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Water pollution is a prominent and urgent environmental problem that represents a significant challenge in solving the water resource crisis. The ability to choose an optimal environmental policy can provide support for decision makers to effectively control water pollution. This study presents an agent-based model (ABM) approach involving two classes of agents, agricultural household agents and factory agents, to simulate pollutant discharge, and discusses the effectiveness of the whole system and subsystems under multiple policy scenarios involving a combination of environmental tax (ET) and payments for environmental services (PES). This idea is applied to the Shanmei Reservoir watershed, one of the important reservoirs watersheds in China. The results showed that: (1) the ABM represented well pollutant discharge scenarios where Nash coefficient (NSE) values were greater than 0.76; (2) though ET and PES policies were both effective in reducing water pollution, PES was more effective at reducing pollution from households, while ET was more effective at controlling industrial pollution emissions; (3) considering the environmental costs and general effect of the system, a medium degree of PES for agricultural household agents and a medium degree of ET for factory agents were found to be optimal for controlling water pollution in this watershed. A differential compensation mechanism and the introduction of market incentives were recommended to reduce the financial burden of the government. The results also demonstrated that ABM was helpful for choosing an effective policy to control pollution emissions and realizing environmental objectives and socio-economic co-benefits. The model structure and parameters should be optimized in specific cases because of the uncertainty of partial parameters and the neglect of the consumption process. These findings could be helpful for providing guidelines for water pollution control and sustainable water management in China.

Optimizing Policy for Balanced Industrial Profit and Water Pollution Control under a Complex Socioecological System Using a Multiagent-Based Model