<p>Understanding the link between unseasonal land cover changes and CO₂ emissions can indicate the decarbonization progress of a region, but limited modeling tools exist for analysis in near-real-time. Here, we developed a modeling framework to reveal a strong and robust relationship between the two quantities. By applying the Butterworth filter, unseasonal changes in land cover and fuel-consuming sectors are extracted for Autoregressive Distributed Lag regression analysis in major economies. Among all investigated economies, Russia has demonstrated the strongest co-relationship (R-squared value of 0.730) between unseasonal CO₂ emissions and land cover changes, indicative of its heavy reliance on fossil fuels. Both Brazil (1200 km²/MtCO₂e on average) and Russia (10,700 km²/MtCO₂e) exhibit greatest sensitivity in land cover changes to CO₂ emission changes. This research provides an effective tool to assess the coupling between unseasonal land cover change and CO₂ emitting economic activities, presenting an alternative indicator to monitor decarbonization in real-time.</p>