Spatial-temporal evolution of carbon storage and its driving factors in the Shanxi section of the Yellow River Basin, China

IF 2.6 3区环境科学与生态学 Q2 ECOLOGY

Ecological Modelling Pub Date : 2025-02-08 DOI:10.1016/j.ecolmodel.2025.111039

Jiakai Ma, Zixuan Hao, Yaqi Shen, Zhilei Zhen

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引用次数: 0

Abstract

The Yellow River Basin (YRB) holds plays a crucial role in China's socioeconomic development and ecological security, and it is also a key strategic area for achieving carbon reduction targets. This study focused on the Shanxi section of the Yellow River Basin (SYRB), utilizing the PLUS-InVEST model to assess changes in carbon storage (CS) from 2000 to 2020. Then, the Structural Equation Modeling (SEM) and optimal parameters-based geographical detector (OPGD) models were employed to explore the impact of various driving factors on carbon storage changes. Finally, the study predicted land use/cover (LULC) changes by 2040 and the distribution characteristics of CS under different development scenarios. The results showed that the CS in the SYRB has been diminishing from 2000 to 2020, with a reduction of 4.08 × 10⁶ t. The LULC changes had the most critical effect on CS, and then the elevation, annual precipitation, and net primary productivity also played an important impact on CS. Moreover, the interaction of LULC and other driving factors presented the strongest impact on CS. By 2040, the CS in the SYRB under the natural development, ecological protection, urban development, and comprehensive development scenarios were 16.68 × 10⁸, 16.74 × 10⁸, 16.63 × 10⁸, and 16.69 × 10⁸ t, respectively. Local Moran's I indicated that the High-High values of CS accumulation were predominantly found in the mountainous areas in 2040, while the Low-Low values of CS accumulation were mainly focused in the urban areas in the middle of the SYRB. This study emphasized the importance of human activities and the natural environment in achieving carbon neutrality, and that we should take ecological conservation measures to increase the level of regional CS.

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黄河流域山西段碳储量时空演变及其驱动因素

黄河流域在中国的社会经济发展和生态安全中发挥着至关重要的作用，也是实现碳减排目标的关键战略区域。本研究以黄河流域山西段为研究对象，利用PLUS-InVEST模型对2000 - 2020年黄河流域碳储量变化进行了评估。然后，采用结构方程模型（SEM）和基于最优参数的地理探测器（OPGD）模型，探讨各驱动因素对碳储量变化的影响。最后，预测了2040年中国土地利用/覆被（LULC）变化趋势及不同发展情景下土地利用/覆被的分布特征。结果表明：2000 ~ 2020年，sysyb的CS呈减少趋势，减少幅度为4.08 × 106 t，其中LULC变化对CS的影响最为关键，其次是海拔、年降水量和净初级生产力。此外，LULC与其他驱动因素的交互作用对CS的影响最大。到2040年，自然开发、生态保护、城市开发和综合开发情景下，长江流域生态承载力分别为16.68 × 108 t、16.74 × 108 t、16.63 × 108 t和16.69 × 108 t。局部Moran’s I表明，2040年高-高CS积累值主要集中在山区，低-低CS积累值主要集中在syb中部的城市地区。本研究强调了人类活动和自然环境对实现碳中和的重要性，并指出应采取生态保护措施提高区域碳中和水平。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Modelling 环境科学-生态学

CiteScore

5.60

自引率

6.50%

发文量

259

审稿时长

69 days

期刊介绍： The journal is concerned with the use of mathematical models and systems analysis for the description of ecological processes and for the sustainable management of resources. Human activity and well-being are dependent on and integrated with the functioning of ecosystems and the services they provide. We aim to understand these basic ecosystem functions using mathematical and conceptual modelling, systems analysis, thermodynamics, computer simulations, and ecological theory. This leads to a preference for process-based models embedded in theory with explicit causative agents as opposed to strictly statistical or correlative descriptions. These modelling methods can be applied to a wide spectrum of issues ranging from basic ecology to human ecology to socio-ecological systems. The journal welcomes research articles, short communications, review articles, letters to the editor, book reviews, and other communications. The journal also supports the activities of the [International Society of Ecological Modelling (ISEM)](http://www.isemna.org/).