Challenge Area:
Promoting sustainable consumer behaviour.

Problem Statement:
Consumers often lack awareness of the environmental impact of their purchasing decisions, leading to unsustainable consumption patterns and increased waste.

Solution Concept:
Develop an AI-powered mobile app that uses machine learning to analyze product data (e.g., materials, production methods, carbon footprint) and provides personalized recommendations for sustainable alternatives. The app employs natural language processing (NLP) to scan product descriptions or barcodes, a recommendation system to suggest eco-friendly options, and gamification (e.g., sustainability scores) to encourage greener choices.

Expected Impact:
Influence 50,000 users to reduce their consumption-related carbon footprint by 10% annually and divert 5,000 tons of waste from landfills through better purchasing decisions.

Next Steps:
Collect product lifecycle datasets, develop NLP and recommendation models, prototype the app using React Native, and partner with retailers for data integration and pilot testing.

Challenge Area:
Waste Management

Problem Statement:
Inefficient waste sorting leads to low recycling rates, increasing landfill use.

Solution Concept:
Build an AI-powered waste sorting system using computer vision to classify recyclables from images. The PoC includes data preprocessing for diverse waste images and modular code for model retraining.

Expected Impact:
Boosts recycling rates by 30%, reducing landfill waste by 20%.

Next Steps:
Deploy on robotic sorting systems, support real-time processing, and integrate with municipal waste systems.