Theoretical foundations of time series analysis in green economy

The rapid global shift towards environmentally sustainable development necessitates advanced analyticalmethods to monitor and predict environmental and economic indicators. This paper presents a comprehensive theoreticaland empirical analysis of time series methodologies applied to the green economy. It focuses on renewable energyproduction, green investment flows, and carbon dioxide (CO₂) emissions. Utilizing real-world data from 2010 to 2023,the study employs classical time series techniques, stationarity diagnostics, and ARIMA modeling to uncover trendsand forecast future dynamics. The results confirm that policy-driven strategies have contributed to a consistent risein renewable energy generation and green investment while effectively reducing CO₂ emissions. The ARIMA-basedforecasts offer robust short- and medium-term insights to support informed decision-making in environmental and energypolicy. The findings underscore the importance of statistical forecasting in promoting long-term sustainability and offer aconceptual basis for future research integrating econometric and computational approaches.