Abstract
AbstractClimate policies can have a significant impact on the economy. However, these policies have often been associated with uncertainty. Quantitative assessment of the socioeconomic impact of climate policy uncertainty is equally or perhaps more important than looking at the policies themselves. Using a deep learning algorithm—the MacBERT model—this study constructed indices of Chinese climate policy uncertainty (CCPU) at the national, provincial and city levels for the first time. The CCPU indices are based on the text mining of news published by a set of major newspapers in China. A clear upward trend was found in the indices, demonstrating increasing policy uncertainties in China in addressing climate change. There is also evidence of clear regional heterogeneity in subnational indices. The CCPU dataset can provide a useful source of information for government actors, academics and investors in understanding the dynamics of climate policies in China. These indices can also be used to investigate the empirical relationship between climate policy uncertainty and other socioeconomic factors in China.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference30 articles.
1. WEF. The Global Risk Report 2023. World Economic Forum. https://www.weforum.org/publications/global-risks-report-2023 (2023).
2. Borenstein, S., Bushnell, J., Wolak, F. A. & Zaragoza-Watkins, M. Expecting the unexpected: Emissions uncertainty and environmental market design. Am. Econ. Rev. 109, 3953–3977 (2019).
3. Nordhaus, W. Climate change: The ultimate challenge for economics. Am. Econ. Rev. 109, 1991–2014 (2019).
4. Dorsey, J. Waiting for the courts: Effects of policy uncertainty on pollution and investment. Environ. Resour. Econ. 74, 1453–1496 (2019).
5. Ilhan, E., Sautner, Z. & Vilkov, G. Carbon tail risk. Rev. Financ. Stud. 34, 1540–1571 (2021).