The Impact of Big Data on Corporate Energy Consumption Intensity: AI Technology and ESG Performance

Abstract

ABSTRACT Big data have become an increasingly important production factor in the digital economy. Although the resource‐based view (RBV) emphasizes the rarity of strategic resources, the growing accessibility of big data challenges this traditional perspective. Under conditions of data abundance, firms differ not in their access to data but in their AI‐enabled absorptive capacity to transform data into operational and environmental value. However, little is known about whether data‐driven public policies can improve corporate energy efficiency through this capability. Exploiting China's National Pilot Zones for Big Data (NPZBD) as a quasinatural experiment, we examine the impact of big data policy on corporate energy consumption intensity (ECI) using a staggered difference‐in‐differences approach and a sample of Chinese listed firms from 2011 to 2020. The results show that NPZBD significantly reduces corporate ECI, and the findings remain robust across a series of alternative specifications and identification tests. Further analysis indicates that the policy effect is stronger when firms possess greater AI‐enabled absorptive capacity, better financing capacity, and stronger ESG‐oriented governance. Moreover, the effect is more pronounced among state‐owned enterprises and high‐polluting firms. By distinguishing accessible data resources from AI‐enabled absorptive capacity, this study extends the RBV under conditions of data abundance and provides new evidence on how data‐driven public policies promote corporate energy efficiency and sustainable development.