A Study on the Green Evaluation System of Suppliers for Power Grid Enterprises Based on the Cloud Model
For the low-carbon energy transition, evaluating the supply chain system within power grid enterprises is critical to achieving national goals of sustainable development and green procurement. As a core entity within the energy industry chain, power grid companies present substantial requirements and challenges to their upstream suppliers. Taking the supply system of State Grid Jibei Electric Power Limited as a case study, this study applies a combination of MATLAB, Python, and R software to build up a cloud model for assessing the performance of materials and service providers within the power grid sector. The findings indicate that although many suppliers have made noticeable progress in implementing green management practices, significant deficiencies still persist in corporate information disclosure of carbon reduction efforts, green service capabilities, and product carbon footprints. Besides that, those companies exhibit a clear stratified pattern in evolving green and low-carbon practices, which implies uneven adoption and implementation across different suppliers. This study suggests that prioritizing investments in green technologies for suppliers can efficiently guide corporate performance into sustainable practices. Moreover, there is an urgent need for government intervention through more incentive mechanisms on grid power enterprises to accelerate the green transformation of supply chains and foster broader adoption of low-carbon practices.
Authors
- Zhenfeng Zhao (ORCID: https://orcid.org/0009-0007-0267-6740)
- Xi Li (ORCID: https://orcid.org/0009-0005-6748-0023)
- Z. Liu (ORCID: https://orcid.org/0009-0000-7199-1305)
- Chao Li
- Qing Mu (ORCID: https://orcid.org/0000-0002-1352-3885)
- Peng Li (ORCID: https://orcid.org/0000-0001-5550-3199)
- Hang Su (ORCID: https://orcid.org/0000-0003-2752-5862)
Institutions
- State Grid Corporation of China (China) (CN)
Publication Details
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1145/3718491.3718519
- Primary Topic
- Advanced Decision-Making Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00