Constructing enterprise pollution index and monitoring analysis using electricity big data
Electricity big data, as a new data source, can provide real-time and continuous data on enterprise energy consumption and production activities. However, existing methods have not taken into account the impact of electricity big data on the pollution index of enterprises, resulting in incomplete da...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of XPU
2024-12-01
|
| Series: | Xi'an Gongcheng Daxue xuebao |
| Subjects: | |
| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1521 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850271815233961984 |
|---|---|
| author | XI Zenghui WANG Weibin LU Jiaming QU Haini |
| author_facet | XI Zenghui WANG Weibin LU Jiaming QU Haini |
| author_sort | XI Zenghui |
| collection | DOAJ |
| description | Electricity big data, as a new data source, can provide real-time and continuous data on enterprise energy consumption and production activities. However, existing methods have not taken into account the impact of electricity big data on the pollution index of enterprises, resulting in incomplete data used in research that cannot fully reflect the pollution behavior of enterprises. To this end, a new method for analyzing enterprise pollution index was constructed using power big data in the article. Based on the electricity source and pollutant emission structure of the enterprise, a pollution evaluation index system for the enterprise was constructed, and the importance scale and weight coefficient of each index were determined. The comprehensive index score was calculated using the entropy weight method, which reflected the risk or impact level of the enterprise in terms of pollutant emissions, environmental impact, and resource utilization. The results show that there are certain differences in the pollution index among different enterprises, with the highest pollution index of 0.14 in the chemical industry, and lower pollution indices of 0.04 in the beverage manufacturing and rubber manufacturing industries. The proposed method can effectively display the differences in pollution indices among different enterprises and has reference value. |
| format | Article |
| id | doaj-art-8812a614b87e4eefb3f4f461cbe65dfe |
| institution | OA Journals |
| issn | 1674-649X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Journal of XPU |
| record_format | Article |
| series | Xi'an Gongcheng Daxue xuebao |
| spelling | doaj-art-8812a614b87e4eefb3f4f461cbe65dfe2025-08-20T01:52:06ZzhoEditorial Office of Journal of XPUXi'an Gongcheng Daxue xuebao1674-649X2024-12-01386838910.13338/j.issn.1674-649x.2024.06.011Constructing enterprise pollution index and monitoring analysis using electricity big dataXI Zenghui0WANG Weibin1LU Jiaming2QU Haini3State Grid Shanghai Electric Power Company, Shanghai 200122, ChinaState Grid Shanghai Electric Power Company, Shanghai 200122, ChinaState Grid Shanghai Electric Power Company, Shanghai 200122, ChinaState Grid Shanghai Electric Power Company, Shanghai 200122, ChinaElectricity big data, as a new data source, can provide real-time and continuous data on enterprise energy consumption and production activities. However, existing methods have not taken into account the impact of electricity big data on the pollution index of enterprises, resulting in incomplete data used in research that cannot fully reflect the pollution behavior of enterprises. To this end, a new method for analyzing enterprise pollution index was constructed using power big data in the article. Based on the electricity source and pollutant emission structure of the enterprise, a pollution evaluation index system for the enterprise was constructed, and the importance scale and weight coefficient of each index were determined. The comprehensive index score was calculated using the entropy weight method, which reflected the risk or impact level of the enterprise in terms of pollutant emissions, environmental impact, and resource utilization. The results show that there are certain differences in the pollution index among different enterprises, with the highest pollution index of 0.14 in the chemical industry, and lower pollution indices of 0.04 in the beverage manufacturing and rubber manufacturing industries. The proposed method can effectively display the differences in pollution indices among different enterprises and has reference value.http://journal.xpu.edu.cn/en/#/digest?ArticleID=1521power big dataenterprise pollution indexweight coefficiententropy weight method |
| spellingShingle | XI Zenghui WANG Weibin LU Jiaming QU Haini Constructing enterprise pollution index and monitoring analysis using electricity big data Xi'an Gongcheng Daxue xuebao power big data enterprise pollution index weight coefficient entropy weight method |
| title | Constructing enterprise pollution index and monitoring analysis using electricity big data |
| title_full | Constructing enterprise pollution index and monitoring analysis using electricity big data |
| title_fullStr | Constructing enterprise pollution index and monitoring analysis using electricity big data |
| title_full_unstemmed | Constructing enterprise pollution index and monitoring analysis using electricity big data |
| title_short | Constructing enterprise pollution index and monitoring analysis using electricity big data |
| title_sort | constructing enterprise pollution index and monitoring analysis using electricity big data |
| topic | power big data enterprise pollution index weight coefficient entropy weight method |
| url | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1521 |
| work_keys_str_mv | AT xizenghui constructingenterprisepollutionindexandmonitoringanalysisusingelectricitybigdata AT wangweibin constructingenterprisepollutionindexandmonitoringanalysisusingelectricitybigdata AT lujiaming constructingenterprisepollutionindexandmonitoringanalysisusingelectricitybigdata AT quhaini constructingenterprisepollutionindexandmonitoringanalysisusingelectricitybigdata |