In this study, an inexact optimization modeling approach (IBEM: inexact Beijing energy model) was developed for supporting energy systems planning and air pollution mitigation under uncertainty. This model was based on the integration of multiple inexact optimization techniques, including interval-parameter programming, mixed-integer programming and chance-constrained programming, which make it have strength in dealing with uncertainties presented as both probabilistic distributions and interval numbers. The model could effectively facilitate systematic analysis of complexities associated with energy conversion and utilization, and air pollution mitigation. Particularly, it could help identify optimal patterns of energy resources allocation, as well as capacity expansion options for energy technologies under different air pollution emission reduction schemes. This could not only alleviate air pollution in the city, but also reduce the total system cost that was associated with various energy activities. Based on a two-step solution algorithm, useful solutions were generated, reflecting tradeoffs among environmental and economic conditions, and among different risk violation levels of constraints of the energy system in Beijing city. The interval solutions could then help decision makers identify desired policies for energy management and pollutions reduction. (C) 2011 Published by Elsevier Ltd.