Acta Marisiensis.
Seria Technologica



ISSN 2668-4217
ISSN-L 2668-4217
(Online)


Română

HomeEditorial boardSubmit paperPublication ethicsContactIndexing
Year 2024
Volume 21 (XXXVIII), no 1

Year 2023
Volume 20 (XXXVII), no 1
Volume 20 (XXXVII), no 2

Year 2022
Volume 19 (XXXVI), no 1
Volume 19 (XXXVI), no 2

Year 2021
Volume 18 (XXXV), no 1
Volume 18 (XXXV), no 2

Year 2020
Volume 17 (XXXIV), no 1
Volume 17 (XXXIV), no 2

Year 2019
Volume 16 (XXXIII), no 1
Volume 16 (XXXIII), no 2

Year 2018
Volume 15 (XXXII), no 1
Volume 15 (XXXII), no 2

Year 2017
Volume 14 (XXXI), no 1
Volume 14 (XXXI), no 2

Year 2016
Volume 13 (XXX), no 1
Volume 13 (XXX), no 2

Year 2015
Volume 12 (XXIX), no 1
Volume 12 (XXIX), no 2

Year 2014
Volume 11 (XXVIII), no 1
Volume 11 (XXVIII), no 2

Year 2013
Volume 10 (XXVII), no 1
Volume 10 (XXVII), no 2

Year 2012
Volume 9 (XXVI), no 1
Volume 9 (XXVI), no 2

Year 2011
Volume 8 (XXV), no 1
Volume 8 (XXV), no 2

Year 2010
Volume 7 (XXIV), no 1
Volume 7 (XXIV), no 2

Year 2009
Volume 6 (XXIII)

2022, Volume 19 (XXXVI), no 1

Macroeconomic Predictor for Recovery Rate of Construction and Demolition Waste. a Neural Networks Model for Romania

Author(s):
Argeime LOPEZ, Research & Development Department, Daw Bența Romania, Sâncraiu de Mureș, Romania
Manuela Rozalia GABOR, ”G.E. Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, Romania

Abstract:
The common values of a circular economy are concentrated in decoupling economic growth from resource consumption; resource efficiency; waste management; sharing; reducing greenhouse gas emissions; lifecycle assessments and closing loops. With the increasing cost of natural resources as a real EU scenario, industries will significantly benefit from shifting towards a more circular approach. The aim of this paper is to analyses the waste management actions, especially for construction and demolition sector, in Romania in the EU-28 context by applying statistical methods and neural network modelling to find the best macroeconomic predictor for recovery rate of construction and demolition waste for period 2010 -2020.

DOI: https://doi.org/10.2478/amset-2022-0005

Pages: 22-27

View full article
Update: 19-Jun-2024 © Published by University Press