Empowering sustainable construction: green rating system adoption prediction through machine learning

Green rating systems (GRS) are playing a vital role in achieving sustainable construction practices. These rating systems are helpful in creating and operating green buildings. However, the adoption rate among stakeholders is limited, as they consider implementing rating systems involves perceived costs. Therefore, to predict whether construction stakeholders are likely to adopt GRS, a machine learning (ML)-based approach is used in this study. This study used two different approaches. At first, ML algorithms – logistic regression (LR), random forest, support vector machine, Naïve Bayes, and extreme gradient boosting – are used to predict how construction stakeholders (224) will make a decision to adopt GRS. Then, a web-based application was developed to enable real-time use of the predictive model. It can be observed that LR combined with regularisation and evaluated with K-fold stratification gave an accuracy of 71%. This study underscores the potential highlights of ML in influencing environmentally sound decision making processes in sustainable building.