The AI Energy Governance Paradox: Reconciling artificial intelligence’s sustainability promise with its growing power demand

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Artificial intelligence (AI) is increasingly positioned as a key enabler of global sustainability transitions, supporting energy optimization, climate modeling, and resource-efficient industrial processes. However, the rapid expansion of AI systems introduces a growing contradiction: the computational intensity of modern AI models drives substantial energy consumption, potentially offsetting their environmental benefits. This study conceptualizes this tension as the AI Energy Governance Paradox, the conflict between AI’s sustainability promise and its escalating power demand. Using a governance-informed mixed-methods design, the research combines quantitative scenario modeling, empirical benchmarking of AI workloads, rebound elasticity simulations, and qualitative interviews with AI engineers, policymakers, and sustainability officers. Three trajectories are compared: efficiency-only, governance-regulated, and business-as-usual. The findings show that technological efficiency measures such as model pruning and carbon-aware scheduling can reduce per-task energy use by 22–35%, but rebound effects may erase these gains when governance mechanisms are absent. In contrast, governance-regulated scenarios, including mandatory energy reporting and carbon pricing, reduce projected energy growth by 28% and suppress rebound elasticity below 0.3. The study demonstrates that AI sustainability cannot be achieved through technical optimization alone. Instead, multi-level governance frameworks, spanning corporate carbon accountability, national digital policy integration, and global standards coordination, are required to align AI innovation with climate objectives. Governance thus determines whether AI becomes a climate solution or an additional environmental burden.

Keywords: AI Energy Governance, Green AI, Rebound Effect, Energy Transparency and Accountability, Sustainable AI Policy

JEL Classification: F23, M14, O33

Received: 19.10.2025
Accepted: 30.10.2025

How to cite: Nakajima, R. (2026). The AI Energy Governance Paradox: Reconciling artificial intelligence’s sustainability promise with its growing power demand. In A. Celentano, A. Kostyuk, S. Dell’Atti, & G. Giovando (Eds.), Corporate governance: Multidisciplinary research (pp. 33–37). Virtus Interpress. https://doi.org/10.22495/cgmrp6