Interplay of Industrial Robots, Education, and Environmental Sustainability in United States: A Quantile-Based Investigation

This study explores the dynamic relationship between industrial robots, education, and environmental sustainability in the United States, emphasizing their role in reducing CO2 emissions. The research aims to quantify how automation, human capital, and the energy transition contribute to carbon mitigation within a data-driven, AI-oriented policy framework. Quarterly data spanning 2011Q1–2024Q4 were analyzed using the advanced Quantile-on-Quantile Autoregressive Distributed Lag (QQARDL) model, which captures heterogeneous long- and short-run effects across emission distributions. Results reveal that industrial robot adoption, education, and renewable energy transition significantly reduce emissions, with the strongest effects occurring at both high- and low-emission quantiles. Economic growth and financial development also support decarbonization when complemented by green finance and innovation, while urbanization increases emissions unless aligned with compact urban design and clean energy systems. The findings imply that AI-driven industrial robotics and education jointly foster sustainability through efficiency, innovation, and awareness. Policymakers are encouraged to integrate automation strategies, renewable energy incentives, and sustainability education into climate policy. This study provides empirical evidence supporting the Resource-Based View, highlighting human capital and intelligent automation as strategic assets for achieving long-term carbon neutrality.