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<p>Global land cover mapping is essential for environmental monitoring and earth system modeling, but the high production costs lead to considerable carbon dioxide emissions that are rarely considered. We develop a carbon dioxide emission method for land cover mapping, which takes into account both direct and indirect carbon dioxide emissions associated with the mapping process. The results show that completing annual global land cover mapping at a 30 m resolution generate an estimated 6,880.7 to 10,126.6 tonnes of carbon dioxide emissions. Based on mapping information extracted from publications published between 2014 and 2024, the cumulative emissions from land cover mapping during this period were approximately 3.89 million tonnes. Projections indicate that by 2050, annual emissions could exceed 184.09 million tonnes. To address the carbon effect, we propose three emission reduction strategies: artificial intelligence-assisted collaborative sample labeling, pre-training models with unlabeled data, and model compression techniques, achieving up to a 35% reduction in carbon dioxide emissions. Our results reveal the potential for carbon dioxide emission reduction in land cover mapping, providing a pathway toward sustainable, low-carbon mapping frameworks.</p>

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Carbon dioxide emissions from global land cover mapping are projected to increase by 2050

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月18日

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  • Hengbin Wang,
  • Yu Yao,
  • Yuanyuan Zhao,
  • Shaoming Li,
  • Zhe Liu,
  • Xiaodong Zhang

摘要

Global land cover mapping is essential for environmental monitoring and earth system modeling, but the high production costs lead to considerable carbon dioxide emissions that are rarely considered. We develop a carbon dioxide emission method for land cover mapping, which takes into account both direct and indirect carbon dioxide emissions associated with the mapping process. The results show that completing annual global land cover mapping at a 30 m resolution generate an estimated 6,880.7 to 10,126.6 tonnes of carbon dioxide emissions. Based on mapping information extracted from publications published between 2014 and 2024, the cumulative emissions from land cover mapping during this period were approximately 3.89 million tonnes. Projections indicate that by 2050, annual emissions could exceed 184.09 million tonnes. To address the carbon effect, we propose three emission reduction strategies: artificial intelligence-assisted collaborative sample labeling, pre-training models with unlabeled data, and model compression techniques, achieving up to a 35% reduction in carbon dioxide emissions. Our results reveal the potential for carbon dioxide emission reduction in land cover mapping, providing a pathway toward sustainable, low-carbon mapping frameworks.