The management of electronic waste (e-waste) has become a global environmental and social challenge due to the rapid growth in the consumption of electronic devices and their frequent obsolescence. In Peru, e-waste regulations have been in place since 2012, but the efficient management of this waste remains limited. According to the United Nations Industrial Development Organization, only 3% of e-waste in Latin America is collected and managed properly, leaving 97% uncontrolled, representing an environmental risk and an economic loss of US$1.7 billion annually in recoverable materials. To address this problem, the design of an optimized data architecture is proposed to improve traceability, storage and analysis of data on the generation and disposal of e-waste. The implementation of technologies such as IoT, machine learning and real-time monitoring platforms will facilitate data-driven decision making and improve collection and recycling efficiency. The use of advanced technological infrastructures and social awareness are key elements to improve sustainability and reduce the environmental impact of e-waste.

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Smart Data-Based Architecture for e-waste Management in Perú

  • Antonio Arroyo-Paz,
  • Daniel Alejandro Yucra Sotomayor,
  • Julio Elmer Sotomayor Abarca,
  • Santos Ciriaco Sotelo Antaurco,
  • Gerson Luis Miranda Yancce

摘要

The management of electronic waste (e-waste) has become a global environmental and social challenge due to the rapid growth in the consumption of electronic devices and their frequent obsolescence. In Peru, e-waste regulations have been in place since 2012, but the efficient management of this waste remains limited. According to the United Nations Industrial Development Organization, only 3% of e-waste in Latin America is collected and managed properly, leaving 97% uncontrolled, representing an environmental risk and an economic loss of US$1.7 billion annually in recoverable materials. To address this problem, the design of an optimized data architecture is proposed to improve traceability, storage and analysis of data on the generation and disposal of e-waste. The implementation of technologies such as IoT, machine learning and real-time monitoring platforms will facilitate data-driven decision making and improve collection and recycling efficiency. The use of advanced technological infrastructures and social awareness are key elements to improve sustainability and reduce the environmental impact of e-waste.