Creating a Systematic ESG Scoring System: Conclusion and Bibliography

15 Jun 2024


(1) Aarav Patel, Amity Regional High School – email:;

(2) Peter Gloor, Center for Collective Intelligence, Massachusetts Institute of Technology and Corresponding author – email:

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.


A Sokolov, J Mostovoy, J Ding, L Seco. 2021. Building Machine Learning Systems for Automated ESG Scoring. The Journal of Impact and ESG Investing 1 (3), 39-50

A. M. Shahi, B. Issac, and J. R. Modapothala. 2011. Analysis of supervised text classification algorithms on corporate sustainability reports. In Proceedings of 2011 International Conference on Computer Science and Network Technology, Vol. 1. 96–100

Akbik, Blythe, and Vollgraf. “Contextual String Embeddings for Sequence Labeling.” Proceedings of the 27th International Conference on Computational Linguistics, pages 1638–1649 Santa Fe, New Mexico, USA, August 20-26, 2018

Andrea Venturelli, Fabio Caputo, Rossella Leopizzi, Giovanni Mastroleo, and Chiara Mio. 2017. How can CSR identity be evaluated? A pilot study using a Fuzzy Expert System. Journal of Cleaner Production 141 (2017), 1000 – 1010.

Awad, M., Khanna, R. (2015). Support Vector Regression. In: Efficient Learning Machines. Apress, Berkeley, CA.

Berg, Florian, et al. “Aggregate Confusion: The Divergence of ESG Ratings.” SSRN Electronic Journal, 2019, doi:10.2139/ssrn.3438533.

CDP. (2017, July 10). New report shows just 100 companies are source of over 70% of emissions. Retrieved May 24, 2022, from media/new-report-shows-just-100-companies-are-source-of-over-70-of-emissions

Chen Tianqi, and Guestrin Carlos. “XGBoost: A Scalable Tree Boosting System.” KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, Pages 785-794, doi: 10.1145/2939672.2939785

de Beer D, Matthee M. Approaches to Identify Fake News: A Systematic Literature Review. Integrated Science in Digital Age 2020. 2020 May 5;136:13–22. doi: 10.1007/978-3-030- 49264-9_2. PMCID: PMC7250114.

Oliver Kramer. Dimensionality Reduction with Unsupervised Nearest Neighbors, 2013, Volume 51, ISBN: 978-3-642-38651-0

Drempetic, Samuel, et al. “The Influence of Firm Size on the ESG Score: Corporate Sustainability Ratings Under Review.” Journal of Business Ethics, vol. 167, no. 2, 2019, pp. 333–360., doi:10.1007/s10551-019-04164-1

Gloor, Peter A., et al. “Web Science 2.0: Identifying Trends through Semantic Social Network Analysis.” 2009 International Conference on Computational Science and Engineering, 2009, doi:10.1109/cse.2009.186.

Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278–282).

Jain, M., Sharma, G. D., & Srivastava, M. (2019). Can sustainable investment yield better financial returns: A comparative study of ESG indices and MSCI indices. Risks, 7(1), 15.

Kotsantonis, Sakis, and George Serafeim. “Four Things No One Will Tell You About ESG Data.” Journal of Applied Corporate Finance, vol. 31, no. 2, 2019, pp. 50–58., doi:10.1111/jacf.12346

Pavel Wicher, František Zapletal, and Radim Lenort. 2019. Sustainability performance assessment of industrial corporation using Fuzzy Analytic Network Process. Journal of Cleaner Production 241 (2019).

Pin-Chao Liao, Ni-Ni Xia, Chun-Lin Wu, Xiao-Ling Zhang, and Jui-Lin Yeh. 2017. Communicating the corporate social responsibility (CSR) of international contractors: Content analysis of CSR reporting. Journal of Cleaner Production 156 (2017), 327–336.

Rao, Prashanth. “Fine-Grained Sentiment Analysis in Python (Part 1).” Medium, Towards Data Science, 9 Sept. 2019,

Ryohei Hisano, Didier Sornette, and Takayuki Mizuno. 2020. Prediction of ESG compliance using a heterogeneous information network. Journal of Big Data 7, 1 (2020), 22

S.-J. Lin and M.-F. Hsu. 2018. Decision making by extracting soft information from CSR news report. Technological and Economic Development of Economy 24, 4 (2018), 1344–1361.

S&P Global. (n.d.). ESG Evaluation | S&P Global. Retrieved May 24, 2022, from

Stackpole, Beth. “Why Sustainable Business Needs Better ESG Ratings.” MIT Sloan, 6 Dec. 2021,

shweta-29. “Shweta-29/Companies_ESG_Scraper: This Repository Includes a Tool to Extract Companies' ESG Ratings & Financial Metrics and Load Them on SQL.” GitHub,

T Krappel, A Bogun, D Borth. 2021. Heterogeneous Ensemble for ESG Ratings Prediction. KDD Workshop on Machine Learning in Finance

United Nations Global Compact. (2016). The UN Global Compact-Accenture Strategy CEO Study 2016. Retrieved May 26, 2022, from

United Nations Global Compact. (2019). UN Global Compact - Accenture Strategy 2019 CEO Study – The Decade to Deliver: A Call to Business Action. Retrieved May 26, 2022, from

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.