Advancing new energy industry quality via artificial intelligence-driven integration of ESG principles

Xiang Zhou, Yubo Peng, Xiaojun Sun (), Xiaojing Cao, Zeyu Wang and Jiali Zhang
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Xiang Zhou: Guangzhou University
Yubo Peng: Xi’an Jiaotong University
Xiaojun Sun: Hubei University of Economics
Xiaojing Cao: Wuhan University
Zeyu Wang: Guangzhou University
Jiali Zhang: Guangzhou University

Humanities and Social Sciences Communications, 2025, vol. 12, issue 1, 1-20

Abstract: Abstract This study seeks to investigate the assessment of new energy enterprises’ performance by integrating Environmental Social Governance (ESG) using artificial intelligence, aiming to enhance the high-quality development of the new energy industry. Initially, an ESG-centered performance evaluation system is established for new energy enterprises, encompassing four dimensions: financial, environmental, social, and governance performance. Subsequently, multimodal data is gathered, and deep learning (DL) techniques, specifically Word2Vec and the graph convolutional neural network, are applied to extract and consolidate features from text and images related to performance within these enterprises. This facilitates the classification and identification of key performance indicators, leading to the development of a DL-based performance evaluation model for new energy industry incorporating ESG. The empirical analysis reveals superior performance indicators, achieving a classification accuracy of 90.48%, surpassing the Convolutional Neural Network algorithm. A detailed examination of individual dimensions and overall performance demonstrates relatively high financial performance and a stable upward trend in environmental performance. However, social performance scores exhibit significant fluctuations, particularly in areas related to employees and product responsibility. Consequently, the developed performance evaluation system effectively identifies trends in enterprise development. In subsequent phases, it is recommended to continuously enhance corporate governance mechanisms, internal controls, and risk management.

Date: 2025
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DOI: 10.1057/s41599-025-05800-0

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