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This study offers a comprehensive discussion of the future role of robots and artificial intelligence (AI) in U.S. recycling under different policy environments and its impact on the workforce. The state of recycling in the U.S. is changing rapidly, with techno-economic developments transforming the efficacy and sustainability of recycling and the workforce it employs. This study describes the technical, social, and policy drivers that influence U.S. municipal solid waste (MSW) management and explores pathways for more sustainable outcomes by focusing on different technology options for the sorting of recyclables in material recovery facilities (MRFs). This study presents four distinct scenario storylines for U.S. recycling by 2050 that contrast recycling and robotic futures, particularly with MRFs that maximize material recovery, worker experience, and economic competitiveness, respectively. This study finds that a recycling scenario defined by strong policy support for recycling and the addition of increasingly flexible, collaborative technology in the form of robotics coupled with AI-driven vision systems, offers the greatest potential for better results. Less certain is the role of MRFs by 2050 based on the full cost for public actors and substantial changes in private industry. Insights from this study can directly inform future techno-economic analyses, technology decisions, and policy recommendations. 

Over the past decade, robots have emerged as a new sorting technology for material recovery facilities (MRFs), enabled through dramatic advances in robotics and artificial intelligence (AI). These advances allow robots to become ‘smart’ by coupling them with AI driven vision systems, able to distinguish recyclables by material type. By integrating robotics, MRFs hope to increase sorting speed and accuracy, reduce their residuals, and to become more resilient towards worker shortages. To better understand the economic implications, this study presents a techno-economic analysis of a representative MRF in the U.S. that integrates robotics and compare it to a similar MRF without robotics integration. We compare the metrics net present value, discounted internal rate of return, and payback period for a mid-size MRF with and without robotic integration and add an uncertainty analysis to inform about the most important factors to consider. The results of the techno-economic analysis can inform MRF operators in their future decision-making.