Sensitivity analysis as support for reliable life cycle cost evaluation applied to eleven nearly zero-energy buildings in Europe

•Introduction of operative methodology for reliable life cycle cost analysis coupled with sensitivity analysis.•Identification and assessment of the main uncertainties in Life Cycle Cost calculation.•Demonstration through eleven case studies that uncertainties can lead to variations up to 37% with an average of 26% around the median LCC.•Identification of strengths and weaknesses of sensitivity analysis with differential and elementary effects method.•Approach for balancing effort and level of reliability when implementing a sensitivity analysis. Life cycle cost analysis represents a strategic tool for supporting the decision-making process while designing a new building or a renovation towards a nearly zero-energy target. Nevertheless, one of the main obstacles undermining the wide application of life cycle cost analysis deals with the effort in collecting the whole set of inputs and boundary conditions and the associated reliability of the results. To address the issue, this work compares the application of different sensitivity analysis methodologies on eleven nearly zero-energy buildings with different uses and in several European contexts, highlighting the strengths and weaknesses. Moreover, it introduces and assesses an approach for applying sensitivity analysis in life cycle cost evaluations to find an effective balance between the effort for calculation, data collection and the reliability of life cycle cost. A main result is the demonstration of a sensitivity analysis procedure to identify and evaluate parameters and boundary conditions with the largest impact on the life cycle cost of the analysed buildings, namely, the interest rate, construction and equipment maintenance costs, structural element costs, and electricity prices. These parameters lead to variations in LCC of up to 37%, with an average of 26% around the median. By focusing a more detailed analysis on these parameters, we could assess the potential life cycle cost range due to input uncertainties with a high degree of confidence while keeping efforts for practitioners reasonable.