2020
DOI: 10.3390/ijerph17165880
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Industrial Energy-Related CO2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016

Abstract: As the world’s largest developing country in the world, China consumes a large amount of fossil fuels and this leads to a significant increase in industrial energy-related CO2 emissions (IECEs). The Yangtze River Economic Zone (YREZ), accounting for 21.4% of the total area of China, generates more than 40% of the total national gross domestic product and is an important component of the IECEs from China. However, little is known about the changes in the IECEs and their influencing factors in this area during t… Show more

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Cited by 10 publications
(6 citation statements)
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“…The algorithm is different from the two existing methods mentioned above. In [19], the Wasserstein distance was introduced to GAN (generative adversarial network) training in the field of machine learning, creating the sensational WGAN (Wasserstein generative adversarial network) algorithm, which has been widely used in the field of face recognition, image analysis, and other machine learning training. The algorithm was developed in [20] as an "unbiased feature for arbitrary spatial classification changes," but in the actual measurement process, a certain spatial scale still needs to be chosen for measurement.…”
Section: Review Of the Literaturementioning
confidence: 99%
“…The algorithm is different from the two existing methods mentioned above. In [19], the Wasserstein distance was introduced to GAN (generative adversarial network) training in the field of machine learning, creating the sensational WGAN (Wasserstein generative adversarial network) algorithm, which has been widely used in the field of face recognition, image analysis, and other machine learning training. The algorithm was developed in [20] as an "unbiased feature for arbitrary spatial classification changes," but in the actual measurement process, a certain spatial scale still needs to be chosen for measurement.…”
Section: Review Of the Literaturementioning
confidence: 99%
“…The above studies mainly focused on countries or large-scale areas and achieved good results by analyzing the factors of CO 2 emissions. In addition, LMDI model has also been widely used to analyze the drivers of CO 2 emissions in various industry sectors [ 24 , 25 , 26 , 27 ]. At the same time, many scholars have applied the LMDI model to researching the drivers of CO 2 emissions at the provincial level, Specifically Jiangxi, Jilin, and Liaoning, respectively [ 28 , 29 , 30 ].…”
Section: Literature Reviewmentioning
confidence: 99%
“…The relationship between land use and land cover change (LUCC) and carbon emissions has attracted the attention of scientists worldwide. Previous research on land use carbon emissions both domestically and abroad has primarily focused on the accounting and temporal and spatial characteristics of land use carbon emissions [33,34], the mecha-nisms by which land use affects carbon emissions [35][36][37], and the factors that influence land use carbon emissions [38][39][40]. Researchers have proposed carbon emission coefficient methods for calculating carbon emissions from cultivated land [41][42][43], forest land [44][45][46], grassland [47][48][49], and construction land [50,51], and explored the impact of land use change on carbon emissions.…”
Section: Introductionmentioning
confidence: 99%