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In this research, an interval-parameter two-stage stochastic programming approach was developed for supporting the identification of low-carbon strategies and the post-earthquake reconstruction in the city of Gugangyuan, Sichuan province of China. The approach can effectively deal with uncertainties that can be expressed as both probability density functions and interval numbers without any distribution information. Also, the approach can provide useful decision alternatives for supporting long-term corrections for any plans associated with carbon dioxide emission limits within a city context. The developed approach was applied in the city of Guangyuan. Three scenarios were established including the baseline (i.e., regional planning) as scenario 1, zero carbon emission as scenario 2, and carbon sink as scenario 3. Also, based on practical information and the collected data, three projection values (i.e., low, medium and high levels) for the carbon emission of the entire city were generated and analyzed under varying probability levels (i.e., 20, 60, and 20%). The results indicated that in order to achieve low-carbon post-earthquake development in the city, the priority areas and industries for carbon emission reduction would be Chaotian (the secondary industry) > Wangcang (the secondary industry) > Lizhou (the secondary industry) = Qingchuan (the tertiary industry) > Cangxi (the secondary industry) = Chaotian (the tertiary industry). [ABSTRACT FROM AUTHOR]

In this study, a feasibility-analysis-based inexact fuzzy multistage model (FA-IFMSM) was developed for supporting the planning of energy and environmental systems under multiform uncertainties. In FA-IFMSM, methods of feasibility-analysis-based fuzzy linear programming (FA-FLP) and inexact multistage stochastic programming (IMSP) with recourse were integrated into a general mixed-integer linear programming framework, such that the developed model could tackle uncertainties described in terms of probability distributions, interval values, fuzzy sets, and their hybrid and mixture. Moreover, it could reflect dynamic features of facility-capacity expansions and energy supplies over a multistage context. It could also quantify constraint violation degrees through the adoption of various acceptable degree levels. Then the proposed model was applied to a regional energy and environmental system to illustrate its applicability, where conventional and renewable energy resources and their economic and environmental implications were considered. The results indicated that reasonable solutions were generated. They were helpful for supporting (1) adjustment or justification of allocation patterns of regional energy resources and services; (2) formulation of local policies regarding energy consumption, economic development, and environmental protection; and (3) in-depth analysis of trade-offs among system cost and satisfaction degree, and environmental requirements under multiple uncertainties. [ABSTRACT FROM AUTHOR]

SUMMARY A regional energy system consists of diverse forms of energy. Energy-related issues such as utilization of renewable energy and reduction of greenhouse gas (GHG) emission are confronting decision makers. Meanwhile, various uncertainties and dynamics of the energy system are posing difficulties for the energy system planning, especially for those under multiple stages. In this study, an interval multi-stage stochastic programming regional energy systems planning model (IMSP-REM) was developed to support regional energy systems management and GHG control under uncertainty. The IMSP-REM is a hybrid methodology of inexact optimization and multi-stage stochastic programming. Not only can it handle uncertainties presented as intervals and probability density functions but also reflect dynamics of system conditions over multiple planning stages. The developed IMSP-REM was applied to a hypothetical regional energy system. The results indicate that the IMSP-REM can effectively reflect issues of GHG reduction and renewable energy utilization within an energy system planning framework. In addition, the model has advantages in incorporating multiple uncertainties and dynamics within energy management systems. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]