By Faridat Salifu
As the demand for electronic devices continues to surge, so does the challenge of managing e-waste effectively.
With approximately 50 million tonnes generated annually—more than the combined weight of all commercial airplanes ever made—the need for innovative solutions has never been more pressing. Experts predict that this figure could more than double by 2050 if current trends persist.
Artificial Intelligence (AI) is emerging as a powerful tool in tackling e-waste management.
By enhancing various stages of the process—from collection and sorting to recycling and disposal—AI can significantly improve efficiency and effectiveness.
Research shows that AI can achieve a classification accuracy of 90–97% in identifying e-waste categories, allowing for better planning and optimization of collection efforts.
Machine learning algorithms are being developed to streamline supply chains and recycling processes, managing inventory levels and sorting different electronic components more effectively.
Furthermore, AI systems can monitor and analyze performance data to extend the lifespan of products, potentially reducing the volume of e-waste generated.
However, the rapid advancement of AI technology also presents challenges. The demand for increasingly powerful computational resources leads to older hardware becoming obsolete at a fast pace, contributing to rising e-waste levels.
Additionally, the complexity and cost of gathering data for AI-driven solutions hinder their implementation, particularly in developing countries where informal waste management practices dominate.
Currently, only about 20% of discarded electronics are recycled through organized channels, with the majority ending up in landfills or being improperly managed in informal settings. Even in the European Union, which is regarded as a leader in e-waste recycling, only 35% of e-waste is reported to be properly processed.
The environmental impact of e-waste is severe. Landfilled electronics can contaminate soil and groundwater with hazardous materials such as mercury, lead, bromine, and arsenic. For example, long-term exposure to arsenic, commonly found in microchips, is linked to severe health issues including lung cancer and skin diseases. Lead exposure poses significant risks as well, particularly for children, leading to brain and kidney damage.
As households accumulate more electronic devices, it is crucial to consider what happens to these products once they are discarded. Without significant improvements in e-waste management and recycling practices, the consequences for both human health and the environment will continue to escalate.