Multilevel Index Decomposition of Energy-Related Carbon Emissions and Their Decoupling from Economic Growth in Northwest China

Dong, Jiefang; Deng, Chun; Wang, Xingmin; Zhang, Xiaolei

doi:10.3390/en9090680

Cited by 20 publications

(21 citation statements)

References 73 publications

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“…Secondly, studies performed at the regional/provincial level were all resourceful, including those on Northwest China [20], Yunnan [21], Guangdong [22][23][24], Jiangsu [25,26], Shanghai [27], Chongqing [28,29], and Xinjiang [30]. The factors found to have the greatest effect on carbon emissions were economic activity [20], investment [21], energy density [21,23,24,26], economic growth [23,24], export [21], population scale [24], energy efficiency [20], technical progress [24], industrial structure [22,23,26], and energy Sustainability 2019, 11,7008 3 of 20 mix [24,26]. For example, Dong et al [21] analyzed the energy-related carbon emissions of five provinces in Northwest China from 1995 to 2012 (Shaanxi, Ningxia, Qinghai, Gansu, and Xinjiang) and found that the effect of economic activity produced a significant impact on increased carbon emission.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Prior to 1977, energy density produced a positive effect on carbon emissions but after 1978, it produced an inhibitory effect [30]. In terms of decoupling effect analysis, Dong et al [20] pointed out that in the five northwestern provinces, a strong decoupling effect was only seen from 2007 to 2009, and the rapid economic growth of the five northwestern provinces posed a serious threat to the decoupling process [20].…”

Section: Literature Reviewmentioning

confidence: 99%

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Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

Tseng

2019

Sustainability

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Inner Mongolia has shown both rapid economic growth and a large renewable energy base, this has come about by the introduction of the "Western Development" strategy and renewable energy policy of the Chinese Government. However, this has led to a contradictory situation where both high carbon emission and reduction exist together. The average economic growth of Inner Mongolia reached 15.76% between 2006 and 2016, which caused huge CO 2 emissions. However, promotion of the renewable energy policy (since 2005) resulted in an energy self-sufficiency rate that reached 270.80% by 2016. In this study of the Inner Mongolia carbon emission situation, the logarithmic mean divisia index (LMDI) model was used to analyze the factors affecting carbon emission fluctuations from 2005 to 2016. The decoupling elasticity index was then used to measure the decoupling effect of the economic growth and carbon emissions. The results of this research show that: firstly, CO 2 emissions increased rapidly from 651.03 million tons in 2006 to 1723.24 million tons in 2013. Despite a slight decline in CO 2 emissions, a level above 1600 million tons was maintained between 2014 and 2016. Secondly, the industry sector was the main source of CO 2 emissions in Inner Mongolia, and coal-based fuel played a determining role. Thirdly, in this study, two important contributions were made, including the discovery of two new drivers: labor and emission intensity factors. Further, findings about the effect of the six industrial sectors, economic structure, energy density, and emission intensity factors were also decomposed. It was found that during research period, the population factor, labor factor, and labor productivity factor all had a positive influence on CO 2 emissions, whereas the economic structure factor and emission intensity factor had different impacts on the CO 2 emissions depending on the particular industrial sector. Furthermore, the energy intensity of six industrial sectors contributed to the decrease in aggregate CO 2 emissions. Finally, in this study, it was also found that economic growth and CO 2 growth in Inner Mongolia presented a weak decoupling state. Policy recommendations based on these results have been presented.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

Tseng

2019

Sustainability

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“…For instance, Xinjiang Uygur Autonomous region, the biggest province in China (Fig. 1), has surging energy consumption demands and carbon emissions in the transport sector [7]. In 2013 the "One Belt, One Road" initiative was put forward by China's government [8][9].…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, the stock explanatory limitation of the LMDI method could be eliminated [52]. On the other hand, by combining index decomposition results and the decoupling index (especially the decoupling effort index), we can identify which factors and to what extent enhance or curtail carbon emissions [7,52,53].…”

Section: Introductionmentioning

confidence: 99%

Delinking Indicators on Transport Output and Carbon Emissions in Xinjiang, China

Dong

Huang

et al. 2017

Pol. J. Environ. Stud.

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This paper identifies the driving forces of CO 2 emissions from 1990 to 2014 in Xinjiang's transport sector based on the logarithmic mean divisia index (LMDI) method. Then we introduce the decoupling index to further quantitatively analyze the delinking indicators on the transport sector's growth and environmental pressures. The results indicate that: 1) CO 2 emissions increased significantly with an average annual growth rate of 8.7%. On the contrary, energy intensity has declined constantly over the study period. 2) Economic growth, population size, industrial structure, internal structural and energy mix have proven to contribute to CO 2 emissions increases. Moreover, economic growth plays a critical role in the increment with a contribution of 13.23 million tons, followed by population size and internal structure. 3) Xinjiang's transport witnessed a fluctuating decoupling progress with weak decoupling as the theme. In particular, the decoupling state moved from weak decoupling in 1991-2000 with short-term volatility to weak decoupling in 2001-2010. However, the coupling relationship was strengthened during 2011-2014. 4) Energy intensity is the most important factor for explaining the dissociation in Xinjiang's transport sector. However, internal structural, industrial structure, and population size has turned out to be the obstacles in decoupling progress.

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“…Among various index decomposition analysis methods of the IDA model, logarithmic mean Divisia index method was considered the most suitable and widely applied [48,49]. In addition, studies on decoupling tended to focus more on the exploration of the link between human economic activities and environmental changes [50][51][52], using Tapio method [53].…”

Section: Introductionmentioning

confidence: 99%

The Multilevel Index Decomposition of Energy-Related Carbon Emission and Its Decoupling with Economic Growth in USA

et al. 2016

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Abstract:The United States of America is not only an important energy consuming country, but also in the dominant position of energy for many years. As one of the two largest emitters, the US has always been trying to register a decline in energy-related CO 2 . In order to make a further analysis of the phenomenon, we choose a new decoupling analysis with the multilevel logarithmic mean Divisia index (LMDI) method. This study examined the contribution of factors influencing energy-related carbon emissions in the United States of America during 1990-2014, quantitatively analyzed decoupling indicators of economic development and environmental situations. As is indicated in the results, economy development and activities have a significant effect in increasing carbon emission, however, measures of energy optimization such as the improvement of energy efficiency has played a crucial role in inhibiting the carbon dioxide emission. Furthermore, as is indicated in decoupling relationship, "relative decoupling" and "no decoupling" are the main states during the examined period. In order to better investigate the long-run equilibrium relationship between total carbon dioxide emissions of each effect and the relationship between CO 2 emissions and economic growth, on the basis of a static decomposition analysis, we applied a dynamic analysis method-cointegration test. At last, recommendations and improvement measures aiming at the related issues were put forward.

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Multilevel Index Decomposition of Energy-Related Carbon Emissions and Their Decoupling from Economic Growth in Northwest China

Cited by 20 publications

References 73 publications

Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

Delinking Indicators on Transport Output and Carbon Emissions in Xinjiang, China

The Multilevel Index Decomposition of Energy-Related Carbon Emission and Its Decoupling with Economic Growth in USA

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