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<p>The urgency of addressing climate change has underscored the need for precise carbon management—an approach that precisely monitors, assesses, and manages carbon emissions and sequestration at fine spatial, temporal, and sectoral scales. This perspective paper examines the current state of precise carbon management, highlighting advancements in ground-based observations, remote sensing, process-based model, and machine learning. Despite these innovations, key challenges persist, including data fragmentation and interoperability, limited geographical and temporal monitoring coverage, difficulties in integrating multi-source data sets with varying resolutions, and insufficient public engagement and decision-support infrastructure. To address these barriers, we propose a roadmap that includes the development of standardized data frameworks, expansion of monitoring networks in underrepresented regions, creation of a foundational AI model for carbon data integration, and user-centric decision-support tools to bridge science-policy gaps. Together, these proposed strategies aim to enhance the accuracy, scalability, and transparency of carbon management strategies in support of global climate goals.</p>

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Toward precise carbon management: current status, gaps and future directions

  • 期刊论文

  • 发表日期：

    2026年3月1日

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  • Fangli Wei,
  • Jiang Zhang,
  • Lanhui Wang,
  • Lizhi Jia,
  • Jiameng Chen,
  • Jiapei Wu,
  • Mengfan Wei,
  • Yuanyuan Huang

摘要

The urgency of addressing climate change has underscored the need for precise carbon management—an approach that precisely monitors, assesses, and manages carbon emissions and sequestration at fine spatial, temporal, and sectoral scales. This perspective paper examines the current state of precise carbon management, highlighting advancements in ground-based observations, remote sensing, process-based model, and machine learning. Despite these innovations, key challenges persist, including data fragmentation and interoperability, limited geographical and temporal monitoring coverage, difficulties in integrating multi-source data sets with varying resolutions, and insufficient public engagement and decision-support infrastructure. To address these barriers, we propose a roadmap that includes the development of standardized data frameworks, expansion of monitoring networks in underrepresented regions, creation of a foundational AI model for carbon data integration, and user-centric decision-support tools to bridge science-policy gaps. Together, these proposed strategies aim to enhance the accuracy, scalability, and transparency of carbon management strategies in support of global climate goals.