We compare the most common reduced-form models used for emissions forecasting, point out shortcomings, and suggest improvements. Using a U.S. state-level panel data set of CO2 emissions, we test the performance of existing models against a large universe of potential reduced-form models. We find that leading models in the literature, as well as models selected based on an emissions per capita loss measure or different in-sample selection criteria, perform significantly worse compared to the best model chosen based directly on the out-of-sample loss measure defined over aggregate emissions.

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