High-frequency Density Nowcasts of U.S. State-Level Carbon Dioxide Emissions

TL;DR

This paper develops a high-frequency nowcasting framework using panel MIDAS models to improve real-time estimates of state-level CO2 emissions growth in the U.S., addressing data lags and enhancing policy decision-making.

Contribution

It introduces a novel panel MIDAS-based approach linking energy consumption and CO2 emissions for more timely and accurate emissions nowcasts at the state level.

Findings

Enhanced accuracy of nowcasts over historical benchmarks.

Improved estimates of CO2 emissions growth distribution.

Effective use of high-frequency indicators for emissions prediction.

Abstract

Accurate tracking of anthropogenic carbon dioxide (CO2) emissions is crucial for shaping climate policies and meeting global decarbonization targets. However, energy consumption and emissions data are released annually and with substantial publication lags, hindering timely decision-making. This paper introduces a panel nowcasting framework to produce higher-frequency predictions of the state-level growth rate of per-capita energy consumption and CO2 emissions in the United States (U.S.). Our approach employs a panel mixed-data sampling (MIDAS) model to predict per-capita energy consumption growth, considering quarterly personal income, monthly electricity consumption, and a weekly economic conditions index as predictors. A bridge equation linking per-capita CO2 emissions growth with the nowcasts of energy consumption is estimated using panel quantile regression methods. A pseudo out-of-sample study (2009-2018), simulating the real-time data release calendar, confirms the improved accuracy of our nowcasts with respect to a historical benchmark. Our results suggest that by leveraging the availability of higher-frequency indicators, we not only enhance predictive accuracy for per-capita energy consumption growth but also provide more reliable estimates of the distribution of CO2 emissions growth.