Department de Sentiers (DDS)
Leading Tasks
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).
Responsible For Deliverables
Deliverable D2.5 — LCA results calculated and published using open infrastructure
| Deliverable Number | D2.5 | Lead Beneficiary | DDS |
| Deliverable Name | LCA results calculated and published using open infrastructure | ||
| Type | DEC | Dissemination Level | PU |
| Due Date (month) | M36 | Work Package No | 2 |
| Description | |||
| Not available |
Contributions in WP1
Contributing 2PM 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 2PM 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 1PM 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).
Contributions in WP2
Contributing 3PM to:
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).
Contributing 3PM 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 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 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).