Investigating the factors that affect the time of maximum rejection rate of e-waste using survival analysis. (June 2017)

• Record Type: Journal Article
• Authors: Petridis, Konstantinos; Petridis, Nikolaos; Stiakakis, Emmanouil; Dey, Prasanta
• Is Part Of: Computers & industrial engineering. Volume 108(2017)
• Page Start: 15
• Page End: 26
• Publication Date: 2017-06
• Subjects: WEEE -- E-waste -- E-waste rejection rate -- Survival analysis -- Weibull parametric accelerated failure time model; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.03.014
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Factors affecting positively or negatively e-waste rejection rates are examined. Economic, cultural, demographic factors are considered. Weibull parametric accelerated failure time model is applied. E-waste rejection rate is prolonged by economic disparity and cultural variables. Wealth causes a shorter time of rejection rate. Abstract: This study aims at investigating the factors which influence positively or negatively electronic waste (e-waste) rejection rates. E-waste quantities have been calculated based on historical sales data worldwide and lifespan distribution. The methodology, which is adopted in this paper in order to estimate the effect that economic, cultural, and demographic factors have upon the time at which maximum e-waste rejection is achieved, is a Weibull parametric accelerated failure time model. Considering the event at which the maximum rejection of e-waste takes place as the dependent variable, it is assumed that it is a function of economic (GDP, GINI index, Internet users, exports/imports and prices), demographic (dependency ratio, gender, literacy, no of households), and cultural covariates (masculinity, uncertainty avoidance). The variables are fed to the model after transformation into two major constructs derived from Factor Analysis: the first construct is Wealth (exports, imports, and GDP) and the second is Economic Disparity (no of households, literacy, Internet users, and GINI). The results demonstrate that the time of maximum e-waste rejection rate is prolonged by economic disparity and cultural variables (uncertainty avoidance), while wealth causes a shorter time of rejection rate. The proposed methodology is of great value, as its application could provide useful information in order to develop policies for optimal management of e-waste quantities.