This study explores the long-run relationship among the environmental footprint (EnF), renewable energy consumption, energy use, industrial growth, and urbanization in Saudi Arabia from 1990 to 2023, employing the Autoregressive Distributed Lag (ARDL) model, alongside Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) for robustness checks. Results indicate a significant long-term relationship among the variables, with renewable energy adoption emerging as a crucial factor in reducing carbon emissions. The ARDL bounds test confirms the existence of cointegration, revealing the dynamic interplay among renewable energy, economic growth, and environmental sustainability. The findings show that renewable energy consumption significantly reduces the environmental footprint (CO₂ emissions), supporting Saudi Arabia’s Vision 2030 goals for economic diversification and sustainable development. However, industrial expansion, while critical for economic growth, still contributes to increased emissions, underscoring the need for further investment in clean technologies. The study also highlights the role of urbanization, which, while essential for development, poses challenges for environmental sustainability. Short-term dynamics, represented by the Error Correction Model, indicate a fast adjustment speed toward equilibrium, with deviations corrected by approximately 52% each period. The study offers valuable insights for policymakers aiming to balance industrial growth with environmental protection, emphasizing the need for strategic investments in renewable energy and energy efficiency. This research contributes to the understanding of energy–economy–environment interactions in oil-rich economies, providing a foundation for future studies to explore the impact of advanced technologies and policy interventions on sustainable development

This study explores the long-run relationship between environmental footprint (EnF), renewable energy consumption, energy use, industrial growth, and urbanization in Saudi Arabia from 1990 to 2023, employing the Autoregressive Distributed Lag (ARDL) model, alongside Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) for robustness checks. Results indicate a significant long-term relationship among the variables, with renewable energy adoption emerging as a crucial factor in reducing carbon emissions. The ARDL bounds test confirms the existence of cointegration, re-vealing the dynamic interplay between renewable energy, economic growth, and environmental sustainability. The findings show that renewable energy consumption significantly reduces en-vironmental footprint (CO2 emissions), supporting Saudi Arabia's Vision 2030 goals for economic diversification and sustainable development. However, industrial expansion, while critical for economic growth, still contributes to increased emissions, underscoring the need for further in-vestment in clean technologies. The study also highlights the role of urbanization, which, while essential for development, poses challenges for environmental sustainability. Short-term dynamics, represented by the Error Correction Model, indicate a fast adjustment speed toward equilibrium, with deviations corrected by approximately 52% each period. The study offers valuable insights for policymakers aiming to balance industrial growth with environmental protection, emphasizing the need for strategic investments in renewable energy and energy efficiency. This research contributes to the understanding of energy-economy-environment interactions in oil-rich economies, providing a foundation for future studies to explore the impact of advanced technologies and policy interventions on sustainable development Keywords: Environmental Footprint; Co-integration; Renewable energy; Industrialization; Energy

This study analyzes the dynamic relationships between per capita CO2 emissions, renewable energy consumption, environmental innovation, and trade openness using a Vector Error Correction Model (VECM) over the period 1995–2024. The econometric analysis confirms the existence of a long-run cointegration relationship between these variables, highlighting a stable equilibrium mechanism linking environmental outcomes, technological progress, energy transition, and economic openness. Short-term causality tests reveal asymmetric interactions: environmental innovation is significantly influenced by shocks to renewable energy consumption and trade openness, while CO2 emissions show relative rigidity in the short run. Stability tests confirm the robustness of the model, and variance decomposition highlights the increasing influence of renewable energy on emissions, reflecting the gradual impact of energy transition policies. Environmental innovation emerges as a key driver influencing both renewable energy development and emissions dynamics, highlighting the crucial role of technological progress in achieving sustainability goals. Trade openness exerts an indirect but significant effect, interacting with innovation and energy over longer time horizons.