Key Finding: Significant Positive Impact
The benchmark regression analysis, as detailed in Table 1 (4), confirms that artificial intelligence has a significant positive influence on the ESG performance of manufacturing enterprises. This finding is robust, holding true even after controlling for various firm-specific and environmental factors.
AI, viewed as a technological progression under the Solow model and endogenous growth theory, enhances resource utilization, improves production efficiency, and fosters innovative management. These advancements reduce environmental resource consumption, mitigate negative environmental impacts, contribute to social improvements through multidimensional growth with lower resource inputs, and optimize governance through data-driven decision-making and transparency. Thus, AI deeply integrates with the manufacturing sector to drive green economic efficiency and innovation-driven growth.
Mechanism 1: Optimized Resource Allocation
AI optimizes corporate resource allocation through precise data analysis and predictive modeling, particularly for investment decisions. Machine learning algorithms analyze market trends, industry dynamics, and internal operational data to provide a scientific foundation for investment portfolio optimization. Real-time assessment of potential returns and risks allows for efficient balance between short-term liquidity needs and long-term strategic investments, aligning with modern portfolio theory. This enhanced efficiency reduces environmental costs and strengthens corporate social responsibility, allowing firms to allocate capital to green technology, employee welfare, and supply chain optimization (Table 5).
The regression results indicate that higher resource allocation efficiency amplifies AI's positive impact on ESG performance, confirming its role as a key mechanism.
Mechanism 2: Enhanced Information Transparency
Artificial intelligence significantly enhances information transparency by leveraging IoT and big data analytics for real-time data collection, analysis, and sharing across the manufacturing process. AI-driven algorithms optimize information flow, improving supply chain efficiency and transparency. This reduces information gaps, enhancing market efficiency as per information asymmetry theory.
Increased transparency builds stakeholder trust, optimizes corporate decision-making, and allows firms to effectively identify and manage environmental/social risks, leading to improved internal governance. This fulfills social responsibility and enhances market competitiveness (Table 5).
Business Environment Moderation
A supportive business environment significantly amplifies the positive impact of AI on ESG performance (Table 6, Column 2). A higher degree of openness facilitates the introduction of advanced international technologies and ESG standards, providing external incentives. Increased marketization, through enhanced property rights protection and a more competitive environment, creates conditions for green transformation and corporate social responsibility initiatives.
The interaction coefficient between business environment (BE) and AI is 0.018, significant at the 1% level, confirming that BE positively adjusts the AI-ESG relationship.
Technology Intensity Moderation
Firms with high technological intensity experience a greater positive marginal effect of AI on ESG performance (Table 6, Column 3). High-tech firms can more effectively integrate AI, optimizing production efficiency and resource allocation, which leads to substantial improvements in environmental and social responsibility. These firms typically possess the necessary infrastructure and expertise for effective AI implementation.
The interaction coefficient between technology density level (DOT) and AI is 0.032, significant at the 1% level, demonstrating that DOT positively moderates the AI-ESG relationship.
Strategic Recommendations for AI-Driven ESG
Government Incentives: Implement tiered tax incentives (e.g., 5-8% tax reductions for 15-20% energy reduction) and matching funds (30-50% for SMEs adopting ISO-certified smart energy management).
Manufacturing Best Practices: Deploy AI-driven predictive maintenance to optimize equipment lifespan and reduce waste (12-18%), targeting high-energy consumption sectors. Utilize real-time resource monitoring with IoT for water usage reduction (20-25%) in textile and food processing.
Industry Benchmarks: Develop sector-specific AI-ESG benchmarks by 2025, incorporating metrics like algorithmic carbon footprint and data center energy efficiency. Implement annual certifications by third-party auditors and public scorecards comparing AI implementation maturity across enterprises.
