Prioritization of Barriers for E-Waste Management in India Using Best Worst Method

INTRODUCTION

The growth of e-waste in developing countries is putting a lot of pressure on the prevailing environment. The flow of e-waste causes a threat to human lives; therefore, there is a need of proper waste of electrical and electronic equipment (WEEE) management. To counter this problem, the government is pressurizing the industrialists to implement proper and effective management to dispose of e-waste. This problem of WEEE prevails in mostly developing countries where a huge quantity of e-waste is imported from developed countries because there is high labor cost and strict government norms. With the increasing knowledge about e-waste, the community is becoming aware and the government is implementing necessary policies to overcome the e-waste problem. India is a primary dumping site for WEEE from the developed nations (Gaziulusoy, 2015; Govindan et al., 2019). The government has introduced and implemented a set of various policies in Extended Producer Responsibility (2011). The EPR policies were amended in 2018. The main reason for the failure of the same is lack of implementation and follow up. The reason behind it is the delay in law enforcement on producers or manufacturers, so as to come up with proper management for handling returns and proper disposal of electronic products (Dwivedy and Mittal, 2010). Lack of policies and regulations is a cause for the same. Today about 85 percent of e-waste is recycled by the unorganized sector, which uses traditional ways to recycle. The recycle process opted by them is unfriendly to nature and does a lot harm to animals, humans and plants (Nnorom et al., 2009). As the report of the associated chamber of commerce (ASSOCHAM) states, India is an emerging country that stands fifth position among the developing countries around the world and second position in Asia, and it produces e-waste with a growth of 25 percent a year, which is 18.5 lakh MT of electronic waste by 2016 as compared to the current level of 12.5 lakh MT annually (Ganguly, 2019). Therefore, with this motivation, the present work is to study the challenges that hinder the implementation of e-waste management in an Indian scenario. The research objectives for the study are as follows:

  • 1. Identification of barriers that hinder the implementation of e-waste management in India.
  • 2. Analysis and identification of the influential barrier with the help of BWM approach through experts’ opinions.

To achieve these objectives, this chapter presents a framework for evaluating the normal and governmental barriers that hinder the proper implementation of e-waste management to aid the Indian industries. Initially, a literature survey is carried out to identify the potential normal and governmental barriers. Best worst method (BWM) is used to analyze the shortlisted attributes, determine the weights (relative importance) of these barriers and finally prioritize them.

The novelty of the study, which distinguishes it from the other studies in this field, lies in identifying the normal and governmental barriers and application of BWM. There are many studies in the literature that identify the barriers for e-w'aste management; how'ever, there is lack of studies that identify governmental as well as normal barriers. Kumar and Dixit (2018) identified ten barriers, mostly governmental in nature, and used interpretive structural modeling (ISM) and Decision Making Trail and Evaluation Laboratory (DEMATEL) for understanding the hierarchal and contextual relationship structure among them. They concluded that the lack of public awareness about e-waste recycling and the lack of policies addressing e-waste issues are the root cause barriers in Indian context. Bhatia and Srivastava (2018) used a grey- DEMATEL approach to analyze the interrelationship among barriers for e-w'aste management in India. Satapathy et al. (2018) ranked the barriers for e-waste management in India based on their importance by using Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) analysis. Chauhan et al. (2018) focused on governmental barriers by using an ISM-DEMATEL approach. The findings of the study suggest that the lack of funds, input material and subsidy are the most influential barriers that are needed to be addressed in an Indian context. Pires et al. (2019) analyzed the major challenges to w'aste collection and management in developing and developed countries. The focus of the work was to better understand the growing informal sector in e-waste management. The focus of the literature is to analyze the governmental barriers. While the literature does not particularly discuss the incorporation of normal as well as governmental barriers, this chapter addresses these gaps and focuses on developing a framework for analyzing normal and governmental barriers using BWM. The advantage of using BWM is that it requires fewer comparison matrices as compared to any multicriteria technique. Hence, the basic objective of this study is twofold. First, we aim to identify the major barriers that hinder the proper implementation of e-waste management. In the second phase, a relatively newer technique known as BWM is used to evaluate the weights and ranks of different barriers. The organization of the manuscript is as follows. Section 8.2 presents the research methodology of the BWM methodology. Section 8.3 represents the numerical illustration to validate the proposed model. Section 8.4 addresses the conclusion of the chapter and a brief description of future work.

RESEARCH METHODOLOGY

This section elaborates the research methodology. We have used a relatively newer methodology, BWM, to calculate the weights of the various governmental and normal barriers that hinder the proper management of e-waste in India. The perspectives of the key decision makers (DMs) are used for the overall assessment of the system. In the first phase, we have identified the various governmental and normal barriers using literature survey and interactions with the DMs. In the second phase, we have used BWM to calculate the weights of these barriers. BWM was developed by Jafar Rezaei in 2015 and is a relatively newer approach that conducts structured pair-wise comparisons with relatively lesser information as compared to other Multi Criteria Decision Making (MCDM) techniques (Rezaei, 2015).

8.2.1 Identification of the Barriers

In this phase, the various barriers are identified based on literature survey and discussion with the stakeholders.

8.2.2 Prioritization of the Barriers

The research methodology used in the same is BWM. The steps for the same are:

• Select the most and least prominent barriers.

In this step we have analyzed the most prominent and least prominent barriers that hinder the implementation of e-waste management.

• Compare barriers.

In this step we have compared the most prominent barrier with the others and the least prominent barrier with the other barriers. The score used is 1-9.

The resulting vector of “Best-to-Others” is given as:

where svBi gives the preference of the most prominent barrier over others and svBB = 1.

The resulting vector of “Worst-to-Others” is given as:

where svWl gives the preference of the other barriers over least prominent barriers and SV|W = 1.

• Calculate optimal weights for the barriers.

The aim of this step is to calculate the optimal weighting vector (zT,---,Z9) of the barriers.

The optimal weight of i'h barrier is the one which meets the following requirements:

In order to satisfy this condition, the maximum absolute difference

s *

Щ- - sv/j, and — ,vv„, need to be minimized for all problem.

Zi Zw

Hence, the optimal weighs can be achieved through the following programming problem:

Subject to

Problem (PI) is equivalent to the following linear programming formulation (P2):

Subject to

TABLE 8.1

Consistency Index Table for BWM

VB,

1

2

3

4

5

6

7

8

9

Consistency index (max) 0.00

0.44

1.00

1.63

2.30

3.00

3.73

4.47

5.2

NUMERICAL ILLUSTRATION

Research Desing

We consider a tandem queue network with two nodes: Node 1 and Node 2, which are arranged in series with a dedicated buffer of finite size. Each node is assumed as a single-server queueing system. Three types of service requests are processed within the tandem network. In type 1 service requests, jobs require the processing only at the first node and leave the network after first node service completion. In type 2 service requests, jobs require processing at both nodes in sequence. In type 3 service requests, jobs require only the second node’s service. Jobs may make independent reneging decisions while waiting in the buffer space for the second node. The architectural layout is depicted in Figure 3.1. For the mathematical modeling purpose, we consider the following assumptions and notations.

Application of the Best Worst Methodology (BWM) for the Case

Exhaustive numerical results are presented in this section to describe the behavior of the queueing systems under study. We examine the effect of the system parameters such as arrival probability (a), balking probability (b). feedback probability (v) and the probability that the server is started (0) to serve the customer successfully on some of the crucial performance measures of our model. The values of the parameters are chosen appropriately and the numerical results are obtained by using MATLAB software.

TABLE 8.2

List of Governmental Barriers

Notation

Barriers (GOVERNMENTAL)

References

GR1

There is lack of proper policy that considers all aspects related to e-waste management, including assignment of responsibilities for all stakeholders

Chowrimootoo (2011)

GR2

The policies sometimes lack practical and rational issues, and so the sustainability issue is perhaps not well addressed

Great Lakes Electronics Corporation (2019)

GR3

Competition between formal and informal sectors

Chowrimootoo (2011)

GR4

There is a lack of standard infrastructure in developing to deal with ICT waste

Ryder (2019)

GR5

Lack of technology and skills for the industries involved in the treatment

Chowrimootoo (2011)

GR6

Lack of economic support and consultancy to know where to invest to maximize the value added and obviously improve and maintain the effectiveness of the treatment industry

Great Lakes Electronics Corporation (2019)

GR7

Including limited access to recycling bin containers, unreliable collection service or living in places far from recycling sites

Ryder (2019)

TABLE 8.3

List of Normal Barriers

Notation

Barriers (NORMAL)

References

NB1

Lack of reliable information regarding the amount and categories of e-waste to be treated, which eventually makes it difficult to devise the correct strategy to be used and ultimately to invest correctly in treatment industries

Great Lakes Electronics Corporation (2019)

NB2

Lack of correct standards and procedures for the collection and eventual treatment of e-waste

Ryder (2019)

NB3

Low recycling penetration and low supply of domestic e-waste

OWN

NB4

Attitudes or perceptions, such as thinking recycling is a waste of time

Great Lakes Electronics Corporation (2019)

NB5

Behavioral issues, like people assuming they're too busy to recycle or simply forget about it

Ryder (2019)

NB6

Lack of information on reuse and recycling possibilities

OWN

TABLE 8.4

Best-to-Others and Others-to-Worst Vectors for First Stakeholder

Best-to-Others

NB1

NB2

NB3

NB4

NB5

NB6

NB1

I

3

4

2

6

5

Olhers-to-Worst

NB5

NB1

6

NB2

4

NB3

3

NB4

5

NB5

I

NB6

2

collected using the opinion of the expert panel. The expert panel used for collection of data include three recyclers. We asked the three recyclers to select the prominent and least prominent barriers and give reference comparison values from a 1-9 scale. This data was used to generate weights using the BWM technique as elaborated in section 3.2. This was done separately for government and normal barriers so as to understand the prominent barriers in each category. For the first stakeholder, let the most prominent normal barrier be NB1- “Lack of reliable information regarding the amount and categories of e-waste to be treated, which eventually makes it difficult to devise the correct strategy to be used and ultimately to invest correctly in treatment industries”. The least prominent barrier is NB5 -“Behavioural issues: like people assuming they’re too busy to recycle or simply forget about it”. The next step is to quantify the best-to-others and others-to-worst vectors, which are given in Table 8.4.

On solving this, using the steps of the BWM methodology, we get the following weights NB1 - 0.379^ NB2 - 0.148. NB3 -0.110, NB4 - 0.221. NB5 - 0.53 and NB6 - 0.089. Figure 8.1 gives the graphical representation of these weights.

Graphical representation of optimal weights

FIGURE 8.1 Graphical representation of optimal weights.

TABLE 8.5

Best-to-Others and Others-to-Worst Vectors for Government Barriers

Government Barriers

Best-to-Others

Others-to-Worst

GRl

2, 2, l

5.5,7

GR2

5. 6,5

2,5,3

GR3

6,7,7

1, 1. 1

GR4

I.5,4

6,2,4

GR5

4,3,2

3,6,6

GR6

2, 1,6

5.7,2

GR7

3,4,3

4. 3,5

Similarly, we collect the responses of all the stakeholders for the most prominent and least prominent governmental and normal barriers. Tables 8.5 and 8.6 provide the responses of the stakeholders.

It can be seen that different stakeholders have given different ranking to each of the barriers based on the difficulty faced by them in proper implementation of the e-waste management. In order to find the optimal weight, we follow the steps of the BWM method. The optimal weights are given in Tables 8.6 and 8.7.

TABLE 8.6

Best-to-Others and Others-to-Worst Vectors for Normal Barriers

Normal Barriers

Best-to-Others

Others-to-Worsi

NB1

1,2, 1

6,5,8

NB2

3,5,5

4, 2,5

NB3

4, 3,2

3,4,6

NB4

2, 1,3

5,6,3

NB5

6,6,6

1. 1.2

NB6

5,4,8

2,3, 1

TABLE 8.7

Optimal Weights of Governmental Barriers

Normal Barrier

GRl

GR2

GR3

GR4

GR3

GR6

GR7

Optimal Weight

Stakeholder 1

0.353

0.084

0.041

0.105

0.209

0.070

0.139

0.066

Stakeholder 2

0.217

0.072

0.034

0.087

0.145

0.336

0.109

0.098

Stakeholder 3

0.181

0.072

0.043

0.311

0.091

0.181

0.121

0.052

Average weight

0.250

0.076

0.039

0.167

0.148

0.196

0.123

0.072

TABLE 8.8

Optimal Weights of Normal Barriers

Normal Barrier

NB1

NB2

NB3

NB4

NB5

NB6

Optimal Weight

Stakeholder 1

0.379

0.148

0.111

0.221

0.053

0.089

0.063

Stakeholder 2

0.3S8

0.096

0.239

0.160

0.080

0.037

0.090

Stakeholder 3

0.221

0.089

0.148

0.379

0.053

0.111

0.063

Average weight

0.330

0.111

0.166

0.253

0.062

0.079

0.072

It can be seen that the overall weights are consistent in nature, thus we can accept this ranking.

Proper management of e-waste is a major concern for developing economies like India. With the help of the BWM approach, we have analyzed the most and least prominent barrier that hinders e-waste management. It was found that the most prominent governmental barrier was GR1 - "There is lack of proper policy that considers all aspects related to e-waste management including assignment of responsibilities for all stakeholders”; while normal barrier comes out to be NB1 - "Lack of reliable information regarding the amount and categories of e-waste to be treated, which eventually makes it difficult to devise the correct strategy to be used and ultimately to invest correctly in treatment industries”.

CONCLUSION

With the rapid growth of electronic sector, the problem of e-waste is becoming a major concern for the government. Although the government is undertaking a number of schemes to encounter these problems, there is still a huge gap in the implementation of the same. This is specifically true for mostly developing countries where, in spite of strict government norms, India is still a primary dumping site for WEEE. In this direction, the primary requirement is to understand the major factors that hinder the proper management of e-waste. The identified barriers consist of seven types of governmental barriers as well as six types of normal barriers that have been identified by from National Capital Region (India). This study used a recently developed methodology of BWM for identifying the prominent barrier based on the opinion of the expert panel. The opinions of the various recyclers are considered for validating the study. The most prominent governmental barrier was found to be GR1 - "There is lack of proper policy that considers all aspects related to e-waste management including assignment of responsibilities for all stakeholders”; while normal barrier comes out to be NB1 - “Lack of reliable information regarding the amount and categories of e-waste to be treated, which eventually makes it difficult to devise the correct strategy to be used and ultimately to invest correctly in treatment industries”. Hence, this study provides a framework for the assessment of various barriers that hinder the proper implementation of e-waste management in India.

8.4.1 Limitations and Future Scope

There are a few limitations of this study. Presently, we are using BWM for the prioritization of the barriers. With crisp inputs, we can also incorporate subjectivity into the decision-making process by the used of Fuzzy BWM. A comparative study can also be done between various MCDM techniques like VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Analytic Hierarchy Process (AHP), and Elimination Et Choice Translating Reality (ELECTRE) for this study.

REFERENCES

Bhatia, M.S. and Srivastava, R.K. 2018. Analysis of external barriers to remanufacturing using grey-DEMATEL approach: An Indian perspective. Resources, Conservation and Recycling. 136:79-87.

Chauhan. A., Singh, A. and Jharkharia, S. 2018. An ISM and DEMATEL method approach for the analysis of barriers of waste recycling in India. Journal of the Air and Waste Management Association, 68(2):100—110.

Chowrimootoo. D.J.M. 2011. E-waste: Causes, hazards, barriers and approaches to effective management. https://www.academia.edu/8958150/E-waste_Causes_hazards_barriers_ and_approaches_to_effective_management?auto=download.

Dwivedy, M. and Mittal. R.K. 2010. Estimation of future outflows of e-waste in India. Waste Management. 30(3):483—491.

Ganguly. R. 2019. Aspects of e-waste management in India. In Electronic Waste Pollution. Champaign, IL: Springer. 253-265.

Gaziulusoy, A.I. 2015. A critical review of approaches available for design and innovation teams through the perspective of sustainability science and system innovation theories. Journal of Cleaner Production. 107:366-377.

Govindan. K., Jha. P.C., Agarwal, V. and Darbari, J.D. 2019. Environmental management partner selection for reverse supply chain collaboration: A sustainable approach. Journal of Environmental Management. 236:784-797.

Great Lakes Electronics Corporation. 2019. The top barriers to e-waste recycling and how to solve them. Great Lakes Electronics Corporation (blog). Accessed 9 December 2019, https://www.ewastel.com/top-barriers-to-e-waste-recycling/.

Kumar, A. and Dixit, G. 2018. An analysis of barriers affecting the implementation of e-waste management practices in India: A novel ISM-DEMATEL approach. Sustainable Production and Consumption. 14:36-52.

Nnorom. I.C.. Ohakwe, J. and Osibanjo. 0.2009. Survey of willingness of residents to participate in electronic waste recycling in Nigeria-A case study of mobile phone recycling. Journal of Cleaner Production. 17(18): 1629—1637.

Pires, A., Martinho. G., Rodrigues. S. and Gomes, M.I. 2019. Technical barriers and socioeconomic challenges. In Sustainable Solid Waste Collection and Management. Champaign, IL: Springer. 335-348.

Rezaei, J. 2015. Best-worst multi-criteria decision-making method. Omega. 53:49-57.

Ryder. G. 2019. The world’s e-waste is a huge problem. It’s also a golden opportunity. World Economic Forum (website). Accessed 9 December 2019. https://www.weforum.org/ agenda/2019/01/how-a-circular-approach-can-turn-e-waste-into-a-golden-opportunity/. Satapathy. S.. Garanaik. A. and Kumar, S. 2018. Prioritising the barriers of waste management as per Indian perspective by PROMETHEEII and VIKOR methods. International Journal of Services and Operations Management. 29(4):462-486.

 
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