Unveiling the Link between Short Selling and ESG Performance in China’s Heavily Polluting Industries: A Double Machine Learning Approach

DOI

10.2991/978-94-6463-845-5_66How to use a DOI?

Keywords

Short selling; ESG performance; Double machine learning; Heavy-pollution industries

Abstract

Environmental, social, and governance (ESG) performance is crucial for business transformation in sustainable development, which is consistent with management science’s emphasis on strategic decision-making. We examine the impact of short selling on ESG efficiency using data from Chinese heavy-pollution listed companies from 2010 to 2023. Using data from the Wind and RESSET databases, we combine the double machine learning (DML) and difference-in-differences (DID) approaches. DML can properly determine the causal influence of short selling on ESG efficiency since it is powered by modern computers, which can manage complicated linkages and control confusing variables. DID guarantees the accuracy of the results. According to our findings, short selling greatly improves ESG performance. For robustness, we use computer-aided techniques to resolve DML biases in accordance with management science’s validation requirements. By substituting lasso, gradient boosting, and random forest for neural network regression and modifying the DML sample split ratio, we bolster the validity of our results. This research advances the field of management science by: Theoretically, it makes the connection between short selling and ESG more clear for strategic management; methodologically, it innovates by combining DML and DID; and practically, it helps regulators create sustainable legislation and helps heavy-pollution company managers optimize their ESG plans.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yanting Wu
PY  - 2025
DA  - 2025/09/16
TI  - Unveiling the Link between Short Selling and ESG Performance in China’s Heavily Polluting Industries: A Double Machine Learning Approach
BT  - Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025)
PB  - Atlantis Press
SP  - 646
EP  - 654
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-845-5_66
DO  - 10.2991/978-94-6463-845-5_66
ID  - Wu2025
ER  -