| Case Study Acronym: | AMAFLOW |
| Long Title of the Case Study: | Mapping and valorizing food loss and waste data in the Amsterdam Metropolitan Area (AMA) to improve the circular economy |
| Case Study Main Contact: | Xuezhen Guo |
| Countries involved and main place of the Case Study: | Amsterdam Metropolitan Areas |
| Part of the Food System addressed: | Losses and wastes along the food supply chains in AMA |
Automatically identify and extract the food components from the LMA dataset in as much as possible details to develop a more reliable food loss & waste (FLW) monitor to provide the basis for further data valorization.
APIs will be created combined with the text mining and automatic metrics calculation algorithms to generate the food loss & waste dashboards and maps for AMA. This will ensure we always have the up-to-date data.
Required Data and procedure to estimate the waste composition:
In the ideal situation, we want to have the description of each individual waste flow registered in the LMA database. If this is possible, then we can use text mining to identify the meaningful texts in the large amount of records and categorize them into e.g., different food items.
If the low-level data is not available, we will at least have the names (or even description) of the companies. Since the SBI codes do not cover all the registered companies in the databases (the aggregate data of 2019 show that a large number of flows cannot be categorized using the SBI code). Then we can use text mining to classify the flows (e.g., matching the companies with their web-information). Domain expert knowledge will also be used to improve the accuracy of the machine learning algorithms.
Biodiversity footprint of the FLW in AMA
After having the more reliable food waste database, then we can use it to calculate many other metrics. We will start with calculating the biodiversity footprint of FLW in AMA. We use the local food production and food import data to create a “food sourcing profile” for AMA. Then combining the land use data of the “source country” for each food item with the “food sourcing profile”, we can calculate the biodiversity footprint of the FLW in AMA using the model that specifies the relationship between the land use and biodiversity losses.
Optimize the reverse logistic network
A spatial model combining the regression and optimization techniques will be developed to optimize the reverse logistic networks for the identified high potential waste streams regarding e.g., nutrient values, biodiversity impacts. The model will deliver the optimal locations of the waste collection centers and treatment plants as well as the most suitable operational schedules for waste transportation and processing.
Food Loss and Waste (FLW) is a big issue related to food security, climate change, biodiversity, etc. It has been prioritized in UN sustainable development goals (SDG) target 12.3 to contribute to “ensure sustainable consumption and production patterns”. Since cities are densely populated areas, urban food waste is a hotspot for food waste monitoring and valorisation.
The key expected outcome of this research include an improved FLW monitor in AMA, a biodiversity footprint calculation tool for FLW, as well as a spatial optimization model to design the reverse logistic networks for high-potential FLW streams.
geoFluxus: The data expert which manages the waste databases from LMA and will work together with WFBR to identify FLW flows, improve the data quality and facility modelling with data inputs.
Amsterdam institute for advanced metropolitan solutions: Link to stakeholder in AMA including policy makers, companies, etc.
Amsterdam Municipality: facilitate the development of the monitor and tools by providing policy relevant insights
The food value chain considered in this research covers the stages of “primary production”, “storage”, “transportation”, “processing”, “distribution” and “consumption”. It deals with the food wastes generated in those chain stages and the strategies to recycle and reuse them in AMA.
We have several on going projects that are relevant to this project. In WFBR, we have 4 ongoing projects from the ministry of agriculture about developing the national monitor for FLW in the Netherlands. We also have many “kennisbasis” and “BO” projects addressing food circularity and biodiversity calculations.
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
