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Decomposition Analysis of Sectoral Energy Use in Beijing (1981-2005) Using the LMDI Method 被引量:4
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作者 Liu Chunlan Xie Gaodi Cai Bofeng 《Chinese Journal of Population,Resources and Environment》 北大核心 2008年第2期49-54,共6页
This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005.Sectoral energy use was investigated when regional economic structure change... This paper aims to identify the main driving force for changes of total primary energy consumption in Beijing during the period of 1981-2005.Sectoral energy use was investigated when regional economic structure changed significantly.The changes of total primary energy consumption in Beijing are decomposed into production effects,structural effects and intensity effects using the additive version of the logarithmic mean Divisia index (LMDI) method.Aggregate decomposition analysis showed that the major contributor of total effect was made by the production effect fol- lowed by the intensity effect,and the structural effect was rela- tively insignificant.The total and production effects were all posi- tive.In contrast,the structural effect and intensity effect were all negative.Sectoral decomposition investigation indicated that the most effective way to slow down the growth rate of total primary energy consumption (TPEC) was to reduce the production of the energy-intensive industrial sectors and improving industrial en- ergy intensity.The results show that in this period,Beijing's economy has undergone a transformation from an industrial to a service economy.However,the structures of sectoral energy use have not been changed yet,and energy demand should be in- creasing until the energy-intensive industrial production to be reduced and energy intensity of the region reaches a peak.As sequence energy consumption data of sub-sectors are not available, only the fundamental three sectors are considered:agriculture, industry and service.However,further decomposition into secon- dary and tertiary sectors is definitely needed for detailed investi- gations. 展开更多
关键词 DECOMPOSITION sectoral energy consumption logarithmic mean Divisia index (LMDI) BEIJING
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Quantitative Analysis and Prediction of China's Natural Gas Consumption in Different Sectors Based on Bayesian Network
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作者 Jian CHAI Yabo WANG +3 位作者 Zhaohao WEI Huiting SHI Xiaokong ZHANG Xuejun ZHANG 《Journal of Systems Science and Information》 CSCD 2022年第4期338-353,共16页
In view of the heterogeneity of natural gas consumption in different sectors in China,this paper utilizes Bayesian network(BN)to study the driving factors of natural gas consumption in power generation,chemical and in... In view of the heterogeneity of natural gas consumption in different sectors in China,this paper utilizes Bayesian network(BN)to study the driving factors of natural gas consumption in power generation,chemical and industrial fuel sectors.Combined with Bayesian model averaging(BMA)and scenario analysis,the gas consumption of the three sectors is predicted.The results show that the expansion of urbanization will promote the gas consumption of power generation.The optimization of industrial structure and the increase of industrial gas consumption will enhance the gas consumption of chemical sector.The decrease of energy intensity and the increase of gas consumption for power generation will promote the gas consumption of industrial fuel.Moreover,the direct influencing factors of gas price are urbanization,energy structure and energy intensity.The direct influencing factors of environmental governance intensity are gas price,urbanization,industrial structure,energy intensity and energy structure.In 2025,under the high development scenario,China’s gas consumption for power generation,chemical and industrial fuel sectors will be 66.034,36.552 and 109.414 billion cubic meters respectively.From 2021 to 2025,the average annual growth rates of gas consumption of the three sectors will be 4.82%,2.18%and 4.43%respectively. 展开更多
关键词 Bayesian network influence factors Bayesian model average forecast natural gas consumption in different sectors
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