AI-Driven Energy Efficiency versus AI-Induced Energy Demand: A Dynamic Computable General Equilibrium (CGE) Analysis of Vietnam’s Twin Transition

Artificial Intelligence (AI) acts as a double-edged sword in the energy transition: its deployment in heavy industry generates productivity dividends that reduce energy intensity (the “Green AI” effect), while the data centers and hardware required to run it surge electricity demand and strain emerging-economy power grids (the “Brown AI” effect). This paper quantifies both effects and their interaction for Vietnam using a 23-sector recursive dynamic Computable General Equilibrium (CGE) model calibrated to the 2019 Input-Output Table and simulated through 2035. Three scenarios are evaluated: Green AI (S2), modelled as a Total Factor Productivity (TFP) shock to heavy manufacturing and electricity; Brown AI (S3), modelled as an exogenous investment surge in Information Technology (IT) hardware and services; and a combined Twin Transition (S4). Green AI delivers a compounding GDP dividend of +0.98% by 2030 and +1.79% by 2035, with consumption rising +1.17% and +2.03%. Brown AI is macro-neutral in GDP terms but imposes a consumption cost of 0.42% by 2030 as infrastructure investment crowds out household expenditure. The Twin Transition is approximately macro-additive (GDP +1.06% by 2030 and +1.95% by 2035), with consumption recovering to +1.70% above baseline by 2035. The dominant structural trade-off is distributional: real exchange rate appreciation generated by Green AI’s export surge displaces Textiles ( 5.5%) and Leather and Footwear ( 16.1%), even as Heavy Manufacturing (+12.9%) and IT Hardware (+10.9%) expand substantially. Under S3 alone the Electricity sector expands only +0.10% by 2030 against a 14.87% per year IT investment surge, indicating a binding generation capacity that the Green AI productivity shock relaxes in S4. The Twin Transition is macro-feasible, but its adjustment costs fall unevenly on the manufacturing workforce.