Uncovering Periodic Patterns in Petroleum-Sector CO2 Emissions Using Fast Fourier Transform Analysis

The petroleum industry remains central to global energy supply, yet continues to generate substantial CO₂ emissions that fluctuate in complex, recurring patterns. Traditional trend-based analyses often overlook these underlying dynamics. This study introduces a digital innovation that applies the Fast Fourier Transform (FFT); a signal-processing algorithm rarely used in climate analytics, to uncover hidden periodicities in petroleum-sector CO2 emissions from 1970 to 2023.

Using data from Our World in Data and the Global Carbon Project, emissions from twelve representative countries were analysed across four typologies: high emitters, policy leaders, oil exporters, and climate-vulnerable states. The FFT, Power Spectral Density (PSD), and Continuous Wavelet Transform (CWT) techniques revealed dominant emission cycles at approximately 5.4, 9, 13.5, and 27 years, corresponding to treaty review intervals, political transitions, and oil-market shocks.

The results demonstrate that petroleum-sector emissions are structurally rhythmic rather than random. Recognising these cycles enables smarter timing of climate policies, investment decisions, and decarbonisation efforts. By integrating spectral analytics into digital petroleum data systems, this work provides a novel, data-driven framework for synchronising emissions governance with the rhythm of global energy transitions.