Data-Driven Sustainability: How Machine Learning Uncovers U.S. Economic and Financial Trends

This research investigates the quantitative associations between GDP together with GDP squared while integrating energy consumption and financial accessibility and urbanization to analyze their joint effects on the Load Capacity Factor (LCF) in the United States using an ARDL bounds testing technique within the LCC hypothesis framework. The examination utilizes time series data from 1996 to 2022 to test variable stationarity using several unit root assessments. The results from ARDL demonstrate economic growth together with financial accessibility create positive influences on LCF thus supporting environmental sustainability over longer periods. Moreover, environmental quality becomes worse as economies grow and energy usage increases and rapid urbanization during short-term periods. Multiple confirmation tests including FMOLS, DOLS and CCR techniques together with various diagnostic evaluations enhance the research validity. The study confirms environmental degradation occurs due to urban expansion alongside temporary economic growth and increasing electricity needs based on carbon-intensive fuels which generate pollutants while speeding up world temperature increases. Furthermore, the study utilized ARIMA method to estimate LCF economic trend between 2023 and 2040. These outcomes emphasize the necessity of exploring the simultaneous relationships between economic advancement and energy usage and urban growth patterns in environmental governance in USA.