@article{scholars11082,
            note = {cited By 16},
          volume = {16},
          number = {12},
             doi = {10.1007/s13762-019-02371-x},
           title = {Operational carbon footprint prediction model for conventional tropical housing: a Malaysian prospective},
            year = {2019},
       publisher = {Center for Environmental and Energy Research and Studies},
         journal = {International Journal of Environmental Science and Technology},
           pages = {7817--7826},
            issn = {17351472},
          author = {Gardezi, S. S. S. and Shafiq, N.},
        keywords = {Architectural design; Emission control; Forecasting; Housing; Life cycle; Tropical engineering; Tropics; Virtual reality, Building Information Model - BIM; Efficient predictions; Environmental assessment; Housing sectors; Life Cycle Assessment (LCA); Statistical criterion; Statistical techniques; Tropical regions, Carbon footprint, carbon footprint; environmental assessment; housing; life cycle analysis; model; regression analysis; sustainability; tropical region, Malaysia},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064698738&doi=10.1007\%2fs13762-019-02371-x&partnerID=40&md5=b8d7eab736fefca663fa83b0fc6c82fe},
        abstract = {The current work presents a Malaysian housing sector experience to develop an innovative prediction model for the operational carbon footprint at planning and design stage. Besides life-cycle assessment methodology, statistical technique of multiple regressions incorporated the effects of different identified variables. Three-dimensional parametric models of selected case studies were developed in a virtual environment using building information modeling (BIM). The emergence of multiple regressions, BIM and LCA, in an environmental assessment study for operational phase in a tropical region unlocked a new direction of research. The successful satisfaction and qualification of statistical criterion and tests ensured an efficient prediction model with an acceptable percentage error of {\^A}{$\pm$} 6 between the predicted and observed values. The study aims to contribute to pre-assessments of CO2 levels at an early stage of life-cycle studies for quick sustainable decisions and safe green social developments. {\^A}{\copyright} 2019, Islamic Azad University (IAU).}
}