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<p>A comprehensive understanding of land use carbon metabolism characteristics from the production-living-ecological space (PLES) perspective is crucial for formulating carbon reduction strategies. As the core economic zone of northern China, the Beijing-Tianjin-Hebei (BTH) region faces severe carbon emission pressures due to rapid urbanization and intensive land use transformation. However, focusing solely on carbon metabolism calculation without considering future changes and optimization effects may prevent achieving carbon emission reduction targets. This study assessed carbon emissions and sequestration based on different land use types in PLES, constructed a multi-objective carbon reduction scenario utilizing the Dinamica-EGO model, nondominated sorting genetic algorithm II, and entropy weight-TOPSIS model, and simulated 2035 carbon reduction characteristics by coupling PLES changes. Taking the BTH region as a case study, a methodological framework and corresponding models were established. The results show that from 2000 to 2020, the total carbon emissions in the BTH region increased significantly, presenting a spatial pattern of high emissions in the southeast and low emissions in the northwest. In contrast, the overall carbon sequestration capacity showed a decreasing trend, with stronger capacity in the northwest and weaker capacity in the southeast. The multi-variable 2035 carbon emission reduction prediction model achieved an accuracy of 82.24%. The 2035 carbon reduction plan developed based on this framework outperformed the original land use plan: economic benefits, emission reduction efficiency, spatial compactness, and accessibility are projected to increase by 15.8%, 7.9%, 2.5%, and 8.3%, respectively, while carbon emissions are expected to decrease by 19.04%. The proposed PLES-based framework for carbon metabolism measurement and emission reduction simulation exhibits good applicability in regional spatial emission reduction. These findings contribute to exploring regional carbon dynamics and provide references for governments to formulate carbon reduction policies.</p>

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The spatiotemporal distribution and multi-scenarios for land use carbon metabolism: simulation and carbon reduction from the production-living-ecological space perspective

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月21日

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  • Tian Chao,
  • Wang WenHui,
  • Zhao JinTao

摘要

A comprehensive understanding of land use carbon metabolism characteristics from the production-living-ecological space (PLES) perspective is crucial for formulating carbon reduction strategies. As the core economic zone of northern China, the Beijing-Tianjin-Hebei (BTH) region faces severe carbon emission pressures due to rapid urbanization and intensive land use transformation. However, focusing solely on carbon metabolism calculation without considering future changes and optimization effects may prevent achieving carbon emission reduction targets. This study assessed carbon emissions and sequestration based on different land use types in PLES, constructed a multi-objective carbon reduction scenario utilizing the Dinamica-EGO model, nondominated sorting genetic algorithm II, and entropy weight-TOPSIS model, and simulated 2035 carbon reduction characteristics by coupling PLES changes. Taking the BTH region as a case study, a methodological framework and corresponding models were established. The results show that from 2000 to 2020, the total carbon emissions in the BTH region increased significantly, presenting a spatial pattern of high emissions in the southeast and low emissions in the northwest. In contrast, the overall carbon sequestration capacity showed a decreasing trend, with stronger capacity in the northwest and weaker capacity in the southeast. The multi-variable 2035 carbon emission reduction prediction model achieved an accuracy of 82.24%. The 2035 carbon reduction plan developed based on this framework outperformed the original land use plan: economic benefits, emission reduction efficiency, spatial compactness, and accessibility are projected to increase by 15.8%, 7.9%, 2.5%, and 8.3%, respectively, while carbon emissions are expected to decrease by 19.04%. The proposed PLES-based framework for carbon metabolism measurement and emission reduction simulation exhibits good applicability in regional spatial emission reduction. These findings contribute to exploring regional carbon dynamics and provide references for governments to formulate carbon reduction policies.