
doi: 10.2139/ssrn.4116768
handle: 2078.1/260961
In recent years, the international community has been increasing its efforts to reduce the human footprint on air pollution and global warming. Total CO2 emissions are a key component of global emission, and as such, they are closely monitored by national and supranational entities. This study evaluates the performance of a broad set of forecasting models and their combinations to predict energy’s carbon dioxide releases using an in-sample and out-of-sample analysis. The focus is on the US for the period 1973-2021 using quarterly observations. The results show that economic variables, energy and interannual climate variability indicators help forecast short-/medium- term CO2 emissions. In addition, a combination of models sharpens quantile predictions.
Forecasting Models, Quantile Forecast, Drought Severity, Energy and Nature- related drivers, Climate Change, Economic, CO2 Emissions, Interannual Variability
Forecasting Models, Quantile Forecast, Drought Severity, Energy and Nature- related drivers, Climate Change, Economic, CO2 Emissions, Interannual Variability
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