Table of Links

• Abstract and Introduction
• Related Works
• Purpose
• Methods
• Results
• Discussion
• Conclusion and Bibliography

7. Conclusion

The proposed ESG analysis algorithm can help standardize ESG evaluation for all companies. This is because it limits self-reporting bias by incorporating outside social network analysis for more balanced results. A social-network-based ESG index can also directly show which areas people want to change, which can better focus executive efforts on meaningful change. Additionally, using machine learning, the model can generate a proxy for a company’s social responsibility, which can help determine ESG for smaller companies that do not have analyst coverage. This will help more companies receive ESG ratings in an automated way, which can create a more level playing field between small and large companies and ultimately help more socially responsible firms prevail. Overall, the project can have broad implications for bridging the gap in ESG. This will help rewire large quantities of ESG capital to more sustainable and ethical initiatives.

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