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Cómo reactivar el crecimiento mundial a partir del comercio, la productividad, la reducción de la desigualdad y el empodramiento ecnonómico de la mujer.
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The UK’s rapid vaccination campaign enabled it to start to reopen the economy in the Spring of 2021. With highly accommodative policies, the recovery has been faster than expected. However, capacity constraints and rising price pressures have emerged while new Covid-19 variants have raised new uncertainties. The government has allowed all initial pandemic support programs to sunset but has loosened near-term fiscal policy while specifying a back-loaded medium-term consolidation plan. With continued above-target inflation readings, the BoE made a first move to raise the policy rate in December. Macroprudential policies are returning to more standard risk settings. The near-term growth outlook remains strong, but so too are price pressures, while the financial cycle remains ahead of the economic cycle. The pandemic and Brexit have magnified structural challenges. Real GDP would remain below its pre-pandemic trend by about 2–2¼ percent in the medium term. Risks are considerable in the period ahead, centering on new Covid-19 waves and spillovers from tensions in Eastern Europe.
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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).