A Review of Research on Methodological Conflict Deconstruction and Integration Paths for ESG Rating Heterogeneity

Wanling Ran

doi:10.62051/ijgem.v9n2.01

Authors

Wanling Ran

DOI:

https://doi.org/10.62051/ijgem.v9n2.01

Keywords:

ESG rating heterogeneity, Methodological conflict, Integration path, Indicator standardization, Weight aggregation, Industry suitability

Abstract

This work examines the heterogeneity in ESG ratings, a critical tool for assessing corporate performance across environmental, social, and governance dimensions, which significantly influences capital market resource allocation. Despite widespread adoption, substantial discrepancies exist among major rating agencies, leading to investor confusion, strategic uncertainty for firms, and potential rating arbitrage, thereby undermining pricing efficiency and hampering the effective implementation of Sustainable Development Goals. In this work, we employ a structured framework of theoretical deconstruction, conflict analysis, and path exploration, combining systematic literature review and comparative analysis to clarify the core concept of ESG rating heterogeneity. We investigate the evolution of major ESG rating systems, quantitatively measure discrepancies, and analyze methodological differences in indicator selection, weight allocation, data processing, and rating procedures. Furthermore, a three-stage cross-institutional integration model—indicator standardization, weight aggregation, and result verification—is proposed, with tailored strategies for energy-intensive and financial sectors considering regional policy adjustments. Empirical evaluations demonstrate that the integration framework enhances cross-agency rating consistency, reduces rating deviations, and improves capital market efficiency. This work contributes both theoretically and practically by offering insights into harmonizing ESG ratings, supporting informed investment decisions, and guiding the evolution of ESG systems from diversified competition toward orderly synergy, providing valuable policy implications for regulators, enterprises, and investors.

Downloads

Download data is not yet available.

References

[1] Berg, F., Fabisik, K., & Sautner, Z. (2022). Quantifying ESG rating heterogeneity. Journal of Sustainable Finance & Investment, 12(3), 245-268.

[2] Antonelli, M., De Angelis, T., & Marchini, P. L. (2021). Neural network fusion of ESG ratings: A comparative study. Technological Forecasting and Social Change, 167, 120689.

[3] Zhang, Z., Li, W., & Chen, Y. (2022). The impact of ESG rating heterogeneity on corporate financing costs: An empirical analysis based on the A-share market. Financial Research, (8), 92-108.

[4] Billio, M., Costola, M., Hristova, I., Latino, C., & Pelizzon, L. (2021). ESG ratings: A critical survey. Journal of Banking & Finance, 130, 106257.

[5] Brandon, D., Krueger, P., & Schmidt, P. S. (2023). The divergence in ESG ratings: An analysis of the social dimension. Journal of Business Ethics, 185(3), 567-589.

[6] Capizzi, M., Giovannini, R., & Pesic, V. (2022). ESG ratings: Correlations and discrepancies across dimensions. Corporate Social Responsibility and Environmental Management, 29(4), 906-918.

[7] Christensen, P. H., Serafeim, G., & Sikochi, A. (2022). Too much of a good thing? ESG disclosure and cognitive dissonance. Journal of Business Ethics, 176(2), 327-345.

[8] Cornell, B. (2020). The ESG premium puzzle. Journal of Applied Corporate Finance, *32*(4), 10-18.

[9] Dorfleitner, G., Halbritter, T., & Weber, M. (2015). ESG ratings: A comparison of different approaches. Journal of Asset Management, 16(5), 310-321.

[10] Kempf, A., & Osthoff, P. (2010). The effect of ESG ratings on portfolio performance. Journal of Business Ethics, 95(S1), 131-147.

[11] Liu, X., Wang, Y., & Zhang, L. (2023). A study of ESG disclosure quality and rating heterogeneity. Management World, 39(5), 135-150.

[12] Feng, Y., & Zhang, Q. (2024). ESG disclosure, rating discrepancies and cost of capital: An analysis based on information asymmetry theory. Journal of Corporate Finance, 84, 102478.

[13] Jiang, Y., & Wang, S. (2025). The impact of non-financial information processing differences on ESG rating divergence. Accounting Review, 100(2), 345-362.

[14] Li, H., & Chen, R. (2024). ESG rating discrepancies from the perspective of institutional logic conflict. Strategic Management Journal, 45(3), 567-589.

[15] Wang, Y., & Chen, X. (2021). ESG investing in the Chinese stock market: Performance and risk. Pacific-Basin Finance Journal, 67, 101542.

[16] Ma, W. J., & Yu, B. J. (2023). Analysis of ESG rating discrepancies and influencing factors at home and abroad. China Industrial Economics, 41(4), 156-174.

[17] Sun, J. X., & Liu, H. (2024). Institutional differences and rating discrepancies of ESG rating agencies: An analysis based on the institutional complexity theory. Organization Science, 35(2), 456-475.

[18] Xu, S. B., & Zhao, L. (2024). A study on the impact of differences in ESG rating objectives on rating divergence. Journal of Business Ethics, 185(1), 123-145.

[19] Zhao, J., & Wang, Y. (2024). ESG disclosure quality and rating divergence: A mediation effect test based on information asymmetry. Journal of Accounting and Public Policy, 43, 107135.

[20] Abate, F., Migliavacca, M., & Scalera, F. (2021). ESG performance and mutual fund efficiency: A DEA approach. Journal of Cleaner Production, 295, 126479.

[21] Zumente, I., & Lāce, I. (2023). Convergence of ESG ratings: An exploratory analysis. Finance Research Letters, 52, 103567.

[22] Zhu, X., & Li, Y. (2023). Assessing the consistency of ESG ratings: A comparison of the top 100 companies. Corporate Social Responsibility and Environmental Management, 30(2), 789-801.