Thursday, June 30, 2022
The participants will understand how to use data to measure key parameters of circular economy in cities. Through presentations, short interactive exercises and discussions they will learn how data can help to define actions to improve urban waste management and circularity. The training focusses on the four methods: 1) UN Habitat - Waste Wise Cities Tool (WaCT) which helps cities to evaluate and improve their municipal solid waste management performance. This method is based on the definition of the SDG indicator 11.6.1 and generates critical information and parameters through primary data collection to establish better waste and resource management strategies and action plans, as well as to mobilise funds and engage stakeholders of the waste chain; 2) GIZ – Waste Flow Diagram (WFD) is a rapid and observation-based assessment method building on the WaCT. It was used in about 100 cities to understanding leakage pathways of plastic waste into the environment which is key to develop effective measures to beat plastic pollution; 3) GIZ - Positive Deviance Approach assumes that in every population there are individuals or communities who, despite facing similar challenges and limitations, achieve better results than their peers. This approach focuses on these outliers (or positive deviants) in order to discover unusual practices and strategies that successfully solve complex problems ¬– particularly where conventional solutions failed; and 4) DLR - analyzing morphologic transformations across the globe using Earth observation data: This part of the training, focusses on how to apply Earth observation data and visual image interpretation in combination with in-situ and Google Street View images to derive 3D city models to provide a temporal analysis of built-up Transformation.
The objective of this training event is to showcase and disseminate various data-centered tools and approaches. These can be of benefit for cities for establishing and reporting on a sustainable waste management and circular economy.