novel numerical model for investigating macro factors influencing building energy consumption intensity

Building energy use accounts for 40% of the primary energy consumed worldwide. Therefore, it is essential to explore the key influencing factors and the associated interactions among these factors in order to achieve energy-saving goals. In practical scenarios, the sample size is always limited; thus, conventional models are unable to analyze the interactions among factors, or their results are unstable. To resolve this issue, this paper proposes a method that combines the improved stochastic impacts by regression on population, affluence, and technology (EM-STIRPAT) model and a structural equation modeling (SEM) model using the entropy weight method. The results obtained by analyzing the building energy use in Beijing, which was considered as a case study, indicate that economic level, technical level, industry structure, and education structure have a significant positive impact on building energy consumption intensity (BECI). Based on this analysis, it was found that 94% of the technical level and 39% of the economic level indirectly affect BECI through the energy structure. In addition, through a scenario analysis, it was found that future changes in the age structure of the population will have a smaller impact on BECI than changes in the education structure. Finally, based on the results of this study, policymaking recommendations to reduce building energy consumption are also presented. (c) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.