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In recent years, due to the sluggish recovery of the global economy and the enormous financial pressure, along with the need for economic transformation and upgrading faced by various countries, listed companies are encountering amplified operational stress, thereby gradually unveiling delisting risks (Xing et al., 2020). The risk conundrum of listed companies not only engenders significant losses to the enterprises but also critically affects the interests of investors, creditors, and other stakeholders. If enterprises can predict these risks in advance, they can proactively evade such potential hazards (Xing et al., 2020). With the advent of the new generation of information technology, artificial intelligence (AI) has emerged as a pivotal tool to alleviate the problem of inadequate support from conventional financial management methods for green innovation in enterprises. Hence, it is essential to explore the ways to employ emerging technologies such as AI to extract indicators from the intricate data of the enterprises for risk prediction and prevention, which not only facilitates the enterprises in seizing a larger market share and maneuvering through the intricate economic landscape of dynamic transformations but also propels the circulation of innovation resources and market transformation of green technology innovation products.
Green technological innovation, as a novel form of technological innovation that emphasizes both environmental protection and green economic development, is playing an increasingly pivotal role in enterprise development. However, weak innovation capability has consistently remained the most significant obstacle to the transformation, upgrading, and development of enterprises (Cambria et al., 2018). The level of green technology innovation capability directly affects the degree of pollution generated by the enterprise. As the green technology innovation capability of an enterprise improves, the degree of pollution decreases accordingly. The enhancement of green technology innovation capability is therefore indispensable to the green transformation of the manufacturing industry. Currently, the majority of domestic and foreign scholars’ research on the financial risk prediction mechanism of enterprises is primarily based on financial text data, such as the z-score model. Although these methods have proven to be effective in early warning of financial crisis of listed companies, they have not incorporated risk evaluation indicators related to green finance (Gao et al., 2022). Consequently, they cannot provide a comprehensive measurement of the future overall development of a company. From the perspective of information disclosure, financial data can visually present the company’s present operation status, while the pertinent risk evaluation indicators of green finance can supplement quantitative data, trace indications of changes in the company’s operation, and encourage the company to achieve green technology innovation while maximizing profits. Generally speaking, when a company’s operating conditions change, the financial report disclosure information related to green financial risk assessment indicators will also change correspondingly. We can perceive these signals through text analysis. Meanwhile, the manager will also reveal the company’s outlook for the next one to two years in the financial report information. Hence, incorporating risk evaluation indicators related to green finance would prove beneficial in promoting the sustainable development of the company and predicting financial crises.