The RIS3-MCAT Open Government Platform maps science and innovation projects in Catalonia, financed by European funds (Fuster et al., 2023[73]). Powered by artificial intelligence (deep learning pre-trained classifiers) and via visual maps,1 the RIS3-MCAT classifies projects (currently 5 376, spanning from 2014 until early 2024) according to the systemic transformation to which they contribute (as defined in the RIS3CAT 2030 strategy). In August 2024, the platform identified 639 projects in the “Sustainable mobility and logistics system” category, illustrated in Figure A K.1.
Transforming Catalonia’s Mobility System for Net Zero
Source: Extracted from (Generalitat de Catalunya, 2024[74]), RIS3-MCAT Platform, https://ris3mcat.gencat.cat/.
By combining top-down and bottom-up classifications, key themes such as Robotics and automation, Remote sensing and monitoring, and Sustainable transitions have been identified. These topics, grouped by sector, provide an exploring guide to understanding the thematic intersections and co-occurrences in the field of sustainable mobility and logistics. This cross-classification sheds light on the intersectoral and interdisciplinary nature of some of the projects and provides an entry-point for exploring synergies. Table A K.1 provides an example of the thematic intersections between the RIS3CAT priority areas Sustainable mobility and logistics, Energy and resource system, Industrial system, and Social and health care system.
Note: For more information see (Generalitat de Catalunya, 2024[74]).
Source: Extracted from (Fuster et al., 2023[73]), Towards building a monitoring platform for a challenge-oriented smart specialisation with RIS3-MCAT.
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← 1. The projects are displayed on a 2D canvas, organised based on the semantic similarity between them. This setup creates a ‘geography’ of the R&I activity. It also provides a visual representation of how closely related different themes are, as well as the overlap and connections between classifications. The projects are clustered based on deep learning Topic Modelling (TM) implementation, which aims at discovering the unknown topics underlying a collection of text.