Modelling the correlations of e-waste quantity with economic increase
Graphical abstract
Introduction
Waste from Electrical and Electronic Equipment (WEEE or e-waste) is considered as the fastest increasing stream of waste in the world (Guo and Yan, 2017; Zeng et al., 2017b). The increasing publications in the recent two decades (Fig. S1 in Supplementary content (SC)) indicate that e-waste management has become a global and emerging issue, from developing countries to industrial nations (Awasthi and Li, 2017; Li et al., 2015; Sthiannopkao and Wong, 2013). The generated quantities of e-waste are highlighted owing to its fundamental significance in both new policies definition and process development. In principle, the experts described e-waste generated amounts like a logical effect of the technological progress, especially in developed countries (Song et al., 2016). The main idea was that it is useless trying to estimate future e-waste generation because there are so many factors influencing these amounts that there are very few chances to give a real value (Cucchiella et al., 2016b; Zeng et al., 2016). The same issue can be described for yearly growth rates. The list of obsolete products considered as e-waste is so variegated and numerous that there are too many different customer behaviours to consider for doing a real estimation of trends (Guo and Yan, 2017; Tran et al., 2016). As evidenced in some work (Cucchiella et al., 2015), the disruptive innovation characterizing some technological product, together with new environmental measures and critical materials restrictions, modified the natural obsolescence of some electrical and electronic equipment (EEE), by actively increasing their substitution rate. A typical example reported in the literature is about the technological shift between cathode-ray tube and liquid-crystal display screens (Sun et al., 2016).
Recently, some authors started in studying the possible presence of any mathematical relation among e-waste generated volumes and the anthropogenic behaviour in developed (and developing) countries (Duan et al., 2016; Song et al., 2017). For example, Kumar et al. (2017) evaluated the relationship among e-waste generated volumes, national Gross Domestic Product (GDP) and population. Kusch and Hills (2017) refined the previous results by considering GDP at Purchasing Power Standards (PPS) – instead of usual GDP – for limiting/standardizing the effect of different purchasing powers in different nations taken into account during their work.
GDP-PPS is an artificial currency unit, that analyses factors of each country to define a number on a person's standard of living within that country. For this reason, GDP PPS is better than usual GDP (Coccia, 2010; Dennett, 2014). However, the analysis of data characterizing e-waste volumes, including—collection, reuse and recycling with macro-variables are not well analysed in literature. Given a vast difference between generated and collected volumes subject to both illegal flows of WEEE (Li et al., 2013), absence of standardized measuring systems (Ongondo and Williams, 2011), and population habits (Wang et al., 2011), it is of utmost importance to have two distinct views of the context. In general terms, generated volumes are those amounts that, usually, are estimated through statistical data by experts (Garlapati, 2016; Ongondo et al., 2015). Given the previous issues, real data on these amounts are very challenging to gather. As opposite, collected volumes are those numbers that are measured by national governments and that give a correct idea of the real recycling performance of nations (Nelen et al., 2014; Salhofer et al., 2016). However, both generated and collected volumes refer to waste amounts prior to their treatment. Instead of reuse, recycling and recovery are resorted to waste amounts after their treatment (Robinson, 2009). Reused/recycled numbers refer to wastes that, after treatment, can directly re-enter within the traditional value chain (e.g., plastics, wood, glass, metals). Recovered amounts, instead refer to wastes that – given their physical features – cannot re-enter in the value chain and must be incinerated for the production of green energy (Bovea et al., 2016; Golsteijn and Valencia Martinez, 2017). This way, it is important to distinguish the two measures also when there is a need to define a new performance parameter.
Considering the global challenges subject to e-waste, this paper aims to reach two objectives: (1) the mathematical relationship among economic growth, population, and e-waste amount, concerning the 28 European countries during the year of 2009–2014, will be examined in six case-studies such as GDP PPS and collected amount, GDP PPS and reuse & recycling amount, population and collected amount, population and reuse & recycling amount, GDP PPS per capita and collected amount per capita, GDP PPS per capita and reuse & recycling amount per capita. And (2) the future projection of e-waste amount will be uncovered with a comparison among 28 European countries.
Section snippets
Materials and methods
This section is structured as follows. Section 2.1 will present a general discussion about circular economy principles. Section 2.2 will link these principles with European governmental actions, evidencing current and future strategies regulated by the EU Commission towards the sound management of e-waste. Section 2.3 will demonstrate a state-of-the-art analysis on e-waste management, uncovering the existing literature gaps. Finally, 2.4 Model assumptions, 2.5 Input data will describe the model
Correlation model of e-waste volumes and GDP
Starting by the model assumptions defined in Section 2.4 and input data proposed in Section 2.5 (SC: Tables S2, S4, and S6), a regression model for e-waste (distinguished between collected and reused/recycled) and GDP PPS is evaluated in Fig. 1. The number of data points analysed in this comparison is equal to one hundred and sixty-four for e-waste collected vs GDP PPS and one-hundred and fifty-nine for e-waste recycled/reused vs GDP PPS.
By considering both the graph and the related equations,
Conclusions and implications
This research article reveals the presence of a strong linear correlation among global e-waste generation and GDP. The obtained results indicate that the best fit for data can be reached by comparing e-waste collected volumes and GDP PPS. Besides, the current work evidenced as e-waste plays a relevant role in the global economy and their growth rate depends from human behaviours. Although, many directives and policies were deliberated during the last decades trying to limit and control e-waste
Abbreviations
- CE
circular economy
- EoL
End of Life
- F
F value
- Fcrit
F critical value
- GDP
Gross Domestic Product
- N
number of data points
- P
P-value
- PPP
purchasing power parity
- PPS
Purchasing Power Standards
- R2
coefficient of determination
- WEEE
Waste from Electrical and Electronic Equipment
Acknowledgements
The work is financially supported by Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resources (CCA2017.12), Key Laboratory for Solid Waste Management and Environment Safety (Tsinghua University), Ministry of Education of China (No. SWMES 2015-01), and National Natural Science Foundation of China (71373141).
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