scholarly journals Multi-scale decomposition of energy-related industrial carbon emission by an extended logarithmic mean Divisia index: a case study of Jiangxi, China

2019 ◽  
Vol 12 (8) ◽  
pp. 2161-2186 ◽  
Author(s):  
Junsong Jia ◽  
Huiyong Jian ◽  
Dongming Xie ◽  
Zhongyu Gu ◽  
Chundi Chen
2021 ◽  
Author(s):  
Guobao Xiong ◽  
Junhong Deng ◽  
Baogen Ding

Abstract Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data suggest weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect on emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.


2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiandong Chen ◽  
Ming Gao ◽  
Ding Li ◽  
Malin Song ◽  
Qianjiao Xie ◽  
...  

Although the logarithmic mean Divisia index (LMDI) approach has been widely used in the field of energy and environmental research, it has a shortcoming. Since the LMDI approach only focuses on the base year and reporting year, in situations in which the research period is long, the annual changes during the research period may be difficult to capture. In particular, if there were huge fluctuations in the indicators (such as the energy consumption and carbon emissions) or their drivers during the middle of a research period, a substantial amount of information about the fluctuations will be ignored. Therefore, we propose four extended yearly LMDI approaches, including pure LMDI, weighted LMDI, comprehensive LMDI, and scenario LMDI approaches to better capture fluctuations and compensate for the original LMDI approach’s shortcomings. Additionally, we found that there are mathematical relationships among the four extended LMDI approaches. We further compare these four approaches’ advantages, disadvantages, and applicable situations and analyze a case study on China’s energy consumption based on the four proposed approaches.


Author(s):  
Wang Lijuan

Carbon emission is further intensified as urbanization and industrialization continue to accelerate. China has maintained its rapid economic development and urbanization in the last 2 decades. The development of the construction industry has not only consumed a large number of energy sources but also resulted in significant carbon emissions, causing some environmental damage. Recognizing the major influencing factors of carbon emissions in the construction industry has become a research hotspot to alleviate environmental pollution caused by the construction industry and meet industrial demands for energy saving and emission reduction. In this study, the factors that influence annual carbon emissions of different building types in China from 2011 to 2018 were decomposed by Logarithmic Mean Divisia Index (LMDI) through a case study in Henan Province. The major influencing factors of carbon emissions have been identified. Results demonstrate that the per capita carbon emission in the construction industry in Henan Province remains high from 2011 to 2018, but it decreases year by year. Carbon emissions from the construction industry in Henan Province increase due to economic development and energy structure. Energy efficiency can inhibit carbon emissions from the construction industry in Henan Province. The obtained conclusions have a positive effect on analyzing annual variations in carbon emissions from the construction industry in a region, identifying influencing factors, and proposing specific countermeasures of energy saving and emission reduction.


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