| Title |
Unlocking the potential of surplus food: A blockchain approach to enhance equitable distribution and address food insecurity in Italy |
| ID_Doc |
17403 |
| Authors |
Yu, MT; Principato, L; Formentini, M; Mattia, G; Cicatiello, C; Capoccia, L; Secondi, L |
| Title |
Unlocking the potential of surplus food: A blockchain approach to enhance equitable distribution and address food insecurity in Italy |
| Year |
2024 |
| Published |
|
| DOI |
10.1016/j.seps.2024.101868 |
| Abstract |
Food insecurity and food surplus are two pressing issues that have been exacerbated by the COVID-19 pandemic. This paper presents a novel approach to addressing these problems using blockchain technology. More specifically, data from the Italian Regusto platform are analysed to identify factors that determine the volume and economic value of surplus food redistribution. From a methodological statistical perspective, the performance of Stepwise Multiple Linear Regression and Random Forest are compared in a blockchain data analysis. The effective machine-learning-based analysis reveals that the Regusto platform gives people greater access to the surplus food redistributed by non-profit organizations and suppliers through both donations and sales. The study also highlights the potential effects of redistributing surplus food on the beneficiaries' diets and health and we find that redistributing surplus food can reduce household food insecurity among low-income families and communities measured by food poverty and COVID-19 indexes at different spatial locations. Overall, by offering a unique perspective on these issues through the lens of blockchain technology, our study can inspire further research on how this technology can ensure everyone has enough affordable, nutritious food to lead healthy lives. |
| Author Keywords |
Food poverty; SDGs; Circular supply chain; Machine learning; Blockchain |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:001228833300001 |
| WoS Category |
Economics; Management; Operations Research & Management Science |
| Research Area |
Business & Economics; Operations Research & Management Science |
| PDF |
https://doi.org/10.1016/j.seps.2024.101868
|