Critical data studies meets discard studies: Waste data reflectivity in digital urban waste tracking system

Abstract This article examines the implementation and impact of the Digital Urban Waste Tracking System (DUWTS) in Poland, a smart waste management technology designed to enhance waste segregation practices in urban environments. DUWTS combines public administration and private sector efforts to oversee proper waste disposal through advanced dataveillance technologies. The system utilizes electronic ground containers equipped with sensors and QR codes to weigh and record waste, aiming to improve recycling levels and manage waste data. The purpose of the research is to critically explore the concept of "waste data reflectivity," highlighting the socio-technological implications of data representation in waste management. The major findings reveal significant distortions and errors in data reflection, impacting various stakeholders, including municipalities, waste disposal companies, and residents. The study concludes that while DUWTS represents a technological advancement, it also perpetuates existing challenges in waste management by obscuring true complexities and responsibilities. Integrating critical discard studies and critical data studies, the article provides a nuanced understanding of the data-driven waste management systems' complexities and challenges. Keywords: digitalization, discard studies, waste data, data journey, critical data studies, data reflectivity, dataveillance, Digital Urban Waste Tracking System, smart waste management technology Ethics Approval The research project description, including ethical considerations, data management plan, and consent forms, is available at the Social Data Repository: https://doi.org/10.60894/ANHJNA. Funder Statement This article was written as part of the project AUTO-WELF: Automating Welfare – Algorithmic Infrastructures for Human Flourishing in Europe. The AUTO-WELF project is supported by the National Science Centre, Poland (grant no. 2021/03/Y/HS5/00263) under the CHANSE ERA-NET Co-fund programme, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no. 101004509.