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In Tianjin, water management is a key urban sustainability challenge. Researchers built a dynamic model linking three elements: water resources, economy and environment (WR‑SE‑WE). The model was tested with five policy scenarios: business as usual (BAU), water conservation priority (WCP), social‑economic advancement (SEA), water environmental protection (WEP) and integrated balanced development (IBD).

The main output is the coupling coordination degree (CCD), which measures how well the three pillars align. All scenarios show a rising CCD, but IBD performs best, reaching 0.926 by 2035. It is followed by SEA (0.920), WEP (0.902), BAU (0.880) and finally WCP (0.874).

Each policy has trade‑offs. WCP maximizes water efficiency and cuts pollution but hampers economic activity. WEP secures more clean water but limits GDP growth. SEA drives rapid economic expansion but strains water resources and delays pollution control. IBD blends the strengths of all, balancing savings, growth and protection.

The authors propose four actions: cross‑sector water governance, speeding up the green industry transition, prioritizing reclaimed water, and boosting agricultural efficiency. These steps could steer Tianjin toward a more balanced, high‑quality development path.

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The WR‑SE‑WE model relies on a continuous‑flow dynamic system structure, where key variables include domestic and industrial water demand, groundwater reserves, water imports, sectoral GDP, and pollutant concentrations (BOD, total nitrogen, phosphates). Differential equations describe aquifer recharge rates, evaporation losses, and pollutant emissions as functions of industrial output. For each scenario, policy parameters (e.g., per‑unit water consumption reduction rates, tax incentives for low‑water‑intensity technologies) are calibrated using historical data (2000‑2020) and demographic projections. The CCD is computed via Saaty's harmony formula, combining normalized indices of water availability (WR), economic growth (SE), and environmental quality (WE). Sensitivity analysis reveals CCD is more responsive to changes in the water recycling coefficient (r) than to GDP growth rate (g), indicating that enhancing water reuse directly improves coordination. Moreover, obstacle‑degree analysis pinpoints four critical factors: agricultural inefficiency, reliance on external water sources, untreated industrial emissions, and insufficient treatment infrastructure. Targeted interventions—especially low‑energy desalination and precision agriculture—can cut the obstacle index by 15‑20%, pushing CCD toward higher values even under less favorable scenarios.

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Authors:
Bohong LiKaili LiuWenkai ChenHongbo Liu
Date Published:
Original Article www.mdpi.com
Fact-check verified on 19/05/2026
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Creative Commons License CC BY-NC-SA 4.0

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