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Published on: September 19, 2020
Coupling time-series analysis and agent-based modeling to design non-price demand side management policies for water
Yacong Hu1, Chen Feng1, Bingqian Zhang1
1School of Environment, Tsinghua University, Beijing 100084, China.
Abstract:
Urban water scarcity increasingly calls for demand-side management (DSM) to complement conventional supply-side engineering, yet sustainable water saving is challenging because conservation behavior can decay, and non-price DSM often exhibit threshold effects and cross-policy synergies. This study proposes an integrated framework that combines time-series analysis with agent-based modeling (ABM) to simulate how non-price DSM policies-environmental education and behavioral nudges-jointly promote water-saving behavior. High-resolution dormitory meter data from a leading university were analyzed to extract baseline consumption trends, periodicities, and holiday effects, which were then translated into empirically grounded behavioral rules for agents in the ABM. The calibrated model reproduced the observed campus dynamics with high fidelity (R²=0.96). Key findings include: (1) education-only and nudge-only policies deliver short-term water savings that regress toward a low-level equilibrium due to the ∼15 % endogenous reversion tendency. (2) A combined policy activates an ordered cascade effect: high-quality education first seeds early adopters, generating a conservation signal that is subsequently amplified by nudges across the social network, driving a system-wide shift toward water conservation. (3) Across adoption stages, the combined DSM strategy reduced per capita water usage by 1.8 % to 10.7 %, and increased the share of water-saving students. Threshold analysis reveals that, when the initial non-water saving ratio is 70 %, adding nudges expands the feasible intervention space by 65.3 %, while education quality outweighs coverage for crossing behavioral tipping points. The model results were validated through a sensitivity analysis using the Hornberger-Spear-Young algorithm. This study provides a data-driven framework for evaluating DSM policies and offers a roadmap for designing staged, cascade-oriented policies aimed at achieving water savings.
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