This paper analyzes the relationship between urban waste recycling and innovation systems in Europe. Data from the Global Innovation Index for 34 European countries in the period 2013–2022 were used. To analyze the characteristics of European countries in terms of waste recycling capacity, the k-Means algorithm optimized with the Elbow method and the Silhouette Coefficient was used. The results show that the optimal number of clusters is three. Panel data results show that waste recycling increases with domestic market scale, gross capital formation, and the diffusion of Information and Communication Technologies (ICTs), while it decreases with the infrastructure index, business sophistication index, and the average expenditure on research and development of large companies.
Waste Management and Innovation: Insights from Europe
Lucio Laureti
;Alberto Costantiello
;Fabio Anobile
;
2024-01-01
Abstract
This paper analyzes the relationship between urban waste recycling and innovation systems in Europe. Data from the Global Innovation Index for 34 European countries in the period 2013–2022 were used. To analyze the characteristics of European countries in terms of waste recycling capacity, the k-Means algorithm optimized with the Elbow method and the Silhouette Coefficient was used. The results show that the optimal number of clusters is three. Panel data results show that waste recycling increases with domestic market scale, gross capital formation, and the diffusion of Information and Communication Technologies (ICTs), while it decreases with the infrastructure index, business sophistication index, and the average expenditure on research and development of large companies.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
