Global urbanization causes more environmental stresses in cities and energy efficiency is one of major concerns for urban sustainability. The variable importance techniques have been widely used in building energy analysis to determine key factors influencing building energy use. Most of these applications, however, use only one type of variable importance approaches. Therefore, this paper proposes a procedure of conducting two types of variable importance analysis (predictive and variance-based) to determine robust and effective energy saving measures in urban buildings. These two variable importance methods belong to metamodeling techniques, which can significantly reduce computational cost of building energy simulation models for urban buildings. The predictive importance analysis is based on the prediction errors of metamodels to obtain importance rankings of inputs, while the variance-based variable importance can explore non-linear effects and interactions among input variables based on variance decomposition. The campus buildings are used to demonstrate the application of the method proposed to explore characteristic of heating energy, cooling energy, electricity, and carbon emissions of buildings. The results indicate that the combination of two types of metamodeling variable importance analysis can provide fast and robust analysis to improve energy efficiency of urban buildings. The carbon emissions can be reduced approximately 30％ after using a few of effective energy efficiency measures and more aggressive measures can lead to the 60％ of reduction of carbon emissions. Moreover, this research demonstrates the application of parallel computing to expedite building energy analysis in urban environment since more multi-core computers become increasingly available.