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Jiaxiong Yao and Mr. Yunhui Zhao
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).
Jiaxiong Yao and Mr. Yunhui Zhao

-friendly analytical framework to analyze empirical associations between the adoption of climate policies and changes in CO2 emission. We illustrate how to implement Oxford University’s machine-learning approach for twenty countries using a readily available R package. Our algorithm can be applied to identify potential trend changes or structural breaks in each country’s carbon emissions. We then interpret these results with a well-established, intuitive Kaya Identity, which can shed light on the driving forces of structural changes in emissions (the driving forces include

Jiaxiong Yao and Mr. Yunhui Zhao

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 non-climate 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). JEL Classification

International Monetary Fund. European Dept.

: European Environment Agency; and IMF staff estimates. Greenhouse Gases, Power Sector (2005 = 100) Note: Italy decreased 43% from 2005 to 2019, ranked 11th in EU. Sources: European Environment Agency and IMF staff estimates. Emissions Intensity of GDP (Index, 1990=100) Source: IMF staff estimates. Emissions by Sector, 2019 (Percent) Source: EEA. 3. Most progress so far has relied on improving the energy efficiency of output, with less coming from greening energy sources . The Kaya identity decomposes the evolution of GHG

Gail Cohen, João Tovar Jalles, Mr. Prakash Loungani, and Ricardo Marto

goods and services and for any region), suggesting trade has played a role in reduced emissions. Nonetheless, our exercise does not capture technology transfers arising from trade, which have spurred improvements in energy efficiency and in the production of carbon-intensive goods. Though it is not the aim of this paper, using the Kaya identity, which relates emissions to the energy intensity of output and the carbon intensity of of energy, would point in that direction. 5.2. Revisiting Okun and Kuznets elasticities Our results so far suggest that advanced

Gail Cohen, João Tovar Jalles, Mr. Prakash Loungani, and Ricardo Marto
Recent discussions of the extent of decoupling between greenhouse gas (GHG) emissions and real gross domestic product (GDP) provide mixed evidence and have generated much debate. We show that to get a clear picture of decoupling it is important to distinguish cycles from trends: there is an Environmental Okun's Law (a cyclical relationship between emissions and real GDP) that often obscures the trend relationship between emissions and real GDP. We show that, once the cyclical relationship is accounted for, the trends show evidence of decoupling in richer nations—particularly in European countries, but not yet in emerging markets. The picture changes somewhat, however, if we take into consideration the effects of international trade, that is, if we distinguish between production-based and consumption-based emissions. Once we add in their net emission transfers, the evidence for decoupling among the richer countries gets weaker. The good news is that countries with underlying policy frameworks more supportive of renewable energy and supportive of climate change tend to have greater decoupling between trend emissions and trend GDP, and for both production- and consumption-based emissions.