A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach

A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach

Journal Pre-proof A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach Feng Ming Ts...

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Journal Pre-proof A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach Feng Ming Tsai, Tat-Dat Bui, Ming-Lang Tseng, Kuo-Jui Wu, Anthony SF. Chiu PII:

S0959-6526(19)34610-4

DOI:

https://doi.org/10.1016/j.jclepro.2019.119740

Reference:

JCLP 119740

To appear in:

Journal of Cleaner Production

Received Date: 26 August 2019 Revised Date:

7 December 2019

Accepted Date: 14 December 2019

Please cite this article as: Tsai FM, Bui T-D, Tseng M-L, Wu K-J, Chiu AS, A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/j.jclepro.2019.119740. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach

Authorship Feng Ming Tsai Department of Shipping and Transportation Management, National Taiwan Ocean University, Taiwan E-mail: [email protected] Tat-Dat Bui Department of Shipping and Transportation Management, National Taiwan Ocean University, Taiwan E-mail: [email protected] Ming-Lang Tseng* Institute of Innovation and Circular Economy, Asia University, Taiwan Department of Medical Research, China Medical University Hospital, Taiwan E-mail: [email protected] Kuo-Jui Wu School of Business, Dalian University of Technology, China E-mail: [email protected] Anthony SF Chiu Department of Industrial Engineering, De La Salle University, Manila, Philippines E-mail: [email protected]

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A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach Abstract This study aims to explore integrated solid waste management hierarchical interrelationships using a sustainable balance scorecard approach. The proposed analysis using the fuzzy Delphi method to exclude invalid attributes, interpretive structural modeling to arrange attributes into an extensive hierarchical model, and using a fuzzy decision-making trialand-evaluation laboratory to examine the causal interrelationships among attributes. The solid waste management systems in Vietnam are generally inefficient due to a lack of proper administrative oversight, infrastructure, and adequate resource utilization. Integrated solid waste management is an important provision in public service systems. There is a need to propose and evaluate better management approaches to enhance waste process activities and increase sustainable performance. Collected qualitative information is converted into a crisp value for the evaluation process, and the qualitative data stem from the operations. This study measures 6 aspects and 24 criteria. The results showed that financial investment, stakeholder involvement, and innovation capacity are decisive causal aspects in which stakeholder involvement and innovation capacity are interrelated. The cost efficiency, stakeholder collaboration, flexibility/adaptability to environmental changes, availability of local technical skills, and knowledge acquisition and communication technologies are identified as the linkage criteria that present the highest driving and dependence powers to help decision makers achieve better operational performance. Keywords: Integrated solid waste management; sustainable balanced scorecard; fuzzy Delphi method; interpretive structural modeling; fuzzy decision-making trial-and-evaluation laboratory

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A performance assessment approach for integrated solid waste management using a sustainable balanced scorecard approach 1. Introduction Solid waste management (SWM) has been recognized as one of the most important provisions of the public service system (Phonphoton and Pharino, 2019). SWM not only occupies the largest portion of the state budget but also employs the most individuals among the various public services. However, in most developing countries, including Vietnam, SWM is generally inefficient because of a lack of proper administrative, infrastructure, and adequate resource utilization (Botello-Álvarez et al., 2018). Various programs have been conducted by the relevant solid waste management authorities to address these problems, but most have been unsuccessful (Fernando, 2019). The implementation process involves complicated operations with complex linkages that are impacted by both the quantities and qualities of waste within the system. This situation requires adequate analysis tools and systemic approaches that support decision making and provide a comprehensive representation that considers the interactions between the main elements of SWM and their evolution (Di Nola et al., 2018). Thus, designing a framework with a greater focus on integrated system and sustainability analysis is necessary to develop valid SWM measures. The integrated solid waste management (ISWM) approach seeks to create administrative strategies for efficient waste management and sustainability. Pires et al. (2011) considered the involvement of and consensus between stakeholders and decision makers during their participation in decision-making processes to reach ISWM. Marshall and Farahbakhsh (2013) approached ISWM by identifying weak points that could improve waste management outcomes, including daily routines, lack of knowledge, inadequate institutional frameworks, inequalities in education and social prospects, and cultural values. Cervantes et al. (2018) proposed three attributes that make up sustainability (social, environmental, and economic attributes) and evaluated ISWM based on those attributes. Esmaeilian et al. (2018) and Fernando (2019) emphasized innovative solutions to control waste sources in their approach towards ISWM. Managing municipal solid waste is an enormous challenge that has yet to be adequately addressed because inappropriate operations can have critical environmental impacts and negative economic and social consequences (Aparcana, 2017). Hence, gaps remain regarding how to bridge these efforts and replace unsustainable practices and attitudes with integrated action plans that sustain waste management (Ikhlayel, 2018). Because ISWM is an essential and complex issue, there is a need to propose and evaluate a management approach that achieves better management practices, enhances waste process activities and increases sustainable performance. Nonetheless, an assessment of the sustainable performance of ISWM is still lacking because of the complex characteristics of the stock and the flow of waste management systems; this complexity makes it difficult to design an operational network that can handle varying conditions (Phonphoton and Pharino, 2019). The absence of a holistic understanding of ISWM does not allow measurement of the level of the waste hierarchy, making it incapable of evaluating the performance attributes. The sustainable balanced scorecard (SBSC) approach is an important tool for integrating sustainable attributes into a performance measurement process (Aly and Mansour, 2017). Falle et al. (2016) suggested that SBSC helps to secure and aid 3

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in the integration and coordination of various forms of expert knowledge when assessing sustainable performance. Jassem et al. (2018) proposed additionally applying SBSC to performance perspectives to address sustainability objectives and integrating them into the existing performance status. The SBSC was developed to support an organization’s sustainable performance; however, a lack of clarity still exists on how to achieve SBSC and what exactly constitutes a SBSC attribute (Lu et al., 2018; Jassem et al., 2018). Hence, this study is based on SBSC and investigates how to structure hierarchical frameworks and interrelationships under uncertainty, which has yet to be explored in an ISWM context. The knowledge gained from this study not only supplements the existing information for implementing SBSC but also supports the operational sustainability effort to achieve sustainable ISWM. Because the complex linkages within the system are impacted by both quantity and quality attributes, this study examined the ISWM using both qualitative and quantitative data. A hybrid fuzzy Delphi method (FDM), interpretive structural modelling (ISM), and the fuzzy decisionmaking trial-and-evaluation laboratory (FDEMATEL) method were employed to approach the ISWM based on experts’ linguistic suggestions. The FDM was used to exclude inessential attributes and customize the valid hierarchical relationships based on the experts’ judgments (Tseng and Bui, 2017), and the ISM method was used to arrange attributes into an extensive systemic model (Tseng et al., 2018). The ISM is a strong qualitative tool that can impose an overall structure on a problem involving several attributes with complex relationships (Wu et al., 2017). FDEMATEL was then used to analyze the causal interrelationships among attributes. The decision makers’ qualitative linguistic preferences were transformed into crisp values for analytical investigation (Tseng et al., 2017). This study’s objectives were as follows: • To identify valid and reliable ISWM attributes based on qualitative information. • To develop an ISWM hierarchical framework for measurement. • To analyze the causal interrelationships among ISWM attributes. This study contributes to the literature not only by highlighting the theoretical insights but also by emphasizing empirical results. For the theoretical proposition, this study cultivates the ISWM hierarchical framework to enable decision makers to achieve higher performance. The implementation of SBSC is assumed to complement the study framework by extending existing knowledge and helping practitioners achieve higher operational success. In practice, advanced guidelines are provided based on the identification of critical attributes that enhance the Vietnam ISWM and lead to sustainable development. The remainder of this study is organized as follows. Section 2 discusses the theoretical background of BSC, ISWM, proposed methods and attributes for measurement. Section 3 addresses the case study circumstances and more clearly explains the applied methodologies. The results are presented in Section 4 and implications are discussed in Section 5. Finally, Section 6 presents conclusions, limitations and future studies. 2.

Literature review This section explores the theoretical background of BSC, ISWM. The proposed methods and measured attributes are also discussed.

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2.1. Theoretical background The SBSC was an improvement on the traditional balance scorecard developed by Kaplan and Norton (1992), which requires a balanced set of financial and non-financial measures and the comprehensive participation of all stakeholders regarding operational performance. The SBSC exploits synergies in assessment, preparation, and strategic broadcasting at all stages of the organization. As evolved from the traditional concept, SBSC injects a sustainability point of view into the four major perspectives: finance, initial processes, stakeholders, learning and growth (Alewine and Stone, 2013). As proposed by Epstein and Wisner (2001), the SBSC recognizes sustainability-related objectives and performance measures, and it is an appropriate tool for integrating strategically relevant sustainable development goals. Tseng (2010) developed a sustainable approach in which organizations need learning and growth to innovate and build appropriate strategic internal operations capabilities and efficiencies that deliver specific value to stakeholders, eventually leading to higher financial value. Falle et al. (2016) indicated that the value of the SBSC lies in its ability to bridge the gap between strategic and operative levels to help identify and monitor those sustainable aspects that are essential in securing an organization’s successful performance. Therefore, the development of the SBSC is crucial to ensure that the organization formulates strategic plans and converts them into an operational system suited to the actual circumstances. Lin et al. (2016) employed multi-dimensional problems that comprise various organizational functions and resource integration within sustainable management. Sustainable coordination evaluations are proposed to customize the determined objectives and the extend theme to operational performance. However, these performance measures cannot be considered as comprehensive evaluations because they focus only on the initial performance. These frameworks are neither inclusive nor effective for holistic evaluation, resulting in a lack of SBSC knowledge that pose difficulties to effective decision-making and subsequently seriously limit the organization's ability to perform sustainable performance assessment (Lu et al., 2018; Jassem et al., 2018). As long as sustainable performance goals are part of management control systems, effective sustainability measurement will remain important. The SBSC has been proven to be suitable for evaluating performance of ISWM because sustainability needs to involve all the available possibilities and include synergistic solutions to such management issues. 2.2. Integrated solid waste management ISWM is an approach for preventing, recycling and managing solid waste in ways that most effectively protect human health and the environment (Phonphoton and Pharino, 2019; Fernando, 2019). This encompasses the consideration of local facilities and their demands and conditions when selecting the most appropriate waste management activities that should be applied in specific contexts (Zurbrügg et al., 2012). For instance, Marshall and Farahbakhsh (2013) stated that the ISWM aims to establish an efficient SWM system by incorporating and integrating the interrelated processes along the entire waste management chain. Arıkan (2017) identified that the network should provide systematic solutions to solve the management problem from different points through product designs that function without producing waste. A contemporary and systematic approach involves numerous operational, executive, and managerial decisions regarding waste processing and disposal, selection of waste-treatment 5

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technologies, and waste flow allocation to processing facilities and landfills in a sanitary and environmentally friendly manner (Asefi and Lim, 2017). SWM problems address the capabilities of waste management authorities resulting from a lack of organization, financial resources, excessive system complexities, and multidimensionality (Guerrero et al., 2013). Edalatpour et al. (2018) claimed that waste dissemination, poor collection mechanisms, waste generation and consequent pollution are causing various environmental and public health problems. Di Nola et al. (2018) concluded that a failure to implement management plans to address the waste crisis has demonstrated the necessity of finding dynamic decision-making tools that consider the interactions between the involved attributes and waste treatment evolution. Because ISWM includes many strategic, tactical and operational aspects, it has become an important concern of management authorities. However, the SWM, which was founded on integrated and holistic assessments has only occasionally been well addressed in the existing literature (Ikhlayel, 2018). Hence, this study investigates ISWM development in relation to operational problems. This approach should be considered as an efficacious acceleration of management issues to structure sustainability. In this context, the SBSC have been promoted to assist decision-makers and become a helpful management tool by integrating the optimal management of environmental, social and financial resources to maximize organizational potential by providing an overview of the entire organization to improve waste management efficiency. The consumption of natural resources and the release of waste and pollution are related to various stakeholders' aspects and also affect customers and employees (Tang and Zhou, 2012). In particular, an integrated model is needed for proposing and implementing a management tool suited to the needs of public waste management service providers (Mendes et al., 2012). Therefore, this study claims that assessing sustainable performance allows the integration of a set of measures and results in better outcomes. An SBSC approach that emphasizes strategic implementation is needed to establish and guarantee the efficiency of the ISWM. 2.3. Proposed method This study, based on SBSC, involves structuring hierarchical frameworks and interrelationships under uncertainty. The lack of studies has limited the development and implementation of the SBSC for providing sustainable strategic management (Jassem et al., 2018). Only a few studies have assessed ISWM with regard to SBSC using qualitative information, and proper methods of implementing the framework remain an unsolved problem. The extremely limited and incomplete information makes it difficult to gain insight into the complex problem of ISWM (Guerrero et al., 2013; Um et al., 2018). This study applies a hybrid method of FDM, ISM, and FDEMATEL to investigate the ISWM. On the one hand, FDM is used to eliminate invalid attributes based on experts’ judgments concerning their importance to ISWM. In particular, fuzzy set theory is adopted to convert highuncertainty linguistic preferences into quantitative values that remain based on human preferences and maintain their qualitative characteristics (Sadeghi et al., 2016). The Delphi method is then embraced to integrate all the experts' judgments and exclude invalid attributes (Tseng, 2009). This approach not only allows experts to transfer their judgments based on knowledge and experience but also simplifies a complex problem by addressing the inherent 6

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uncertainty of survey procedures (Tseng et al., 2018). Sanchez-Lezama et al. (2014) applied the Delphi method to reduce uncertainty of expert judgments when examining the accuracy of survey technique and guarantee the quality of survey analyses. Tseng and Bui (2017) increased outcome validity and reliability, reduced expert opinion uncertainty when exploring reinforcing attributes. This method achieves consensus in group decision making and attends to the number of selections by merging or eliminating experts' opinions and helps decision makers reduce the time required to make decisions. The extended hybrid method consisting of ISM and FDEMATEL is employed. In the ISM, attributes are arranged into an extensive systematic model that considers both direct and indirect attributes. Fundamental graph theory is adopted to combine the theoretical, conceptual and computational advantages of addressing complex patterns of logical relations among the attributes to identify the influence, direction and order of a system’s attributes (Wang et al., 2018). Field expert experiences and knowledge gained from practice are used to analyze the complex interrelationships among the proposed attributes and rearrange them into a hierarchical structure (Tseng et al., 2018). The method tackles the problems of attribute dependence, linguistic preferences and hierarchical structure modeling by providing additional valuable information for determining strategic directions. The FDEMATEL method is then employed to examine the causal interrelationships among the attributes by reviewing the qualitative information in the linguistic descriptions provided by experts and generating a causal diagram of the proposed attributes (Wu et al., 2017). First, fuzzy set theory is used to quantify equivocal concepts related to subjective human judgments in an uncertain environment into crisp values, and then the DEMATEL technique is applied, which is designed to build and analyze the interrelationships between complex perspectives as well as to construct inter-correlations among aspects and criteria. Chuang et al. (2013) applied a hybrid ISM and DEMATEL model to address complex, multi-criteria decision-making problems. Tseng et al. (2018) used this technique to evaluate the causal interrelationship and hierarchical inter-relationships of attributes and identify the attribute critical for improvement. Waste management is a complex problem due to the nature of waste and the increasing restrictions on managerial capabilities. The results of this study indicate that the proposed methods are appropriate for assessing ISWM. 2.4. Proposed measured This study derives four fundamental dependency perspectives for SBSC to analyze the ISWM framework, including finance inducement, stakeholder involvement, sustainable internal processes, and learning and growth. The initial set of 70 criteria were previously proposed in the literature for the evaluation process, as shown in Appendix A. The financial perspective (P1) represents financial performance concerns that must be addressed to guarantee the organization's survival and development. Data for this perspective is acquired by monitoring relevant economic benefits, such as return on investment and strength of asset management (Kaplan and Norton, 1992). Zurbrügg et al. (2012) suggested that financial and operational necessities as well as the cost-recovery mechanisms of economic elements are major drivers in sustaining the ISWM. Simatele et al. (2017) claimed that sustainable resource management and proper socio-economic service delivery would be achieved by reducing supplementary costs and extending effectiveness. Local authorities have 7

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increasingly turned to donors for financial help to remain solvent and update older technology (Chimuka and Ogola, 2015). However, failures occur in managing solid waste due to financial issues. Guerrero et al. (2013) stated that insufficient financial resources and legislative regulations limit the safe disposal of waste in well-equipped, engineered landfills. Ikhlayel (2018) declared that huge expenses are required to deliver waste management services, while the absence of sufficient financial support, reluctant payments by service users, limited resources, and lack of the proper uses of economic instruments make waste management systems have vulnerable. Hence, considering such criteria is a prerequisite when building ISWM strategies. The stakeholder’s involvement (P2) addresses the related stakeholders, community demand and identification, and market segments with the purpose of aligning the key measures for success: satisfaction, market share, user service retention and capture, and profitability. Involving stakeholders in the decision-making process when solving solid waste problems is a primary aspect of achieving sustainable waste management (Soltani et al., 2015). Zurbrügg et al. (2012) proposed that the interactions between a wide range of involved stakeholders, various waste systems elements and SWM activities can impact the socioeconomic and natural environment in both positive and negative ways. For instance, Guerrero et al. (2013) claimed that stakeholders such as the local authority, some central government ministries from and private contractors providing services could assist in ISWM by setting up regulations and establishing waste management systems. Vučijak et al. (2016) suggested that stakeholder involvement can provide a wealth of relevant local knowledge that might otherwise be missed and that this information may also lead to more pragmatic benefits. Stakeholder involvement can alter both the perceptions of stakeholder and their interactions, thus assisting environmental efforts. The outcomes of such an approach affect the entire value chain of the system—operational activities, transportation, services distribution, and communication (Mendes et al., 2012); however, stakeholders are likely to introduce perceptions from multiple different viewpoints, which can lead to increased conflict. Cobbinah et al. (2017) claimed that the lack of a well-thought-out ISWM plan involving all major stakeholders, particularly urbanites, remains the bane of efficient ISWM. Hence, ISWM strategies demand a decision support assessment that facilitates communication among stakeholders and helps solve their conflicts. The internal processes (P3) present the set of developed activities and actions for monitoring and analyzing the operational dimension of the organization and to assess the adequacy of internal processes for achieving customer satisfaction and financial optimization (Kaplan and Norton, 1992). Mendes et al. (2012) argued that internal processes usually result from particular client perspectives (stakeholders) identifying driving values for organizational management. Guerrero et al. (2013) indicated that the ISWM depends upon the active participation of both the municipal agency and the citizens to provide suitable waste solutions. In addition, Cervantes et al. (2018) stated that a correlated system is required in which waste management processes are integrated throughout the entire sequence of transportation, processing, recycling, resource and energy recovery and disposal technologies. The effectiveness of SWM depends on how well the internal processes are implemented to guarantee efficiency in collection, treatment and disposal activities (Fratta et al., 2019). The outcome should result in environmental performance benefits favorable to ISWM that help 8

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achieve a sustainable system (Zurbrügg et al., 2012). There are always challenges because communities continually create more waste due to population growth, inefficiencies in resource consumption, inefficient institutional and organizational structures, and poor management oversight (Marshall and Farahbakhsh, 2013). Evaluating the internal processes is necessary because SWM authorities progressively search for ways to maximize profits and perform ISWM more efficiently. Learning and growth (P3) represent intangible human resource values, information and management systems, and managerial processes in creating value for the organization (Kaplan and Norton, 1992). Malinauskaite et al. (2017) suggested that technological advances, innovation, knowledge creation, and cooperation could help waste management organizations reduce risk while also involving the public in the entire management process. Guerrero et al. (2013) reported that citizens who received information and training concerning the benefits of waste management were more active in attending to the systems. However, local waste management authorities lack administrative capabilities and functional knowledge, and the amount of information available to the public is often negligible (Chung and Lo, 2008). Environmental agency workers have little or no specialized background or training in operational skills, which makes SWM ineffective and inefficient (Fernando, 2019). Thus, improving general knowledge and awareness through education and technical training is an important factor in success (Hammed et al., 2018). Innovative solutions for controlling waste require experimentally validated models (Esmaeilian et al., 2018). This study develops performance measures to accomplish the ISWM objectives using SBSC. 3.

Method This section discusses Vietnam’s ISWM case background. The evaluation processes of FDM, ISM, FDEMATEL are explained in detail. The proposed analytical steps used in this study are also provided. 3.1. Case background The Vietnamese environment administration reported that the total solid waste generated in the country for 2019 is approximately 38,000 tons/day with an average increase of 1016%/year (Vietnamese environment administration, 2018). The average collection rate is now approximately 85.3% and continues to increase. The ISWM has recently received much attention from all levels and sectors; integrated management methods, solutions for reducing, reusing, recycling and recovering energy from waste have been given some initial attention. However, the amount of solid waste waiting for treatment is quite large, and waste buried in temporary and open landfills has long been a source of environmental pollution that negatively affects health and human activities. Environmental pollution at gathering points is quite serious, and because this aspect is often intermingled with residential areas and involves main roads, asynchronous connections between collection and transfer stations are not conducive to urbanization. Although the treatment rate of solid waste in the country has increased, the large amount of solid waste generated, management complexity and limited capabilities remain causes of underperformance in ISWM systems. Lack of guidance, inspection and oversight by specialized agencies and local authorities, has resulted in a failure to deal strictly with violations in accordance with regulations, which has resulted in long-term environmental violations and 9

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caused serious environmental pollution. The investment in and development planning of waste treatment zones has been neglected in some areas, even though large budgets are provided. The levels of integration and synergy within the SWM system are weak and unsustainable. Therefore, accomplishing ISWM is important in improving the effectiveness of SWM and for improving environmental conditions in Vietnam. This approach would not only help decision makers in their decision-making processes but also increase sustainable performance. Proposing a specific ISWM framework that can help find strategic solutions and effective management mechanisms is urgent. 3.2. Fuzzy Delphi method Ishikawa et al. (1993) proposed the integration of fuzzy set theory and the traditional Delphi method to solve uncertainties regarding experts’ preferences expressed through language to improve the proficiency of their judgments. The combination offers advantages that reduce the number of interviewees, survey times and costs, and help optimize experts’ knowledge (Lee et al., 2018). The significant value of attribute is evaluated by respondent as = ; ; , = 1,2,3, … , ; = 1,2,3, … , . Next, the weight of attribute is calculated as = / ∏ ; ; , where = , = , and = . The triangular fuzzy numbers are then transformed from the original linguistic terms, as shown in Table 1. (INSERT Table 1 here- Transformation table for linguistic terms)

The convex combination value = − − − , =

is generated by a cut using: − , = 1,2,3, … , .

(1)

The value can range from 0 to 1 based on whether the experts are positive or negative perceivers. The value = 0.5 denotes the median condition. Then, the exact value of is calculated as follows: =" , = #$ + 1 − # & , (2) where # represents the positivity level of a decision maker and is used to stabilize the primary decisions among the group of experts. Finally, ' = ∑ ) / serves as a threshold for screening the most important attributes. When ≥ ', attribute is accepted; otherwise, it should be eliminated. 3.3. Interpretive structural modeling The ISM method is applied to collect expert opinions in a number of management practices, including brainstorming, data mining to develop the hierarchical levels and interrelationships among the attributes (Tseng et al., 2018). Thus, four symbols are employed to identify the alleviation among two attributes ( and ): V: attribute helps alleviate attribute but not in the other direction. (3) A: attribute helps alleviate attribute but not in the other direction. (4) X: attribute and help to improve each others. (5) O: no relations exists between and . (6)

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These symbols constitute a structural self-interaction matrix illustrating experts’ linguistic judgments and that must be converted into binary code by substituting instructions to obtain a reachability matrix. The conversion of the reachability matrix is performed as follows: $ , , , & → , = 1,0 ; - = 0,1 ; . = 1,1 ; / = 0,0 . (7) Then, the reachability and antecedent sets are developed in sequence to gather the individual reachability matrices into a total reachability matrix. Here, 0 1 = 2345 6 ×8

characterizes the 9: expert's individual reachability matrix; thus, the total reachability matrix 0 ; is calculated by the following equation: (8) 0 ; = <345 + 345= + ⋯ + 3451 ?, , = 1,2,3, … , . 1 ; When 0 > 0.5, the gathered relationship is considered to be 1; otherwise, it is 0. Afterward, the reachability 0′ and antecedent ,′ set are derived from the total reachability matrix using the following equation: E E E E E E 34 = 1, 0 B = C3 D , 3=D , … , 3 D F; 35 = 1, ,B = C3 G , 3=G , … , 3 G F. (9) Consequently, the intersection set H′ is computed using H B = 0 B ∩ ,B . (10) The intersection set is derived from coinciding criteria and reveals the number of criteria with higher value for addressing the upper levels in the ISM hierarchy. The criteria at the upper hierarchy levels cannot facilitate the criteria in even higher levels. After the highest level has been confirmed, the criteria are detached from the others. This process is repeated until all the criteria have been addressed. 3.4. FDEMATEL FDEMATEL uses the defuzzification technique to transform qualitative information into fuzzy linguistic data. The process generates crisp values from fuzzy numbers. Then, the left and right values are computed by the minimum and maximum fuzzy numbers. The fuzzy L L L membership functions J̃45 = J̃ L45 , J̃=45 , J̃M45 are used to generate the total weighted values. The crisp values are then presented in a total direct relation matrix, allowing a diagram to be drawn to simplify the analytical results. The cause and effect groups containing certain attributes represent the structured interrelationships and important effects. A set of attributes is proposed, N = OP1, P2, P3, ⋯ , P Q, and certain pairwise interrelationships are used to create the mathematical relations. In particular, this study obtained and accumulated crisp values using linguistic scales of VL (very low influence), L (low influence), M (moderate influence), HI (high influence), and VHI (very high influence), as presented in Table 2. Assuming that R experts participated in the L evaluation process, the J̃45 signifies the fuzzy weight of the 9: attribute's effect on the 9: attribute as assessed by expert R 9: . (INSERT Table 2 here- TFN linguistic scale)

The fuzzy numbers are simplified as follows: L L ?=S N =
X X TUVW Y84 TUVW



,

X X T[VW Y84 T[VW



11

,

X X T\VW Y84 T\VW



] ,

(11)

474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510

where ∆=

L JM45 −

e.

The left _ and right 3_ normalized values are calculated using < _45 , 3_45 ? = `

X aT[VW

,

X aT\VW

X X X X b caT[VW YaTUVW d b caT\VW YaT[VW d

e.

(12)

The total normalized crisp values f_ are generated as follows: L f_45 =

X X X [ $ghVW b YghVW dc ihVW &

.

X X YghVW cihVW

(13)

The synthetic value notation used to accumulate the individual perceptiveness from the R respondentsis: J̃45L =

[ \ \ jkU VW ljkVW ljkVW l⋯ljkVW X

.

(14)

A pairwise comparison is used to acquire the × initial matrix of direct relation Hm , in L which J̃45L represents the effect level of attribute on attribute , modified as Hm = $J̃45 & × . The normalized direct relation matrix n is created as follows: n = o ⊗ Hm . (15) o = stu ∑X T̃ X UrVrX WqU VW

From the normalized direct relation matrix, the interrelationship matrix v is obtained by: v =n H−n Y , (16) where v is $w45 & × , = 1,2, ⋯ . The values of the driving power and dependence power x are obtained from the sums of the row and column values of the interrelationship matrix by applying the following equations. α = $∑4Y w45 & × = $w4 & × (17) β = $∑5Y w45 & × = $w5 & × . (18) The attributes are positioned in the cause-effect diagram derived from $ α + β , α − β &, which represent the horizontal and vertical axes, respectively. Here, (α + β) represents the importance of attributes: the higher the (α + β) value of an attribute is, the more important it is. The attributes are grouped into cause-and-effect groups based on their α − β values, which are positive or negative. When an α − β is positive, the attribute is allocated to the cause group; otherwise, it is allocated to the effect group. For the criteria, a visual diagram is arranged into four quadrants: an autonomous quadrant that denotes criteria with weak driving and dependence power; a dependent quadrant that includes criteria with weak driving power but strong dependence power criteria; an independent quadrant that includes criteria with weak dependence power but strong driving power; and a linkage quadrant that includes criteria with both strong driving and dependence power (Tseng et al., 2018).

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3.5. Proposed analytical steps The method is implemented in three stages. In the first stage, a theoretical assessment and the proposed ISWM attributes are collected from the literature and from expert consultants, respectively. Then, 2-rounds FDM are conducted to exclude invalid attributes. The second stage uses the ISM to arrange the attributes resulting from stage 1 into a hierarchical framework. The FDEMATEL is adopted to assess the ISWM hierarchical framework by examining interrelationship and identifying the critical attributes for operational improvement in the third stage. This study included a group of 12 expert participants: five members from the academic sector, three members from related government agencies, and four members from waste treatment organizations. The expert participants had an average of 10 years of experience in studying and practicing SWM. The evaluation process is as follows: 1. The probable attributes of ISWM barriers are collected from the literature. Then, the proposed attributes are finalized by the experts through a group discussion. 2. Two rounds of FDM are employed to refine the essential attributes by applying Equations (1)-(2). A questionnaire is formed after each round and used by the experts to complete additional assessments based on the proposed attributes. 3. From the criteria set resulting from stage 1, the contextual relationships of the criteria are assessed based on the experts’ opinions by applying Equations (3)–(6). The qualitative judgments are transformed into binary codes following Equation (7) to create the individual reachability matrices. The total reachability matrix is aggregated using the average method presented in Equation (8) to avoid extreme values when judging the relationships. This process results in the final reachability matrix. The reachability and antecedent sets are characterized from the total reachability matrix using Equations (9) and (10). Finally, the hierarchical model of the criteria is constructed, and the final digraph is developed using transitivity. 4. The hierarchical framework is then used to collect qualitative information judgments from the experts. 5. The resulting qualitative information is translated into TFNs and then converted into comparable values. Fuzzy defuzzification is conducted using Equations(11)–(13). The resulting crisp values are utilized by Equation to generate an initial direct relation. The total DEMATEL interrelation matrix is computed using Equation (18) and mapped, resulting in a graphic diagram of the causal effect for ISWM aspects and criteria.

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4.

Result This section reports the analytical results of this study. The ISWM hierarchical framework is revealed, and the critical attributes are identified for further implications. 4.1. Fuzzy Delphi method Seventy criteria regarding the four perspectives of BSC were introduced for FDM evaluation. The results FDM rounds 1 and 2 are presented in Appendices B–E along with their weights and the threshold for excluding attributes. In round 1, the initial set of ISWM (see Appendix A) was evaluated based on experts’ judgments. Subsequently, the linguistic terms were transformed into their corresponding triangular fuzzy numbers as shown in Table 1. The FDM was applied to 13

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refine the important attributes (see Appendix B) with a threshold of 0.644. Forty-four criteria were accepted and forwarded to the round-2 evaluation step (see Appendix C). The 44 remaining criteria were employed in round 2 of FDM to refine the important criteria for the next stage of the analytical process; the acceptance threshold was 0.671 (see Appendix D for details). Appendix E shows the 24 final criteria that were accepted and renamed. 4.2. Interpretive structural modeling The structural self-interaction matrix is presented in Table 3. The interactions between the criteria are described using 4 symbols, and this qualitative information must be converted into quantitative binary code data using the proposed substitution instructions, as shown in Table 4. The table includes areas that assist with the contrasting areas separated by the gray dashed line. The area below the line denotes the relationship from criterion to criterion , while the area above the line represents the relationship from criterion to criterion . Table 5 displays the intersection set between the coinciding reachability matrix and the antecedent matrix. The 24 criteria are arranged into eight levels and grouped into 6 major aspects that represent areas that can improve the operational performance of ISWM, as shown in Figure 1. These aspects include financial investments (A1), economic benefits (A2), sustainable stakeholder cooperation (A3), ecoefficiency (A4), environmental performance (A5), and innovation capacity (A6), as presented in Table 6. (INSERT Table 3 here - Structural self-interaction matrix of the criteria) (INSERT Table 4 here - Reachability matrix of the criteria) (INSERT Table 5 here - Intersection set of the criteria) (INSERT Table 6 here - ISWM hierarchical framework) (INSERT Figure 1 here - The ISWM hierarchical framework)

4.3. Fuzzy DEMATEL Based on the ISM hierarchical framework, the experts evaluate the aspects’ interrelationships using the provided linguistic scales. A sample of the fuzzy direct relation matrix and its defuzzification is depicted in Table 7. The average crisp value from each respondent is computed into the initial direction matrix as presented in Table 8. Then, the total interrelationship matrix is generated accompanied by causal interrelationships among aspects, as shown in Table 9. As a result, Figure 2 depicts the cause-effect diagram based on the + x and − x axes for financial investment (A1), stakeholder involvement (A3), and innovation capacity (A6) belongs to the cause group, while economic benefits (A2), eco-efficiency (A4), and environmental performance (A5) are in the effect group. In particular, sustainable stakeholder cooperation (A3) and innovation capacity (A6) are considered the most important and strongest aspects of ISWM and have potential driving effects throughout the system. (INSERT Table 7 here - TFNs, fuzzy direct relation matrix and defuzzification for the aspects – Respondent 1) 14

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(INSERT Table 8 here - Initial direction matrix for the aspects) (INSERT Table 9 here - Total interrelationship matrix and cause-and-effect interrelationships

among the aspects.) (INSERT Figure 2 here - Causal interrelationships among the aspects)

The initial direction matrix and the total interrelationship matrix for the criteria are presented in Tables 10 and 11. The cause and effect interrelationships among the criteria are shown in Table 12, and the generated driving and dependence power diagram is shown in Figure 3. The diagram divides the criteria into four quadrants. The autonomous quadrant includes C7, C9, C13, C14, and C15, which show very limited effects on others and are rather disconnected from the system. The dependent quadrant consists of C4, C5, C11, C12, and C22, which have less influence within the system but are easily affected by other criteria. The independent quadrant includes C2, C6, C10, C16, C17, C18, and C23, which displays considerable driving power but do not have as tight a connection to the framework. The last quadrant presents the linkage criteria, which have both high driving and dependence powers. In particular, cost efficiency (C1), collaboration among stakeholders (C9), flexibility/adaptability to environmental changes (C20), the availability of local technical skills (C21), and knowledge acquisition and communication technologies (C24) have continuing effects on and responses to other criteria within the framework. Thus, by enhancing these criteria, the other criteria can also be improved. These criteria represent the most important criteria that are critical to focus on. (INSERT Table 10 here - Initial direction matrix of the criteria.) (INSERT Table 11 here - Interrelationship matrix of the criteria) (INSERT Table 12 here - Cause-and-effect groupings among the criteria) (INSERT Figure 3 here - Causal diagram of the criteria)

5. Implications This section discusses the theoretical insights gained from the results. The managerial implications offers practical guidelines for enhancing ISWM performance. 5.1. Theoretical implications This study deepens the literature by providing theoretical contributions to both ISWM and SBSC theory. Its derivation from the 4 perspectives of SBSC has allowed this study to approach a comprehensive measure for higher ISWM performance. The approach forms a hierarchical framework by creating an efficient instrument for constructing a better network that emphasizes strategic implementation to establish and guarantee ISWM efficacy. The 24 criteria are arranged into eight levels and grouped into 6 major aspects that represent areas that can improve the operational performance of ISWM including financial investments (A1), economic benefits (A2), sustainable stakeholder cooperation (A3), eco-efficiency (A4), environmental performance (A5), and innovation capacity (A6). This study finds that financial support (A1), 15

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stakeholder involvement (A3), and innovation capacity (A6) are the main aspects that control the ISWM. In particular, the system is strongly influenced by sustainable stakeholder cooperation (A3) and innovation capacity (A6) because these aspects bond with each other to assess sustainability in driving eco-efficiency and environmental performance. The result shows that innovation capacity (A1) is the most critical aspect because it has the strongest interrelationship among others. This aspect refers to an organization's ability to exploit advanced technology, knowledge creation, and cooperation that help waste management authorities reduce risk and improve management efficiency (Malinauskaite et al., 2017). Innovation for controlling waste requires the production of experimentally validated models regarding solid waste management (Esmaeilian et al., 2018). Hence, focusing on studies applicable to planning and constructing the ISWM; organizing training to strengthen SWM capabilities in terms of institutional frameworks, management mechanisms, technology and clarifying the responsibilities of related parties is critical. Concentrating on waste management research, innovating in waste management techniques, providing common standards for infrastructure and facilities to handle waste, collection equipment, transportation and waste treatment technology is required. Proposing a unified administrative model with integrated processes as well as management solutions and solid waste treatment technology is particularly important in effectively controlling and solving the problems of environmental pollution caused by solid waste. Sustainable stakeholder cooperation (A3) was confirmed as playing an important role in ISWM due to its high importance value in the framework. This aspect can help control and limit pollution by contributing to a legislation framework and the establishment of waste management systems (Guerrero et al., 2013). Each stakeholder has distinct roles and interests in implementing the ISWM that must be considered during collaboration during efforts to improve the waste management network. Stakeholder involvement provides a wealth of relevant local knowledge that might otherwise be missed, and this information may also lead to more pragmatic benefits (Vučijak et al., 2016). However, arbitrary manifestations, in particular, a lack of coordination between the stakeholders, can cause fragmentation of the ISWM. These manifestations occur not only between public-private partnerships, social aspects, households, businesses, management agencies, localities, and regions but also between stages (emission, collection, sorting, transport, treatment, and landfill). Hence, education, advocacy, raising awareness among stakeholders, and economic solutions such as lucrative contracts, financial incentives, inspections, and administrative fines are all needed. Socialization in the field of ISWM can mobilize all the stakeholder resources for the purpose of sustainable development. Financial support (A1) is another critical causal aspect in the ISWM framework. Requirements for sustainable financial mechanisms include depreciation, and investments to cover operations and maintenance costs, and are key in ensuring robust and reliable waste management network operation (Zurbrügg et al., 2012). Financial resources for ISWM are increasingly diversified. Investment Capital for the construction of integrated solid waste treatment facilities and auxiliary works can be supported by the central budget, local authorities, foreign aid, long-term loans and other lawful capital sources. Increasingly, recurring financial help is available to support continual operations and maintenance (Chimuka and Ogola, 2015). However, this diversified financial support is still insufficient and unstable. Hence, it is necessary to take steps to diversify investment sources for SWM and treatment, 16

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strengthening international mobilization, and maintaining the sustainability of investment sources to ensure consistent operations and processes. A complete financial mechanism must be in place for ISWM to mobilize stakeholder sponsorships. 5.2. Managerial implications This study indicates that the linkage criteria, including cost efficiency (C1), collaboration among stakeholders (C9), flexibility/adaptability to environment change (C20), local technical skills availability (C21), and knowledge acquisition and communication technologies (C24), substantially interact with other criteria. After such impacts, other criteria may be influenced or vice versa. Thus, these criteria are considered critical attributes that help decision makers improve overall network performance. The cost efficiency (C1) aspect of ISWM is related to minimizing cost, increasing recycling, preventing waste from entering landfills, and maximizing benefits. Cost efficiency can be achieved by implementing eco-friendly and rational operations. These are expressed through the economic solutions implementation and achieve benefits by reducing the cost of waste treatment, reducing generated waste, improving and renovating production technologies, and applying internal management measures, thereby reducing raw material and fuel losses, improving productivity, product quality, and decreasing expenditures on input costs such as materials and energy. Furthermore, using waste as a resource for input material and energy recovery activities is proposed to achieve higher productivity and better environmental protection. Waste classification can capture large amounts of waste capable of being recycled and converting it into useful sources of materials. In addition, reducing waste at the source helps collectors save during collection and saves costs for ISWM. The decision making criterion in ISWM is profitability; when the benefits are greater than the costs, the organization can reduce costs by reducing the input of new materials in the production process. Benefits can be gained from the recovery and sale of energy and recycling materials from waste on reusable trading markets which by increases economic efficiency and forms an effective solution that can for reduce the state budget waste management cost burden through the application of tax policies and waste charges on polluters and service users. The results of this study are necessary to develop SWM and management strategies to attract all the stakeholders to participate and improve comprehensive development to achieve effective ISWM. Regarding collaboration among stakeholders (C9), responsible state management agencies and stakeholders must coordinate and consider all the important factors in advance when selecting technical criteria and creating plans for solid waste treatment to ensure the effectiveness and feasibility of a SWM system. Thus, there is a need to establish, coordinate and maintain long-term education and training and to develop infrastructure. The government and development partners should prioritize Building capacity and improving education and training at all levels when planning large and long-term ISWM programs in Vietnam. In addition, this study recommends improving and completing transparent legal pathways and waste management policies to encourage collaboration and creating a mutually beneficial environment to enable win-win relationships among stakeholders. This study also suggests communicating and educating communities to become conscious of the dangers of inadequate waste disposal to encourage them to join in inspecting and detecting hazardous waste sources. Coordination between state management agencies, technicians, biologists and 17

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the public in monitoring, developing waste treatment technology and instituting environmental protection policies are needed. Vietnam lacks the mechanisms and procedures for appropriate prevention, response, and remedy of waste crises due to natural disasters or environmental incidents from production operations. No detailed instructions exist; when an incident occurs, it is almost impossible to perform effectively. Hence, studying and proposing prevention and response mechanisms for dynamic ISWM is both necessary and urgent on from the aspect of flexibility/adaptability to environment changes (C20). A more flexible and responsive institutional, financial and technical framework needs to be put in place to encourage creativity and introduce practical solutions for local use. Extending training, rehearsals, and disseminating information on incident prevention and response to officials and employees of establishments, and concerned organizations, and individuals is recommended. Forecasting risk and waste crises and planning key infrastructure projects such as wastewater collection systems, needs to be flexible, adaptable and have a reduced impact on resources. Optimal design decisions concerning waste management infrastructure for urgent and priority measures, alarm modes, notifications, and evacuation and mobilization procedures for both human resources and equipment are also important in the prevention of and response to future waste crisis scenarios. Regardless of the context, no SWM initiative is suitable for every community or city; therefore, local technical skills availability (C21) needs to be addressed based on the current state of waste management and the resources of each locality. Recent assessments of practical implementation of ISWM in Vietnam have emphasized the selection of high-tech treatment solutions and the consumption of inappropriate energy. Moreover, divergent treatment processes can be a more sustainable option. Hence, local authorities need to identify complete and flexible information about the range of available treatment solutions for specific geographical-residential conditions under specific financial contexts. The ability to choose from limited guidelines should not be constrained, but the selection should be based on the most appropriate available technology in terms of financial capacity and in accordance with local social and environmental conditions. Planning for the construction of hygienic solid waste disposal sites and landfill sites in conformity with local socio-economic development is urgent. An appropriate technology is one in which the technology has the lowest cost (investment and operating costs), and is technically and legally feasible: these characteristics ensure the effectiveness of pollution treatments and their acceptability to the community. Some resources exist for improving ISWM that can be quickly enhanced, that are less dependent on financial capacity, and that can function as a source of information for integrated networks. Knowledge acquisition and communication technologies (C24) include systematic prerequisites and lend importance to the system operation through information exchange and bidirectional feedback. The ISWM implementations in Vietnam currently lack this important criterion. To select appropriate standards and technology, having data on the current status of local SWM is vital. The statistical data must include generation sources, quantities, and composition of solid waste. In addition, the current treatment technology status, financial resources, stakeholder participation, institutions and policies/regulations are necessary to identify the challenges and opportunities of SWM systems and identify solutions. Developing and promulgating synchronous guiding circulars, and supplementing and completing the contents that guide the implementation of the mechanisms to create competitive and 18

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transparent mechanisms and mobilize the resources of the entire society in the collection, transportation and treatment of solid waste is recommended. A coordination between stockholders to build a database on SWM, exploit, exchange and provision relevant information on SWM with multi-dimensional assessment is proposed. The application of high-tech solutions, such as a blockchain system for ISWM to improve system quality, transparency, and trustworthy is another idea stemming from this study. 6. Conclusions Although various SWM plans have been implemented, they have not been successful. Derived from SBSC, in this study, a set of 70 criteria are proposed to address adequate tooling and systemic approaches to support decision making and to reveal the comprehensive interaction network among the attributes. This study applied a hybrid method to the ISWM in Vietnam. Using the FDM to first exclude invalid attributes, the ISM method was then used to arrange the important attributes into an extensive hierarchical model, and the FDEMATEL was applied to examine the causal interrelationships among the attributes. The fuzzy linguistic experiences of experts were converted into crisp values for the evaluation process to overcome the uncertainty and complexity of ISWM. Both qualitative information and quantitative data were employed. This study formed a hierarchical ISWM framework that covered 6 aspects and 24 criteria. The results showed that financial investment, sustainable stakeholder cooperation, and innovation capacity are decisive causal aspects that influence economic benefits, eco-efficiency, and environmental performance in the effect group. Sustainable stakeholder cooperation and innovation capacity are important because they have the strongest interaction power within the network. Five of 24 criteria were identified as linkage criteria that present the highest driving and dependence powers, including cost efficiency, collaboration among stakeholders, flexibility/adaptability to environment changes, the availability of local technical skills, and knowledge acquisition and communication technologies. These attributes can successfully help decision-makers achieve more efficient ISWM operational performance. Hence, this study contributes to the literature by examining the hierarchical framework and revealing the critical attributes need to achieve better organizational performance. The implementation of BSC is assumed to embrace the study framework, thus extending existing knowledge and helping practitioners achieve higher operational success. Sustainable stakeholder cooperation and innovation capacity are found to have close interrelationships for assessing sustainability and driving eco-efficiency and environmental performance. Advanced guidelines based on the linkage criteria of cost efficiency, collaboration among stakeholders, flexibility/adaptability to environment changes, the availability of local technical skills, and knowledge acquisition and communication technologies are provided for practicing practitioners to foster improvements in decision-making processes and promote sustainable performance. However, this study still has existing limitations. The proposed attributes were extracted from the literature and used expert consultants, both of which might limit the comprehensiveness of the framework. Future studies should add related attributes to the existing framework to deepen the present work. The number of expert respondents was limited to 12, which could have caused bias in the study results due to their specific knowledge, 19

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experience and acquaintance with the sector; thus, enlarging the number of respondents in future study is suggested to overcome this problem. Furthermore, this study applies the BSC to evaluate the ISWM: applying a different theoretical framework to enrich the literature is also encouraged in future studies.

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Acknowledgement This study is partially supported by Ministry of Science and Technology, Taiwan, The grant number is 108-2221-E-468 -004 -MY2

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t%E1%BA%A1i-Vi%E1%BB%87t-Nam.aspx 45. Vučijak, B., Kurtagić, S. M., & Silajdžić, I. (2016). Multicriteria decision making in selecting best solid waste management scenario: a municipal case study from Bosnia and Herzegovina. Journal of Cleaner Production, 130, 166 174. 46. Wang, L., Ma, L., Wu, K. J., Chiu, A. S., & Nathaphan, S. (2018). Applying fuzzy interpretive structural modeling to evaluate responsible consumption and production under uncertainty. Industrial Management & Data Systems, 118(2), 432-462. 47. Wu, K. J., Liao, C. J., Tseng, M. L., Lim, M. K., Hu, J., & Tan, K. (2017). Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties. Journal of Cleaner Production, 142, 663-676. 48. Zurbrügg, C., Gfrerer, M., Ashadi, H., Brenner, W., & Küper, D. (2012). Determinants of sustainability in solid waste management–The Gianyar Waste Recovery Project in Indonesia. Waste Management, 32(11), 2126-2133.

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Appendix A. List of proposed criteria Perspectives

P1

P2

Financial inducement

Stakeholders involvement

Criteria C1 Cost efficiency C2 Cost for waste recovery process C3 Operating costs C4 Transportation costs C5 Land demand C6 Raising fund and donation C7 Management expenditures C8 Equipment (distance, fuel consumption, time, etc.) and personnel (workers) payment C9 Market potential for the byproducts C10 Resources recycling C11 Income from sold recyclables C12 Fees for collection services against waste produced C13 Energy/emergy (possible overlap with an LCA) C14 Recovery, demand, or material flow (valorization) C15 Financial return (sales of recyclables, energy production, collections, etc.) and financial income C16 Sharing economy, circular economy models C17 Collaboration among stakeholders C18 Citizen’s participation and green behavior C19 People’s awareness and training C20 Leaders interest in environmental issues C21 Extent of stakeholders’ involvement in decision-making process C22 Stakeholder groups C23 Stakeholders hierarchy level C24 Relationship among stakeholders C25 Government Local authorities C26 Number of formal sector C27 Number of Informal sector C28 national and local government synergy C29 Joint awareness programs C30 Service user willingness to pay. C31 Stakeholders’ willingness participation in solutions. C32 Priorities for solid waste. C33 Solid waste management interesting C34 Coordination and cooperation C35 Central government support

References

Zurbrügg et al., 2012; Vučijak et al 2016; Arıkan et al, 2017; Esmaeilian et al., 2018

Esmaeilian et al., 2018, Fernando,2019; Soltani et al., 2015; Ikhlayel, 2018; Guerrero et al., 2013

P3

P4

Sustainable internal processes

Learning and growth

C36 C37 C38 C39 C40 C41 C42 C43 C44 C45 C46 C47 C48 C49 C50 C51 C52 C53 C54 C55 C56 C57 C58 C59 C60 C61 C62 C63 C64 C65 C66 C67 C68 C69 C70

Natural resources consumption level Energy consumption efficiency Effectiveness and limitations on waste generation Environmental control systemization Raw materials recovery rate Waste recovery rate Recycling rate Energy recovery rate Composting, recycling, or incineration (rate and separation at source) Promoting GHG emissions reduction through bio-waste diversion Environmental legislation compliance Emissions limitation Environmental feasibility Toxic waste materials removal Methane reduction Portion of hazardous waste treated Reduction of environmental impacts Health impacts reduction Waste prevention and minimization Waste diversion Waste generation and classification Local skills for design and construction Local skills for operation and maintenance External knowledge sharing and exchange level Performance level considering expected goals Changing conditions changing flexibility (adaptability) Local based solutions Municipal waste administrators Knowledge Local technical skills availability Space availability for the accommodation of possible new equipment Access to disposal sound facilities Information sharing and open data Waste treatment information security and citizen privacy Intelligent and connected devices Knowledge acquisition and communication technologies

Zurbrügg et al., 2012; Guerrero et al., 2013; Vučijak et al 2016; Ikhlayel, 2018

Pires et al., 2011; Zurbrügg et al., 2012; Ikhlayel, 2018

Zurbrügg et al., 2012; Guerrero et al., 2013, Chung and Lo, 2008; Ikhlayel, 2018; Esmaeilian et al., 2018

Appendix B. FDM round 1 screening out for criteria Initial criteria C1 0.017 C2 (0.054) C3 0.262 C4 (0.231) C5 0.625 C6 (0.000) C7 (0.086) C8 (0.022) C9 0.000 C10 0.037 C11 0.000 C12 0.000 C13 0.000 C14 (0.030) C15 (0.030) C16 0.031 C17 0.285 C18 (0.022) C19 (0.446) C20 (0.075) C21 (0.358) C22 (0.388) C23 (0.253) C24 0.262 C25 (0.379) C26 0.000 C27 (0.054) C28 0.337 C29 0.007 C30 (0.050) C31 0.021 C32 0.625 C33 0.317 C34 (0.311) C35 (0.040) C36 (0.319) C37 (0.007) C38 (0.379) C39 (0.268) C40 0.273 C41 (0.002) C42 0.034 C43 0.005 C44 (0.013) C45 0.000 C46 (0.017) C47 (0.328) C48 0.000 C49 0.002 C50 (0.054) C51 (0.353)

0.858 0.929 0.988 0.731 1.000 0.875 0.961 0.897 0.500 0.838 0.500 0.500 0.500 0.905 0.905 0.844 0.965 0.897 0.946 0.950 0.858 0.888 0.753 0.988 0.879 0.500 0.929 0.913 0.868 0.925 0.854 1.000 0.933 0.811 0.915 0.819 0.882 0.879 0.768 0.977 0.877 0.841 0.870 0.888 0.500 0.892 0.828 0.500 0.873 0.929 0.853

0.655 0.703 0.825 0.487 0.917 0.667 0.724 0.681 0.333 0.642 0.333 0.333 0.333 0.687 0.687 0.646 0.810 0.681 0.630 0.717 0.572 0.592 0.502 0.825 0.586 0.333 0.703 0.775 0.662 0.700 0.653 0.917 0.789 0.541 0.693 0.546 0.672 0.586 0.512 0.818 0.668 0.644 0.663 0.675 0.333 0.678 0.552 0.333 0.666 0.703 0.568

Decision Accepted Accepted Accepted Unaccepted Accepted Accepted Accepted Accepted Unaccepted Unaccepted Unaccepted Unaccepted Unaccepted Accepted Accepted Accepted Accepted Accepted Unaccepted Accepted Unaccepted Unaccepted Unaccepted Accepted Unaccepted Unaccepted Accepted Accepted Accepted Accepted Accepted Accepted Accepted Unaccepted Accepted Unaccepted Accepted Unaccepted Unaccepted Accepted Accepted Unaccepted Accepted Accepted Unaccepted Accepted Unaccepted Unaccepted Accepted Accepted Unaccepted

C52 C53 C54 C55 C56 C57 C58 C59 C60 C61 C62 C63 C64 C65 C66 C67 C68 C69 C70 Threshold

(0.021) (0.024) 0.285 (0.301) (0.040) 0.262 (0.004) 0.337 (0.379) (0.004) (0.326) 0.037 (0.064) 0.262 (0.245) 0.625 (0.000) (0.086) (0.046)

0.896 0.899 0.965 0.801 0.915 0.988 0.879 0.913 0.879 0.879 0.826 0.838 0.939 0.988 0.745 1.000 0.875 0.961 0.921

0.680 0.683 0.810 0.534 0.693 0.825 0.669 0.775 0.586 0.669 0.551 0.642 0.710 0.825 0.497 0.917 0.667 0.724 0.697 0.644

Accepted Accepted Accepted Unaccepted Accepted Accepted Accepted Accepted Unaccepted Accepted Unaccepted Unaccepted Accepted Accepted Unaccepted Accepted Accepted Accepted Accepted

Appendix C. List of FDM - round 1 criteria result Perspectives P1 Financial inducement

P2

Stakeholder involvement

P3

Sustainable internal processes

Criteria C1 Cost efficiency C2 Cost for waste recovery process C3 Operating costs C5 Land demand C6 Raising fund and donation C7 Management expenditures C8 Equipment (distance, fuel consumption, time, etc.) and personnel (workers) payment C14 Recovery, demand, or material flow (valorization) C15 Financial return (sales of recyclables, energy production, collections, etc.) and financial income C16 Sharing economy, circular economy models C17 Collaboration among stakeholders C18 Citizen’s participation and green behavior C20 Leaders interest in environmental issues C24 Relationship among stakeholders C27 Number of Informal sector C28 national and local government synergy C29 Joint awareness programs C30 Service user willingness to pay. C31 Stakeholders’ willingness participation in solutions. C32 Priorities for solid waste. C33 Solid waste management interesting C35 Central government support C37 Energy consumption efficiency C40 Raw materials recovery rate C41 Waste recovery rate C43 Energy recovery rate C44 Composting, recycling, or incineration (rate and separation at source) C46 Environmental legislation compliance C49 Toxic waste materials removal C50 Methane reduction C52 Reduction of environmental impacts C53 Health impacts reduction C54 Waste prevention and minimization

P4

Learning and growth

C56 C57 C58 C59 C61 C64 C65 C67 C68 C69 C70

Waste generation and classification Local skills for design and construction Local skills for operation and maintenance External knowledge sharing and exchange level Flexibility/adaptability for environment changing Local technical skills availability space availability for the accommodation of possible new equipment Information sharing and open data Waste treatment information security and citizen privacy Intelligent and connected devices Knowledge acquisition and communication technologies

Appendix D. FDM round 2 screening out for criteria Initial barriers C1 C2 C3 C5 C6 C7 C8 C14 C15 C16 C17 C18 C20 C24 C27 C28 C29 C30 C31 C32 C33 C35 C37 C40 C41 C43 C44 C46 C49 C50 C52 C53 C54 C56 C57 C58 C59 C61 C64 C65 C67 C68 C69 C70 Threshold

(0.054) 0.337 0.007 (0.050) 0.021 0.625 0.317 (0.054) (0.040) (0.044) (0.007) (0.379) (0.268) 0.273 (0.002) 0.034 0.005 (0.013) 0.000 (0.017) (0.328) 0.000 0.002 (0.054) (0.353) (0.021) (0.024) 0.285 (0.301) (0.040) 0.262 (0.004) 0.337 (0.379) (0.004) (0.326) 0.037 (0.064) 0.262 (0.245) 0.625 (0.000) (0.086) (0.046)

0.929 0.913 0.868 0.925 0.854 1.000 0.933 0.929 0.915 0.919 0.882 0.879 0.768 0.977 0.877 0.841 0.870 0.888 0.500 0.892 0.828 0.500 0.873 0.929 0.853 0.896 0.899 0.965 0.801 0.915 0.988 0.879 0.913 0.879 0.879 0.826 0.838 0.939 0.988 0.745 1.000 0.875 0.961 0.921

0.703 0.775 0.662 0.700 0.653 0.917 0.789 0.703 0.693 0.696 0.672 0.586 0.512 0.818 0.668 0.644 0.663 0.675 0.333 0.678 0.552 0.333 0.666 0.703 0.568 0.680 0.683 0.810 0.534 0.693 0.825 0.669 0.775 0.586 0.669 0.551 0.642 0.710 0.825 0.497 0.917 0.667 0.724 0.697 0.671

Decision Accepted Accepted Unaccepted Accepted Unaccepted Accepted Accepted Accepted Accepted Accepted Accepted Unaccepted Unaccepted Accepted Unaccepted Unaccepted Unaccepted Accepted Unaccepted Accepted Unaccepted Unaccepted Unaccepted Accepted Unaccepted Accepted Accepted Accepted Unaccepted Accepted Accepted Unaccepted Accepted Unaccepted Unaccepted Unaccepted Unaccepted Accepted Accepted Unaccepted Accepted Unaccepted Accepted Accepted

Appendix E. List of FDM - round 2 criteria result Perspectives P1 Financial inducement

P2

Stakeholder involvement

P3

Sustainable internal processes

P4

Learning and growth

Initial criteria C1 C2 C5 C7 C8 C14 C15 C16 C17 C24 C30 C32 C40 C43 C44 C46 C50 C52 C54 C61 C64 C67 C69 C70

Renamed C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24

Cost efficiency Cost for waste recovery process Land demand Management expenditures Equipment (distance, fuel consumption, time, etc.) and personnel (workers) payment Recovery, demand, or material flow (valorization) Financial return (sales of recyclables, energy production, collections, etc.) and financial income Sharing economy, circular economy models Collaboration among stakeholders Relationship among stakeholders Service user willingness to pay. Priorities for solid waste. Raw materials recovery rate Energy recovery rate Composting, recycling, or incineration (rate and separation at source) Environmental legislation compliance Methane reduction Reduction of environmental impacts Waste prevention and minimization Flexibility/adaptability for environment changing Local technical skills availability Information sharing and open data Intelligent and connected devices Knowledge acquisition and communication technologies

Table 1. Transformation table of linguistic terms. Linguistic terms Corresponding (performance/importance) triangular fuzzy numbers Extreme (0.75, 1.0, 1.0) Demonstrated (0.5, 0.75, 1.0) Strong (0.25, 0.5, 0.75) Moderate (0, 0.25, 0.5) Equal (0, 0, 0.25) Triangular fuzzy membership functions for performance/importance Table 2. TFNs linguistic scale Scale Linguistic variable VL Very low influence L Low influence M Moderate influence H High influence VH Very high influence

Corresponding triangular fuzzy number (TFNs) (0.0, 0.1, 0.3) (0.1, 0.3, 0.5) (0.3, 0.5, 0.7) (0.5, 0.7, 0.9) (0.7, 0.9, 1.0)

Table 3. Contextual relationships matrix of criteria C24 C23 C22 C21 C20 C19 C18 C17 C16 C15 C14 C13 C12 C11 C10 C9 C8 C7 C6 C5 C4 C3 C2 C1

C1 X X X V X V V V V O O O X X X X V V V V A A A -

C2 X X X X V V V V V O O O X X X X V V V A A A -

C3 A A A A A O V O V O A O A A A X V A V A A -

C4 X X X X X V V V V O O O X X X X V V V A -

C5 A V A A A A V V V O A O A A A A O O O -

C6 V X X X X A V V V V O O X X X X V A -

C7 V X X X V V V V V O V O X X X X V -

C8 X X X X X V V V V O V V X X X X -

C9 X X X X X V V V V O O O X X X -

C10 X X A X A V V V V O O O X X -

C11 V X A X X V V V V V V V V -

C12 X X X V X V V V V O O O -

C13 X X X X X V V V V O O -

C14 X X X X V V V V V V -

C15 V A A V A A V V V -

C16 X X X X X V V V -

C17 X X X X X V V -

C18 X X X X X V -

C19 V X X V X -

C20 X X X X -

C21 X X X -

C22 X X -

C23 X -

C24 -

Table 4. Reachability matrix of criteria C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24

C1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 1

C2 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1

C3 0 0 1 1 1 0 1 0 1 1 1 1 0 1 0 0 0 0 0 1 1 1 1 1

C4 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1

C5 1 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 1

C6 1 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0

C7 1 1 0 1 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0

C8 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1

C9 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1

C10 1 1 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1

C11 1 1 0 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0

C12 1 1 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 1

C13 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 1

C14 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 1

C15 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 1 0

C16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1

C17 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1

C18 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1

C19 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0

C20 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1

C21 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

C22 1 1 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1

C23 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1

C24 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Table 5. Intersection set of criteria C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24

C1 1 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 1 0 1

C2 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1

C3 0 0 1 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0 0 1 1 0 1 1

C4 0 0 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1

C5 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0

C6 1 1 1 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0

C7 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0

C8 1 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1

C9 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1

C10 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 1

C11 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0

C12 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1

C13 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 1

C14 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1

C15 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 0

C16 0 0 1 0 1 1 1 0 1 1 0 1 0 0 1 1 0 0 0 1 1 1 1 1

C17 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1

C18 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 1 0 1 1

C19 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 1 1 1 1 1 0 1 1 0

C20 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1

C21 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1

C22 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 0 1 1

C23 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 1 1 0 1 1 1 1 0

C24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1

Amount 7 7 7 7 7 8 8 8 12 12 10 12 4 4 4 5 5 5 5 13 17 13 17 17

Level 3 3 3 3 3 4 4 4 6 6 5 6 1 1 1 2 2 2 2 7 8 7 8 8

Table 6. ISWM hierarchical framework Perspectives

P1

A1

Financial investment

A2

Economic benefit

A3

Sustainable stakeholder cooperation

A4

Eco-efficiency

A5

Environmental performance

A6

Innovation capacity

Financial inducement

P2

Stakeholder involvement

P3

Sustainable internal processes

P4

Aspects

Learning and growth

Criteria C1 Cost efficiency C2 Cost for waste recovery process C3 Land demand C4 Management expenditures C5 Equipment (distance, fuel consumption, time, etc.) and personnel (workers) payment C6 Recovery, demand, or material flow (valorization) C7 Financial return (sales of recyclables, energy production, collections, etc.) and financial income C8 Sharing economy, circular economy models C9 Collaboration among stakeholders C10 Relationship among stakeholders C11 Service user willingness to pay. C12 Priorities for solid waste. C13 Raw materials recovery rate C14 Energy recovery rate C15 Composting, recycling, or incineration (rate and separation at source) C16 Environmental legislation compliance C17 Methane reduction C18 Reduction of environmental impacts C19 Waste prevention and minimization C20 Flexibility/adaptability for environment changing C21 Local technical skills availability C22 Information sharing and open data C23 Intelligent and connected devices C24 Knowledge acquisition and communication technologies

Table 7. TFNs, fuzzy direct relation matrix and defuzzification for aspects – Respondent 1

A1 A2 A3 A4 A5 A6

[1.000 [0.700 [0.700 [0.300 [0.100 [0.700 ̃ [1.000 [0.667 [0.667 [0.222 [0.000 [0.667

A1 1.000 0.900 0.900 0.500 0.300 0.900 ̃ 0.778 0.667 0.667 0.222 0.000 0.667

A1 A2 A3 A4 A5 A6

1.000 0.667 0.667 0.222 0.000 0.667

0.714 0.625 0.625 0.222 0.000 0.625

A1 A2 A3 A4 A5 A6

0.714 0.639 0.639 0.222 0.000 0.639

A1 A2 A3 A4 A5 A6

1.000] 1.000] 1.000] 0.700] 0.500] 1.000] ̃ 0.556] 0.556] 0.556] 0.222] 0.000] 0.556]

[0.300 [1.000 [0.500 [0.300 [0.300 [0.300 ̃ [0.000 [1.000 [0.286 [0.000 [0.000 [0.000

A2 0.500 1.000 0.700 0.500 0.500 0.500 ̃ 0.000 0.714 0.286 0.000 0.000 0.000

0.000 1.000 0.286 0.000 0.000 0.000

0.000 0.600 0.286 0.000 0.000 0.000

0.700] 1.000] 0.900] 0.700] 0.700] 0.700] ̃ 0.000] 0.429] 0.286] 0.000] 0.000] 0.000]

0.000 0.600 0.286 0.000 0.000 0.000

[0.300 [0.500 [1.000 [0.500 [0.300 [0.700 ̃ [0.000 [0.286 [1.000 [0.286 [0.000 [0.571

A3 0.500 0.700 1.000 0.700 0.500 0.900 ̃ 0.000 0.286 0.714 0.286 0.000 0.571

0.000 0.286 1.000 0.286 0.000 0.571

0.000 0.286 0.600 0.286 0.000 0.500

0.700] 0.900] 1.000] 0.900] 0.700] 1.000] ̃ 0.000] 0.286] 0.429] 0.286] 0.000] 0.429]

0.000 0.286 0.600 0.286 0.000 0.533

[0.300 [0.300 [0.700 [1.000 [0.100 [0.700 ̃ [0.222 [0.222 [0.667 [1.000 [0.000 [0.667

A4 0.500 0.500 0.900 1.000 0.300 0.900 ̃ 0.222 0.222 0.667 0.778 0.000 0.667

0.222 0.222 0.667 1.000 0.000 0.667

0.222 0.222 0.625 0.714 0.000 0.625

0.700] 0.700] 1.000] 1.000] 0.500] 1.000] ̃ 0.222] 0.222] 0.556] 0.556] 0.000] 0.556]

0.222 0.222 0.639 0.714 0.000 0.639

[0.700 [0.300 [0.700 [0.300 [1.000 [0.700 ̃ [0.571 [0.000 [0.571 [0.000 [1.000 [0.571

A5 0.900 0.500 0.900 0.500 1.000 0.900 ̃ 0.571 0.000 0.571 0.000 0.714 0.571

0.571 0.000 0.571 0.000 1.000 0.571

0.500 0.000 0.500 0.000 0.600 0.500

0.533 0.000 0.533 0.000 0.600 0.533

Table 8. Initial direction matrix for aspects A1

A2

A3

A4

A5

A6

1.000] 0.700] 1.000] 0.700] 1.000] 1.000] ̃ 0.429] 0.000] 0.429] 0.000] 0.429] 0.429]

[0.700 [0.100 [0.300 [0.100 [0.300 [1.000 ̃ [0.667 [0.000 [0.222 [0.000 [0.222 [1.000

A6 0.900 0.300 0.500 0.300 0.500 1.000 ̃ 0.667 0.000 0.222 0.000 0.222 0.778

0.667 0.000 0.222 0.000 0.222 1.000

0.625 0.000 0.222 0.000 0.222 0.714

0.639 0.000 0.222 0.000 0.222 0.714

1.000] 0.500] 0.700] 0.500] 0.700] 1.000] ̃ 0.556] 0.000] 0.222] 0.000] 0.222] 0.556]

A1 A2 A3 A4 A5 A6

0.721 0.448 0.614 0.462 0.364 0.489

0.554 0.710 0.529 0.460 0.380 0.570

0.459 0.431 0.708 0.467 0.446 0.602

0.459 0.508 0.540 0.706 0.477 0.511

0.504 0.431 0.570 0.413 0.713 0.556

0.513 0.471 0.444 0.333 0.433 0.721

Table 9. Total interrelationship matrix and cause-and-effect interrelationship among aspects. A1 A2 A3 A4 A5 A6 A1 1.641 1.641 1.564 1.611 1.616 1.487 9.561 9.197 A2 1.456 1.584 1.455 1.523 1.488 1.378 8.884 9.519 A3 1.695 1.720 1.726 1.726 1.725 1.543 10.136 9.227 A4 1.383 1.425 1.387 1.504 1.402 1.260 8.360 9.529 A5 1.338 1.385 1.369 1.420 1.485 1.281 8.277 9.466 A6 1.684 1.763 1.725 1.746 1.750 1.656 10.324 8.605

+ 18.758 18.402 19.363 17.889 17.744 18.930

− 0.364 (0.635) 0.910 (1.169) (1.189) 1.719

Table 10. Initial direction matrix for criteria. C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24

C1 0.701 0.484 0.361 0.374 0.347 0.346 0.274 0.391 0.451 0.333 0.379 0.306 0.303 0.364 0.457 0.452 0.364 0.377 0.377 0.343 0.422 0.289 0.318 0.405

C2 0.572 0.755 0.326 0.216 0.306 0.233 0.323 0.337 0.487 0.487 0.450 0.434 0.399 0.418 0.399 0.514 0.450 0.487 0.180 0.426 0.446 0.471 0.452 0.587

C3 0.344 0.403 0.767 0.511 0.314 0.296 0.333 0.292 0.312 0.203 0.241 0.152 0.151 0.203 0.187 0.400 0.292 0.386 0.257 0.487 0.579 0.326 0.309 0.429

C4 0.471 0.404 0.204 0.767 0.387 0.296 0.351 0.330 0.312 0.224 0.208 0.240 0.188 0.224 0.153 0.450 0.344 0.365 0.297 0.490 0.563 0.362 0.312 0.431

C5 0.352 0.317 0.299 0.584 0.803 0.336 0.388 0.405 0.389 0.371 0.372 0.283 0.247 0.320 0.265 0.422 0.267 0.299 0.339 0.423 0.478 0.424 0.386 0.525

C6 0.432 0.347 0.206 0.331 0.437 0.791 0.434 0.349 0.418 0.419 0.438 0.400 0.295 0.312 0.294 0.558 0.490 0.456 0.349 0.557 0.540 0.486 0.522 0.535

C7 0.385 0.548 0.361 0.362 0.356 0.453 0.773 0.459 0.458 0.389 0.401 0.389 0.320 0.372 0.230 0.483 0.461 0.430 0.321 0.414 0.510 0.309 0.444 0.544

C8 0.470 0.349 0.387 0.332 0.349 0.423 0.275 0.767 0.506 0.348 0.384 0.401 0.203 0.328 0.170 0.332 0.333 0.365 0.294 0.433 0.420 0.381 0.385 0.452

C9 0.381 0.384 0.347 0.332 0.367 0.456 0.437 0.333 0.767 0.386 0.461 0.442 0.384 0.405 0.297 0.387 0.387 0.347 0.280 0.540 0.400 0.456 0.437 0.454

C10 0.539 0.347 0.367 0.261 0.387 0.455 0.260 0.438 0.441 0.767 0.474 0.438 0.439 0.419 0.292 0.437 0.509 0.403 0.297 0.433 0.472 0.487 0.453 0.488

C11 0.422 0.314 0.314 0.314 0.351 0.333 0.368 0.385 0.439 0.389 0.763 0.435 0.332 0.366 0.330 0.369 0.383 0.421 0.350 0.470 0.473 0.350 0.367 0.471

C12 0.522 0.381 0.242 0.208 0.259 0.259 0.312 0.261 0.350 0.457 0.473 0.767 0.385 0.315 0.294 0.418 0.294 0.297 0.368 0.541 0.507 0.490 0.419 0.486

C13 0.464 0.464 0.409 0.427 0.336 0.373 0.427 0.336 0.445 0.445 0.427 0.445 1.000 0.355 0.245 0.391 0.391 0.373 0.245 0.445 0.318 0.373 0.409 0.391

C14 0.507 0.459 0.386 0.512 0.418 0.386 0.464 0.298 0.493 0.366 0.389 0.386 0.391 0.743 0.386 0.386 0.389 0.316 0.264 0.439 0.491 0.336 0.473 0.382

C15 0.418 0.334 0.371 0.475 0.421 0.316 0.409 0.245 0.509 0.334 0.373 0.371 0.352 0.439 0.743 0.457 0.336 0.282 0.243 0.503 0.421 0.282 0.418 0.400

C16 0.503 0.435 0.455 0.417 0.454 0.494 0.494 0.330 0.440 0.458 0.530 0.526 0.441 0.577 0.576 0.767 0.441 0.368 0.368 0.487 0.502 0.364 0.452 0.453

C17 0.408 0.426 0.428 0.420 0.528 0.485 0.340 0.352 0.478 0.521 0.484 0.432 0.376 0.395 0.392 0.360 0.779 0.545 0.317 0.460 0.405 0.232 0.371 0.421

C18 0.448 0.403 0.368 0.365 0.421 0.422 0.297 0.347 0.509 0.424 0.495 0.530 0.422 0.440 0.406 0.418 0.438 0.767 0.382 0.488 0.490 0.439 0.455 0.543

C19 0.388 0.361 0.377 0.329 0.356 0.371 0.342 0.424 0.443 0.390 0.390 0.357 0.377 0.428 0.378 0.304 0.395 0.445 0.761 0.428 0.433 0.346 0.446 0.410

C20 0.497 0.430 0.396 0.446 0.430 0.430 0.252 0.337 0.442 0.341 0.325 0.375 0.380 0.325 0.273 0.431 0.377 0.341 0.393 0.755 0.376 0.396 0.452 0.516

C21 0.431 0.321 0.252 0.341 0.465 0.466 0.393 0.412 0.393 0.483 0.324 0.360 0.251 0.305 0.305 0.375 0.430 0.377 0.377 0.447 0.753 0.398 0.399 0.483

C22 0.430 0.382 0.385 0.328 0.347 0.460 0.437 0.257 0.470 0.454 0.369 0.316 0.260 0.278 0.314 0.349 0.333 0.316 0.280 0.435 0.438 0.765 0.407 0.542

C23 0.500 0.417 0.469 0.344 0.419 0.422 0.350 0.366 0.475 0.455 0.406 0.424 0.440 0.441 0.457 0.478 0.442 0.406 0.460 0.330 0.348 0.387 0.767 0.491

C24 0.473 0.459 0.409 0.353 0.249 0.464 0.322 0.426 0.481 0.450 0.340 0.376 0.286 0.413 0.413 0.465 0.359 0.359 0.345 0.356 0.551 0.528 0.416 0.777

C22 0.241 0.215 0.196 0.198 0.204 0.221 0.203 0.185 0.242 0.222 0.213 0.203 0.179 0.192 0.179 0.222 0.207 0.202 0.173 0.242 0.247 0.246 0.224 0.262

C23 0.273 0.241 0.224 0.221 0.232 0.239 0.216 0.214 0.267 0.245 0.240 0.235 0.215 0.228 0.211 0.257 0.239 0.231 0.208 0.258 0.264 0.234 0.279 0.284

C24 0.261 0.236 0.210 0.214 0.208 0.235 0.206 0.212 0.258 0.236 0.225 0.222 0.193 0.217 0.200 0.247 0.223 0.219 0.191 0.251 0.273 0.239 0.239 0.299

Table 11. Interrelationship matrix of criteria C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24

C1 0.262 0.221 0.191 0.200 0.201 0.207 0.186 0.194 0.237 0.208 0.211 0.199 0.180 0.197 0.190 0.228 0.207 0.204 0.179 0.230 0.242 0.200 0.213 0.246

C2 0.273 0.265 0.206 0.204 0.216 0.216 0.208 0.206 0.262 0.242 0.238 0.231 0.207 0.221 0.201 0.254 0.234 0.233 0.178 0.260 0.266 0.236 0.245 0.286

C3 0.206 0.193 0.208 0.193 0.178 0.181 0.172 0.166 0.200 0.174 0.177 0.164 0.147 0.162 0.147 0.201 0.179 0.185 0.151 0.219 0.232 0.183 0.189 0.223

C4 0.223 0.198 0.161 0.219 0.189 0.186 0.177 0.174 0.205 0.181 0.179 0.177 0.155 0.169 0.148 0.210 0.188 0.187 0.159 0.225 0.235 0.190 0.195 0.229

C5 0.232 0.207 0.186 0.220 0.243 0.207 0.197 0.196 0.232 0.212 0.211 0.198 0.175 0.194 0.172 0.226 0.198 0.198 0.177 0.238 0.248 0.213 0.220 0.258

C6 0.266 0.234 0.199 0.219 0.233 0.272 0.223 0.213 0.261 0.241 0.242 0.233 0.201 0.216 0.196 0.264 0.243 0.235 0.198 0.277 0.281 0.243 0.257 0.287

C7 0.256 0.247 0.209 0.217 0.221 0.237 0.248 0.218 0.259 0.233 0.234 0.227 0.199 0.216 0.185 0.252 0.235 0.229 0.191 0.259 0.273 0.222 0.245 0.282

C8 0.238 0.206 0.190 0.193 0.198 0.211 0.183 0.225 0.238 0.206 0.209 0.205 0.168 0.191 0.161 0.214 0.201 0.200 0.170 0.235 0.238 0.206 0.216 0.247

C9 0.248 0.226 0.201 0.208 0.216 0.230 0.213 0.200 0.279 0.226 0.232 0.225 0.200 0.213 0.186 0.236 0.222 0.214 0.182 0.263 0.255 0.229 0.237 0.266

C10 0.272 0.231 0.211 0.210 0.226 0.239 0.204 0.218 0.260 0.269 0.242 0.233 0.212 0.222 0.193 0.249 0.241 0.228 0.190 0.263 0.271 0.240 0.248 0.279

C11 0.244 0.212 0.192 0.200 0.207 0.212 0.200 0.199 0.242 0.219 0.252 0.217 0.188 0.203 0.183 0.226 0.214 0.214 0.182 0.248 0.253 0.212 0.223 0.259

C12 0.249 0.214 0.182 0.186 0.195 0.201 0.191 0.184 0.230 0.222 0.222 0.243 0.191 0.195 0.177 0.227 0.202 0.199 0.181 0.250 0.252 0.221 0.224 0.256

C13 0.257 0.235 0.208 0.218 0.214 0.223 0.213 0.202 0.252 0.233 0.230 0.226 0.257 0.209 0.182 0.238 0.223 0.218 0.179 0.256 0.248 0.222 0.236 0.262

C14 0.263 0.236 0.208 0.228 0.224 0.227 0.218 0.200 0.258 0.227 0.228 0.222 0.203 0.246 0.196 0.240 0.225 0.214 0.183 0.257 0.267 0.220 0.244 0.263

C15 0.242 0.213 0.196 0.214 0.212 0.209 0.202 0.185 0.247 0.212 0.215 0.209 0.189 0.208 0.218 0.233 0.208 0.199 0.171 0.250 0.247 0.204 0.226 0.251

C16 0.291 0.259 0.237 0.243 0.251 0.262 0.244 0.226 0.282 0.261 0.267 0.260 0.229 0.255 0.235 0.300 0.255 0.243 0.213 0.291 0.297 0.248 0.268 0.300

C17 0.261 0.239 0.217 0.226 0.240 0.242 0.212 0.211 0.264 0.248 0.244 0.233 0.207 0.221 0.202 0.244 0.266 0.242 0.193 0.267 0.267 0.217 0.241 0.274

C18 0.273 0.244 0.218 0.227 0.237 0.244 0.215 0.217 0.275 0.247 0.252 0.249 0.218 0.232 0.210 0.256 0.242 0.268 0.205 0.277 0.282 0.243 0.256 0.294

C19 0.244 0.219 0.201 0.204 0.211 0.218 0.200 0.205 0.246 0.222 0.222 0.213 0.195 0.211 0.190 0.224 0.218 0.219 0.221 0.248 0.253 0.214 0.234 0.257

C20 0.255 0.226 0.203 0.216 0.218 0.224 0.193 0.198 0.246 0.218 0.216 0.215 0.196 0.202 0.181 0.236 0.217 0.210 0.189 0.278 0.249 0.220 0.235 0.268

C21 0.246 0.213 0.187 0.203 0.219 0.225 0.203 0.202 0.239 0.229 0.213 0.211 0.181 0.198 0.181 0.228 0.219 0.211 0.186 0.247 0.279 0.217 0.227 0.261

Table 12. Cause-and-effect group among criteria. + C1 6.075 5.034 11.109 C2 5.432 5.587 11.019 C3 4.839 4.430 9.269 C4 5.081 4.561 9.642 C5 5.192 5.058 10.250 C6 5.368 5.734 11.101 C7 4.927 5.594 10.520 C8 4.851 4.950 9.800 C9 5.982 5.407 11.390 C10 5.435 5.654 11.089 C11 5.414 5.200 10.614 C12 5.250 5.095 10.345 C13 4.687 5.439 10.125 C14 5.020 5.498 10.518 C15 4.523 5.159 9.682 C16 5.712 6.218 11.929 C17 5.306 5.677 10.983 C18 5.202 5.880 11.082 C19 4.449 5.292 9.741 C20 6.089 5.308 11.398 C21 6.218 5.228 11.446 C22 5.317 5.115 10.432 C23 5.622 5.755 11.377 C24 6.393 5.515 11.907

− 1.041 (0.155) 0.409 0.520 0.134 (0.366) (0.667) (0.099) 0.575 (0.219) 0.214 0.155 (0.752) (0.478) (0.636) (0.506) (0.370) (0.678) (0.843) 0.781 0.991 0.202 (0.133) 0.878

Levels

ISWM hierarchical framework

1

Eco efficiency

2

Environmental performance

Raw materials recovery rate (C13)

Environmental legislation compliance (C16)

Methane reduction (C17)

Cost efficiency (C1)

3

Energy recovery rate (C14)

Reduction of environmental impacts (C18)

Cost for waste recovery process (C2)

Waste prevention and minimization (C19)

Land demand (C3)

Financial investment

Management expenditures (C4)

4

Composting, recycling, or incineration (rate and separation at source) (C15)

Economic benefits

Recovery, demand, or material flow (valorization) (C6)

Equipment (distance, fuel consumption, time, etc.) and personnel (workers) payment (C5)

Financial return (sales of recyclables, energy production, collections, etc.) and financial income (C7)

Sharing economy, circular economy models (C8)

Service user willingness to pay (C11)

5 Sustainable stakeholder cooperation

Collaboration among stakeholdes (C9)

6

Relationship among stakeholders (C10)

Changing conditions changing flexibility (adaptability) (C20)

7

Priorities for solid waste (C12)

Information sharing and open data (C22)

Innovation capacity

8

Local technical skills availability (C21)

Figure 1. The ISWM hierarchical framework.

Intelligent and connected devices (C23)

Knowledge acquisition and communication technologies (C24)

A6 - Innovation capacity

A3 - sustainable takeholders' cooperation

A1 - Financial supports

A2 - Economic benefits

A5 - Environmental A4 - eco-efficiency performance

Figure 2. Causal interrelationship among aspects

Figure 3. Causal diagram for criteria

Weak Medium Strong

Highlights • • • • •

This study aims to explore ISWM using a sustainable balance scorecard Fuzzy Delphi method is to exclude the invalid attributes. ISM arranges attributes into an extensive ISWM hierarchical model Fuzzy DEMATEL presents the causal interrelationships The financial investment and stakeholder involvement capacity are the decisive attributes.

Authors’ Contributions •

Feng Ming Tsai is conceptualized the concept



Tat-Dat Bui is on 1st draft



Ming-Lang Tseng* is final checking on the manuscript



Kuo-Jui Wu is on 1st draft



Anthony SF Chiu is for English checking

Conflict of interests

Dear Editor

This manuscript is free of Conflict of interests

Regards

Authors