An interval-fuzzy two-stage stochastic programming model for planning carbon dioxide trading under uncertainty

In this study, an IFTSP (interval-fuzzy two-stage stochastic programming) method is developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed IFTSP incorporates techniques of interval fuzzy linear programming and two-stage stochastic programming within a general optimization framework, which can effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions and discrete intervals. The IFTSP cannot only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated CO2 mitigation policies and the associated economic implications. The developed model is applied to a case study of CO2-emission trading planning of industry systems under uncertainty, where three trading schemes are considered based on different trading participants. The results indicate that reasonable solutions have been generated. They are help for supporting: (a) formulation of desired GHG (greenhouse gas) mitigation policies under various economic and system-reliability constraints, (b) selection of the desired CO2-emission trading pattern, and (c) in-depth analysis of tradeoffs among system benefit, satisfaction degree, and CO2 mitigation under multiple uncertainties.