川西南攀西地区土地利用与碳储量动态分析:多情景下的时空演变与驱动力

IF 5.6 Q1 ENVIRONMENTAL SCIENCES
Zhiquan Zhou , Jiangtao Xiao , Lei Huang , Ruoying Song , Ping Ren
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引用次数: 0

摘要

准确评估和预测陆地碳储量(CS)的变化至关重要,特别是考虑到日益复杂的土地利用/土地覆盖(LULC)模式。这些努力对于中国实现碳峰值和碳中和的双重目标至关重要。西南山区作为国家碳战略的重点区域,是重要的生态屏障和主要的碳排放中心。应用PLUS、InVEST和GeoDetector模型,系统分析了2000 - 2035年攀西生态敏感区土地利用价值和土地利用价值在多种政策情景下的时空变化及其驱动机制。结果表明:①2000 - 2020年,攀西地区耕地面积减少4.25 × 106 t,主要是城市化进程加快和建设用地扩张导致耕地和草地面积减少,耕地和草地面积空间失衡加剧;(2)情景预测表明,只有生态保护情景(EPS)显著增加了区域碳排放,在2030年和2035年分别增加0.17 × 106 t和4.37 × 106 t,生态恢复措施的长期效益显著;(3) NDVI(56%)、人口(44%)和海拔(37%)是主要驱动因子,其中NDVI和人口的交互作用最强(q = 0.77)。通过将当地政策因素纳入情景分析,本研究增强了模型的实际相关性。研究结果对优化区域土地管理、平衡生态保护与经济发展、支持中国实现双碳目标具有重要的指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of land use and carbon storage dynamics in the Panxi region, Southwest Sichuan, China: Spatiotemporal evolution and driving forces in multiple scenarios
Accurately assessing and predicting changes in terrestrial carbon storage (CS) is essential, especially given increasingly complex land use/land cover (LULC) patterns. Such efforts are crucial for China to achieve its dual goals of carbon peaking and carbon neutrality. As a key region within the national carbon strategy, the mountainous areas of Southwest China serve as important ecological barriers and major CS. In this study, we applied the PLUS, InVEST, and GeoDetector models to systematically analyze the spatiotemporal changes and driving mechanisms of LULC and CS in the ecologically sensitive Panxi region under multiple policy scenarios from 2000 to 2035. The results show: (1) From 2000 to 2020, CS in Panxi decreased by 4.25 × 106 t, mainly due to accelerated urbanization and construction land expansion, resulting in the loss of cultivated land and grassland, and worsening spatial imbalance in CS; (2) Scenario projections indicate that only the Ecological Protection Scenario (EPS) significantly increases regional CS, with gains of 0.17 × 106 t and 4.37 × 106 t in 2030 and 2035, respectively, highlighting the long-term benefits of ecological restoration measures; (3) NDVI (56 %), population (44 %), and altitude (37 %) are identified as the main driving factors, with the interaction between NDVI and population showing the strongest effect (q = 0.77). By integrating local policy considerations into scenario analysis, this study enhances the practical relevance of the modeling. These findings provide important guidance for optimizing regional land management, balancing ecological protection and economic development, and supporting China's dual carbon targets.
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来源期刊
Environmental and Sustainability Indicators
Environmental and Sustainability Indicators Environmental Science-Environmental Science (miscellaneous)
CiteScore
7.80
自引率
2.30%
发文量
49
审稿时长
57 days
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