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A proposed sustainable and digital
collection and classification center
model to manage e-waste in
emerging economies
Yigit Kazancoglu, Melisa Ozbiltekin and Yesim Deniz Ozkan Ozen
Department of International Logistics Management, Yasar University,
Izmir, Turkey, and
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Received 3 February 2020
Revised 17 February 2020
Accepted 10 March 2020
Muhittin Sagnak
Department of Information and Document Management,
Izmir Katip Celebi University, Izmir, Turkey
Abstract
Purpose – This study aims to propose an electronic waste collection and classification system to enhance
social, environmental and economic sustainability by integrating data-driven technologies in emerging
economies.
Design/methodology/approach – GM (1, 1) model under grey prediction is used in this study in order to
estimate the trend of the amount of collected electronic waste in emerging economies.
Findings – It is revealed that the amount of collected electronic waste is increasing day by day, and within the
framework of sustainability in the process of collecting and classification of electronic waste, digital
technologies were found to be lacking. It has been determined that this deficiency, together with the increasing
amount of electronic waste, has caused environmental, social and economic damage to emerging economies.
Originality/value – The main originality of this study is integrating electronic waste collection and
classification processes with data-driven technologies and sustainability, which is a relatively new subject.
Keywords Digitalization, Data-driven technologies, Sustainability, Waste management, Electronic waste,
Forecasting, Grey method
Paper type Research paper
1. Introduction
Digitalization within the supply chain aims to create a fully optimized supply chain by
integrating digital data with processes (Lopez Alvarez et al., 2008), and it helps in reducing
waste and increasing productivity in supply chain management (Rovetta et al., 2009). Digital
supply chains include features such as integrated application, logistics visibility, smart
warehousing and autonomous logistics (Osburg and Lohrmann, 2017) as well as the
digitalization of supply chain management directly related to waste management processes
(Rada et al., 2013; Barata et al., 2018).
As one of the global problems under waste management, electronic waste (e-waste) is a
significant issue, and it gains more attention (Schumacher and Agbemabiese, 2019) due to
sustainability concerns, including all dimensions: environmental, economic and social.
According to Global E-Waste Monitor Report (2017), almost 45 million tons of e-waste were
produced in 2016, and only 20% of these wastes could be collected. E-waste is the fastest
growing type of waste in terms of environmental effect on the world (Islam and Huda, 2020).
There are many valuable materials that can be gained from recycling activities of e-waste;
however, the harmful substances in the e-waste need to be treated before the e-waste is
destroyed (Kumar and Holuszko, 2017; Althaf et al., 2019; Islam and Huda, 2020). Moreover,
sustainability should be integrated with the supply chain management which includes end-
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pp. 267-291
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of-life (EoL) products and recycling operations (Linton et al., 2007; Luthra and
Mangla, 2018).
Especially in emerging countries, e-waste is a residual problem (Garlapati, 2016; Ikhlayel,
2018) due to poor treatment processes and lack of knowledge (Kumar and Holuszko, 2017).
Due to the lack of safety considerations within recycling process of hazardous electronic
waste and lack of relevant protocol about classification of wastes in emerging countries, lots
of EoL products are recycled without classification or are thrown away (Frazzoli et al., 2010;
Garlapati, 2016). Moreover, as a result of the lack of regular and effective legislation,
emerging countries can be vulnerable to health risks arising from chemicals in electronic
waste (Frazzoli et al., 2010). In emerging countries, there is no adequate and appropriate
infrastructure for safe recycling activities for electronic wastes, and therefore, e-waste is
usually kept at home (hibernation) or disposed directly in landfills.
In order to understand the severeness of e-waste in emerging countries, it is essential to
develop a systematic approach to determine further applications for managing e-waste in
emerging countries. In that manner, reverse logistics may contribute to managing e-waste
collection. Hence, in order to manage the reverse logistics activities, proper roadmaps are
essential. Thus, to comprehend the current state and to anticipate the future, the initial step of
roadmaps should be forecasting. From this point of view, the first research question is
structured as:
RQ1. What shall be the anticipated e-waste collection figures in the long run within
emerging economy context?
In particular, collection of e-waste is a growing problem in emerging economies since there is
a lack of systematic approaches and improper technologies. Especially, the most significant
problem is caused by the lack of classification and collection process of e-waste in emerging
economies. In contrast to developed countries, in emerging economies, e-wastes are collected
and recycled without any classification (Kumar and Holuszko, 2017; Leader et al., 2018), and
thus the useful and valuable parts, components and even products are either incinerated or
landfilled (Needhidasan et al., 2014). Therefore, it can be said that there is a need for a
systematic collection and classification mechanism, which is the crucial part of reverse
logistics, to manage environmental, economic and social concerns.
With this view, the second research question is presented as:
Based on the previous research question, digitalization should also be integrated to ewaste management practices in emerging economies in order to deal with the environmental
and technological challenges. By considering literature, it can be seen that there is a gap in
electrical and electronic waste sectors, especially in the collection process based on track-andtrace technologies and smart collection systems (Lopez Alvarez et al., 2008; Rada et al., 2013;
Keim, 2017; Martin and Leurent, 2017). Furthermore, in the collection process, digitization
should be designed to leverage support and synchronize transformations toward
sustainability (Keim, 2017). Therefore, in order to create innovations in the world of waste
management and to enter the digital age, adopting innovations and using appropriate
technology have become very important under the triple bottom line (TBL) concept (Osburg,
and Lohrmann, 2017).
RQ2. How shall the current state of e-waste collection system be improved by
digitalization to enhance sustainability within emerging economy context?
Based on these research questions, implementation of this study was done in one of the
emerging economies, Turkey, which struggles with the e-waste classification and collection
problem. Thus, firstly, the amount of e-waste collection in Turkey is predicted for the next
four years by using the grey prediction method. Secondly, a proposed sustainable and digital
collection and classification center model to manage e-waste in emerging economies is
developed according to the results of the implementation, and the data-driven technologies
and TBL-based benefits of the proposed sustainable collection and classification center are
identified.
Following the introduction, the background related to e-waste collection problem in
emerging economies and e-waste collection problem in Turkey is discussed. In section 3, the
need for forecasting in e-waste collection is explained. In sections 4 and 5, methodology and
implementation of the study are explained, respectively. In section 6, an analysis of the
current state of e-waste collection in Turkey is presented. In section 7, a sustainable collection
and classification center model based on digitalization benefits for e-waste is proposed.
Finally, in section 8, concluding remarks are summarized.
2. Theoretical background: e-waste collection problem in emerging economies
Electronic waste is a growing problem in emerging economies like Pakistan, India, Nigeria
and Vietnam (Park et al., 2017; Garlapati, 2016; Ikhlayel, 2018). E-waste production is
increasing in emerging economies that do not have sufficient resources and technology to
properly destroy it (Park et al., 2017). Many developed countries send electronic waste to
emerging economies for disposal or processing (Asia and Africa) (Garlapati, 2016). The
developed countries find the process cheaper and easier (Garlapati, 2016). It is an opportunity
for emerging economies to obtain useful materials from the recycling of electronic wastes, but
the fact that electronic wastes cannot be properly recycled or disposed of as classified is the
biggest source of harm (Heeks et al., 2015; Garlapati, 2016). Improper dismantling or
incineration of these products for the recovery of materials from electronic products
adversely affects both employees and inhabitants and causes air, water and soil
contamination (Orlins and Guan, 2016; Park et al., 2017). This means that because of
unsafe disposal and nonclassification of e-waste, lots of hazardous chemicals are released
during disposal and processing (Park et al., 2017).
The excessive concentration of innovative technologies in the development of electronic
equipment results in rapid aging that causes large amounts of e-waste. The proper collection
and classification of valuable and dangerous components is the most important criterion to
develop an environmentally friendly and economic recycling system for e-waste (Garlapati,
2016). In most emerging economies, there are no well-established processes and effective
regulations based on sustainable criteria for separation, collection and disposal of wastes that
takes into account sustainable criteria for its separation, storage, collection, transport and
disposal (Mundada et al., 2004; Nnorom and Osibanjo, 2008).
Especially e-waste collection is major problem for emerging economies (Park et al., 2017).
Produced e-waste has to be stored due to the lack of collection systems, safe disposal and
suitable recycling facilities in emerging economies (Ikhlayel, 2018). In addition, waste
management authorities and experts have concerns about the lack of waste separation, safe
disposal and appropriate recycling (Gu et al., 2016; Ikhlayel, 2018). In emerging economies,
some factors are considered in the collection of electronic waste. These are e-waste volumes
from abroad, e-waste regulations of countries and activities in the informal e-wasteprocessing sectors. These collectors are not supported by the government (Gu et al., 2016).
Most emerging economies face difficulties in e-waste management, such as in disposal and
collection; e-wastes are collected and disposed of in uncontrolled sites illegally
(Ikhlayel, 2018).
E-waste collection systems are divided into two groups: “collective system” and “clearing
house system” (Park et al., 2017; Sthiannopkao and Wong, 2013). A collective system is based
on nongovernmental and non-profit organizations which are founded by trade associations
(Park et al., 2017). This type of e-waste collection system is based on channelization and
sorting of each type of e-waste for reverse logistics activities. On the other hand, “the clearing
house system” involves establishing waste collection points and governmental registrations
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(Kumar and Holuszko, 2017; Park et al., 2017; Sthiannopkao and Wong, 2013). In addition,
logistics processes of e-waste collection involve three types of collection: “municipal
collection” is a without-cost system in which any amount of waste can be accepted from
people, “in-store retailer take-back system” is based on repeating purchases and can be free
and “direct producer take-back system” is based on business customers which involves
replacement purchase (Park et al., 2017; Sthiannopkao and Wong, 2013).
Furthermore, in emerging economies, unskilled workers are responsible for the collection
of electronic waste (Kumar and Holuszko, 2017). This collection process involves door-to-door
operation. If the collected waste is not valuable for recycling operations, these wastes are
disposed of or burned. This unconscious separation causes huge damage to the environment
and affects human health (Gu et al., 2016; Ikhlayel, 2018; Kumar and Holuszko, 2017).
Although there are some studies advocating that the electronic waste collection method in
emerging economies can be helpful to decrease collection costs (Park et al., 2017), the
collection methods are harmful for human health (Gu et al., 2016).
This study especially focuses on Turkey, one of the emerging economies that has e-waste
classification and collection problem.
2.1 E-waste collection in emerging economies: Turkey
Recently, with the rapid advancement of technology, the usage period of electrical and
electronic devices is shortened. With this rapid progression has emerged the concept of
e-waste. Due to the increase in e-waste, some legal regulations and policies promote the
development of the transition process. On May 22, 2012, in Turkey, Official Gazette reprinted
the “Regulation on Control of Waste in Electrical and Electronic Equipment (WEEE)” to
provide information about the e-waste legislation, the effect of the waste and the possible
measures to deal with it. The purpose of this regulation is to organize the legal and technical
guidelines for the reduction of the amount of disposed waste through repair, reuse,
remanufacturing, refurbishing or recycling activities, to protect the environment and human
health from production to the final disposal of electrical and electronic items and to decrease
the electronic waste, which is one of the most common wastes (Ozturk, 2015).
According to the Regulation on Control of Waste in Electrical and Electronic Equipment
published in May 2012, both companies and municipalities in Turkey are obliged to collect ewaste. Also, e-waste producers are obliged to establish a system for the collection, processing
and disposal of waste. Regarding e-waste management in Turkey, there are three authorized
organizations: ELDAY (Electric and Electronic Recycling and Waste Management
Association), TUBISAD (Association of Information Technology Industrialists) and AGID
(Lighting Equipment Manufacturers Association) (Ozturk, 2015). Turkey has a young
population, and therefore use of technology is continuously increasing in the country (Ozturk,
2015). While the number of processing plants was 21 in 2011 (Ilgar, 2016; Balde et al., 2017), it
increased to 71 in 2016 (Ministry of Environment and Urbanization, 2017).
Like other emerging economies, Turkey has been facing a growing e-waste problem in recent
years. Especially, the collection of e-waste in Turkey is not regulated effectively by the firms and
government. Like other emerging economies, in Turkey, e-waste is collected without any
classification (Park et al., 2017). The consumer can dispose of their electronic products in any of
the following ways: hibernation, discarding and other collection methods (Gu et al., 2016).
Hibernation has different meaning in the literature such as “permanent hoarding”
(nonused electronic products at home) (Haig et al., 2011), “household storage” (Jang and
Mincheol, 2010) or “dead storage period” (Wilson et al., 2017). Especially in Turkey,
consumers keep their electronic products in their homes instead of giving them for recycling.
Moreover, some consumers in Turkey prefer to donate their electronic products under
various campaigns of firms of nongovernmental organizations such as Migros, TEGV and
TUBISAD. On the other hand, unfortunately, lots of consumers throw their electronic
products in the rubbish bins.
There are five main collectors in e-waste collection management in Turkey, like in other
emerging economies: seller, firms or enterprises, stationary collection points which include
piggy banks, bins or containers, second-hand markets and peddler or waste collector (Gu
et al., 2016). These are directly linked to the consumers in Turkey. Firstly, consumers can
contribute to recycling by giving the electronic product to the seller under the “old for new”
method (Wang et al., 2013). Moreover, when consumers purchase new products, they can give
the old one without any charge to the dealer. The seller also acts as a distributor (Garlapati,
2016; Gu et al., 2016). It is responsible for distributing the product brought by the consumer to
the dismantling centers. Moreover, the e-waste which has huge volume can be collected by
firms or licensed enterprises at specific periods by vehicles as a mobile collection method
(door-to-door) and then taken to a middleman (transshipment center) for distributing to
dismantling companies or to backyard recycling (Gu et al., 2016; Ikhlayel, 2018; Kumar and
Holuszko, 2017). Furthermore, the consumer stationary collection points include bins or
containers organized by TUBISAD, ELDAY, the government or civil institutions in Turkey
(Ozturk, 2015). According to the WEEE regulations in Turkey, all producers are obliged to
collect and recycle old products in the market share. The process can be conducted through
an organization authorized by Ministry of Environment and Urbanization (TUBISAD in
Turkey). The consumer can bring electronic products to these bins for sending them to the
middleman (transshipment center) (Ikhlayel, 2018). Therefore, in Turkey, many companies
and organizations such as municipalities began to collect e-waste; in fact, in this regard, a
“market” structure has been formed. The other type of collection of e-waste is the secondhand market (Khurrum et al., 2011; Gu et al., 2016). These second-hand market products are
sent to the middleman to be exported as second-hand items to other Third World countries;
the electronic circuits of these waste products are removed and disposed of in the open,
dissolving the precious metals in acids in undesired conditions, threatening human and
environmental health (Ladou and Lovegrove, 2008; Gu et al., 2016; Schumacher and
Agbemabiese, 2019). Moreover, peddler/waste collectors are individual collectors in Turkey
who collect electronic products from door to door by paying a small amount to the product
owners. They deliver these products to backyard recyclers for money.
In the following section, the framework of digital technologies in e-waste collection system
is explained in detail.
3. Digital technologies in e-waste collection
The digitalization, which is a new trend, brings new developments all over the world in many
ways. The digitalization provides new application areas for preventing, reducing, improving
resource recovery, achieving disposal standards and greatly reducing pollution and
environmental impacts from industries (Abdel-Shafy and Mansour, 2018).
The digitalization, which incorporates technology into daily lives, creates opportunities
for social transformation by making progress in fields such as robotics, autonomous tools
and smart vehicles. Adopting data-driven technologies enables the waste sector to redefine
its processes in the new world (TWI2050, 2019).
Moreover, it provides new ways not only for environmental protection and waste
reduction, but also for promoting stakeholder awareness, consumer engagement and
producer responsibility on a global scale (Balde et al., 2017). In addition, the digitalization
process will redefine the meaning of “waste” and provide robotic solutions and driver-free
collection patterns, particularly by reshaping, especially in electronic waste management and
recycling (Martin and Leurent, 2017).
Digitalization is crucial for the waste industry, from waste prevention to smart logistics, to
advanced resource recovery (DEFRA, 2018). However, the application of this term is different
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between developed and developing countries. Therefore, there is a gap between developing
and developed countries (Balde et al., 2017). While developing countries face difficulties in
waste management such as wrong recycling process, insufficient collection services and open
burning, developed countries are willing to be innovative in waste management process
(Balde et al., 2017). Therefore, it is more important to know and adopt the concept of
digitalization, especially in developing countries (TWI2050, 2019).
Especially in the e-waste industry, the digitalization of the industry brings innovation to
traditional waste management system (ILO, 2014). Although technology has increased
rapidly in recent years, its use for the waste management sector, especially electronic waste
industry, has been increasing slowly (Abdel-Shafy and Mansour, 2018).
Particularly in the waste collection process, there are problems caused by the lack of
adoption of digital technology such as cost and time optimization (Yukalang et al., 2017).
Electronic waste management includes processes such as production, resource allocation,
storage, collection, processing and recovery and incineration (Lopez Alvarez et al., 2008). The
implementation of digital technologies to e-waste collection process provides traceability of
process and recovery in costs and time spent per collation station (Rada et al., 2013).
Therefore, following new technologies and implementing them into waste management
are essential for achieving sustainable solutions under the TBL approach.
In the following section, reasons to forecast the amount of e-waste collection are explained
in detail.
4. Forecasting in e-waste collection
Due to the growth of the electronic sector, current technological revolutions, changes in
consumer trends and the increase in the use of electronic products, the amount of e-waste is
constantly increasing. Moreover, the technological developments make electrical and
electronic products cheaper and easily procurable (Khurrum et al., 2011; Needhidasan et al.,
2014; Garlapati, 2016). Therefore, consumers can change their products with new ones, instead
of getting them repaired (Khurrum et al., 2011). Furthermore, electronic waste includes more
than 1,000 substances such as Pb, Be and Cd. When the substances are disposed of, they
negatively impact the environment and indirectly human health (Awasthi et al., 2016; Kumar
and Holuszko, 2017; Maheswari et al., 2019). The rapid increase in the amount of e-waste causes
significant risks and problems for both emerging and developed countries.
In addition, collection of e-waste is a critical issue in emerging economies because of the
lack of regulations and treatment processes (Shevchenko et al., 2019). While in developed
countries e-waste is collected separately and disposed or recycled according to the category of
waste, in emerging economies, there is a lack of collection system based on categories of
e-waste, and hence the useful products are disposed of (Leader et al., 2018). Moreover, in
emerging economies, there are structural problems about collection and recycling process of
e-wastes. Therefore, to find sustainable solutions for the problem, it is crucial to know the
amount of collected e-waste in the long term (Mmreki et al., 2018).
It is important to know the amount of electronic waste to be collected in emerging
economies in order to prevent social, economic and environmental problems that may arise
when considering the lack of appropriate infrastructure facilities and disposing of useable
materials as waste (Mmreki et al., 2018; Park et al., 2017; Leader et al., 2018).
In the following section, the literature review about methods used for e-waste prediction is
explained in detail.
4.1 Methods used for e-waste prediction
There are many studies about e-waste generation and e-waste estimation in the literature.
To start with, Balde et al. (2017) published “Global E-Waste Monitor 2017,” which is a
collaborative effort of the United Nations University, the International Telecommunication
Union (ITU) and the International Solid Waste Association (ISWA). The report gives
general information about the situation of e-waste for countries. As stated in the “Global
E-Waste Monitor 2017,” e-waste in emerging economies plays a crucial role in global e-waste
problem.
To start with, Borthakur and Govind (2017) studied the consumers’ e-waste disposal
behavior and awareness in India. Also, Roldan (2017), an International Telecommunication
Union (ITU) expert, prepared a report about e-waste management policies and regulatory
framework for Jamaica. The report includes e-waste generation from specific electric and
electronic equipment (EEE): mobile phones, personal computers (PCs; desktops and laptops),
TV sets, cathode-ray-tube (CRT) and flat-panel display monitors. Moreover, G€ok et al. (2017)
carried out a study about consumer behavior and policy for e-waste in Aksaray and Nigde,
which are cities of Turkey. They evaluated the level of knowledge about e-waste and e-waste
management perspectives. Rasnan et al. (2016) studied about e-waste management in three
Asian countries. The study includes three serious problems for e-waste: exponential increase
in total amount, environmental degradation and health complications.
Under investigation of e-waste problem, there are many studies about estimation of the
amount of e-waste in the literature. Nguyen et al. (2009) researched about forecasting the
amount of waste of five types of electronic appliances in Vietnam: color televisions,
refrigerators, washing machines, air conditioners and PCs. They tried to estimate future
amount of e-waste by using a survey method with users of electronic appliances. Also, there
are several studies to predict amount of e-waste in different countries by using material flow
analysis (MFA) (Jain and Sareen (2006); Yoshida et al., 2009; Dwivedy and Mittal, 2010; Zhang
et al., 2011; Yedla, 2015). Similarly, Oguchi et al. (2008) focused on finding the quantity of ICT
products in Japan by using product flow analysis.
Yang et al. (2018) studied prediction of the quantity of obsolete computers by using a logistic
model. Kim et al. (2013) predicted the quantity of e-waste for eight products in South Korea by
using questionnaires and population balance model. On the other hand, there are many studies
using the market supply method which includes production and sales data to predict the
amount of e-waste in different countries (Liu et al., 2006; Chung et al., 2011; Barman et al., 2017).
In 2009, Walk predicted the amount of CRT devices in Germany between 1995 and 2020 by
using a case study and following Weibull distribution. Moreover, Mmreki et al. (2012)
estimated the amount of e-waste in Botswana by using the Stanford method. Similarly,
Mmreki et al. (2018) made a prediction about quantity of e-waste in Botswana by using the
GM (1,1) model (IFC, 2007). Xiang and Daoling (2007) used a different approach which is grey
fuzzy dynamic model to estimate solid waste in China. In Table 1, literature summary of
articles about e-waste estimation is given.
In order to deal with the consequences of this rapid increase, the first step should be to
predict the amount of e-waste that will be collected in the future. Therefore, it is important to
forecast the amount of e-waste that would be collected to analyze the impact of this drastic
increase on the environment, society and economy.
4.2 Reasons to use grey prediction
Traditional forecasting methods, such as Box–Jenkins methods, regression analysis and
neural networks, have been mainly used for known and accessible historical data sets
(Chiang, 1997; Tien, 2009). In addition, fuzzy time series methods focused on data with
linguistic uncertainty and past observation data set (Song and Li, 2008). Instead of using
traditional forecasting methods or fuzzy time series, the grey prediction method is more
practical when there are limited and discrete data to obtain information accuracy (Tien, 2009).
The grey system theory is based on chaotic, fluctuated and uncertain systems (Liu and
Forrest, 2007).
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Method
Focused area
MFA (material flow analysis)
Estimation of e-waste in Delphi
Grey fuzzy dynamic model
Forecasting solid waste in China
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Jain and Sareen
(2006)
Xiang and Daoling
(2007)
Oguchi et al. (2008)
Nguyen et al. (2009)
Yoshida et al. (2009)
Yang et al. (2018)
Walk (2009)
Yu et al. (2010)
Zhang et al. (2011)
Product flow analysis
Questionnaire
MFA (material flow analysis)
Logistic model
Case study
Logistic model
MFA (material flow analysis)
Mmreki et al. (2012)
Zu and Cheng (2012)
Kim et al. (2013)
Stanford method
Grey prediction
Population balance model and
questionnaire
GM (1,1)
SYE-waste model
Market supply
Estimation of ICT product in Japan
Estimation of e-waste in Vietnam
Forecasting discarded computers in Japan
Forecasting computer waste in the USA
Estimation of the CRT devices in Germany
Obsolete PCs in global
Prediction of obsolete household products in
China
Estimation of e-waste in Botswana
Prediction of e-waste in China
Estimation of e-waste for eight products in
South Korea
Estimation of e-waste in Botswana
E-waste estimation (Material Flow)
Estimation of e-waste in Guwahati
Mmreki et al. (2018)
Table 1.
Literature summary of Yedla (2015)
Barman et al. (2017)
e-waste prediction
Furthermore, since the grey prediction model is suitable when there are chaotic, complex and
uncertain data sets, it is suitable to use the method (Zhang et al., 2011). In Turkey, there is a
chaotic situation due to the turbulence effect of the regulation, and in addition to that, limited
data sets exist to make prediction of the amount of e-waste. Therefore, the grey prediction
model is practical when there is an ambiguous system (Chen and Chang, 2000).
Moreover, the prediction methods have to be applied while considering “saturated or
emerging market” conditions for a study (Ikhlayel, 2016). The grey prediction method has
been mainly used for chaotic environment in emerging economies having limited data.
The GM (1,1) model works as the core system and hence is the most crucial part of the grey
prediction method(Yang et al., 2018). Furthermore, the GM (1,1) model is useful for limited
data sets and can work for small amount of data (Bao et al., 2015). It has several advantages
when it is an uncertain system and when less statistical analysis is required, which refers to
the normal distribution (Liu and Yang, 2015). Therefore, in this study, the GM (1,1) model is
used to predict the amount of e-waste in Turkey taking into consideration Turkey’s condition.
In the following section, grey prediction is explained in detail.
5. Methodology: grey prediction
Ju-long Deng developed grey system theory in 1982 (Slavek and Jovic, 2012). The grey system
defines incomplete and missing information. Grey system theory provides a
multidisciplinary approach that can work in cases where information is incomplete and
inadequate (Xiang and Daoliang, 2007). The grey model has demonstrated the practicality of
using an inadequate database because it can identify such an unknown system and can
predict efficiently based on a few variables (Morita et al., 2002). Therefore, it is more practical
than other traditional methods (Huang, 1994; Liu et al., 2006; K€ose and Taşçı, 2015).
In this study, the grey model (GM (1, 1)) is used to predict the amount of collected e-waste in
Turkey due to the chaotic and ambiguous system there. According to Guo et al. (2015), to
calculate predicted data (x03 Þ with the GM (1,1) rolling model, the actual data set (x01 ; x02 Þ
is used (Guo et al., 2015). According to Liu and Forrest (2007), K€ose and Taşçı (2015), Hui
et al. (2013) and Mostafaei (2012), in the GM (1, 1) model, every new data can be used in
prediction. Therefore, the model is more practical and understandable than other traditional
prediction models when there is a chaotic system and limited data (Mostafaei and Kordnoori,
2012). The GM (1, 1) model needs only four recent sample data to make prediction (Guo
et al., 2015).
There are six main steps in the GM (1, 1) model and those are presented below:
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Step 1: The first stage includes the utilization of the initial data set. The original data set is
shown as:
xð0Þ ¼ xð0Þ ð1Þ; xð0Þ ð2Þ; . . . xð0Þ ðnÞ ; n ≥ 4
(1)
275
Then subsequent one is calculated as
xð0Þ ði; kÞ ¼ xð0Þ ðiÞ; xð0Þ ði þ 1Þ; . . . xð0Þ ðkÞÞ
(2)
Equation (3) is given below
xð0Þ ði; kÞ ¼ xð0Þ ð1Þ; xð0Þ ð2Þ; . . . xð0Þ ðkÞ ;
i¼1
(3)
The presented sequence is subjected to the accumulating generation operation (AGO), which
refers to the cumulative sum of x0 series, wherein the following sequence x1 is found.
Step 2: x0 series changes monotonically to increasing x1 series by using AGO in the
second step. Sigma is utilized in the equation, which is shown below, in order to calculate
x1.
x1k ¼
k
X
x0 i ; i ¼ 1; 2; . . . ; n
(4)
i¼1
x1 is found as given below, after implementing the AGO formula:
x1k ¼ x11 ; x12 ; . . . ; x1n
(5)
Step 3: After obtaining x1k series, z1k should be calculated. The generated mean sequence z1k
of x1k is calculated as follows.
z1k ¼ 0:5x1k þ 0:5x1ðk1Þ ;
k ¼ 1; 2; . . . ; n
(6)
By using the given formula, z1k is found as follows:
z1k ¼ 0:5x1k þ 0:5x1ðk−1Þ
(7)
Step 4: Analytical solution of the corresponding grey equation can be calculated by
using parameters a and b, after structuring the required GM model (Chen et al., 2008).
In this study, the least square method is used to calculate a and b. Equations are given
below:
Using Equation (8), all values are substituted as Equation (9).
b ¼ x0ðkÞ þ aZk1
x0ð2Þ ¼ aZ21 þ b
x0ð3Þ ¼ aZ31 þ b
(8)
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x0ðnÞ ¼ aZn1 þ b
(9)
In order to find a and b, below matrices should be structured by using the given formula:
z12
z13
x02
x03
276
Y ¼
x0n
B¼
1
1
−z1n
(10)
1
After that, the matrix method is used to find a and b parameters by Equation (11).
−1 T B :Y
α ¼ ½a; bT ¼ BT B
(11)
Step 5: Grey differential equation is needed to calculate the predicted value of the initial
data at time ðk þ 1Þ.
b
b
x1ðkþ1Þ ¼ x01 e−ak þ
(12)
a
a
Inverse AGO is essential in order to control the calculated data by using Equation (13)
(Kayacan et al., 2010).
Step 6: For the 6th step of the GM (1, 1) model, future values of the initial data set are found.
x0ðkþ1Þ ¼ x1ðkþ1Þ x1k ;
k ¼ 1; 2; 3; 4
b
x0ðkþ1Þ ¼ ð1 ea Þ x0 ð1Þ eak ;
a
(13)
k ¼ 1; 2; 3; 4
Step 7: Error analysis in the GM (1,1) model
In order to determine the error between the predicted and the actual value, error analysis is
needed in the GM (1,1) model. Given equation should be used when k 5 l, lþ1, . . . , n-1 (Yılmaz
and Yılmaz, 2013) for calculating percent error average, where x0k shows the initial value and
0
b
x_ k shows the predicted value of the data set (Wen, 2004).
_x0 x0 b
k
ðkÞ
(14)
eðk þ 1Þ ¼ 0
3100%
xðkÞ 6. Implementation of the study
In the implementation of the study, the amount of e-waste is predicted to highlight how
crucial it is for Turkey. Therefore, the grey prediction method is used.
As shown in Figure 1, the amount of e-waste collected in Turkey is increasing. After the
publication of e-waste regulation in 2012, the amount of waste collected has increased rapidly.
While the amount of collected e-waste was 6,000 tons, it reached 9,500 tons in 2013 (Ministry
of Environment and Urbanization, 2017). With rapid increase, it reached 22,000 tons in 2014,
22,800 tons in 2015, 55,000 tons in 2016 and 58,000 tons in 2017 (Ministry of Environment and
Urbanization, 2017). Moreover, the value in 2018 is considered to be approximately 80,000
tons (Ministry of Environment and Urbanization, 2017). Figure 1 shows the amount of
e-waste collected between 2011 and 2018.
Therefore, it is crucial to know the future e-waste amount and the plan of industries or
people in the production. The GM (1, 1) method is used for estimation when there is a chaotic
and ambiguous situation and also limited availability of data on collected e-waste quantity
(Mmreki et al., 2018). Therefore, the amount of electrical and electronic equipment is predicted
by the GM (1, 1) model for the next three years after 2018 in Turkey.
The process of the application is explained step by step below.
The actual nonnegative data series called as “ X(0)” is given below
Xð0Þ ¼ ð9500; 22000; 28000; 55000; 58000; 80000Þ
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277
The new X (1) series is calculated from the cumulative sum of series (0) which is AGO.
X ð1Þ ¼ ð9500; 31500; 59500; 114500; 172500; 252500Þ
In the following sequence z1k of x1k is found as
ð1Þ
ZðkÞ ¼ ð20500; 45500; 87000; 143500; 212500Þ
The least square method is obtained to find a and b values by using Equation (11). In the
following step, by using Equation (10), X, Y and B matrices are calculated.
20500 1 22000 45500 1 28000 Y ¼ 55000 B ¼ 87000 1 143500 1 58000 212500 1 80000 Before using Equation (11), (BT.B)-1 is calculated.
T 75808000000 509000 B B ¼ 509000
5
T −1 0
0; 00000848 B B
¼ 0; 00000424 0; 63194925 Ton
Equation (11) is calculated to find a and b values and the results are as below:
a ¼ 0:30
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
E-Waste
2011
8000
2012
6000
2013
9500
2014
22000
2015
28000
2016
55000
2017
58000
Note(s): 2018* approximate data of the amount of collected e-waste in Turkey
(Ministry of Environment and Urbanization, 2017)
2018*
80000
Figure 1.
The amount of e-waste
collected in Turkey
(ton) (Ministry of
Environment and
Urbanization, 2017
(National Waste
Management and
Action Plan,
2016–2023)
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b ¼ 18492; 54
e ¼ 2:7183
After calculations, the predicted values, which are calculated by Equation (13), are shown in
Table 2. As shown in Table 2, it is expected that the amount of collected e-waste increases
further.
Table 3 shows actual and predicted values of the amount of e-waste collected in Turkey in
2011–2020, and the error analysis for these years.
As represented in Table 3, the amount of e-waste collected in Turkey continues to increase
until 2021. While the approximate amount of e-waste in 2017 was 58,000 and in 2018 was
80,000 tons, it is expected that the amount of e-waste in 2021 will reach 196,485 tons.
Especially after 2018, this increase will accelerate from 80,000 tons to 108,753 tons in 2019.
In addition, another expected fraction will be from 108,753 tons in 2019 to 196,485 tons
in 2021.
In addition, as stated in Table 3, error analysis shows the error rate between the actual
values and predicted values when the prediction is made by the grey prediction model.
In the following part, Table 4 shows the traditional error results and defined as
Equation (14).
After calculating traditional error analysis for 2013–2018, average relative error is found
as 18%. Moreover, Figure 2 shows the comparison between actual and predicted data for
2013–2021.
As stated in Figure 2, the amount of e-waste collected in Turkey is increasing year by year.
The increase accelerated after the regulation in 2012. While the amount of e-waste collected
was 58,000 tons in 2017 and approximately 80,000 tons in 2018, it is expected to be 108,753
tons in 2019. It is expected to be 146,179 tons in 2020, and 196,485 tons in 2021. Therefore, to
Table 2.
2019
Predicted values of
amount of collected ewaste in Turkey (tons) 108,753
Years
2013
2014
2015
2016
Table 3.
2017
Actual and predicted
2018*
values of e-waste
2019
collected in Turkey,
and the error analysis 2020
between 2011 and 2020 2021
Table 4.
Traditional error
analysis (Δk)
2020
2021
146,179
196,485
Actual value
X0k
Predicted value
X1k
Errors
« (k)
9,500
22,000
28,000
55,000
58,000
80,000
–
–
–
9,500
24,787
33,317
44,783
60,194
80,909
108,753
146,179
196,485
0
287,18
5317,33
10216,99
2194,43
909,47
2013
2014
2015
2016
2017
2018
0% (Accepted)
13%
18 %
18%
4%
1%
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Comparison Between Actual and Predicted Data
2021
2020
2019
279
2018
Predicted Data
2017
Actual Data
2016
2015
Figure 2.
Comparison between
actual and predicted
data for 2011–2020
2014
2013
0
50000
100000
150000
200000
250000
cope up with the rapid increase, sustainable solutions which are based on economic,
environmental and social solutions should be taken into consideration.
In the following section, the current situation in e-waste collection in Turkey is explained.
7. Current-state analysis for e-waste collection in Turkey
As mentioned in the previous sections, the increase in the amount of e-waste collected after
the regulation in 2012 has economic, social and environmental impacts in Turkey. Therefore,
to cope up with these increases, sustainable solutions should be taken into consideration for
circular economy.
In contrast to the current application in Turkey, the only way to reduce the amount of ewaste is not only a recycling classification, but also refurbishing, recycling or reusing
classifications (K€ose et al., 2007; Goren and Ozden, 2011; Arslan et al., 2012; Akkucuk, 2016).
Nonclassification of products causes environmental, economic and social damage
(Akkucuk, 2016; PAGEV, 2018). From an economic point of view, nonclassification causes
loss of value-added products and increases collection costs. Also, while it has effects on
human health from a social point of view, it has environmental consequences in terms of
pollution.
With the regulation and increase in the amount of e-waste in Turkey, not only
municipalities but also firms are obliged to take part in all collection, recycling and
disposal waste processes. In Turkey, in the current situation, products are sent to recycling
without any classification (Akkucuk, 2016; PAGEV, 2018). Therefore, classification-based
collection centers should be taken into consideration for circular economy. In the proposed
system, the products are divided into classes according to whether they are suitable for
refurbishing, reusing or recycling and then sent to the centers where they will be further
processed.
As mentioned above, the failure to classify products according to their operations
(refurbished, reused, recycled, etc.) causes environmental, economic and social problems
(Shingkuma and Managi, 2010). In Figure 3, the current situation in the e-waste supply chain
is represented. Without any classification, e-waste is collected by different methods and sent
to recycling facilities regardless of the class of the product. In addition to hibernation,
donation or disposal, e-waste in Turkey can be collected by five main collectors such as seller,
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Donation
Hibernation
Dismantling Company
Producer
280
Consumer
Seller
Firms/Enterprises
Stationary
Collection Points
Transshipment Center
Secondhand
Markets
Backyard Recycler
Peddlers
Dispose
Raw Material
Figure 3.
Current situation in
e-waste collection in
Turkey
Not recycled
Transshipment
Direct Route
firms/enterprises, stationary collection points which include piggy banks, bins or containers,
second-hand markets and peddler/waste collector. As mentioned in the “Theoretical
Background” section, consumers can keep their EoL electronic products, donate them under
some campaigns organized by firms or dispose them directly in rubbish bins. Beside these
activities, e-wastes are collected by sellers under the concept of “old for new,” firms/
enterprises utilize door-to-door operations to collect e-waste accumulated by the consumer
and licensed enterprises and government at specific periods collected it in vehicles (Guptha
and Shekar, 2009). In these methods, the consumer is passive, and the collector predominantly
plays an important role. Moreover, consumers can bring their e-waste to stationary collection
points organized by TUBISAD, ELDAY, the government or civil institutions in Turkey
(Ozturk, 2015). In the operation, people leave the waste in the piggy bank, bins or containers
(Guptha and Shekar, 2009). However, these wastes are sent to transshipment centers without
recycling types of wastes. In addition, firms can export e-waste to Third World countries to
remove or disassemble harmful materials, which is the most detrimental process for people
who are living in the Third World countries. Furthermore, peddlers can collect them from
consumers. E-waste is collected by peddlers and disposed of in informal ways without
considering devastating effects on environment and people. In addition, the process does not
only affect environment and people’s health, but also there is critical loss of value for precious
materials in these electronic products. E-waste which is collected by sellers is sent to
dismantling companies, while that collected by firms/enterprises or stationary collection
points is sent to collection or transshipment centers. These transshipment centers send ewaste without any classification of recycling process of e-waste. Therefore, e-waste that can
be reused or remanufactured are recycled. Moreover, e-waste collected by peddlers is sent to
backyard recyclers (informal recycling centers) or collection centers. In backyard recycling,
due to lack of recycling process of e-waste, harmful materials that are toxic to human health
and environment are released.
In the following section, the proposed model for e-waste collection in Turkey is explained.
8. Proposed Sustainable Collection and Classification Center Model
The lack of classification in the current situation causes degradation of materials and results
in missing opportunity of obtaining value-added products at the final stage. Furthermore,
there are many parties including municipalities, recycling companies and public/private
organizations that are acting independently from each other in order to satisfy the
requirements of the regulation. Therefore, each party tries to manage the whole supply chain,
starting from e-waste collection until recycling. Due to increased and unnecessary number of
trips, unutilized vehicles and excess amount of distance traveled, transportation costs, CO2
emissions and traffic congestion increase. In addition, the lack of appropriate collection and
classification practices increases the risk of contamination of hazardous materials during
storage and transportation phases.
According to literature review, e-waste collection and recycling process do not consider
lean management which helps in waste reduction, cost saving and sustainable improvement
in emerging economies (Kurdve et al., 2014; Manzouri et al., 2014). Moreover, lean concept
includes generation and collection of waste which covers e-waste to decrease unnecessary
waste (Kurdve et al., 2014). Therefore, to have sustainability of e-waste collection, the
flowchart of collection process has to be lean (Kayanda, 2017). Furthermore, governments/
organizations can use lean management processes in e-waste activities to increase
environmental, social and economic benefits (Fercoq et al., 2016).
Therefore, in order to overcome the problems related to collection centers in emerging
economies that were mentioned in the previous section, as in line with the literature, lean and
sustainable approaches should be implemented. In Figure 4, the proposed model is
implemented by eliminating peddlers, transshipment centers and backyard recyclers and
adding sustainable classification and collection centers for a leaner and sustainable process.
In Figure 4, the proposed model has sustainable collection and classification center, which
classifies e-waste as refurbishable/remanufacturable, reusable and recyclable. The classified
products are sent from the collection and classification center according to their features so
that transport costs and carbon emissions are reduced, and harmful substances that occur
Donation
Hibernation
Consumer
281
Dismantling
Company
Factories or
industries used
for
refurbishing
Seller
Producer
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Firms/Enterprises
Stationary Collection
Points
Secondhand Markets
Sustainable
Classification
and Collection
Center
Factories or
industries used
for reusing
Factories or
industries used
for recycling
Dispose
Raw Material
Not recycled
Transshipment
Direct Route
Figure 4.
Proposed sustainable
collection and
classification center
model for e-waste
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during transport and storage are decreased. Moreover, loss of products that could be
refurbished and reused is decreased. Digitalization can be integrated into this proposed
model by using different data-driven technologies for different processes. In order to improve
the processes in the e-waste collection system, stakeholders should make decisions by
considering huge amount of data to improve TBL gains (Raut et al., 2019).
Expanding the scope of collection and classification centers by following data-driven
technologies provides not only economic benefits, but also social and environmental benefits
based on TBL concept. With respect to this, in Table 5, recommended benefits of data-driven
and TBL-based collection and classification centers are shown.
E-waste recovery is an important factor not only for human health, but also for prevention
of resource waste and efficient and economic use of country’s resources in terms of the
precious metals it contains. According to the 2016 Global E-Waste Report, in terms of e-waste
generation, Turkey was ranked 17th among 177 countries (Balde et al., 2017). When the ewaste is collected and processed appropriately, Turkey will save approximately EURO 767m
(Balde et al., 2017).
New technological developments related to product life cycle management can be used in
order to predict the EoL of the product, and this may contribute to the e-waste management
practices in terms of sustainability. Digitalization covers new innovations in industries,
providing social, economic and environmental benefits such as reducing air pollution,
improving working standards, reducing waste and so forth by new technologies such as
smart vehicles, robotics and sensors (Abdel-Shafy and Mansour, 2018). Thanks to digital
innovations such as robotic systems, smart tracking, sensors, RFID applications, mobile apps
and autonomous vehicle, waste management operations become more sustainable and
beneficial for future. Especially, waste collection is one of the most lost stages and needs to be
integrated with digitalization (Balde et al., 2017).
Data-driven and TBL-based benefits of sustainable collection and
classification center
Table 5.
Data-driven and TBLbased benefits of
proposed sustainable
collection and
classification center
Efficient sorting and separating of e-waste by robotic bins
Waste collection by driverless cars
Decrease in transportation cost by smart-vehicles
Prevention of leakage of harmful chemicals during the storage and
the transportation due to lack of classification by real-time
monitoring systems
Ensuring occupational safety (leakage of harmful chemicals during
the storage and transportation) by sensors
Prevention of degradation of materials that could be refurbished or
reused during storage and transportation by robotic systems
Reducing the loss of products that could be refurbished or reused
by smart tracking
Decreasing CO2 emissions in transportation by smart routing
Decreasing the number of trips and traffic congestion by mobile
apps and software programs
Decreasing collection cost by smart planning through big data
management
Increasing vehicle utilization by an autonomous vehicle
Increasing the awareness of EoL activities by using mobile apps
and social media
Route optimization and waste separation by using RFID and GPS
integration
Social
U
Environment
U
U
Economic
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
U
Robotic bins, which are more popular in waste collection systems, provide a fully
automated system that sorts and separates e-waste by reverse logistics activities rather than
multiple community trash cans and bagged street garbage piles (Balde et al., 2017). Therefore,
having efficient sorting and separation of e-waste by robotic bins contributes social and
economic benefits in the e-waste collection system.
Moreover, driverless cars, which are an innovative and safer transport method
considering the environmental concerns, are one of the new technologies in the concept of
digitalization (Lopez-Lambas and Alonso, 2019; Hasan et al., 2020). Thus, driverless cars
provide benefits such as time optimization, minimization in staff costs and flexibility in
changing routes in the e-waste collection system.
Furthermore, lots of difficulties are faced in the collection of waste from different locations
and recycling stations, such as cost of operations, planning routes and delaying in collection
times (Balde et al., 2017). Along with new technological developments, vehicle production is
shifted to being “smart” (Esmaeilian et al., 2018). Smart vehicles enable monitoring,
controlling and data transfer in waste collection process and provide economic benefits by
decreasing transportation costs.
In addition, real-time monitoring systems provide environmental protection tools by
controlling pollution (Sayed et al., 2019) caused by waste management. E-waste contains more
than 1,000 substances, the majority of which are toxic (Awasthi et al., 2016). It creates serious
environmental pollution in the areas of destruction or storage. Some harmful substances in ewaste are mercury, chromium, barium, beryllium and cadmium. These substances directly
affect environment, and monitoring and controlling the leakage is essential in the e-waste
collection system to prevent environmental concerns (Sayed et al., 2019). Therefore, it is
essential to prevent leakage of harmful chemicals during the storage and the transportation
due to lack of classification by using real-time monitoring systems.
Moreover, the sensors provide monitoring employees in terms of health and safety, and at
the center of every device in the IoT system (Balde et al., 2017). At the same time, workforce is
exposed to toxic chemicals during the storage and transportation of e-waste. In order to avoid
occupational hazards, monitoring the occupational health and safety by using sensor-based
technologies is crucial in the waste management system (Esmaeilian et al., 2018). Therefore,
ensuring occupational safety (leakage of harmful chemicals during the storage and
transportation) by sensors and the classification of e-wastes is a useful solution for
occupational safety.
In addition, robotic systems can remove, store and transport dangerous and valuable
items from waste by using artificial intelligence (SIENNA, 2018). By using robotic systems,
the proposed collection and classification center helps prevent the degradation of materials
that could be refurbished or reused during storage and transportation. It contributes to the
environment by avoiding emission of chemicals into air, water or ground caused by storage
and transportation of waste collection process.
With the advancement of technology and digitalization, instead of traditional solutions, a
digital waste-monitoring solution is needed to record all waste movements (Balde et al., 2017).
This can be achieved with “smart tracking” applications. Smart tracking tracks movements
of waste collection and avoids loss of products by recording and tracking data (DEFRA,
2018). Reducing the loss of products that could be refurbished or reused by smart tracking
provides not only environmental but also economic benefits; it prevents losses caused by the
lack of sorting and separating of e-waste.
Smart routing provides automated management of route optimization of waste collection
based on predefined precise data on waste collection vehicles, warehouses and landfills
(Hrabec et al., 2019). The development of the waste collection system provides not only route
optimization but also prevents traffic congestion, long travel distance and delays. In other
words, the digitally driven TBL-based benefits of proposed sustainable collection and
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classification center are decrease in CO2 emissions by smart routing and decrease in the
number of trips and traveled distance. Therefore, these benefits in the transportation improve
the vehicle utilization. These improvements will contribute to the environment.
Moreover, in the waste collection sector, mobile apps and software can communicate with
citizens during the waste management process (Mavropoulos et al., 2013). Thanks to mobile
apps and software, efficiency in the e-waste collection process is achieved by creating route
optimization and collecting information (Balde et al., 2017). Therefore, the digitally driven
TBL-based benefits of proposed sustainable collection and classification center are decrease
in the number of trips and traffic congestion, using mobile apps and software programs,
which contribute to not only the social dimension but also environment and economic
dimensions.
Smart planning covers smart traffic, smart vehicle utilization, smart separating of waste,
smart occupational safety and so forth, and the system is operated by gathering data on
waste collection operations from sensors or IoT technologies and so on. (Lu et al., 2018). Since
it is essential to plan e-waste collection operations considering cost limitations, smart
planning through big data management is needed. It provides sustainable solutions for
economy through big data management (Babar and Arif, 2017). In the e-waste collection
system, thanks to smart planning through big data management, collection costs of e-waste
decrease.
Moreover, autonomous vehicles, which are becoming more and more common day by day,
are beneficial on streets for providing economic and environmental benefits. Autonomous
vehicles not only benefit from the vehicle utilization, but also provide more efficient handling,
safer collection process and better working conditions for drivers (Fagnant and Kockelman,
2014). Therefore, the digitally driven TBL-based benefits of proposed sustainable collection
and classification center give opportunity to increase vehicle utilization by an autonomous
vehicle. Therefore, it creates environmental and economic benefits with decreasing carbon
emission and cost of transportation while e-waste is collected.
Furthermore, social media and mobile apps are one of the most effective ways of
increasing awareness in the society. For waste management, it is essential to create
awareness about EoL products, and this can be done through social media and mobile apps
(Gaudillat et al., 2018), thus ensuring social and economic benefits.
Moreover, the integration of radio frequency identification (RFID) and the global
positioning system (GPS) has become important for the waste monitoring process (Balde
et al., 2017). RFID and GPS identification systems track the transactions in real time, enabling
vehicle location and route optimization. Information such as where and what kind of waste is
taken into the system is facilitated for separation. Therefore, the integration is essential for
waste separation and route optimization.
To sum up, the digital implications will enhance the proposed model in several aspects
such as maximizing productivity, minimization of costs, reducing losses and wastes in supply
chain operations, increasing occupational safety and health, decreasing environmental
impact and increasing awareness of society.
Conclusion
E-waste is a globally growing problem due to rapid technological developments and
shortening product life cycles. Especially in emerging economies, regulations are used to
promote proper e-waste treatment modes to gain environmental, social and economic
advantages. This study is conducted in one of an emerging economy, Turkey, where
regulation on control of e-waste items was published in 2012. In order to manage e-waste
problems in the long run and to understand the potential effects of e-waste problems in the
context of digitalization under the sustainability dimensions and to see the impacts of the e-
waste regulations, the grey prediction method is used to forecast the amount of e-waste
collected for the next four years after 2016. Results show that the amount of collected e-waste
will increase. However, due to lack of collection and classification practices, and to deal with
the current chaotic and ambiguous situation in e-waste management in Turkey, a new
sustainable collection and classification center model, based on digital technologies under
TBL concept, is proposed for e-waste management in line with the expected increase in the
amount of e-waste. Current practices in Turkey are solely based on recycling without
digitalization; however, with the proposed model, refurbishing / remanufacturing and
reusing will be imposed as a tool to deal with adverse impacts of e-waste.
As mentioned before, both the municipalities and electronic product-producing
companies are obliged for the collection and recycling stages. However, technology is
developing rapidly and digitalization is gaining importance. Therefore, digitalization
should result in benefits for waste collection stages. Digitalization benefits not only the
environment, but also the economy and society. This proposed center will provide social,
economic and environmental benefits by implementing data-driven technologies such as
collecting waste in driverless vehicles, reducing transportation cost with smart vehicles,
preventing leakage of harmful chemicals during storage and transportation due to lack of
classification with real-time monitoring systems, ensuring occupational safety (leakage of
harmful chemicals during storage and transportation), prevention of deterioration of
materials that can be renewed or reused during storage and transportation by robotic
systems, reduction of product loss that can be renewed or reused with smart monitoring,
reducing CO2 emissions in transportation with smart guidance, reducing the number of trips
and traffic jams with mobile application and software programs, increasing the usage of
vehicles with an autonomous vehicle and increasing awareness of EoL products using
mobile applications and social media.
Therefore, the proposed digital and TBL-based collection and classification centers can be
co-invested and even managed by municipalities and companies to achieve the efficient use of
resources in a digital and sustainable manner.
The proposed model requires substantial managerial and digital background in terms of
awareness, know-how and accumulation of necessary data. In addition, it also necessitates an
initial investment which can challenge the investors or the government. Furthermore, lack of
knowledge related to data-driven technologies in sustainable solutions can limit the potential
applications in emerging economies. The lack of governmental rules and regulations may
emerge as a barrier for the proposed system; thus, it may require certain time span to modify
and revise these rules and regulations. The proposed model needs to be clearly defined and
promoted among stakeholders in order to show its mutual benefits and to gain their support.
For future studies, the proposed model shall be implemented and disseminated to waste
management practices for various sectors. In addition, the asserted benefits can be validated
with real-life applications. Another research topic would be to integrate circular approaches
with the proposed model in order to embrace closed-loop supply chains. Comparative studies
can be conducted among emerging economies in order to reveal the common and
differentiating issues among them. The tools and techniques of digitization shall be
embedded within the proposed model in future studies.
References
Abdel-Shafy, H.I. and Mansour, M.S.M. (2018), “Solid waste issue: sources, composition, disposal,
recycling, and valorization”, Egyptian Journal of Petroleum, Vol. 27 No. 4, pp. 1275-1290.
Akkucuk, U. (2016), Handbook of Research on Waste Management Techniques for Sustainability, IGI
GLobal Publisher, Pennsylvania.
Manage
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Althaf, S., Babbitt, C.W. and Chen, R. (2019), “Forecasting electronic waste flows for effective circular
economy planning”, Resources, Conservation and Recycling, Vol. 151, p. 104362.
Arslan, H., Çoşgun, N. and Salgın, B. (2012), Construction and demolition waste management in
Turkey. Inbook: waste Management- an integrated vision, Chapter 12. Published by, InTech,
pp. 313-332.
Awasthi, A.K., Zeng, X. and Li, J. (2016), “Relationship between e-waste recycling and human health
risk in India: a critical review”, Environmental Science and Pollution Research, Vol. 23,
pp. 18945-18946.
Babar, M. and Arif, F. (2017), “Smart urban planning using big data analytics-based internet of
things”, UbiComp/ISWC 2017- Adjunct Proceedings of the 2017 ACM International Joint
Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM
International Symposium on Wearable Computers, (April 2018), pp. 397-402.
Balde, C.P., Forti, V., Gray, V., Kuehr, R. and Stegmann, P. (2017), The Global E-Waste Monitor – 2017,
United Nations University (UNU), International Telecommunication Union (ITU) &
International Solid Waste Association (ISWA), Bonn/Geneva/Vienna.
Bao, C.Z., Mayila, M., Ye, Z.H., Wang, J., Jin, M., He, W. and Chen, K. (2015), “Forecasting and
analysing the disease burden of aged population in China, based on the 2010 global burden of
disease study”, Int J Environ Res Public Health, Vol. 12, pp. 7172-7184.
Barata, J., Rupino Da Cunha, P. and Stal, J. (2018), “Mobile supply chain management in the Industry
4.0 era: an annotated bibliography and guide for future research”, Journal of Enterprise
Information Management, Vol. 31 No. 1, pp. 173-192.
Barman, R., Gayan, P.R. and Choudhury, A. (2017), “Forecasting of potential electronic waste in
guwahati”, International Journal of Innovative Research in Science, Engineering and Technology,
Vol. 6 No. 3, pp. 4145-4151.
Borthakur, A. and Govind, M. (2017), “Emerging trends in consumers’ E-waste disposal behaviour and
awareness: a worldwide overview with special focus on India”, ScienceDirect, Vol. 117 No. B,
pp. 102-113.
Chen, H.W. and Chang, N.B. (2000), “Prediction of solid waste generation via grey fuzzy dynamic
modeling [PDF file]”, Resources, Conservation and Recycling, Vol. 29 No. 1, pp. 1-18.
Chen, C., Chen, H.L. and Chen, S.P. (2008), “Forecasting of foreign exchange rates of taiwan’s major
trading partners by novel nonlinear grey Bernoulli model NGBM (1,1)”, Communications in
Nonlinear Science and Numerical Simulation, Vol. 13, pp. 1194-1204.
Chiang, K.S. (1997), The Introduction of Grey Theory, Gauli Publishing, Taipei.
Chung, S.S., Lau, K.Y. and Zhang, C. (2011), “Generation of and control measures for, E-waste in Hong
Kong”, Waste Management, Vol. 31 No. 3, pp. 544-554.
DEFRA (2018), Our Waste, Our Resources: A Strategy for England, HM Government, pp. 1-146,
available at: www.nationalarchives.gov.uk/doc/open-government-licence/version/3/oremailPSI@
nationalarchives.gsi.gov.uk.
Dwivedy, M. and Mittal, R.K. (2010), “Estimation of future outflows of E-waste in India”, Waste
Management, Vol. 30 No. 3, pp. 483-491.
Esmaeilian, B., Wang, B., Lewis, K., Duarte, F., Ratti, C. and Behdad, S. (2018), “The future of waste
management in smart and sustainable cities: a review and concept paper”, aste Management,
Vol. 81, October, pp. 177-195.
Fagnant, D.J. and Kockelman, K.M. (2014), “The travel and environmental implications of shared
autonomous vehicles, using agent-based model scenarios”, Transportation Research Part C:
Emerging Technologies, Vol. 40, pp. 1-13.
Fercoq, A., Lamouri, S. and Carbone, V. (2016), “Lean/Green integration focused on waste reduction
techniques”, Journal of Cleaner Production, Vol. 137, pp. 567-578.
Frazzoli, C., Orisakwe, O.E., Dragone, R. and Mantovani, A. (2010), “Diagnostic health risk assessment
of electronic waste on the general population in developing countries’ scenarios”,
Environmental Impact Assessment Review, Vol. 30 No. 6, pp. 388-399.
Garlapati, V.K. (2016), “E-waste in India and developed countries: management, recycling, business
and biotechnological initiatives”, Renewable and Sustainable Energy Reviews, Vol. 54,
pp. 874-881.
Gaudillat, P., Antonopoulos, I.S., Canfora, P. and Dri, M. (2018), “European commission. Joint research
centre. and S, A. I”, Best Environmental Management Practice for the Waste Management Sector
Learning from frontrunners, In Bemp 2018, ISBN 978-92-79-80361-1, doi: 10.2760/50247,
JRC111059.
G€ok, G., Tulun, Ş. and G€
urb€
uz, O.A. (2017), Consumer Behaviour and Policy about E-Waste in Aksaray
and Nigde Cities, Turkey, CLEAN – Soil, Air, Water.
Goren, S. and Ozden, F. (2011), “Regulation of waste and waste management in Turkey”, Waste
Management and Research, Vol. 29 No. 4, pp. 433-441.
Gu, Y., Wu, Y., Wang, H. and Zuo, T. (2016), “The stability and profitability of the informal WEEE
collector in developing countries: a case study of China”, Resources, Conservation and Recycling,
Vol. 107, pp. 18-26.
Guo, X., Liu, S., Wu, L. and Tang, L. (2015), “A grey NGM (1,1, k) self-memory coupling prediction
model for energy consumption prediction”, The Scientific World Journal, Vol. 2014, p. 12.
Guptha, C.K.N. and Shekar, G.L. (2009), “Electronic waste management system in Bangalore – a
review”, JK Journal of Management and Technology, Vol. 1, pp. 11-24.
Haig, S., Morrish, L. and Morton, R. (2011), Market Flows of WEEE Materials. Final Report into the
Study of Market Flows of WEEE Materials, Including Development of a Mass Balance Model,
WRAP, Oxon.
Hasan, U., Whyte, A. and Jassmi, H.A. (2020), A review of the transformation of road transport
systems: are we ready for the next step in artificially intelligent sustainable Transport? From
the public public, Vol. 3 No. 1, p. 21.
Heeks, R., Subramanian, L. and Jones, C. (2015), “Understanding e-waste management in developing
countries: strategies, determinants, and policy implications in the Indian ICT sector”,
Information Technology for Development, Vol. 21 No. 4, pp. 653-667, available at: https://doi.
org/10.1080/02681102.2014.88654.
Hrabec, D., Senland, P., Nevrly, V., Popela, P., Hoff, A., Somplak, R. and Pavlas, M. (2019), “Quantitypredictive vehicle routing problem for smart waste collection”, Chemical Engineering
Transactions, Vol. 76, pp. 1249-1254.
Huang, B.K. (1994), Computer Simulation Analysis of Biological and Agricultural Systems, CRC Press,
Florida, p. 472.
Hui, H., Li, F. and Shi, Y. (2013), “An optimal multivariable grey model for logistics demand forecast”,
International Journal of Innovative Computing, Information and Control, Vol. 9 No. 7, pp. 1349-4198.
IFC (2007), Environmental, Health, and Safety Guidelines for Waste Management Facilities,
International Finance Corporation-World Bank Group, WorldBank, Pennsylvania, pp. 1-36.
Ikhlayel, M. (2016), “Differences of methods to estimate generation of waste electrical and electronic
equipment for developing countries: Jordan as a case study”, Resources, Conservation and
Recycling, Vol. 108, pp. 134-139.
Ikhlayel, M. (2018), “An integrated approach to establish e-waste management systems for developing
countries”, Journal of Cleaner Production, Vol. 170, pp. 119-130.
Ilgar, M. (2016), 19-20 October 2016 Turkey Exitcom Recycling Presentation, Exitcom, Turkey,
available
at:
http://www.turktay.com/sunumlar/70e75e5f62b92be6b39d9e243d72f960.pdf
(accessed 2 May 2019).
Manage
e-waste in
emerging
economies
287
JEIM
34,1
ILO, COOP and SECTOR (2014), Tackling Informality in E-Waste Management: The Potential of
Cooperative Enterprises, International Labour Organization, Geneva, available at: http://www.ilo.
org/wcmsp5/groups/public/—ed_dialogue/—sector/documents/publication/wcms_315228.pdf.
Islam, M.T. and Huda, N. (2020), “Assessing the recycling potential of “unregulated” e-waste in
Australia”, Resources, Conservation and Recycling, Vol. 152, (October 2019), p. 104526.
288
Jain, A. and Sareen, R. (2006), “E-waste assessment methodology and validation in India”, Journal of
Material Cycles and Waste Management, Vol. 8, pp. 40-45.
Jang, Y.-C. and Mincheol, K. (2010), “Management of used and end-of-life mobile phones in Korea: a
review”, Resources, Conservation and Recycling, Vol. 55, pp. 11-19.
Kayacan, E., Ulutaş, B. and Kaynak, O. (2010), “Grey system theory-based models in time series
prediction”, Expert Systems with Applications, Vol. 37, pp. 1784-1789.
Kayanda, A. (2017), “Analysis of supply chains in the recycling of electronic waste. Resource
efficiency within recycling supply chains”, MG110X Degree Project in Industrial Production,
KTH Industrial engineering and management Industrial production SE-100 44 STOCKHOLM,
Vol. 2014, p. 28.
Keim, R.G. (2017), “The future is now”, Journal of Clinical Orthodontics: Journal of Clinical
Orthodontics, Vol. 51 No. 1, pp. 9-10.
Khurrum, M., Bhutta, S., Omar, A. and Yang, X. (2011), “Electronic waste: a growing concern in
today’s environment”, Economics Research International, Vol. 2011, p. 8.
Kim, S., Oguchi, M., Yoshida, A. and Terazono, A. (2013), “Estimating the amount of WEEE generated
in South Korea by using the population balance model”, Waste Management, Vol. 33 No. 2,
pp. 474-483.
K€ose, E. and Taşçı, L. (2015), “Prediction of the vertical displacement on the crest of keban dam”,
Journal of Grey System, Vol. 27 No. 1, pp. 12-20.
€ Ayaz, S. and K€oro
K€ose, O.,
glu, B. (2007), “Waste management in Turkey, national regulations and
evaluation of implementation results performance audit report”, Turkish Court of Accounts,
Vol. 2017.
Kumar, A. and Holuszko, M. (2017), “E-waste: an overview on generation, collection, legislation and
recycling practices”, Resources, Conservation and Recycling, Vol. 122, pp. 32-42.
Kurdve, M., Shahbazi, S., Wendin, M., Bengtsson, C. and Wiktorsson, M. (2014), “Waste flow mapping
to improve sustainability of waste management: a case study approach”, Journal of Cleaner
Production, Vol. 98 No. 1, pp. 301-315.
Ladou, J. and Lovegrove, S. (2008), “Export of electronics equipment waste”, International Journal of
Occupational and Environmental Health, Vol. 14 No. 1, pp. 1-10.
Leader, A., Gaustad, G., Tomaszewski, B. and Babbitt, W., C. (2018), “The consequences of electronic
waste post-disaster: a case study of flooding in bonn, Germany”, Sustainability, Vol. 10, pp.
4193-4207.
Linton, J.D., Klassen, R. and Jayaraman, V. (2007), “Sustainable supply chains: an introduction”,
Journal of Operations Management, Vol. 25 No. 6, pp. 1075-1082.
Liu, S. and Forrest, J. (2007), “The current developing status on grey system theory”, Journal of Grey
System, Vol. 2, pp. 111-123.
Liu, C. and Yang, C. (2015), “The GM (1,1) model optimized by using translation transformation
method and its application of rural residents’ consumption in China”, Journal of Systems Science
and Information, Vol. 3 No. 2, pp. 184-192.
Liu, X., Tanaka, M. and Matsui, Y. (2006), “Generation amount prediction and material flow analysis
of electronic waste: a case study in beijing, China”, Waste Management and Research, Vol. 24,
pp. 434-445.
Lopez Alvarez, J.V., Aguilar Larrucea, M., Fernandez-Carrion Quero, S. and Jimenez del Valle, A.
(2008), “Optimizing the collection of used paper from small businesses through GIS techniques:
the Leganes case (Madrid, Spain)”, Waste Management, Vol. 28 No. 2, pp. 282-293.
Lopez-Lambas, M.E. and Alonso, A. (2019), “The driverless bus: an analysis of public perceptions and
acceptability”, Sustainability, Vol. 11 No. 18, pp. 1-15.
Manage
e-waste in
emerging
economies
Lu, J.W., Chang, N.B., Zhu, F., Hai, J. and Liao, L. (2018), Smart and Green Urban Solid Waste Collection
System for Differentiated Collection with Integrated Sensor Networks, 15th International
Conference on Networking, Sensing and Control (ICNSC), Zhuhai, pp. 1-5.
289
Luthra, S. and Mangla, S.K. (2018), “Evaluating challenges to Industry 4.0 initiatives for supply chain
sustainability in emerging economies”, Process Safety and Environmental Protection, Vol. 117,
pp. 168-179.
Maheswari, H., Yudoko, G. and Adhiutama, A. (2019), “Government and intermediary business
engagement for controlling electronic waste in Indonesia: a sustainable reverse logistics theory
through customer value chain analysis”, Sustainability, Vol. 11 No. 3, pp. 732-752.
Manzouri, M., Ab-Rahman, M.N., Zain, C.R. and Jamsari, E.A. (2014), “Increasing production and
eliminating waste through lean tools and techniques for halal food companies”, Sustainability,
Vol. 6, pp. 9179-9204.
Martin, C. and Leurent, H. (2017), Technology and Innovation for the Future of Production: Accelerating
Value Creation, World Economic Forum, Geneva, pp. 1-38.
Mavropoulos, A., Anthouli, A. and Tsakona, M. (2013), “Mobile applications and waste management:
recycling, personal behavior”, Logistics, Vol. 1, pp. 1-32.
Ministry of Environment and Urbanization (2017), National Waste Management and Action Plan,
(2016 – 2023), Turkish Government, Turkey, available at: http://webdosya.csb.gov.tr/db/cygm/
haberler/ulusal_at-k_yonet-m–eylem_plan–20180328154824.pdf.
Mmreki, D., Li, B. and Wang, L. (2012), “Estimation of waste electronic and electrical equipment
arising in Botswana-A case study of gaborone city”, International Journal of Environmental
Sciences, Vol. 3 No. 1, pp. 441-452.
Mmreki, D., Li, B., Hossain, U. and Meng, L. (2018), “Prediction of e-waste generation based on grey
model (1,1) and management in botswana”, Environmental Engineering and Management
Journal, Vol. 17 No. 11, pp. 2515-2780.
Morita, H., Kaise, T., Tamura, Y. and Iwamoto, S. (2002), “Interval prediction of annual maximum
demand using grey dynamic model”, International Journal of Electrical Power and Energy
Systems, Vol. 18 No. 7, pp. 409-413.
Mostafaei, H. and Kordnoori, S. (2012), “Hybrid grey forecasting model for Iran’s energy consumption
and supply”, International Journal of Energy Economics and Policy, Vol. 2 No. 3, pp. 97-102.
Mundada, M.N., Kumar, S. and Shekdar, A.V. (2004), “E-waste: a new challenge for waste management
in India”, International Journal of Environmental Studies, Vol. 61, pp. 265-279.
Needhidasan, S., Samuel, M. and Chidambaram, R. (2014), “Electronic waste – an emerging threat to
the environment of urban India”, Journal of Environmental Health Science and Engineering, Vol.
12 No. 1, pp. 74-97.
Nguyen, D.Q., Yamasue, E., Okumura, H. and Ishihara, K. (2009), “Use and disposal of large home
electronic appliances in Vietnam”, Journal of Material Cycles and Waste Management, Vol. 11,
pp. 358-366.
Nnorom, I.C. and Osibanjo, O. (2008), “Electronic waste (e-waste): material flows and management
practices in Nigeria”, Waste Management, Vol. 28, pp. 1472-1479.
Oguchi, M., Kameya, T., Yagi, S. and Urano, K., (2008), “Product flow analysis of various consumer
durables in Japan”, Resources, Conservation and Recycling, Vol. 52 No. 3, pp. 463-480.
Orlins, S. and Guan, D. (2016), “China’s toxic informal e-waste recycling: local approaches to a global
environmental problem”, Journal of Cleaner Production, Vol. 114, (2016), pp. 71-80.
JEIM
34,1
Osburg, T. and Lohrmann, C. (2017), Sustainability in a Digital World: New Opportunities through New
Technologies, Springer.
Ozturk, T. (2015), “Generation and management of electrical–electronic waste (E-Waste) in Turkey”,
Journal of Material Cycles and Waste Management, Vol. 17 No. 3, pp. 411-421.
PAGEV (2018), “Plastic industry is important for Turkey’s competitiveness, sustainable development,
innovation and for a low-carbon economy”, Interview.
290
Park, J.K., Hoerning, L., Watry, S., Burgett, T. and Matthias, S. (2017), “Effects of electronic waste on
developing countries”, Advance Recycling Waste Management, Vol. 2 No. 2, p. 128.
Rada, E.C., Ragazzi, M. and Fedrizzi, P. (2013), “Web-GIS oriented systems viability for municipal
solid waste selective collection optimization in developed and transient economies”, Waste
Management, Vol. 33 No. 4, pp. 785-792.
Rasnan, M.I., Mohamed, A.F., Goh, C.T. and Watanabe, K. (2016), “Sustainable E-waste management
in asia: analysis of practices in Japan, taiwan and Malaysia”, Journal of Environmental
Assessment Policy and Management, Vol. 18, p. 1650023.
Raut, R.D., Mangla, S.K., Narwane, V.S., Gardas, B.B., Priyadarshinee, P. and Narkhede, B.E. (2019),
“Linking big data analytics and operational sustainability practices for sustainable business
management”, Journal of Cleaner Production, Vol. 224, pp. 10-24.
Roldan, J.M. (2017), E-waste Management Policy and Regulatory Framework for Saint Lucia,
Telecommunications Management Group, TMG.
Rovetta, A., Xiumin, F., Vicentini, F., Minghua, Z., Giusti, A. and Qichang, H. (2009), “Early detection
and evaluation of waste through sensorized containers for a collection monitoring application”,
Waste Management, Vol. 29 No. 12, pp. 2939-2949.
Sayed, E., Ahmed, A. and Yousef, M.E. (2019), “Internet of things in smart environment: concept,
applications, challenges, and future directions”, Vol. 134, July, pp. 1-51.
Schumacher, K.A. and Agbemabiese, L. (2019), “Towards comprehensive e-waste legislation in the
United States: design considerations based on quantitative and qualitative assessments”,
Resources, Conservation and Recycling, Vol. 149, June, pp. 605-621.
Shevchenko, T., Laitala, K. and Danko, Y. (2019), “Understanding consumer E-waste recycling behavior:
introducing a new economic incentive to increase the collection rates”, Sustainability, Vol. 11, p. 2656.
Shingkuma, T. and Managi, S. (2010), “On the effectiveness of a license scheme for E-waste recycling:
the challenge of China and India”, Environmental Impact Assessment Review, Vol. 4, pp. 262-267.
SIENNA (2018), State-of-the-art Review [WP4 – AI and Robotics], European Commision Project,
available at: https://www.sienna-project.eu/digitalAssets/787/c_787382-l_1-k_sienna-d4.1-stateof-the-art-review–final-v.04-.pdf.
Slavek, N. and Jovic, A. (2012), “Application of grey system theory to software projects ranking”,
ATKAFF, Vol. 53 No. 3, pp. 284-293.
Song, H. and Li, G. (2008), “Tourism demand modelling and forecasting - a review of recent research”,
Tourism Management, Vol. 29 No. 2, pp. 203-220.
Sthiannopkao, S. and Wong, M.H. (2013), “Handling e-waste in developed and developing countries:
initiatives, practices, and consequences”, The Science of the Total Environment, Vols 463-464,
pp. 1147-1153.
Tien, T.L. (2009), “A new grey prediction model FGM (1, 1)”, Mathematical and Computer Modelling,
Vol. 49, pp. 1416-1426.
TWI2050 - The World in 2050 (2019), The Digital Revolution and Sustainable Development:
Opportunities and Challenges, Report prepared by The World in 2050 initiative, International
Institute for Applied Systems Analysis (IIASA) , Laxenburg, available at: www.twi2050.org.
Walk, W. (2009), “Forecasting quantities of disused household crt appliances – a regional case study
approach and its application to Baden-W€
urttemberg”, Waste Management, Vol. 29 No. 2, pp. 945-951.
Wang, F., Kuehr, R., Ahlquist, D. and Li, J. (2013), “E-waste in China: a country report”, Step Green
Paper Series, United Nations University, ISSN: 2219-6579.
Wen, K.L. (2004), Grey Systems Modelling and Prediction, Yang’s Scientific Press, Springer, p. 4.
Wilson, G.T., Smalley, G., Suckling, J.R., Lilley, D., Lee, J. and Mawle, R. (2017), “The hibernating
mobile phone: dead storage as a barrier to efficient electronic waste recovery”, Waste
Management, Vol. 60, pp. 521-533.
Xiang, Z. and Daoliang, L. (2007), “Forecasting municipal solid waste generation based on grey fuzzy
dynamic modelling”, Int. Conf. on Energy, Environment, Ecosystems and Sustainable
Development, Agios Nikolaos, Greece, July, pp. 24-26.
Yang, X., Zou, J., Kong, D. and Jiang, G. (2018), “The analysis of GM (1, 1) grey model to predict the
incidence trend of typhoid and paratyphoid fevers in Wuhan City, China”, Medicine, Vol. 97
No. 34, p. e11787.
Yedla, S. (2015), “Development of a methodology for electronic waste estimation: a material flow
analysis-based SYE-Waste model”, Waste Management and Research, Vol. 34 No. 1, pp. 81-86.
Yılmaz, H. and Yılmaz, M. (2013), “Forecasting CO2 emissions for Turkey by using the grey prediction
method”, Sigma, Vol. 31, pp. 141-148.
Yoshida, A., Tasaki, T. and Terazono, A. (2009), “Material flow analysis of used PCs in Japan”, Waste
Management, Vol. 29 No. 5, pp. 1602-1614.
Yu, J., Williams, E., Ju, M. and Yang, Y. (2010), “Forecasting global generation of obsolete pcs”,
Environmental Science and Technology, Vol. 44 No. 9, pp. 3232-3237.
Yukalang, N., Clarke, B. and Ross, K. (2017), “Barriers to effective municipal solid waste management
in a rapidly urbanizing area in Thailand”, International Journal of Environmental Research and
Public Health, Vol. 14 No. 9, pp. 9-14.
Zhang, L., Yuan, Z. and Bi, J. (2011), “Predicting future quantities of obsolete household appliances in
nanjing by a stock-based model”, Resources, Conservation and Recycling, Vol. 55 No. 11,
pp. 1087-1094.
Zu, G. and Cheng, S. (2012), “Study on the production and trend of the electronic waste in China based
on grey prediction model”, Advanced Materials Research, Vol. 599, pp. 551-555.
Further reading
European Commission (2011), Communication from the Commission to the European Parliament, the
Council, the European Economic and Social Committee and the Committee of the Regions,
Roadmap to a Resource Efficient Europe, EEA, Copenhagen.
Eurostat (2018), Municipal Waste Statistics, Website Link, EU, available at: http://ec.europa.eu/
eurostat/statisticsexplained/index.php/Municipal_waste_statistics (accessed 2 May 2018).
Corresponding author
Melisa Ozbiltekin can be contacted at: melisa.ozbiltekin@yasar.edu.tr
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