Journal of Innovation & Knowledge (Jan 2024)

Quantification of the impact of innovations in industry and infrastructure for sustainable circular economy production and consumption

  • Marinko Skare,
  • Beata Gavurova,
  • Martin Rigelsky

Journal volume & issue
Vol. 9, no. 1
p. 100456

Abstract

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The primary aim of this study was to quantify the impact of industry and infrastructure innovations on sustainable production and consumption within the circular economy (CE) in European Union (EU) countries. From the perspective of the Sustainable Development Goals (SDGs), the relationships between indicators representing SDG 9 and SDG 12 were examined. To achieve this, data from Eurostat for the period 2010–2021 were analyzed using regression and cluster analysis. The analyses revealed significant differences among EU countries in the areas investigated. The Netherlands and Belgium were among the highest-rated countries in terms of the examined relationships. Denmark excelled in industrial and infrastructure innovations, while Romania ranked among the lowest. A year-on-year decrease since 2010 was observed for several indicators, including the circular material use rate and the public transport ratio. Developed countries such as Finland and Luxembourg experienced a recent decrease in circular material use rate. A significant relationship was identified between the circular material use rate and industry and infrastructure innovations. Countries such as Romania, Portugal, Croatia, and Cyprus were in the worst positions. The results of the study are beneficial for policymakers focused on transitioning economies to CE, as well as for experts in business environments, educational policies, and regional development. These results support the development of benchmarking indicators at national and international levels, facilitating the creation of composite models for multidimensional analysis implementation. The findings are relevant for political strategists at both regional and international levels and may provide valuable insights for analytical and research teams designing predictive models.

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