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High-rise timber residential towers (≥eight-stories) represent a burgeoning and auspicious sector, predominantly due to their capability to provide significant ecological and financial advantages throughout their lifecycle. Like numerous other building types, spatial optimization in high-rise timber residential structures stands as a pivotal design factor essential for project viability. Presently, there exists no comprehensive investigation on space efficiency in such towers. This study analyzed data from 51 case studies to enhance understanding of the design considerations influencing space efficiency in high-rise timber residential towers. Key findings included (1) the average space efficiency within the examined cases was recorded at 83%, exhibiting variances ranging from 70% to 93% across different cases, (2) the average percentage of core area to gross floor area (GFA) was calculated at 10%, demonstrating fluctuations within the range of 4% to 21% across diverse scenarios, and (3) no notable distinction was observed in the effect of various core planning strategies on spatial efficiency when properly designed, and similar conclusions were drawn regarding building forms and structural materials. This research will aid in formulating design guidelines tailored for various stakeholders such as architectural designers involved in high-rise residential timber building developments.

Gross floor area is defined as the product of number of building stories and its base area. Gross floor area acquisition is the core problem to estimate floor area ratio, which is an important indicator for many geographical analyses. High data acquisition cost or inherent defect of methods for existing gross floor area acquisition methods limit their applications in a wide range. In this paper we proposed three instance-wise gross floor area estimation methods in various degrees of end-to-end learning from monocular optical images based on the NoS R-CNN, which is a deep convolutional neural network to estimate the number of building stories. To the best of our knowledge, this is the first attempt to estimate instance-wise gross floor area from monocular optical satellite images. For comparing the performance of the proposed three methods, experiments on our dataset from nine cities in China were carried out, and the results were analyzed in detail in order to explore the reasons for the performance gap between the different methods. The results show that there is an inverse relationship between the model performance and the degree of end-to-end learning for base area estimation task and gross floor area estimation task. The quantitative and qualitative evaluations of the proposed methods indicate that the performances of proposed methods for accurate GFA estimation are promising for potential applications using large-scale remote sensing images. The proposed methods provide a new perspective for gross floor area/floor area ratio estimation and downstream tasks such as population estimation, living conditions assessment, etc.

Even if a building has the same building area or number of floors, the effect on construction safety varies depending on the building shape, and thus, safety management cost (SMC) should be calculated differently. If the effect of the building shape on the SMC is clearly analyzed and reflected, a reasonable SMC could be calculated. This study analyzes building shape’s impact on SMC, including apartment buildings’ impact. Following the data collection from 21 projects for this study, an analysis was conducted using the independent variables of the building perimeter (BP), building floor area (BA), and the building shape factor (BSF), and the dependent variable of SMC. As a result of analyzing the correlation between the three main factors and SMC, it was found that the BP, BSF, and BA have a very strong positive Pearson correlation coefficient of 0.876, 0.801, and 0.792, respectively. In the future, the results of this study can be used as supporting data for improving the safety management cost-related system and will develop into a more reliable model through continuous data accumulation and utility verification.

In modern skyscraper architecture, the preference for incorporating tapered building configurations is on the rise, constituting a prominent trend in the industry, particularly due to their structural and aerodynamic benefits. The efficient utilization of space is a critical consideration in the design of tapered skyscrapers, holding significant importance for sustainability. Nevertheless, the existing body of scholarly work falls short in providing an all-encompassing investigation into the space efficiency of super-tall towers featuring tapered configurations, despite their prevalent adoption. This research endeavors to rectify this notable void by undertaking an exhaustive examination of data derived from 40 case studies. The key findings are as follows: (1) average space efficiency was about 72%, with values fluctuating between a minimum of 55% and a maximum of 84%; (2) average ratio of core area to the gross floor area (GFA) registered about 26%, encompassing a spectrum ranging from 11% to 38%; (3) most tapered skyscrapers employed a central core design, primarily tailored for mixed-use purposes; (4) an outriggered frame system was the prevailing structural system, while composite materials were the most commonly used structural materials; and (5) significant differences in the influence of function and load-bearing systems on the space efficiency of tapered towers were not observed. The author anticipates that these results will offer valuable direction, particularly to architectural designers, as they work towards advancing the sustainable development of tapered skyscrapers.

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the “beautiful countryside” construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide.

Annual greenhouse gas (GHG) emissions from residential energy use in the United States peaked in 2005 at 1.26 Gt CO 2-eq yr −1 , and have since decreased at an average annual rate of 2% yr −1 to 0.96 Gt CO 2-eq yr −1 in 2019. In this article we decompose changes in US residential energy supply and GHG emissions over the period 1990–2015 into relevant drivers for four end-use categories. The chosen drivers encompass changing demographics, housing characteristics, energy end-use intensities, and generation efficiency and GHG intensity of electricity. Reductions in household size, growth in heated floor area per house, and increased access to space cooling are the main drivers of increases in energy and GHG emissions after population growth. Growing shares of newer homes, and reductions in intensity of energy use per capita, household, or floor area have produced moderate primary energy and GHG emission reductions, but improved generation efficiency and decarbonization of electricity supply have brought about far bigger primary energy and GHG emission reductions. Continued decline of residential emissions from electrification of residential energy and decarbonization of electricity supply can be expected, but not fast enough to limit climate change to 1.5 °C warming. US residential final energy demand will therefore need to decline in absolute terms to meet such a target. However, without changes in the age distribution, type mix, or average size of housing, improvements in energy efficiency are unlikely to outweigh growth in the number of households from population growth and further household size reductions.

This paper reports on modelling outcomes for improvements to building energy performance in Indonesia. Long-term climate effects due to building energy demand and carbon emissions are also considered. The global change assessment model (GCAM) was used to generate the related end-user building energy data, including socioeconomics, for urban areas of Indonesia. As a comprehensive study, the total life cycle of carbon in the building sector and the concept of zero-carbon buildings, including energy efficiency, zero-emissions electricity and fuel-switching options, were considered. Building shell conductance (U-value) of the building envelope, floor area ratio (FAR), air conditioner (AC) efficiency, electrical appliance (APL) efficiency, rooftop photovoltaic (PV) performance and ground source heat pump (GSHP) systems were considered as parameters to mitigate carbon emissions under the operational energy category in the GCAM. Carbon mitigation associated with the cement production process was considered in the raw material category. Urban population and labour productivity in Indonesia were used as base inputs with projected growth rates to 2050 determined from the available literature. Low growth rate ‘LowRate’ and high growth rate ‘HighRate’ were considered as variable inputs for U-value, FAR, AC efficiency, APLs efficiency and PV capacity factor to model emissions mitigation. The energy consumption of the GSHP was compared to the conventional reverse cycle ACs to identify the potential of the GSHP as a fuel-switching option. In the GCAM, the benchmark (base case scenario) data set was generated based on input parameters (urban population and labour productivity rate) only for the residential building sector in Indonesia. Total potential carbon emissions mitigation was found to be 432 Mt CO2-e for the residential building sector in Indonesia over 2020–2050. It was found that an average of 24% carbon emissions mitigation could be achieved by 2020–2030 and 76% by 2031–2050.

Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of 20 million 2G mobile phone users in downtown Shanghai and the land use data of urban public service facilities, this study explores the spatiotemporal correlation between population density and public service facilities’ locations in downtown Shanghai and its variation laws. The correlation between individual population density at day vs. night and urban public service facilities distribution was also examined from a dynamic perspective. The results show a correlation between service facilities’ locations and urban population density at different times of the day. As a result, the average population density observed over a long period of time (day-time periodicity or longer) with census data or remote sensing data does not directly correlation with the distribution of public service facilities despite its correlation with public service facilities distribution. Among them, there is a significant spatial correlation between public service facilities and daytime population density and a significant spatial correlation between non-public service facilities and night-time population density. The spatial and temporal changes in the relationship between urban population density and service facilities is due to changing crowd behavior; however, the density of specific types of behavior is the real factor that affects the layout of urban public service facilities. The results show that mobile signaling data and land use data of service facilities are of great value for studying the spatiotemporal correlations between urban population density and service facilities.

Construction industry is well known as the major source of environmental issues while public awareness has raised upon time due to significant impacts due environmental problems. Project refurbishment has raised as one of the solutions to reduce the environmental problems as it reuses and recycles existing building by carry out adequate renovation and refurbishment to make the old or abandoned building reusable again. However, there are consent on the factors such as the impacts of additional floor area and design of opening onto overall energy consumption in refurbished buildings.A qualitative method has been employed in this study where a total of 5 respondents who are working in Pulau Pinang refurbishment projects.From the results, design of opening is agreed as the main contributor to total energy consumption in refurbished building while factor of additional floor area merelygain agreement from respondents. This study has the novelty in the context of refurbishment project in Pulau Pinang asit can be a useful guideline to the designers or practitioners who are designing refurbishment projects for historical city such as Georgetown and Malacca.

Air pollution, such as particulate matter (PM), and extreme weather are causing increasingly complex problems and socioeconomic damage in urban environments year-round. This study predicts extreme weather and air pollution changes that occur in urban street canyons as the basic data necessary for research on energy conservation. Changes in PM and microclimate elements based on the change in floor area ratio are analyzed. In addition, the effects of microclimate elements on the distribution of PM factors are examined. Based on the change in floor area ratio, high-concentration PM was negatively correlated with PM2.5, PM10, O3, NO2, NOx, Ta, Tmrt, and Tsurface. Extreme heat was observed to be negatively correlated with Tmrt and Tsurface, and extreme cold negatively correlated with PM2.5, PM10, NO2, and NOx. The higher the floor area ratio, the higher the wind speed (WS), indicating a positive correlation between the two factors. Ta, Tmrt, and Tsurface were observed to be negatively correlated with PM2.5, PM10, NO2, and NOx. WS showed negative correlations with PM2.5, PM10, NO2, and NOx. The results of this study can be used as basic data for the derivation of evaluation indices and to determine prediction and response strategies with respect to a combination of extreme weather and air pollution to ensure a suitable and sustainable quality of life. This study helps predict energy loads according to urban street canyon structures and examines whether trees and green walls are effective in reducing extreme weather and air pollution and saving energy.