4.4 Article

The Estimation of Environmental Kuznets Curve in China: Nonparametric Panel Approach

Journal

COMPUTATIONAL ECONOMICS
Volume 46, Issue 3, Pages 405-420

Publisher

SPRINGER
DOI: 10.1007/s10614-015-9486-7

Keywords

Industrial pollution; Economic growth; Environmental Kuznets curve; Nonlinear studies

Funding

  1. Chang Jiang Scholars Program of Education Ministry
  2. National Social Science Foundation [14ZDB144, 12AZD047]
  3. National Natural Science Foundation [71173048]
  4. Shanghai Leading Talent Project
  5. Fudan Zhuo-Shi Talent Plan

Ask authors/readers for more resources

In this paper, the panel data of industrial CO emissions among 31 provincial regions in China from 1985 to 2010 are utilized to analyze the nonlinear relationship between industrial pollution and economic development level based on nonparametric method for testing and verifying the environmental Kuznets hypothesis of carbon dioxide (CKC) in China. The industrial carbon dioxide emissions and GDP per capita are as measures of the industrial pollution and the level of economic development. The nonlinear methods have much more flexibility than linear model because there is no linear hypothesis, which may lead to the problem of model misspecification. Thus, nonlinear methods can obtain more accurate and effective results. Our results show that the nonlinear relationship between the industrial carbon dioxide emissions and the level of economic development has an inverted-U shape, which is the pattern of CKC curves. Moreover, Shanghai, Beijing and Tianjin have crossed the CKC inflection point and are now in developmental stage of environmental pollution reduced since 2005, 2006 and 2008 respectively. However, other areas are still in the earlier stage of environmental pollution, economic growth is accompanied by the tendency towards worsening environmental pollution.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available