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The increasing volume of electronic waste (e-waste) creates significant environmental and economic challenges which demands practical management strategies. Life Cycle Assessment (LCA) has been known as a principal tool for evaluating the environmental impact of e-waste recycling and disposal methods. However, its application is hampered by inconsistencies in methodology, data limitations, and variations in system boundaries. This study provides a review of current LCA tools used in e-waste analysis and identifies gaps and opportunities for improvement. It categorizes studies into three groups: studies that applied LCA to product and process optimization, impact evaluation, and policy development. Findings reveal that LCA has been helpful in assessing the sustainability of different recycling strategies. However, significant variations exist in methodological approaches and data accuracy. Challenges such as the lack of standardized LCA protocols, the limited availability of regionspecific impact data, and inconsistencies in assessment methodologies are still barriers to its widespread adoption. Finally, the study discusses emerging trends in LCA aimed at addressing current gaps, including the incorporation of machine learning and artificial intelligence for predictive modeling, dynamic impact assessment frameworks, and the role of real-time data collection via IoT-based sensors.
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This content will become publicly available on September 3, 2026
Living Sustainability: In-Context Interactive Environmental Impact Communication
Climate change demands urgent action, yet understanding the environmental impact (EI) of everyday objects and activities remains challenging for the general public. While Life Cycle Assessment (LCA) offers a comprehensive framework for EI analysis, its traditional implementation requires extensive domain expertise, structured input data, and significant time investment, creating barriers for non-experts seeking real-time sustainability insights. Here we present the first autonomous sustainability assessment tool that bridges this gap by transforming unstructured natural language descriptions into in-context, interactive EI visualizations. Our approach combines language modeling and AI agents, and achieves >97% accuracy in transforming natural language into a data abstraction designed for simplified LCA modeling. The system employs a non-parametric datastore to integrate proprietary LCA databases while maintaining data source attribution and allowing personalized source management. We demonstrate through case studies that our system achieves results within 11% of traditional full LCA, while accelerating from hours of expert time to real-time. We conducted a formative elicitation study (N=6) to inform the design objectives of such EI communication augmentation tools. We implemented and deployed the tool as a Chromium browser extension and further evaluated it through a user study (N=12). This work represents a significant step toward democratizing access to environmental impact information for the general public with zero LCA expertise.
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- PAR ID:
- 10653043
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 9
- Issue:
- 3
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 42
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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