ORIGINAL RESEARCH
Regional Distribution of Carbon Intensity
and its Driving Factors in China: An Empirical
Study Based on Provincial Data
Rongwei Wu1, 2, Jiefang Dong3, Liang Zhou4, 5, Lu Zhang1, 2
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography,
Chinese Academy of Sciences, Urumqi 830011, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Department of Economics and Management, Yuncheng University, Yuncheng 044000, China
4Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
5Institute of Geographic Science and Natural Resources Research, CAS, Beijing 100101, China
Chinese Academy of Sciences, Urumqi 830011, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Department of Economics and Management, Yuncheng University, Yuncheng 044000, China
4Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
5Institute of Geographic Science and Natural Resources Research, CAS, Beijing 100101, China
Submission date: 2017-05-18
Final revision date: 2017-08-07
Acceptance date: 2017-08-10
Online publication date: 2018-02-05
Publication date: 2018-03-12
Pol. J. Environ. Stud. 2018;27(3):1331–1341
KEYWORDS
TOPICS
ABSTRACT
The regional distribution and driving factors of total carbon emissions have been the focus of considerable research. However, carbon intensity rather than total carbon emissions has been selected as the emissions reduction index in China. The Chinese government has committed to reducing carbon intensity by 60-65% from 2005 levels. Currently, limited academic attention has been given to the regional distribution and driving factors of carbon intensity. To explore the means of achieving the carbon intensity target in China, Gini coefficients were employed in this paper to investigate regional differences in carbon intensity across 30 provinces from 1995 to 2014. Moreover, the FGLS (feasible generalized least squares) method was applied to identify the key influencing factors of carbon intensity at the national and three regional levels. The results indicate that:
1. Chinese inter-provincial Gini coefficients of carbon intensity have increased steadily in recent years, which indicates that the difference in carbon intensity between provinces in China has widened.
2. Economic growth, foreign direct investment, and trade openness were negatively correlated with carbon intensity. Conversely, coal consumption, industrial proportion, and urbanization were positively correlated with carbon intensity. Moreover, urbanization has proven to be the most important factor affecting China’s carbon intensity.
3. The dominant cause of carbon intensity varies by region. In particular, the dominant cause of carbon intensity in low- and medium-level regions is urbanization. However, the dominant cause of carbon intensity in high-level regions is coal consumption.
4. Based on these empirical findings, policy recommendations to reduce carbon intensity were proposed. In summary, the improvement of urbanization quality in both low- and medium-level regions is urgently needed. However, optimizing the energy structure is essential to carbon intensity reduction in high-level regions.
1. Chinese inter-provincial Gini coefficients of carbon intensity have increased steadily in recent years, which indicates that the difference in carbon intensity between provinces in China has widened.
2. Economic growth, foreign direct investment, and trade openness were negatively correlated with carbon intensity. Conversely, coal consumption, industrial proportion, and urbanization were positively correlated with carbon intensity. Moreover, urbanization has proven to be the most important factor affecting China’s carbon intensity.
3. The dominant cause of carbon intensity varies by region. In particular, the dominant cause of carbon intensity in low- and medium-level regions is urbanization. However, the dominant cause of carbon intensity in high-level regions is coal consumption.
4. Based on these empirical findings, policy recommendations to reduce carbon intensity were proposed. In summary, the improvement of urbanization quality in both low- and medium-level regions is urgently needed. However, optimizing the energy structure is essential to carbon intensity reduction in high-level regions.
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