Inexact stochastic optimization model for industrial water resources allocation under considering pollution charges and revenue-risk control

Abstract

In this study, an inexact two-stage stochastic downside risk-aversion programming is developed for regional industrial water resources allocation under considering system return-risk and various environment control strategies. In the model, interval-parameter programming, two-stage stochastic programming, and downside risk measure are introduced into an integrated framework for reflecting the complexity and uncertainty of industrial system, and avoiding the expected revenue risk. The method could not only reflect industrial water resources allocation characteristic among multiple users and suppliers, but also provide an effective linkage between economic cost and the system stability. The model is applied to a real case of industrial water resources allocation management in Chongqing city, China, where regional industrial system has faced with lots of difficulties and complexities in water resources utilization and water environmental protection. The impact of pollutants emission reduction and risk-aversion attitude on water resources allocation for different industry sectors, system benefits, and pollutants emissions were analyzed. The results indicated that the total pollutants emission amount control and the expected revenue risk can be used as effective measures for regional industry structure adjustment from terminal environmental and macro-economic perspective. The model has a significant value for regional industrial water optimization allocation under uncertainty to achieve the maximum economic benefits and the effective utilization of multiple water resources.