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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Yao, Jiaxiong.
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|a Structural Breaks in Carbon Emissions :
|b A Machine Learning Analysis /
|c Jiaxiong Yao, Yunhui Zhao.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2022.
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|a 1 online resource (47 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a To reach the global net-zero goal, the level of carbon emissions has to fall substantially at speed rarely seen in history, highlighting the need to identify structural breaks in carbon emission patterns and understand forces that could bring about such breaks. In this paper, we identify and analyze structural breaks using machine learning methodologies. We find that downward trend shifts in carbon emissions since 1965 are rare, and most trend shifts are associated with non-climate structural factors (such as a change in the economic structure) rather than with climate policies. While we do not explicitly analyze the optimal mix between climate and non-climate policies, our findings highlight the importance of the nonclimate policies in reducing carbon emissions. On the methodology front, our paper contributes to the climate toolbox by identifying country-specific structural breaks in emissions for top 20 emitters based on a user-friendly machine-learning tool and interpreting the results using a decomposition of carbon emission ( Kaya Identity).
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|a Mode of access: Internet
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|a Climate
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|a Econometric Modeling
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|a Natural Disasters and Their Management
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|a Panel Data Models
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|a Spatio-Temporal Models
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|a Solomon Islands
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|a Zhao, Yunhui.
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|a IMF Working Papers; Working Paper ;
|v No. 2022/009
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2022/009/001.2022.issue-009-en.xml
|z IMF e-Library
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