Heterogeneous environmental regulations and carbon emission efficiency in China: A perspective of resource endowment

捐赠 中国 资源(消歧) 自然资源经济学 可持续发展 波特假说 面板数据 经济 环境法规 环境经济学 环境资源管理 业务 地理 生态学 政治学 计算机网络 计算机科学 法学 考古 计量经济学 生物
作者
Jiazhan Gao,Guihong Hua,AbidAli Randhawa,Baofeng Huo
出处
期刊:Energy & Environment [SAGE Publishing]
被引量:2
标识
DOI:10.1177/0958305x241270274
摘要

China, as the world's largest carbon emitter, is striving for green transformation through the implementation of various environmental policies. This study employs panel data from 30 Chinese provinces between 2000 and 2022 to analyze in-depth the heterogeneous effects of three types of environmental regulations. The findings reveal a U-shaped relationship between both general public environmental regulation (GER) and mandatory environmental regulation (MER) and carbon emission efficiency (CEE). Conversely, stimulating environmental regulations (SERs) exhibit an inverted U-shaped relationship with CEE. Mechanism analysis further reveals that environmental regulations enhance CEE by promoting industrial structural upgrades and technological innovation. Notably, SERs are particularly effective in improving the CEE in resource-rich and moderately resourced provinces. However, GER exhibits a masking effect on the pathway of technological innovation, indicating potential inefficiencies in its implementation. Moreover, heterogeneity analysis demonstrates that mandatory environmental regulation has a more pronounced impact on improving the CEE in resource-rich and moderately resourced provinces, whereas this impact is relatively weaker in resource-poor provinces. This finding underscores the importance of tailoring environmental policies to the specific resource characteristics of different regions. The insights from this study offer critical guidance for policymakers in designing and implementing differentiated environmental regulation policies, particularly in advancing China’s transition toward a sustainable, green, and low-carbon future.
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
欣喜沛芹发布了新的文献求助10
刚刚
来个肉盒子完成签到 ,获得积分10
刚刚
trap完成签到,获得积分10
刚刚
1秒前
2秒前
3秒前
香蕉觅云应助hilda采纳,获得10
4秒前
Plank发布了新的文献求助10
5秒前
6秒前
雪白的灵凡完成签到,获得积分10
7秒前
WilliamYuan应助lucy采纳,获得10
8秒前
9秒前
9秒前
David发布了新的文献求助30
10秒前
xxxxxwww应助N7采纳,获得10
10秒前
xiaxia发布了新的文献求助10
10秒前
11秒前
Owen应助雪白的灵凡采纳,获得10
11秒前
13秒前
顾矜应助阔达的向南采纳,获得10
13秒前
mouxq发布了新的文献求助10
14秒前
汉堡包应助Adelinelili采纳,获得30
15秒前
无算浮白完成签到,获得积分10
15秒前
嘎嘎嘎发布了新的文献求助10
16秒前
共享精神应助如意的晓旋采纳,获得10
16秒前
17秒前
冷酷保温杯完成签到,获得积分10
18秒前
cfy完成签到,获得积分10
19秒前
圆圆完成签到 ,获得积分10
19秒前
kk完成签到 ,获得积分10
19秒前
simons发布了新的文献求助20
19秒前
彭于晏应助Dissapper采纳,获得10
20秒前
20秒前
CipherSage应助near采纳,获得10
22秒前
田様应助dreamode采纳,获得10
23秒前
23秒前
灰灰发布了新的文献求助10
23秒前
虫虫发布了新的文献求助10
23秒前
23秒前
23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 6357100
求助须知:如何正确求助?哪些是违规求助? 8171731
关于积分的说明 17205670
捐赠科研通 5412803
什么是DOI,文献DOI怎么找? 2864774
邀请新用户注册赠送积分活动 1842223
关于科研通互助平台的介绍 1690446