测绘学报 ›› 2024, Vol. 53 ›› Issue (8): 1480-1492.doi: 10.11947/j.AGCS.2024.20230324

• 时空大数据的地理认知专栏 • 上一篇    下一篇

土地利用碳收支精细化核算与时空特征分析

李佳(), 焦利民()   

  1. 武汉大学资源与环境科学学院,湖北 武汉 430079
  • 收稿日期:2023-08-07 发布日期:2024-09-25
  • 通讯作者: 焦利民 E-mail:jialee@whu.edu.cn;lmjiao@whu.edu.cn
  • 作者简介:李佳(1997—),男,博士生,主要研究方向为土地利用与可持续发展。E-mail:jialee@whu.edu.cn
  • 基金资助:
    国家自然科学基金(42371423);教育部人文社会科学基金(21YJC790006);中央高校基本科研业务费专项资金(2042023kfyq04);中国人民大学教育基金会林增杰土地科学发展基金优秀学术论文资助项目(2023)

Refined accounting and spatio-temporal characteristics of land use carbon budget

Jia LI(), Limin JIAO()   

  1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
  • Received:2023-08-07 Published:2024-09-25
  • Contact: Limin JIAO E-mail:jialee@whu.edu.cn;lmjiao@whu.edu.cn
  • About author:LI Jia (1997—), male, PhD candidate, majors in land use and sustainable development. E-mail: jialee@whu.edu.cn
  • Supported by:
    The National Natural Science Foundation of China(42371423);The Ministry of Education of Humanities and Social Science Project(21YJC790006);The Fundamental Research Funds for the Central Universities(2042023kfyq04);The Outstanding Academic Paper Grant Program of the Lin Zengjie Land Science Development Fund, Educational Foundation of Renmin University of China(2023)

摘要:

探索精细化的土地利用碳收支核算方法,分析当前低碳国土空间格局状况,对于推动土地低碳利用并助力实现“双碳”目标具有重要现实意义。本文基于地理大数据精细化核算了2008—2020年中国土地利用碳收支,并对其时空趋势特征进行分析。研究结论如下:①从时序上看,土地利用碳排放逐年上升,碳汇量年度变化较小;其中,建设用地是土地利用最大的碳源;农业用地已实现碳达峰;生态用地碳汇量目前仅能抵消7%左右的碳排放;土壤碳库多数年份表现为净碳损失。②从空间上看,空间集聚特征明显,锁定效应较强;碳排放总量方面,建设用地碳排放高值城市多以资源型、经济发达城市为主;农业用地碳排放高值区主要分布在粮食主产区;生态用地高碳汇区主要分布在“胡焕庸线”两侧及东南地区;土壤碳库碳损失量整体上东部较高,西部较低;③基于土地利用分析城市碳达峰状态,结果表明碳达峰在任何类型城市都有可能实现,主动达峰、被动达峰、平台期、未达峰城市数量分别占比10%、5%、31%及54%,城市层面的碳达峰压力仍然巨大。未来可以基于地理大数据,推动土地资源的精细化管理,针对不同地区、不同用地类型碳收支情况制定低碳土地利用转型策略,助力实现低碳国土空间优化与碳中和。

关键词: 土地利用碳收支, 精细化, 碳核算, 碳达峰, 时空特征

Abstract:

This research delves into refining the method of carbon budget accounting for land use, which is crucial for advancing low-carbon land utilization and aiding in achieving China's dual carbon goals. Utilizing geospatial big data, the study accounts for the carbon budget of China's land use from 2008 to 2020 across various land use types and examines their spatio-temporal trends. Key findings reveal a consistent annual increase in carbon emissions from land use, with minor changes in carbon sequestration. Construction land represents the predominant carbon source, while agricultural land has reached its carbon peak. Ecological land offsets only about 7% of carbon emissions, and soil carbon stocks are mostly experiencing net losses. The study also highlights significant spatial clustering and lock-in effects, with high carbon emissions from construction land often found in resource-rich or economically developed cities, and major grain-producing areas showing high agricultural carbon emissions. High carbon sequestration areas in ecological lands are located on both sides of the "Hu Line" and in the southeast, with soil carbon stock losses generally higher in the east. The study underscores the immense pressure of achieving carbon peak in land use across various city types, with active and passive peaks, plateau phases, and cities yet to reach their carbon peak constituting 10%, 5%, 31%, and 54%, respectively. The findings advocate for the adoption of geospatial big data-driven refined management of land resources, formulating low-carbon land use transition strategies tailored to different regions and land use types, thereby facilitating the optimization of low-carbon national land space and carbon neutrality.

Key words: land use carbon budget, refinement, carbon accounting, carbon peak, spatio-temporal characteristics

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