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).
fall substantially at speed rarely seen in history. 2 3 Identifying structural breaks in carbon emission patterns and understanding forces that could bring about such breaks are important goals. In this paper, we identify and analyze these structural breaks using machinelearningmethodologies and shed light on potential game-changing policies for mitigating emissions. Our analysis focuses on the top 20 carbon emitters, which account for 80 percent of cumulative global carbon dioxide emissions.
We take an agnostic approach to examine trend changes and structural
Executive Board, or IMF management.
ABSTRACT : 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 machinelearningmethodologies. 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
International Monetary Fund. Strategy, Policy, & Review Department
-financial risks has been developed in the context of the work on the Integrated Policy Framework.
Crisis prediction models. These models evaluate likelihoods of crises originating in different sectors (e.g., external, fiscal and financial). The new crisis prediction models use state-of-the-art machinelearningmethodologies that underpin the VE ( IMF, 2021 ), and related models for predicting fiscal stress (using more traditional techniques) inform safety thresholds for debt levels in the DSA ( IMF, 2018 and IMF, 2021 ). While these methods cannot be used to generate
International Monetary Fund. Strategy, Policy, & Review Department
The coverage of risks has become more systematic since the Global Financial Crisis (GFC): staff reports now regularly identify major risks and provide an assessment of their likelihood and economic impact, summarized in Risk Assessment Matrices (RAM). But still limited attention is paid to the range of possible outcomes. Also, risk identification is useful only so much as to inform policy design to preemptively respond to relevant risks and/or better prepare for them. In this regard, policy recommendations in surveillance could be richer in considering various risk management approaches. To this end, progress is needed on two dimensions: • Increasing emphasis on the range of potential outcomes to improve policy design. • Encouraging more proactive policy advice on how to manage risks. Efforts should continue to leverage internal and external resources to support risk analysis and advice in surveillance.
countries for which our machine-learningmethodologies are not readily applicable, we present a preliminary heatmap map and provide suggestive evidence about the degree of potential pressures of rising housing costs on headline inflation, which serves as a first step for more rigorous analyses of the inflationary pressures in these countries.
Operationally, our paper highlights the need to carefully incorporate housing prices into the headline inflation forecasts. In view of the potential lagged responses of the housing cost components (rent and OOHC, if applicable) to
Inflation has been rising during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices. We present some stylized facts pointing to house prices as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices. We then apply machine learning methods to forecast inflation in two housing components (rent and owner-occupied housing cost) of the headline inflation and draw tentative inferences about inflationary impact. Our results suggest that for most of these countries, the housing components could have a relatively large and sustained contribution to headline inflation, as inflation is just starting to reflect the higher house prices. Methodologically, for the vast majority of countries we analyze, machine-learning models outperform the VAR model, suggesting some potential value for incorporating such models into inflation forecasting.
This paper presents a set of collaborative filtering algorithms that produce product recommendations to diversify and optimize a country's export structure in support of sustainable long-term growth. The recommendation system is able to accurately predict the historical trends in export content and structure for high-growth countries, such as China, India, Poland, and Chile, over 20-year spans. As a contemporary case study, the system is applied to Paraguay, to create recommendations for the country's export diversification strategy.
commonalities and differences of recommendations for countries that may share certain characteristics. In addition, the merit of the recommender system could be further tested, by applying it to a wide range of countries, identifying the divergence between recommended and actual export structures, and correlating the divergence with manifested economic outcomes.
Wider adoption of machinelearningmethodologies in international trade studies likely faces two challenges, which also apply to the adoption of machine learning tools in empirical economic research in general
in guiding practical decisions in more complex scenarios.
In this paper, we try to combine the merits of both worlds to shed some light on the importance of export structure evolution in the growth and income convergence process. We leveraged machinelearningmethodology to characterize the complex patterns in countries’ latent comparative advantages and create export recommendations accordingly. We then use a standard linear regression model to evaluate the soundness of these recommendations by asking whether a country’s growth performance is better if they had