The driving forces of China׳s energy use from 1992 to 2010: An empirical study of input–output and structural decomposition analysis

Coal consumption and carbon dioxide emissions from coal combustion in China are attracting increasing attention worldwide. Between 1990 and 2013, the coal consumption in Shandong Province increased by approximately 5.29 times. Meanwhile, the proportion of coal consumption of Shandong Province to China rose from 7.6% to 10.8%, and to the world, it rose from 1.8% to 5.5%. Identifying the drivers of coal consumption in Shandong Province is vital for developing effective environmental policies. This paper uses the Vector Autoregression model to analyze the influencing factors of coal consumption in Shandong Province. The results show that industrialization plays a dominant role in increasing coal consumption. Conversely, coal efficiency is the key factor to curtailing coal consumption. Although there is a rebound effect of coal efficiency in the short term, from a long-term perspective, coal efficiency will reduce coal consumption gradually. Both economic growth and urbanization have a significant effect on coal consumption in Shandong Province. In addition, the substitution effect of oil to coal has not yet met expectations. These findings are important for relevant authorities in Shandong in developing appropriate policies to halt the growth of coal consumption.

“…This result is in line with [25,26,[64][65][66][67]. The impulse response function shows there exists a rebound effect of coal intensity in Shandong province.…”

Section: Discussionsupporting

confidence: 73%

Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model

Deng

Dong

2016

“…The introduction of the extended IO table (i.e., the Hybrid Input-Output Table) allowed application of SDA framework to be extended to study changes in energy consumption (e.g., energy use [22,25], energy intensity [38], etc.) and environmental issues (e.g., carbon emissions [15,21,39,40], water resources [35,41,42], mercury emissions [43], PM2.5 emissions [44], environmental pressure [45], etc.).…”

Section: Structural Decomposition Analysis (Sda)mentioning

confidence: 99%

“…Minx et al used structural decomposition analysis to update Peters' previous analysis of China's carbon emissions and found that efficiency improvements have largely offset additional CO2 emissions from increased final consumption with special focus on the period 2002 to 2007 [21]. Xie investigated the driving forces of China's energy use from 1992 to 2010; results show that three-quarters of energy consumption changes came from investment activity between 2007 and 2010 [22]. All of these studies highlighted the efforts of efficiency gains for curbing carbon emissions, and found that the rapid economic development and increasing exports growth had great positive effects on carbon emissions in China.…”

Section: Introductionmentioning

confidence: 99%

Structural Decomposition Analysis of Carbon Emissions and Policy Recommendations for Energy Sustainability in Xinjiang

Wang

2015

“…[12] and Butnar and [13] analyzed the effect of international trade, internal demand, external demand, and other factors on energy consumption and carbon emission in Brazil. This method is also widely used in the analysis of China's industrial and regional energy consumption and carbon emissions [14,15], as well as analysis at the urban level [16].…”

Section: Mtmentioning

confidence: 99%

Using LMDI to Analyze the Decoupling of Carbon Dioxide Emissions from China’s Heavy Industry

Lin

Kui

2017

Abstract:China is facing huge pressure on CO 2 emissions reduction. The heavy industry accounts for over 60% of China's total energy consumption, and thus leads to a large number of energy-related carbon emissions. This paper adopts the Log Mean Divisia Index (LMDI) method based on the extended Kaya identity to explore the influencing factors of CO 2 emissions from China's heavy industry; we calculate the trend of decoupling by presenting a theoretical framework for decoupling. The results show that labor productivity, energy intensity, and industry scale are the main factors affecting CO 2 emissions in the heavy industry. The improvement of labor productivity is the main cause of the increase in CO 2 emissions, while the decline in energy intensity leads to CO 2 emissions reduction, and the industry scale has different effects in different periods. Results from the decoupling analysis show that efforts made on carbon emission reduction, to a certain extent, achieved the desired outcome but still need to be strengthened.