Planning energy and environmental systems associated with air pollutants mitigation under uncertainty

Air pollution has been one of the world's worst pollution problems. This study aims to present a two-stage stochastic fuzzy programming (TSFP) method for air pollutants mitigation within energy and environmental systems. In TSFP model, fuzzy possibilistic programming (FPP) is introduced into a two-stage stochastic programming (TSP) framework, which could tackle uncertainties reflected by possibilistic distributions and fuzzy membership functions associated with energy process and optimization solutions. The proposed TSFP is applied to air quality management within energy and environmental systems to clarify its applicability under different scenarios. The results of case study are beneficial for decision-maker to achieve rational energy resource distribution and identify desired policies for pollutants mitigation through cost-environment tradeoff