Knowledge of influencing factors of industrial carbon emissions (ICE) is crucial to the efforts of reducing anthropogenic greenhouse gas emissions. In this paper, main factors responsible for the ICE in Shanghai between 1996 and 2007 were identified and quantitatively analyzed using the Log-Mean Divisia Index method. It was found that the industrial output was the main driving force of ICE. The decline in energy intensity and the adjustment of energy and industrial structure are major determinants for reduction of ICE, with the former alone accounting for 90% of the reduction. To better investigate the relative contribution of different industrial sectors and their changes over time, we divided the study period into two equal time intervals and analyzed some high-carbon emission sectors. The results suggested that the intensity of energy use should be reduced further, for it was far higher than the world average. Adjustment of industrial structure by developing low-carbon emission industries is more crucial than energy mix.
1. Introduction
Global warming is considered one of the most pressing threats to the existence of human race. According to the IPCC Fourth Assessment Report [1], most of the observed increase in global annual average temperatures since the mid-20th century is very likely attributed to the observed increase in anthropogenic GHG, Greenhouse Gas, concentrations. Among them, carbon emissions from the fossil fuel energy consumption are the main source of anthropogenic GHG. As an important energy consumption sector, the industrial energy consumption (IEC) comprises roughly 40% of overall global energy use. In China, the terminal IEC amounts to about 60% of the total terminal energy consumption during the period of 1980–2005 [2], and its carbon emissions comprise 65% of the total from all kinds of energy consumption. While producing 5% of the national GDP (Gross Domestic Product) within a region of less than 0.1% of the land area of China, Shanghai is a huge energy consumer due to its large population and rapid economic development. As with the case at the national level, Shanghai’s IEC accounted for 58.2% of the total energy consumption, with electricity consumption making up 65.8% of IEC in 2007 [3]. In comparison, the carbon emissions from IEC take up 60% of the total from all sorts of energy consumption in Shanghai.
This paper is concerned about the question of what factors have the most influence on industrial carbon emissions (ICE) related to energy consumption in Shanghai. Knowledge of ICE is essential to the decision makers in countermeasure development and strategic planning to realize the goal of national carbon emission reduction. The paper intended to use decomposition analysis to disentangle distinct components behind historical carbon emission data in order to identify the factors that may have caused the observed changes. This same method has been used to analyze the carbon emission in the U.S. economy from 1972 to 1982, manufacturing carbon emission in 10 OECD(Organization for Economic Co-operation and Development) countries, ICE of European Union and carbon emission in APEC(Asia Pacific Economic Co-operation) countries [4], [5], [6], [7]. In general, there are two kinds of decomposition techniques. One is based on input-output tables and known as structural decomposition analysis (SDA). The other uses aggregate data at the sector-level and known as index decomposition analysis (IDA). SDA is capable of more refined decomposition of economic and technological effects, whereas IDA is capable of more detailed time and country studies because of the availability of data. In addition, IDA is characterized by a greater variety of indicator forms, mathematical (additive and multiplicative) specifications and indices [8]. Considering the attainable data for the industries in Shanghai, IDA is more applicable for our study.
Even within the IDA group, many methods are available for quantifying the impacts of factorial changes on the aggregating sectors. Among them, methods based on the Divisia index and those based on the Laspeyres index are well accepted, and in each case a number of different methods have been proposed by researchers. In this study, the log mean Divisia index method (LMDI) was chosen because of its leaving no residuals in the analysis and its time-reversal and factor reversal properties. LMDI performs well especially when there is large variation in the values of variables, and it can deal with the zero values in the dataset while other methods cannot [9], [10]. Several studies have used LMDI to investigate carbon emissions at regional and national scales [2], [4], [11].
Many studies with regard to ICE have been done using decomposition analysis, and main influencing factors are energy intensity, energy mix, industrial structure and industrial output. In this paper, to better investigate the relative contribution of different industrial sectors and their changes over time, we divided the study period into two time intervals and analyzed main high-carbon emission sectors. The rest of the paper is organized as follows. The decomposition method of LMDI and the factors of carbon emissions are described in Section 2, followed by a presentation of the data sources in Section 3. Section 4 discusses the decomposition results of ICE from 1996 to 2007 in Shanghai. Conclusions are provided in Section 5.
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