Analysis of barriers in e-waste management in developing economy: An integrated MCDM approach

: An increasing quantity of electronic waste (e-waste) is not a new cause of concern, and it has been causing trouble 31 throughout the world. This waste comprises of valuable metals along with harmful compounds that leads to 32 harmful environmental consequences. Managing this kind of waste in developing economies is difficult due to 33 presence of lot of hindrance in the process. Therefore, the goal of this research work is to determine the barriers 34 while taking expert opinions and through available literature, and subsequently prioritize them to address the 35 challenges in e-waste management. Moreover, this study reflects a hybrid Fuzzy DEcision-MAking Trail and 36 Evaluation Laboratory (F-DEMATEL) and Fuzzy Interpretive Structural Modeling (F-ISM) approaches applied 37 for determining the interrelationship between the barriers. The F-DEMATEL technique facilitates in identifying 38 influential and influenced barriers and categorizes them into causal or effect groups. Performance data obtained 39 from this combined approach is applied to determine an overall rank for 15 identified barriers. In addition, a fuzzy 40 Matrice d'impacts Croisés Multiplication Appliquée an un Classeement (F-MICMAC) analysis is exercised to sort 41 them into dependent or driving factor. The findings suggest that the underlying cause barriers include ‘ lack of 42 customer awareness about return ’, ‘ less policies addressing e-waste problem ’, ‘lack of long - term planning’, and 43 ‘ insensitiveness of public towards environmental issues. The methodology is integrated with fuzzy logic to take 44 uncertainty in the data gathered into consideration. This approach aids policymakers and decision-makers in 45 determining the barriers' mutual relationships and interconnections. 46


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The electronic sector is currently one of the fastest expanding markets in developing countries. Rapid economic 49 expansion is driving up demand for and usage of electronic devices. Increased e-waste is caused by urbanization, 50 rapid technical progress, lowering pricing, and the replacement of electronic gadgets with new ones based on user 51 behaviors. As a result, the manufacturing of electronics items has become more difficult, resulting in a significant 52 increase in harmful e-waste (Dwivedy and Mittal, 2010). This e-waste comprises toxic metals and harmful 53 compounds which have a detrimental effect both on humans and environment. Inappropriate waste treatment 54 results in resource depletion and significant loss to the environment and economic (Menikpura et al., 2014).

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Understanding the relevance and impact of the e-waste surge, the developed economies have created mandatory 56 e-waste handling legislation. As a result of inadequate environmental policies, low -cost labor, and other 57 considerations, these wastes are sent to underdeveloped countries for recycling (Garlapati, 2016;Wakolbinger et      All the barriers considered in the study are displayed in Table 1.  The managing methods are limited owing inefficient of technological infrastructure for recycling, collection and storage facilities, and sufficient transportation facilities for the waste generated. Patil & kant (2014) Low safety and security of information system (J) Less advanced technology, as well as the safety and security of information systems, are major barriers in adopting these practices. (Gunasekaran & Nagai, 2004) Less investment in warehouses (K) Due to a lack of funding, there is no suitable warehouse training or testing for hazardous substance restrictions or ewaste recycling enterprises. To establish e-waste management practices, a large initial financial expenditure would be required for recycling plants, collecting centers, training, and awareness programmes. Processing the waste and creating a recycled product would require a cutting-edge technology which will be costly.

Scheinberg et al, 2011
Inadequate funds to recycle e-waste (N) A lot of money is needed for recycling the waste. Kumar

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Managing e-waste is a complex problem which requires an in-depth analysis of the various factors involved, 102 prompting the development of many MCDM models in the past two decades. Srivastava and Sharma (2015) 103 employed ISM technique to formulate the relationship between the identified factors for e-waste management.

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 Applying F-DEMATEL and expert input, the finalized barriers were evaluated to separate them into 132 cause-and-effect groups.

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 Adopting F-ISM and expert input, the finalized barriers were evaluated to examine the contextual 134 relationships between them and to comprehend their hierarchical relationships.

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 Discussing the managerial implications of e-waste management policies in developing nations to identify 136 the issues related to their implementation. Therefore, in this study, a hybrid F-ISM and F-DEMATEL approach is applied to address uncertainty and 138 inconsistency in the gathered data. Moreover, this study considers an exhaustive list of 15 barriers that have a 139 significant character to play in implementation of e-waste management. which has not been carried out previously.    Step I: Development of the fuzzy direct-relation matrix

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To determine the type of relationships between n criteria, a n x n matrix is initially created. The impact of row  Table 2.
All experts must fill out the matrix if several experts' perspectives are to be considered. Moreover, a direct relation 157 matrix z was created by calculating the arithmetic mean of all the experts' opinions, as stated in Equation 1. Step II: Normalize the fuzzy direct-relation matrix.

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Equation 2 can be introduced to get the normalized fuzzy direct-relation matrix. 162

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Equation 3 can be applied to calculate r value.

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Step III: Calculate the total-relation matrix with fuzziness.

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Equation 4 can be applied to calculate the fuzzy total-relation matrix in step 3 168 Equation (5) (6) and (7) may be applied to calculate each factor of the fuzzy total-relation matrix, which 169 is written as ̃a b = (l ab " , m ab " , u ab " ).

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For defuzzification, converting fuzzy data into crisp scores (CFCS) process is adopted. First, the fuzzy values are 175 converted to crisp values using a technique similar to that used for calculating the right and left scores using fuzzy 176 max and min values respectively. Finally, the total score is obtained using the membership functions as a weighted

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Normalized values for lower and upper boundary conditions are calculated by utilizing Equations (12) and (13) 189 respectively.
Crisp values are the result of the CFCS algorithm.

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Total normalized crisp values derived using Equation 14 are shown in Table 3. 195 Step V: Decide on a value for the threshold.  Table 4 below.

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Step VI: Create a causal relationship diagram using the final output.

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The following step is performed to get the values in rows and columns of the total-relation matrix (T) (in step 4).

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Equations (15) and (16) can be used to get the sum of rows (D) and columns (R): Step VII: Analyze the outcomes 208 As illustrated in Figure 3 and Table 8, each element can be assessed using the following criteria.:   Step VIII: On a reachability matrix, level partitioning is used to acquire information about the arrangement of 227 barriers in a degree-clever manner (Warfield, 1973). For all the barriers produced from the final reachability 228 matrix shown in 10

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Step IX: The diagraph is developed by arranging all of the barriers graphically in levels and drawing directed 236 links to demonstrate relationships in the reachability matrix. By deleting the transitive relationships grade by grade 237 and analyzing their interpretation from the expert's perspective, a simpler version of the preliminary diagraph can 238 be obtained. The most effective association with the most crucial interpretation is kept.

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Step X: Interaction matrix: The final digraph yields a binary interplay matrix that depicts all interactions with a 240 single access.

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Step XI: Digraph formation and conversion: The digraph is converted to an ISM, and the miles are examined for 242 conceptual conflicts. By replacing element nodes with charges, the resulting digraph from step X is turned into an 243 ISM. Finally, we evaluate the ISM version to look for inconsistencies.

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 Autonomous: having a low driving and dependent power.

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 Dependent: having a low driving but a high dependence power.

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 Linkage: having a lot of driving and depending on power.

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 Independent: having a high driving but a low dependence power.

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The driving and dependency powers are traditionally calculated using the final reachability matrix. In this matrix, 254 the vertical sum gives the dependent power and the horizontal sum of individual barriers gives the driving power.

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The steps in F-MICMAC analysis are as follows:

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Step I: Binary direct reachability matrix: Due to the fact that a factor has no reachability, the diagonal components 257 of the initial reachability matrix are replaced by zero.

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Step II: Fuzzy direct relationships matrix: Traditional MICMAC analysis appears to be less sensitive because it 259 is based on a binary relationship between the variables. Furthermore, many factors distribute unevenly and

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In the eighth stage of multiplication, the driving and dependency powers alternately repeat, resulting in the fuzzy 278 stabilized matrix are shown in Table 9.  Table 8 is also graphically represented in the form of a causal diagram as shown in Figure 3. The

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The F-ISM approach was employed in the current study to exploit conceptual and computational leverage to

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represented the barrier's rank. A hierarchical ISM model was created using the levels assigned to various barriers.

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The barriers are further linked by arrows based on the relationships found in the initial reachability matrix. The

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ISM model shows that barrier O is the most significant barrier for implementation of e-waste management. As a 307 result, barriers evaluating environmental concerns for implementation of e-waste were found to have a 308 considerable impact on other barriers. Subsequently, F-MICMAC was applied to check the intensity of the 309 correlation between dependency and driving powers of barriers. Figure 5 shows a fuzzy MICMAC analysis. The

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It is evident form Table 8 that there are six influential barriers that lie in the causal group as obtained from F-DEMATEL method. Further, it is observed that the outcomes of both integrated MCDM approaches for the four 352 barriers are most influence e-waste management implementation.

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The goal of this study is to examine into the numerous barriers that come up when implementing e-waste 354 management. Few studies have been established on e-waste management barriers, and no study has been done to 355 establish a structured relationship between the barriers for e-waste management in fuzzy environment with 356 integrated MCDM approach. F-ISM and F-DEMATEL analyses are also applied to examine the interrelation of 357 these barriers. However, e-waste management is a critical matter that requires the decision-maker's full attention.

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Determining which barrier is the most/least essential in terms of sustainability-oriented barrier is difficult because 359 each barrier has its value as a whole. In such circumstances, understanding both, contextual as well as the cause- in many developing countries. The findings of this study will make a substantial contribution to the research on 366 this. Governments may take these effects into consideration and make important policy changes to address these 367 concerns.

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 Government policies and frameworks' importance: In defining long term successful e-waste management 394 implementation within an economy, the government plays a critical role. Dealing with infrastructure 395 (e.g., reliable energy) and political limits requires government backing and a regulatory framework.

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In developing economies, such as India's large cities, e-waste management is currently one of the most difficult 412 challenges. In order to comply with global environmental regulations, e-waste management is in high demand. As 413 a result, we identified twelve significant barriers that needed to be assessed; these barriers were assessed using F-

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DEMATEL methodologies to frame long-term flexible decision-making strategies. The development of value is evident that the current study aims to overcome these barriers. As a result, for successful e-waste management 417 implementation, this study applied an integrated F-DEMATEL and F-ISM-based approach to a real-world MCDM 418 problems.

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The implementation of e-waste management is exceedingly complex, with an inter structure. According to the 420 findings of the study, the most causal and influential barriers in integrated F-DEMATEL and F-ISM methods are 421 " lack of customer awareness about return," "less policies addressing e-waste problem," "lack of long-term 422 planning," and "insensitiveness of public towards environmental issues." As a result, a greater emphasis on the 423 successful implementation of e-waste management in developing economies is required. The establishment of a 424 cause or prominent group barriers is essential, as these have the ability to influence the entire structure.