China Climate Change News Indices

The daily climate change news indices for China from 2010 to 2023 are provided. Initially, based on nearly 4.9 million news articles published by nine Chinese newspapers (People's Daily, Guangming Daily, Xinhua Daily Telegraph, China News Service, Science and Technology Daily, Science Times, China Energy News, China Environment News, and Global Times) from January 2010 to November 2022, the indices were constructed using the Latent Dirichlet Allocation (LDA) model, encompassing five distinct themes: natural disasters, climate governance, energy transition, climate cooperation, and climate communication. Subsequently, the indices were updated to 2023 utilizing a naive Bayes model.Cite: Ma, D., Zhang, Y., Ji, Q., Zhao, W. L., & Zhai, P. (2024). Heterogeneous impacts of climate change news on China's financial markets. International Review of Financial Analysis, 91, 103007.</p

China's city level CPU index

CPU dataset of 293 cities in China (excluding Hong Kong, Macao, and Taiwan data.2412000-2023), including monthly and annual data (“China's city level CPU index, 2000-2023”).</p

Dataset of newspaper

The six newspapers are chosen as the main sources for constructing the CCPU index: People’s Daily, Guangming Daily, Economic Daily, Global Times, Science and Technology Daily, and China News Service. The newspaper data are collected from the Wisenews database between January 2000 and December 2022. 1755826 newspaper data are stored in news_six_all.csv.</p

China's CPU index

A dataset on china's climate policy uncertainty index</p

Code

# Installation Dependencies```pip install -r requirements.txt```# DataWe use the newspaper data are collected from the Wisenews database between January 2000 and December 2022.# Pretraining ModelWe use [RoBERTa-base](https://huggingface.co/xlm-roberta-base), [MacBERT-base](https://huggingface.co/hfl/chinese-macbert-base), [PERT-base](https://huggingface.co/hfl/chinese-pert-base), [LERT-base](https://huggingface.co/hfl/chinese-lert-base) as the pretraining model.# TrainAfter modifying the data path and pretraining model path, execute```bash train.sh```# ResultPretraining Model | Accuracy | Checkpoint:-------------------------:|:-------------------------:|:-------------------------:roberta | 93.04 | [link](https://pan.baidu.com/s/1WhSAp374W7bQiiY6En5mPA?pwd=7v4j):7v4jmacbert | 93.33 | [link](https://pan.baidu.com/s/1ngY0RmeHr5rWrp980TYNyA?pwd=5pwk):5pwkpert | 92.87 | [link](https://pan.baidu.com/s/1hkEmVmFpPYFvwV5ztUVPhw):8frplert | 93.27 | [link](https://pan.baidu.com/s/19hJgILlEFv_T8fPnH4TsHQ?pwd=2tbc):2tbc# InferenceAfter modifying the data path and pretraining model path, execute```python infer.py```</p

China's provincial CPU index

CPU dataset of 31 provinces in China (excluding data from Hong Kong, Macao, andTaiwan; 2000-2023), including monthly and annual indices (“China's provincial climate policy uncertainty index,2000-2023")</p