Holistic analysis and prediction of life cycle cost for vertical greenery systems in Singapore

Recent decades have witnessed the emerging and development of vertical greenery systems (VGS) or green walls as an alternative to enhance the quality of urban environments, especially for high-density cities. Although previous studies have calculated the life cycle cost (LCC) of VGS, the conventional method is time-consuming and requires a large amount of detailed data, which is unable and unpractical to obtain LCC of new VGS projects by collecting data of only several key variables of VGS. Hence, after computing and analyzing the LCC of 40 cases from different projects with various types, sizes, and other attributes, our study established a prediction model for the LCC of VGS using the data from forty cases of three types of VGS. Multiple linear regression (MLR), MLR with stepwise regression, and orthogonal partial least square (OPLS) models were built and compared. And the OPLS model was selected as the prediction model as it has relatively good prediction performance and can avoid the collinearity issue encountered by the other two methods. Furthermore, the combined OPLS model for three types of VGS was separated into three individual models with improved total prediction accuracy (85.4%–95.5%). In addition, key variables affecting the LCC of different types of VGS were also identified including salary, plant density, plant price, etc. Applications of LCC prediction models for VGS for various stakeholders were discussed to conclude the study. •Compared the life cycle cost of different vertical greenery systems in Singapore.•Stepwise MLR and orthogonal PLS regression were compared for prediction performance.•Developed separate prediction models for each type of green wall systems (85.4–95.5%).•Identified and quantified factors significantly affecting the life cycle cost of VGS.