A new panel for analyzing the impact factors on carbon emission: A regional perspective in China. (February 2019)

• Record Type: Journal Article
• Authors: Wu, Ya; Shen, Liyin; Zhang, Yu; Shuai, Chenyang; Yan, Hang; Lou, Yingli; Ye, Gui
• Is Part Of: Ecological indicators. Volume 97(2019)
• Page Start: 260
• Page End: 268
• Publication Date: 2019-02
• Subjects: Carbon emission -- Low carbon economy -- Two-dimensional panel framework -- Impact factors -- STIRPAT model -- Panel analysis; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2018.10.006
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: An economic performance-carbon index panel framework for classifying different regions is established. Carbon emission are decomposed into four impact factors. The understanding of impact factors offers valuable references for adopting low carbon economy measures. Abstract: As the largest carbon emitter in the world, China is under pressure in addressing carbon emission reduction. It is important for the country to understand the impact factors on emission so that proper mitigation measures can be taken. Previous studies have analyzed emission factors through classifying regions mainly by a single economic index or carbon index, whereas this study introduces a two-dimensional panel framework to classify regions. These two dimensions are per capita GDP and carbon intensity. Based on this panel framework, the 30 provinces in China are classified into four regions, namely, high economy and high carbon intensity (Region I ), high economy and low carbon intensity (Region II ), low economy and low carbon intensity (Region III ), and low economy and high carbon intensity (Region IV ). The STIRPAT (stochastic impacts by regression on population, affluence, and technology) model and the panel cointegration approach are employed to analyze the impacts of various carbon emission factors in the four classified regions. The results show that the most significant factor for Region I is industrial structure, and that for Regions II, III and IV is energy intensity, population size and per capita GDP respectively. The research results can help decision makers adopt effective emission control measures. The two-dimensional panel framework introduced can be extended for application to examine the emission impact factors at regional or national levels in other countries.