Investigating the factors that affect the time of maximum rejection rate of e-waste using survival analysis

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), and cultural covariates (literacy, 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 (size 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.

Examining country development indicators and e-waste under the moderating effect of country development levels and e-waste policy

“…We checked any direct influence of internet penetration (H3) and urban population (H6) on e-waste generation, but we found no significant effects. These findings are contradictory to the assumptions that internet penetration (Petridis et al, 2017) or urbanization (Asongu, 2018) will lead to the higher usage of technical gadgets and mobiles phones, leading to higher levels of e-waste production. Taking the analysis further, we checked the mediating roles of internet penetration (H4) and urban population (H7) within the model and did not notice any significant influence.…”

“…It is seen that the growth in GDPPC is associated with the growth in urbanization, which leads to a higher e-waste production (Kusch and Hills, 2017). The internet penetration leads to the higher usage of technical gadgets and mobile phones (Kalia, 2019), which is associated with the higher levels of e-waste production (Petridis et al, 2017). It is seen that the economies have grown faster after the advent of the internet, and the growth of the urban population also surged resulting in a higher level of e-waste production (Zhang and Meng, 2019;Zhu et al, 2018).…”

“…A higher literacy rate is associated with the higher usage of gadgets and gizmos, which in a way leads to higher e-waste production (Popli et al, 2021). Higher economic activities are associated with higher e-waste production (Petridis et al, 2017). A higher literacy rate paves the road to higher usage of Country development indicators and e-waste mobile gadgets (Zia, 2020a), which is one of the biggest contributors to e-waste (Galang and Ballesteros, 2018).…”

The path to sustainable municipal solid waste management: Do human development, energy efficiency, and income inequality matter?

“…In recent years, little research has begun to examine the relationship between income inequality or economic inequality and waste generation. Petridis et al (2017) investigate the effect of economic inequality and cultural factors on the waste rejection rate for 34 countries in the 1984–2012 period using the Weibull parametric accelerated failure time model. The results show that economic disparity prolongs the waste rejection rate, while an increase in wealth shortens the rejection rate.…”