Global decarbonization corresponding with unseasonal land cover change

Kehan He, Lixing Wang and Zhu Liu ()
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Kehan He: The University of Hong Kong
Lixing Wang: Tsinghua University
Zhu Liu: International Research Center of Big Data for Sustainable Development Goals

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-63144-4

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