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Artificial Intelligence and Corporate Energy Efficiency: A Pathway to Sustainable Manufacturing—Evidence from Chinese Firms

Year: 2026

Type: article

Abstract: In the context of global efforts to achieve sustainable development and carbon neutrality, improving industrial energy efficiency has become a cornerstone of green transformation. This study examines how corporate AI-related disclosure intensity is associated with sustainable energy efficiency in manufacturing firms. Using panel data on Chinese A-share listed manufacturing firms from 2012 to 2024, we measure corporate energy efficiency using energy intensity based on standard coal. AI-related disclosure intensity is constructed from the frequency of AI-related terms in firms’ annual reports and logarithmically transformed. Based on panel regression, process innovation association analysis, moderation analysis, and ownership-based subgroup analysis, this study finds that AI-related disclosure intensity is negatively associated with energy intensity. Higher AI-related disclosure intensity is also linked to a higher share of process innovation, indicating enhanced attention to production process improvement. Data element utilization weakens the negative association between AI-related disclosure intensity and energy intensity, suggesting a moderating effect. The negative relationship persists across both state-owned and non-state-owned firms. These findings contribute to the understanding of how AI-related disclosure intensity reflects corporate energy management and promotes sustainable manufacturing. They offer actionable implications for firms and policymakers seeking to advance green innovation and achieve a sustainable economy.

Source: Sustainability

DOI: 10.3390/su182010559

Authors: Yahan Shi, Yinchao Zhang, Xizhe Xu, Xiaofeng Xu, Da Tong