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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).
Mr. Michael Keen, Ian W.H. Parry, and Mr. James Roaf
This paper assesses the rationale, design, and impacts of border carbon adjustments (BCAs). Large disparities in carbon pricing between countries raise concerns about competitiveness and emissions leakage. BCAs are potentially the most effective domestic instrument for addressing these challenges—but design details are critical. For example, limiting coverage of the BCA to energy-intensive, trade-exposed industries facilitates administration, and initially benchmarking BCAs on domestic emissions intensities would ease the transition for trading partners with emission-intensive production. It is also important to consider how to apply BCAs across countries with different approaches to emissions mitigation. BCAs alone do not solve the free-rider problem in carbon pricing, but might be a step to an effective international carbon price floor.