Quantifying the spatiotemporal dynamics and impact factors of China's county-level carbon emissions using ESTDA and spatial econometric models

温室气体 溢出效应 人均 环境科学 空间相关性 单位(环理论) 空间分析 面板数据 计量经济学 空间计量经济学 碳纤维 人口 计量经济模型 自然资源经济学 地理 经济地理学 经济 统计 数学 生态学 人口学 复合数 生物 遥感 社会学 数学教育 微观经济学 算法
作者
Xiaojie Liu,Xiaobin Jin,Xiuli Luo,Yinkang Zhou
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:410: 137203-137203 被引量:86
标识
DOI:10.1016/j.jclepro.2023.137203
摘要

China currently has the largest total CO2 emissions and is one of the countries that has made the most efforts to cut these. Anthropogenic emissions from county-level districts play a pivotal role in meeting carbon neutral due to the favorable breakdown of reduction targets into sub-national units, whereas limited work has been done related to those features and determinants. Here, we attempted to quantify the spatiotemporal dynamics of county carbon emissions and their drivers in China between 2000 and 2020 using an exploratory space-time data analysis (ESTDA) and spatial econometric method based on a remote sensing image inversion dataset. The results showed that emissions per capita and per unit of GDP in Chinese counties took on drastically opposite stances amid a trend of increasing carbon emissions. Meanwhile, their carbon emissions exhibited a pronounced regional disparity and spatially positive autocorrelation, characterized by sharp spatial heterogeneity and clustering. Local indicators of spatial association implied that the patterns of county carbon emissions had certain spatial integration, and that locally correlated forms were represented by deep path dependence and spatial locking effects. Besides, a range of panel regression models provided evidence of endogenous interactions of county carbon emissions, notably where every 1% increase in the neighboring emissions induced a local emissions increase by at least 0.4%. Various factors employed not only exerted a direct impact on local carbon emissions, but had spatial spillover effects on neighboring districts. Of these, economic level and industrial structure presented a significantly positive relationship with carbon emissions, while population clustering, financial input and technological advance had clear inhibitory effects. Our findings cast fresh light on the importance of the socioeconomic diversity of a district and its neighbors for government policy decisions related to carbon abatement at the county level.
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
Black完成签到,获得积分10
刚刚
SciGPT应助哈哈哈采纳,获得10
刚刚
哈哈完成签到 ,获得积分10
刚刚
1秒前
Yiran发布了新的文献求助10
1秒前
思源应助阳仔采纳,获得10
1秒前
爆米花应助清新的紫蓝采纳,获得10
2秒前
林一发布了新的文献求助10
2秒前
feitan发布了新的文献求助10
3秒前
wanci应助温暖的乌龟采纳,获得10
3秒前
初景发布了新的文献求助30
3秒前
3秒前
852应助LiuZheng采纳,获得10
3秒前
4秒前
完美世界应助冲冲ccc采纳,获得10
4秒前
4秒前
FashionBoy应助李静静采纳,获得10
4秒前
CodeCraft应助zhaohaocheng采纳,获得10
5秒前
爱听歌安彤完成签到,获得积分10
5秒前
司思完成签到,获得积分10
6秒前
6秒前
北野发布了新的文献求助10
6秒前
6秒前
sanqiuguizi发布了新的文献求助10
6秒前
Changtraigiaochi完成签到,获得积分10
6秒前
盐水虾哟哟哟完成签到,获得积分10
6秒前
冷酷的踏歌完成签到,获得积分10
6秒前
lkk完成签到,获得积分10
7秒前
飞飞鱼完成签到,获得积分10
7秒前
搜集达人应助慧子采纳,获得10
8秒前
AJ2发布了新的文献求助10
9秒前
顺利汉堡完成签到 ,获得积分10
9秒前
zp关闭了zp文献求助
9秒前
公西傲蕾完成签到,获得积分10
10秒前
LZ发布了新的文献求助10
10秒前
1234发布了新的文献求助10
10秒前
乐乐应助酷酷采纳,获得10
10秒前
善良傲晴完成签到,获得积分10
11秒前
12秒前
Nole应助yht采纳,获得10
12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
Navigating Normative Orders. Interdisciplinary Perspectives 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
CLSI VET01S-2024 Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals (7th Ed) 500
A Case Study on Hotels as Noncongregate Emergency Living Accommodations for Returning Citizens 500
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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7756500
求助须知:如何正确求助?哪些是违规求助? 9302923
关于积分的说明 20272149
捐赠科研通 7339879
什么是DOI,文献DOI怎么找? 3311562
关于科研通互助平台的介绍 2462414
邀请新用户注册赠送积分活动 2325064