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Nearly 60% of electrical energy use in Hong Kong is for space conditioning during summer months. The paper investigates the impact of design-related variables on outdoor micro level daytime heat island effect in residential developments in Hong Kong. The paper hypothesizes that the differences in outdoor temperatures within and between residential developments can be explained by the impact of design-related variables on the overall environment. Case studies of three large housing estates reveal urban heat island effect (UHI) in the order of 1.5 °C within an estate, and 1.0 °C between estates. The results indicate that energy efficient designs can be achieved by manipulating surface albedo, sky view factor and total height to floor area ratio (building massing) while maximizing cross ventilation. [Copyright &y& Elsevier]

Increase in cooling energy usage of the residential sector in Hong Kong has been moderate in the most recent ten years (2004–2013). Factors and drivers responsible for this are of interest to policy makers in search of performance improvement but such information is not available in extant literature. This paper reviews the policy instruments introduced in Hong Kong during this period of time and examines the different engineering factors contributing to improvements in cooling energy efficiency. Whether the policy instruments have been instrumental in bringing about changes in engineering design and thus improvement in cooling energy efficiency is evaluated on the basis of the equipment and building envelope characteristics of 64 residential developments in Hong Kong. Different statistical analyses reveal that the use of more energy efficient room air-conditioning units (COP), smaller window-to-wall ratios (WWR) and walls with higher U-values are the engineering factors (WallU) that have contributed to moderation of growth of energy consumption; these were driven by policies introduced by the Hong Kong Government. Regarding the sensitivity of the three engineering factors, it was found that COP was the highest amounting to 0.455, followed in descending order by WallU and WWR. Their sensitivities were 0.289 and 0.006, respectively. [ABSTRACT FROM AUTHOR]

This paper firstly presents a case study on analyzing the electrical load profiles recorded in a Hong Kong hotel over a period of 12 months to examine the potential energy saving opportunities (ESOs) in its building services installations. The case study is followed by a report of the survey results of energy and water use in 36 quality hotels in Hong Kong. Finally, a regression analysis where the surveyed energy and water use data were correlated to a number of hotel background or operational parameters to search for suitable energy and water use performance explanatory indicators is presented. The survey results depict a diversified energy and water use situation in Hong Kong’s hotels, but the regression analysis indicates that, while some of the correlations obtained are weak, there do exist a few strong energy and water explanatory indicators. [Copyright &y& Elsevier]