With industrialization and urbanization, China faces enormous challenges from energy security and environmental issues. To address these challenges, it is imperative to establish a green accounting system for economic growth and to measure the uncertainty of China's green GDP (GGDP) growth from a risk management perspective. With this in mind, we follow the idea of growth-at-risk (GaR) to propose the concept of green GaR (GGaR) and extend it to the mixed-frequency data environment. Specifically, we first measure China's annual GGDP using the System of Environmental Economic Accounting (SEEA), then construct China's monthly green financial index by a mixed-frequency dynamic factor model (MF-DFM), and finally monitor China's GGaR from 2008M1 to 2021M12 with the mixed data sampling-quantile regression (MIDAS-QR) method. The main findings are as follows: First, the proportion of China's GGDP to traditional GDP gradually increases from 81.97% in 2008 to 89.34% in 2021, which illustrates that the negative environmental externalities caused by China's economic growth are gradually decreasing. Second, the high-frequency GGaR has favorable predictive performance and is significantly superior to the common-frequency GGaR at most quantiles. Third, the high-frequency GGaR has good nowcasting performance, and its 90% and 95% confidence intervals include true value for all prediction horizons. Furthermore, it can provide early warning of economic downturns through probability density prediction. Overall, our main contribution lies in constructing a quantitative assessment and high-frequency monitoring of China's GGDP growth risk, which provides an effective tool for investors and companies to predict risk, and a reference for the Chinese government to better formulate sustainable development strategies.
Keywords: Green GDP; Green finance; Growth-at-risk; MIDAS-QR; Nowcasting; Skewed t-distribution.
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.