An insight into the Italian chemical sector: How to make it green and efficient. (10th August 2020)

• Record Type: Journal Article
• Authors: Arbolino, Roberta; Boffardi, Raffaele; Ioppolo, Giuseppe
• Is Part Of: Journal of cleaner production. Volume 264(2020)
• Publication Date: 2020-08-10
• Subjects: Circular economy -- Economic and institutional determinants -- Chemical sector -- Composite indicator; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.121674
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The present research moves from the consideration that the implementation and the dissemination of circular economy processes is a hotspot in European ecosystem management and that EU countries must converge towards the same targets. Considering the relevance of chemical sector economic and environmental impact, we aim at unveiling the economic and institutional determinants of sectoral performances in terms of circular economy and resource efficiency. By means an OECD procedure, we built a Circular Economy Index for the chemical sector. Following, a panel data model has been applied on 20 Italian regions in order to test two "economic" hypotheses and a "politico-institutional" one. This allowed us to capture the features most likely to affect circular performances within the regions. The findings reveal that regional socio-economic background plays a fundamental role in improving firm performance level, while Institutions and sectoral economic dynamic are not strong flywheel of circular economy. Highlights: The circular economy implementation has been evaluated concerning the chemical sector. The procedure has been tested on twenty Italian regions. The background features improving CE industrial processes have been assessed. The CE performance has been estimated by means a composite indicator. A composite index has been built by using a PCA Analysis.