| Title |
New evidence about artificial intelligence and eco-investment as boosters of the circular economy |
| ID_Doc |
2395 |
| Authors |
Platon, V; Pavelescu, FM; Antonescu, D; Constantinescu, A; Frone, S; Surugiu, M; Mazilescu, R; Popa, F |
| Title |
New evidence about artificial intelligence and eco-investment as boosters of the circular economy |
| Year |
2024 |
| Published |
|
| DOI |
10.1016/j.eti.2024.103685 |
| Abstract |
The circular economy (CE) is a key component in achieving SDG-12. A number of features could greatly enhance and quicken the circular economy. Artificial intelligence (AI) and eco-investment are two of the most important ones. The primary objective of this article is to assess the impact of artificial intelligence and eco-investment on the development of circular economy in the context of Sustainable Development Goal (SDG) - 12. The authors designed a panel regression model to ascertain the influence on CE. In this model, CE is the dependent variable, whereas AI and ecoinvestment are the independent variables. Over a ten-year period, data and information from 27 EU member states were employed in the modelling. The strong results demonstrate that, albeit having a lesser impact than eco-investment, AI has a substantial impact on CE. The model created exhibits flexibility that permits the estimation of distinct equations for any country under consideration. One crucial finding is that there are slow processes that may take many years to complete in order to meet deadlines and require a substantial sum of money. One more point of attention is the replacement that occurs between the two independent variables. Up to a certain point, it is feasible to replace one variable with another while accounting for unique features of every country. The paper highlights the significance of AI and how it can help us get closer to SDG-12 and accelerate the CE. |
| Author Keywords |
SDG-12; Circular economy; Artificial intelligence; Eco-investment; Regression panel data; Country specificity; Heterogeneity |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001250753000001 |
| WoS Category |
Biotechnology & Applied Microbiology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Biotechnology & Applied Microbiology; Engineering; Environmental Sciences & Ecology |
| PDF |
https://doi.org/10.1016/j.eti.2024.103685
|