This paper analyzes the reasons for regional variations in industrial CO2 emissions mitigation. First, regional industrial CO2 emissions during the “11th Five-Year Plan” period are calculated based on the presented method. Then a two-level perfect decomposition method, LMDI, is used to find the nature of the factors that influence the changes in energy-related industrial CO2 emissions in nine economic regions in China. The changes of industrial CO2 emissions are decomposed into energy emission factor effect, energy structure effect, energy intensity effect, industrial structure effect and economic output effect. As the results suggest, rapid growth of industry is the most important factor responsible for the increase in CO2 emissions. The adjustment of both industrial structure and energy structure contributes to the increase of CO2 emissions slightly. Energy consumption per unit GDP is the most important measure of CO2 emissions and the energy emission factor by itself also makes a weeny contribution to CO2 reduction as a result of electricity generation efficiency enhancement.
The next 15–20 years are expected to be an important period for China's social and economic development, as controlling greenhouse gasses (GHG), especially CO2emissions, will be the key to sustained development. In recent years, the high rate of economic growth has been accompanied by an equally high growth in fossil energy consumption, particularly because of the coal-dominated structure of the economy; China's CO2 emissions are now the highest in the world. According to IEA (2008), China was the world's largest emitter of CO2 in 2007. Not surprisingly, it faces increasing pressure from the international community to curb CO2 emissions. In the total CO2 emissions, industrial energy-related CO2 emissions account for over 90% of the total. Therefore, study of the driving forces governing industrial CO2 emissions has been of considerable interest to researchers and policymakers.
2. Literature review
Several studies have examined CO2 emissions in many countries. Claudia and Leticia (1998) presented an in-depth decomposition analysis of driving factors of CO2 emissions in the Mexican cement industry using the Refined Laspeyres Index method, and the results showed that energy intensity effect plays a significant role in the reduction of CO2 emissions while structure and output effect are found to be primarily responsible for driving CO2emissions growth. Lee and Oh (2006) decomposed the changes of CO2 emissions in APEC countries over time based on the logarithmic mean Divisia decomposition approach. The major findings were that growth in per capita GDP and population are the two dominant contributors to the increase in CO2 emissions in most cases. For reducing emissions, energy efficiency and fuel switching are found to be the two most promising areas for possible cooperation between APEC member countries. Hatzigeorgiou, Polatidis and Haralambopoulos (2008) applied both the Arithmetic Mean Divisia Index (AMDI) and the Logarithmic Mean Divisia Index (LMDI) techniques to analyze energy-related CO2 emissions in Greece from 1990 to 2002. In this paper changes in CO2 emissions are decomposed into four factors: income effect, energy intensity effect, fuel share effect and population effect. Claudia and Leticia (2010) also analyze energy and CO2 emission trends in Mexico's iron and steel industry during the period 1970–2006 by using international comparisons and Log mean Divisia index, in order to examine CO2 emissions related to energy use and production process. Gürkan (2011) performs a decomposition analysis based on Laspeyres Index method to identify factors that influenced changes in carbon dioxide emissions in Turkey from 1970 to 2006. He found that output effect contributes the most to CO2 emissions. Hammond and Norman (2012) has presented a decomposition analysis of energy-related carbon emissions by UK manufacturing industries; the authors segregate contributions of changes in output, industrial structure, energy intensity, fuel mix and electricity emission factor to the reduction in carbon emissions and show that the primary reason for the fall in emissions was reduction in energy intensity.
The existing literature about China's industrial CO2 emissions can be traced back to earlier use of the Divisia index method for the study of CO2 emissions intensity in 12 Asian countries, including China, by Shrestha and Timilsina (1996). Zhang (2003) analyzed the reasons for industrial energy intensity reduction in the 1990s. Wang, Chen, and Zou (2005)decomposed CO2 emissions in China during the period 1957–2000, based on LMDI index method, which is possibly the longest time span covered by a research effort in this context. Wu, Kaneko, and Matsuoka (2005) investigated the evolution of energy-related CO2emissions in China from 1985 to 1999, using the newly proposed three-level “perfect decomposition” method and provincially aggregated data. Afterwards Liu, Fan, Wu, and Wei (2007) analyzed the change of industrial carbon emissions from 36 industry sectors in China over the period 1998 to 2005, based on time series decomposition of LMDI. Zhang, Mu, Ning, and Song (2009) presented a decomposition analysis of CO2 emissions in China for the period 1991 to 2006 by using the Laspeyres index method, and in this paper changes in CO2 emissions are decomposed into four factors: output effect, energy intensity effect, fuel share effect and energy emission factor effect. Song and Lu (2009) adopted a two-stage LMDI model from 1990 to 2005 time series data of China, and then divided the factors that influence energy-related CO2 emissions into four aspects, and concluded that differences in the pattern of economic growth during the four stages are an important cause of fluctuations in carbon emissions. Wang and William (2010) provided an overview of the nonferrous metal industry in China from a CO2 emissions reduction perspective and addressed energy usage disaggregated by energy carrier and by province, but the paper considered only direct CO2emissions, without considering power consumption, which causes indirect CO2 emissions. Guo (2010) decomposed China's carbon emissions from 1995 to 2007 at industrial and regional levels using the LMDI index method.
The above literature review shows that the index decomposition analysis (IDA) method has been widely used in the study of CO2 emissions, by both domestic and foreign scholars. Among IDA methods, two methods that have been used most often are the Laspeyres index method and the Divisia index method. The Laspeyres method was first introduced by Howarth, Schipper, Duerr, and Strom (1991) and Park (1992) and has been extensively used in several decomposition studies since then. The major problem encountered with conventional Laspeyres method has been the large residual term found in most applications and that a significant part of the examined changes is left unexplained (Greening et al., 1997). In order to assign the residuals, Sun (1998) proposed the refined Laspeyres on the basis of the “jointly created and equally distributed” principle. Divisia index methods mainly include AMDI and LMDI. Boyd, Hanson, and Sterner (1988) introduced the AMDI method, but computational problems may arise in the application of AMDI when the data set contains zero values. Ang, Zhang, and Choi (1998) compared various index decomposition analysis methods and introduced the LMDI method to handle cases with zero values in the data set. Ang (2004) concluded that the LMDI method was the preferred method, due to its theoretical foundation, adaptability, ease of use and results' interpretation, along with some other desirable properties in the context of decomposition analysis. Ang (2005) provided a practical guide to LMDI, based on the change of Canada's industrial energy consumption and CO2 emission. It was pointed out that the results based on LMDI decomposition did not contain an unexplained residual term, and that all zeros in the data set could be replaced by a small positive constant (Ang and Liu, 2007a, Ang and Liu, 2007b). Wood and Lenzen (2006) argued that the LMDI strategy was not necessarily robust because it produces significant errors when applied in the decomposition of a data set containing a large number of zeros. At last, Ang and Liu, 2007a, Ang and Liu, 2007b gave eight strategies to handle zero values and negative values in LMDI decomposition approach, eliminating the only insufficiency of LMDI in practical use. Therefore, in the current decomposition system, the modified LMDI method has been recognized as the most widespread, practical and accurate IDA method.
This paper mainly makes two contributions to the literature on investigation of China's CO2emissions. First, we not only considered the direct CO2 emissions released by fixed energy, but also indirect emissions by heat and electricity, lest we greatly underestimate the actual amount of CO2 emissions by an industry segment. Second, we decompose the total industrial CO2 emissions into nine economic regions during the “11th Five-Year Plan” period and analyze the underlying reasons and compare differences between regions based on the two-level LMDI perfect decomposition method. This provides important information about change in carbon emissions in China's various regions and the reasons thereof, during the “12th Five-Year Plan” period (2011–2015).
The rest of the paper is organized as follows. In Section 3 we estimate CO2 emissions for 9 regions of China in detail. Section 4 focuses on the model of driving factors decomposition in CO2 emissions. Section 5 presents the estimation results and specification search. The last section is devoted to conclusions.
3. Estimating regional industrial CO2 emissions
3.1. Economic regions in China
On the basis of regional revitalization program guidance in China, this paper divides China into nine economic regions (Table 1). The regional revitalization program comprises “The regional planning of Yangtze River Delta Region” (2010), “The reformation and development planning outline of Pearl River Delta Region” (2008), “The integrated development planning of Bohai Rim Economic Region (underway)” (2011), “Some comments of the Central Committee of the CCP and the State Council on the rise of Midland Region” (2006), “The revitalizing planning of Northeast Region” (2007), “Some comments of the State Council on supporting Fujian to build Economic Zone on the Western Coast of the Taiwan Straits” (2009), “The regional planning of Chengyu Economic Region” (2011), “The 11th planning of West Development Strategy” (2006) and “Some comments of the State Council on promoting to build Hainan International Travel Island” (2009).
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