Mapping European innovation and policy landscapes using deep learning: Technologies for sustainable wastewater management

Sustainable Development Goals (SDGs), established by the United Nations in 2015, chart a global course toward sustainability, with Goal 6 specifically targeting the optimization of water resources to ensure widespread access to clean water and sanitation. The objective of this SDG mandates innovative approaches to managing wastewater, which is a critical component for the preservation of public health and our environment. Recent advancements across the technological landscape, including the Internet of Things (IoT), Computer Vision, and Artificial Intelligence (AI), have ushered in a new era for wastewater management, offering unprecedented capabilities in monitoring, data collection, and analysis at scale. These technologies have the potential to revolutionize traditional practices, enhance process efficiency, and play a crucial role in achieving SDG 6. This chapter explores the evolution of wastewater management strategies within the context of the regulation framework of the European Union. The methodology employs a comprehensive analysis and empirical review of 5550 patent grants collected from the years 2016–23 using KeyBERT and topic modeling to identify and categorize relevant patents. We then assess the alignment of these patents with the Urban Wastewater Treatment Directive of 2014 using cosine similarity metrics to evaluate the contextual relevance and technological alignment between them and select the most relevant patents. The analysis reveals an interest in digital innovations, as evidenced by the surge in patent grants in wastewater management. However, there is a gap between the potential of these theoretical advancements and their real-world implementation since only 68 patents show a high similarity rating with the policy document. Even in those cases, there is a weak similarity between the patents and the policy document, with most patents from outside European regions. This gap signifies not just a technological challenge but also highlights areas requiring European policy intervention, cross-sector collaboration, and knowledge sharing to bridge the divide between innovation and application. The proposed framework in the chapter will serve as a template for stakeholders in the industry to better align innovations to the needs proposed by experts in the government and to better draft streamlined policies.