BlockTrace: A Decentralized Model for Tracking and Managing E-Waste Lifecycle


Authors : Dr. Benciya Abdul Jaleel

Volume/Issue : Volume 10 - 2025, Issue 11 - November


Google Scholar : https://tinyurl.com/yv6ddbd5

Scribd : https://tinyurl.com/5n86mfyu

DOI : https://doi.org/10.38124/ijisrt/25nov843

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Abstract : The rapid rise in discarded electronic devices, commonly referred to as e-waste, presents serious hurdles for both environmental policy and municipal waste programmes. In response, this study puts forward BlockTrace, a decentralised blockchain platform designed to monitor each stage of an electronics product's life, from factory floor to recycling bin. By creating an immutable digital ledger, the system intends to strengthen transparency, hold manufacturers and consumers accountable, and streamline recovery operations. Using a quantitative approach, the paper defines specific performance indicators and describes the data flows needed for deployment, showing how researchers can apply R-based statistical tools to conduct life-cycle assessments, verify regulatory compliance, and generate predictive forecasts. Although actual adoption and the collection of real-world data lie outside the current investigation, the proposed model offers a conceptual blueprint that future field trials can test and refine.

Keywords : E-Waste Management, Blockchain Technology, Decentralized Systems, Life-Cycle Assessment, Environmental Sustainability, R Programming, Quantitative Analysis, Digital Ledger, Predictive Modeling, Regulatory Compliance.

References :

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The rapid rise in discarded electronic devices, commonly referred to as e-waste, presents serious hurdles for both environmental policy and municipal waste programmes. In response, this study puts forward BlockTrace, a decentralised blockchain platform designed to monitor each stage of an electronics product's life, from factory floor to recycling bin. By creating an immutable digital ledger, the system intends to strengthen transparency, hold manufacturers and consumers accountable, and streamline recovery operations. Using a quantitative approach, the paper defines specific performance indicators and describes the data flows needed for deployment, showing how researchers can apply R-based statistical tools to conduct life-cycle assessments, verify regulatory compliance, and generate predictive forecasts. Although actual adoption and the collection of real-world data lie outside the current investigation, the proposed model offers a conceptual blueprint that future field trials can test and refine.

Keywords : E-Waste Management, Blockchain Technology, Decentralized Systems, Life-Cycle Assessment, Environmental Sustainability, R Programming, Quantitative Analysis, Digital Ledger, Predictive Modeling, Regulatory Compliance.

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Paper Submission Last Date
30 - November - 2025

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