Simulation study reveals rising water demand could induce a $16 trillion global GDP loss by 2050, highlighting the protective capacity of efficiency reforms and AI allocation.
Water scarcity threatens global stability, with demand set to surge 20–30% by 2050, pushing withdrawals from 4600 km3 yr−1 today to 5500–6000 km3 yr−1 under rising demographic and climatic pressures. This study presents a sequentially coupled econometric–hydrological–reduced-form economic framework that couples water supply dynamics with demand forecasting and macroeconomic impact assessment. Agriculture dominates current withdrawals at 70% (FAO AQUASTAT), followed by industry (20%) and domestic use (10%). Monte Carlo simulations (n = 1000) identify critical regional hotspots: Asia (stress ratio = 1.06), the Middle East (1.18), and Africa (0.99). The reduced-form economic module uses a target-calibrated scarcity elasticity (ε = 0.1865) applied against a fixed economic reference threshold (4600 km3 yr−1). This internally calibrated parameter yields a first-order GDP loss estimate of approximately $16.0 trillion under the high-demand (+30%) 2050 scenario (6000 km3 yr−1 demand), equivalent to 5.5% of projected 2050 global GDP ($290 trillion, PwC 2017 baseline). The resulting magnitude is broadly consistent with the order of GDP impacts discussed by OECD (2012) and GCEW (2024), although neither publication reports this specific elasticity value. This is not an independently predicted outcome; it is a calibrated scenario estimate produced by a reduced-form damage function designed to reproduce first-order magnitudes consistent with published structural model results. Mitigation strategies including efficiency improvements, pricing reforms, and AI-driven allocation can reduce demand by up to 40%, which within the model’s mathematical structure reduces the calibrated economic loss to zero. Sectoral water distribution is addressed through continuous linear programming with proportional rationing. This framework advances transparent, reproducible scenario-based understanding and informs policy decisions aimed at mitigating future water scarcity challenges globally, while explicitly acknowledging limitations relative to full structural CGE models and empirically estimated panel econometric models.
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Soufiane Haddout (2026) studied this question.
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