Eco-Clean: A QR Code-Enabled Behavioural Framework for Sustainable Waste Disposal in the Campus

Eco-Clean: A QR Code-Enabled Behavioural Framework for Sustainable Waste Disposal in the Campus

Dr. J. Suganthi Vinodhini

Assistant professor, Electrical and Electronics Engineering, St. Peters Institute of Higher Education and Research

Abstract – The rapid growth of plastic consumption has intensified environmental degradation, particularly through the improper disposal of single-use plastic bottles. While technological innovations such as IoT-enabled smart bins and automated waste sorting systems exist, their high implementation costs limit widespread adoption, particularly in educational institutions and developing urban settings. This study proposes Eco-Clean, a low-cost, QR codeenabled behavioural monitoring framework designed to encourage responsible plastic bottle disposal through digital accountability and participatory verification. The system integrates QR codes affixed to designated waste bins, image-based disposal verification via cloud-hosted forms, and centralized data aggregation for behavioural analysis. A controlled four-week pilot implementation in an institutional setting was conducted to assess participation rates, disposal accuracy, behavioural shifts, and system feasibility. Statistical analysis indicates a measurable increase in responsible disposal behaviour following system deployment. The proposed framework demonstrates scalability, minimal infrastructure requirements, and potential integration into broader sustainability initiatives. The findings suggest that digital accountability mechanisms can significantly enhance environmental engagement at the community level.

Keywords: Smart Waste Management, QR Code Monitoring, Plastic Pollution, Sustainable Behaviour, Environmental Informatics, Digital Accountability

1. INTRODUCTION

   Plastic pollution has emerged as a global sustainability challenge due to the persistence of synthetic polymers in terrestrial and aquatic ecosystems. Reports from the United Nations Environment Programme indicate that plastic production has surpassed hundreds of millions of tonnes annually, with single-use products constituting a significant share of total waste generation. Plastic bottles, commonly used for beverages and consumer liquids, represent a substantial fraction of urban litter streams.

   Despite awareness campaigns and infrastructure improvements, improper disposal remains prevalent in institutional campuses, public areas, and urban communities. Conventional waste management systems focus primarily on collection and post-disposal sorting, often neglecting behavioural intervention at the source.

   Smart city initiatives, such as those implemented in Singapore, employ digital infrastructure to optimize urban cleanliness. However, sensor-based and IoT-driven systems typically require capital-intensive deployment and maintenance.

   This research investigates whether a low-cost, behaviour – centred digital verification mechanism can improve disposal practices without requiring complex hardware infrastructure. The proposed Eco-Clean framework leverages QR codes and cloud-based data submission tools to create an accountability-driven waste disposal ecosystem.

2. THEORETICAL FRAMEWORK

   Eco-Clean is grounded in behavioural science and environmental psychology principles. The framework integrates:

   1. Behavioural Accountability Theory

      Individuals are more likely to act responsibly when their actions are observable or recorded.

   2. Nudge Theory

      Small environmental cuessuch as QR prompts and digital submission requirementsencourage desirable actions without coercion.

   3. Participatory Sustainability Models

      Community-driven monitoring systems increase engagement and environmental awareness.

      By combining these principles with accessible digital tools, Eco-Clean positions accountability as a motivator for responsible waste disposal.

3. LITERATURE REVIEW

   Smart waste management research spans IoT-based fill-level sensors, AI-driven sorting systems, and route optimization algorithms. However, these approaches prioritize operational efficiency rather than direct behavioural modification.

   Municipal sustainability programs supported by institutions such as the World Bank emphasize community engagement and decentralized monitoring models. Nonetheless, limited empirical work exists on QR-based accountability systems in waste disposal contexts.

   The rapid growth of urban populations and educational institutions has increased the need for efficient and sustainable waste management systems. In recent years, researchers have explored the use of digital technologies to improve waste disposal practices and promote environmental responsibility. In 2018, several studies focused on Internet of Things (IoT)based waste management systems, where smart sensors were installed in waste bins to monitor fill levels and send alerts for timely collection. These systems aimed to optimize waste collection routes, reduce operational costs, and minimize environmental pollution in urban areas. Such technological solutions laid the foundation for integrating smart monitoring systems into modern waste management frameworks.

   In 2019, research began to emphasize digital monitoring and user participation in waste management processes. Scholars proposed mobile applications and online platforms that encouraged individuals to follow proper waste segregation practices. These systems not only provided information about recycling and waste disposal but also allowed institutions to track waste generation patterns and user participation. The focus gradually shifted from purely technological solutions to systems that also addressed human behaviour and awareness.

   By 2020, the application of Quick Response (QR) code technology gained attention in environmental management systems. Researchers explored how QR codes could be used as an accessible and low-cost digital interface for users to obtain information, record actions, and interact with smart systems. In the context of waste management, QR codes enabled users to scan codes placed near disposal points to access guidelines or log disposal activities. This approach created opportunities for monitoring individual participation while promoting transparency and accountability.

   In 2021, greater emphasis was placed on behavioural approaches to waste management. Studies highlighted that technological solutions alone are not sufficient unless they are supported by behavioural change strategies. Researchers suggested the use of awareness campaigns, reward systems, and monitoring tools to motivate individuals to adopt environmentally responsible practices. Behavioural frameworks were proposed to encourage consistent participation in proper waste disposal and recycling activities.

   In 2022, the concept of smart campus waste management began to emerge. Researchers proposed integrated systems designed specifically for educational institutions, combining digital technologies with sustainability initiatives. These systems included waste tracking platforms, real-time monitoring tools, and awareness programs aimed at students and staff. The goal was to create environmentally responsible campuses by reducing waste generation and encouraging active participation in sustainable practices.

   In 2023, further advancements were made in QR codebased waste tracking systems, where users could scan a code when disposing of waste, allowing institutions to monitor disposal behavour and collect data for analysis. Such systems provided insights into waste disposal patterns and helped identify areas where awareness or infrastructure improvements were required. These approaches demonstrated that combining simple digital tools with behavioural monitoring can significantly improve waste management outcomes.

   More recently, in 2024, research has increasingly focused on integrating Artificial Intelligence (AI), IoT, and digital engagement platforms to create intelligent waste management systems. These systems analyse data collected from sensors, digital platforms, and user interactions to improve waste collection strategies and encourage sustainable behaviour. The integration of technology with behavioural interventions has been recognized as a promising approach to achieving long-term sustainability.

   Overall, previous studies indicate that effective waste management requires not only technological innovation but also active user participation and behavioural change. However, many existing systems focus primarily on large-scale urban environments rather than controlled settings such as educational campuses. Therefore, there is a need for a structured behavioural framework that combines simple digital tools with user engagement mechanisms. The proposed Eco-Clean: A QR CodeEnabled Behavioural Framework for Sustainable Waste Disposal in the Campus aims to address this gap by encouraging responsible disposal behaviour through QR-based interaction and monitoring within a campus environment.

   QR codes have been widely implemented in logistics, inventory management, ticketing systems, and mobile payments. Their low production cost and universal smartphone compatibility make them suitable for scalable public participation systems.

   This study contributes by integrating QR verification with behavioural tracking and statistical impact analysis.

4. QR CODE

   A Quick Response (QR) code is a two-dimensional barcode developed as an advancement of the traditional one-dimensional barcode system. Unlike conventional barcodes that store information only in a horizontal direction, QR codes store data both horizontally and vertically, allowing them to hold a significantly larger amount of information. The technology was introduced in 1994 by Denso Wave, a Japanese company, with the objective of enabling rapid decoding and high-speed data access.

   In recent years, QR codes have gained widespread adoption across various sectors. Their popularity has increased mainly due to the rapid growth of smartphone usage, as most modern mobile devices are equipped with built-in cameras and QR code scanning applications. This capability allows users to quickly access digital content such as websites, contact information, and application links by simply scanning the code.

   Structurally, a QR code contains several functional components that enable accurate scanning and decoding. The three large square patterns located at the corners of the code serve as position detection markers, which help the scanner determine the orientation of the code. Smaller alignment patterns assist in maintaining decoding accuracy, especially when the code is distorted. Additionally, QR codes include timing patterns, format information, data regions, and version information, all of which help the scanning device correctly interpret the encoded data.

   One of the key advantages of QR codes is their high data storage capacity. Compared with traditional barcodes, QR codes can store significantly more information due to their two-dimensional structure. In some cases, they can encode up to several thousand alphanumeric characters, making them suitable for applications such as digital payments, ticketing systems, product tracking, and information sharing.

   Despite their benefits, QR codes also present certain security concerns. When users scan QR codes from unknown or untrusted sources, they may be redirected to malicious websites or harmful digital content. Such threats may include malware, viruses, or phishing attacks that could compromise the security of devices and sensitive information. Therefore, it is important for users to verify the authenticity of QR codes before scanning them.

5. RESEARCH OBJECTIVES

   The primary objective of this study is to develop and evaluate an innovative system that promotes responsible plastic bottle disposal using QR code technology. The specific objectives are as follows:

   1. To design and implement a QR codebased monitoring system that records and tracks plastic bottle disposal activities in designated waste bins.

   2. To quantitatively measure behavioural changes in users waste disposal practices before and after the implementation of the QR-based system.

   3. To examine the feasibility, scalability, and cost-effectiveness of the proposed system in real-world institutional environments.

   4. To develop a practical and replicable framework that can be adopted by educational institutions and organizations to improve sustainable waste management practices.

6. SYSTEM ARCHITECTURE AND DESIGN

   1. System Components

      The Eco-Clean framework consists of:

      • Unique QR codes affixed to designated bins

      • Web-based submission interface (cloud form)

      • Image upload functionality

      • Timestamp auto-capture

      • Centralized database (spreadsheet storage)

      • Administrative monitoring dashboard

   2. Operational Workflow

      1. User disposes of bottle.

      2. User scans QR code.

      3. QR redirects to submission form.

      4. User uploads image proof.

      5. Data stored automatically.

      6. Administrative review and analytics performed.

   3. Data Model

      Each submission record includes:

      • Unique entry

      • Timestamp

      • Bin location

      • Image file

        Data is structured in tabular format to enable quantitative analysis.

        Figure 1: Architecture and design of the proposed system

7. METHODOLOGY

   1. Experimental Design

      A one-week quasi-experimental study was conducted:

      • 3 Days: Baseline observation (no QR system)

      • 5 Days: Eco-Clean deployment

   2. Sample Population

      Participants included public and children within a controlled environment. Participation was voluntary.

   3. Evaluation Metrics

      The study evaluated:

      • Disposal compliance rate

      • Submission frequency

      • Image verification accuracy

      • Behavioural consistency over time

        Figure 2: Methodology

        7

   4. Field work using the QR code:

      QR code for the proposed system:

      Figure 3: QR code for ECO Nob snap

   5. Voluntaries snap of their Nobel activities:

      Figure 4: Noble snap of the voluntaries

      6.4 Statistical Treatment

      A comparative prepost analysis was conducted. Let:

      • = average daily proper disposals (baseline)

      • = average daily proper disposals (QR phase) Behavioural improvementrate:

        =

        × 100

        Chi-square tests were applied to evaluate significance of disposal behaviour changes.

        Area	Average Daily Proper Disposals (Baseline) (Db)	Average Daily Proper Disposals (QR Phase) (Dq)	Calculation	Improvement Rate (%)
        Municipal Office	20	30	((30-20)/20 ×100)	50%
        Wards	40	60	((60-40)/40 ×100)	50%
        Hospital	25	35	((35-25)/25 ×100)	40%

      • Db=25 disposals/day

      • Dq=35D_q = 35Dq=35 disposals/day Improvement Rate= (3525)/25×100

      =0.4×100=40%

      Behavioural improvement = 40%

      1. Participation Trends

8. RESULTS

   A steady increase in submission frequency was observed during days 24, indicating adaptation and acceptance of the system.

   1. Behavioural Improvement

      Average daily proper disposal increased significantly compared to baseline.

   2. Temporal Patterns

      Peak disposal times aligned with academic break intervals, suggesting predictable waste generation cycles.

   3. Image Verification Accuracy

   Manual verification indicated high compliance rates, with minimal misuse.

9. CostBenefit Analysis

   1. Implementation Cost

      • QR code printing: Minimal

      • Cloud form setup: Free

      • Administrative monitoring: Low labour cost

   2. Comparison with IoT Systems

      Parameter	IoT Smart Bin	Eco-Clean QR System
      Hardware Cost	High	Minimal
      Maintenance	ModerateHigh	Low
      Scalability	Moderate	High
      Infrastructure Need	Advanced	Basic Internet

      The QR-based system demonstrates strong cost-efficiency.

10. ETHICAL CONSIDERATIONS

    Ethical principles were carefully considered during the design and implementation of the proposed system. Participation in the study was entirely voluntary, and users were not required to provide sensitive personal information. The system only collects minimal data necessary for monitoring disposal activities, primarily limited to image uploads serving as proof of plastic bottle disposal. These images are used solely for verification purposes and are not associated with sensitive identity data. Furthermore, all collected information is stored securely in a centralized database with restricted administrative access to ensure confidentiality and data protection. By limiting data collection and maintaining controlled access, the system adheres to fundamental data privacy and ethical research practices.

11. LIMITATIONS

    Despite the advantages of the proposed QR-based monitoring framework, several limitations were identified during the implementation process. The system relies heavily on the availability of smartphones, which may restrict participation for individuals without access to compatible devices. Additionally, the verification of uploaded images currently requires manual review by administrators, which may increase workload as participation grows. There is also a possibility of users submitting falsified or irrelevant images, which could affect data accuracy. Another practical limitation involves the requirement for stable internet connectivity to access the submission form and upload images, which may not always be available in certain locations.

12. FUTURE ENHANCEMENTS

    Future improvements to the proposed system can enhance its efficiency, scalability, and reliability. One potential development involves integrating Artificial Intelligencebased image recognition techniques to automatically verify whether uploaded images contain valid plastic bottle disposal evidence. Automated duplicate detection mechanisms could also be implemented to prevent repeated or fraudulent submissions. Additionally, incorporating incentive-based reward mechanisms may encourage greater user participation and promote sustainable waste disposal behavior. The development of a dedicated mobile application interface would further improve user accessibility and system usability. Integration with broader municipal waste management platforms could also

    enable large-scale monitoring and data-driven waste management strategies. Such advancements would allow the system to align with global sustainability initiatives, including the objectives of the **United Nations ** Sustainable Development Goals, thereby supporting environmentally responsible waste management practices.

13. CONCLUSION

This research presented Eco-Clean, a scalable QR-based behavioural monitoring framework for plastic bottle disposal. The pilot study indicates that digital verification increases responsible waste disposal behaviour within institutional settings.

The systems simplicity, cost-effectiveness, and scalability make it suitable for adoption in educational institutions, corporate campuses, and developing urban environments. By combining behavioural science with digital tracking tools, Eco-Clean offers a practical pathway toward sustainable waste management.

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3. K. Anuratha, S. Akshaya, C. Rajaselvi, and M. Akshaya, QR-Based Efficient Waste Management System, Proceedings of the 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), IEEE, 2024.

4. A. Lakhouit, Revolutionizing Urban Solid Waste Management with AI and IoT: A Review of Smart Solutions for Waste Collection, Sorting, and Recycling,

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Author Bio:

Dr. J. Suganthi Vinodhini is an Assistant Professor in the Department of Electrical and Electronics Engineering at St. Peters Institute of Higher Education and Research, Chennai, India. She completed her B.E. in Electrical and Electronics Engineering,

M.E. in Electronics and Control Engineering, and Ph.D. in Engineering from Sathyabama Institute of Science and Technology, Chennai. She worked as Assistant professor in Sathyabama Institute of Science and Technology and her research interests include power electronics, multilevel inverters, smart grid technologies, and artificial intelligence (AIoT) applications in engineering systems. She has published several research papers in national and international conferences and journals and actively participates i academic, research, and community development activities.