| Due to the impact of human activities, energy consumption, global greenhouse effect and water environmental pollution is more and more serious, and hinders the economic development and social progress; therefore, environmental system planning is of significance for achieving regional society and economic sustainable development in many municipalities. However, the uncertainties that are inherent in the planning process and the complex interactions among various uncertain parameters are challenging the capabilities of traditional determistic models and approaches. Therefore, the objective of this study is to apply inexact optimization methods to energy system's environmental management and water environmental planning to deal with such complexities and uncertainties. In this study, an interval fixed-mix stochastic programming model is developed for supporting air pollution control and greenhouse gas emissions reduction management in energy and environmental system under uncertainties; and considers the complexity of environmental system in Binhai New area, an interval chance-constrained programming method is developed for supporting water pollution control and water resources management, based on Monte-Carlo technique and stochastic analysis. The results indicate that the interval fixed-mix stochastic programming model could effectively reflect uncertainties of a long-term greenhouse gas emissions reduction management systems, and provide decision makers with scientific basis for decision making; the interval chance-constrained programming model could effectively reflect complexities of the study systems, obtain several optimized schemes under differrent allowable water pollutant emission scenarios, and provide useful information for decision makers regarding the tradeoffs between allowable water pollutant emission and economic benefit, so as to obtain a optimal strategy for regional economic-environmental system sustainable development.
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