Terrain-vegetation interactions regulate carbon storage heterogeneity in the rapidly urbanizing Chaoshan coastal region
急速に都市化する潮汕沿岸地域における地形-植生相互作用が炭素貯留の不均一性を制御する (AI 翻訳)
Zili Xiong, Song Yao, H J LIU
🤖 gxceed AI 要約
日本語
本研究は、急速に都市化する中国潮汕地域を対象に、2000年から2020年までの土地利用データを用いて炭素貯留量の空間的不均一性を分析した。PLUSモデルで将来の土地利用を予測し、InVESTモデルで炭素貯留量を評価した結果、NDVIとDEMの相互作用が重要な要因であることが分かった。低炭素開発シナリオが炭素損失を最も抑制することを示し、地域別の対策を提案している。
English
This study analyzes carbon storage heterogeneity in the rapidly urbanizing Chaoshan region of China using land-use data from 2000 to 2020. It employs the PLUS model for future land-use simulation and the InVEST model for carbon storage assessment, finding that NDVI and DEM interaction is a key driver. The low-carbon development scenario best mitigates carbon loss, and spatially targeted strategies are proposed.
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
日本においても、都市化に伴う炭素貯留量の変化は重要な課題である。本研究で用いられたPLUSモデルやInVESTモデルは、日本の沿岸都市地域における炭素貯留評価にも応用可能であり、特にSSBJや統合報告書における土地利用変化の影響評価に示唆を与える。
In the global GX context
This study provides a quantitative framework for assessing carbon storage changes under different development scenarios, relevant to global urban sustainability. The combination of PLUS and InVEST models can be applied in other coastal urban areas, offering insights for climate disclosure frameworks like TCFD and ISSB regarding land-use impacts.
👥 読者別の含意
🔬研究者:Researchers can adopt the model-based approach for analyzing carbon storage heterogeneity and scenario simulations in urbanizing regions.
🏛政策担当者:Policymakers can utilize the spatially targeted strategies for balancing development and carbon conservation, especially in coastal zones.
📄 Abstract（原文）
Land use/cover change (LUCC) alters vegetation structure and carbon sequestration capacity, thereby reshaping regional carbon cycling. However, the mechanisms driving the spatial heterogeneity of carbon storage (CS) in rapidly urbanizing coastal regions remain insufficiently understood. In this study, the relationship between land use and CS was quantified using land-use data from 2000, 2010, and 2020 for the Chaoshan region. Future land-use patterns were simulated under three development scenarios using the PLUS model, CS dynamics were evaluated with the InVEST model, and the dominant drivers of CS spatial heterogeneity were identified using the Geographical Detector. The results indicate that between 2000 and 2020, artificial surfaces expanded significantly (889.23 km²), while cultivated land decreased substantially (782.98 km²), and wetlands nearly disappeared. Spatially, CS exhibited a pattern of low storage in the southeast and southwest and high storage in the central and north, a distribution primarily influenced by NDVI, DEM, and Slope. Notably, a strong interaction between DEM and NDVI (q = 0.62) highlights a critical terrain-vegetation synergy in shaping CS patterns. By 2030, CS is projected to decline under all three development scenarios, with the lowest reduction under the low-carbon development (LCD) scenario, emphasizing its significant advantage in mitigating carbon loss. This study proposes spatially targeted strategies integrating forest enhancement in high-carbon mountainous regions, compact urbanization with cropland protection in southeastern areas, and coastal-riparian wetland restoration, underpinned by carbon compensation mechanisms to balance development with ecological conservation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1038/s41598-026-60527-5first seen 2026-07-10 05:29:52
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。