Double Machine Learning - Enabled Analysis of How Digital Transformation Shapes ESG Performance

Digital transformation (DT), propelled by emerging technologies, signifies a strategic realignment of business operations, with the goal of enhancing organizational performance and competitive advantages. It involves the application of innovative digital technologies to comprehensively reshape business models and service delivery mechanisms. Key drivers of DT encompass big data, cloud computing, artificial intelligence, and block chain. In the context of global sustainability imperatives and China's dual carbon targets, this study empirically explores the mechanisms through which information technology - driven digital transformation improves corporate environmental, social, and governance (ESG) performance. Based on 11,800 firm - year observations of Chinese listed companies in heavy - polluting industries from 2010 to 2023, a double machine learning (DML) model is established, and tools such as Stata 19 are employed for data management, statistical analysis and programming. The findings demonstrate that digital transformation significantly enhances corporate ESG performance, with a more pronounced effect on state - owned enterprises. Replacing DML algorithms, including methods like lasso, gradient boosting, and neural network regression, shows that the research findings stay consistent across various model configurations. The present research stands out with its three pivotal contributions. Theoretically, it validates the positive impact of digital transformation on ESG performance for heavy - polluting listed companies in China, enriching the relevant theoretical system. Methodologically, the establishment of the DML model and the empirical analysis framework provide a new approach for similar research. Practically, it offers actionable strategies for emerging economies to integrate information technology into ESG governance, facilitating the construction of a sustainable development model characterized by “technology - enabled empowerment, market - oriented regulation, and environmental synergy,” and promoting the realization of China's “Dual Carbon” goals and broader sustainable development objectives.

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Published
2025-05-23
DOI
https://doi.org/10.1145/3746972.3747001
Primary Topic
Innovation Diffusion and Forecasting
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article
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Double Machine Learning - Enabled Analysis of How Digital Transformation Shapes ESG Performance

Yukun Wu
Innovation Diffusion and Forecasting
article

Double Machine Learning - Enabled Analysis of How Digital Transformation Shapes ESG Performance

Yukun Wu
article en

Abstract

Digital transformation (DT), propelled by emerging technologies, signifies a strategic realignment of business operations, with the goal of enhancing organizational performance and competitive advantages. It involves the application of innovative digital technologies to comprehensively reshape business models and service delivery mechanisms. Key drivers of DT encompass big data, cloud computing, artificial intelligence, and block chain. In the context of global sustainability imperatives and China's dual carbon targets, this study empirically explores the mechanisms through which information technology - driven digital transformation improves corporate environmental, social, and governance (ESG) performance. Based on 11,800 firm - year observations of Chinese listed companies in heavy - polluting industries from 2010 to 2023, a double machine learning (DML) model is established, and tools such as Stata 19 are employed for data management, statistical analysis and programming. The findings demonstrate that digital transformation significantly enhances corporate ESG performance, with a more pronounced effect on state - owned enterprises. Replacing DML algorithms, including methods like lasso, gradient boosting, and neural network regression, shows that the research findings stay consistent across various model configurations. The present research stands out with its three pivotal contributions. Theoretically, it validates the positive impact of digital transformation on ESG performance for heavy - polluting listed companies in China, enriching the relevant theoretical system. Methodologically, the establishment of the DML model and the empirical analysis framework provide a new approach for similar research. Practically, it offers actionable strategies for emerging economies to integrate information technology into ESG governance, facilitating the construction of a sustainable development model characterized by “technology - enabled empowerment, market - oriented regulation, and environmental synergy,” and promoting the realization of China's “Dual Carbon” goals and broader sustainable development objectives.

Beijing Normal-Hong Kong Baptist University (CN), Zhuhai Institute of Advanced Technology (CN)
Openalex Percentile: Top 78%
Innovation Diffusion and Forecasting
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Double Machine Learning - Enabled Analysis of How Digital Transformation Shapes ESG Performance — Yukun Wu · (2025) | TGRS Research Map | TGRS