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Journal of Cleaner Production
Article . 2015 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Potential impacts of industrial structure on energy consumption and CO2 emission: a case study of Beijing

Authors: Mi, Zhi-Fu; Pan, Su-Yan; Yu, Hao; Wei, Yi-Ming;

Potential impacts of industrial structure on energy consumption and CO2 emission: a case study of Beijing

Abstract

An optimization model is developed based on the Input–Output model to assess the potential impacts of industrial structure on the energy consumption and CO2 emission. The method is applied to a case study of industrial structure adjustment in Beijing, China. Results demonstrate that industrial structure adjustment has great potential of energy conservation and carbon reduction. When the average annual growth rate of GDP is 8.29% from 2010 to 2020, industrial structure adjustment can save energy by 39.42% (50.06 million tons of standard coal equivalent), and reduce CO2 emission by 46.06% (96.31 million tons) in Beijing in 2020. Second, Beijing had better strive to develop several low energy intensive and low carbon intensive sectors, such as information transmission, computer service and software, and finance. Third, energy intensity is possible to decrease without negatively affecting economic growth by reasonable industrial structure adjustment. Four, compared to “intensity targets”, “total amount targets” are more effective on the energy conservation and carbon reduction, but have much greater negative effects on economic growth. Therefore, it needs to be balanced between “total amount targets” and “intensity targets”.

Country
United Kingdom
Related Organizations
Keywords

670, 330, Industrial structure, Energy consumption, CO2 emission, Input-output, Optimization model, jel: jel:C61, jel: jel:Q41

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    383
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    Top 0.1%
    influence
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
383
Top 0.1%
Top 1%
Top 0.1%
Green
bronze