Study on carbon sink of cropland and influencing factors: A multiscale analysis based on geographical weighted regression model

回归分析 碳汇 环境科学 回归 统计 水槽(地理) 计量经济学 地理加权回归模型 数学 地理 地图学 地质学 气候变化 海洋学
作者
Shixiong Song,Mingli Kong,Mingjian Su,Yongxi Ma
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:447: 141455-141455 被引量:13
标识
DOI:10.1016/j.jclepro.2024.141455
摘要

Analyzing the carbon sink of cropland and its relationship with influencing factors is of great significance to improve carbon sink and develop sustainable agriculture. However, fewer studies evaluated the spatial relationship at multiple scales. In this study, we first quantified the carbon sink of cropland in China in 2020 using empirical model. Then, we used the Geographical Weighted Regression model to quantify the relationship between carbon sink of cropland and influencing factors at the national, agricultural zone and provincial scales. Finally, we discussed potential ways to enhance carbon sink of cropland. The results found that the total carbon sink of cropland in 2020 was in surplus, with 2.56 billion tons, showing a spatial distribution of "high north and low south". There was significant spatial heterogeneity in the relationship between carbon sink of cropland and influencing factors. Labor size and agricultural inputs were most closely related to carbon sink of cropland. The areas with significant correlation were 116.99 and 108.79 million ha, respectively, or 93.17% and 86.64% of the total cropland. Carbon sink of cropland will have strong economic value, with 1.17 trillion yuan in China in 2020, which can increase farmers' per capita income by 1.34%. In order to enhance the carbon sink of cropland, we suggest that China's agricultural sector should reduce farmers' production costs, further improve the carbon trading platform for realizing the economic value of carbon sink of cropland, and then promote the sustainable development of agriculture.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
莫琳完成签到 ,获得积分10
2秒前
夨坕发布了新的文献求助10
5秒前
6秒前
南风未眠完成签到,获得积分10
7秒前
无极微光应助细腻听白采纳,获得50
7秒前
灰灰发布了新的文献求助10
10秒前
21完成签到,获得积分10
11秒前
11秒前
科研通AI6.1应助Deepsleep采纳,获得10
15秒前
swimming完成签到 ,获得积分10
17秒前
19秒前
夨坕完成签到,获得积分10
20秒前
来日可追发布了新的文献求助10
22秒前
我的Diy发布了新的文献求助10
23秒前
24秒前
赤峰发布了新的文献求助10
26秒前
one777完成签到,获得积分10
27秒前
28秒前
28秒前
汉堡包应助科研通管家采纳,获得10
28秒前
28秒前
28秒前
28秒前
28秒前
28秒前
28秒前
科研通AI2S应助科研通管家采纳,获得10
28秒前
28秒前
28秒前
无极微光应助科研通管家采纳,获得20
28秒前
miss张应助科研通管家采纳,获得10
29秒前
29秒前
菜园子发布了新的文献求助30
30秒前
33秒前
34秒前
李恺强完成签到,获得积分10
36秒前
hello_25baby完成签到,获得积分10
40秒前
王建平完成签到 ,获得积分10
41秒前
shmily完成签到,获得积分10
41秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
信任代码:AI 时代的传播重构 450
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6357297
求助须知:如何正确求助?哪些是违规求助? 8171997
关于积分的说明 17206526
捐赠科研通 5412966
什么是DOI,文献DOI怎么找? 2864858
邀请新用户注册赠送积分活动 1842270
关于科研通互助平台的介绍 1690520