“双碳”目标下湖北省耕地碳流生态网络形成机制与演变特征
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金面上项目(42271284);教育部人文社会科学研究规划基金(22YJA790088)


The formation mechanism and change characteristics of the ecological network of carbon flow of cultivated land in Hubei Province under the "dual carbon" targets
Author:
Affiliation:

Fund Project:

National Natural Science Foundation of China, No.42271284; Humanities and Social Sciences Foundation of MOE, No.22YJA790088

  • 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:

    作为人工-自然耦合的地理系统,耕地碳流生态网络的紊乱严重影响了区域碳平衡。"双碳"目标下,探究耕地碳流生态网络有助于推动其健康运转,对实现碳中和目标具有重要意义。基于湖北省土地利用数据和社会经济数据,从复杂地理系统视角,采用生态网络分析和GIS分析方法,在剖析2000-2020年湖北省耕地碳流生态网络的形成机制的基础上,实证研究了该网络的空间格局和内部结构的演变特征。研究表明:(1)湖北省耕地碳收支为净排放状态,整体呈现明显增长态势,导致耕地碳流密度高且递增趋势明显,并且耕地净碳排放和耕地碳流密度的区域差异显著。(2)湖北省耕地碳流生态网络整体紊乱,碳流转移呈现消极净碳流态势,网络中积极碳流明显小于消极碳流,对维护区域碳平衡产生负面作用。(3)网络碳流的分布重心集中在天门市的北部,范围主要覆盖以天门市为中心的湖北省中部偏南部地区,并且总体上呈现"西北-东南"分布格局。(4)基于网络结构分析,湖北省耕地碳流生态网络在土地利用碳流系统中发挥重要作用,凸显了优化湖北省耕地碳流生态网络的紧迫性。研究结果可为国土空间低碳优化提供新研究视角,以期为推动耕地碳流生态网络可持续发展、加快实现碳中和目标提供参考。

    Abstract:

    As an artificial-natural geographical system, the ecological network of carbon flow of cultivated land plays a crucial role in the regional carbon cycle balance. Disturbances to this network can significantly impact carbon equilibrium, potentially leading to long-term environmental consequences. Under the "dual carbon" targets (carbon peak and carbon neutrality), exploring the ecological network of carbon flow in cultivated land is essential for promoting its healthy operation and achieving carbon neutrality. This study focuses on Hubei Province, a critical agricultural region in central China, utilizing land-use data and socio-economic data from 2000 to 2020. Adopting a complex geographic system perspective, we employed a combination of ecological network analysis (ENA) and Geographic Information System (GIS) methods to examine the formation mechanism of the ecological network of carbon flow of cultivated land. On this foundation, we conducted an empirical study on the evolutionary characteristics of spatial patterns and the internal structure of this network. Our research revealed several key findings: (1) The carbon budget of cultivated land in Hubei Province exhibited net carbon emissions, demonstrating a substantial overall growth trend. Consequently, the carbon flow density of cultivated land was high with a pronounced increasing trend. The net carbon emissions and carbon flow density displayed significant regional variations. (2) The ecological network of carbon flow of cultivated land was found to be generally disordered. Carbon flow transfers predominantly showed a negative net carbon flow, with positive carbon flows significantly outweighed by negative carbon flows, negatively impacting regional carbon balance maintenance. (3) The distribution center of carbon flow in the network was concentrated in the northern part of Tianmen City. The distribution range mainly covers the south-central part of Hubei Province, with Tianmen City as the center, while the carbon flow exhibited a "northwest-southeast" spatial pattern. (4) Network structure analysis revealed the critical role of the ecological network of carbon flow of cultivated land in the land-use carbon flow system of Hubei Province. This finding emphasizes the urgent need for targeted optimization of the local ecological network of carbon flow of cultivated land to enhance carbon sequestration and mitigate carbon emissions. This research offers a novel perspective for low-carbon optimization of land use, providing valuable insights for policymakers and land managers. By understanding the dynamics of the ecological network of carbon flow of cultivated land, relevant government departments can develop more effective strategies for the sustainable development of the ecological network of carbon flow of cultivated land and the realization of carbon neutrality.

    参考文献
    相似文献
    引证文献
引用本文

张路,林学涵,葛硕,赵蓓蓓,张茂茂,李源.“双碳”目标下湖北省耕地碳流生态网络形成机制与演变特征.生态学报,2025,45(4):1599~1612

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: