| Title |
Prediction of Post-COVID-19 economic and environmental policy and recovery based on recurrent neural network and long short-term memory network |
| ID_Doc |
69273 |
| Authors |
Hu, H; Xiong, SZ; Chen, Y; Ye, L; Zhao, SL; Qian, K; De Domenici, MC |
| Title |
Prediction of Post-COVID-19 economic and environmental policy and recovery based on recurrent neural network and long short-term memory network |
| Year |
2022 |
| Published |
Environmental Research Communications, 4.0, 11 |
| DOI |
10.1088/2515-7620/ac9bd8 |
| Abstract |
COVID-19 has brought significant impacts on the global economy and environment. The Global Economic-and-environmental Policy Uncertainty (GEPU) index is a critical indicator to measure the uncertainty of global economic policies. Its prediction provides evidence for the good prospect of global economic and environmental policy and recovery. This is the first study using the monthly data of GEPU from January 1997 to January 2022 to predict the GEPU index after the COVID-19 pandemic. Both Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models have been adopted to predict the GEPU. In general, the RNN outperforms the LSTM networks, and most results suggest that the GEPU index will remain stable or decline in the coming year. A few results point to the possibility of a short-term increase in GEPU, but still far from its two peaks during the first year of the COVID-19 pandemic. This forecast confirms that the impact of the epidemic on global economic and environmental policy will continue to wane. Lower economic and environmental policy uncertainty facilitates global economic and environmental recovery. Economic recovery brings more opportunities and a stable macroeconomic environment, which is a positive sign for both investors and businesses. Meanwhile, for the ecological environment, the declining GEPU index marks a gradual reduction in the direct impact of policy uncertainty on sustainable development, but the indirect environmental impact of uncertainty may remain in the long run. Our prediction also provides a reference for subsequent policy formulation and related research. |
| Author Keywords |
recurrent neural network; long short-term memory; time series prediction; post-COVID-19; global economic-and-environmental policy uncertainty; recovery; economic-and-environmental policy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000878386700001 |
| WoS Category |
Environmental Sciences |
| Research Area |
Environmental Sciences & Ecology |
| PDF |
https://iopscience.iop.org/article/10.1088/2515-7620/ac9bd8/pdf
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