NIE 2025 | Expert Interviews: Corporate ESG Management and the Integration of AI with ESG

Q1: Against the backdrop of the 'dual carbon' goal and increasingly stringent ESG disclosures, what are the significance of companies conducting ESG management?

Li Jing pointed out that enterprises are undergoing a profound transformation in their understanding of ESG. In the past, many companies treated ESG merely as a compliance requirement, considering it an additional burden. However, today ESG is gradually becoming an important engine for value creation. She emphasized that in addition to traditional financial statements, companies should also pay attention to another 'non-financial statement'. This 'statement' presents the company's efforts in environmental protection, social responsibility, long-term development, and green transformation, which will ultimately be translated into natural capital and brand assets.

Taking manufacturing as an example, in the past, companies focused on cost control and production efficiency. However, now, carbon footprint management and emission reduction capabilities have become key elements in measuring competitiveness. By proactively disclosing carbon emission information, optimizing supply chain management, and enhancing ESG governance levels, companies can not only gain favor from the capital market but also establish a long-term, stable, and responsible brand image in public perception. This transformation indicates that ESG is no longer just a cost center but is becoming a value center that drives long-term value and competitive advantage.

Q2: How do Chinese enterprises find a balance between international standards and local practices, and build an ESG system that suits their own needs?

When discussing this issue, Li Jing stated that China is undergoing a transformation from an 'follower' to a 'leader' in the field of ESG. Especially in green finance and carbon trading, China has ranked among the top in the world, creating favorable conditions for enterprises to establish ESG systems that comply with international norms while highlighting local characteristics.

She pointed out that enterprises must first possess quantifiable, benchmarkable, and traceable ESG data, which is a prerequisite for winning recognition from the capital market. No matter which market they choose to list on, high-quality quantitative disclosure has become a mandatory requirement. Secondly, enterprises should combine their industry characteristics and development stages to create unique ESG stories. For example, the manufacturing industry can demonstrate its advantages through green supply chain management and energy conservation and emission reduction. Finally, in terms of governance frameworks, enterprises need to strike a balance between following international standards and reflecting Chinese characteristics. Highlight differentiation based on compliance, gradually forming an indicator system that can represent the industry and national characteristics. She emphasized that this balance not only helps improve compliance levels but also highlights the unique value of Chinese enterprises in international competition.

Q3: At different stages of development, what challenges do enterprises face in advancing ESG initiatives?

During the Pre-IPO phase, the key lies in establishing a comprehensive data collection mechanism as early as possible and initially forming a governance framework by comparing historical data with industry benchmarks. This phase often determines whether a company can gain recognition from the capital market in the future. After entering the initial listing stage, compliant disclosure becomes a core task. Companies not only need to meet regulatory requirements but also demonstrate strategic thinking during the disclosure process, incorporating ESG into the company's long-term development planning. As companies grow to a certain scale, challenges gradually shift towards supply chain and climate risk management. For example, European market customers often require supply chain partners to disclose complete carbon footprints, forcing companies to promote green supply chain management globally and strengthen their ability to cope with climate risks. When companies grow into multinational groups, complexity further increases, and traditional management methods are no longer sufficient. At this point, it is necessary to introduce digital and information-based ESG management systems, achieving cross-regional and cross-subsidiary data integration and governance coordination through platforms such as ERP, thereby enhancing compliance and management efficiency.

Li Jing emphasized that enterprises at different stages have different focuses in practice, but the core goal is always to continuously enhance sustainable competitiveness through systematic management of ESG.

Q4: The rapid development of artificial intelligence is changing various industries. So, what role can AI play in the ESG field? At the same time, what challenges may it face?

Regarding the application of AI in ESG, Li Jing holds a positive yet cautious attitude. She states that AI, especially large models, shows great potential in data processing and efficiency improvement, enabling enterprises to collect ESG data, conduct carbon accounting, and disclose information more quickly and accurately. EY is also actively exploring this direction, such as developing intelligent Q&A mini-programs and gradually building an ESG knowledge base to provide more convenient support for enterprises in their daily learning and management.

At the same time, she also emphasized that the implementation of AI still faces many challenges. Firstly, the training and operation of large models require a significant amount of computing power, and enterprises must strengthen relevant management. Secondly, the accuracy of AI highly depends on data quality; if the corpus or input data is not rigorous enough, deviations or even 'AI hallucinations' can occur, affecting the scientific nature of decision-making. In addition, although AI can significantly improve efficiency, it still struggles to replace customized and emotional communication in human-computer interaction, which requires enterprises to seek a balance between efficiency and humanistic care. Finally, when AI is applied in sensitive areas such as carbon accounting and rating, algorithm transparency and ethical review mechanisms are particularly crucial. Li Jing pointed out that only under the premise of scientific governance and prudent supervision can AI truly become a powerful booster for promoting ESG development.

Q5: In the long run, what value will AI-powered ESG bring to enterprises?

In answering this question, Li Jing put forward a widely circulated view: 'AI will not replace humans, but those who do not know how to use AI.' She believes that this view also applies to the ESG field. For businesses, AI can not only improve the efficiency of data collection, analysis, and disclosure, reduce labor costs, but also create new value in environmental governance, social responsibility, and governance optimization. More importantly, AI is helping businesses elevate their strategic level. Through intelligent systems, businesses can better plan their ESG paths globally, achieving cross-border compliance and integrated management. Looking ahead, with the gradual improvement of green computing power, intelligent management platforms, and ethical governance frameworks, the integration of AI with ESG is expected to become the core engine driving high-quality sustainable development for enterprises.