Economic growth and carbon emissions: Estimation of a panel threshold model for the transition process in China

面板数据 人均 国内生产总值 中国 城市化 北京 经济 环境污染 人口 自然资源经济学 地理 经济增长 环境保护 计量经济学 人口学 考古 社会学
作者
Zhiguang Song
出处
期刊:Journal of Cleaner Production [Elsevier]
卷期号:278: 123773-123773 被引量:74
标识
DOI:10.1016/j.jclepro.2020.123773
摘要

In recent years, the correlation between economic growth and environmental pollution has become noticeably contentious in China, which is experiencing the process of transitions in various social and economic sectors, for example, slowing economic growth, reformations to industrial and capital structure, ongoing urbanization, aggravated aging population and a series of environmental challenges, which are also known as a “New Normal” era in the 2010s. In the present study, a test is conducted on the non-linear relationship between economy and environment, which are represented as real GDP (Gross Domestic Product) per capita and carbon emissions per capita, respectively. Based on the data of a non-dynamic panel that covers 30 Chinese provinces from 2001 to 2016, the threshold regression model is applied with fixed effects to understand how the mentioned transitions impact on such relationship as structural breaks, in particular since its “New Normal” era. In brief, according to the estimation results, economic growth with a sustained high level of investment in technology (e.g., Northwest and Southwest China) and environmental protection activities are of empirical significance to cutting down on carbon emissions in China. Moreover, given the emissions reduction during China’s economic growth, “Economically significant provinces” concentrated in East China (e.g., Beijing, Tianjin, Shanghai, Jiangsu and Shandong in the present study) ought to develop and transfer their energy structure in a more renewable manner. In the meantime, “Energy abundant provinces” located in inland China are supposed to develop clean mining technology and strengthen their construction of energy delivery channel, which complies with an efficient market mechanism for cross-regional transaction in renewable energy from the perspective of sustainability.

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
歪歪发布了新的文献求助50
1秒前
共享精神应助adalu采纳,获得10
2秒前
2秒前
tingalan应助xkyasc采纳,获得10
2秒前
Orange应助颖火虫666采纳,获得10
3秒前
Jason完成签到,获得积分10
3秒前
4秒前
成就的菀发布了新的文献求助10
5秒前
wanci应助ZHANG采纳,获得10
6秒前
共享精神应助勤劳樱采纳,获得10
8秒前
8秒前
9秒前
11秒前
12秒前
13秒前
13秒前
13秒前
13秒前
14秒前
14秒前
郭腾完成签到,获得积分20
14秒前
uracil97完成签到,获得积分10
14秒前
15秒前
科研通AI2S应助睡不醒的网采纳,获得10
15秒前
科研通AI2S应助TARS采纳,获得10
15秒前
慕青应助孟琳朋采纳,获得10
15秒前
盘尼西林发布了新的文献求助10
16秒前
洛熙发布了新的文献求助10
16秒前
汉堡王发布了新的文献求助10
16秒前
尚欣雨完成签到 ,获得积分10
17秒前
小毛发布了新的文献求助10
17秒前
accept发布了新的文献求助10
18秒前
青空发布了新的文献求助50
18秒前
18秒前
爱笑映菡发布了新的文献求助10
19秒前
jaslek发布了新的文献求助10
19秒前
深情安青应助沈格采纳,获得10
20秒前
仙林AK47发布了新的文献求助10
21秒前
Owen应助patti采纳,获得10
22秒前
天天快乐应助柒柒的小熊采纳,获得10
24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
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Les Mantodea de guyane 2500
VASCULITIS(血管炎)Rheumatic Disease Clinics (Clinics Review Articles) —— 《风湿病临床》(临床综述文章) 1000
Feldspar inclusion dating of ceramics and burnt stones 1000
What is the Future of Psychotherapy in a Digital Age? 801
The Psychological Quest for Meaning 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5971777
求助须知:如何正确求助?哪些是违规求助? 7289297
关于积分的说明 15992554
捐赠科研通 5109654
什么是DOI,文献DOI怎么找? 2744087
邀请新用户注册赠送积分活动 1709830
关于科研通互助平台的介绍 1621780