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The objective of this study is to develop the Freight Trip Generation (FTG) models concerning establishment classification process for estimating freight trip activity. Establishment Based Freight Survey (EBFS) was performed to collect various supply chain variables involved in the urban freight system. Extensive data of 647 samples were collected from Tiruchirappalli city, India. Economic activity analysis shows that pure receiver and intermediate establishments do not follow the uniform freight trip activity among the industrial segments. Hence, a suitable methodology for the establishment classification process is developed using traditional and machine learning (ML) techniques to classify the establishment into an intermediate and pure receiver before estimating the freight trip activity. Supply chain variables like employment, FTA, industrial segment, mode of the commercial vehicle, type of delivery, number of suppliers, and gross floor area play a crucial role in the establishment classification process. Then, employment-based FTG models are developed with the classified data using linear and non-linear functional forms for each industrial segment. Finally, it is observed that FTG models result enhanced, when the establishment classification process is performed as an initial stage, with lower error and accurate estimation of urban freight trip activity.

Nowadays energy has a very important role in the life of human civilization. One of the energy that is still widely used and almostcannot be reduced from the community is electrical energy. Energy audit is one of the processes in managing energy use to bec ome more efficient. The purpose of this study was to determine the potential for energy savings that can be carried out optimally and accurately in Cold Storage, Malang Regency. This study uses data collection methods with Web-based Power Quality Meter PM5110 and account data from energy use during the period June 2020 to May 2021. Data retrieval using a Web-based Power Quality Meter PM5110 which is carried out for 24 hours on weekdays, then the total consumption of kWh in one day reached 119,538 kWh, while the total usage of kWh in one month is 3586.14 kWh for the month of June and for the total usage for the period of one year is 43631.37 kWh. Regarding the value of energy consumption kWh in a period of one year, it can be generated by dividing the total energy consumed by the building in one year by the total gross floor area of the building. Based on the results of the IKE calculation in the Cold Storage Building, Malang Regency from the results before the recommendation of 34.91 kWh/m2/year and for the result after the recommendation is 174,841 kWh/m2/year, which is included in the very efficient category according to the ASEAN USAID 1987 standard. For energy saving opportunities in the lighting system and cooling system, it can be done by using energy-efficient lamps and changing the operating pattern in the cooling room.

Preparing architectural programs according to internationally approved approaches it follows a highly efficient design and affordable cost, most countries of the world use multiple approaches to preparing space programs of projects during the architectural programming stage. However in Egypt still depends on traditional methods of calculating spaces during preparation those programs and the consequent low quality of the architectural end product, the area is one of the important element that is required to be calculated accurately to ensure an appropriate quality for the project at low cost, so we need to research for a suitable technique to calculate the optimal solution for the gross area that achieves a high quality of the project , so the researchers suggest using linear programming technique that its results are the value of this variables: (net area, services area, area of communication and vertical and horizontal connection element. The research aims minimize the gross area throw some constrains such as standards, requirements, budget, and (min , max) limits for area, etc. then use the simplex method to find the optimal values of the gross area components , that represent the independent variable of study and measure the change on the dependent variables as ( the efficiency and cost of the project). the study be applied on a hypothetical space program, and the results of the optimal solutions targeted for spaces , that achieve the highest efficiency for the project at a cost commensurate with the project budget , this study concluded that the linear programming technique can be used to calculate the area when preparing space programs for architectural projects before the design, and the success of the proposed technique in calculating the optimal solutions that achieve the quality of the project at an early stage during the architectural programming stage before starting the schematic design for the project.

State of the art in understanding the urban freight system is quite challenging in developing countries, and India is one of them. Consequently, there is a paucity of knowledge in comprehending the urban freight system's supply chain characteristics and its impact on operation, infrastructure, and the environment. Therefore, this study aims to analyze the supply chain characteristics, estimate the freight trips and freight temporal travel patterns for different industrial sectors in the various establishments at a microscopic level. Using an establishment-based freight survey (EBFS) form, a total of 647 establishment data was collected from Tiruchirappalli, India. This study initially investigated the dynamic and robustness of the supply chain characteristics such as establishment, commodity, vehicular movements, and delivery patterns involved in the urban freight system at the microscopic level. Then, correlation tests were performed to understand the relationship between supply chain characteristics and freight trips at different confidence intervals. Business size variables (employment and gross floor area) were used to estimate the freight trip movement and generate a freight trip rate table for intermediate and pure receiver establishment. Freight trips rates for different industrial sectors were estimated using multiple classification analysis method and compared to various goodness of fit measures. Eventually, the urban freight temporal travel pattern was assessed and visualized by hourly travel distribution of the freight movements during regular working days in the various industrial sector for the Indian context.

Given the rapidly increasing number of air-conditioned buildings, the electricity demand in the Kingdom of Saudi Arabia has increased during the past decade. Efficient thermal insulation is extremely important for energy efficiency and sustainability, especially with the country’s hot-arid climate. This study explores the law of diminishing returns when improving the conservation level of residential buildings by using case study simulation. Specifically, this study aims to identify alternative positions for the insulation material and the optimum thickness for the three proposed strategies (on the roof only, on the walls only and on the roof and walls) in accordance with the energy efficiency index defined in the thermal insulation regulation of the KSA. Moreover, this study uses the life cycle cost model to manage the optimum number of insulation levels. This study also uses DesignBuilder energy simulation tool to estimate the energy performance and the environmental impact of a sample prototype villa with a gross area of 238 m2 in Najran City. The optimum insulation thickness is defined based on the cost benefits of the extruded polystyrene material (XPS) over its lifetime. Results show that the optimum insulation thicknesses of 8, 4 and 6 cm must be applied on the roof only, the walls only and the roof and walls. These alternatives can lead to reductions of 19.14%, 7.51% and 29.77% on annual energy consumption, respectively. A substantial reduction on CO2 emission is also achieved. Finally, the payback period in the three optimum alternatives are 3.73, 12.14 and 6.39 years, respectively.

Construction waste generation is a crucial issue in the construction industry worldwide which gives significant negative impact towards the environment, economy and society. New Zealand (NZ) is no exception with the construction industry being responsible for up to 50% of landfill waste. The quantification of construction waste generation is critically important to manage its negative impacts effectively. Due to the differences in materials, technologies and processes in the construction projects, NZ requires more specific data to quantify waste generation accurately. This study analysed waste quantities from 159 detached residential buildings to quantify waste generation rate per gross floor area (WGR). The WGR is 32. 2 kg/m2 which sits mid-range compared to other international studies. A regression model for predicting waste and trend analysis were conducted to identify the highly correlated design features with waste generation. Findings of this study are useful to determine the impact of design decisions on waste generation for typical NZ builds. Although the findings primarily aimed at the NZ construction industry, the results can apply to detached timber-framed homes overseas.

Another reviewer of one of Princeton’s “On … ” series has drawn attention to the smallness and slimness of the volume: On Freedom has only just over 116 small pages—a gross area of less than 1.6m2....

A pushover-based seismic risk assessment and loss estimation methodology for masonry buildings is introduced. It enables estimation of loss by various performance measures such as the probability of exceeding a designated economic loss, the expected annual loss, and the expected loss given a seismic intensity. The methodology enables the estimation of the economic loss directly from the results of structural analysis, which combines pushover analysis and incremental dynamic analysis of an equivalent SDOF model. The use of the methodology is demonstrated by means of two variants of a three-storey masonry building both of which have the same geometry, but they are built, respectively, from hollow clay masonry (model H) and solid brick masonry (model S). The probability of collapse given the selected design earthquake corresponding to a return period of 475 years was found to be negligible for model H, which indicates the proper behaviour of such a structure when designed according to the current building codes. However, the corresponding probability of collapse of model S was very high (46%). The expected total loss given the design earthquake was estimated to amount to 28 000 % and 290 000 %, respectively, for models H and S. The expected annual loss per 100 m2 of gross floor area was estimated to amount to 75 % and 191 %, respectively, for models H and S. For the presented examples, it was also observed that nonstructural elements contributed more than 50% of the total loss.

In this study, the energy consumption of three government and three private office buildings in Lucknow was investigated, and the energy performance index (EPI) for each building was determined. The main purpose of this research was to assess the energy usage of the buildings and identify factors affecting the energy usage. An analysis was performed using data from an energy audit of government buildings, electricity bills of private office buildings, and an on-site visit to determine building envelope materials and its systems. The annual energy consumption of buildings has been evaluated through EPI. The EPI, measured in kilowatt hour per square meter per year, is annual energy consumption in kilowatt hours divided by the gross floor area of the building in square meters. In this study, the energy benchmark for day-time-use office buildings in composite climate specified by Energy Conservation Building Code (ECBC) has been compared with the energy consumption of the selected buildings. Consequently, it has been found that the average EPI of the selected buildings was close to the national energy benchmark indicated by ECBC. Moreover, factors causing inefficient energy consumption were determined, and solutions for consistent energy savings are suggested for buildings in composite climate.

Kota Bandung merupakan kota pertama di Indonesia yang telah menerapkan kebijakan insentif green building. Insentif yang akan diberikan kepada pemilik bangunan berupa insentif tambahan jumlah lapis lantai dan pengurangan pajak bumi dan bangunan. Sejak 2016, pemerintah Kota Bandung belum pernah melakukan monitoring, dan evaluasi tentang penerapan kebijakan insentif tersebut sehingga menyebabkan ketidaktahuan akan hasil pelaksanaannya selama ini. Penelitian ini bertujuan untuk mengetahui sejauh mana penerapan kebijakan insentif di Kota Bandung, termasuk mengetahui permasalahan yang dihadapi sehingga dapat dipikirkan solusinya. Pengumpulan data dilakukan melalui wawancara mendalam dengan target responden yang memiliki kaitan langsung dengan penerapan kebijakan insentif green building di Kota Bandung, antara lain Dinas Penataan Ruang (Distaru) Kota Bandung, Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu (DPMPTSP) Kota Bandung, Badan Pengelolaan dan Pendapatan Daerah (BPPD) Kota Bandung, Tim Ahli Bangunan Gedung (TABG) dan Tim Penyusun Peraturan Wali Kota Bandung Nomor 1023 Tahun 2016 tentang Bangunan Gedung Hijau. Hasil penelitian membuktikan bahwa sampai saat ini belum ada insentif green building yang dikeluarkan oleh wali kota karena bangunan yang ada belum memenuhi persyaratan green building bintang 2 dan 3 sesuai dengan peraturan yang berlaku. Diperoleh juga hasil bahwa peraturan insentif yang ada belum mengatur tentang besaran, bentuk, waktu pemberian dan masa berlaku/durasi dengan lebih rinci. Oleh karena itu, meskipun ada bangunan yang telah memenuhi persyaratan green building, pemilik bangunan/pengembang tersebut belum dapat memohon insentif green building. Rekomendasi yang dapat diberikan adalah program sosialisasi yang harus dilakukan terhadap pihak pemerintah maupun masyarakat, dan pembuatan peraturan lanjutan yang mengatur detail kebijakan insentif.

Freight studies have traditionally used business size measures such as employment and gross floor area as predictors of freight generation, giving very limited attention to the effects of an establishment’s age on freight demand. This study uses establishment-based freight survey data collected in seven cities of Kerala, India, to analyze the impacts of the age of an establishment on its freight demand. This is achieved by grouping the establishments into pre-specified classes with relatively homogeneous freight demand pattern. This classification is based on a data-driven a posteriori segmentation of industrial classes. These groups are further divided into sub-groups based on founding year of an establishment. As the business age increases, establishments grow in business size indicators and hence the changes in productivity can be either because of age or because of size or both. It is very important to separate the effects of these two indicators. The Blinder–Oaxaca decomposition method is used in this study to disentangle the effects of size and age on freight production and freight trip production. Age is found to be a fundamental driver of freight demand in younger establishments while the explanatory power of business size variables in explaining freight demand diminishes with age. The study findings illustrate the potential omitted variable bias that can occur when freight demand is estimated using business size indicators, without controlling for the business age differences.

A trip generation manual and database are important for transportation planners and engineers to forecast new trip generation for any new development. Nowadays, many petrol stations have fast-food restaurant outlets. However, this land use category has yet to be included in the Malaysian Trip Generation Manual. Therefore, this study attempted to develop a new trip generation model for the new category of “petrol station with convenience store and fast-food restaurant”. Significant factors influencing the trip generation were also determined. Manual vehicle counts at the selected sites were conducted for 3 h during morning, afternoon and evening peak hours. Regression analysis was used in this study to develop the model. A simple trip generation model based on the independent variable number of restaurant seats showed a greater value for the coefficient of determination, R2, compared with the independent variables gross floor area in thousand square feet and number of pumps. The multivariable trip generation model using three independent variables generated the highest R2 among all of the models but was still below a satisfactory level. Further study is needed to improve the model for this new land use category. We must ensure more accuracy in trip generation estimation for future planning and development.

Domestic hot water (DHW) system energy losses are an important part of energy consumption in newly built or in reconstructed apartment buildings. To reach nZEB or low energy building targets (renovation cases) we should take these losses into account during the design phase. These losses depend on room and water temperature, insulation and length of pipes and water circulation strategy. The target of our study is to develop a method which can be used in the early stages of design in primary energy calculations. We are also interested in how much of these losses cannot be utilised as internal heat gain and how much heat loss depends on the level of energy performance of the building. We used detailed DHW system heat loss measurements and simulations from an nZEB apartment building and annual heat loss data from a total of 22 apartment buildings. Our study showed that EN 15316-3 standard equations for pipe length give more than a twice the pipe length in basements. We recommend that for pipe length calculation in basements, a calculation based on the building’s gross area should be used and for pipe length in vertical shafts, a building’s heating area-based calculation should be used. Our study also showed that up to 33% of pipe heat losses can be utilised as internal heat gain in energy renovated apartment buildings but in unheated basements this figure drops to 30% and in shafts rises to 40% for an average loss (thermal pipe insulation thickness 40 mm) of 10.8 W/m and 5.1 W/m. Unutilised delivered energy loss from DHW systems in smaller apartment buildings can be up to 12.1 kWh/(m2·a) and in bigger apartment buildings not less than 5.5 kWh/(m2·a) (40 mm thermal pipe insulation).

Effective mitigation of greenhouse gas (GHG) emissions in the buildings sector requires a full understanding of the factors influencing emissions over the life-cycle of buildings, particularly in places where large additions to the building stock are expected. Currently, little is known about what affects the GHG emissions of buildings located in warmer climates, a typical situation for many emerging economies. This paper presents a study of emissions from Brazilian office buildings using building archetypes. A sensitivity analysis explores possible parameter ranges, various contributions to life-cycle impacts and their key drivers. For each of the 1000 building variations in the sample, the emissions were calculated using a life-cycle assessment. Multivariate regression analysis enabled the study of the results’ sensitivity to 10 parameters, influencing building operation, design and others. The emissions ranged from 20 to 106 kg CO2-eq/m2 gross floor area and year. Electricity mix, climate and cooling efficiency were the most impactful parameters, but building component service time was also significant. Policy relevance Emerging economies are expected to rapidly increase their building stock and energy use, particularly for cooling in the coming decades. The findings show the key factors influencing the GHG emissions of office buildings in warm climates, typical for many emerging economies, such as Brazil. For effective mitigation, priority should be placed on reducing the carbon intensity of electricity and encouraging highly efficient heating, ventilation and air-conditioning systems. Policymakers may want to offer incentives for office buildings with a combination of natural ventilation and mechanical cooling, because they were less emission-intensive in every investigated city. The benefits are the biggest for buildings in which a high proportion of windows can be opened for natural ventilation.

Urban planning relies on the definition, modelling and evaluation of multidimensional phenomena for informed decision-making. Urban building energy modelling, for instance, usually requires knowledge about the energy use profile and surface area of each use that takes place within a building. We do not have a detailed understanding of such information for mixed-use developments, which are gaining prominence in urban planning. In this paper, we developed a methodology to quantitatively define the characteristics of mixed-use developments using archetypes of programme profiles (ratios of each programme type) of a city’s mixed-use plots. We applied our methodology in Singapore, resulting in 163 mixed-use zoning archetypes using Singapore’s master plan data and Google Maps API data. In a case study, we demonstrated how these archetypes can be used to provide more detailed data for urban building energy modelling, including energy demand forecasts and energy supply system design. To enable future automation of the workflow, the archetype definitions were represented and stored as a machine-readable ontology. This ontology can later be extended with for example, the mobility properties of archetypes; thus, enabling the archetypes' use in other urban planning applications beyond building energy modelling., Environment and Planning B: Urban Analytics and City Science, 50 (6), ISSN:2399-8083, ISSN:2399-8091

The purpose. To determine link of economic and constructive characteristics of capital equipment for growing hexapods and to find out demands which minimize technological cost price of its manufacture. Methods. Theoretical studies are lead on known procedure of parametric technical-economical analysis. As basic approach to solution of optimization problem they selected determination of ranges of values of technical parameters which minimize cost price without evaluation, actually, their values. At development of mathematical models the conjecture is accepted that monetary expenditures for materials and wages for manufacture of certain device are considered directly proportional area of its envelopment or length of its ridges. Results. Technical-economical analysis of capital equipment for growing hexapods (cells and boxes) for the purpose of determination of their optimum squares for minimization of technological cost price of manufacture of the equipment is lead. Mathematical model of cost price of manufacture of the equipment as function of geometrical sizes of cell and box is developed. For minimization of the specified cost price independent parameters on which optimization is carried out are selected, namely: amount of cells in a box, effective area of one cell, gross area of a box. All other parameters are regulated by technical requirements and are considered set. Model study shows that inside the set range of squares the objective function has no extreme, as in the specified range its first derivative always negative (it is not equal to null). Therefore the extreme (minimum) is attained only on boundary of the set range of squares, that is at peak figures of squares of cell and box. Conclusions. Cost of equipment, which is evaluated through technological cost of its manufacture, will be minimum at the greatest possible values of squares of cell and box. Limitations of these values will depend on technical requirements: ergonomics, strength, etc. At increase of sizes of cell from 25x25 cm up to 25x50 cm the reduced expenditures on materials for manufacture drop on 6%, and on payment — on 25%. At increase of productivity on 62% technological cost price of complex is drops on 2%. Approaches justified in study essentially improve economic indicators of complex.

The majority of the space and domestic hot water heating needs of houses in cold climates can be supplied by solar energy, but only if long-term (seasonal) storage is employed to enable solar energy collected during the summer and autumn to be used during winter. Most seasonal storage applications to date have been for community-scale systems, although there have been a few experimental and simulation studies of building-scale applications, which offer reduced losses from transmission networks due to the proximity between building-mounted solar collectors and the seasonal store. A simulation-based study has been performed to design a building-scale solar thermal system with seasonal storage for a research house with a heated floor area of approximately 150 m2. Parametric simulations revealed that a solar fraction exceeding 90% could be achieved for many combinations of solar collector area and seasonal store volume. A solar collector gross area of 41.6 m2 and a seasonal store volume of 36 m3 were chosen for this installation, which is expected to achieve a solar fraction in the range of 87–98%.

The circular economy agenda makes it paramount for construction supply chains to reduce material waste. Although a collaborative platform called Building Information Modelling (BIM) offers a means of supply chains integration, it has not been efficiently upscaled for delivering waste efficient building designs. This study, therefore, develops a BIM-based computational tool for building waste analytics and reporting in the construction supply chains. A Construction Waste (CW) prediction model using Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed and integrated into Autodesk Revit BIM platform. The model development process reveals that “Gross Floor Area” and “Construction type” are the two key predictors for CW. The results of the study show that the tool offers useful insights into CW minimisation opportunities. The study makes a huge contribution to CW management practices by developing a computational approach to CW measurement. The contribution of the study is fundamental because achieving accurate waste prediction is crucial to waste prevention through adequate design principles and BIM.

Cadastral mapping of favela’s agglomerated buildings in informal settlements at Level of Detail 1 (LoD1) usually requires specific surveys and extensive manual data processing. Therefore, there is a demand for including the favelas in the city map production on the basis of Lidar surveys, as well as the detection of their vertical growth. However, the currently developed algorithms for automatically extracting buildings from airborne Lidar data have mainly been tested only for regular building reconstruction. This study aims to develop a Lidar data processing pipeline enabling to compute metrics related to intraurban informal settlements. To do so, we present a procedure to generate favela’s buildings delineation, height, floors’ number and built area and apply them to six case studies in favela typo-morphologies. We conducted an exploratory analysis in order to obtain the adequate parameters of the processing pipeline and its evaluation, using open source, free license and self-developed software. The results are compared to reference data from the manual stereo plotting, achieving a quality index in the building reconstruction about 70%. We also calculated the growth density, measured by gross Floor Area Ratio index inside settlement, revealing values from 29% to 74% considering different time periods.

This study provides a life cycle assessment (LCA) of a sustainably designed office building to be built in Thailand. The building has a gross floor area of 6300 m2 and a lifetime of 50 years. An analysis of four different energy supply scenarios was performed to evaluate methods for reducing grid electricity demand with the goal of optimizing renewable energy usage and minimizing environmental impacts. The scenarios included: (1) the conventional, grid-dependent building, followed by (2) the addition of a rooftop photovoltaic (PV) system, (3) a PV system with lithium-ion battery storage, and (4) a PV system with an ice storage system. Scenarios 3 and 4 were included in this study as 16% of the electricity from PV was overproduced during the weekends when the building was unoccupied. The results show that scenarios 2, 3, and 4 reduced operational grid consumption by 33%, 37.8%, and 37.9% but increased metal depletion potential by 23.9%, 34.4%, and 29.0%, respectively. Ice storage led to the greatest reduction in lifetime environmental impacts. Efficient production and utilization of renewable energy in buildings is vital to reducing nonrenewable fuel dependence; however, it is necessary to minimize metal depletion in the implementation of such technologies.

We present an integer program (IP) for urban design that (1) maximizes the floor area; (2) fills building volume with room templates; (3) encodes translational symmetry in urban layout; and (4) constructs economical urban routes. Regardless that integer programming is intensively studied in operational research (OR), its role in solving geometrical and topological problems in urban design was overlooked. Based on a regular grid, our 0–1 IP formulates the sunlight-gain rules, which give urban sites their shapes, especially for residential projects. With predefined plot templates, the gross floor area (volume) within a given site can be maximized under various sunlight requirements. Subsequently, the IP fills each building volume with 2D/3D room templates. Finally, an IP-based algorithm constructs routes that connect all plots and the site’s entrances to public transportation. Both the classical Steiner tree model and the latest coverage network model are extended to create reasonable routes. In addition, this work extends the concept of special ordered sets (SOS) to encode translational symmetry in urban layouts. Encoding layout symmetry can benefit from the solvers’ SOS2 mechanism in the Branch-and-Bound search algorithm. The results indicate that traditional decision making for cities could be partially automated by IP and an abundance of valid solutions are available for designers.

The issue of water resources using which make the best using stayed one of the most important issue which Egyptian society suffer from on the two levels individual and national . The research depended for a achieving his goals on the discriptive and statistical method which representative on linear programmar method and the researcher depended on the data which be published by research associations such as the central agency for the general mobilization and statistics and ministry of agriculture and reclaimation the lands. The research showed that the limitations of crops combination are (the agricultural land – the water resources) and the constraints which be used in linear programmar model are (crops area- feddan return net) and by the looking for the actual crops combination we can observe that it consist of (42 crops divided into (17 winter crops – 13 summer crops- 6 nile crops- 6 fruits) and from the research results showed that the gross water amount was (46271.5) million m3 and the water amount for the winter crops was (14870.7) million m3 and the water amount for the summer crops was (23552.6) million m3 whereas the nile crops was (1041) million m3 and the fruits was (6807.2) million m3 and which related to the crops area the results showed that the gross area was (13678254.7) feddan and the winter crops was (6613977.2) feddan whereas the summer crops was (5325536.7) feddan and the nile crops was (392740.8) and which related to the fruits was (1246000) feddan

Convenience stores are known to have higher Energy Use Intensities (EUIs) than most other commercial building types. However, convenience store energy performance analyses have not been systematically investigated. Moreover, current national convenience store benchmarks are inconsistent from one data base to another due to lack of relevant energy data. This paper assesses the energy performance of more than five hundred convenience stores in the same fleet in Eastern United States. The investigation aims to establish benchmarks for convenience stores with and without gasoline pumping stations. The main factors influencing the actual convenience store building energy use were determined. Multiple regression models were developed to find relationships between annual energy consumption per unit area and gross floor area, customer transaction count, building operating year, etc. The statistically characterized results are meant to serve as a reference data set in comparing the whole-building energy performance index for buildings of similar functionality. The statistical correlation analyses can strengthen the robust prediction of energy performance in convenience stores, enabling proper assessment of energy conservation measurements (ECMs) for convenience store operations, as well as guiding additional such energy performance studies.

Even though urban land-use change simulations provide useful information for decision makers, planning is generally weakly integrated into land-change modelling. However, the increasingly digitally available zoning data from statutory planning offers new opportunities. This study aims to reveal the potential effectiveness of statutory planning in terms of sustainable urban development by integrating zoning regulations in a multi-scenario simulation. Specifically, the gross floor area that can be built per parcel, as defined in the zoning plan, supports the allocation of varying degrees of urban densities. Using the CLUmondo modelling framework that couples cellular automata and multivariate logistic regression, we simulated urban growth in Madrid, Barcelona, Valencia, and Zaragoza Spanish Functional Urban Areas from 2012 to 2030 in four scenarios. The scenarios reflect the degree of planning intervention, ranging from high intervention to unrestricted development, while consider Spanish legislation and urban agenda 2030 sustainability goals. Simulations shows that by shifting growth to zones with urbanization projects almost 4200 ha of grassland and cropland could be saved from overbuilding, and 3800 ha by shifting it to zones without urbanization project. The simulation results provide critical information to support decision-makers and planners in revising plans and designing new plans to achieve sustainable urban development.

Funding Information: This work was supported by the EIT Climate-KIC project “ReWood II” [grant number: TC_2.1.5_190346_P279-1A]; the Forest Value ERA.net project “InfutUReWood”, which is financially supported by the Finnish Ministry of the Environment and others [grant number VN/4236/2018]; and the Finnish Foundation for Technology Promotion (Tekniikan Edistämissäätiö). Special thanks to Fredric Mosley for his support during data collection. Publisher Copyright: © 2021 The Authors The aims of this study were to quantify the amount of wood in residential houses in Finland in 2017 that could be available for cascading, and to characterize the age distribution and gross floor area of the houses in the stock. Through a bottom-up material stock analysis, the mass of wood and the gross floor area of buildings in each building type and construction period were estimated. The study found that 10 million tons of wood are contained in the structures of residential houses built before 1969, equivalent to around 59% of the stock. Since much of this stock is nearing end of life, this material should soon become available for cascading so providing a significant potential resource. It was also found that, overall, the structural parts of residential houses embody 17.5 million tons of wood, of which around 9 million tons is, theoretically, reusable and recyclable. However, for effective reuse and recycling, further analysis of the quality, type and future availability of recovered wood is required. The current results could be used for material stock and flow analyses to help planning for the use of recovered wood. Further research is needed to fill in gaps in the time-series of the number and gross floor area of buildings constructed and their average gross floor area. Moreover, a material intensity analysis of Finnish buildings is needed to better quantify the wood used.

This study analyzes construction productivity based on the construction duration per floor and per gross area over 20 years (1996–2015) and compares the results among the United States, United Kingdom, South Korea, and Japan, which have similar sizes of total construction investment and market risk. Although construction labor productivity is widely used to analyze and compare construction productivity among countries, it does not consider the changed construction duration caused by levels of investment and technology. Therefore, construction duration per floor and gross area was selected analyze and compare construction productivity in this paper. Regular and non-modular buildings with a total of five or more floors and a basement are collected during the analysis period (1996–2015). The total number of collected buildings is 800 and it includes buildings in the United States (194), the United Kingdom (186), South Korea (322) and Japan (98). Construction duration, increase rate and standard deviation are then compared between each country. Finally, factors that influence construction duration are derived and additionally considered to explain and adjust the trends and changes of construction productivity related to construction duration in the four countries. The productivity of the United States is the highest, but the difference between it and other countries decreases steadily because the increase rate of the construction duration in the United stated is larger than those of other countries. Then, the factors influencing the construction duration are derived as a learning effect by the number of ground floors and gross area, as well as the rate of constructed buildings with a first basement floor for efficient productivity management. The rate of the first basement floor influences both the construction duration per floor and per gross area. This study contributes to the field by explaining the productivity change based on the construction duration and proposing the key management point of the productivity by deriving the influence factors

Based on data collected in 20 A-level high-rise commercial concrete buildings in Hong Kong, the research successfully established a probability density function model, which is used to describe the carbon emissions profile of a commercial building. Results indicate that the superstructure of a commercial building, on average, had a footprint of 226.65 kg CO2/m2 and 10.6 kg CO2/m2 separately in the material use stage and transportation stages. It also evaluates the carbon emissions of various building elements and divides them into three levels according to the magnitude of their contribution. The results show that upper floor construction and external wall in Tier 1 contribute nearly 80% of emissions and should be of great concern. In addition to the probability density function model, a regression model was also successfully established in the study to predict carbon emissions. Research has shown that building layers and gross floor area can predict carbon emissions per unit area, and there is a positive relationship between the independent variable and the dependent variable. The regression model can help building designers determine design options to reduce carbon emissions in the early stages of design.

Demolition waste (DW) faces a significant challenge due to massive urban development and renovation activities. It is therefore imperative to accurately estimate and predict the generation of DW. However, the previous research on the generation of DW was mainly aimed at the single building. The delayed time that buildings are demolished was not taken into account in the previous time predication model established. In this study, an accurate model for predicting the generation of DW during the large-scale urban renewal process was proposed. Case studies were conducted in a rapidly developing Special Economic Zone of Shenzhen in South China, and the dismantling of 459 urban renewal projects planned to be demolished between 2010 and 2016 was systematically investigated based on field study and GOOGLE EARTH. In addition, GOOGLE EARTH was used to calculate the lag time of DW generation and BIGEMAP was used to determine the gross floor area of urban renewal projects. Also, the estimation and prediction model of the quantity, generation time of DW was further established. The Houhai Cun urban renewal project was chosen as a representative case. The predicted results showed that it would generate 28277.88 tons of DW in November 2022. The generation location was predicted on southeast side of Dengliang Road, Yuehai Street, Nanshan District, Shenzhen. The findings of this study provide the valuable reference for the number, scale and location of DW comprehensive utilization enterprises and landfills. Besides, the prediction model considering the lag time of the DW generation is much in line with the actual situation, which improves the accuracy of the research method.

Investigation of the key drivers of building energy consumption at the city scale could assist in informing energy efficiency policies. This study proposed a data-driven approach integrating regularization and quantile regression methods. This approach not only identified the key drivers of energy use by group lasso but also moved beyond the typical model of estimating average effects by quantile regression, quantifying the effects of the key drivers under low, medium and high consumption levels. The effectiveness of the proposed approach was evaluated based on a dataset of more than 2000 hospital and education buildings in Chongqing, China. The key drivers of these two types of buildings were found to be mainly related to building characteristics, including gross floor area, number of occupancies and cooling type. Additionally, the quantile regression results showed that most of the key drivers imposed clear heterogeneous effects on energy consumption throughout the quantiles. Compared with the other variables, gross floor area exerts the greatest positive effect on energy use across all the quantiles. The proposed approach can provide a general solution to understanding the effects of key drivers on energy consumption, and the empirical findings could benefit policy design for energy conservation.

Ein Großteil aller Altbauten, der etwa ein Viertel der Wohnungen in Österreich ausmacht, wurde während der Gründerzeit errichtet. Typische Merkmale sind Raumhöhen bis zu vier Metern, Außenwände aus Vollziegelmauern und aufwendig gestaltete Straßenfassaden. Jene Bauwerke sind hinsichtlich ihrer energetischen und thermischen Eigenschaften den heutigen Niedrigenergie- und Passivhausstandards deutlich unterlegen, wodurch ein hoher Heizwärmebedarf entsteht und infolgedessen eine höhere CO2-Produktion. Der Energieverbrauch erfolgt grundsätzlich über die Wärmeabgabe der Baustoffe, die sich aus den Lüftungs- und Transmissionswärmeverlusten zusammensetzt. Die Lösung hierfür ist die Sanierung, da auf diese Weise der Energiebedarf und Kohlendioxidausstoß minimiert werden. Zusätzlich entstehen Wohnungen im Dachgeschoß, wodurch der Wohnungsknappheit entgegengewirkt wird. Im Zuge dieser Bachelorarbeit wurden die Wärmeverluste in den maßgebendsten Bereichen, den Stiegenhäusern, dreier Gründerzeithäuser berechnet und Temperatur- und Feuchtemessungen durchgeführt, welche die Grundlage für die exakte Ermittlung bildeten. Auf Basis der aktuellen OIB Richtlinie 6 und ÖNORM B 8110-6 wurde ein vereinfachtes Berechnungsverfahren erstellt. Durch die Endergebnisse wurde erkannt, dass das Stiegenhaus aufgrund des hohen Rauminhaltes und durch das Angrenzen an die Innenhoffassade ausschlaggebend für die Wärmeverluste eines Gründerzeithauses ist. Dieses Problem kann durch die korrekte Wärmedämmung der opaken Bauteile und Verwendung effizienter Fenstersysteme verhindert werden. Zusätzlich wird das Stiegenhaus im Zuge der Sanierung in die konditionierte Hülle aufgenommen, wodurch das Verhältnis zwischen Fläche und Volumen und daraus resultierend der Heizwärmebedarf verringert wird. Im Endeffekt entsteht ein thermisch optimiertes Bauwerk, das von den heutigen Standards des Neubaus nicht zu unterscheiden ist. The majority of all old buildings, which make up around a quarter of the apartments in Austria, were built during the early days of the Gründerzeit. Typical features are room heights of up to four meters, exterior walls made of solid brick and elaborately designed street facades. These buildings are significantly inferior to today's low-energy and passive house standards in terms of their energetic and thermal properties, which results in a high heating requirement and consequently higher CO2 production. The high energy consumption is basically due to the heat dissipation of the building materials, which is made up of the ventilation and transmission heat losses. The solution for this is renovation, as this minimizes energy consumption and carbon dioxide emissions. Additionally, apartments are being built on the top floor, which counteracts the housing shortage. In this bachelor thesis, the heat losses in the most important area, the staircase, three Gründerzeit houses were determined, and temperature and humidity measurements were carried out, which provided the basis for the exact determination. Based on the current OIB guideline 6 and ÖNORM B 8110-6, a simplified calculation method could be created. The final results revealed that the stairwell, due to the large volume of space and the fact that it is next to the inner courtyard facade, is decisive for the heat loss of a Gründerzeit house. This problem can be avoided by properly insulating the opaque components and using efficient window systems. In addition, the stairwell will be included in the conditioned shell during the course of the renovation, which will reduce the ratio between area and volume and ultimately the heating requirement. In the end, a thermally optimized structure is created, which can’t be distinguished from today's standards of new buildings.

The sustainable development of cities, by focusing on abandoned or degraded areas, represent the key driver that is able to remedy to damages that are produced by uncontrolled urban growth. The aim of the work is to highlight usefulness of the Cost-Revenue Analysis (CRA) in decision-making process concerning the enhancement of a military complex located in the city of Rome (Italy). In particular, existence of conditions that allow the Public Administration (PA) to request private entrepreneur additional operations to those established by the law is intended to examine, ensuring financial convenience of both subjects involved. The results achieved demonstrate feasibility of initiative, but also existence of wide margins for financial convenience of private entrepreneur that allow the PA to request additional financial resources or the direct realization of the initiatives to be intended to public functions. Furthermore, the influence of combination of the intended uses on urban planning loads and financial budget of the initiative is tested by analyzing the variation of share of gross floor area that can be attributed to each intended use. The adoption of the CRA can be implemented by subjects involved in the early stage of redevelopment initiatives in order to provide the size of the amount of financial resources that are necessary.

In line with EU Directives, European building portfolio owners are required to bring their stock to nearly Zero Energy Building (nZEB) standards by 2050. To fulfil this goal in a timely and cost-effective manner, they will need to have a comprehensive understanding of their buildings’ condition, as well as consistent information on viable energy and low-carbon technology measures. Currently, in Europe, there is a lack of knowledge of what energy efficiency measures are being implemented in residential buildings. It is also unknown what are the decision-making processes behind the selection of these measures. On this basis, the aim of this study is to shed light on (1) what energy efficiency measures are currently carried out across European building portfolio owners (BPOs), (2) how are these measures selected (i.e. decision-making processes and information sources), and (3) what data would be needed to foster the uptake of low carbon energy efficiency technologies. The applied methodology combines desk research on scientific and grey literature, with findings in the field of building maintenance & operation. The later based on semi-structured interviews with 23 selected private and public BPOs across 7 European countries: Sweden, UK, Germany, Denmark, Italy, Spain and Switzerland. Results show that the most often implemented actions are the maintenance of the roof and the upgrade of the heating system. Measures are decided based on a combined planned and “reactive” (i.e. problem/solution) approach. The data that is typically used by BPOs is basic building information, such as gross floor area or year built. Although currently unavailable, the most often solicited evidence by the BPOS in favour of energy efficiency and low carbon technology measures is related to energy consumption and other user data.

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.

An accurate understanding of the effect of energy efficiency policies is a key to achieving national goals. This study aims to analyze the effects of building energy efficiency certificates (BEEC) using the data on 222,813 non-residential buildings in the Korea national building energy database. First, the actual source energy use intensities (EUIs) were compared with the simulated source EUIs of certified buildings. The actual source EUIs were larger than the simulated source EUIs, and the difference became greater as BEEC level rose. For instance, the gap was 15.5% in buildings with BEEC level 2 in the central region, whereas it was 63.0% in buildings with BEEC level 1++. Second, the source EUIs of certified and non-certified buildings were then compared, revealing that the source EUI for heating energy of certified buildings was 33.8% to 58.1% smaller than that of non-certified buildings. This suggests that BEEC contributes greatly to reducing heating energy usage. However, the source EUI for baseload and cooling energy consumption was larger in buildings with BEEC level 1 and 2 than in non-certified buildings. The result shows that a significant amount of energy that cannot be taken into account in the scope of BEEC is being used in non-residential buildings. Nevertheless, there was an apparent decrease in the source EUI for the certified buildings with BEEC level 1+ or higher. This indicates that in buildings with BEEC level 1+ or higher, energy saving technologies offset the increased baseload and cooling energy and are effective at reducing energy consumption. This in turn suggests that it is appropriate for the government’s plan to require all buildings with a gross floor area of more than 1000m2 to obtain a BEEC level of 1++ or higher from 2025.

This paper presents a set of freight generation (FG) models for seven cities in Kerala, India. Models were formulated by considering business size variables that capture establishment freight activity. Model estimation results revealed that business size indicators such as number of employees and gross floor area explained the freight generation well. Number of years in business (YB) was identified as another variable and considered in model specification to estimate FG. Detailed investigation of single variable FG models suggested that employment-based models are suitable for cities with dense commercial activities and higher land value, while area-based models better represented FG in cities with medium level of urbanization. Area appears to be a skewed indicator for representing business size in cities with dense commercial activities. In this case, where acquiring area is difficult, employment may be a better representative of growth in freight activity. As an extension of city specific FG models, three types of combined FG models were developed to provide quantitative statistical evidence for differences in model specifications across the cities. The statistical findings from these models suggest that freight activities are influenced by the interaction of establishment characteristics and its location. Interaction effect is more prominent when area is used to represent the business size. Since a systematic commodity flow survey practice is absent in India, planners and policy makers can be benefited from this study while making decisions on freight specific investment schemes and freight operation strategies. The interaction FG models discussed subsequently in this study may be utilized in transportation planning application for the state, regional and corridor level network capacity needs.

This paper estimates traffic attracted to schools using attributes that are usually available in the early stages of urban planning, namely the neighborhoods’ average income and lot area. The paper also compares the prediction of trips using attributes that are more informative such as the gross floor area, the number of students and classrooms. The number of students and floor area provided the best trip prediction models, while the neighborhoods’ income and lot area produced reasonable models, but not as robust, for the early planning stage. The observed school trips were compared to the trips estimated by the developed models. Moreover, the observed trips were compared with trips estimated using three models commonly used in the study area, namely the Institute of Transportation Engineers’ (ITE) rates from the USA, Riyadh City rates from Saudi Arabia, and Abu Dhabi rates from the United Arab Emirates. The developed detailed models provided the best results in the study area followed by the ITE rates. The Abu Dhabi rates were also reasonable and better than the Riyadh rates.

The national building codes set requirements for building energy efficiency in many countries. The purpose of this study was to improve understanding on the measured and calculated energy efficiency of Finnish schools and daycare centers. The study analyzed the energy consumption of 134 schools and 71 daycare centers and compared the regulatory building permit calculations to measured values for 18 case buildings. According to the results, the specific electricity consumption (kW h/(m2 a)) has increased in schools but not in daycare centers. The heating energy consumption was lower in schools, but this might be explained by that they had clearly larger gross floor area than daycare centers. When compared to the technical requirements in the building code, the actual heating energy consumption has decreased less than what the changes in the building code would suggest. The building energy consumption calculated for building permits with the monthly calculation method and standard use clearly underestimated the measured building energy consumption. The differences were larger in heating energy than in electricity consumption. In conclusion, different regulatory limit values should be considered for the two building types. The calculation methods and input data should be analyzed to ensure that they truly guide towards cost-optimal design choices. submittedVersion

A life cycle assessment (LCA) is conducted on a low-energy industrial building under construction in Thailand. The building has a gross floor area of 14,938 m2 and a 20-year lifetime. As energy-saving initiatives need to expand beyond the established domain of low-energy residential and commercial buildings, this study demonstrates the successful application of active and passive energy-saving measures to a large, energy-efficient industrial building—the first to be surveyed by an LCA. LED lighting, minimal air conditioning, and passive ventilation architecture reduce operation phase burdens. As a result, the manufacturing phase yields largest impacts in primary energy demand (71%), global warming potential (60%), and four other environmental impact categories. This is largely attributable to steel and concrete production and a higher embodied energy quantity per material. Additionally, four scenarios—a base case, recycling case, photovoltaic system scenario, and combined recycling/photovoltaic scenario—are simulated to evaluate strategies for further energy reduction. Analysis indicates that significant life cycle energy savings can be achieved through recycling (29%) and a rooftop PV system (64%). The combination of both enhancements compensates for all manufactured material embodied energies and results in a building with zero or sub-zero total life cycle energy demand. Buildings that are already low-energy can further reduce environmental impacts through inclusion of the aforementioned approaches in design and implementation.

Buildings, due to their significant environmental footprint, are major contributors to global energy use and carbon emission. Although buildings consume more than 50% of raw materials in the construction sector of Sri Lanka, there is a notable lack of building energy and carbon related studies based on the country. The present study assesses the embodied carbon of a commercial office building in Sri Lanka, focusing on the material production phase of the building life cycle. The embodied carbon in the material production phase was found to be 629.6 kgCO2/m2 of the gross floor area of the building. Reinforced concrete and clay bricks are the major carbon emitting materials contributing to more than 70% of the total embodied carbon. It was found that in selecting building materials, both the mass materials and high carbon emitting materials should be given special attention. The study identified several important strategies for the reduction of embodied energy and carbon of buildings in Sri Lanka. Taking a proactive approach in mitigating embodied energy and carbon impacts of buildings will lead to energy efficient and low-carbon buildings, enabling Sri Lanka to take part in overcoming the global environmental challenges in future.

This study aims to develop prediction models for HVAC related energy saving in office buildings. The data-driven modelling makes use of data gathered from several energy audit reports. These reports entail building and energy consumption data for 56 office buildings in Singapore. The two models are developed using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). The methodology to select the most appropriate input variables forms the essence of this study. This variable selection procedure involves 819,150 iterations, taking all possible combinations of the 14 input variables to determine the most accurate model. The dependent variable is taken as the change in energy use intensity (EUI, measured in kWh/m2.year) between pre- and post-retrofit conditions. The results show that the ANN model is more accurate with a mean absolute percentage error (MAPE) of 14.8%. The best combination of variables to achieve this comprises of gross floor area (GFA), air-conditioning energy consumption, operational hours and chiller plant efficiency. The information on these four variables, along with the prediction model can be used to predict HVAC related energy savings in office buildings to be retrofitted.

This study aims to determine key building variables influencing energy consumption in air-conditioned office buildings. The study is based in Singapore which entails tropical climatic conditions. The analysis is based on assessment of several energy audit reports concerning pre- and post-retrofit data from 56 office buildings. A list of 14 building variables, extracted from these reports form the superset. These are systematically analyzed further to derive key variables influencing energy consumption and retrofitting decisions. For this purpose, a robust iterative process is developed utilizing k-means clustering. This process tests all combinations of the 14 variables against change in energy use intensity (EUI, measured as kWh/m2.year) for pre- and post-retrofit conditions. The results indicate that the best set of variables consists of: 1) gross floor area (GFA), 2) non-air-conditioning energy consumption, 3) average chiller plant efficiency, and 4) installed capacity of chillers. This information can be utilized to explore energy saving potential of office buildings that need to be retrofitted. The resultant clusters can also be used to benchmark buildings based on pre-retrofit conditions and energy saving potential.

Understanding regional building energy patterns is the prerequisite to efficiently and effectively promote sustainable urban development. Previous studies have proposed various data-driven methods to investigate the relationship between building energy consumption and hundreds of potential influencing features. However, it is difficult to include all potential features in one single model since either some data could be unavailable or the model would be too complex. To identify the critical features, this study develops a data-driven random forest (RF) based framework with a dataset of Taipei City, consisting of 24,764 buildings in 881 city-blocks, to model the relationship between city-block-level building-oriented features and building energy consumption. The RF model is found to outperform other machine learning models including logistic regression, k-nearest neighborhood, support vector machine, and decision tree models in the predictive accuracy of the classification problem. Seven critical features related to the built year of buildings, building gross floor area, building density, and the ratio of commercial buildings in the block are identified from the 59 city-block-level building-oriented features. The developed framework could refine the features adopted in regional building energy models, and policymakers and city planners would get practical implications from the identified critical features.