Speaker
Description
The war in Europe, in Ukraine, once again highlights that the battles of the great powers are fought for territories and resources. In this context, the circular economy comes to respond to the increased needs for resources and capabilities, putting into a new, integrative paradigm, everything we knew about the economy and society until then. Circularity strategies, such as recycling, reuse, redesign, remanufacturing, etc. (10 R-Strategies), although they are still insignificant aspects within some private and public policies, will mean more and more in the fight for resources, and will show who the winners of the battle for the future are. In this framework, the article analyzes econometrically, based on Eurostat data, for the period 2010-2023, the implications of some circularity indicators, including SDGs, on the recycling rate of waste from electrical and electronic equipment at the level of the European Union with 27 states (EU27). Thus, in methodology although initially several indicators were calculated within the 10 R-Strategies, through repeated simplifications, only one indicator was chosen from them, the most relevant for the model, namely: Share of recovered waste from collected waste (%) (SRWEEEC). The data source is Eurostat, the data are systematized as panel for all EU27 and the analysis period is 2010-2023. The method is ordinary least squares estimation, and a correlation matrix is also created to better explain the links between the selected variables. The number of observations obtained is 378 and thus the information volume can be considered relevant. The results highlight that 10 R-Strategies along with indicators related to the development of the circularity field (added value, employees, trade, investments) have significant implications on the recycling rate of electrical and electronic equipment waste. The chosen explanatory variables relate to circularity strategies, economic development and especially the circular economy environment related to innovation and competitiveness. These, according to the chosen model, can explain 66.5% of the situation of recycling electrical and electronic waste (RRWEEE).
Key words: circular economy, WEEE, impact, correlation matrix, regression
JEL classification: C33, F64, N54, O44, Q53
