Nowcasting U.S. state-level CO2 emissions and energy consumption
提出面板即时预测方法，利用经济数据和电力消费等高频信息，提前两年预测美国各州CO2排放和能源消费增长，为地方碳减排政策提供及时参考。
This paper proposes panel nowcasting methods to obtain timely predictions of CO2 emissions and energy consumption growth across all U.S. states. This is crucial, not least because of the increasing role of sub-national carbon abatement policies but also due to the very delayed publication of the data. Since the state-level CO2 data are constructed from energy consumption data, we propose a new panel bridge equation method. We use a mixed frequency set-up where economic data are first used to predict energy consumption growth. This is then used to predict CO2 emissions growth while allowing for cross-sectional dependence across states using estimated factors. We evaluate the models’ performance using an out-of-sample forecasting study. We find that nowcasts improve when incorporating timely data like electricity consumption relative to a simple benchmark. These gains are sizeable in many states, even around two years before the data are eventually released. In predicting CO2 emissions growth, nowcast accuracy gains are also notable well before the data release, especially after the current year’s energy consumption data are used in making the prediction.