The impact of renewable energy, banking credit, and industrial robots on environmental sustainability in the top five robot installing countries

This study investigates the asymmetric impact of renewable energy use (RENEW), Bank Domestic Credit (BDS), and Industrial Robots (ROBOTS) on environmental sustainability, measured by the ecological footprint (EF) and greenhouse gas emissions (GHG), in the five leading robot-installing countries (China, Japan, the United States, Germany, and South Korea) from 2011 to 2023. Given the confirmed cross-sectional dependence and slope heterogeneity, advanced panel data techniques were employed, including panel cointegration and the method of moments quantile regression (MMQR) model as the primary estimation technique, supplemented by FMOLS and Robust Least Squares for robustness checks. Despite increasing academic interest, the current research primarily analyzes renewable energy, financial development, and industrial automation in isolation, predominantly utilizing mean-based methodologies that neglect distributional variation. This study fills the existing gap by presenting the inaugural integrated empirical framework that concurrently evaluates the asymmetric impacts of these three structural forces on environmental sustainability in the foremost robot-installing economies, employing a quantile-based methodology that captures nonlinear dynamics across varying pollution regimes. The MMQR findings indicate significant variability across environmental quantiles. GDPPC and BDS consistently diminish both EF and GHG, indicating pro-environmental impacts of income and financial growth, aligning with a post-turning-point EKC interpretation rather than a formal EKC analysis. RENEW exhibits a more pronounced pollution-reducing effect at elevated quantiles, suggesting that renewable energy is most efficacious in severely degraded environments. Conversely, ROBOTS routinely increase EF and GHG emissions, indicating that automation currently exacerbates environmental strain. Granger causality results further validate the dynamic relationships among the variables. The results endorse more stringent green criteria for automation and greater use of bank loans to expedite transitions to renewable energy.