Analisis Tren Konsentrasi Karbon Dioksida (CO₂) di Mauna Loa Menggunakan Model Exponential Smoothing State Space (ETS)

Abstract

Carbon dioxide  levels in the atmosphere continue to draw global attention due to their impact on climate change. At Mauna Loa Observatory,  concentrations show a long-term increasing trend accompanied by relatively stable seasonal fluctuations. To better understand these dynamics, this study employs the Exponential Smoothing State Space (ETS) method using R Studio. The ETS model effectively captures both long-term trends and seasonal patterns, providing reliable and accurate forecasts. Residual analysis indicates that the model meets the assumptions of random errors without significant autocorrelation, confirming the validity of the predictions. Future projections suggest that  levels will continue to rise by several ppm each year, highlighting that atmospheric carbon accumulation has not yet stabilized. These findings underscore the importance of global efforts to reduce carbon emissions and implement data-driven environmental policies. In addition, individual and collective awareness is crucial to minimize harmful human activities and to preserve the Earth for future generations. This study offers a solid scientific basis for climate change mitigation strategies and for ongoing monitoring of  concentrations.