Impact of Artificial Intelligence Capabilities on Plastic Waste Recycling Efficiency

Abstract

This growing amount of plastic waste is a serious environmental problem and more efficient and innovative recycling solutions are needed to address it. The research in this paper focuses on how artificial intelligence (AI) affects the efficiency of plastic waste recycling. Specifically, the research considers Automation as an intermediate operational factor, and investigates the impact of AI-Based Sorting, Data Availability, Real-Time Monitoring, and Government Policy on Recycling Efficiency. The research method used was quantitative with a sample of 160 respondents who were taken using a structured questionnaire. Partial Least Square Structural Equation Modeling (PLS-SEM) was used in analyzing the proposed model with the software of SmartPLS. The results show that Data Availability and Real-Time Monitoring have a positive impact on Recycling Efficiency, and AI-Based Sorting and Government Policy do not directly impact the results of recycling. In addition, AI-Based Sorting, Data Availability, and Government Policy also have significant contributions to Automation, while Real-Time Monitoring has no significant impact. The result shows that there is no significant difference in Recycling Efficiency due to the implementation of Automation. Reliability and validity tests were conducted and the thresholds for internal consistency, convergent and discriminant validity were satisfied. The model also proved to be reasonably predictive and explanatory. This investigation builds on the current studies on AI empowering sustainability, which help to provide empirical evidence to improve recycling efficiency. The research results can help policy makers, waste management companies, and technology creators to design data-driven and intelligent systems for plastic waste recycling.