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<p>China's commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060 highlights the vital role of terrestrial ecosystems and urban agglomerations in the national carbon balance. However, understanding the complex dynamics within urban agglomerations remains limited. To address this gap, we developed an innovative modeling framework that combines Double-layers System Dynamics (DSD), Convolutional Neural Networks (CNN), and Cellular Automata (CA). This integrated model analyzes interaction patterns and response mechanisms within sub-urban agglomerations, projecting land use changes for three sub-urban agglomerations in the Triangle of Central China under economic priority development (EDP) and double carbon constraint (DCC) scenarios for 2030 and 2060. Utilizing an enhanced Net Ecological Productivity (NEP) model, we evaluated the impacts of projected climate variations and land use strategies over 2020–2060, incorporating Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) alongside land management policies. The results demonstrate that achieving carbon neutrality in terrestrial ecosystems in the Central Triangle by 2060 is challenging, but strict carbon emission control and land use management can effectively increase carbon sinks. These findings provide valuable insights for policymaking, emphasizing the importance of integrated carbon emission and land use to support China's carbon neutrality objectives.</p>

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A predictive modeling framework for carbon sink-source dynamics in urban agglomerations: a study of the Triangle of Central China

  • 期刊论文

  • 发表日期：

    2025年6月3日

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  • Youcheng Song,
  • Haijun Wang,
  • Xiaoxu Cao,
  • Cuifang Chen,
  • Xi Yang,
  • Bin Zhang,
  • Liangyu Xiang

摘要

China's commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060 highlights the vital role of terrestrial ecosystems and urban agglomerations in the national carbon balance. However, understanding the complex dynamics within urban agglomerations remains limited. To address this gap, we developed an innovative modeling framework that combines Double-layers System Dynamics (DSD), Convolutional Neural Networks (CNN), and Cellular Automata (CA). This integrated model analyzes interaction patterns and response mechanisms within sub-urban agglomerations, projecting land use changes for three sub-urban agglomerations in the Triangle of Central China under economic priority development (EDP) and double carbon constraint (DCC) scenarios for 2030 and 2060. Utilizing an enhanced Net Ecological Productivity (NEP) model, we evaluated the impacts of projected climate variations and land use strategies over 2020–2060, incorporating Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) alongside land management policies. The results demonstrate that achieving carbon neutrality in terrestrial ecosystems in the Central Triangle by 2060 is challenging, but strict carbon emission control and land use management can effectively increase carbon sinks. These findings provide valuable insights for policymaking, emphasizing the importance of integrated carbon emission and land use to support China's carbon neutrality objectives.