Power shortage index for 218 Chinese cities
- Contributors: Dongmei Guo, Qin Li, Peng Liu, Xunpeng Shi, Jian Yu
- Abstract: This paper uses the text analysis method to construct a city-level power shortage index. We selected daily newspapers from 218 prefecture-level city as the data source and used a combination of selected high-frequency words with expert investigation to screen out basic terms related to power shortage. The following 20 keywords were identified: peak scheduling management, power generation, waste heat power generation, off-peak, grid disconnection, power rationing, switching off, orderly power consumption, tripping, peak avoidance, disconnection, plant power consumption, staggered peak avoidance, pull road, accident electricity, overload, transferring power supply, security of electricity, power rationing, and load transfer.
Mr. Jian Yu and his collaborators present a power shortage index to characterize the city-level power outages for 218 Chinese cities from 2001 to 2017. Please cite the following papers when using this data:
- Guo, D., Li, Q., Liu, P., Shi, X., Yu, J., 2023. Power shortage and firm performance: Evidence from a Chinese city power shortage index, Energy Economics, Vol.119, No.106593.
经中央财经大学俞剑老师授权,CnOpenData建立了该数据的展示区及数据索引,便于学者浏览。
数据下载请点击Power shortage index for 218 Chinese cities。
数据应用指南
Visual representation of the word frequencies of power shortage keywords

时间区间
2001-2017
字段展示
| Economic policy uncertainty (EPU) index |
|---|
| city |
| year |
| power shortage index with 5 keywords |
| power shortage index with 20 keywords |
| planned power shortage index |
| unplanned power shortage index |
样本数据
| province_code | province_name | year | China's provincial EPU index |
|---|---|---|---|
| 11 | 北京 | 2017 | 68.1627655 |
| 12 | 天津 | 2017 | 139.3325806 |
| 13 | 河北 | 2017 | 79.15668488 |
| 14 | 山西 | 2017 | 189.1946869 |
| 15 | 内蒙古 | 2017 | 76.54681396 |
| 21 | 辽宁 | 2017 | 91.38005066 |
| 22 | 吉林 | 2017 | 89.98912048 |
| 23 | 黑龙江 | 2017 | 7.519604683 |
| 31 | 上海 | 2017 | 80.68917847 |
| 32 | 江苏 | 2017 | 47.75826263 |
| 33 | 浙江 | 2017 | 97.14289093 |
| 34 | 安徽 | 2017 | 111.6440353 |
| 35 | 福建 | 2017 | 160.7241058 |
| 36 | 江西 | 2017 | 102.082283 |
| 37 | 山东 | 2017 | 78.49134827 |
| 41 | 河南 | 2017 | 96.05151367 |
| 42 | 湖北 | 2017 | 99.25975037 |
| 43 | 湖南 | 2017 | 54.22574615 |
| 44 | 广东 | 2017 | 56.38425064 |
| 45 | 广西 | 2017 | 118.8451614 |
| 46 | 海南 | 2017 | 57.19842529 |
| 50 | 重庆 | 2017 | 107.0997543 |
| 51 | 四川 | 2017 | 118.5892792 |
| 52 | 贵州 | 2017 | 90.86172485 |
| 53 | 云南 | 2017 | 38.87081146 |
| 54 | 西藏 | 2017 | 163.8305817 |
| 61 | 陕西 | 2017 | 99.39268494 |
| 62 | 甘肃 | 2017 | 137.8933868 |
| 63 | 青海 | 2017 | 79.26584625 |
| 64 | 宁夏 | 2017 | 102.7975769 |
| 65 | 新疆 | 2017 | 46.38435364 |
| 11 | 北京 | 2016 | 47.85101318 |
| 12 | 天津 | 2016 | 143.1006927 |
| 13 | 河北 | 2016 | 115.6779938 |
| 14 | 山西 | 2016 | 98.37980652 |
| 15 | 内蒙古 | 2016 | 108.1842651 |
| 21 | 辽宁 | 2016 | 80.5438385 |
| 22 | 吉林 | 2016 | 104.2610016 |
| 23 | 黑龙江 | 2016 | 81.40262604 |
| 31 | 上海 | 2016 | 128.4771423 |
| 32 | 江苏 | 2016 | 43.71399307 |
| 33 | 浙江 | 2016 | 90.4801178 |
| 34 | 安徽 | 2016 | 125.6614151 |
| 35 | 福建 | 2016 | 94.90132904 |
| 36 | 江西 | 2016 | 113.8047638 |
| 37 | 山东 | 2016 | 55.01879883 |
| 41 | 河南 | 2016 | 86.23591614 |
| 42 | 湖北 | 2016 | 139.5249634 |
| 43 | 湖南 | 2016 | 58.70393753 |
| 44 | 广东 | 2016 | 57.20541382 |
参考文献
- Guo, D., Li, Q., Liu, P., Shi, X., Yu, J., 2023. Power shortage and firm performance: Evidence from a Chinese city power shortage index, Energy Economics, Vol.119, No.106593.
数据更新频率
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本文使用文本分析方法构建了一个涵盖218个地级城市的电力短缺指数,基于2001年至2017年每日新闻报道,结合高频词筛选和专家调查,确定了与电力短缺相关的20个关键词,如峰谷管理、发电量、余热发电等。
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