Global decarbonization corresponding with unseasonal land cover change.
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- Record sourced from PubMed, PMID 40849298.
- Also identified by DOI 10.1038/s41467-025-63144-4 and PMC identifier 12375125.
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Abstract
Understanding the link between unseasonal land cover changes and CO<sub>2</sub> 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 CO<sub>2</sub> emissions and land cover changes, indicative of its heavy reliance on fossil fuels. Both Brazil (1200 km<sup>2</sup>/MtCO<sub>2</sub>e on average) and Russia (10,700 km<sup>2</sup>/MtCO<sub>2</sub>e) exhibit greatest sensitivity in land cover changes to CO<sub>2</sub> emission changes. This research provides an effective tool to assess the coupling between unseasonal land cover change and CO<sub>2</sub> emitting economic activities, presenting an alternative indicator to monitor decarbonization in real-time.