# ESG Rating Disagreement and Corporate Total Factor Productivity:Inference and Prediction

arXiv: https://arxiv.org/abs/2408.13895 · HTML: https://mlsanity.com/paper/2408.13895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)

## Built on by (most cited first, 1 of 1)

- [Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA](https://mlsanity.com/paper/2604.22239/) (2026)

## Abstract (arXiv)

This paper examines how ESG rating disagreement (Dis) affects corporate total factor productivity (TFP) in China based on data of A-share listed companies from 2015 to 2022. We find that Dis reduces TFP, especially in state-owned, non-capital-intensive, low-pollution and high-tech firms, green innovation strengthens the dampening effect of Dis on TFP, and that Dis lowers corporate TFP by increasing financing constraints and weakening human capital. Furthermore, XGBoost regression demonstrates that Dis plays a significant role in predicting TFP, with SHAP showing that the dampening effect of ESG rating disagreement on TFP is still pronounced in firms with large Dis values.