A Blockchain-Enhanced Neural Network Framework for secure e-waste forecasting in smart cities

The exponential growth of electronic waste (e-waste) represents one of the most critical environmental challenges faced by contemporary urban systems. Accurate forecasting of e-waste generation is essential for supporting sustainable decision-making within smart cities. This study proposes the Blockchain-Enhanced Neural Network Framework (BENNF), a conceptual and architectural framework designed to enable secure, transparent, and scalable e-waste forecasting through the integration of artificial intelligence, big data analytics, and blockchain technology. The proposed framework is structured into three interconnected layers: (i) a data acquisition and preprocessing layer based on big data pipelines (Apache Spark and Hadoop); (ii) a prediction layer employing multilayer neural networks trained on socioeconomic and environmental variables; and (iii) a blockchain layer that ensures data integrity, transparency, and traceability through smart contracts and a Proof-of-Authority consensus mechanism. Unlike fully deployed empirical systems, BENNF is presented as a system-level and design-oriented framework, aimed at strengthening digital governance and trust in data-driven environmental management. The framework aligns with the Sustainable Development Goals (SDGs) 12 and 13, promoting responsible consumption, circular economy principles, and climate-resilient urban planning. Its potential applicability in urban contexts such as Guayaquil, Ecuador, highlights its scalability and relevance for sustainable smart city initiatives.