effort by contributors: GLOBE (2PM), NILU (1PM), NILUAB (1PM), ULEI (1PM), UOY (1PM), COOL (1PM). Timing: M1-48. DDMXX this should not appear since we should not re-initailize again in tasks.ts. To support EU disclosure regulation a continuous effort will be dedicated to integrating the insights acquired from this and adjacent portfolio projects into the development relevant sustainability reporting standard setters, including EFRAG and others, e.g. GRI and/or ISSB considering evolving policy contexts. Particular attention will be given to supporting European Sustainability Reporting Standards (ESRS), focussing on the sector-specific standards for the food and beverages sector. Two team members from Climate & Company are seconded to EFRAG’s writing team for the sector standards Agriculture, Farming and Fishing as well as Food and Beverages. They will be able to include significant insights from GREENGROCER into shaping the disclosure requirements of the latter. While EFRAG remains a key standard setter to engage, we recommend broadening our engagement to include other relevant standard setters, particularly GRI and potentially ISSB. This broadened approach is especially important considering the EU Commission’s Omnibus Proposal, which increases the relative relevance of voluntary, internationally recognized standards like GRI. Including those stakeholders, too, could enhance the project’s relevance and impact. The development of the Food and Beverages standard relies on leveraging scientific insights into the ESG impacts of food and beverage systems to formulate disclosure requirements that are both ambitious in addressing the needs of people and the planet, while also being practical and feasible for companies to report on. Furthermore, the insights of this project will help to create meaningful and comparable data that is used by financial institutions to make informed investment decisions, necessary for the sustainable transition. Milestones: M5.2 Introduction of GreenGrocer activities to the ESRS working group (M24). In this task we will create an online platform (exact format TBD) that will highlight and help match innovative solutions developed in the project and challenges and information gaps in sustainability disclosure field, especially within regard to the food sector. By explicitly showcasing both solutions and challenges, we can better match innovative approaches with existing needs and help project outputs to take policy needs into consideration more easily and timely. This platform would serve as an interface between solution providers (e.g. researchers from the project consortium) and those that need them (e.g. standard setters). While standard-setters (EFRAG and others) are an important audience, we envision the platform and its content being accessible and informative to a broader group, including companies aiming to improve their sustainability reporting and the financial sector developing financing solutions for the transition. Similarly, we could also showcase relevant solutions beyond the project through the platform. While active engagement by different groups directly on / through the platform would be valuable, the platform can also function as a resource for us to extract insights and share them with relevant contacts. Contributes to:
D5.3 (M22).
This task is linked to the following milestones: MS5.2 (M24).
Contributions in WP6
Contributing 4PM to:
T6.5: Data Management and Open Science
Lead: NILU (8PM).
Effort by contributors: UOY (4PM), ULEI (2PM). Timing: M1-49. In this task, we prepare an overarching data management plan (DMP) for the project. We will solicit guidance from the experienced University of York Library Open Science team if required. The DMP will be developed in compliance with the EC guidelines on data management in Horizon Europe projects (cf. section 2.2). The DMP will be updated periodically. Contributes to:
D6.1 (M6), D6.5 (M24), D6.6 (M48).
Contributing 3PM 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).
Contributing 2PM 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 2PM to:
T6.4: Exploitation strategies and business cases
Lead: NILU (9PM).
Effort by contributors: UOY (2PM), ULEI (1PM), SEI (1PM), UOXF (1PM), ZHAW (1PM), INTEC (0.5PM). Timing: M1-49. GREENGROCER has a distinct set of exploitable outcomes. In this task, we will develop the initial exploitation routes for data products, tools, methodologies, and standardisation activities. Starting out from key exploitation routes, we will develop business model canvases for each of the GREENGROCER outcomes, starting by identifying target markets (e.g. science to science, science to business, B2B or B2C), value propositions, and potential value capture models (e.g. freemium, SaaS -models). For data products developed under an open source licence, this task has clear links with the Data management and open science practices (T6.5) and communication and dissemination strategies. Contributes to:
D6.2 (M9), D6.7 (M46).
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.