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ID:
070309
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ID:
089836
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| Publication |
London, Routledge, 2008.
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| Description |
174p.Hbk
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| Standard Number |
9780415418515
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Copies: C:1/I:0,R:0,Q:0
Circulation
| Accession# | Call# | Current Location | Status | Policy | Location |
| 054305 | 658.570951/SUN 054305 | Main | On Shelf | General | |
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| 3 |
ID:
070308
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ID:
190647
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| Summary/Abstract |
Since China is the world's largest carbon emitter with a high dependence on coal and a constantly upgrading industrial structure, therefore, we study which of these two factors can affect carbon emissions more significantly. In this study, the Cross-sectional augmented error correction method (CS-ECM) is used to investigate the impact of coal consumption and industrial structure on carbon emissions in 30 provinces of China from 2000 to 2019. For robustness check, the common dynamic process augmented mean group (AMG) is also adopted. The study suggests that in the short term, both coal consumption and industrial structure have no significant impact on carbon emissions. In the long term, coal consumption plays a decisive role in reducing carbon emissions, while the impact of industrial structure is still not. According to the CS-ECM approach, in the long run, a 1% rise in coal consumption increases carbon emissions by 1.057%; it indicates that coal consumption is the more important factor for the increase of long-term carbon emission. Government should adopt necessitating coal consumption-control measures to avoid further deterioration of carbon emissions.
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