Looking ahead: Forecasting total energy carbon dioxide emissions

Bernardina Algieri, Leonardo Iania () and Arturo Leccadito
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Leonardo Iania: Université catholique de Louvain, LIDAM/LFIN, Belgium

No 2024007, LIDAM Reprints LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

Abstract: In recent years, the international community has been increasing its efforts to reduce the human footprint on air pollution and global warming. Total carbon dioxide (CO2) releases are a crucial component of global greenhouse gas emissions, and as such, they are closely monitored at the national and supranational levels. This study presents different models to forecast energy CO2 emissions for the US in the period 1972–2021, using quarterly observations. In an in-sample and out-of-sample analysis, the study assesses the accuracy of thirteen forecasting models (and their combinations), considering an extensive set of potential predictors (more than 260) that include macroeconomic, nature-related factors and different survey data and compares them to traditional benchmarks. To reduce the high-dimensionality of the potential predictors, the study uses a new class of factor models in addition to the classical principal component analysis. The results show that economic variables, market sentiment and nature-related indicators, especially drought and Antarctic wind indicators, help forecast short/medium-term CO2 emissions. In addition, some combinations of models tend to improve out-of-sample predictions.

Keywords: CO2 emissions; Forecasting models; Quantile factors; Economic; Sentiment and nature-related drivers (search for similar items in EconPapers)
JEL-codes: C53 C55 E71 Q47 Q53 (search for similar items in EconPapers)
Pages: 14
Date: 2024-01-01
Note: In: Cleaner Environmental Systems, 2023, vol. 9, 100112
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Persistent link: https://EconPapers.repec.org/RePEc:ajf:louvlr:2024007

DOI: 10.1016/j.cesys.2023.100112

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