Optimizing Carbon Footprint Management in Power Enterprises Using AI
Author Information
Author(s): Wu Liangzheng, Li Kaiman, Huang Yan, Wan Zhengdong, Tan Jieren
Primary Institution: Energy Development Research Institute, China Southern Power Grid, Guangzhou, China
Hypothesis
Can artificial intelligence improve the carbon footprint management model of power enterprises?
Conclusion
The study demonstrates that AI can significantly enhance the accuracy and effectiveness of carbon footprint management in power enterprises.
Supporting Evidence
- AI technology can process large amounts of carbon emission data quickly and accurately.
- The study identified key factors influencing carbon emissions and opportunities for reduction.
- Case studies showed significant improvements in carbon management for participating companies.
- The DSR model effectively captured the driving factors and responses to carbon emissions.
- Entropy weight-TOPSIS method provided a reliable evaluation framework for carbon audits.
Takeaway
This study shows that using smart technology can help power companies track and reduce their carbon emissions better, making the planet healthier.
Methodology
The study used a carbon footprint calculation model based on big data and AI, employing machine learning algorithms to analyze carbon emission data.
Limitations
The model may not fully adapt to different energy structures and regulatory environments in various countries.
Digital Object Identifier (DOI)
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