Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region
Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region
Jing-min Wang, Yu-fang Shi, Xue Zhao, and Xue-ting Zhang
Department of Economics Management, North China Electric Power University, No. 689 Huadian Road, Baoding 071003, China
Correspondence should be addressed to Yu-fang Shi; moc.361@29_fys
Received 23 December 2016; Revised 18 April 2017; Accepted 10 May 2017; Published 6 July 2017
Academic Editor: Aimé Lay-Ekuakille
Copyright © 2017 Jing-min Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: () Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. () The carbon factor for CO2/energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. () The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. () The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.
1. Introduction
With global warming increasing rapidly, the issue of the adverse impact of the greenhouse gas with which each country is confronted is serious. China is the largest developing country in the world with rapid economic development accompanying the augmentation of energy consumption and carbon emissions. With the increase in international pressure, China actively responds to climate change issues and determines to develop low-carbon economy. As one of the members of the United Nations Framework Convention on Climate Change (UNFCCC), China possesses an ambitious goal to reduce carbon intensity by 60–65% by 2030, from the level of 2005 in the Intended Nationally Determined Contribution (INDC) in 2015.
The Beijing-Tianjin-Hebei region, part of the large Bohai Bay economic zone, is the most developed economic zone and the largest industrial cluster in North China. Following the success of the Yangtze River Delta (YRD) and the Pearl River Delta (PRD), Beijing-Tianjin-Hebei is the third megaregion of China [1]. The integration of Beijing-Tianjin-Hebei region has become a major national strategy, defining its function of the area as world-class city group with the core of capital. The gradual expansion of economic scale in Beijing-Tianjin-Hebei region gives impetus to the augmentation of energy consumption and carbon emissions [2, 3], which directly lead to low economic benefits, environmental pollution, and other problems [4]. The industrial transition is imperative and the optimization of energy structure is urgent. It requires regional coordinated development [5]. In addition, there are few studies about energy-related carbon emissions in Beijing-Tianjin-Hebei region, which affects the degree of research on integrated development.
Currently, there are two most widely used methods to decompose carbon emission factors, namely, structural decomposition analysis (SDA) and index decomposition analysis (IDA) [6–8]. SDA is used to analyze the influencing factors by using the input-output tables in specific years. Compared to the SDA method, the application of IDA model is more extensive, which mainly contains the Laspeyres index and the Divisia index. The Laspeyres index approach is easier to understand without the “zero-value” problem, but the decomposition results have large residual terms [9]. Within the Divisia index, the Logarithmic Mean Divisia Index (LMDI) is extensively applied in factor decomposition of energy-related carbon emissions because it has the perfect decomposition and has no unexplained residuals [10, 11], which was first proposed by Ang and Zhang [12]. The LMDI method has been used in different countries for researching carbon emission, such as France [13], Latin American [14], Ireland [15], Portugal [16], United States [17], European Union [18], Spain [19], South Korea [20], and China [21–28]. Based on the index decomposition methods, the main conclusion is that economic growth was r (...truncated)