Automated Organization Profile保定理工学院
保定理工学院
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.6 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This article takes A-share listed manufacturing companies in China as the research sample, and the data mainly comes from Wind database, CSMAR database, and annual reports of listed companies. Due to the time constraints of the sources of carbon emissions from listed companies and the level of financing constraints in the manufacturing industry, this article selects the period from 2000 to 2022 as the research interval. There are a total of 26099 observations, including variables such as id, year, CO2, CO2 growth rate, fossil fuel, emissions from biomass fuel combustion, Escaping emissions from raw material extraction, Escaping emissions from oil and gas systems, Indirect carbon emissions from power transfer in and out, Production process emissions, KZ, Eastern, Western, Central, High tech industries, Heavy polarization industries, size, lev, ROA, p/b ratio, TobinQ, and Employ. In order to avoid the influence of outliers, this article refers to existing research practices and processes the raw data as follows: (1) using EXCEL to remove financial industry data; (2) Use Excel tools to remove samples with delisting risks and delisting warnings; (3) Use STATA16.0 to eliminate missing values in the sample; (4) Use EXCEL tools to dimensionless some data using methods such as natural logarithm and normalization; (5) Use EXCEL tools to perform tail reduction on some data.
Authors
- Shuangbin, Tao ;
- Tianyu, Li