A Factor Decomposition on China’s Carbon Emission from 1997 to 2012 Based on IPAT-LMDI Model

Mathematical Problems in Engineering, May 2015

We probe into the key factors that possess significant effects on China’s CO2 emissions during 1997–2012 on the basis of IPAT-LMDI model. Carbon dioxide emissions are specifically decomposed into CO2 emission intensity, energy structure, energy intensity, industrial structure, economic output, and population scale effects. Results indicate that the paramount driving factors that resulted in the growth of CO2 emissions are economic output, population scale, and energy structure. In contrast, energy intensity and industrial structure generally play an outstanding role in reducing emissions. This paper constructs a new weight assessment system by introducing “contribution value-significant factor-effect coefficient” to replace “contribution value-contribution rate” in the previous literature. According to the most significant positive effect and the most negative effect from the conclusion, we point out the effective policies that can not only accelerate the target of “China’s carbon emissions per unit of GDP could be cut down by 40–45% by 2020, from 2005 levels,” but also have crucial significance on the low-carbon economic development strategy of China.

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A Factor Decomposition on China’s Carbon Emission from 1997 to 2012 Based on IPAT-LMDI Model

A Factor Decomposition on China’s Carbon Emission from 1997 to 2012 Based on IPAT-LMDI Model Wei Li, Ya-Bo Shen, and Hui-Xia Zhang Department of Economic Management, North China Electric Power University, No. 689 Huadian Road, Baoding 071003, China Received 23 January 2015; Accepted 13 May 2015 Academic Editor: Matteo Gaeta Copyright © 2015 Wei Li 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 We probe into the key factors that possess significant effects on China’s CO2 emissions during 1997–2012 on the basis of IPAT-LMDI model. Carbon dioxide emissions are specifically decomposed into CO2 emission intensity, energy structure, energy intensity, industrial structure, economic output, and population scale effects. Results indicate that the paramount driving factors that resulted in the growth of CO2 emissions are economic output, population scale, and energy structure. In contrast, energy intensity and industrial structure generally play an outstanding role in reducing emissions. This paper constructs a new weight assessment system by introducing “contribution value-significant factor-effect coefficient” to replace “contribution value-contribution rate” in the previous literature. According to the most significant positive effect and the most negative effect from the conclusion, we point out the effective policies that can not only accelerate the target of “China’s carbon emissions per unit of GDP could be cut down by 40–45% by 2020, from 2005 levels,” but also have crucial significance on the low-carbon economic development strategy of China. 1. Introduction Nowadays, the world economy is in a period of transition to a low-carbon economy, while China is facing a stage of rapid development of industrialization and urbanization. Economic growth gives rise to the augmentation of energy consumption and CO2 emissions [1]. China is the largest developing country in the world with the rapid economic development. China’s GDP has increased (from 7225.34 to 29533.57 billion Yuan) sharply at the average annual rate of 9.84%, and the per capita GDP has increased (from 5873.91 to 21865.47 Yuan) dramatically at the average annual rate of 9.16% [2]. The total GDP has run up to 5878.61 billion dollars by 2010 exceeding Japan, turning into the second largest economy in the world. At the same time, China’s primary energy consumption raised sharply from 1.38 to 3.62 billion ton coal equivalent (tec) during 1997–2012 [2]. With the persistent focus on climate change from all countries in the world, carbon emissions have captured growing attention of our government. China’s carbon emissions keep on rising fleetly with relevant energy consumption level, from 3528.02 million tons in 1997 to 9774.47 million tons in 2012. China’s energy-related carbon emissions have reached 7.03 billion tons by 2008, surpassing the United States to become the world’s largest emitter for the first time. That makes China’s international pressure increase in response to climate change issues. China possesses an ambitious goal to reduce carbon emissions intensity by 40–45% of the 2005 level by 2020. Carbon emissions will constraint China’s energy consumption in the process of the increased economy. The IPAT identity was first proposed by Professors Ehrlich and Holdren to depict the influence on the environment of growing population [3, 4]. Nakicenovic used a reformulation of this IPAT identity known as the Kaya equation to be the basis for the GHG emissions calculations, projections, and scenarios conducted by the Intergovernmental Panel on Climate Change [5]. Two main methods are used for decomposition of carbon emission factors: structural decomposition analysis (SDA) and index decomposition analysis (IDA) [6, 7]. In recent years, many scholars have done multitude research on carbon emissions with the SDA and they found a host of practical significant conclusions [8–11]. Ang put forward the log Mean Divisia Index in the first place [12]. The log Mean Divisia Index (LMDI) can not only be used to decompose multiple factors with zero residual errors, but it can also be used to solve the problem of insufficient data. With this method, carbon emissions can be resolved quantitatively into several effects [13, 14]. Ang and Wang et al. came to the conclusion by using the LMDI method that economic growth is a leading cause of carbon emissions and energy intensity is seen as essential effect if China’s carbon emissions are looked forward to being reduced over the long term [15, 16]. Chunbo, Tunc, and Claudia explained the primary element affecting the change of China’s carbon emissions. Turkey and Mexico applied the LMDI approach to decompose carbon emissions [17–19]. Paul and Bhattacharya pointed out that energy intensity, industrial structure, the change of GDP, and emis (...truncated)


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Wei Li, Ya-Bo Shen, Hui-Xia Zhang. A Factor Decomposition on China’s Carbon Emission from 1997 to 2012 Based on IPAT-LMDI Model, Mathematical Problems in Engineering, 2015, 2015, DOI: 10.1155/2015/943758