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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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Ubiquitous Life Cycle Assessment (U-LCA): A Proposed Concept for Environmental and Social Impact Assessment of Industry 4.0
Smart manufacturing in an Industry 4.0 setting requires developing unique infrastructures for sensing, wired and wireless communications, cyber-space computations and information tracking. While an exponential growth in smart infrastructures may impose drastic burdens on the environment, the conventional Life Cycle Assessment (LCA) techniques are incapable of quantifying such impacts. Therefore, there is a gap between advances in the manufacturing domain and the environmental assessment field. The capabilities offered by smart manufacturing can be applied to LCA with the aim of providing advanced impact assessment, and decision-making mechanisms that match the needs of its manufacturing counterpart.
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- Award ID(s):
- 1705621
- PAR ID:
- 10065238
- Date Published:
- Journal Name:
- Manufacturing letters
- Volume:
- 15
- ISSN:
- 2213-8463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1016/j.mfglet.2017.12.012
