Carbon Storage Response to Land Use Change and SSP-RCP Scenario Simulation: A Case Study of Coastal Area in China
土地利用変化に対する炭素貯蔵応答とSSP-RCPシナリオシミュレーション:中国沿岸域の事例研究 (AI 翻訳)
Zenglin Hu, Luodan Cao, Jialin Li, Ruiqing Liu
🤖 gxceed AI 要約
日本語
中国沿岸域の2000~2024年の土地利用・炭素貯蔵の時空間動態を分析し、2050年の複数シナリオをシミュレーション。森林減少が炭素損失の主因であり、持続可能なSSP1-2.6が炭素貯蔵安定に最適であることを示した。XGBoost-SHAPとGAMを用いて、人為活動の非線形影響が増大していることを明らかにした。
English
This study analyzes spatiotemporal dynamics of LULC and carbon storage in China's coastal regions (2000-2024) and simulates 2050 multi-scenario trajectories using SSP-RCPs. Forest loss is the dominant driver of carbon loss, with SSP1-2.6 being the most favorable pathway. Using XGBoost-SHAP and GAMs, it reveals increasing nonlinear impacts of human activities on carbon storage.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国沿岸域の事例だが、日本でも土地利用変化と炭素貯蔵の関係は重要。特にSSBJでの自然資本開示や、日本の「ダブルカーボン」目標に対する示唆を含む。ただし、直接的な開示基準への応用は限定的。
In the global GX context
While focused on China, this paper's methodology for linking land use change to carbon storage under different SSP-RCP scenarios is relevant for global climate mitigation planning. It demonstrates how machine learning (XGBoost) can quantify drivers of carbon storage change, a technique applicable to disclosure frameworks like TCFD or TNFD.
👥 読者別の含意
🔬研究者:Provides a robust framework for integrating land-use and carbon storage modeling with scenario analysis, useful for climate impact studies.
🏢実務担当者:Offers insights on how land-use decisions affect carbon storage, relevant for corporate natural capital accounting and offset strategies.
🏛政策担当者:Highlights the importance of sustainable land-use pathways (SSP1-2.6) for maintaining carbon storage, informing spatial planning policies.
📄 Abstract(原文)
Land use/land cover (LULC) is one of the core driving factors affecting terrestrial ecosystem carbon storage and exacerbating global warming. As an area with the most intense land–sea interactions, China’s coastal zone has experienced drastic LULC transition and carbon storage fluctuations during the rapid urbanization process. Based on the InVEST model, this study analyzes the spatiotemporal dynamics of LULC and carbon storage (CS) in China’s coastal regions from 2000 to 2024, and simulated multi-scenario carbon storage trajectories for 2050 integrating the SSP-RCP scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, the XGBoost-SHAP and generalized additive models (GAMs) were introduced to deeply analyze the nonlinear characteristics and temporal heterogeneity of the driving mechanisms of CS evolution. The results show the following: (1) During the study period, the LULC structure of the coastal region was dominated by cropland and forestland consistently accounting for over 85%, but exhibited a competitive pattern characterized by the continuous expansion of built-up land severely squeezing ecological spaces. (2) The total regional CS showed an overall phased downward trend, accompanied by increasing fragmentation of high carbon sink areas. Notably, as the core carbon pool, the reduction in forest area was the dominant factor causing regional net carbon losses. (3) CS remained relatively stable under SSP1-2.6, representing a sustainable development pathway with low greenhouse gas emissions. In contrast, SSP2-4.5, SSP3-7.0, and SSP5-8.5 exhibited more pronounced declines in carbon storage by 2050, indicating that SSP1-2.6 is the most favorable pathway for maintaining long-term carbon storage stability in China’s coastal regions. (4) The driving mechanism of CS has undergone a profound shift from being dominated by natural ecological baselines to human activities. Land use intensity (LUI) has emerged as the strongest predictor in the model, and the nonlinear impacts of human activities have grown increasingly complex over time. This study highlights the complex impacts of high-intensity human disturbances on the coastal carbon cycle, providing a scientific basis for formulating differentiated carbon management strategies and adaptive spatial land-use planning oriented toward the “Dual Carbon” goals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/land15071137first seen 2026-06-27 05:04:46
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