Analysing challenges to smart waste management for a sustainable circular economy in developing countries: a fuzzy DEMATEL study

PurposeWaste can be converted to a high-value asset if treated properly with smart solutions. The purpose of this research is to identify critical barriers hindering smart waste management (SWM) implementation in developing economies using comparative analysis and a mixed-method approach. The objective of this work is to provide exhaustive insight including the smart cities projects to discuss the deferring parameters toward IoT-enabled waste management systems.Design/methodology/approachTo accomplish the objective, the present study followed mixed-method approach consisting of two phases: In the first qualitative phase, barriers in the adoption of IoT (Internet of Things) for SWM were identified using extensive literature review and discussion with selected experts. In the second phase, the quantitative analysis using the Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) method was performed on the selected barriers. The fuzzy DEMATEL methodology helps in prioritizing the most significant causal barrier by separating them into the cause-effect group. The comparative analysis was used to understand two different perceptions. To provide more detailed insight on the problems faced while implementing SWM in developing economies.FindingsThe results disclose that “Lack of government strict regulatory policies,” “Lack of proper financial planning” and “Lack of benchmarking processes” are the most critical causal barriers toward IoT-enabled SWM implementation that are hindering the vision of efficient and effective waste management system. Also, “Difficulty in implementing innovative technologies” and “Absence of Dynamic Scheduling and Routing” fall under the potential causal category. The effect barriers include “Lack of awareness among the community,” “Lack of source segregation and recycling commitment” and “Lack of service provider” as concluded in results considering the comparative analysis. The results can aid the policy-makers and stakeholders to identify the significant barriers toward a sustainable circular economy and mitigate them when implementing IoT-enable waste practices. Also, it assists to proactively build programs, policies, campaigns and other measures to attain a zero-waste economy.Research limitations/implicationsThe research is focused on the context of India but it provides new details which can be helpful for other developing economies to relate. The research addresses the call for studies from public-sector and citizen’s perspectives to understand the acknowledgment of SWM systems and critical success factors using qualitative and exploratory method analysis.Practical implicationsThe practical implications of the study include strict regulatory policies and guidelines for SWM acceptance, proper financial administration and benchmarking waste-recycling practices (prominent causal barriers). The practical implication of the results includes assistance in smart city projects in handling barriers proactively. The “Lack of Benchmarking processes” provides a critical application to standardized recycling practices in developing economies to improve the quality of the recyclable material/product. The comparative analysis also provides in-depth reflection toward the causal barriers from both the perspective which can help the government and stakeholders to work in a unified manner and establish an efficient waste management system. The results also conclude the need for targeted training programs and workshops for field implementation of innovative technologies to overcome the causal barrier. Moreover, policy-makers should focus to improve source segregation and recycling practices and ensure dedicated communication campaigns like Swachh Bharat Abhiyan to change the behavioral functioning of the community regarding waste. Lastly, developing economies struggle with the adequacy of resources to establish SWM systems, hence the authors conclude that proper financial planning is required at the ground level for smart city projects to overcome the spillover effects.Social implicationsThe social implications of the study include a reduction in pollution and efficient handling of waste resulting in a healthier and cleaner environment using IoT technology. Also, the results assist decision-makers in developing economies like India to establish smart city projects initiatives effectively to improve the quality of life. It proposes to establish standardized recycling processes for the better quality of recyclables and help in attaining a sustainable circular economy.Originality/valueThe research is novel as it provides comprehensive and comparative information regarding the barriers deferring SWM including the field barriers. To our consideration, the present study serves the first to address the comparative analysis of barriers in IoT-enabled waste systems and establish the relationship from both the perspective in middle-lower income economies. The study also suggests that the effect barriers can be overcome automatically by mitigating the causal barriers in the long run.


Introduction
The vision of achieving sustainable development which provide improved quality of life for citizens and more efficient management of resources need revolutionary changes. The increasing population in countries have increased waste quantity which is the major concern. The unsuccessful and inefficient operations of waste management in cities have several reasons like collecting, disposing, routing vehicles and pollution that need to be monitored (Jacobsen et al., 2018). The technologies are envisioned to change the situation of urban development and provide aspiration towards efficient circular economy. Circular economy (CE) is referred as efficient use of natural resources. As the world realizes the necessity for sustainable utilization of resources the conventional model of linear economy (extract-make-dispose) has been transformed into circular economy (Zaman, 2015).
Circular economy aims to adopt practices that reduce the waste generation and establish a closed-loop ecosystem for efficient utilization and consumption of resources (Korhonen et al. 2018). Moreover, Sustainable circular economic (SCE) model supports both the changes of designing and consumption perception, to produce products that can be reused, remanufactured and recycled. The existing literature also depicts the influence of waste management in achieving economic, social and environmental sustainability in long term is crucial (Xu et al. 2018). Cole et al. (2014) discussed the concept of zero-waste (ZW) management which provide waste prevention, valuation of all resources originated from wastes, high recycling levels and certain behavioural changes. Moh (2017) emphasis the potential of source segregation and recycling practices to attain circular economy concept.
Hence the concept of circular economy and zero waste management goes side by side to create value from waste. The employment of smart technologies can help to attain the vision by involving infrastructure assistance and management decision-making. With IoT (Internet of thing) embedded system and increasing number of internet users in urban and rural areas, a transparent system with real time optimized waste collection can be achieved. The ongoing research in this domain mainly focuses on describing the technical assistance involved and their implementation, while it need to be examine through inter-disciplinary studies including engineering, urban sciences, ecological, economical and ethical domains as well. The present economy demands waste to be seen as resource. A 'resource' that can be recycled, reused and re-utilized in a manner where it leads maximum value recovery. Smart waste management define the waste handling with a transparent and technological methods. In smart cities, waste management is the inflow of waste value cycle in a flexible and efficient manner with real time monitoring. IoT allows the waste collection system to become connected and optimized while reducing costs and minimizing open dumping issues. An IoT based prototype works with sensors to measure the waste volume in containers or waste bins continuously, with facility to exchange information over the Internet. The data is collected and transmitted over the internet where intelligent and optimized algorithms are used to dynamically optimize the waste collection system. The results help in selecting better optimized route for waste collection and ensuring that waste is collected without affecting the environment and transported to proper segregation plants where disposal and recycling is done. The Table 1. Represent the table listing the technologies involved in smart waste management.

Technologies
Application in smart waste management IoT sensor including NFC sensors, RFID tags and GPS sensors IoT is responsible for data collection, sensing, storing and processing by connecting the physical or virtual device to the internet. IoT sensors can facilitate real-time monitoring of the smart trash bins which provide information of each bin status and geographical location.
Solar powered trash collectors using ultrasonic sensors Solar powered bins automatically compresses the trash into compact packet and ultrasonic sensors helps in bin tracking and optimization.
highly infectious waste (Himabindu et al. 2015). Also, when the world is fighting against COVID-19 pandemic, handling of waste is a serious issue that need to be improved. Electronic waste (all electronic gadgets) is estimated 50 million tonnes being discarded annually worldwide (IISD, 2019). Such huge quantity of waste need proper recycling while disposing. The solution to these issues were proposed via integration smart technologies such as IoT (Internet of Things) to waste management system. Table 2 (Appendix) shows the types, composition and sources of municipal solid waste (MSW).
Research regarding smart cities have already been done by many organization and been implemented as well. India launched smart city project in 2015 investing 98,000 crore including the adoption of smart waste management practices in selected cities, but still the improvement at ground level is low hence showing the need to examine the situation more deeply. Also, implementation of smart technologies at ground level in developing economies is still in the embryonic stage and deals with complex issues (Aljerf, 2018). The present research aims to analyse the developing economies such as India to adopt smart technologies and foresee the shortcomings. The study provide comprehensive insight about the barriers faced in the smart waste management implementation considering perception from real ground situation and to identify the interrelationship among the barriers. To achieve the aim Fuzzy DEMATEL method is considered as it facilitate to identify the cause-effect relationship among the barriers in a complex system. It is a visual structural model which helps to find the critical parameters in a system, divide them into cause and effect group and rank them for a strategic decision making. Therefore the present research was prepared to inform the decision-makers about the specific barriers that are hindering in smart waste management implementation. The research addressed the following two main objectives: • To identify the significant barriers to the smart waste management implementation for an efficient CE in India • To recognize how the key barriers are interacting and influencing the present stakeholders and how to make effective measures to improve the conditions for ecologically and economically sound waste management system in India.
The rest of the paper is arranged as follows: Section I presents systematic literature review and data collection procedure. Section II consist methodology and importance of Fuzzy DEMATEL analysis. Section 3 discussed the results and summary obtained from the analysis. Section 4 discuss results and its managerial implications. Section 5 concluded the paper with the future suggestions and scope.

Literature Review on cities those have adopted smart city concept
Researches have shown that smart waste management adoption has been an open area for identifying major barriers that hinder its adoption. The below section comprises list of studies that highlight the benefits of adopting smart city concept including smart waste management around the world. There is limited knowledge on smart city implication and its long term benefits on environment The smart city concept deal with human/decision smartness rather than technological smartness.
Implementation of correct decisions and policies are critical aspect in smart urban development Changing the view from technology centric to decision /policy centric Smart cities are able to achieve ecological sustainability solving urban waste issues The mentioned studies put forward the need to adopt smart technologies concept to provide a better and sustainable future and to achieve the vision of efficient waste management system. Foreseeing the best time to collect garbage as well as synchronizing the number of vehicles collecting from the containers are of significant value, real time information about the containers also optimize the process of collecting waste. Therefore the developing economies need to focus on deferring parameters that lead to inefficient implementation of smart technologies. An extensive literature review is done to identify the prominent barriers while implementing smart waste management. Lack of collaboration from supply chain channels also contribute in weak CE model adoption.
The need of integrating CE in supply chain of food waste must be established.
Research is needed to analyse the impact of IoT, Big Data Analytic and blockchains in establishing effective circular supply chain management especially in handling waste.
Effective recycling models should be adopted to extract maximum value from the waste   Table 5.   Masood and Ahmad 2020;

Lack of awareness
The above listed barriers were selected for further analysis with validation from the expert's in the study. The barriers were analysed using Fuzzy DEMATEL method to differentiate among cause-effect groups. The output of the method facilitates to identify the most causal barriers which can be mitigated with proper insight. To overcome the gap of comprehensive study the experts were included from different similar background to analyse the results from both the perception. The literature intend to make understand the managers and policy makers the importance of underline barriers influential and influenced impact.

Methodology
The fuzzy DEMATEL methodology is employed for this study to visualise the causal-effect relationship among the barriers. DEMATEL can also be used to reveal the overall degree of influence wielded by the respective factors Venkatesh et al. (2017). ISM and AHP can also be employed for similar study but it is not possible to determine the cause and effect relationship (Mangla et al. 2015 of human judgements and to deal with ambiguity many scholars use DEMATEL with fuzzy logic as fuzzy set theory supplies solution to human uncertainties in these complex system. Fuzzy set theory is helpful in the situation which is unclear, vague and probabilistic in nature, and very useful in the process of decision-making through linguistic expressions (Ehsani et al. 2016). The present research applied the following steps, to analyse the barriers to smart waste management.

Step 1: Construct a pair-wise comparison matrix
In this step the participant are asked to assess the impact of the barrier i on the barrier j on the scale of 0 to 4 (0= no influence, 1= very low influence, 2= low influence, 3= high influence and 4= very high influence). The information gathered is presented in the form a pair wise comparison matrix.
Step 2: Generate the fuzzy direct-relation matrix In order to identify the model of the relations among the n criteria, an n × n matrix is first generated. The influence of the element in each row exerted on the element in each column of this matrix can be represented a fuzzy number. The response from multiple experts is converted into direct-relation matrix z using arithmetic mean.
Where, z n1 is the value obtained by arithmetic mean and then converted into linguistic value using Table 1 The following table shows the fuzzy scale used in the model. Step 2: Normalize the fuzzy direct-relation matrix The normalized fuzzy direct-relation matrix can be obtained using the following formula (Source): Where l, m and u are the fuzzy values identified from fuzzy direct relation matrix Where, � is the normalized fuzzy value obtained for fuzzy direct relation matrix Step 3: Determine the fuzzy total-relation matrix To calculate the total-relation matrix the following formula is used: Where , k is a constant defined to infinity If each element of the fuzzy total-relation matrix is expressed as ̃i j = �l ij " , m ij " , u ij " � it can be calculated as follows: Hence, the normalized matrix inverse is first calculated, and then it is subtracted from the matrix I (identity matrix), and finally the normalised matrix is multiplied by the resulting matrix. Step Where l, m, u are corresponding fuzzy values in each cell and Calculating the upper and lower bounds of normalized values: (5) 3. Calculating total normalized crisp values: ……….. (6) 4. Combined crisp values: …………. (7) From eq. (a) to (d) are used to calculate crisp value to form total-relation matrix.

Step 5: Final output and create a causal relation diagram
The next step is to find out the sum of each row and each column of T (in step 4). The sum of rows (D) and columns (R) can be calculated as follows: Then, the values of D+R and D-R can be calculated by D and R, where D+R represent the degree of importance of factor i in the entire system and D-R represent net effects that factor i contributes to the system. The horizontal vector (D + R) represents the degree of importance between each factor plays in the entire system. In other words, (D + R) indicates both factor i's impact on the whole system and other system factors' impact on the factor. Whereas, the vertical vector (D-R) represents the degree of a factor's influence on system. In general, the positive value of D-R represents a causal variable, and the negative value of D-R represents an effect. in terms of degree of importance Case illustration applyintg DEMATEL is discuused in the next section.

Case illustration
The first phase include qualitative measure doing a systematic literature review and identifying the major barriers in smart waste management as well as interviewing the participants with open end questions about the knowledge of the topic. Before conducting the interviews the participants were provided with information sheet explaining the concepts of smart waste management and the related future opportunities in brief. A total 6 participants from different background participated in the study, participants belong to technology provider perspective i.e. companies that provide smart technology application in India and remaining working in the 'smart city' project initiated by government (Agra Smart City). To ensure the data efficacy and accuracy, the study include participants that had multiple years of experience in smart technologies and waste management system. The research participants shared their experience voluntarily and supported the research in active manner. All the included participants were at managerial level and presently working in the waste management sector. The participant was interviewed in March 2020-April 2020 either by an e-mail conversation or over the phone. Social business platform i.e. LinkedIn was used to reach the participants, each interview lasted for approx. 30-40 min for providing details insight about the study.

Data collection
The

DEMATEL application
The results are observed on the basis of D+R and D-R values calculated for both the perception input. The expert's input from the questionnaire are evaluated separately using the steps and equations descried in Section 3.. The calculative matrix for each step are placed in appendices for both the perception. For Government project perception Table I, II, III and IV include the matrix respectively. Similarly, for Technology provider perception Table V, VI, VII and VIII represent the matrix to calculate the final result. The below section discuss the results obtained from each perspective separately followed with summary to results and its managerial implications.

Results from the Government project's Perspective
The final cause-effect relation values from the total-relation matrix of the DEMATEL analysis (government perspective) are given in Table 5 cluster based planning also turn out to be a causal parameter as proper planning ensures the success of the whole project and its efficiency to achieve the goal.
Also, lack of government strict regulation has a significant effect on resistance of people to change. And lack of proper financial planning effect absence of dynamic routing and scheduling as apparently they can hinder new technological improvement in smart waste management system setup. Moreover, absence of regulatory policies can cause resistance from people to change their attitude about waste management. Lack of Government strict policies can also be the cause behind the limited integration of private service providers towards this issue. The results also reflected that lack of awareness among community is an effect barrier, and is influenced by lack of cluster-based planning and lack of government strict rules towards the waste monitoring. Clearly, the attitude and indulgence of people can be changed if the key causal barrier H9 (Lack of government regulatory strict policies) can be address effectively. To conclude the major causal barriers are lack of government strict regulating policies, lack of proper financial planning, lack of benchmarking processes, Difficulty in implementing innovative technologies and lack of cluster-based planning, and if the causal barriers are mitigated then the effects will also get improved according to the results. adoption is lack of government strict regulatory policies that is justified as if people take the waste management issue seriously as they do for other resources like electricity, the present scenario will improve in India. Fig. 2 depicts the D-R data that indicate lack of financial support and lack of benchmarking processes are the most influential barriers where absence of dynamic scheduling and routing, resistance of people to change and lack of government strict regulatory policies falls under the causal parameter. This suggest that from technology contributor perspective the main cause behind the slow adoption of smart technologies lies in both lack of financial support and lack in benchmarking processes. To employ smart waste management efficiently in India the financial support should be made available in equity manner. Moreover lack of benchmarking processes also turn out to be an important causal barriers indicating application of standardised methods while implementing smart technologies is an important parameter. With proper allocation of funds and standardised processes the smart waste management implementation can be done in an improved manner. The results also indicated that resistance of people towards new technologies and lack of strict government policies influence the implementation process, as if people will not become open to adopt new technologies there will be an resistance in implementation at ground level hence proper contribution within community level is important. The barriers that comes under the effect category are ' lack of cluster-based planning', 'lack of awareness among communities', 'difficulty in implementing technologies', 'lack of segregation and recycling at source' and lack of service provider. The conclusion can be made as with proper allocation of funds and standardised processes, the effects barriers such as private service providers and proper cluster-based planning can be addressed.

Discussion and Summary
The results obtained indicated similarities between technology contributor and government project perspective i.e. 'lack of government strict policies' is common and one of the significant adoption barrier. Both the stakeholders agreed that 'lack of proper financial planning' is an important causal barrier further lack of government strict regulatory policies and lack of benchmarking processes are also among the potential influential barriers that need to be mitigated to eliminate the later effects in the system. From technology contributor viewpoint 'Lack of awareness among people' is one of the important barrier in adopting smart waste management. People need to be get aware about the long term benefits, as without knowing they will not consider to accept it at ground level. The general attitude towards social issues like waste management need to be changed as the emerging technology of IoT is tech-social platform that can provide diverse benefits. The Government project participants gave more weightage towards 'Difficulty in Implementing Technology' and that is probably due to the fact that the participants are not expert in smart enabling technologies, so they sense more difficulty in technology dimensions. Moreover, hindrances from people as well as lack of exposure towards such smart platform is also responsible for present scenario. In India, the smart projects are proposed for various metropolitan cities but the implementation at ground level is still at challenging phase. While interviewing, participants revealed that while implementing smart waste management they face many issues such as inadequate resources, poor quality equipment's, improper planning and lack of people support. Hence the respondents gave major importance to 'Lack of source segregation and recycling commitment' as a hindering barrier towards the project. This can also be justified as to have an efficient system people need to participate actively into such processes and the acceptance has to be from both the sides. Both the stakeholders believe that regulatory policies is reasonable because waste management require strict measures and policies to be implemented so as to make active changes in the present system making it focussed for the community. The attitude of people towards waste problems also depends upon the policies formulated by government making it compulsory to adopt measures to reduce and recycle waste at individual level. Further cluster based planning is generally lacked in major technology projects as these projects fail to identify socio-economic barriers while implementation. In contrast technology contributor rated 'Absence of Dynamic scheduling and routing' and 'Resistance of people to change' into causal groups indicating that technicians are working to overcome the limitation of IoT applications and the present barriers in large project implementation is lack of dynamic algorithms to enhance real time monitoring. Both the participants has rated 'Lack of awareness among community' as an effect barrier indicating that effective government policies can make people aware about new technologies and change their traditional methods to handle waste. Moreover, 'lack of source segregation and recycling' and 'lack of service provider' are also rated under effect groups indicating that these barriers can be overcome if the potential causal barriers are mitigated. The finding about the 'Difficulty in implementing technology' barrier also require special attention because it was identified as a causal barrier by government participants and an effect barrier by technology participant suggesting that technological challenges are not a problem from their perspective rather dynamic scheduling and routing and resistance from people are a more prominent cause. The second conclusion was to handle lack of proper financial planning and benchmarking (prominent causal barriers). Guerrero et al. (2013) stated that waste services include high costs and expenditures as any other service in cities. Therefore proper administration of the provided funds are essential for an efficient sustainable waste management system. There is a huge amount proposed by the government in Smart City project, but they lack the proper distribution of funds in the project. From the government participant's interaction it was found that funds are misused and mismanaged in various aspects e.g. for the implementation of sensor for each area in Agra a private company was responsible however after the implementation the sensor does not work they were supposed to be but the private company did get the payment from the project via direct transfer from the government. Hence there was low quality after service and management by the firms due to the reason they get funds easily without crosschecking. Therefore proper distribution of funds should be done at each level of the project so that misusing of money is avoided as the resource in developing countries is limited and has to be used wisely. Whereas benchmarking is always considered as an important management tool to surpass the performance goals by learning from best practices and understanding the methods by which they are achieved. Solid waste benchmarking main purpose is to compare common elements in solid waste systems and to follow waste management from generation to disposal (NSWB, 2011). In 2010 the Ministry of Urban Development Government of India (MoUD) launched a Service Level Benchmarking (SLB) for managing solid waste management, storm water, wastewater and water supply (Service Level Benchmarking, 2010). The concept was proposed to help certain municipalities to predict the potential for economically efficient recycling and able to achieve better results. In Global scenario the recent National Sword Policy of China has declined the intake of contaminated recyclables. The policy includes a contamination limit of 0.5% on all other waste imports. Countries from high income, low income and middle income dump their recyclable waste to China recycling system with recent implication sword policy has created a tension that where this whole waste is going to get treated. It is estimated that by the year 2030, 111 million MT of plastic waste will be displaced due to this policy (Greene, 2018). The problem can be overcome if the knowledge about recycling standards and quality recycling is achieved in developing economies. One of the way to improve the recycling process is to standardized processes. Innovative and economically sustainable methods has to be adopted by countries to practice recycling. Countries like Germany, Australia, United States of America have started adopting innovative recycling systems to manage a quality recycled products (Luczak, 2020). Hence Benchmarking the recycling practices in developing countries like India can help to implement best practices for recycling the waste from the initial level. With new standards of recyclable waste acceptance the countries have to improve the quality of recycling as well as focusing on better practices.
The third preposition drawn from the results suggest that in order to overcome the difficulty in implementing technology and innovative methods. Based on ground details of India an expert teams should be incorporated with projects on site so that they can give technical solutions to ground problems. On proposed prepositions the research indicated that strong enforcement of regulation and technology incorporated policies regarding waste management systems can result in better situation (Leal Filho et al. 2016). In comparison, municipal solid waste management of Germany have incorporated regulatory measures for collecting packaging waste, waste paper and bio-waste separately and strict ban on landfilling of non-pre-treated waste has been implemented in cities. Hence the role of government regulatory policies and awareness programs can be learned by many similar countries those have mastered the waste management system (Ahluwalia and Patel, 2018). The present government policies in India lacks the integration of smart technologies platform, circular sustainability, benchmarked recycling practices and waste management, hence the research propose strong relationship among barriers that are hindering the future smart world as well as provide roadmap to formulate policies keeping the stated barriers in mind.

Research Implications
The critical causal barriers impose fundamental importance on the system as they effect it for long-term. The Indian government must take active measures to mitigate the barriers in implementing smart enabling technologies at ground level with maximum efficiency. The present research consist holistic insights of the barriers faced while implicating these project at ground level, hence policy-makers can decide accordingly the gaps that are present while the implementation process. Proper allocations of funds and strict regulatory measures including active participations of the community are the key enablers to enhance the quality of life with clean and sustainable environment. The vision of waste as an unwanted utility need to be change within the government system making it more sustainable and treating it as valuable resource. Many programs within India have managed to treat waste as an useful resource making the whole recycling process more efficient and valuable. In last decade India has made progress regarding waste management measures incorporating sustainable environment in its future vision. The Swachh Bharat Abhiyan is one of the largest program implemented all over India creating awareness about the importance of handling waste efficiency. The integration of IoT enabled infrastructure in waste management process can further provide an advanced digitalized and connected platform to monitor the system efficiently. The IoT and related big data applications can play a key role in improving the process of waste sustainability development in future. The sensor based technology of IoT technology can collect large masses of urban data that can serve as an input to Big Data applications. Furthermore decision-makers can extract useful knowledge from the IoT data to provide relevant services in order to optimize urban processes and efficiency of ecosystem (Bibri, 2018). Also the advantages of smart enabling technologies should also be incorporated in present education system to aware the generation about the scope of the future smart cities. The prominent barriers revealed in the present study require immediate action from the government as well as research aspect so as to mitigate the long term effects on the systems. The overall barriers including difficulty in implementing technologies, lack of source segregation and recycling and lack of cluster based planning also inform the policy framing management to actively mitigate potential causal barriers to improve the long term adoption. The Indian government may consider programs and projects offering certain financial assistance to participants to further encourage the use and research in smart enabling technologies for improving the performance of the waste handling system.

Conclusion
India lags the approach to overcome the field barriers knowing that they differ from the mentioned one's. These barriers have interdependencies on each other.
The research provide several contribution to the literature providing detailed insight on smart waste management project in India. Another contribution was the work has been prior in the field of comparative smart waste management studies considering the barriers from different perspective. Though smart enabling technologies bear the potential to improve the performance of the waste management system but still many developing countries struggle with barriers that hinder the implementation process. This research is well-timed as the present economy requires more digitalised solutions to handle the social utilities namely waste management, air quality monitoring etc. The research adopted a mixed methods approach to finalize the results firstly the barriers were selected through systematic literature review and then a quantitative approach was used to identify the cause-effect relationship among each other. The analysis was implemented considering responses from two different perspectives including government projects representatives and Technology contributor's in India. The research is focused within the context of India but it provides new details which can be helpful for other developing economies to relate. As the circular economy implementation becomes more necessary to provide better management of resources in long term, so well the barriers towards incorporating smart enabling technologies need to eliminate at initial level. The policy-makers can use the results for further programmes and policies consisting strict measure towards waste management system. Educational and awareness policies can initiate active participation from the community to accept the smart waste management implementation. Charge free smart dustbins can be implemented in rural areas to provide more engagement from the people i.e. community centric approach. Also, the research used the quantitative method to address the barrier and accordingly given priority. The barriers turn to be in two main categories namely cause and effect groups. The analysis believed that if causal barriers are eliminated by the management the effect barriers will get minimize accordingly. The present study can be referred by other developing countries to analyse the situation of waste management and focus on causal barriers identified in the study.
Moreover to overcome the limitations future work can focus towards collecting data from other relevant sources including surveys and various field stakeholders dealing with smart waste solutions to provide further results regarding the critical parameters. The research included representative from industry and government perspective further studies can be done including sectors who have implemented smart waste management and can provide real experience with various issues encountered after implementing smart waste management. The qualitative analysis can further be done using other qualitative methods like Delphi Method to provide the identification of barriers more exclusively.

Availability of Data and Materials
The data was collected from two different domains including expert's from Smart City Project and expert's from Technical domain including companies like IoTracX and others. The data provided below is used to complete the proposed methodology in the paper. The scale was defined in the questionnaire for the expert's.