https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39499 2 emissions: urbanisation, R&D, population size, GDP, and energy use. The study used quarterly data throughout 1971Q1–2014Q4 to develop, calibrate, and validate the models. Each model was developed using 140 observations and validated on 36 observations. In tuning each ML model for comparative purposes, 10-fold with cross-validation approach was used in selecting the optimal hyperparameters and their associated arguments. The results indicate that the random forest (RF) model attained the highest coefficient of determination (R²) of 99.88%, followed by the k-nearest neighbour (KNN) (99.87%), extreme gradient boosting (XGBoost) (99.77%), decision tree (DT) (99.63%), adaptive boosting (AdaBoost) (99.56%), and the support vector regression (SVR) model (97.67%). Overall, the RF algorithm is the best performing ML algorithm in accurately predicting building-related CO₂ emissions, whereas the best algorithm in terms of time efficiency is the DT algorithm. The KNN model is highly recommended when practitioners want to have accurate predictions in a timely manner. RF, KNN, and DT models could be added to the toolkits of environmental policymakers to provide high-quality forecasts and patterns of building-related CO₂ emissions in an accurate and real-time manner.]]> Tue 09 Aug 2022 14:38:12 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15925 Sat 24 Mar 2018 08:26:11 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15918 Sat 24 Mar 2018 08:26:10 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15917 Sat 24 Mar 2018 08:26:08 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15966 Sat 24 Mar 2018 08:23:38 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18384 Sat 24 Mar 2018 07:52:37 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25625 Sat 24 Mar 2018 07:28:15 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24677 case study, literature review, modeling, questionnaire survey and comparison; and the popular research topics include PPP models and their application, risk management, financing and economic issues, legal and procurement issues, government regulation and guarantee. Comparison analysis results show that in terms of order of popularity, there are more similarities in the research topics than research methods in Chinese and international journals. The outcomes of this paper contribute to the PPP body of knowledge through summarizing PPP research in Chinese journals and international journals in the context of China. In addition, the findings are valuable for Chinese researchers to embark future research on PPP. This paper also breaks the language barrier and enriches western researchers a better understanding of the research status of PPP in the context of China.]]> Sat 24 Mar 2018 07:10:51 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:49727 Mon 29 May 2023 16:23:44 AEST ]]>