人口-土地城镇化协同下陆地植被碳汇的时空分异及驱动机制:基于GWRF和SEM混合方法研究
DOI:
作者:
作者单位:

1.湖南工商大学;2.常德学院;3.上海立信会计金融学院;4.上海财经大学;5.中国科学院生态环境研究中心

作者简介:

通讯作者:

中图分类号:

基金项目:

国家社会科学基金后期资助项目(24FJYB051);国家社会科学基金重大项目(22 ZD051,23 ZD067);湖南省自然科学基金面上项目(2024JJ5117)


Spatial-temporal heterogeneity and driving mechanisms of terrestrial vegetation carbon sinks under the synergy of population and land urbanization: A study based on the GWRF and SEM hybrid method
Author:
Affiliation:

1.Hunan University Of Technology and Business;2.Shanghai Lixin University of Accounting and Finance;3.Shanghai University of Finance and Economics;4.Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 文章评论
    摘要:

    人口与土地城镇化发展不均衡引致的“人地分离”冲突,已成为制约中国陆地生态系统碳汇功能提升的核心阻滞。本研究构建地理加权随机森林与结构方程模型的混合分析框架,系统解析城市陆地植被碳汇的时空分异规律及其复杂的因果驱动机制。研究发现:(1)中国城市植被碳汇呈现“南北高、中部低”分布格局,华中地区、华北地区及华东地区北部等地呈现“低基数、高增长”的追赶型态势,华南地区东部和华东地区南部则面临“高本底、负增长”的可持续性挑战。(2)土地城镇化是抑制植被碳汇的核心因素,其负向效应显著强于人口城镇化,且随时间推移持续增强。(3)人口城镇化的直接负向效应可被人居环境改善等间接路径缓解,总路径系数由-0.1175减弱至-0.0556;而土地城镇化的负向效应则因其无序扩张而被间接路径放大,总路径系数由-0.1847增强至-0.2983。(4)不同类型城市植被碳汇的驱动机制表现出根本性差异,在人地失调型城市,人口城镇化展现出正向驱动潜力且总路径系数为0.0618,而土地城镇化则始终呈现抑制效应;在人地协调型城市,人口与土地城镇化均呈强负向抑制,意味着城市发展进入了内涵式精细化绿色发展的新阶段。本研究为制定差异化的城市碳中和路径提供了关键依据,且应以锚定土地集约利用与人口合理再分布为关键抓手,在严控土地无序扩张的同时适度引导“人地失调”型城市的人口集聚,从而推动新型城镇化与“双碳”目标深度协同。

    Abstract:

    In this study, we address the "mismatch between population and land" conflict, resulting from unbalanced population and land urbanization, which has become a key impediment to enhancing carbon sink functionality in China's terrestrial ecosystems. We establish a hybrid analytical framework combining Geographically Weighted Random Forest and Structural Equation Modeling to systematically analyze the spatial-temporal heterogeneity and complex causal driving mechanisms of China's terrestrial vegetation carbon sinks. Our findings reveal several key points: (1) The carbon sink of urban vegetation in China exhibits a distribution pattern of "high in the south and north, low in the middle". Regions such as Central China, North China, and the northern part of East China show a catching-up trend of "low base, high growth". Meanwhile, the eastern part of South China and the southern part of East China face sustainability challenges of "high background, negative growth". (2) Land urbanization is the core driver suppressing vegetation carbon sinks, with its negative impact significantly stronger than that of population urbanization, and its marginal effect continuously strengthening over time. (3) The impact pathways of urbanization are dual-natured. The direct negative impact of population urbanization is moderated by its indirect positive effects, with the total path coefficient weakening from -0.1175 to -0.0556. In contrast, the negative effect of land urbanization is amplified by its indirect pathways due to disorderly expansion, with the total path coefficient increasing from -0.1847 to -0.2983. (4) The driving mechanisms differ fundamentally across cities with different development models. In "population-land mismatched" cities, population urbanization shows a unique positive driving potential, with a total path coefficient of 0.0618, while land urbanization remains the core inhibitory factor. In "population-land coordinated" mature cities, both types of urbanization transition into strong negative suppressors, marking a new stage of urban development that must rely on intensive and refined green development. Our conclusions provide empirical support for building differentiated urban carbon-neutral pathways, suggesting that while strictly controlling disorderly land expansion, appropriately guiding population agglomeration in "population-land mismatched" cities can provide a scientific basis for synergistically advancing new-type urbanization and the national "dual carbon" strategy.

    参考文献
    相似文献
    引证文献
引用本文

吴伟平,刘星宇,黄历,吴客形,王振军,王辰星.人口-土地城镇化协同下陆地植被碳汇的时空分异及驱动机制:基于GWRF和SEM混合方法研究.生态学报,,(). http://dx. doi. org/[doi]

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数: