What can arise from the unusual combination between Artificial Intelligence and waste dumps in the global South? Can we start from the problem of plastic pollution in emerging countries to design a cutting-edge solution?
The dimensions of the issue are significant. To date, a country like South Africa recycles only 21% of the plastic produced, pouring the remaining 79% into the environment. In absence of a structured municipal waste management system, collection is carried out by 90,000 informal collectors. They do not receive a fair social or economic recognition for the service offered.
Thanks to a grant from P4G, AISent and Oxfam have designed a digital solution for the disposal of household waste, to strengthen environmental protection, citizen involvement and social development. The monitoring of exchanges between waste pickers, collection centers and recycling companies takes place through Plus App, an online platform that allows the functionality, scalability and replicability of the system, and the control of transparency along all the steps of the value chain.
Plus App equips all the players with a virtual profile and allows the matching of supply and demand needs, the optimal planning of routes, and, ultimately, the reduction of intermediaries, to avoid exploitation mechanisms and extract the maximum value from waste.
Plus App also encourages financial literacy: the waste collection and disposal process allows each link in the chain to earn some virtual credits, which can then be converted into real money.
Technically, the platform consists of a Django backend, which manages data storage and access using a rule-based approach. In addition, a frontend developed in Vue.js and Ionic allows the collection centers to have a clear and user-friendly view of the information.
Plus App is an AI-enabled solution: through the analysis of the data collected by the system, it is possible to identify the most common products in order to optimize their purchase prices, provide graphs of interest related to the social and environmental impact, or identify frequent patterns for identifying anomalies in the system.