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 ± 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.
Similar content being viewed by others
References
Arghira N, Hawarah L, Ploix S, Jacomino M (2012) Prediction of appliances energy use in smart homes. Energy 48:128–134
Asdrubali F, Baldassarri C, Fthenakis V (2013) Life cycle analysis in the construction sector: guiding the optimization of conventional Italian buildings. Energy Build 64:73–89
Azari R (2019) Chapter 5—life cycle energy consumption of buildings; embodied + operational. In: Tam VWY, Le KN (eds) Sustainable construction technologies. Butterworth-Heinemann, Oxford, pp 123–144
Basbagill J, Flager F, Lepech M, Fischer M (2013) Application of life-cycle assessment to early stage building design for reduced embodied environmental impacts. Build Environ 60:81–92
Blengini GA, Di Carlo T (2010) The changing role of life cycle phases, subsystems and materials in the LCA of low energy buildings. Energy Build 42:869–880
Blom I, Itard L, Meijer A (2010a) LCA-based environmental assessment of the use and maintenance of heating and ventilation systems in Dutch dwellings. Build Environ 45:2362–2372
Blom I, Itard L, Meijer A (2010b) Environmental impact of dwellings in use: maintenance of façade components. Build Environ 45:2526–2538
Chang Y, Ries RJ, Wang Y (2010) The embodied energy and environmental emissions of construction projects in China: an economic input–output LCA model. Energy Policy 38:6597–6603
Chau C, Hui W, Ng W, Powell G (2012) Assessment of CO2 emissions reduction in high-rise concrete office buildings using different material use options. Resour Conserv Recycl 61:22–34
Commission E (2014) Malaysia energy statistics handbook. Energy Commission, Malaysia
Crawford RH (2011) Towards a comprehensive approach to zero-emissions housing. Archit Sci Rev 54:277–284
Denwigwe IH, Babatunde OM, Babatunde DE, Akintunde TJ, Akinbulire TO (2019) A technical review on methods and tools for evaluation of energy footprints, impact on buildings and environment. In: Muthu SS (ed) Energy footprints of the bio-refinery, hotel, and building sectors. Springer, Singapore, pp 47–81
Dixit MK, Fernández-Solís JL, Lavy S, Culp CH (2012) Need for an embodied energy measurement protocol for buildings: a review paper. Renew Sustain Energy Rev 16:3730–3743
Dodoo A, Gustavsson L, Sathre R (2014) Lifecycle carbon implications of conventional and low-energy multi-storey timber building systems. Energy Build 82:194–210
Ekici BB, Aksoy UT (2009) Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw 40:356–362
Esin T, Yüksek İ (2013) Sustainable resource utilisation in the production of building materials. Int J Sustain Build Technol Urban Dev 4:141–145
Fenner AE, Kibert CJ, Woo J, Morque S, Razkenari M, Hakim H et al (2018) The carbon footprint of buildings: a review of methodologies and applications. Renew Sustain Energy Rev 94:1142–1152
Fesanghary M, Asadi S, Geem ZW (2012) Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm. Build Environ 49:245–250
Franzoni E (2011) Materials selection for green buildings: which tools for engineers and architects? Procedia Eng 21:883–890
Goggins J, Keane T, Kelly A (2010) The assessment of embodied energy in typical reinforced concrete building structures in Ireland. Energy Build 42:735–744
Hertwich EG (2005) Life cycle approaches to sustainable consumption: a critical review. Environ Sci Technol 39:4673–4684
Hinnells M, Layberry R, Curtis D, Shea A (2008) Transforming UK non-residential buildings: achieving a 60% cut in CO2 emissions by 2050. In: Improving energy efficiency in commercial buildings conference (IEECB’08), Frankfurt
Huang Y, Niu J-L, Chung T-M (2012) Energy and carbon emission payback analysis for energy-efficient retrofitting in buildings—overhang shading option. Energy Build 44:94–103
Huang B, Xing K, Pullen S (2017) Energy and carbon performance evaluation for buildings and urban precincts: review and a new modelling concept. J Clean Prod 163:24–35
I. 14040 (2006) 14040: environmental management—life cycle assessment—principles and framework. British Standards Institution, London
I. E. Agency (2015) CO2 emissions from fuel consumptions
Karatasou S, Santamouris M, Geros V (2006) Modeling and predicting building’s energy use with artificial neural networks: methods and results. Energy Build 38:949–958
Kellenberger D, Althaus H-J (2009) Relevance of simplifications in LCA of building components. Build Environ 44:818–825
Kim Y-W, Bae J (2010) Assessing the environmental impacts of a lean supply system: case study of high-rise condominium construction in Korea. J Archit Eng 16:144–150
Kleinbaum DG, Kupper LL, Nizam A, Muller KE (2008) Applied regression analysis and other multivariable methods, 4th edn. Duxbury Press, Duxbury
Negishi K, Tiruta-Barna L, Schiopu N, Lebert A, Chevalier J (2018) An operational methodology for applying dynamic life cycle assessment to buildings. Build Environ 144:611–621
Pons O, Wadel G (2011) Environmental impacts of prefabricated school buildings in Catalonia. Habitat Int 35:553–563
Ramesh T, Prakash R, Shukla KK (2010) Life cycle energy analysis of buildings: an overview. Energy Build 42:1592–1600
Ren Z, Foliente G, Chan W-Y, Chen D, Ambrose M, Paevere P (2013) A model for predicting household end-use energy consumption and greenhouse gas emissions in Australia. Int J Sustain Build Technol Urban Dev 4:210–228
Roufechaei KM, Bakar AHA, Tabassi AA (2014) Energy-efficient design for sustainable housing development. J Clean Prod 65:380–388
Sartori I, Hestnes AG (2007) Energy use in the life cycle of conventional and low-energy buildings: a review article. Energy Build 39:249–257
Scheuer C, Keoleian GA, Reppe P (2003) Life cycle energy and environmental performance of a new university building: modeling challenges and design implications. Energy Build 35:1049–1064
Shafiq N, Nurrudin MF, Gardezi SSS, Kamaruzzaman AB (2015) Carbon footprint assessment of a typical low rise office building in Malaysia using building information modelling (BIM). Int J Sustain Build Technol Urban Dev 6(3):157–172
Shimoda Y, Yamaguchi Y, Okamura T, Taniguchi A, Yamaguchi Y (2010) Prediction of greenhouse gas reduction potential in Japanese residential sector by residential energy end-use model. Appl Energy 87:1944–1952
Thormark C (2002) A low energy building in a life cycle—its embodied energy, energy need for operation and recycling potential. Build Environ 37:429–435
Tsai W-H, Lin S-J, Liu J-Y, Lin W-R, Lee K-C (2011) Incorporating life cycle assessments into building project decision-making: an energy consumption and CO2 emission perspective. Energy 36:3022–3029
Tuominen P, Holopainen R, Eskola L, Jokisalo J, Airaksinen M (2014) Calculation method and tool for assessing energy consumption in the building stock. Build Environ 75:153–160
Wu HJ, Yuan ZW, Zhang L, Bi J (2012) Life cycle energy consumption and CO2 emission of an office building in China. Int J Life Cycle Assess 17:105–118
Yang J, Rivard H, Zmeureanu R (2005) On-line building energy prediction using adaptive artificial neural networks. Energy Build 37:1250–1259
Acknowledgements
The authors acknowledge the support of CUST, Islamabad Pakistan, and UTP, Malaysia, for successful completion of this study.
Funding
Funding was provided by Ministry of Education (Higher Education Department), Malaysia (MyRA Incentive Grant (0153AB-J11)).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Editorial responsibility: Zhenyao Shen.
Rights and permissions
About this article
Cite this article
Gardezi, S.S.S., Shafiq, N. Operational carbon footprint prediction model for conventional tropical housing: a Malaysian prospective. Int. J. Environ. Sci. Technol. 16, 7817–7826 (2019). https://doi.org/10.1007/s13762-019-02371-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13762-019-02371-x