Using fuzzy cognitive maps to identify better policy strategies to valorize organic waste flows: An Italian case study

Morone P., YILAN G. , Imbert E.

Journal of Cleaner Production, vol.319, 2021 (Journal Indexed in SCI Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 319
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jclepro.2021.128722
  • Title of Journal : Journal of Cleaner Production
  • Keywords: Circular bioeconomy, Italy, Organic fraction of municipal solid waste (OFMSW), Policy mixes, Policy scenarios


© 2021 Elsevier LtdIn Europe, there is a vast amount of municipal waste available. The organic fraction of municipal solid waste (OFMSW) represents a particularly valuable part of this waste, due to its potential to be employed to produce a range of value-added products. While several studies have addressed the utilization of the OFMSW in the Italian context, an overall picture of how the circular bioeconomy (CBE) model is being implemented in Italy is lacking. Accordingly, the present study investigated the status quo of the Italian bioeconomy sector, focusing on the use of the OFMSW as feedstock. The research aimed at increasing our understanding of barriers to the effective adoption of the CBE and identifying effective policy strategies. Specifically, a fuzzy cognitive mapping technique using an artificial neural network model was used to assess the impact of both single policy measures and policy mixes on a sample of selected outcomes, including human health, the environment, profitability and biorefinery approach. The results clearly showed that excessive bureaucracy, linear logic and technology-based solutions ignoring the complex characteristics of waste planning activities were the most important variables influencing the implementation of the CBE in Italy. Moreover, the results suggested that a policy mix combining economic and financial support policies for sustainable activities alongside improvements to waste collection systems could generate the highest positive effect on all considered outcomes.