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Buildings consume up to 40% of the total global energy. By the year 2030, the consumption is expected to increase to 50%. In Malaysia, buildings consume a total of 48% of the electricity generated in the country. Commercial buildings consume up to 38,645 Giga watts (GWh) while Residential buildings consume 24,709 Gwh. Demand for electricity in the country is expected to rise from 91,539 GWh in the year 2007 to 108,732 GWh in 2011. By the year 2020, the energy demand in Malaysia is expected to reach 116 Million tons of oil equivalents (Mtoe). Carbon dioxide (CO2) emission in the country has increased by 221% ,which lists the nation at 26th among the top 30 greenhouse gas emitters in the world.  Literature studies indicate more than 50% of this energy is used in buildings for occupants comfort (air conditioning and refrigeration). Energy consumptions by residential occupants can be minimized if energy usage is considered. This paper aimed at reviewing some literatures on energy consumption in the residential buildings in Malaysia and suggests ways of improving the energy usage by the occupants.

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Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction implementation is quite limited. There is also a shortage of methodologies for identifying key drivers. To address these challenges, this paper presents a list of all essential drivers within three aspects of sustainability (social, economic, and environmental) and proposes a novel methodology to rank the drivers and identify the key drivers for successful and sustainable lean construction implementation. In this regard, the entropy weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed in this research. Subsequently, an empirical study was conducted within the Malaysian construction industry to demonstrate the proposed method. Moreover, sensitivity analysis and comparison with the existing method were engaged to validate the stability and accuracy of the achieved results. The significant results obtained in this study are as follows: presenting, verifying and ranking of 63 important drivers; identifying 22 key drivers; proposing an MCDM model of key drivers. The outcomes show that the proposed method in this study is an effective and accurate tool that could help managers make better decisions.

Global economic trends have shown the progression of social inequalities and environmental deterioration in the grey economy. New economic practices and policies need to be developed in order to achieve the sustainable development goals (SDGs). A green economy (GE) has a correlative role with the implementation of sustainable development (SD), which could revive the grey economy, human well-being, and social equity, as well as substantially decrease environmental risks and ecological scarcities. This study aims to develop a hybrid methodological and mathematical approach to prioritize the most effective variables from classified GE and SDGs criteria (23 criteria) to implement SD. This study has deliberated over the Decision making trial and evaluation laboratory (DEMATEL) technique for considering interconnections among numerous criteria to collect the most effective variables (12 criteria) based on three pillars (3Ps) of SD. Likewise, the analytic network process (ANP) technique ranked these effective variables by considering their network relations based on three indicators. Lastly, integration was used to finalize and prioritize the most effective variables based on their weight from the ANP technique. This study will highlight the green economy with exclusive environmental issues and sustainable growth as the greatest effective variables among GE and SDGs criteria for SD implementation.

Oil and gas construction projects are complex and risky because of their dynamic environment. Furthermore, rising global energy demand has increased the need for trustworthy risk assessment models for such projects that can provide adequate and precise policy planning. Traditional risk assessments in oil and gas construction projects do not consider the interrelationships of factors in the best-fit models. The Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Processes (ANP), called the DEMATEL-ANP approach, have been applied to other research disciplines to address this shortcoming. This method is able to construct a structural relationship among the different influence factors to visualize complex correlations. Thus, the purpose of this study is to showcase the DEMTAL-ANP risk assessment model to assess the overall risk factors of OGC projects. This study thus identifies the crucial risk criteria of such projects. Data were collected in 2016 through interviews with experts active in OGC projects in Iran. DEMATEL in this situation is used to determine the interdependencies' relative strengths among the risks. The ANP method is applied to assess the relative importance of the risk factors and to determine the best strategy for implementation of a risk management program. The results presented in this study are a novel adaptation of the risk assessment methodology to OGC projects that determines the important risk factors that directly affect the project success, which in turn helps in formulation of policies for ensuring reliable energy supply planning.

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