A decomposition analysis of energy-related CO2 emissions in Chinese six high-energy intensive industries

Du, Gang; Sun, Chuanwang; Ouyang, Xiaoling; Zhang, Chi

doi:10.1016/j.jclepro.2018.02.304

Cited by 111 publications

(37 citation statements)

References 40 publications

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“…Furthermore, the method of structural decomposition analysis (SDA) enables researchers to examine the driving forces of resources, emissions and other physical quantities (e.g., Hoekstra and Van Den Bergh, 2002;Ang, 2012, 2017;Arto and Dietzenbacher, 2014;Malik et al, 2016;Deng and Xu, 2017;Wang et al, 2017a). By using this method, energy, air pollutants and carbon emissions in China have been widely analyzed (e.g., Kagawa and Inamura, 2004;Peters et al, 2007;Guan et al, 2008Guan et al, , 2009Guan et al, , 2014Zhang, 2009;Xu et al, 2011;Zhang and Qi, 2011;Feng et al, 2012Feng et al, , 2017Xie, 2014;Zeng et al, 2014;Deng et al, 2016;Yuan and Zhao, 2016;Jiao et al, 2017;Liu and Liang, 2017;Mi et al, 2017aMi et al, , 2017bShi et al, 2017;Wei et al, 2017;Zhao et al, 2017;Du et al, 2018a;Zhang et al, 2018b). Despite of the existing literature on GHG emissions accounting, the current understanding of China's CH 4 emissions from both consumption-and income-based perspectives is insufficient.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic determinants of China's growing CH4 emissions

Chen

Guan

et al. 2018

Journal of Environmental Management

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Reducing CH emissions is a major global challenge, owing to the world-wide rise in emissions and concentration of CH in the atmosphere, especially in the past decade. China has been the greatest contributor to global anthropogenic CH emissions for a long time, but current understanding towards its growing emissions is insufficient. This paper aims to link China's CH emissions during 2005-2012 to their socioeconomic determinants by combining input-output models with structural decomposition analysis from both the consumption and income perspectives. Results show that changes in household consumption and income were the leading drivers of the CH growth in China, while changes in efficiency remained the strongest factor offsetting CH emissions. After 2007, with the global financial crisis and economic stimulus plans, embodied emissions from exports plunged but those from capital formation increased rapidly. The enabled emissions in employee compensation increased steadily over time, whereas emissions induced from firms' net surplus decreased gradually, reflecting the reform on income distribution. In addition, at the sectoral level, consumption and capital formation respectively were the greatest drivers of embodied CH emission changes from agriculture and manufacturing, while employee compensation largely determined the enabled emission changes across all industrial sectors. The growth of CH emissions in China was profoundly affected by the macroeconomic situation and the changes of economic structure. Examining economic drivers of anthropogenic CH emissions can help formulate comprehensive mitigation policies and actions associated with economic production, supply and consumption.

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Section: Introductionmentioning

confidence: 99%

Socioeconomic determinants of China's growing CH4 emissions

Chen

Guan

et al. 2018

Journal of Environmental Management

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“…Overall, Light industry (IS4), IS6, Nonmetals (IS7), IS8, and IS9 were major contributors to a city's industrial FEU. The energy-intensive industries, including IS6, IS7, and IS8, should be the hot spots [19,50,59]. For Beijing, IS8 and IS10 induced changes in industrial FEU most positively and negatively, respectively (see Figure 6).…”

Section: Determinants Of Changes In Sectoral Final Energy Usementioning

confidence: 98%

“…Given the merits of the LMDI approach over other IDA approaches with regards to the capability to successfully handle zero values and residuals and the computational simplicity, the LMDI approach is considered as the most accurate and practical IDA approach [13,17,18]. Therefore, it has been wildly used in measurement of the driving factors of changes in energy use and emissions at international, national, regional, provincial, city, and industrial levels [5,19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Some contributions were concentrated on decomposing China's industrial CO 2 emission, which dominated the total energy-related carbon emissions of economic activities [9,57,58]. Further, the range of industrial sectors was narrowed to six energy intensive industrial sectors [19,59]. China was also divided into three regions and 30 provinces with different classification of sectors to analyze the spatial and sectoral disparities in driving forces of emissions [8,14,[60][61][62][63].…”

Section: Introductionmentioning

confidence: 99%

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Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

et al. 2019

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With continuous industrialization and urbanization, cities have become the dominator of energy consumption, to which industry is making leading contribution among all sectors. Given the insufficiency in comparative study on the drivers of energy use across cities at multisector level, this study selected seven representative cities in China to quantify and analyze the contributions of factors to changes in final energy use (FEU) in industrial aggregate and sectoral levels by using Logarithmic Mean Divisia Index method. Disparities in the drivers of industrial FEU across cities were explicitly revealed within two stages (2005–2010 and 2010–2015). Some key findings are presented as follows. Alongside the increase in industrial output of seven cities within two stages, the variation trends in industrial FEU are different. Industrial output effect (contribution rate 16.7% ~ 184.0%) and energy intensity effect (contribution rate −8.6% ~ −76.5%) contributed to the increase in aggregate FEU positively and negatively, respectively. Beijing had the largest contribution share of industrial structure effect (−24.4% and −12.8%), followed by Shenyang and Xi’an. Contributions of energy intensity effect and industrial output effect for Chemicals, Nonmetals, Metals, and Manufacture of equipment were much larger than those of other sectors. The results revealed that production technological innovations, phase-out of outdated capacities of energy intensive industries, and industrial restructuring are crucial for reduction in industrial FEU of cities. This study also provided reference to reasonable industrial layout among cities and exertion of technological advantages from a national perspective.

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“…The study found that economic growth and energy intensity are the main factors leading to the increase of carbon dioxide emissions and the industrial structure is the main factor for carbon dioxide reduction. Du et al [28] studied the driving factors of China's high-energy-intensive industries and energy-related carbon dioxide emission changes by using the LMDI decomposition method. The study found that the expansion of industrial scale was the leading force explaining CO 2 emissions change in China's high-energy-intensive industries.…”

Section: Energy Consumption and Factors That Affect Carbon Emissionsmentioning

confidence: 99%

Analysis of CO2 Emissions in China’s Manufacturing Industry Based on Extended Logarithmic Mean Division Index Decomposition

et al. 2019

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China is the world’s largest emitter of CO2. As the largest sector of China’s fossil energy consumption and carbon emissions, manufacturing plays an important role in achieving emission reduction targets in China. Using the extended logarithmic mean division index (LMDI) decomposition model, this paper decomposed the factors that affect the CO2 emissions of China’s manufacturing industry into eight effects. The results show the following: (1) China’s manufacturing CO2 emissions increased from 1.91 billion tons in 1995 to 6.25 billion tons in 2015, with an average annual growth rate of 6%. Ferrous metal smelting and rolling were the largest sources of carbon dioxide emissions, followed by chemical raw materials and products and then non-metallic minerals. (2) During the research period, the industrial activity effects were the most important factor leading to increased CO2 emissions in manufacturing and energy intensity was the most important factor in promoting the reduction of CO2 emissions from manufacturing. The investment intensity was the second most influential factor leading to the increase in China’s manufacturing CO2 emissions after the industrial scale and this even exceeded the industrial activity effect in some time periods (2000–2005). R&D efficiency and R&D intensity were shown to have significant roles in reducing CO2 emissions in China’s manufacturing industry. The input of R&D innovation factors is an effective way to achieve emission reductions in China’s manufacturing industry. (3) There were differences in the driving factors of CO2 emissions in the manufacturing industry in different periods that were closely related to the international and domestic economic development environment and the relevant policies of the Chinese government regarding energy conservation and emission reduction. (4) Sub-sector research found that the factors that affect the reduction of CO2 emissions in various industries appear to be differentiated. This paper has important policy significance to allow the Chinese government to implement effective energy-saving and emission reduction measures and to reduce CO2 emissions from the manufacturing industry.

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A decomposition analysis of energy-related CO2 emissions in Chinese six high-energy intensive industries

Cited by 111 publications

References 40 publications

Socioeconomic determinants of China's growing CH4 emissions

Socioeconomic determinants of China's growing CH4 emissions

Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

Analysis of CO2 Emissions in China’s Manufacturing Industry Based on Extended Logarithmic Mean Division Index Decomposition

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