Digitizing Talent, Intensifying Emissions: A Review of the Rebound Effect in AI-Driven Human Resource Management Systems

Abstract

-The integration of artificial intelligence into human resource management has been widely championed as a pathway to organizational efficiency and, increasingly, as a tool for advancing sustainability objectives through digital transformation. This review challenges the prevailing assumption that AI-enabled HR systems inherently reduce environmental impact by examining the phenomenon through the lens of the Jevons paradox. Synthesizing evidence from engineering informatics, energy economics, and strategic human resource management, we argue that efficiency gains in algorithmic HR processes generate rebound effects that may partially or fully offset anticipated resource savings. The review identifies three mechanisms through which digital HR intensifies resource consumption: the scaling effect, wherein reduced marginal costs drive increased transaction volumes; the complexity effect, wherein ever-expanding datasets and model architectures demand escalating computational resources; and the oversight effect, wherein algorithmic outputs generate new categories of human labor for validation and correction. We propose the Net-HR Carbon Account framework as a methodological response, integrating Life Cycle Assessment principles with HR process analytics. The review concludes that sustainable HR scholarship must move beyond surface-level digitization metrics toward a systemic accounting of the energy, water, and human costs embedded in AI-driven talent management.