Global decarbonization corresponding with unseasonal land cover change

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 t...

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Main Authors: Kehan HE, Lixing WANG, Zhu LIU
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-63144-4
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author Kehan HE
Lixing WANG
Zhu LIU
author_facet Kehan HE
Lixing WANG
Zhu LIU
author_sort Kehan HE
collection DOAJ
description 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.
format Article
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institution Kabale University
issn 2041-1723
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publishDate 2025-08-01
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spelling doaj-art-5552389f3d5d40aea8e0b46a8fcc41ae2025-08-24T11:38:39ZengNature PortfolioNature Communications2041-17232025-08-0116111110.1038/s41467-025-63144-4Global decarbonization corresponding with unseasonal land cover changeKehan HE0Lixing WANG1Zhu LIU2Institute for Climate and Carbon Neutrality, The University of Hong KongDepartment of Earth System Science, Tsinghua UniversityInternational Research Center of Big Data for Sustainable Development GoalsAbstract 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.https://doi.org/10.1038/s41467-025-63144-4
spellingShingle Kehan HE
Lixing WANG
Zhu LIU
Global decarbonization corresponding with unseasonal land cover change
Nature Communications
title Global decarbonization corresponding with unseasonal land cover change
title_full Global decarbonization corresponding with unseasonal land cover change
title_fullStr Global decarbonization corresponding with unseasonal land cover change
title_full_unstemmed Global decarbonization corresponding with unseasonal land cover change
title_short Global decarbonization corresponding with unseasonal land cover change
title_sort global decarbonization corresponding with unseasonal land cover change
url https://doi.org/10.1038/s41467-025-63144-4
work_keys_str_mv AT kehanhe globaldecarbonizationcorrespondingwithunseasonallandcoverchange
AT lixingwang globaldecarbonizationcorrespondingwithunseasonallandcoverchange
AT zhuliu globaldecarbonizationcorrespondingwithunseasonallandcoverchange